OXFORD
Geographic Information Systems Applications in Natural Resource Management
1
Second Edition
Geographic Information Systems Applications in Natural Resource Management
Michael G. Wing Pete Bettinger
OXFORD UNIVERS ITY PRESS UNIVERSITY
2
OXFORD U:-:lvr SIV I RSITV ASITY Pitt's!> l'Rt'SS
rt' 204, Don Mills, Omario OH5 8H Sampson Mcw$, Mews. Sui Suite O lllario M3 M3C OHS www.oupcanada.com Oxford Uni University ress is a department of the Uni\'cr.)ity versiry ('Press Unive rsity of Oxfo rd. rd . II furt hers (he rsity's objective objccrivc of excellence in resea rch. rch . scholarship. if urthcrs rhe Unive rsiry's scholarship, :md cd uc.1.r ion by puhlishing worldwidee in and cduc uion publi shing worldwid
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Librnry Li brary ~nd and Archives Canada Cataloguing in Puhlic:1 Publication lioll Wing. Mid13d M ichael G
Gcogr.tphic SYSWllS : applications in forestry and n:1IlIral nafUr.l.1 Geographic information synems: resources management J/ M Michael ichael G. G. W Wing ing & Pete Pele Benin I3cltinger. ger.- -lnd 2nd cd c:d.. Previous Pr('ViollS: cds. t'd<. by Pete Beninger and i\'li Mi chad chael C. G. Wing. Wing. 19-5426 10-6 ISBN 978-0- 19-542610-6
1. I. Forests and forestryfor~try- Remme Remote sensing. )cllSing. 2. ;uur.aJ alura.! resources-Re r('~urccs-Rcmou: mme sensin g. 3. systems. I. Beltinger. Ben inger. Pete. rete. 1962- II. Title. Title:.. sensing. J . Geographic information systellls. SD387. R4W562008
634.9·028 634.9 '028
C2008-902309-9
Cove r image: Philip & Karen KJreu Smith/Geny milh fGercy Images. 345345 - 121110 12 11 10 Thi s book is pri med on permanent This permant'nt (acid·free) (ac.id, rree) paper p:tpt:r Primed Print cd in C Canada an ada
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3
Contents List of Tables Preface
XIV
xv
Part 1 Introduction to Geographic Information Systems. Spatial Databases. and Map Design I Chapter 1
Geographic Information Systems Objectives
2
2
What is a Geographic Infonnation System? A Brief History of GIS
3
4
Why Use GIS in Natural Resource Management Organizations? GIS Technology
8
Data collection processes and inpuc devices Manual map digirizing 10 Scanning 10 Remme sensing II Phologrammerry 13 Field data collection 1G Dara s[Qrage rechnology 19 D.ta ma nip ulation and display 19
O utput Devices 20 Prinrers and ploners Screen displays 21 Grap hic images 22 Tabular outpur 22 G IS software programs
Summary
7
8
20
22
24
Applications 24 References
25 4
vi
Contents
Chapter 2
GIS Databases: Map Projections, Structures, and Scale Objectives
27
The Shope and Size of the Earth Ellipsoids, Geoids, and Datums
27 28
The Geographical Coordinate System Map Projections
30
32
Common Types of Map Projections Planar Coordinate Systems GIS Database Structures Raster data srruc[Ure
33
34 38
38
Sa«llite imagery 38 Digital elev'Hio n models 39 Digital orthophotographs 39 Digital raster graphics 40 ar io nal Map Accuracy Standards Vector data st ru cture
Topology
43
44
45
Comparing raster and vector data structures Alternative data S(fuc£ures
48
D ynam ic segmentat ion ofli near nerworks
Regions
50 50
Scale and Resolution of Spatial Databases
Chapter 3
51
51
Applications References
49
50
Obtaining Spatial Data
Summary
47
48
Triangular Irregular Nerwork
Metadata
52 53
Acquiring, Creating, and Editing GIS Databases Objectives
27
54
54
Acquiring GIS Databases Creating GIS Databases Editing GIS Databases Editing anribuccs
55 57
59
60
Editing spatial position
61 5
Contents Checking for missing da,a
62
Checking for inconsistent data
62
Sources of Error in GIS Databases Types of Error in GIS Databases Summary
63 64
67
Applications References Chapter 4
vii
67 70
71
Map Design Objectives
71
Map Components
72
Symbology 72 Direction
73
Scale
74 Legend 74 Locational inset
75
76
N eadine
Annotation
76
Typography 76 Color and contrast
77
AnciHary information
78
Caveats and disclaimers
Map Types
79
80
Reference maps
80
Thematic maps
81
O,her types of maps 84 The DeSign Loop
85
Common Map Problems Summary
86 87
Applications References
85
88
Part 2 Applying GIS to Natural Resource Management Chapter 5
Selecting Landscape Features Objectives
89
90
90 6
viii
Contents
Selecting Landscape Features from a GIS Database Selecting one feature manually
91
91
Selecting many features manually
91
Selecting all of the features in a GIS database
92
Select ing none of the features in a GIS database
92
Selecting features based on some database cr iteria
92
Single critcrion queries 94 Multiple criteria queries 94 Selecting features from a previously selected set of features
95
Inverting a selection 97 Example 1: Find the landscape features in one GIS database by using single and mulriple criteria queries and by selecting features from a previously selected set of features 98 Selecting features within some proximity of other features
99
Example I: Find rhe landscape featu res in one G IS database (har are
inside la ndscape features (polygons) contai ned in anmher G IS database
99
Example 2: Find rhe landsca pe fea [tlres in o ne GIS database that are cl ose to the landscape features conta ined within anothe r GIS database
100
Exam ple 3: Find landscape featu res from one G IS darabase that are adjace nt ro other landscape featu res in the sa me G IS database
Advanced query applications Syntax errors
Summary
102
102
102
Applications References
Chapter 6
101
103 105
Obtaining Infonnation about a Specific Geographic Region Objectives
106
106
The Process of Clipping Landscape Features
107
Obtaining information about vegetation reso urces within riparian zones Obtaining informati on about soil resources within an ownership Obtaining information about roads within a forest Obtaining information about streams within a forest
The Process of Erasing Landscape Features
109
110
III I 13
I 14
Obtaining information about vegetation resources o utside of riparian zones
Summary
116
Applications References
115
I 16 118
7
Contents
Chapter 7
Buffering Landscape Features Objectives
119
119
How a Buffer Process Works
120
Buffering Streams and Creating Riparian Areas Fixed-width buffers
122
123
Variable-width buffers
123
Buffering Owl Nest Locations
124
Buffering the Inside of Landscape Features
125
Buffering Concentric Rings around Landscape Features Buffering Shorelines
127
128
Applications References
Chapter 8
125
126
Other Reasons for Using Buffering Processes Summary
128 130
Combining and Splitting Landscape Features, and Merging GIS Databases 132 Objectives
132
Combining Landscape Features
132
Contiguous, similar landscape features
135
Multiple spatial rep resentations within a single landscape feature o r record
Overlapping polygo ns
Merging GIS Databases
138
140
Determinin g how much land area is unrestricted
Summary
140
142
Applications References
142 143
Associating Spatial and Non-spatial Databases Objectives
136
137
Splitting Landscape Features
Chapter 9
ix
144
144
Joining Non-spatial Databases with GIS Databases One-to-one join processes One-to-many joi ns
145
145
147 8
x
Contents Many-to-one (or many-to-many) joins 147 Example 1: Determining [he number of hardwood sa wm ills in a stare Example 2: Determining sawmill em ploymenr in a counry 149
Joining Two Spatial GIS Databases
148
ISO
Making Joined Data a Permanent Part of the Target (Destination) Table Linking or Relating Tables Summary
153
154
Applications References
Chapter 10
153
ISS
156
Updating GIS Databases Objectives
157
157
The Need for Keeping GIS Databases Updated
158
Example 1: Updating a forest stand GIS database managed by a forest management company 159 Exam ple 2: Updating a streams GIS database managed by a state agency
160
Updating an Existing GIS Database by Adding New Landscape Features Updating a stands GIS database Updating a trails GIS database
161
161 162
Updating an Existing GIS Database by Modifying Existing Landscape Features and Attributes 166 Ed_iring the spatial position of landscape features using digitaJ
ortbophotographs
166
Updating the tabular 3rrribures using a join process
Summary
167
Applications References
Chapter 11
168 169
Overlay Processes Objectives
167
170
170
Intersect Processes Identity Processes Union Processes
171 174 175
Incorporating Point and Line GIS Databases into an Overlay Analysis Applying Overlay Techniques to Point and Line Databases Additional Overlay Considerations
178
179
180 9
Contents Summary
181
Applications
183
References
Chapter 12
182
Synthesis of Techniques Applied to Advanced Topics Objectives
184
Land Classification
185
Recreation Opportunity Spectrum
188
Habitat Suitability Model with a Road Edge Effect Summary
References
194 19S 195
Raster GIS Database Analysis Objectives
197
197
Digital Elevation ElevaHon Models (DEMs)
Elevation Contours
Slope Class Maps
197
198
Shaded Relief Maps
200 20 I
Interaction with Vector GIS Databases Interarnon Viewshed Analysis
Chapter 14
208
210
Applications References
211
212
Raster GIS Database Analysis II Objectives
202
20S 205
Watershed Delineation Summary
191
193
Applications
Chapter 13
184
213
213
Raster Data Analysis
213
Raster Analysis Software Parameters Distance Funrnons Functions
214
Statistical Summary Search Functions Funrnons Density Functions
213
21S 215
216 10
xi xl
xii
Contents Contents
Raster Reclassilication Reclassification
Raster Map Algebra
217
218
Database Structure Conversions
218
Getting Started with the ArcGIS Spatial Analyst
219
Determining the Most Efficient Route to a Destination
220
Creating a Density Surface for the Number of Trees Per Acre Summary
223
Applications References
223 224
Part 3 Contemporary Issues in GIS Chapter 15 Trends Tre nds in GIS Technology Objectives
225
226
226
Integrated Raster/Vector RasterNector Software
226
Linkage of GI GIS S Databases with Auxiliary Digital Data High Resolution GIS Databases
Web-based Geograpruc Geographic Information Systems
Data Retrieval via the Internet
227
228
Distribution of GIS Capabilities to Field Offices
229 230
230
Portable Devices to Capture, Display, and Update GIS Data Standards for the Exchange of GIS Databases Legal Issues Related to GIS
GIS Education
Chapter 16
231
234
234
235
Applications References Re ferences
231
232
GIS Interoperability and Open Internet Access
Summary
221
235 235
Institutional C Challenges halle nges and Opportunitie Opportunitiess Related to GIS Objectives
237
237
Sharing GIS Databases with Other Natural Resource Organizations Sharing GIS Databases within a Natural Resource Organization
237
239 11
Contents
Distribution of GIS Capabilities to Field Offices Technical and Institutional Challenges Benefits of Implementing a GIS Program Successful GIS Implementation Summary
241 243
243
243
Applications References
Chapter 17
240
244 244
Certification and Licensing of GIS Users Objectives
245
245
Current Certification Programs The NCEES Model Law
246
247
The Need for GIS Certification and Licensing
248
GIS Community Response to Certification and Licensing MAPPS Lawsuit Summary
249
251
Applications References
249
251 251
Appendix A GIS Related Terminology
253
Appendix B GIS Related Professional Organizations and Journals Appendix C
GIS Software Developers Index
260
263
264
12
xiii
List of Tables 1.1
Com mo n sizes of map Outpur fro m plQners Common OUtP'" from plouers 2 211
1.2
Common rypes of ics image Out OUtpUt o f graph graphics put files fi les
2. 2.11
M ap scales and associa associated Map ted
2.2
Comparison of raste rasterr and vecto srru c(Ures vectorr dara doH3 Stru Ctures
3. 3.1J
TypiC'JJ ted with Typ ical informarion info rmat io n associa assoc iated w ith a GIS GIS d;nabase data base request reques t
3.2
Auribures of sl.lI1ds stands in rhe Dani Daniel nds GIS Attributes el Pickerr l'ickeoo sra Stands GIS darabase database
3.3
Exa mple Roo calculario n for fo r Grs coo rd inares Example Roorr Mea Meann Square quare Erro r (RMSE) (R.'v1SE) ealculaoion GI'S coordinates
5.1
A rimber lim ber .srand t.lnd darabase da tabase
6. 6.11
A subse rhe tabu ra bular da,. co ntained in the th e GIS rh ar resulted resulred from cl ip ping subS«r of the lar da ta contained .IS database that clipping Brow racr stands within wit hin 50-meIer 50-merer stream st ream buffers 110 Brownn T ract
6.2
Length Lengrh and rype of o f road within wi thi n the rhe roads GIS database darabase developed develo ped fo r the rhe Brown Traer Tract
6.3
Le ngrh and rype of road wirhin with in the rhe boundary bou ndary of ,he racr Length Ihe Brow Brownn T raer
6.4
Length and T ype of sore.ms srrea ms within th SHea rn s GIS database rac( Lengrh rype rhee Streams cb ra bas. used by rhe Ihe Brown T Traer
6.5
Lenglh and rype of strea Length ms within undary of rhe meams withi n the bo boundary the Brown Brown T racr (3Ct
7 .1 7.1
Ten hypmher eir st rea m class. length. length , and width Te n hypor her iicaall streams soreams and th rheir meam
7.2
T en hypor her ical streams eir stream class, length,. wi dth, and buffer Ten hypothetical meams and th their class. lengrh widd,. buffer distance disrancc
7.3 7.3
Stale of Oregon riparia n managemem area policy State O regon riparian
7 .4 7.4
Samp le stream reaches represenred Trdc[ strcarns strea ms GIS GIS darabase. data base, their th eir Sample represented in the Brown Trace cha racreristics. and resulring characreristics. n:sulring buffer buffer width 124
8. 8. 1
Res ul ts of combi co mbi ning rwo sra Results sta nds
9.1
rial database ASCIIIItcxt rexr file format fo rmat i1lustr:oti illustrari ng com ma-del imired ,bra data ma-delimited A non-spa non-spatial darabase in AS
9.2
Spadal joi n ooprions prions by targer source Spatial mge t and sou rce fea ture rure (y ty pe
10.1
fo r updaring upd at ing GIS data bases A sam pling of reaso ns for GIS databases
10.2
Inp urs and process thar ist a GIS rim ca n be used to to ass assist GI database update Inp
10.3
Attribu res of o r sm sra nds in a 32 .38 hecra re (80 acre) land la nd purchase adjacenr Attributes 32.3 hectare pur h. < adjace nt foresr foreSt 16 1
I 1.1 11.1
or f land allocation categories in research resea rch plot locarions wi thin rhe Frequency distriburio dist ributionn o plol locacions within Brow ract 179 Brownn T tact
11.2
Frequency disrri distribution nd allocatio allocario n caregorit;S burio n orla o fland ca tegories in in retll relario srrea m segments segmenrs wi thi n the ionn ro to stream within tile: Tract Brown Brown T tact 180
12.1 12. 1
An exam ple of a management-related managemenr-related land c1assiflcarion classi fica ri on sysrem ex.m pIe sYStem
12.2
A subset with spatia l considerations co nsiderations for fo r deline-J.ling delineat in g recreationa recreationall opportunity su bsel of rules wi th sp3rial spec rrum (ROS) classes 189 spectrum
13. 1
OutpUt slope val va luut::s es fo managemenr units O UtPUt of percent percell[ stope forr managemem
or
22
ar ia na! Map Accu Accuracy ri zonta l accu racy ational racy Standards fo r ho horizontal accuracy
44
47
55 61 66
93
11 I 122
I 12 11 3
11 I 144
12 1 122
124
136 145 145
151 15 1
158 159 10 to
[he the Daniel Pickert Pickett
185
203 13
Preface his second edition of Gtogmphic Applications Natural Rtsourct r-r-his G~ogrtlphic Infonnatioll Infonnatiol1 Sysums: SysuJns: AppL icatiolls ill NOITlml Rtsourre Management is intended 1. Mnnagtmt!1lt. inrended for inrroducrory courses in geographic information systems or or campmer computer applications Ihat that address add ress (opics copies relared related TO [0 natural narural resource manage-mem. management. The emphasis of the rhe book is on geogrdphical geographical information informacion systems (G (C IS) applications app lications in naru -~ ral fal resource reso urce managemelli. management. GIS GIS rools lOols are now considered core corC' technologies tec hnologies for natural resource organiZ.1rions become parr pare of daY-fo-day day-co-day acrivities activities in many pans of dlC rhe organizations and have b«ome addition , many narural natural resou reso urce S[Uworld. In addirion. rce programs in higher education require [hat srudems dents comple[e comple[(~ at least leasl one course rha tha lt comai contai ns significant signific.1nt rre'llmem rrearmenr of GIS. We provide derailed discussions and examples of orGIS GIS opera o perations rions such as querying, buffering, clipping, cl ipping, and overlay ana analysis lysis (and ot 01 hers), as well as background information on the history of GIS, GIS. database creation creation,, ediring, editing, acquisition. and map development. T The he 3pplicarions applicarions provided world, although primary extended (0 in this rhis book can be exrended to any region in the rhe wo rld. altho ugh the rhe prima ry emphasis is on Nonh North America. America, as 3S portrayed by numerous examples of narural natural resource management scenariOS. scenarios. The conreOiS contents of rhis this book were determined largely (hrough through our experiences ex periences in na[Ural natural research, as well as our ex(ensive resource management and research. extensive instructional experience over lhe the ones oncs we presenr present in this book have rhe previous decade. Many appiic.1lions applications similar to ro (he resou rce professionals (as well as by the aurhors) authors) as parr of their been performed by natural resource normal job (asks tasks in private oorganizadons rganizations and public ag('ncies. agencies. goa l of this book is to introduce Sludent students,. field personnel. personnel, biologists, aand orher The goal nd other natural resource resou rce professionals to ro the most common GIS applica rions ti ons and principles asson31l1ral [he mOSt 3SS0ciared ciated with managing naturaJ natural resources. resou rces. Therefore. Therefore, the book focuses mainly on GIS applir:uher Ihan than on o n GIS theory. We would be remiss, however, if we did not nor provide cations J'
T
14
xvi xvi
Preface applicarions typicall [0 field pro protessionals tede ral, stare, p rovincial, app li ca ti o ns typica fessionals associated with federal. sta te. provi ncial. or priV(ltC' narural natural resource organizations. organizatio ns. vare applicarions of otG. IS to natural narural resource managemelH, have provided To illustrare illustrate the applications management. we have: tourr sets ot GIS rhe hypotherical Daniel toresr, fOll of G I databases. The flrsr first ser set reterences re fere nces the hypotimical D aniel Picken Pickett lo rest. m iliar ro {"hose who rses in foresr m3nagemenr, ir is one rhar fami li ar to those wh o have )ulve taken cou co urses manage melH. as it thar may be fa ont' of th one thee landsca pes lIsed used to illustrare managemem alrern alternatives Foust iliustr.Hc management atives in the book Fort'st Mfl1Jflgtmt11l (D (Davis et al.. aI., 2001) . The second set references reterences a fictional fores, foresl called the Mal/agflllcm avis e[ represe ms a more realistic landscape and includes a digiTrace Brown T racr. The Brown Tract representS [al rhe resources being managed managed.. The thi rd nh opho togrn ph so that th ar users can acrually acrllally see so< the T he third ,al oonhophorograph represents land uscs uses in Saskarchewan, rep resents da(:l Setr represems askarchewan. while rhe founh represe nt s milliocarions mill locations dara se and counties southern US. acrual GIS data, bur cou m ies of the rhe sourhern U . These databases were derived from aem al G IS data. signi fican tl y by the m make them suitable suitab le fo r lise were modified significantly dl C' authors to use in th thiiss texr. tocr. Each E...1ch of these sets of Gl websi(e has red by rega n State [ate ot GIS data can be accessed through a websire hosred by Oregon University (hrrp:/ /www.fo resrry.o tegonstate.edu/gisbook). regollsrare.edu/gisbook). Univers ity (lmp:llwww.forestry.o Parr 1I provides readers not nor on ly with rhe hhistory isrory and development of GIS. GIS, bur also with wirh ,he a common pecrive on GIS. ing perspecrive on C IS. Too ootten ften people us usin g GIS have lirrle fortorco mmon la nguage and pers mall training; rraining; instead insre:ld ., they gain ga in knowledge and skills skil ls through lrial-and-error ma applications, Irial-and-erro r applications. sho rr co other means. We want [0 disco discourage rhe effons selturses, o r th rough orher \Vlc do nor wa m to urage the efio n s ot of self· shorr courses, through mOl users; however, usuall y have an abridged ab ridged perspective oonn the rhe his history m a l iva ted red CIS GIS IIsers; however. they rhey usually lOry of GIS, srructures are diffe rent, and in othe otherr rel reiared hope rhat C I . how and why dara data strucrures difFerenr. ated mpies. topics. We ho pe (hat communication naruml ir rda reiares GIS processes and com munication among naru ral resource professionals as iI' tes to ro GIS a nd rC'1uests will w ill thus requests roved with a more rasks [0 rhu s be imp improved mo re thorough rhoro ugh pers pective, pec rive. allowing allowi ng work lasks to accomplis hed more efficie ntly. nrl y. be acco mplished Pan 2 emphasizes GIS ope rations rario ns and introduces inrroduccs reade r~.ldc rs lO of the: ro many ot the most powerful ful and com commonly GIS app lica lications monly lIsed used GIS tions in narura namrall resource reso urce ma nagemenr. Each chaprer cha pu~r Pa rr 2 inr roduces GIS tech niq ues, an d [he thenn provides app licatio ns related relared to the techrechim roclllces niques, and applicatio ro rhe in Pan niques. The nceprs introduced in Part Pan 2 are initially inirially related (Q to the managemenr mana ge ment and use T he co concepts vec(Q r GIS d:uabases. darabases. The concepts build upon culminare of vecto upo n themselves, themselves. and culm ina te in a synthesymhc~ chapter 12. Chaprers provide presented in chaprer C hap ters 13 and 14 prov ide [rearments rreat ments sis of advanced analyses presemed raste r GIS GIS database uses in narural naru ral reso resource of raster urce managemenl. m:m age menr. Parr rhe book introduces a number of ropics wpies relared related w GIS Pan 3 of the to the rhe tre rre nds in the use ot of Gl in na narural resource challenges and opponunities Faced by those o rganilUra) resou rce management, manage ment. {he rhe chaJlenges o ppo rruni li es faced 'latio des iring to use GIS ing processes, and the ngoing and ,IS to ass isr in dec ision-mak isio n-making rh e o ongoing za[io ns desiring contentious to ce certification licensi ng of CIS GIS users. use rs. The appendices rhe cOl1len[ious issues relared related 10 rrifl ca rio n and licensing appe nd ices ot of the terms, a summary of organ academic o rgan izations iz.u ions and acade mi c book provide users with wirh :'a1 glossary of [erms, journ als associated wirh with (he Ihe lise GIS, and refe references (0 {he lhe devdopers developers ot most co comrences [0 of the rhe mOSt mjournals use of GIS. softwa re prog rams. mon GIS sofrwa progra ms. This book is dedicated (Q challenged Tbis to those students studems who have chall enged LIS us to ddevelop evelo p coursecO ll r.sedirectlyy 'to0 [he rhe GIS tasks likely perform .5 as natural rei ates directl r..ks (hat tha t they will willlikcly narural resource wo rk thar rhat relares manage rs.
References Davis. L.S .• .• Johnso n. K.N .• Bellinge r. rP.S ....• & H owa oward. 1). Form Johnson. Berringer. rd. T.E. (200 (2001). Form lIIaul/gem",/: mOllogelllellf: socifllvfllues ed .). H ilI. To slistain sustain tcological. ~colqgicfll, tconomic, t rvl/omie, and find socia/ llaluts (4th (4(h ed. ). New York: McGrawMcG raw-HilI.
15
Part 1
Introduction to Geographic Information Systems, Spatial Databases, and Map Design
e ho pe Pa re 1 of GIS AppiicatiollS in Natural Resouras prov ides readers wirh a commo n la ng uage a nd pe rspecrive o n geogra phic in fo rma t io n sys tems (G IS). Frequently, peo pl e usin g G IS have li rrle fo rmaJ tra ining, and they ga in rh eir kn owled ge and skills either thro ugh shorr courses or rh rough [hei r own ini{iarive. Whil e these selfmociv3 rcd effo rts 3rc laudable, they lIsually result in an ab ridged perspecrive on lhe histOry o f GIS. how and why data srrllc{tl res di ffe r. and m her rel ated topi cs. Co mmunicat io n among narum l reso urce p rofessio nals as it rel ates [0 G IS processes and req ucs[s shoul d be improved with an informed perspecri ve of G IS, enco uragin g work tasks to be acco mplished mo re e ffect ively. In chapte r I , rhe historical development of GIS an d the va rious tools you mi ght use ro crea re C IS da tabases are exa m ined . The focus of chapte r 2 is an esse nti al ro pic fo r C IS: data. C haprer 2 begins by describing the ways in w hich we ca n quant ify and measure the Ea rth 's size a nd shape, and how resulrs from these meth ods ca n be inco rpo rated inco C IS. C hapter 2 also includes materia l o n ho w data can be stru ctured within C IS and o utlin es [he o ptions (har a re avai lable fo r t h ~e pu rposes. In add itio n, so me o f rh e mo re co m mo n so urces of dara for developin g o r augment in g spat ial databases are presented in chapte r 2. C hapter 3 builds upon the data theme im rodu ced in chapter 2 by exa minin g how o rganiza ri o ns mi ght acquire or develop darabases, and d iscuss ing issues rel ated ro database ed itin g and pme nt ial errors. C hapter 4 delves into ca rtogra phy; o ne of many supporting d isci plines from whi ch G IS has evolved bur also o ne of rhe ce mral ways in whi ch C IS resul ts ca n be comm un ica ted ro ot hers. In addit io n, chapte r 4 incl udes a tho ro ugh d iscussio n of rhe co ncepts and co mpo nem s that may lead to a successful map. whil e at the sa me time identifyi ng so me co mmo n pi tfa lls lO avo id in rhe ma p crea ti o n process.
W
16
Chapter 1
Geographic Information Systems Geographic Information Systems (G IS) are now core technology fo r many natu ral resource o rganizatio ns and are also app lied in disci plines throughout society. The initial applications of GIS that demo nstrated so me of rhe power and potential of (his spacial technology, however, were within namra! resou rce applications (Wing & Beninger, 2003). In one ofrhe firsr papers on the use of GIS in narural resource manageme nr, de Sreiguer and Giles (1981) describe the potenrial uses of GIS in naru ra l resou rce managemenr. In adapting o ne of their inrroducrory remarks [0 rhe presenr day, you will find rhe releva nce of G IS ro narlIfal resou rce management clearly sta red: A natural reso urce ma nage r is often ca ll ed upon to
selecr an area of land
to
designate as cririca l wildlife
habir3r, as a pmenria l area to implemem a timber harvest, or as an area to reco mmend a silviculrural rrear mem, o r to evalu ate a landscape under a lternarive managemem policies. The manager desc ribes (0 (he GIS the charac teristics of the ideal a rea in terms of forest s(fu cwrai cond itions, soils, or (Opograph y. Within seco nds the manage r receives gra phic a nd rabu lar infor matio n ro loca re the a ppropriate man age ment area s, or to co mpare alre rn ative policies. (de Stei g uer & G iles, 198 1, p. 734)
managers {Q consider rhe impacts of d ifferenr policies o r act ions in a more efficienr manner, usually savi ng time and money. For ma ny na[Ural resou rce ma nagement o rganizations, G IS has beco me a n irreplacea ble rool to assis t in [he day-to-d ay manage menr of la nd , wate r, an d ot her resources. The applica ti o ns of GIS vary widel y amo ng o rganiza tions and may ra nge from usin g GIS primarily as a mapping tool to lIsing C IS to model pol icy aire rn atives rhat may impact landscape fearures during rh e nexr 100 years and beyo nd. Rega rdless of how a narural reso urce manage menr o rga nizat io n plans to use G IS, understanding rhe potential appl ica ti ons of C IS to na tural reso urce manage menr is essenrial for natural resource professionals. This text is designed to int roduce readers to GIS concepts an d princi ples a nd to provide exam ples of how [0 apply rhi s knowledge in a narural reso urce manage ment co nrex[. The introdu cto ry chapler begins by describ in g the rools an d technol ogy thar co mpri se a GIS, and illusrrari ng why G IS has beco m e so impo rtalll for many o rganizat ions. A brief hi slOry of the evo lu tion of G IS a nd identification of significanr conr ribmors to G IS development is then provided. Toward the end of this chapter rh e key co mpo nent of any successful G IS (s parial dara ) is discussed.
Objectives Obviollsly, natura l reso urce managers ca n perfo rm th e sa me task of idenr ifYi ng appropriate managemenr areas o r of analyzi ng policies by examining se ts of paper o r myla r maps, bur rhe process beco mes mu ch mo re efficienr 'lOd acc urate when perfo rmed with G IS. Furrher, analyses of the impact of alternative policies are faci litated , allo wing
This chapter represenrs an introdu ction to GIS concepts, roo ls, technology, history, and significant co ntribu rors. Given that the focus of this book is on rh e appl icatio ns of C IS ro natu ral reso urce mana ge ment, what is provided in (his c hap te r is a co nden sed version of these [opics. 17
Chapler Chapter 1 Geographic Inlormabon Informafion Systems Nevenheless, Nevertheless. at ar the rhe co nclusion nclu sion of this cha chapte pter. r, readers should understand and be able to discllss discuss the perrinent as peas of the following topics: rop ics: aspeclS
1. I. [he the reasons why GIS usc: use is IS pr~valenl prevalenr in In natural resource management. 22.. the rhe evolution of the development of GIS technology and key figu figures. res. tech niqu es and 3. the common spatial data collecrion techniques input devices rhat are available, inpur available. G IS OUlptH omput processes thar are rypical typical in 4. the commo n GIS natural narural resource managemenr, man:l gemellt. a nd - rhe broad rypes orGI of G IS sofrwa software re tim rhar are avai lable. la ble. 5.
What is a Geographic Information System? A geog' geographic raphic information sy system tern consisrs of the necessary {ools ro allow you to collecr, collect, organize. maniprools and services lO ulate, inrerprer, interpret. and display geographic geogrJphic information. A GIS than JUSt tlit': rhe h'lrdware hardware and software is more {han softwa re familiar hlmiliar (Q to mosr people; it extends most exte nds to lhe the staff who operate the system. rem, the r.he databases, dat.bases. [he the physical facilities, fuci lities. and lhe the o rganirgan izational commirment commitmelll necessary {O ro make ir it all work. A GI GIS can be defined by how it is used (e.g (e.g.,.• a land informarion information sysrcm. a narural natural resou resource management information infor matio n syssystem, rce managemenr tem), by what it comains contains (spadaJly (sparially distincr distinct fearurcs. features, acrivactivities. iciest or events defined as points. points, lines. lines, polygons, polygons. or raster cells). by its irs capabilities capabiliries (a powerful set SCI of 1001s tools for grid cells), sroring. retrieving. transforming, displaying collecting. collccling. storing, rransforming. and displ:tying sparial data)., or by ils its role in an organization organizarion (a (~ map prospa rial dara) duction sysrem. syste m . a spadal spatial aanalysis nal ysis system. a system sysl~m for assisting in making decisions regard ingg basic geographic geograph ic regardin quesrions such as: Where is it? What is il? questions it? Why is it (here?). mere?). The co core re component of a GIS however is ...a daradataconmins a geographic camponelll. base Ihar thar contains componenr. We wi willll discuss geographic dara data in more detail shordy. GIS can also be defined as geograph geographic ic info information rmation scie nce (GIScience). GIScience involves the identification science idemiflcation ro GIS .1 use, affect its ils and srudy of issues that are rdated 10 implementation, and that implemenr:uion. lhar arise from iu irs applicarion applic;l(ion (Goodchild. (Goodchi ld, 1992). In short. short, GIScience both borh encourages lIsers rec hn ology in pro(0 IInderstand understand the benefits of GIS tec users 10 viding a powerful set of ana lysis (ools tools and encourages users ro to view Ihe rhe techno rechno logy as pan of a broader discipline rh:H promo prommcs geographical inking a nd problem solvi ng tes geogmp hiC'JI th thinking that society. The development deveiopmem of strategies strJtegies as being useful to sociery. GIScience is an outgrowth of the faCt FaCt that rhac GIS tec technology hnology is avai lable 10 roday than ever before. ro more users today before, and [hat that
3
sparial categorization spatial categorizarion and analysis analys is is applicab applicable le (Q to many societal issues and problems. pe rceived or used. it the Regardless of how a GIS is perceived il is (he inlegraliion on of Ihe rools and services ,hat imegrar rhe various variolls (ools rhat leads (0 to a successful GIS. Although other ot her sofrware software programs perliS-like tasks rasks (e.g., darabase management, manilgemCnt, graphics, graphics. form GIS-like or compurer computer ass assiSted isred drafting [CAD] [CAD) software), sofrwa re). a GIS is unique in its irs abiLiry ability (0 to allow lIsers users to ro create. crC'dtC, maintain. maintain, and :lnd ana lyze lyu geographic or o r spa spatial rial dara. data. The term sp spatial atial thar a database not nor only describes landscape d ata implies that candidon, composirion. composition, SUUClUre stru cture of features fe-dlures (e.g., (e.g .• condition. forests), bur forests). but also includes a geographic reference (0 co where manipulate features Features can be found. A GIS allows you to manipulare :tnd and display spatia sp:niall d:lIa data so fllat that quesrions questions regarding a resource a nd its conditions am can be answered. A GIS. when capable of analyzing ana lyzing a la large rge volume of properly, is cap.ble used properly. spatial spa rial data quickly and providing graphical graphical and tabular results. A GIS GIS stores sto res spa spatial rial data in a digital database file; file; file may be ref
me
18
4
Part 1 Introduclion to Geographic Information Systems, Spatial Databases, and Map Design
In many ways. college and university srudems are examp les of a living, breat hing GIS. Each day YOli ven{Ufe from your home inro rhe world. and make deci-
sions about where you are going. how you will get rhere, and w hat YOLI will do when you arrive. For instance, as a rypical srudenr. you probably have a route that you usually rake [0 campus. Chances 3rc that you have designed this route over rime and based on your experiences. so thar you can arrive as quickly and easily as possible. Perhaps you have included a stop ar your favorite coffee shop in your roure. If you have a e.u, rhen you might e1eer to dr ive, and depending on the rime of day. you might alter your usual route (0 avoid traffic. Road consrrllcrion may force you to alter your route for a few days or weeks. You will make orher adjustments (Q avoid unfo reseen delays. Once you
links [0 a database management system and are often limired in their abiliry (Q srore and analyze descripdve information abom fearures, whereas GIS software programs generally have srrong links (Q a database managemenr system. CAD spadal modell ing ca pab ili ties are also limited, whereas G IS conrains a wide variery of spatial modelling capabi liries (rhese will be examined in larer chaprers of this book). The field of compurer cartography emphasizes map production, and while rhe databases used may be similar to those lIsed in GIS, computer cartography generally purs less emphasis on rhe non-graphic arrribures of spadal landscape fearures than does GIS. Database managemelH software programs have rhe abiliry to store and manage locarion and attribure data of landscape fearures, bur rhey genera ll y lack the powe r ro display the locations and characte risdcs of feamres. Visua l capabi liries are fundamelHal qualities within most GIS, CAD, and canography soff\vare programs. Statistical programs are lIsll
ar rive on campus, you will have to find a parking space, and then walk another roure to get to your hrsr class. Of course, you might have decided rhat the rroubles wirh parking make riding a bike (Q campus more attractive, bur then you will still need to design a rome for the bike trip. The choices you make just to get to school in the morning require you to analyze muhiple layers of spatial informarion abom you r presenr locatio n, you r desti nation, and rhe interven ing influential factors. In shon, as you solve your daily rransponation challenge, you are acring as a GI S. This rype of example, in which location is a key component in decision making, can be applied (Q many activities that people engage in, ranging from how best (Q cross the street, to navigating a downhill skiing or snowboarding course, ro arranging trips to other counrries.
integrate G IS-like funcrional ity in some of their modu les. Finall y, rem ore sensing-related software programs generally focus on the manipulation and management of rasrer GIS dara derived from satellites, scanners, or Olher photo. g raph ic devices; rhey have a limired capabiliry ro handle vector GIS databases, which tend to be more commonly used within natural resource management organizations.
A Brief History of GIS As previously mentioned, GIS is uniq ue from other software programs in its inregrat ive ability that enables you to process, catalog, map, and analyze spalial data. Spatial data have been collected and maintained for millennia, with records of pro perry boundary surveys for raxarion purposes in Egypr daring back ro abour 1400 Be. Ir is only wid,in the pasr 40 years, however, th:u sociery has learned how to digitally capture. mainrain, and analyze spatial dara. Although the term 'geographic information system' was firsr used in the 1960s, overlay analysis has been demonsrrared through manual techniques for over 200 years. Overlay analysis is the process of analyz,ing mu ltiple layers of information simulta neously to address management issues. The layers rep resent different rypes of information bur are reiared to each orher in that the informadon is drawn from a common landscape area (Figure 1.1). GIS allows you to drape, or overlay, rhe layers on top of one anorher and to combine all pans into a new, imegrared layer thar contains all or some of 19
Chapter 1 Geographic Information Systems
"
.
Stand Types Figure 1.1
Hydrology
Roads
5
....-........ .
Topography
",
Composite Layers
G IS lhC'lne overlay.
rhe pans of the original layers. depending on rhe rype of ove rlay selected by the user. The new inregrared layer allows liS to examine the sparial relationsh ips of rhe in formation contained in rhe o ri ginal layers. Although digitally-based GIS has been available for a relatively shorr period in his[Qry. (here is a significanr hisrory of ana lysts using the overlay approach th rough manual techniques. During rhe American Revolution, rhe French carrograph er Louis-Alexandre Berrhier overlaid multiple maps to analyze (roop movemenrs (Wolf & Ghi lani, 2002). In 1854, Dr John Snow conducted a spa rial analysis by compari ng rhe locations of cholera deaths ro well locations in London. His analysis revealed rhar well warer drawn from specific wells was a means of spreading cholera infecrions. The first wr inen description of how ro precisely combine multiple maps rhrough a manual overla y process appea red in a 1954 rex( titled Town and Country Planning Textbook by Jacqueline Tyrwhitt (Ste initz et aI. , 1976). In 1964, Ian McHarg described how ro lise a series of rransparent overlays m derermine the suirabiliry of areas for developmenr in New York 's Staren Island. By using a transparent overlay fo r each layer of interest (soi ls, forests, parks, erc.) and blackin g-our rhe areas on each overlay rhar presented development impedimems. the layers could be overlaid and rhe final suitable areas defined. McHarg ( 1969) later published examples of hi s ove rl ay rechniques in his sem inal book, D~sign with Natllr~, which continues ro be sold throughour the world. in rile early stages of the development of GIS rechnology, rwo fac rs were evidenr: {here was little geograph ic or spada I dam to work with, and rhe rechnology ro srore and manipulate rh e dara was rudimentary (by mday's standards), Some may argue t har GIS technology has nor evolved very much in the passing years simply because
many of the compurarional processes used roday we re inirially developed in rhe 1980s, however, advancements in computer technology and rhe increasing availabil iry of GIS darabases indicare orherwise. In addirion, a growing number of people throughollr sociery have heard of G IS {even though rhey may often confuse irs purpose w irh that of a similar acronym, GPS [global positioning systems]} . We provide a brief history below of rhe developmenr of'digitall y-based GIS, and note that many of irs advancements were made by innovarors and scienrisrs throughout North America, During rhe 1960s, orga nizarions in the Un ited States (i ncludin g the US Geological Survey and rhe US Deparrment of Agriculture's Natural Resource Conservarion Service) began m create GIS darabases of topography and land cover ( Lon gley et aI. , 200 I). Srudents and resea rche rs began ro write compute r programs and design hardware devices (such as the precursor ro roday's digitizing tab le) rhar would allow you (0 rrace the outlines of landscape fearures on hard co py thematic maps and rransfer them inro a digital formaL These early programs were designed ro handle specific [asks and were ofren limited in scope. As programmers began ro bring these algorirhms rogerher ro creare more versatile, powerfu l software programs. rhe era of com purer mapping applications began. Early examples of mapp in g programs include IMGRID, CAM, and SYMAP (Clarke, 2001). In conjuncrion with rhe developmenr of sofrware programs. other organizarions began ro assemble GIS darabases for mapping and analyz in g fearures of inreresr to public agencies, The firsr example was rh e GIS darabase created by the US Centra l Intelligence Agency (CIA) and was called rhe ' World Dara Bank', Spa rial layers in the GIS database included coas tlines. major ri ve rs. and polirical borders from arollnd the world, The US Census Bureau designed a merhodology for linking census info r20
6
Part 1 Introduction to Geographic Information Systems, Systems, Spatial Databases, Databases, and Map Design
mauon maUDn to loca locatio tions ns in preparation prepararion for the rhe 1970 US ce nsus. The 1970 US Censlis CenSllS was the rhe first census ce nsus tbar that was mai led, and rhe oonly mailed, nl y piece of information informa tio n (har that was returned rerurned [0 to refe reference rence rhe location lacarion of rhe respo respondem ndem was rhe address. add ress. The Ce Census nsus Bureau. Bureau, however. was faced with rhe challenge of matching marching [he rhe response respo nse add addresses resses (Q to a map so that char rhe spa spatia riall di disrriburi srributi ons of responses could co uld be mapped and aanalyzed. nalyzed. The Census Bureau developed a system sYSlem known as DIME (Dua (Duall lnd Ind epe nd ndent en r Map Encoding) in response respo nse to (Q this rhis challenge, challenge. which nor nm only creared reco rds of all srreets, created digiral digi ta l records streets, bur bm also associared associated addresses [a ro srreer st reet locations. locarions. The DIM E system syste m allowed rhe Censlis Censlls Bureau [Q (Q understand understa nd which streets st reets were connected to which orher necred other srreets, streets, and wha wharl lanclsc;-t landscape pe features fealUres were we re adjacent to each sstreer. tree!. This T his method of associating n:presemarions of landsca landscape pe features (Q to other orher the digital represemarions la ndscape nd sca pe featu res was a c ri ritical rical advan advance ce because it if ca [ionn of spadaJ enabled the idenrifi idenrificado spacial relationships within a digital enviro envi ronment. nment. The descriptio descriptionn or characterization of the th e spatial relattio io nships between berween landscape features feat ures in a G GIS IS database is refe rred [Q to as topology. Topology is an imporra referred importam nr conCO I1cept with respect to GIS ap applications plicatio ns and a nd will be discussed in more detail in chaprer chapter 2. T Topology o pology manages objects and requires requ ires objecrs objects to be organized orga ni zed and analyzed according to ro their location and with respect respecr widl with proximiry proximity to ro othe otherr objects. objecrs. The topological characteristics of dara data structu res -allow allow a determinacion. determinatio n, for example, Structures example. of how water travels warer rravels through a stream necwo network. rk, the con necti nectiviry vity other roads. of roads in a forest to ro orher road s, or dl thee idemification id cnrificar ion of forest stands sta nds [hal that share a border with other othe r fores forestt stands. stand s. rhe basis of many resource These relationships form the analyses thaI that take locationa locationall posidon position into imo account in ng techniques. problem solvi solving rcchniques. The DIME syste m was the rhe predecessor to T IGER (To pologicall y Integrated Geog Geographic ra phic Encod ing in g and a nd Referen cin g System) Referencing ysrem) files. which were inrroduced inr roduced by the US Census Bureau in 1988, aand nd are sdJl still used today [0 to distribute spacially-referenced d istribute spatiallyre ferenced ce nsus and boundary data. ity of TIGER flfi les instrumental in proThe avai labil lability ies was insrrumemal mOting U . The US Geological Geologica l Surv
USGS has made features from finer-resolution 1:24,000 scale maps ava ilable for small portions ponions or of rhe co COUntry um ry.. The USGS has si since nce become a worldwide leader in mapping land cover resources reso urces and making maps avai ava ilable lable in borh hardcopy and digital format. both To manage and a nd ana analyze lyze spatial data dara for their jurisdicjurisdi lions. Canad £ions. Canadian ian and US orga organizations nizations began co to develop sofn. ... are rhe 1960s. One of the th e most mosr ambisofrwa re programs in the tious rio us and noteworthy of these rhese systems was the rhe Canada Geographic Geograph ic Informat io n System (eGIs), (eGIS), which, in 1964, \vas created creared under (he was the guidance of Roger Tomlinson. Tom linson. A chance mee ting. on a p!;H1e, plane. between berween Tom Tomlinson li nson and chan ce meering, Canada 's Minister Ministe r of Agriculture Agriculrure resulted in Tomlinson rhe creation of a national narional etTon effort ro inventory overseei ng the Canada's land resources, resources. and developing ~Ia sofrwa softwa re program to quanr ifY existing aanndd potential porelllia l land lIses. uses. The ro 'luantiry CG GIS IS is recognized as being the first na t io ional-level nal -level G GIS. IS. and thus thu s Tomlinson continues to ra receive recognition recogn irion as a GIS pioneer for his effo eilo rts. rts, Other early landmark efforts efforrs in the evolution evo lution of GIS in include clude the developmem of the Land Use and Narural Natural Resource Resou rce In Invenrory ventory System (LUN R) in New York (LUNR) Yo rk in 1967, aand nd the rhe development of the MinneSOta Minnesota Land La nd Management System (MLM IS) in 1969. The success of these rhese early systems sysrems and need for furrher further refinemenrs ni zed by a group grou p of faculty
t Geographic InformaMn Information Systems Chapter 1
tlnd a nd user use r illlerfacc; ilHerface; they rhey are now offered ofTered as differenr licenses within ArcGI lice nses wirhin ArcGIS.. witnessed [he The 1980s also wimessed the proliferation proli femion of [he rhe microcompmer. mday's version of rhe com purer, coday's compucer (PC). the personal computer response, sofrware manufacmrers [0 produce GI. GIS In response. manufacrure rs began to softwa re programs thar rhar could operare rhe microcomsofrwa operate on the microcom· Ii" of GI GIS sofrware manuF..cAppe nd ix C for a list manu",cpurer (see Appendix [urers). Co rporation was for formed, rurers) . In 1986, 1986. Maplnfo Corporarion med. and subsequently rhe world's firsr major deskrop desktop vecsubsequemly developed [he mr tor GI GIS software sof[Ware program for the rhe Pc. PC. Soon afte afre rwards, rwards. raster GIS soff\vare sof£\vare programs, IORISI, programs. such as IDRJ I. began co to rasrer appea r. Some softwa re programs, such as the appear. orne software me raster GIS program GRA GRASS, utilize a software architectu archirecmre S. utilize: re thai rhal was works[3rion computer platforms. developed for workstation Orher significant deveiopmelHs developments in GIS included rhe Other eme rge nce of Gl GIS-relared cmergence -related conferences and publications. The first AuroCano Conference was held in 1974 and esrablish ,he helped [0 to establish rhe GIS research agenda. One ne of [he lhe first compilarions of avajlable available mapping programs was firsr compilations Internationa l Geog raphical Union in rhe International published by the 1974. Bmic Basic Rmdillgs Readings ill in Geographic G~ograpbic Information Infomuuion SystemsSysum.s-collection of papers thar G IS technology-\va_ rechnology-was a collecrion rhat discussed GI published in 1984. 1984, and inl 986 [he firsr textbook wrinen in 1986 rhe first spe ifically for GIS. specifically GIS, Principln Principles of Geographic Infonnanoll C
7
expecr suppOrt and lrdining training rdated rel ared to ro specific (;15 G IS software programs, programs. and expect expecr [hat (ha[ sofnvare softwa re will be mosdy me of irs release [Q general publ ic. perrecred by [he perfected rhe ririme ro [he rhe general public. Funher, Further, as GIS darabases are a re shared sha red amongsr organiza.1 dalabases standardize da[a dara fo rmars rm ars is evidem, evident, lions. rhe need ro standardiu tions, because dara Iransformarions transformations ca cann require an exrensive commirmem commirmenr of resou rces and may lead to ro flawed Aawed results if no nO{r done correctly. correcdy. Sociery fonunare roday, today. on one hand. ro have a variSociety iis forrunate va rieryof .IS software sofrware programs from which to choose. On ery of GIS the eval uari ng which of these programs beSt best Ihe orher mher hand, ha nd. evaluating rhe needs of a naru narural reso urce managemenr management organisu its the ral resource ric. This faCt mct posed a significant significam chal· chalzarion zation is problema problematic. lenge even in the crea rio n of (his cre-cllion this book. Since each oorganrgan~ (nar ural resOllrce resource managemem izatio managemenl., as well as izarionl1 (narural academic) may lise software program, program. we use a different GIS sof£\vare rhis book boo k as a general reference refe rence for decided (0 design {his ry pical types of applicadescribing. lhe rypical or GIS :1pplic:ldescribing, in genera generall., [he tions Faced by field-level professio professionals nals associa,ed assoc ia red wirh with natrions ",ced organizarions. T Therefore, herefore, speural resource management organizations. cific cifi C examples of how to ro address ad dress each eac h applica application rion described in rhis book. book, [hose rhose lha, rhar are related relared (0 to specific sof£\va re programs, ava ilable through GIS software programs. will be made 3vailabl< orher means (e.g (e.g., other .• a book-related book-relared websire at al W\,VW. www. fores[ry.oregonsra[e.edu/gisbook). roresrry.orego nsrare.edu/gisbook).
Why Use GIS in Natural Resource Management Organizations? to see GI GIS usC'd used [Q to assist assisr managers make It is commonplace [0 roday's nalural natural rcsource resource managemC'J1[ managemenr environenviron~ decisions in loday's co be submined submiaed LO ro ment. For exam ple, maps are requ ired ro example. required srate agencies in [he US in support suppo rr of forest mansmre the wesrern western U rrh America. America, pesticide agemem plans. In most mosr areas ofNo ofNonh pcsricide age ment plans. proposed acriviry. dClail rhe propos~d OIcciviry. as well plans requi re a map to derail the nearby nea rby homes and warer water resources. reso urces. While maps may as Lhe be hand-drawn in a handful of natu narural o rga nsrill he ral resource resollfce orga still iza[ions. pro C'Sses ro be amo3U[Qizariol1s, GIS allows map producrion processes repeated. reducing a lengrhy exe rcise mated and repeated, l~nglllY drafting exercise [0 on ly a few short sho rr minUles minmes and li kely producing far to only fa r more reliable resuils. In addition. all ows some processes (to0 resuirs. In addir ion, GIS allows be accomplished that rhat wou would ld normally [ax taX a person's analyTica l abiliries. lytica abilir ies. For example. example, a~ n:ttur..tl narural resource management southern US considering a fertilfe rcilmenr organization in the sourhern ization project, pro ject, yet operaring with a limited budget. izadon operd(ing wirh budget, may ro loc3re areas (sra (sta nds) thal rhar would benlocate those dlOSC forested arca5 ben need to efit (he growth From a fen efir mosr in rerms of rhe growrh of rhe forest foresr from fert i1ilappli carion in order to ro make efficiem ization application efficient use of their assume rh ar rhe sta s[a nds musr budget. bud get. Irf you were to :1 $Sume lh:H must be
22
8
Part 1 Introduction to Geographic Information Systems. Systems, Spatial Databases. Databases, and Map Design
cenainn soil dominated by pine [ree rree species. species, and located on cerrai the enormOuS enormous (ask [ask fitced rypes, you can imagine [he faced by a large 500,000 acres) if paper maps (soils and landowner (> 500.000 stands) sta nds) were rhe only (eSOlIrCe resource available for analysis. A (his would have required severa) several days to process such as [his complete with paper maps. maps, bur but mighr might require only a few fe\v minures when performed within GIS. minmcs GIS in natura ma nageThe application of GIS naturall resource management ndard pracrice during men[ organizadons organizations has become sta standard practice duri ng the rs (Wing & Beninger. Berringer, 2003) rarr of ,he the rhe lasr las, 10 yea years 2003).. ran reason for widesp read lise is because of rhe efficiencies ror this widespread hinred at bur also lt of comillucd ca mi ll ucd technologtech no loghinted m above, bUt a lso a resu result' hardware rdware and software. ical advances in computer ha Compurer conti nue {Q [0 decline whi le processing Com pUler prices conri power and srorage efficiency ( 0 grow. A smrage efficie ncy have conLinued continued {Q software programs progrAms have also emerged, wide variety of GIS sofrvvare emerged. rhe [rend sof[\va re program design has been to and the (rend in GIS softwa make programs more user-friendly while. perhaps. sacrific ing efficiency ficing rarions. efficie ncy of ope operacions. the primary reasons for the rhe growth use One of lhe growrh of GIS u", rhat the rhe in natural resource management organizations is thaI collection analys is of landscape measuremems measuremenrs is funco llection and analysis oflandscape damenral for mosr ral resource analysis and managedamemal most naru namraJ ment acciviries. acriviries. GIS allows )'ou yo u to work with measuremCni measurement menl informarion lO information to facilitate mapping and modelling lando r {Q suppor[ Ihe rhe eval uat ion of managescape featu res or to supporr menr policies. For example. m ighr be interested inrerested in mem example, you might vegetation resou rces with in a determining the extenr exrcnr of vegerarion resources within w ildlife habitar within a naruwatershed, rhe amounr watershed . the amOllnt of wildlife habilar wirhin nalural area, potenriai impacts of cha nges in ripar area , or the potencial ripariian an managemenr policies. pol ic ies. GIS facil facilitates managemelll it ates an efficient dTicient explor.nion of rile ration rhe informarion information relaled relared to narural natural resources. reso urces. Alrhough many narural resou rce managemenr organAlthough izations employ a GIS expert (guru, cenrralizadons (gllru . manager) ar a centralized GIS GI S office, these rhese people are arc often oFten ove rl oaded wirh to offer sustained assistance {Q work and unable 10 oHer stls rained 3ssiS[ance ro field perrhe expc expe rts, n s, particularly parricularly if iF they have sonnel. IInn some cases, the narural resource a computer background as opposed ro a~I natural background,, may be unaware of (he rhe common types rypes of background GIS plicatio ns in na natural G IS ap plications tu ral reso urce management. When additionally thar many man y I1:ltural natural resource you 3dditiona lly consider that management organizations desire new employees to have a G IS background. background, lbe rhe advantages advanrages for ,hose those involved in nalural familiar ith the naLUral resource managemem m ~m agemenl to ro be fami liar w idl pQ[ential uses of GI G IS are clear. This fami liariry shou ld potential F..miliariry [he ability to communicare communicate using us ing basic GISinclude the GI ~ reiared terminology and a nd the CIS rei31ed [he abi liry lity to ro perform basic GI processes. such as viewing data dara and making maps of feaprocesses, teamres of interesr. interest. cures
Recem graduates of many university-level natural Recenr leasr one course resource management programs complete alle;)sr com plete at involving GIS. GI . Geospar GeospariaJ ial skills are currently cu rrend)' in-demand in narural resource nagement, and aare part of an managemcnr, re seen sC'en as pan resou rce ma thar ar wi willll experience conrin~ l ed growth for experience: co nrinued eme rgi ng ind ustry th ,he near future the futu re (Wing & Sessions, essions, 2007). One survey of nalnatural resource managemenr Lassoie, management employers (Brown & L.assoie. another indicates rhar indicales cllat 1998) supportS supporrs ,hese these assenions, assertio ns, while anomer ne-drly expeCted new employees nea rly half of industrial employers expected ro have obtained GIS undergraduate to GlS experiences during their undergraduare educalion (Sample et education What skills should students e, al., al., 1999). Wha, to graduating? Merry et ddevelop evelop a proficiency prior 10 grad uating? Merr), el al. (2007) surveyed su rveyed reeent recent graduares graduates who were employed in natna,managemenr-related positions posirions and found that rhar ural resource man3gemenr-relared ESRJ's ArcMap and ArcView sof[\va soFrwa re products were the mOSt software packages. Olhers Othe rs rypes of mOSI commonly used GIS sof,ware software producls products were also being used (e.g., Map lnfo , sof"V'dre (e.g. , Maplnfo. Earth, Delorme, Landmark Sysrems' Systems' SoloFidd Google Eanh. SoloFieid CE, CEo (U1d mercia.l software packages from Davey Resoufces). and com commercial Resources). and learning how to use at least ll make leasr one o ne program p rogram wi will adapting Jdapring to the use of others relatively reiariveiy easy. Basic Bas ic GIS operarions adons (heads-up arrributes, (heads~up digirizing, digitizing, manual editi ng of anribures. manuaJ editing of spatial pos irions, and querying of tabular manual positions. artribures) were lhe most frequently frequen tl y lIsed used GIS processes. anributes) the moSl More complex processes, such as combining and erasing fea[tires. spatia l que ries, were also mode rarely used by (Ures. a nd spatial queries, moderately recem rypes of products recem graduates recelll graduates. Of the rypcs created locational maps, managemenr decicreat~ with GIS. ,IS. basic 1000donai maps. management sion-related sion-reialed maps (i.e., (i.e .. planting maps), maps). and GIS databases (e.g.,.. prescribed fire locations. easemems. invalocado ns. conservation conservarion easemenlS. {e.g co mmon. sive species distriburions, disrriburions. soil maps) maps} were me rhe mosr common.
'0
GIS Technology Operating a GIS G I requires working with a wide range of technology acquiring. the rechnology and possessing. or acquiring, rhe skills necessa ry CO to understand unde rstand and manipulare manipulate the rechno logy. sary rh e techno chapter. a desc ription of rhe During the rhe next part of this chapter, description mponents of G GIS presenred. various technological co omponenls IS is presemed. not be important [Q be conAlthough it may nor imporr:mr for GIS users (0 expens in all GIS-related !echnologies. technologies, fam iliariry sidered experts F..milia ri ry with the various components componenrs may help understand hdp you undersrand how rhe the components componenrs are integrated.
Data collection processes and input devices Despite (he sofrwa re , Despire the rapid advances of GIS hardware and sofrware. one of the rhe primary challenges ch311enges for fOf organizations using GIS I 23
Chapter 1 Geographic Information Systems
relines ro the developmenr and maimenance ot G IS da[abases. Collecting spatial data, preparing the data for GIS use, and documenting rhese processes conri nu e [0 co mprise the majoriry ofbudgers allocated fo r GIS processes. Sparial data qualiry is cemral [0 successtul GIS implementarion and analys is. Dara are often described in re rms ot thei r precision and accuracy, (wo terms that are otren confused. Precisio n relates ro rhe degree ot specificity {Q which a measuremenr is desc ribed. A measurement that is described wit h mulr iple decimal places, such as an a rea measu rement ot 2.6789 hec ta res, is co nsidered a ve ry precise measurement. It rhis measuremenr were derived trom a prope rty bou ndary survey whe re distances were gathered by cou ncin g paces. and a ngles were meas ured using a handheld compass, you might quesrio n the accuracy ot [he measuremem; however, it is ina rguably presenred in a highly precise manner. Precision can also be described in rerms ot rhe relative consistency among a set of measu rements. For insrance, if the measurements relared ro a property bou ndary were measured multiple rimes wilh a sophisticated surveyi ng insrrumenr and rhe resu lting va riario n amo ng measu rem ents was small , YOli could rhen describe rhe measuremems as being relarively precise. Acc u.racy refers ro rhe abiliry of a meas urement to describe a landscape feature's [ru e lacarion. size, or condi[ion. Accuracy is typically described in rerms of a range o r varia nce [hat derails a duesho ld within which we would expect to find the likely value. The assessmem of accuracy anemprs to answe r rhe following question: H ow close are the measurements to their t rue val ue? Examples of accuracy levels include distance meas uremems of ± 0.5 m or angle measuremenrs of ± 1 seco nd. Yo u can have measuremenrs rhat a re borh highl y precise a nd acc urare (Figu re 1.2, rarr A), highly precise without being very accu rare (Figure 1.2, Pan B), nor very precise. bm accurare (Figure 1.2. Pan C), or neirher precise nor accurate (Figu re 1.2, rart D). Accuracy and precision may also be stared in relative terms. Suppose rhe lengrh of a sr ream is measured (\vice wirh a I ~O-foo t meral rape, resulr ing in measurements of 232.7 and 232.5 feet. The average length of the srream is 232.6 feet. If the meta l tape was previously broken, say at the I O-foot mark. a nd spliced back rogerher, reducing rhe dfecr ive lengrh of the rape (Q 99.9 feel , (he rel at ive aCCllracy and precision of (he measuremenrs can be calcu lated. Since rhe tape was used about 2.3 rimes when measuring Ihe stream, and the broken part of rhe rape was used each
A
B
c
D
9
Figure 1.2 Examples of accuracy and precision. Pan A shows accur:HC and precise locations of €lara aro und rhe circle centcr; Pan B shows predse bUi nor vcry accurare dar. ; Pan C shows acc urau~ bur not very precise data; and Part D sho...., neither precise nor :accurate d:ata around the circle center.
rime, rhe accuracy of rh e measuremenrs is aboUt 2.3 X 0 .1 foot = 0.23 foot off of the value you might have expecred wirh an unbroken rape. You could exp ress the relative precision of rhe measurements as (232.7 - 232.5) 1 232.6 = I: 1, 163 and the relative accuracy of [he tape as 0.1 I 100.0 = I: 1,000. One advantage of using rel ative accuracy is rhar it prov ides an assessmenr of rhe expecred porrion of erro r given some measu red amount. This allows for lhe reladve comparison of rhese errors berween differenr measured irems and locations. Relative accuracy can also provide a means of stating o r assessing re<Juired o r mi nimal mappin g accuracies. The U Federal Geodetic Contro l Subcommittee (FG e S) (J 984) and N a tural Resources Canada (J 978) fo llow this practice whereby rhe relar ive precision ca n be carego ri zed as acceptable or unacceprable. given a desired measuremenr accuracy. It is important rhar G IS use rs are aware of [he disrin ction berween precision and accu racy. parricularly when co nside ring rhe value of using a C IS database in an analysis fh ar leads lO a management decis ion . These terms, despire [heir com mon usage. imply information about diffe renr qu alifies of a measuremen( o r acriviry. Alrhough acc ura cy and p recisio n impl y differenr cha rac rerisrics, 24
10
Part 1 Introduction 10 Geographic Information Systems, Spatial Databases, and Map Design
many lise these te rms inrerchangeably and. as a resu lt, incorrecdy. There are many ways (0 create and co ll ect data, and all methods require va rying degrees of skill and organizadonal commit menr, The following sec rions describe some of rhe most commo n medlOds for creatin g GIS databases.
ro human error and variarion. Today, digitizing is sti ll a necessary function for many namral resource managemenr organizations bur reli ance on this technique has dramatically decreased as orher dara collection merhods have emerged or become refin ed.
Scanning Manual map digitizing The abi lity (0 manually encode vecto r maps using a digitizing tab le (Figure 1.3) and associated software has been ava ilable since the late I 960s. Paper o r mylar maps are raped down to a digitizing rabie, in which is embedded a fine mesh of copper wire. Known reference points on rhe maps are idclHified using rhe digitizing table 's ' puck' (s imilar ro a compurer mouse), which sends a signal ro rhe wire mesh within rhe rable. Once rhe referen ce poincs have been idemiried, all orher landscape fearures can be encoded in a Cartesian coord inare sysrem and related ro rhe reference poinrs. For poim fe-drures. rhis requires lining up the cross-hairs of the puck wirh the poim locarions and identifying rhe points. For line and polygon fearures. ir involves rracing rhe boundaries of rhe lines or polygon boundaries. noring each c hange in a line's direcrion. Features can be recorded with eithe r 'srream mode' o r ' poinr mode' referencing processes. In stream mode. the spa rial locatio n of the digitizing puck is recorded ar either regular time intervals (e.g., every seco nd) or regular dis-
Scann ing involves rhe exam ination of maps by a computer process dlat seeks to ident ify (a nd convert to digital form) changes in map colo r or rone. which idemi fy landscape fearures. Flat-bed scanne rs allow a picture or map, such as an aerial pho(Qgraph o r a to pographic map. to be co nvened ro a digita l form. The resuhing images a re described by rhe rasrer data srrucCllre, and include pixels
or grid cells that may be encoded (o r attribured) dilterenriy. depending o n how the scanne r imerprets the color
or rone of each feature. Scanners (Figu re 1.4) generally move systematically across a picture or map. and record rhe reAecrance values of the (Ones or colo rs for each grid cell. Scanned images rend ro look very much like rhe pictures or maps that were scanned, yer there is lIsually some difference in qualifY due [0 the size of the g rid cells assumed in rhe sca nning process, rhe qualiry of the pic(Ure or map. or the qualiry of rhe scan ner.
ranees inrervals (e.g., every 0.25 inch). In poinr mode, rhe spalia llocario n of rhe digirizing puck. and hence rhe locarion oflandsca pe fe'drures. is recorded every time a button on rhe puck is pushed. Manual digitizing of maps can be a redious process and. like many orher tasks thar are done by hand. subjecr
Figun 1.3 Digitizing table:.
Figure 1.4 Small format
---
~-~
.
JCln n~r.
25
Chapter t Geographic Information Systems A second method of scan ni ng involves the use of d igiral cameras. An array of phoroderectors located within digital cameras allows you to caprure and store an image. The images are saved with a raster data srructure and can be tra nsfe rred ro a computer system and rhen used in a manner si mil ar to the sca nned images mentioned above. DigitaJ cameras can be synchron ized with CPS rece ive rs so that a coordinare value and elevation a re potenrially associared with each image.
Remote sensing Remote sensing involves rhe use of a sensor that is nOt in physical contact with its subject of interest (Avery & Berlin , 1992). Ir can include a wide variery of rechniques, and in faCt, capturing images with a digitaJ camera rheorerically uses remme sensing technology, since the camera is nor necessarily in contact with the image being co ll ecred (the landscape). However, when discussing remore sensing technology in natural resource ma nagemem, the use of satellites or cameras mounted on airplanes is frequently referenced. Remore sensing devices capture electromagnetic energy, generated by the sun or perhaps by some other device, such as a radar eminer, that is reflected off of landscape features. Most sa tellite senso rs a re designed to record the reflectance of light or heat from objects on the Earth 's surface. These e1ectromagneric reflecrances are reco rded by rhe sensors in terms of their wavelength of energy, as described by rhe e1ectromagneric specrrum. The electromagnetic wavelengths are then convened to a digi(al formar and transmirred back to a computer for processin g and inte rpolation. Satelli tes such as rhe Landsat
Thematic Mapper™ series can capture wide swaths of rhe Earth's su rface ( 185 km, or 115 miles), and rhus have rhe potencial to reco rd vast amounts of informarion over a shorr time period. The launching and operation of sa tellires for dara collection has increasingly been cond ucted by private o rganizations, and as a result many different forms ofhighly accurate and precise data are becoming available, such as the I In resolurion IKONOS satell ite data (Land Info Worldwide Mappi ng, LLC, 2006). Alrhough rhese advances in remote sensing technology have in creased the variety of products available to cons um ers, the cOSt of acq uiring and processing sa tellite-collected data is sri II prohibirive for many organizarions. D igiral cameras ca n be mounred on airplanes (Figu re 1.5) , and can genera ll y p rovide higher reso lurion images than that provided by satellites, yet rhis distincrio n is getring less clear with each passing year. It is also possible to mount digiral cameras on smaller, remore cont rolled a ircrafr, and to synch ro nize color or infrared photograp hy with CPS measuremems. This coupli ng of technology may become more widespread in the futu re. A relarively new rechnology called LiDAR (light derection and ranging) has emerged that allows for rhe collection of ropographic or elevation data . LiDAR sysrems are rypically mounred on an aircralT (although ground-based plat fo rms are also used), and include a laser, an inertial navigation sysrem, a CPS receiver, and an on-board compurer fo r dara processing. LiDAR measuremenr rechnology allows scienriscs [0 remotely sense and create digital models oflandscape features such as vegera ti on, ropography, and strucru res. LiDAR techno logy has been applied
On-board computer
Camera field of view - - -..... Figure 1.5
Digi tal camera mounled on airpla ne.
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26
12
Part 1 Introductioo to Geographic Information Systems, Spatial Databases, and Map Design
[0 natural resources (Q meas ure foresl ca no py str ucrure, inventory, and bio mass (Reutebuch et aI., 2005). LiDAR airborne lase r sca nnin g invo lves directing discrete pulses of light onro a landscape in o rder ro rerurn rhe posirions and dimen sions orJandscape features. A LiDAR lighr pulse is emined from a u ansmi [(er as rhe aircraft moves and rravds unli l it reac hes a solid o bject (Figure 1.6). Depending on the type, density, and reAecci viry of 3n o bject, the li ght pulse is either refl ected back ro an airborne se nsor o r conrinues (0 deflect off of other objeclS unril it reaches a solid surface. such as [he ground. T ypica lly, up [Q fou r reflecred va lues ca n be retu rned from a si ngle pulse. T he combi nation of repear (e(Urns ca n be fused with orher multiple reru m pulses [Q create a rhreedim ens io nal visuali za cio n of la ndscape fearures. The round-trip lravel rime of individual lighr pulses is measured and sro red by an airborne se nsor rhar is coo rdin ared w irh an o n-board globa l posi t ioning system (C PS) . By comparing rhe rerum rime ro rhe speed of light, rhe d isra nce ro the gro und or the landsca pe fear ure ca n be calculaled. The couplin g of pu lse se nso r and CPS measurements res ults in the geo-referenci ng of retu rn pulses so thar coordinates (lo ngilllde and latitude) and height (elevat ion) are assoc iared with each returned pulse. In addition, rhe iner-
tial nav iga tion sys rem [racks [he irregul arities of rhe ai rcrdft's Aight path and att itude (yaw, pitch, and roll) and all informarion is collected and processed by the on-boa rd com puter. Up to 150,000 pulses per second ca n be generated with contemporary LiDAR systems (YII et aI., 2006). This rapid pulse rate leads to LiDAR databases of hundreds of gigabytes for even modest sized areas (e.g. 4,000 hal . Image p rocessin g sofr""are can convert rhe millions of rerurn pulses rhat are ryp ical of LiDAR da ta projects into twO and rhree-dimensional represenrarions of lan dscape characrerisrics including streams, roads, an d vegetation. In addition ro posirion a nd heigh t measuremelHs, rhe reflecrance inrensiry of each LiDAR pulse is measured and sro red with rhe geo-referenced information. Researchers have rece nrly recognized (har rhe sr ren gdl of reflecrance inrensiry values can pOlenrially provide descriptive info rmarion abom landscape features. Reflecra nce intensity is the rat io o f srre ngdl of ('he refl ected pulse ro thal of the emined pulse. The reAecra nce inrensiry informarion is a spectral signa[ure and ca n be used [0 derermine the na[ure of landscape objens. There are few pu blished studi es of usi ng LiDAR reflectance inrensiry values for na rural resou rce appl ications bur researchers ha ve used LiDAR ro investigare differences between con ife ro us and deciduous
laser ~ scanner
28
..
24 20
§: 16 ;: ~ 12
'"
8 4 0 0
50
100
Number of laser shots Figun 1.6 LiDAR system o n ai rcraft (cou rtes)' Dr Jason Drake. US Forest Service ).
27
Chapter 1 Geographic Information Systems
In ou r sociery, electromagnetic energy is generated by a va ri ety of sou rces, including su nl amps, fi res, microwaves, radio [Owers, rada r detectors. and lase rs. Devices or techniques for capturing this e nergy can ~ characterized as passive or act ive. Passive data cap[Ure techn iques. suc h as aer ial photography o r LandsarTM, record electromagnetic energy that is naturally emitted or reAected. The most obvious narural producer of e1ecrromagneric energy is the sun. The sun produces eiecrromagneric energy at multiple wavelengrhs, some of which a re visible to the human eye. Devices that employ rada r or lase r rec hn o logy (fansmir elecrromagnecic e nergy a nd record rhe
rrees (So ng et aI., 2002) and <0 dete rmin e rree healrh (McCombs et aI. , 2003). While these prior research findings have had modeSt success in app lyi ng LiDAR reAectance intensities, the more powerful emitters and sensors that are rypical of contemporary LiDAR equipmell[ may provide descriptive information such as tree species and healrh, land cove r rype, o r eype of structure fo r mapped positions. LiDAR has shown great porenrial in foresrry a nd narural resou rce applica rions, nOt only in generating high-reso lution digital elevation models (DEMs), but also in measuring stand strucrural co nd itions. Although the COSt of acquiring LiDAR data is still prohibitive for many o rganizations. large areas can lx Aown ar a COSt of abom $ 1 per acre; COS[5 are expected to decrease in th e furure. Photogramme t ry Phorogram metry is perhaps the primary method used for rhe creation of spacial data in forestry and narural resource management, alr hough LiDAR acquisition is gaining steadily in its app lication. Within the US, many of the products produced by the US Geological Survey (USGS), including elevation surfaces and other represemarions of natural resources, were derived from phorogrammerric techniques. Photogrammetry ca n be defined as the act of collecting measuremenrs from rhe image of an object o r resource. T his tech n ique dates back to the mid-nineteemh century. soon after the first phorograph was created (Wolf & Dewitt, 2000). Through va ri ous tec hniques, pholOg rammetry fac ilitates the interpretatio n and
13
amounr of time ir rakes for rhe energy (Q rerurn; rhese are defined as acrive techn iques. The enti re range of electromagnetic energy is known as rhe electromagneric spectru m. The range of electromagneric ene rgy humans can see is ca lled the visible porrion of the elecrromagneric specrr um , which co nta ins wavelengrhs between 0.4 and 0.7 mm. Other portions of the spectrum that are nOt visible to hum a ns include rhe cosmic, ultravioler , infrared, microwave, and radar wavelengths. Mosr digital imagery developed from remore sensi ng devices makes use of rhe visible and infrared portions (0.4- 0.9 mm) of the elecrromagnetic spect rum .
meas u re ment of features capt ured on photogra phs. Phorog rammetry requires a firm understanding of photography, stro ng quantit3rive skills, and ar rimes, creariviry; successful interpretacio n so metimes becomes an a rt. Phorogrammerry offers several advantages ove r ground-based dara collectio n techniques. Usi ng airc rafts, phorographs ca n be ta ken of a reas at heiglHs th at mi ght ordinarily be inaccessible by orhe r devices. Large la nd scape areas can be capru red. creati ng a permanent reco rd of a reso urce ar the time of data collection. Photographs can also be used for hisrorical resea rch because it is relatively easy [0 reexamine a ph otograph, as opposed to reviewing a field survey of a reso urce, which genera ll y is d ifficulr to reproduce. The accuracy. speed of acquisir ion, a nd cos r of photog ra mmer ri c products are consra nri y improv ing, thus phorogra mmerry remains a popular mer hod for collecting spatial dara an d for the creation of GIS darabases. Digital methods ofcapruring images, however, are steadil y rep lacing those rha t use phowgraphic
film. The mOS t crucial physical co mpo nenr of phorogra mmerry is the photographic system utilized. Single lens cameras are most common, a nd a rypical frame measures 23 X 23 cm (9 X 9 in.). The camera lens is held ar a fixed distance, o r focal length, from rhe frame. Knowing this distance is critical in facili tating furu re measu rements from photOgraphs (Figure 1.7) . The most common focal length is 152.4 mm (6 in.), bur orher lengths are also used (90, 210, and 305 mm ). Photogra phic images a re cap(ured when a shuner near (he lens is opened, momemarily 28
14
Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design Principal point
Film surface
graph
+
[Q
all ow yo u
[0
define the geometric ce nrer of the
phorograph. Ae rial phorographs are described by rhe angle in which f ocal length (I)
they were cap mred: ve rtica l o r ob lique. A venical aer ial phmograph is o ne where rhe position of the camera axis is a nearly perpendicu lar orie ntat ion [0 rhe grou nd su rface. An ob liq ue ae ri al pholOgraph is one where the positio n of rhe camera axis is located somewhere between a vertica l
Height (H-h)
and hor izontal or ientat io n ro rhe gro und . For measu rement purposes. most phOlogram merrisrs prefer verr ica l
phorographs (Figure 1.8) while obliqu e phorographs are
Ground Surface Figurr: 1.7
A ~ rial phot og~p h y geometry.
allowi ng lighr ro srrike rhe fi lm su rface . Fiducial ma rksusually in the form of four or eight markings located in the sides or corners of the photograph margins-are projecred onto the film during rhe ex pos ure of rhe phoro-
Figur~ J.8
A~riaJ photogra ph.
mos rl y useful fo r imerprerive purposes. Verrical aeria l phorographs usually capture images at regular imervals along a consisrenr heading. known as a flight line. This systema ti c data collecrio n ap proach is followed ro ensure tora l coverage of a resource. Flight lines are usually designed so rhat subsequent photographs will have an ove rlap of 60 per ce nL In addirio n. phorographs captured on adjacent Aighr lines should have an ove rl ap
or 30 per cem. An advamage or crearing ove rl ap ping phorographs is thar YOll can see landsca pe feat ures in Ste reo with a stereosco pe (Figure 1.9) when s illluh aneously viewin g phorographs ca prured of rhe same area yet a[ di f-
29
Chapter t Geographic Information Systems
15
phoro distances ca n be co nvened to ground dislances by
multiplyi ng the pho to distance by the scale. For exa mple. assu me that the distan ce between rwo points o n a phmo distan ce was 2.5 in ches, rh e di stance berween rhe same [\-Yo points o n a map was 4 .5 inches, and thar the map
scale was I :24.000. The scale of the photo is (2.5 /4 .5) X 24.000 = 13.333. o r expressed as a rati o I: 13.333. Ana lytical phmogrammerry involves the use of mathemat ics to precisely defi ne the locarions of landsca pe features on srereo pairs of phorograp hs. Srereop lotters are often used in analytical photogrammetry 10 register and
measure photOgraphs (Figu re 1.10). There are several di fferenr rypes of srereoploners; newer models inrerfilce w irh a co mputer ro increase the speed of d:Ha creat io n and correc ri o n. Once rh e pho rogra phic images are placed o n the ste reo plo ner, lights projecred from differem angles are
Figure: 1.9 Mirror $le:re:osc:opt.
directed throu gh the photOgrd phs. The lights are adjusted ferem angles. In additio n. phorograph ic mosaics can be more eas ily c reated when using overlapping phorogra phs.
so thar a srereomodel is formed from rhe overlapping
T he scale of an ae rial photograph is desc ribed by the
rhe srereo plotrer o peraro r brings the srereo model into focus, landscape featu res can be measured and mapped, and a porenrial GIS database is created. The accu racy of measurements obtained rhrough anaJytica l phorogrammerry is usually expressed as a rario of the camera heigh I involved in rhe imaging process. Accuracy levels of
rari o o f phmo dista nces ro grou nd distances. A sca le can
be calculated for any point on a pho tOgraph by usi ng the following formula:
s= f (H -h)
areas of me projected images o n the photOgraphs. Once
arou nd 1/12. 000 of the camera height are typica l. For came ra heiglHs of 12,000 fr , [his rranslares into an accu-
where 5 is Ihe sca le, l is the ca mera focal length. H is the heig ht of rhe ca mera above a co mro l sur face. such as mean sea level (Figure 1.7), and IJ is the poinr's e1evalion.
Variables f H. and h. all need to be stated in [he same
racy of abo ut
±
I ft.
A relarively new produc t th ar is developed from ae ri al
photographs is a digit al orthophotogtap h. While an orthophotograph is derived fro m aerial photographs. the
uni ts of meas uremen t. If the focal lengt h were 6", th e
he ight of the ca mera 2.000'. and the height of the point in question 450'. th e absol ute scale would be 0.5 / (2000 - 450) = 0.000323. Expressed as a rel at ive sca le (t he inverse of rhe abso lure scale). these meas uremenrs repre-
sent 1:3 100. If a map is ava ilable of the photographed area. scale can be derived withom using the focall engrh and ca mera height. bur instead by co mparing the photo distance with the map disrance berween twO points. T he fo llowin g formu la ca n then be used: photo disca nce
photo scale = ( - - - - - , - - - - ) X map scale. map dista nce The distances lIsed in this formula must be in the same units, and the phoro scale wi ll reAecr rhe average elevation between rhe tw O points. Once rhe phoro sca le is known ,
Figure: 1.10 S l e: re:op l otl~ r.
30
16
Part 1 Introductioo to Geographic Information Systems, Spatial Databases, and Map Design
relief displacemenr inherent in rhe phorographs is minimized. and measurements of landscape fearures can be taken direcdy from rhe orrhophomgraph withom the
need ro r displacement corrections. To create a digital orthophomgraph, scanned aerial phomgraphs an d digital e leva tion models (OEMs) are required . Orrhophorographs a nd DEMs a rc discussed in mo re derail in cha pter 2. Field data collection Field coll ectio n techniques for the c rearion of G IS databases have adva nced tremendously over rhe past 20 yea rs and a re now fully enmeshed in rhe digital age. Increasingl y, field dara collection processes in natural reso urce e nvironm enrs are using digital data collection techniques (Wing & Kellogg, 2004) . Field collection techniques were once limited [0 m anu a l [ools that required physical skill on the part of the operamr and, depending on rhe ins trument, technica l compete ncy equivalenr to that possessed by a p rofessiona l land surveyor (Kavanagh & Bird, 2000) . Field c rews would lise meral or synthetic rapes co measure distances between objects. and clinometers or level guns [0 determine gradients and elevation differences. Approximate angles co uld be derermined from compass readings, and more precise a ngl e measurements were calcula ted from rransirs or rheodolites. Measurements were recorded in field norebooks a nd processed in an office se ning. Post-process in g and adjustment of the data we re almost always necessary {Q ensure [har data collection and insrrumenr errors were acco tllHed for and balanced (hroughom rhe measurements. These practices a re sri ll com mon and approp riate tOday fo r many field crews who are invo lved in collectin g spada l data for forestry and natural resource purposes. Alth ough technica l competency with d igita l instrumentarion and an understanding of measuremenr error and co rrections a re necessa ry skills for field crews using this techno logy, spatia l data can be collected and processed wiLh an efficiency and precisio n thar far surpasses other manual field measu remem techniques. Electronic distance measu ring devices (EDMs) were first developed abour 50 yea rs ago and represented a major breakthrough in data collection (Wo lf & Ghilani, 2002). These devices measure the amounr of time ir rook a beam of electromagnetic energy to (ravel from an instrument, (0 a reflective su rface. and back. With (his information, a distance ca n be calculated. Currenr technology includes rhe abiliry IO nor only ca pture distance measurements. bur also the angles berween objects. In addirion, th e
measurementS a re s{Qred in a digiral dambase. In some cases, these measuremenrs can be highly accurate, providing positions that are within centimeters, or less, of their [rue locarions va lues. Total S[3rions a nd laser range finders (Figure 1.11) are examples of tools that make ir possible for field crews to sigh t and 's hoor' d istant o bjects. Typically, these insrrumenrs require thar a reflective surface be placed on the object of inrerest so that a beam can be projeC[ed onro the surface and returned for measurement. Measurements include not only horizontal disrances and a ngles, bur also rhe elevation difference from rh e ins trument's position. Aurom arica lly storing rhe measuremenrs within the surveying instruments eliminares rhe potenrial errors rh ar may a ri se when data recorded by hand on field forms is transferred to a G IS database. Another technology [har ha s become bmh more affordable and more useable in recent yea rs is that of global positio ning systems (GPS). GPS requires [har a receiver, locared on the Earth's surface, collect and record sign als transmitted by satellites o rbiting the Earth (Figure 1.12). Many narural resource professionals conside r GPS receivers ro be a source of frusrrar ion but recenr evidence suggests (har so me GPS receivers are ca pable of reliably collec ting measuremelHs under ca nopy. The com mon limiring factor for GPS appl ica tion s in narural resources has been that lines-of-s iglu between GPS rece ivers on-(heground and space-based satellite sysrcms have been obscured by ca nopy co ndition s. [Opographic barriers. or some combi nation thereof. A GPS receive r calculates a position by being ab le to receive signals from at leas t four sa tellites, wid1 more sa rel-
Fig ure 1.11
Laser rangt" fi ndc r.
31
Chapter 1 Geographic Information Systems
17
Satellite
Figure 1.1 2 GPS schenl2lic.
lites leading ro berrer data collection opponu nities. GPS receivers (Figure 1.13) calculate rhe amou nt of time it takes each signal to travel from the satelli re. The GPS receiver uses information contained in the signals ro calculare rhe range (dis£an ce) berween the rece iver and all satellites in com munication. Ranges arc lIsed to estimate a posirio n through rrilare ration. Satellite signal quality and rel iabiliry for measuremeIH determinacion depends on sarellite availabil ity and geometry of avai lable satell ites in relarion ro the GPS receiver. Satellite signal qua li ry is esrimated as a Posirion Dilution of Precision (POOr) statisric. Mission planning softw are is designed ro idenrify the potemiaHy best or preferred data collection rimes for GPS. Mission planning softwa re ca n calculate an expected POOl> statistic and potemially ava ilab le number of satellires fo r a field sire. La rger va lues of POOP (> 8) infer diminished satell ite geometry and measu rement reliability with values of 6 or below being preferred for data collecrion (Kennedy. 2002). Measuremenr variability and error for GPS receivers can be inrroduced by atmospheric interference of satellite signa ls, ti min g errors between sarellires and rhe CPS receiver, the rotation of the Eanh, and satellire orbiral
____
FigUR 1. 13 GPS receiver and antenna.
parrerns (Leick. 2004). A porrion of these errors can be es timated and removed through the process of differential co rrection. Differential co rrecdon uses a fixed CPS base station at a known locatio n rhat co nrinuously co mpares calculated CPS-derived posirions to its own location. C PS base stalion locations are derermined through repeated measurements tha t lead ro an accurate an d precise dete rmination. Calculated differences between rhe known location and the GPS-derived locations serve as a correction facror that can also be applied to other GI)S receivers that are collecting measurements nearby. Anmhe r pote m ial so urce of error for GPS receivers is that of mulriparh. Multipath erro rs occur when satell ite signals reAect off of Olher objects before rea ching a C PS receiver. This can inrroduce positional er rors into the measuremems (Figure 1.14). These errors generall y must
--------------~L.-----~-:I-li~-t-h-er-r~----~~
Trail
location Figure 1. 14 Example of multipath error in da12 collccttd through GPS.
32
18
Systems, Spatial Databases. Databases, and Map Design Part 1 Introduction to Geographic Information Systems.
be removed manually bur some so me CPS GPS manufacru rers offer ofTer
3re designed [0 software romines mat ma r are [0 defect delee, and reduce mulriparh errors. multipath er rors.
Anorher way in which to [0 reduce CPS GPS receiver measurement ro collect muhiple measurements at m enr erro errorr is (0 collecr multiple al single locations. a process known as precise po int positioning. locations, point ate dete determinamal a coordin coordjnarc rm inatells us that Statistical probability [dis tion based on the rhe average of multiple measurements measuremenrs sho uld be more reliabl t ha n trhhat o n a ssingle should reli ablee (han ar bbased ased on in gle measurement. measuremc:nt. satellite the difficulty difficulry in receivi ng sa<elli (. sigIn addition to rhe nals in natural resource reso urce settings senings in fo rested rrai n, res ted te teridi n. until ndy the re has been only on ly one sarellirc satellite sysrem system relatively rece recently users worl worldwide. sarell ire sysavailab le to uscrs dwid e. This primary satdlite <em is ,he AVSTAR (Navigatio tem the NAVSTAR (N av igarionn Satellite Tracking and Department Ra nging) system sYStem operated opera red by the rhe US Depanme nt of Ranging) Defense D efense (000). (0 00). NAV NAVSTAR TAR became avai lable in the early eady 1980sj it has :Ia minimum of 24 o peratio nal satell sa tell ires ites at 19805; satellite anyone cime rime and makes ma kes sa rellire signals freely and a nd co ndnntinava ilable to dwide. U Until 200 I, rh e uously available ro GPS users worl worldwide. ntil 2001, DoD , in the interest security, inrenrionally 000, me im eres{ of national narional secu riry, ilHenr ionall y satellll i« ite signals so rha e rrors scrambled CPS GPS sate thatt random erro rs prevented urate locatio locario n information informarion from laccurate rrom being co colve illed acc scrambl in g process was known as selective se l~ctive lecred. lected . The scrambling uld lead to measuremem availabili(y (SA) ( A) and co could measurement e rrors of availability 100 III m or o r more more.. The random erro rs co could uld be removed re moved by mapping- and survey-grade C sofrware through duough CPPS' receiver software differential S ince 200 1, selective select ive ava il abi lility ty difTerenrial co rrecrion. rrection. Since d several several new sa satellire systems and tellit e sys tems have has been removed an become operatio o pe ratio nal. There is no guarantee, guaramee, however, thar that ilability will remai futu re. selective availabiliry remainn off in the rhe fumre. selecr ive ava In additio addit io n to NAVSTAR, NAV TAR, (here there are now Space Based (SBAS)) (hat thar can provide co nvenAugmentarion Systems (SBA conventional real-time re-dl-rime differential diff'ereOlial corrections corrt:crions to CPS GPS receivers as 3S rhey real-ri me diffe re ntial correcthey collect data. dara. Convenrional real-dme differential tion (CIA) accessible uisition (C/A) tion uses the more easily access ible coarse/acq uisi sa rellire s ign als A I(hhough ough phase ","e1l itc signa ls rathe r3rherr rhan than phase p hase code. cod e. Alt forr accurate accura te CPS meascode signals have greare grearerr potenrial pore n da l fo uremems, nuous and uni nte rrupted sarellite satellite signals are co nrinuous merrupted urements, conri terrupted satellite satellire signals are uni nrerrupted req uired. requi red. Conrinuous onri nuous and unin ofte n difficlllr difficulr fO to maimain maint ain unde underr forest ca nopy and a nd in o f len BAS derives measurement correction correcrion facuneven Ilneven rerrain termin.. SBAS potemial armos· ror seve ral po tors for tenrial (;1'5 CPS eerror rro r sOllTces so urces includi ng atmossatellite rrerence of signals. time ti me sequences for satelli te pheric inrerference pheri inte o rbital rdliite te orb ital pat(di sta nce) estimates. and sa tell signal range (disra ncc) enim:ues. ary SBAS for many lIsers in North Ameri America terns. T he prim p rimary mall Y users ca Aviat ion Administrarion's Administ ration's Wide Area is Ihe the US Federal Aviation Augmenrarion System (WAAS). In 2008, four WAAS sarelAugmentation sa tellites were in oorbir rbi( with at ar least (wo tWO being operational, opermionai. with Iires
satellires furure yea rs. Altho Although more s3fel lites expected in fumr. ugh a single WAAS satell sate llite receiver to apply WAA ire signal is required fo r a CPS GI'S (eceiver real -rime factors, receptio rea l-ti me co rrec tion factors. receptionn from additional WAAS satellite up should recepsarellite signals ca cann provide a back backup rion sa tellite become unavailable. Other SBAS rrom one satellite tion from include the European Geostatio Geostationary nary Navigation Overlay
Sysrem ite-based )'Stem (EGNOS) (EG OS) and the Japanese MTSAT Sarell Sa tel lite-based Augmentation orne conte co nremporary mporary GPS Augmenration System (MSAS). Some rece ivers can SHAS. C,ln opera te w ith all SBAS. receivers
Orher GPS sa satellite ASS (Global tellite systems include GLO GLONASS Navigation .. e1lire System), which was developed by ,he the Navigar ion Satellite
GLO ASSS was made fully fu ll y Russ ia n military. Although G LO NAS Ru ssian operat ional with 1996, it has been inconoperarional wirh 24 satellites in 1996. sisrcnr operatio nal satellites. sistent with wich regard (0 to rhe the number of operationalsatdlites. systtem em , ca ll ed e d Ga lil eo. is under In ad didon. anorher 'Inorher sys Galileo. addition, th rough the European Space pace Age Agency development through ncy and is expected to be operational operatio nal in 2008. The key co mponem o f a CPS com ponent of GPS from a user's perspective the CPS ver. CPS separa ted is (har that of the' GP recei receiver. GPS receivers receive rs can be separated measurement by measu reme m accuracy and price into three broad ri es: survey, grades or catego categories: survey. mapping, and consumer
(Wing & Kellogg, 2004). Accuracy in this distinction is the difference di ffe re nce berween ected measuremenr [he between a CPS-coll CPS-collected measurement and me true location of rhe CPS receiver when w hen it coll ected the the measuremem. st accura te and expensive ex pens ive C PS measuremenc. The mo most accurate CPS receivers are survey grade and ca n calcul calcu late ate positions posi tions to wit hin one o ne cm of true location locarion when used co rrectly. within em ot correctly. full-fearu red of Survey-grade CPS GP receivers are rhe the mosr most full-fearured rhe ree receiver grades and enable users to differentially the th three co rrect In addition correcr collected dat;}. data. In addirion ro to the rel atively high COst thann $10,000) COSt of survey-grade GPS (rypically (rypica liy greater tha o perato r profic ie ncy wirh so ftware operaro p roficiency with {h rhee hardwa h ardwa re aand nd sofrwa re iver use applicatio ns is necessary. Survey-grade urvey-grade CPS G PS rece receiver applications rce applica ti o ns has bee resource ap plicalions beenn very limited in natural resou because beca use of the [he del icare icate na[Ure I'w[U re or o f the rhe equipmenl eq uipme nr and rhe require mcnc satellite reception in o rder (Q nt for sustained sarellire ro requi reme derive measurements efflciendy. efficiently. Su urvey-grade accurvey-grdde CPS GI)S ;ICCUan thar are likely g rearer rearcr th rhan rhat required req u ired for many ma llY na[Umll uracies arc ral resource app lications. licarions. GI'S falls into the rhe second seco nd Mapping- or resource-grade CPS the three GPS CPS categories and ca n be purchased for level of ,he $1 ,500-$10,000, depending $1,500-$10,000, dependin g on o n tthe he features a nd the rhe rn:lOu facturer. r.,ctll rer. These T hese G GPS a re also a lso so me merimes manu PS receive rs are times ed GiS-grade G iS-grade GPS. Ma ny mapping-grade GPS have ca lllled Many associa ted software for diffe renrial associared rcmial correction. Manufacrucer esr im atcs of posilional Manufacrurer eSlima[es posilional accuracy accu racy are 1-5 III m depending on the receive ti on an d mapping receiverr configura configurarion and application. esrimares oftt'n often reAec app licadon. Accuracy estimates reAecrr rhe best-case bc=st~casc ectionn scenarios, wh ich may nor nO( be possib possibllee in data co ll ecrio sce narios, which 33
Chapter Systems hapler 1 I Geographic Information Syslems
forested rorested enviro environments; nmems; rhere there have been several studies on ,his this issue. Sigrisr igrist et er al. 3J. (1999) found fo und positional accuracies be"veen during belween 3.8 and 8.8 m durin g leaf-off leaf-ofT and a nd between berween 12.3 conditions mixed-and 25.6 m during leaf-on co nditions within a mixed hardwood forest during selective availabiliry. aesser and availability. Naesset Jonmeister (2002) reported positional Jonmdsler posidonal errors be[\veen berween 0.5 (Piun sitchmsis). sitch",;;s). Liu (2002) aand nd 5.6 m in sitka spruce (Pian hard-tested several mapping grade receivers under dense hard cano py and reponed averdge average posidonal positional errors of wood canopy measurement 4.0 ITI. m. Wing and Karsky (2005) found measuremenr accuracies belWeen berween 1I and 4 m depend depending accurf1cies ing oon n the amount canopy re a nd tthe he type of CPS configu ration. of ca nopy closu clos ure rarion. aJ. (2005) twed variety BolSlad et tesled a va ri ery of mapping-grade Bolstad et oj. GPS receiver configurAtions configurations :Ind and found accuracies accurac ies bcrween between Grs 2.4 and 4.5 m under ca no py in deciduous and redunde r forest canopy forests . Wing el severalI mapp mappingpine foresls. et al. (in (i n press) tesred resred severn ingconfigurations determined accuracies grade GPS con figura tions and determ ined acc uracies from 0 . 1 and 1.2 m in you young foresr aand POst-processed r. of 0.1 ng forcsl nd post-processed da data respectively. closed canopy conditions, respect ivel y. GPS receivers are a re rhe the leaS! accu rate least accurare Consumer-grade GP GPS receivers PS g rades with receive rs and most affordable of rhe C costing between $50$50-$750. cos ring berween $750_ This price range may ma y be att ractive for many potential porenrial lIsers bur several seve ral disadvand isadvananracrive users but tages GPS receivers rages must be considered. Consumer-grade CPS don't operators ro ser rhresholds for don 't allow o perators LO se t minimum rluesholds sa tellite relli re signal qua li ty through rhrough the rhe establishmel1l establishment of a minimum POOP level as a quality conlrol. camrol. Mission planning software is usually nor included with wirh consumer GP CPS receivers and some poim so me do not enable users to conduct poilH averaging ro determine a single position. posicion. While most ave rag in g to Whil e mosl co n urner GPS affo afford to srore measurerd users the ability LO store mc:asureconsumer menrs common me ll( S individually, a co mmon srorage limir limit of 500 poinrs can limir rhe amount of rime a.1 co consumer CPS poims limit the nsum e r GPS cann be used in the field befo re the rhe receive receiver ca receiverr memful l. Differential Differentia l co correction ities through th rough o ry is full. rreclion capab capabiillities avail(-processing techn iques are not generall ildata po posr-processing techniques generallyy ava to consumer able lO co nsumer grade CPS. Like survey grade GPS receiver accuracy. consu consumer mer CPS accuracy G P receiver accu racy in forested settings sc::rtings has been et al. aJ. (2005) lesled tested the reponed rhe reported in previous srudies. studies. Wing er consumer positionJI reliabil ity of six s ix co nsumer grade posirional accuracies accurac ies and reliabiliry CPS ivers within several se veral dirrerenr lorest types GPS rece receivers different forest rypes an andd reponed me;:asurement mete rs of true wi thinn 10 meters measure ment accuracies accu rac ies wirhi position co nifer canopy GIIlOPY and wi thin 5 merers posirion under dense conifer within meters depending consumer ca nopy. depend ing on the [he:: type of co nsumer under partial canopy. Average merr CPS GPS grade CPS receiver. Ave rage accuracies accumcies of o f' co nsu nsume be[\veen 6.5 and 7. 7.11 m under dense primarily receivers between hardwood c.mopies canopies were reponed by Bolstad Bolsrad er al. (200S). Althou gh lhe rypical reported average accuracies the typical (2005) . Although
19
reported by Ithese studies (5 (S to 10m) may be acceptable hese srudies for many natural nalllral resou resource CPS rce applications. consumer GP receive r limitations, inability ro to set minireceiver limirarions, including the inabiliry tel lite qual iry ity standards, the possibility of poinr mum sa lel rhe possibiliry different ial correccion averaging. and rhe lack of dilTerenrial correction procedures. dures, mllsr must be co nsidered. nsidered .
D ata techn o logy a ta slorage sto rage technology Commonly, G GIS databases co consisr quantir ies of COlllmonly. IS darabases nsist of large quanrities data thal dala that muSt be slored Stored and replicaled r
20
Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design
marion a nd comp uting syste m } operating system . Perso nal compute rs a re now rhe primary plarfo rm upon
Output devices
which
G IS darabases a nd [he resulrs of G IS analyses can be presented in a varie[), of manners , both in gra ph ica l and tabula r fo rm . Fo r examp le, when examining the impacts of altern ative riparia n manage m ent policies on a landscape, ir may be important to present data in ta bular fo rm (Q describe the potential eco no mi c impacts of alte rn atives, and in grap hical form to visually display th e area each policy affects. A number of co mmon om pur devi ces thar ca n be used to presenr GIS analys is res ults are d escribed in the nex t few sectio ns.
to
urilize GIS sofr\,l
GIS databases. Even though rhe distinctio n between perso nal co mputers and worksrario ns has beco me blu rred , co mpu te rs ca n be chamcrerized by crite ria [hat co nstan d y chan ge w idl adva nces in co mpme r techno logy, such as speed , memory, and rhe o perating system. Key com ponems within a com pu te r include random access memory (RAM), cent ral processing unitS (C PUs), and storage devices (hard drives o r o prical discs). Windows" and
Linu operating systems have beco me mu ch more co mmon (han U IX- run computers. Most GIS software designers now focus t hei r development effo n s on Wind ows
Prin ters and p lotters T he m ost obvious output d evices associa ted with GIS are primers and pio((ers. Fifteen yea rs ago, line printers were co mmon peripherals to computer systems. Their o utput qualiry was low and [he range of symbol s and co lors was limited. Today, a va riety of color and black and white prinrers are avai lable (hal' ca n produce hi gh qua li ty maps. Thus the ca rtographi c creariviry of G IS users is virtu al ly unhindered. Printers are generally class ified as lase r o r in k jer, dependin g on how ink is transfe rred (Q paper. Laser printers a re mo re expensive ($ 100-$2,000) rhan ink jer prinre rs « $500), and rhey require re usable laser ca rtridges. H o wever, (hey generally p roduce Outp Ut products that are more stab le. with regard (Q ex posure ro moisture, than ink jet printers. In k jet printers require disposa ble ink ca rtrid ges and the o mput products produced are more easi ly affected by exposure to mo isture.
1- • A GIS wo rkstatio n usually includes a hi ghe r-e nd compurer with a fast processor and large amountS of RAM a nd imernai disk sto rage space. H oweve r, fo r the type of wo rk pe rfor med by field for este rs an d natural resource m anagers, a typica l pe rsonal com puter (PC), or even a laptop co mpmer, will gene rall y suffi ce to facil irate rhe use of desktop G IS softwa re such as ArcG1S or Map ln fo. What are [he desired cha racrer isrics of a PC com pute r [hat might allow you to use d eskrop GIS sofrwa re ' Obviousl y a fasr processo r
wou ld be beneficia l: rhe faster rhe processor, t he quicker a p rocess is complered. such as buffering o r overlayin g. In addir io n, 1 GB of RAJ"t is perhaps rhe m inimum RAM necessary to easily handle large processing rasks. And finally, a 100-140 G B ha rd drive is perhaps the minimum size necessary to srore GIS d atabases YO ll mi ght develo p and use over rhe useful life of a personal co mpllter (2-3 years). If LiDA R o r o rhe r la rge r-size G IS databases are to be used , the m inimum ha rd drive size mighr begin ar 200 GB.
35
Chapter 1 Geographic Information Systems The main drawback with moSt primers is the format of the ourpm. which is generall y limited to BIh" X II " or 1 I" X 14" media. Plouers allow GIS users to produce maps of a variery of sizes (Ta ble 1.1 ). Plo([ers, however, are genera ll y more expensive than primers. {hough they are now ava ilable for prices beginning at $ \000; they also typically req uire special paper and ink cartridges. One way {Q ca regorize planers is to use a vecto r/ras ter analogy. Venor plotters incl ude (hose termed as Aad>ed or drum. Vector plotters draw lines using planing pens of differelll colors and can prod uce some very precisely d rawn maps. Raster plotters include those termed electrostatic, laser, ink jel, and warm wax. Electrostatic ploners use an a rray of elect ric contaClS (> 100 per inch) that ap ply a charge ro paper, which then co mes in CO IHacr with nega ri vely charged to ner to produce images. The technology that is used in laser and ink jet plotters is similar (0 thar in laser and ink jet primers-each featu re on rhe map is drawn pixel by pi xel. Warm wax ploners are simila r to ink jet plouers bur rhe resultin g producrs have a glossy appeara nce. Some of these primers and planers. in co njun ction with h igh-qu ality
TABLE l.l
Common sizes of map output from plotte rs
Map siu
D im t:RsiORs
ANSI A
805''' X 11.0" (2 16 mm x 279 mm)
ANSI B
11.0" x
1 7.0 ~
ANSI C
17.0"
22.0" (432 mm
ANSID
22.0" X 34.0" (559 mm X 864 nun)
ANSIE
34.0" X 44.0" (864 mm X 1118 mm)
~
28.0" X 40.0" (7 11 mm X 1016 mm)
X
21
paper, enable G IS users ro produce photograp hi c-qual ity oucpur products. A tho rough examinarion at the needs at a n organization is warranted prior ro making a decisio n rega rd ing an in vest ment in primers or plotters. Screen displays A more rudimemary set of outp m products trom C IS are those relared to rhe image displays YO ll can view on a computer sc reen. These processes co nsist of capturing inform atio n (data o r maps) d isplayed on [he sc reen at a computer and tempora rily sro rin g {he information in a digita l database. A number of methods a re avai lable to ca pcu re co mp uter sc reen images. The resolution a nd derail of rh e resulting captu red image, however, depends on the method used for image ca pture. Screen displays are someti mes ca ptured and saved as image flies, and ar ot her ti mes simp ly sto red in the com purer's 'clipboard', and thu s a re available for past in g into a variety of othe r software programs. Fo r exam ple, mOSt personal co mpmers allow users [0 save what may be displayed on a compmer sc ree n by pressing (all at o nce) the Alt a nd Pmt Scm butrons on a compute r keyboa rd . This Stores (he entire image di splayed o n th e sc ree n ro the compu ter's clipboard, JUSt like if yo u we re copying text in a word processing prog ram. Then, YO ll ca n paste the ca ptu red image imo either a word processing or graphi cs softwa re program (Figure 1.15). One potential drawback is thar screen
(279 mm x 432 nun) X
SS9 mm)
~"'J.o .. ,
..... _
.. _ _ ...
,_
f"(.Oo~ _ _ ~ _ _
,::,x
._...;;0
.-,-
o-
ANSI
ISOM
8.3"
ISOAJ
11.7" X 16S (297
ISOA2
16.5"
ISOAI
23.4" X .n. I" (594 mm X 841 mm)
ISOAO
33. 1"
ISO B4
9.S" X 13.9" (250 mm X JS3 mm )
ISO B3
13.9" X 19.7" (353 mm X 500 mm)
ISO B2
19.7" X 27.S" (500 mm X 707 mOl)
ISOBI
27.S"
X
X
X
X
11.7" (210 mm
X
297 mm)
mm X
23.4" (420 mill
46.8" (S4 1 mm
X
X
r
420 mm ) 594 mm)
IIS9mm)
39.4" (707 mm X 1000 mm)
ANSI . Ametican Nalional SI:lndards InSfiWIt:
ISO", International Standards Organizalion
~
.. "_.-- -------------"
Fig urt: 1. 15 Scrt:c:n d is play.
36
22
Part t Introduction to Geographic Information Systems, Spatial Databases, and Map Design
ca prures a re raster images. eve n tho ugh YO li m ay be 3ncmpling {Q caplU re a represenration of a vector GIS darabase displayed on [he screen. Graphic images In addition {Q sc ree n cap rures , most GIS sof~\Ia re programs allow lIse rs (Q direcrly store images viewed on the screen as inclependenr compmcr files. These products are
also raster images. yet they
3rc
TABLE 1.2
Common types of graphics image output files
FiI ~ CIt~ nsion
Description Windows® bilmap formal Corel DtaW® formal
cgm
Computer graphics metafile fo rmal
emf
Windows® Enhanced M etafile fornu.t
'P'
Encapsu lated PostScript format
gif
Graphics interchange format
jps
JPEG
PC'
Macill{Qsh® PICT formal
AutoCad@ digiml exc hange f..le format
slighdy difFerem ('han rhe
scree n displays desc ri bed above in [hac, generally, only rhe map image is ca prured and stored (Figu re 1.16) , and nor everything else fhar may be vis ible on [he scree n. These images can be sro red in a w ide va riety of fo rm ats (Tab le 1.2) depending on rhe availabili ry within [he GIS sofrware program bein g used . H oweve r, transferring grap hi c images from one system (e.g., GIS) ro anot her (gra phics editing programs) or vice ve rsa ca n somerimes be problema dc because offo rm ar inconsistenc ies. In addition , [he size of rhe resu lring graphic image files will vary depending on the formar lIsed ro save rhe image. Tabular output As you mi ght assu me, rab ula r ourpuc consists of tabl es or sets of data (numbers, rex ,) derived direcrly from a GIS database or from rhe resul, of a GIS a nalysis. While maps are engagi ng- rh ey draw peo ple in and all ow rhem ro visua lize rhe qualifies and conditions of a landscape-rabular dara are also importanr fo r illustra ring non -spa rial information. For exam ple, while developing a map of habita, qualiry fo r the sporred owl (Strix occidmln/i,) may provide an in re resfing and compelling view of a land -
Fil~
interchange format
PC paintbrush formal png
Ilo rtable network graphics form:J1
,g,
Targa format
tif
Tagged image file forl11:1.1
wmf
Windows@ metafile formal
wpg
WordPerfcct«l graphics format
scape, decisio n- makers might also be inre resred in how mu ch land of h ighe r quality habirat ex ists. O r, as alrernarive riparian ma nagemem policies are evaluated, [he effect (e.g .• rhe area or timber vol um e wirhin rhe riparian man age ment areas) will likely be of inre rest [Q decision makers. Tabula r da ta fro m GIS a nalyses ca n be di s played direcrly on a map. or d rawn rogerher imo an independelll table for incorporation wirhin a report.
GIS software programs
Figure 1. 16
Graphic imag~ .
There are many GIS softwa re programs available to natura l resource managemenr orga ni za rio ns raday. The co mmeI1[S provided in rhis secrion are general in natu re, however a lisr of dle co mmon GIS so ftware programs is provided in Appendix C. GIS softwa re programs are categorized in a number of ways. One characterizat ion is based on w hich of rhe twO co mmon dara slrucm res (raste r or vecto r) is accommo~ dated. Rasrer and vecror data st ru ctures is disc ussed in more deprh in chapler 2. GIS software programs have also been c haracrer ized by rhe ope rat in g sys tem used . For example, GIS sofrwa re programs developed for UNIX wo rksta tions were once considered 'workstario n GIS sofrware\ and GIS softwa re programs developed for pes were 37
Chapter 1 Geographic Information Systems considered 'I)C 'PC GIS GIS sofrware' sofrware',. This distinction diS[incrion has essenessen~ riaJly tial ly disappeared d isappea red as wori<sration workstarion GIS C IS sofrwa sofrware re is now used an d U UN IX opeT
23
ing field dara data collecrion collectio n processes. processes, o r La to perfo perform rm waterdelineation and ana shed delinearion analysis lysis processe processes wirh with aDEM. a OEM. If If,••afrer fter pondering th rhese ese issues. issues, the rhe choi choice ce of a GIS software in g a sofnva re program r('mains remai ns unclear. perhaps choos choosing GIS software sofnva re program (har that seems co ro have the capabiliry capabili ty to txpan ex pand d wirh with tht t he needs of an orga o rganizarion ni za tion would be a good de ision. In creasingl y, GIS ,IS software so ftware programs are a re decisio n. Inc reasingly. desi gned in a modular manner. ma nner, which allows users use rs co to designed purchase sepa separate rate software sof['\vare modules intended [0 to work GIS program progra m.. Users then [hen purchase rhe the base with a base GI GIS program and oonly nl y tthose hose modules Iha, that they deem necessary necessary.. becoming Mainren ming more typicaJ ty pical Maintenaanncee cha rges are a re also beco fo G IS soFtware prog rams, and are often overlooked overloo ked software program s. :Hld forr CIS narural when natu ra) resource managemem managemenr organizations create a nnu al maimerheir budgets. Users can either purchase annual mainre!lance suppOrt for a3 GIS GIS software program, oorr pay for nance SUppOfl arise. An annual fee is eas easier ier to techn ical sup suppOrt technical pOrt as issues arist. predict in clude in rhe the budger, budge r, bur a per-incident per-incidenr pred ict,, and to include fee may resuh result in a lower lowe r [Oral roral COSt cOSt (depending on rhe the amount of suppo su ppo rt rr a user needs). needs). For some so me G GIS IS softwa software re ;1mounr maimena nce fees cover product upgrades, and programs, mainrenance so as nC\'V new vers versions io ns of the rhe sofrware soflware are rel released. eased. rhey they may af no cosr ro [hose rhose wirh annua annuall maimenance availa ble :H be availd agreements. As an alternative, al terna t ive, on online line GIS GIS user suppOrt agreemems. avai lable for users with lilimil mired techgrou ps are ed access ro rechgroups a re available nica1 nical support suppOrt from the sofrware developer. Reliance Reli ance on these groups grou ps ca n result res ulr in an inexpensive inex pe nsive and ofte n rapid sofrware Frware and
24
Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design
Summary This introdu crory chapter described the history of GIS developmenr. why GIS is imporranr in natural resource management, and rhe wide variety of inpur and output devices associated with G IS. In addidon. a num ber of issues related (Q rhe selectio n and purchase of GIS sof{~ ware programs were ourlined; hopefully (his summary of issues will stimulate discussion amo ng those considering rhe development of a GIS system within a narural resource organization. The applications of GIS to narural resource
man agemenr iss ues will vary widely among organizations, however, und erstanding the capabilities and porcnria l use of GIS is essenrial fo r na tura l resou rce management professionals. The following applications, as with those in subsequenr chapte rs, are inrended ro provide srude nts with a taste of the typical types of GIS requestS posed to field fo reste rs. biologists. and other professio nals fami li ar with GIS. who work in narural resource management field offices.
Applications 1.1 Developing the s pecifications for a GIS system. You have been as ked by the Distr ict Manage r of you r natural resource management o rga nization. Jane Lerner, ro develop the specificat ions for a computer (Q be purchased and used for, among other things, GIS analysis and map production in a narura l resource managemenr orga niza tion field office. Use resources avai lable on the Internet to design a com purer sys rem (har would be ca pable of run ning deskrop GIS software in a forestry or natural resource field office. a) What are the specifications of rhe compute r system [hat you would recommend. and how much might it cost? b) If yoll r budget were limited to $2,500 (max imum), how might your recommendation change? c) You have been asked to decide whether marc RAM or video memory wou ld be a berrer investment for a G IS com purer system. What is the difference between these (wo types of memory and which would you select? 1.2 Terminology. The District Manager of you r nam ral resource managemenr o rganization, Steve Sm ith, is unEulliliar with a numbe r of terms related to GIS. H e has heard these term s distribllIed freely during staff meetings, and during one of your weekJy reviews he asks you to help him understand what they mean. Briefly describe for him GIScience, [Opology. and overlay analysis. 1.3 C haracterizing GIS software systems. Your supervisor, John Darling, has heard rhe terms 'workstation ' and ' desktop ' GIS sofrware. bur remains confused about how they diffe r. Explain to him the differences between the twO types of GIS software.
1.4 History of GIS. While on vacation and VISlfll1g yo ur relatives. you find that the conversation around rhe dinner table has rurned to the rypes of work you perform in your role as a narural resource manager. Describe for your relatives, many of whom have never heard of GIS, the orig in of G IS. how GIS has evo lved into irs current form. a nd how YOll mighr use GIS in narural resource management. 1.5 GIS pioneers. IdentifY and list the noteworrhy contribut io ns of so meone who has made significant contri butions to the development of GIS. 1.6 GIS data. Identi fY and describe o ne of the GIS databases described in [his chapter thar contained data ror the resources of an entire counrry or for a portion of the world. 1.7 A between map to between
question of scale. YOll measure the distance twO owl nests o n a 1:24,000 scale ropographic be 6 cm. Whar is (he ac(Ual g round disrance the nesrs?
1.8 Scale revis ited . YOli have measured (he distance bel"ween two campgrou nd s on a ropographic map to be 2 cm . From a field vis it, you know that rhe co rresponding g rou nd distance berween rhe campgrounds is 1 km. What is th e sca le of the topographic map' 1.9 GIS software. List and briefly describe three GIS softwa re packages (hat were avai lable prior to 1990. 1.10 Relat ive error. YOll measure the perimeter of a field plor wirh a meta l rape an d derermine a tota l 39
Chapler 1 Geographic Information Syslems perimeter of 134.5 meters. Your instrllcror tells you that he used a (Otal station and determined rhar rhe (Oral perimete r is actually 136.2 meters. a) Whar is rhe closure error between your measure~ menr and yo ur insrrucror's? b) Whar is rhe relative precision of your meraJ tape measurements?
1.11 GIS d ata fro m above. You have been asked
(Q
develop a database of your counry that co ntains elevation and landform informatio n. Whar are three remore se ns~ ing data co ll ectio n tech niques [har would be used (0 develop rhis database and what are rhe relative strengths and weaknesses of each ?
1. 12 GIS d ata from the ground. You have been asked (0 create a GIS database that conrains the boundaries of a set of tree stand boundaries in a research forest. Describe th ree approaches (0 co ll ecting this data and the relative strengths an d weaknesses of each approach. 1. 13 GPS consideration s. A friend of you rs has recendy purchased a nC\v GPS receiver from a local departmelH Store for $75 and has told YOll that she is excited thar she will be able ro collect coo rdinates of fearures that repre~
25
sem 'exact loeadons' on the Earth's surface. What advice would you offer her aboUT rhe measu rement accuracy of a
$75 GPS receiver? 1.14 Data input d evi ces. The Bu reau of Land Management has hired you as a fo restry technicia n. Your supervisor is aware that you have a background in GIS. and asks for your input regarding the technology that can be used to develop a vegetation GIS database. Describe th ree options. and their srrengrhs and weaknesses in terms of collecting data and developing a G IS database. 1. 15 Data dh play o ptions. It is Friday afternoon in a narural resou rce organization's field office. As you are day~ dreaming abour the fo rthcoming weekend's events, your supervisor enters your office and rells you rhat he has a meeting Monday morning with a neighboring landowner [0 describe rhe management alrernatives for a pordon of the forest you r organ izatio n manages. A fC\v graph ics that desc ribe rhe alrernatives under co nsideration would be beneficial to the meeting, and maps are the obvious choice of ourpur products to engage [he public. However, the color plo[[er in your office is not working. Describe three m her methods for examin ing ourpur from GIS that mighr be useful for your supervisor's Monday meeting.
References Avery, T.E, & Berlin , G.L. ( 1992). Fllndammtais of remote Jeming and airphoto interpretation (5 th ed .). New York: Macmillan Publishing Company. Berna rd , A.M. , & Prisley, S.P. (2005). Digital mappi ng alternatives: GIS for the busy forester. journal of
Formry, 103(4), 163-8. Bolsead, P., Je nks, A., Berkin, J., Horne, K. , & Reading, W.H. (2005) . A compa rison of auto nomous, WAAS , real- time, and post~p rocessed global posit ioning sys~ terns (GPS) accuracies in nonhern forests. Northern jOllmal ofApplitd Formry, 22( I ), 5- 1 \. Brown , T.L., & Lassoie, J.P. (1998). Entry-level competency and sk ill requiremenrs for foresters . journal of
Forestry, 96(2), 8- 14. Burrough , P. (1986). Principl.s of geographical information sysums for land resourus assessment. Oxford: Oxford Universiry Press.
Burrough , P.A. , & McDonnell, R.A. (1998). Principk, of geographical information sysums. Oxford: Oxford Universiry Press.
Clarke, K.C. (200 I). Getting "aned with geographic information sy"",u (3 rd ed.). New Jersey: Prem ice H all, Inc. de Steiguer, J.E., & Giles, R.H. (1981). Introduction to compute ri zed la n d~i n fo r marion systems. JournaL of Formry, 79,734-7. Goodchild, M .F. (1992) . Geograph ical informatio n science,lmernatiollaL jOlirnaL ofGeographiCtll lnformation Sy"",u, 6(1),31--45. Kava nag h, B.F. , & Bird, S.J.G. (2000). SlIrvqing: Principl.s and practim (5th ed.). New Jersey: Prentice Hall , Inc. Kennedy, M. (2002). The global positioning system and GIS. London and New York: Taylor and Francis. Land Info Worldwide Mapping, LLC. (2006) . IKONOS high-ruollllion ,aftllift imagery. Highlands Ranch , CO: Land Info Worldwide Mapping, LLC. Retrieved April 21 , 2007 , from http: //www.lan dinfo. com/ s(lrprices.htm.
Leick, A. (2004). CPS ,at"li" survqing. Hoboken , NJ: John Wiley & Sons. 40
26
Part 1 Introduclion Introduction to Geographic Information Systems, Spatial Databases, Databases, and Map Map Design
Liu , c.J. ,j. (2002). Effecrs EffectS of seleerive selective availability ava ilability oonn GPS CPS pos irioning accu racy. racy. Sourban Sou/bun Journal of Appli~d positioning Forestry, Formry, 26(3), 140140-5.. Longley, P.A P.A..., Goodchild, Goodchi ld, M.F., M.F. , Magui re, D.j D.J ..., & Rhind, D.W. (200 1). Ceographic Rhind. CeogroplJir illformolioll information 'ystems systems find science. flnd s(i~nct. New York: John Wi Wiley ley and Sons, Sons. Inc. McHarg, 1969). Design with 1II1I/m. Ilfltllrt . New York: M Harg. I.L. ((1969). Dnigll wi," Joh Inc. johnn Wiley and Sons, ons. Inc. Me McCombs, ombs, J.W., j .W., Robe"s, Roberts, S.D S.D.,.• & Evans. Evans, D.L. (2003). Influence of fusing LiDAR and multispectral Innuence ll1uhispecrral imagery
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o n remorely on remo tely sensed es eS(im rim ates ~HCS of stand srand den density s it y and Jnd mean tree lfee height mean heigh I in in a managed loblolly pine plantaplanra-
Wing, M.G M .G.,.• & Bettinger. Berringer, P. (2003). GI C IS:: An updared updated primer on mana gemel1l rool. journal o n a powerful management tool. Jo urnal of Fomtry. Formry, 101 101(4),4-8. (4),4-8. Wing, M.G. Kellogg, L.D . (2004). Locating and Wing. M.G.,, & Kell ogg. L.D.
tion tion,. Form Sci",u. Sci",,,, 49(3), 49(3),457-66. 457-66, Merry, Beninger, P P., Cl un er, M .•., Hepinstall, Merry. K.L., Berringer. .• Cluner. Hepinstall. J., j .• & Nibbelink, N. N.P. P. (2007) . An assessment of geographic geogra ph ic informalion system (C info rmarion sysrem (G IS) skills used by field-Ievd field-level na(nallIral ural resollrce resource ma managers. nagers. journal of Forestry, Forestry. 105(7)' 105(7), 364-70. Naesset. E. E.., & Jonmeister, NaesSCI, jonmeister. T. (2002). Assessing poilll poinr accuracy accu racy of ot DGPS under under fo foresr rest ca no nopy py before data dara acqu isition, fie ld , an and postprocessing. acqui ilion. in rh th e field. d afrer after postprocessing. S{(lIIdi1lllVioll Scandinavian journal jot/mol of Forest Foml R",orrh. ReunrriJ, 17. 17, 3 351-8. 1-8. arura l Resources Ca Canada. (1978).. SptcijicariollS Narural nada. (1978) Sptcificnli01lS olld and recOlmlltlldllliollJ onmundntiom for control surll9s surveys and Ilnd survey Sf/TVt] markus. markers. Onawa, ON: Canada Centre OWlwa. Cemre for fo r Remore Remote Sensing. Peucker, T,K T.K..., & Chrisman. Ch risman, N. (1975). Cartographic C.nographic data structures. structures. Am"imll Americoll Cartograph", Cllrrogrllpher. 2(1), 5--69. dara 55-69. Reutebuch, .E.. Reutebuch. .E., Andersen, H.-E., & McG.ughey, McGaughey, R.J. R.j. (2005). Lighr ng (Ll DAR): An Ligh t detection detectio n and rangi ranging (L1DAR): emerging rool venrory. Journal lool fo forr multipl muhiplee resource in invclHory.Joumal
ofFomtry, 103(6),286--92. ofFomrry, 103(6),286-92. Sample, V.A., Rin ggold,, P.c.. P.c., Block. Block, N.E., & Giltmier. Gil rmi er, Sample. Ringgold j.W. W. (1999). Forestry educarion: education: .dapting adapting to ro the rhe J. changing demands. dem.nds. jOlmlll1 ch.nging jOllrnal of Foresrry. Forestry, 97(9), 44-10. 10. Sigrist, igrisr, P.. P., oppin. oppin, P. P.•, & Hermy. Herm y, M M.. (I (1999). 999). Impact Impac r or of forest Forest canopy on qualiry and accuracy ofcp. of CPS measu measurerements. IIIf~r/lllfiolllll Imt'rnational Journal journal ofRemou Remote Sensing. ~nsillg. 20( 18). 18),
3595-610. Song, ong. j.-H J.- H .,.• Han. Han, S.-H S.- H .,.• Yu, Yu , K K., .. & Kim , Y.-I. (2002) . Assessing rhe lhe possibi possibiliry li ty of or land-cover classifica classification tion us ing UDAR dala. Inrernational IlHern:uional Society ociery of using LIDAR intensiry inren siry data.
drawn overlays: Their history and an d prospecrive prospective uses.
LOlldSCIlp' LflIu/scap' IlrriJirechrr,. Architectllre, 66(5) 66(5),444-55. ,444-55. U US Federal Geoderic eodetic Control Control Comm Committee ittee (FCeS). (FGeS). (1984). Siamiords Iptcificoriolll for geodetic g,odnic cOlltrol Stalldards I11ld alld 'p"ijicariollS networks. lI
mobile mapping rechniques techniques for forestry applicar applic.1rions. ions. C,ographic IlIformOlioll C,ograpiJic IlIformatioll Srimm. Scimm, 10(2). 10(2), I175-82. 5--.s2. M.G.,.. Eklund , A., Wing, M.G A.• & Kellogg, L.D L.D.. (2005). ons um er grade global onsumer global pos positioning itio nin g syste system m (CPS) (GI'S)
acc uracy and rel reliabili iability. ty. jOllrlllri jOllmal of Fomtry. Fomtry, 103(4) 103(4),, 169-73. Karsky, R. R. (2006). Standard Srandard and real.• & Karsky. Wing, M.G ., rime accumey time accuracy and reliability of a map mapping-grade ping-grade GI'S CPS in a co conifero niferous us western wes[ern Oregon forest. foresr. Western \Y/~Slem Journal journal
Fomtry,. 21(4), 2 1(4), 222-7. ofApplied FomlrJ' Wi ng, M.G M.G.,.. & Sessions, J. rechnolWing. j. (2007). Geosparial Geospatialleehnology ed education. Formry. 105(4), 173173-88.. jourtlol ofFowtry. ucllion. jOlinUlI W ing, M.G M.G.,.. Eklund A.,.. & K.rsky, Karsky, R. (In press). Wing, Eklund., A pres ). H orizonra Hori zonta l measure measurement menr pe performance rformance of five mappin g-grade GPS rece ive r co nfigurations nfi gllradons in s(,vc:ral seve ral ping-grade CPS receiver foresred fore ted se((ings. se((ings. W",,," \\'It'SltrJI jOlmlol jOllmal ofAppli_d Applied Fomtry. Wolf. Wolf, P.R., P.R .• & Dewitt, B.A. B.A. (2000). Elm,,,,,, EI,mmts of pho10grfllllllJelry: togrfllllm,try: \\'Iilh With applicatiollS applicfllioJlS ill GIS (3rd (3 rd ed.). New York: McGraw-H McGraw-HilI. ilI. Wolf, Wolf. P.R., P.R.. & Ghil.n;' Ghila ni, .0. (2002) (2002).. Elmtmurry Elemmtary ",rorymrvrying: illgo' An introduction iutToduClioll to geomntics geolnlllics (1 (I Olh O[ h cd.). ed.). Englewood Cliffs, ClilTs. NJ: j: Prentice Prenrice Hall, Hall , Inc. Yu. Yu , X,. X., Hyyppii. H yy ppa, J. j.,, Kukko. Kukko, A. A.., Malramo, Maltamo, M., M .. & Kaaninen , H. (2006). Change Kaarlinen, Cha nge dete detection rion techniques fo r canopy heighl height growrh growth measurel1lC'I1[S measuremems using airborne laser scanner dat:t. dara. Phologrllmmtfrir Photogrammetric Engineering Engillt'l'ring
alld fwd RtII,olt Remote Stilling. S""ing, 72( 72(12), 12), 1339-48. 1339--48.
41
Chapter 2
GIS Databases: Map Projections, Structures, and Scale Objectives This chapre r imrocluces rhe concepts of map projections and dara srrucru res. After compierin g (his chap rer. readers should have an understanding of th e following top ics rdared ro rhe sr rucru re and composition of GIS darabases: I . the defi nirion of a map projection, and rhe components rhat comprise a projeccion. 2. the compone nts a nd characrc ri sri cs of a ras rer dara structure, 3. the componems an d cha racrerisrics of a vec[Q r clara structu re, 4. the purpose and srrucrure of meradar3, 5. the likely so urces of GIS databases that describe natu ral resources with in North America,
G. rhe rypes of info rmation available on a typ ical
[OPO-
graphic map. an d 7. the definirion of scale and reso lm ion as rhey relate to GIS databases. Perform in g G IS processes a nd analyses in suppo rt of natural resource management decisions requires obta inin g and wo rking with spatial databases. Many GIS lIsers find rhar they spend a g rear deal of time and efforr acquiring and modifying GIS darabases ro ensu re rhar rhe most su itab le and approp ri ate data is be in g used in subsequenr ana lyses. One of the g reat challenges in working wirh fea(Ures locared on rhe su rface of the Earth is thar the Earth is very irregularly shaped , and is in a constant sta re of
change. When you arrempr to create a n.vo-dimensional represenration of the Earth (as is typically represenred on maps), rhe Earrh 's irregu larities musr be ad dressed. Di fferent map project ions and pro jecr ion components have been creared so {hat data from the Ea rth 's surfa,c e ca n be displayed on ma ps and othe r fl at su rfaces. Understanding [har s par ial dara can be re prese n ted through any numb
The Shape and Size of the Earth GIS software programs are designed to work with data describing rh e Ean h's featu res. (Q provide merhods fo r fearu re measurements, and ro allow comparisons of fearures of interest. A number of options exist by which you can coll ect, stru cture, and access GIS data. Spatial data use rs, however, must always be cognizant that represenratio n of landscape fea rures o n n.vo-dimensional surfaces. such as maps or co mputer monitors, are subject to disrortio n based on the sp herical sha pe or the Earth. These 42
30
Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design
poi nr loeadons within a datum , rhe greater the potential of rhe darum
[Q
acr as a reliable surface upon which you
Hundreds of darums have been developed [0
working with spatial dara in
onh America use NAD27,
NAD83, o r an adj usred NAD83 darum. A co mm on error
can reference other landscape features. the Earth, many o f which are specific
M any agencies and organ izations th at are involved in
to
describe
a pani cular cou n-
try or region. Within Noreh America, [wo darums are pro minenr: the arc h American Datum of 192 7
among users of GIS, es pecial ly those who have acqui red clara from a number of differen ce so urces, is in forgercing to convert their darabases to a common da(Um. In rerrns of co mpari son, in the US , landsca pe fe-d. rures re feren ced in
( AD2? ) an d rhe Norrh American Darum of 1983 (NA D83). Anothe r, WGS84, is co mmonl y used in co nju ncrion wir h GPS dara collecrion effom. The NAD83
barh
and WGS84 are very si m ilar, an d are sometimes used
ha rd to derec r visually when using G IS to view large reso urce areas. Th is overs ighr ca n o bviou sly lead to inac-
interchangeably, although this practice may nor be suitable for applicarions tha t require high data accu racy levels; rh e NAD83 was designed fo r Norrh America whereas rhe WGS84 rakes a global approach in representing rhe Earlh . Differences berween AD83 and WGS84 a re in rhe neighborhood of 1 co 2 m within rhe conrerminous US. The primary differe nces berween NAD2? and NAD83 darums are rhe number of longirude and latitude locations (hat we re measured (0 creare each darum , and rh e way in wh ich rhe measu red locario ns are re fere nced (0 rh e
AD2? and NAD83 will appea r up ro 40 m offser
from each other in latitude and as many as 100 m off in
longitude. T hese differences vary by region and may be
cu ra te analys is resulrs . The discussion of da(U ms, {Q rhis poim , has focused on rhose relared to horizomal sur faces. When worki ng wim elevation dara, such as a DEM , GIS users musr also be awa re thar daru ms have also been developed [Q desc ri be rhe verrical dimension . A verrical datum allows us ro derermine where '0' eievarion begins and rhe heighrs of orher objecrs locared eirh er above o r below this poinr.
surfa ce of rhe Ea rth . Abo ur 25,000 poinr locarions were
The Narional Geoderic Verrical Darum of 1929 ( GVD29) was esrablished from 26 gauging srarions in
lI sed to crea re rhe NAD27 datum , each of which was referenced to a cemrallocari o n- the Meades Carde Ran ch
rhe US an d Ca nada and was a direcr efTorr in determining rhe posido n or mean sea level. The Norrh Ame rica n
loca ted in Kansas. Some 270,000 locatio ns were used (Q c reate rhe NA D83 datum. Instead of referencin g loca rion s to a cem ralloca ri on on the Earrh 's surface, loeadons are referenced to the cem er of rhe Ea rth 's mass . The NAD83 datum has become the preferred datum for use in No rth Ameri ca alrhough man y GIS databases co minue to co n-
Vert ical Darum of 1988 (NAVD88) used addirional meas-
ra in landsca pe fearures described by rhe NA D2? datum. ricular datum. For insrance, rhe Cla rke Ellipsoid of 1866
uremenrs and adjusrm en rs provided a m o re reliable mea ns of esrabli shing eleva ti on surfaces and fo r rhi s reason NAVD88 has beco me (he preferred verrical darum.
was desi gned [0 describe rhe landsca pe fea tures of Nonh Ame rica, and is co mm on ly used in conju ncr ion wit h rhe
Elevarion d ifferences berween NGVD29 and NAV D88 in rhe US could di ffe r by as mu ch as 1.5 m in some areas.
Geoids and ellipsoids are ohen associa red with a par-
uremenrs fro m a large numbe r of elevario n pro files (Q c reate a single sea level co mcol surface. Between 1929 and
1988, ove r 600,000 km oflevel profiles were com plered, and changes that had occurred ro ex irin g elevation benchmarks were (aken inro acco unr. These addirionai meas-
NAD2? darum. Borh GRS80 a nd WGS84 rake a more global app roach and are thus bener sui led for desc ribin g wo rldwi de sur Fd.ces. Wh ereas GRS8 0 is co mmonly associ-
ared wirh NAD83, WGS84 ca n be rhoughr o f as barh an ell ipsoid and hor izomal datum. D atums are so metimes upd ared [Q renecr additio nal co mrol measuremems, shifrs in rhe Earrh's lan dm asses, or dara co rrecrio ns. When a da tum is updated, the most recenr yea r in which data we re coHecred is often appe nded
ro rhe datum name. As an exa mple. AD83/91 would indical< rim rhe NAD83 darum has been adj usted wirh addi rional dara rhar were collecred rhrough 199 1. These darum adjusrmenrs are o ften small (o n rhe order of cenrimerers, o r less) and are so merimes referred ro as darum realizatio ns.
The Geographical Coordinate System N ow thar a desc rip tio n of [he size and s hape or rhe Earth's surface has been prese nted , an enri o n is rurned (Q rhe merhods by w hic h landseape fea rures are locared on rhe Earrh's surface. Rene D escartes. a sevenreenrh-cen-
lU ry French marhematician and philosopher, devised one of rhe fi rsr wrinen methods fo r loca ring landscape fe-d(Ures o n a planar surface. D esca rres superi mposed rwo axes, o rienred perpendicular ro one anorher, with gradations alo ng bot h axes to create eq ual distance inrervals (Figure 2.4). The horizonral axis is rermed {he x-axis and 43
Chapter 2 GIS Databases: Map Projections, Structures, and Scale
31
90· North lalilude 9
8
7
2,6 6 y
0" latitude
5 4
3
30' 5,60' E
2 6,1 90' 5001h la1i1ude
o
2
3
4
5
6
7
8
9
x
meridian
Figure 2.5 GC'ogr"phic coordinalC's as determ ined from angular distance from the cenler of the Earth and referenced to the equator and prime meridian.
Figur~ 2.4 Exampl~ of point locations as idcmtific=d by Cartesian coordinate geometry.
[he ve rr ical is [he y-axis. The (ocarian of any po int o n rhe planar surface covered by rhis rype of gr id can be defined wirh respec( to rhe inrerval lines that it inrersec(s or chat ir most closely neighbors. This basis of determinin g locarion is known as a Ca rtes ian coo rcl inare sysrem . The most common coo rdin ate system is rhe sysrem of larirude and longirude. so meti mes refer red ro as rhe geograph ic coo rd inare sys tem . Although [his system can be rega rd ed as having x- and y-axes. angular measurements miller than distance-orienred imervals. esta blish rhe axes and imcrsecrions. The geograp hic coordinate sysrem has an or igi n at rhe ce nrer of the Earth and co ncains a ser of perpendicular Jines running rhrough the cemer [Q approximate the x- and y-axes of rhe Ca rtes ian coo rd inare sysrem. The o rienrario n of rhe perpendicular lin es is based on the rota ri an of rhe Earth . The Earth sp ins on an axis rhat, if ex rended, coi ncides very closely wirh rhe Norrh Sta r (Polaris}-th is ax is is called the axis of rotarian. Th is ro rari o n axis di vides rhe Earrh in half [Q creare a line of longitude that approx imares rhe y-axis. A line perpendicu lar to the lin e o f longitude fa lls a lo ng th e equatOr (Ea rth 's widest exrenr) ro crea re a line of lar itude that is co nce pruall y si mil ar ro rhe x-axis. Laritudes are expressed ro a maxi mum of 90°, in a no[(h o r sourh direction from Ihe equaror with the equamr deno[ing 0° (F igure 2.5) . Trave ll ing 90° norrh from the eq uaro r would leave YO ll ar rhe mosr norrhern poi nt of rhe Eart h and would be noted as 90· N. Sim ilarly a position half'vay between the South
Pole and rhe equ ato r would be referen ced as 45· S. The equator and o rh er lines oflatitude that parallel the equator a re also called parallels. Altho ugh the axis of roratio n splits the Earth in half, a reference line must be established from which coord in ares can srart. This reference line is referred ro as [he prime meridian, and altho ugh there are dozens in exisrence, rhe most widely recogni zed prime meridi an circles rhe globe while passing across the Brirish Royal Observdtory loca ted in Greenwich, En gland. Longiwde mea suremems a re made from this reference line an d a re designared from 0° ro 180°, in a wesrern o r eas rern di reccion. North America is loca red in a regio n (h ar is wesr of rhe prime meridian and is correcrly described as falli ng inro an area o f negarive lon gi tud e (a ll areas rh ar ex tend wirh in 180° west of rhe prime meridian) , alrhough many ma ps rhal are regional o r loca l with lIsually omir rhe negarive sig n. Other lines tim pass through the Norrh and So uth Po les (0 acr as guides a nd mark prominent longirude differences from rhe prime meridian are simpl y called meridians. The co nce prual co ll ecrion o f meridians an d parallels superimposed on the Ea rth 's surface is known as a grar.icule. The geogra phi c coo rdin a[e system ca n be used to locate any po int on [he Ea rth 's surFace. To achieve a hi gh level of precis io n wh en locating la nds cape fea rures, degrees are funher subdivided imo minures and seconds. There are 60 minutes (noted by ') wirhin each degree, and 60 seco nds (no red by") within each minme. A loca[ion rh ar is desc ribed as 38°30' laritude would ind icare a 44
34
Part t Introduction to Geographic Information Systems, Spatial Databases, and Map Design
LZI
LL
'\\
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\.
~
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\~
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II
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a. Mercator
""'"
\.
\~
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17
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b. Transverse Mercator
Figure 2.8 Thf: o ricn U.lio n of the Mercator and Tra nsverse MUC2tOr to the p rojection cylinder.
related [Q ae rial navigation. meteorological uses, and [Opographic maps. The emphasis is usually placed on mid-Iarlrude features of the world, such as those found in rhe conterminolls US. Detailed applicarions of this projection system should focus on smaller land areas, since
mainraining angular imegriry across large areas is difficult. Equal area or equ ivalent projections are wel l suited for mainraining rhe relative size and shape of landscape features when size comparisons are of imerest. Equal area projections preserve the siz.e and shape of landscape featu res bur sacrifice linear or d isrance relationships in doing so. A tenet of map projection techniques and an importam distinction berween equal area and conformal projec(ions is rhat areas and angles cannor be maimained simultaneollsly-you mUSt decide which is more important [0 your work. One example of (he equal area projection is the Albers ' equal a rea projecri on. T his projection is widely used and is typica ll y based on a secam conic map surface. Similar (0 rhe Lambert's conformal co nic projection. mid-latitude areas, which have extensive east-wesr orie n[adons, are bener candidates. This projection system has been selected by many US agencies as a base map projection. The Lambert equal area projection is anomer co mmonly used equal area projection , however, i[ is based on an az.imutha l map surFace. Azimurhal projecrions are llseful for maintain ing direction on a mapped surface. Az immhal projectio ns can be based on one (ra ll gem) or [wo (seca m) points of reference. Wirh one poilU of reference. distortion w ill occu r radially from the reference point but directions near the reference poim should remain rrue. For this reason, the az imutha l projectio n is appropriate for maps that have reladvely the same amoum of area in nonh-sourh and east-west o rientatio ns. When using two points of refer-
ence. direcrions emanating from either reference point should be true. The azimuthal equidistant project ion offers rhe unique abi lity of maintaining uniform direction and distance from reference poims. Azimuthal projecrions are usefu l for demonstrating the shortest route between tWO poims (Robinson et aI., 1995). Applications include those related to air navigation romes, radio wave ranges, and the description of celestial bodies. Azimuthal projecrion approaches include Lambert's equal area, ste reographic, orthographic, and gnomic. \'Q'hen pondering which projection sysrem ro use to descr ibe GIS databases, you sho uld consider the size of the area being managed, and whether maimaining direction o r area is more important (Cla rke , 2001). ProjeClion distortions and the resulring analytical errors can become magnified as the size of a management area inc reases. A conformal o r azimuthal projection should be considered when navigational or other directional properties are importanr. If maimaining the size, shape, and dis rribu rion oflandscape featu res is important. an equal area projection should be employed.
Planar Coordinate Systems Now that the process of taking shapes located on the surface of a sphere and projecti ng rhem on a flat su rface has been discussed. it is rime to explo re the coordinate systems that are useful in order (0 locare landscape features on a flat surface. These systems are known as plana r coordinates. or rectangu lar coordi nates. Previously, [he framewo rk for examining plane coordinates was introduced with [he concep( of the Cartesian coordinate sys[em . Th is same framework appl ies to planar coo rdina res, wirh a few minor mod ificatio ns. Fo r example, depending on the type of planar coordinate systems. coordinates are so metimes referred to as east in gs or nonhi n gs. An eaSling measu res distance easr of the coo rdi nate sysrem's origin whi le a northi ng measures distance north of the origin. These are usually specified by following the ' right-up' approac h; easrings are numerically organized so rhal positive measuremems begin ar the o rigin and increase to [he right (to rhe east) of the origin, while northings a re numerically o rga n ized so that positive measu rements begin ar the origin and inc rease up (to the north) of the origin. One inconvenience of this approach is rhar if a coo rdinate system 's origin is in the middle of a landscape, negarive eas tings and northings may occur, since some of (he landscape is (0 the west' a nd somh of the origin. These negative coordinares might complicate the calculation of 45
Chapter 2 GIS Databases: Map Projections, Structures, and Scale
is established for each zone so that the central meridian of each zone has an easting of 500,000 meters. This arrangemenr ensures rhar all easrings are positive. and that areas of zones can overlap, if needed. As the name implies, the UTM coord inate system uses the Mercaro r projectio n ro minimize distortion. The level of accu racy in the sysrem is assumed ro be one part in every 2,500 (Robinson er aI. , 1995). Another ve rsion of the UTM is th e military grid version. The mili ta ry grid version utilizes many princi ples of the UTM, yet di vid es each zone inro rows, and each row covers 8° oflatirude. Rows are denoted usi ng rhe letters C ro X wirh X occupyi ng the no nhern lati tude between 72° and 84° latitude. The military UTM can be used to further define blocks of zones into 100,000 meter sq uares. The state plane coo rdinate system (SPC) was developed in the 1930s by the US Coast and Geoderic Survey (k now n today as the US C hart an d Geodetic Survey), which crea ted a unique set of planar coordinates for each of the 50 Uni ted Srates. The SPC was originally designed fo r land surveying purposes, so that location mo numems co uld be permanently established. Unde r this system, mosr stares are spli t inro a smaller set of zones depending o n rhe size and shape of rhe stare. For in sta nce, Florida has twO zones and Ca lifornia has four. The SPC system uses eith er a Lambert's conforma l conic or Transverse Mercaror projection , the choice of which is usually influenced by rhe dimensions of the sta le (Lamben is lIsed for
distances and areas with in GIS softwa re. and rhey also make manual calculations more cha1lenging. As a remedy to these ci rcums[3nces. false origin s or False castings can be constructed to preve nt nega ti ve coordinates. This involves shifting rhe coo rdinate grid's numeric origin from the cemcr of a landscape ro rhe lower lefr corner (the fdrrhes( poim west and so urh locatio n o f rhe landscape), o r JUSt outside rhe lower left corner, so that all areas of rhe landscape are IOC3ted east and north of rhe origi n. and can be represenred by positive coordinate va lues. The most co mm o n coordinarc system in the US and Ca nada is that of lhe U niversal Transverse Mercato r (UTM). which has even been lI sed to describe rhe surface of Mars (Cla rke, 200 I). T he UTM system has been used for remOle sensi ng, foresrry, and topographic map applications. and if has been used in many ocher countries duc. in parr co its world-wide applicabili ty and relative simplicity. The UTM system d ivides rhe Earth into 60 vertical lo nes, each zone covering 6° oflongirude. The zo nes 3re numbered I-GO starring at 180 0 longirude (rhe inrernadonal dare line) and proceeding eastward. The ren zones lhac cover rhe conterminous US and Canada a re illusrrared in Figure 2.9. The system ex rends no rrh wa rd (0 84°N iarirude, and sourhward 80 0 S lari[t1cle. A uni versal polar stereographic (UPS) grid system is used for th e polar regions. Coord in ares for each zone scan af rhe equator for areas covering rhe nonhern hemisphere and at 80 0 S laritud e fo r areas in rhe southern hemisphere. A fa lse o rigin
,"
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longitud~ lin~s
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46
36
Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design
large wcsr-eaS( dimensions relative to north-south) and rhe dimensions of the zones of each srare. The level of accuracy of rhe SPC system is approximately olle parr in
and so on. The metes and bounds sysrem is inadequate roday because of the subjecrive and transiem narure of physical features; however, many of the original surveys were described in this manner and can be seen in properry maps associated with land surveyed prior ro abom 1830. In some of [he original US colonies. metes and bounds were used in [he Head right systems char were developed [Q distribure land ro setders. Other more regular systems of describing land, such as rhe lorrery system used [0 describe abom two-thirds of the State of Georgia, followed (Cad le. 1991). Unforeseen problems wirh rhese
10,000. Mosr of rhe o riginal surveys in the US and Canada were described by metes (rhe act of metering. measuring, and ass igning by measure a straigh t course) and bounds (a refe rence [0 general property boundaries), These were systems of describing real esrare (hat wefe based on English
ammon Law, and involved describing rhe boundaries of by physical land feafllres such as streams, trees,
a property
a. First Standard Parallel North
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10
11
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23
24
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27
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2. 10 Origin (a), township (b). and
~ction
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NW 114 , NE 1/4, Seclion 17 NW1I4 NE 1/4
NE 1/4 NE 1/4
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E 112 SE 114
NW 114
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Su("\'~Y Syst~m.
47
Chapter Projections, Structures, and C hapter 2 GIS Databases: Map Projections. and Scale Scale
37
early ea rly surveying systems included includ ed ex((nsive extensive fraud. fraud . surveying in g errors, a lack of consisrency co nsisten cy in the surveying processes, hostil ity fro from n~tives. m nat ives. and an d remoteness of the rhe (ccrain. te rrain. As a hostility resu lt. several rectangul ar sysrems systems of describing land were resulr, reCtangular wore proposed, rangi ranging ng from 6 or 7 square miles to ro 1,000 or or 2,400 acres each (Ladell, (Ladell , 1993). Thes< These rectan recrangular gular lOWI1townships were mea meant nr CO fO fac facilitate ilitate the rhe development of communities (Srew:m, (Stewart, 1979). Th recrangular US Public Thee rectangular Publ ic Land La nd Survey System CSlablished in 1785 by ,he the US (PLSS) system was established
mi les square, miles squa re, and is further fu rther divided into ineo sections, secrions. with each section measuring one o ne square mile; m ilei {here there are 36 seccions wirhin within a rowns wwnship. Sect ion s are numbered 1-36 [ions hip. Seccions with [he the 361h 36th secrion sect ion being ar ar ,he rhe lower lowe r ri righr-hand glu-h and corner ner of a [Ownship. [Qwnship. Sections Sectio ns can ca n be apportioned appo rrioned inca in to smaller compo nems such as quaner quarter sections. sectio ns. half secsmalle r components tio ns, oorr quarrer quarter quarrer seCTions. sections. In {he co nve ntions, the naming convention, rhe the smallest smaJlesr component is named nrsc, first, starting scarr ing with rhe portion of a section thai a piece of land resides, the porrion secti o n rhar res id es, then the rownship. ipal meridian. principal rownship, range, and name of the princ
Congress as a national nalionaJ sys system tem for the meas measuremenl uremenr and subdividing of public lands. Approximarely Approximately 75 per cem ce nt of the U US was subjeCt su bject to measurement measuremenr by rhe PLSS. The
An example would be the NWI/4, NE 114, Sec,ion Sectio n 17, Ihe NWII4, T2N., R3W, Meridian. T2N R3W , M, Mt Diablo Meridia n. Use of ,he [he recta rec,angular ngular
origina l 13 colonies, which com origin.1 composed posed a significanl significant holding, were nor not included in thi [hiss system sysrem because rhey they had already al ready been invenroried inventoried ,hrough through meres and bounds syslems. tems. In In 1872 a similar simil ar system, sysrem , the rhe Dominion Land Survey, urvey. was creared for adminisrration adm inisrrarion of the Dominion
Lands of western objectives o f ,Ihe he PLSS aand nd L1nds weSlern Canada. Ca nada. The objec' ives of Dominion Land Survey were [0 ro quantitatively quanrirariveiy measure
previously non-planed land. land , create c reate land port pa rdons io ns th
system systems is limited limired within CIS GIS to visualizing survey sysrem sys rem themes, themes. but it is likely. likely , especia esp~cia ll y in namra narurall resource reso urce ap plicar ions, that you you will encounter enco umer th this applicadons. is system as your particularly work wilh GIS progresses. This wo rk wirh T his is particu larl y (Cue true for fo r projects that rhat involve invo lve property properry ownership ownersh ip issues issues., as most
boundaries cove red by We me properry locarions locations and boundar ies in areas covered rectangular rectangu lar systems sys Lems are described desc ribed using us ing sections. sections, tOwntOWI1sh ips, ranges. and principal merid ships. meridians. ians. At some poim po int you YO LI may be reqllired required co ro re-p re~p rojec rojecrt ow nership ne rsh ip bounda boundaries ries rhe rectangu derived from the recrangular lar sysrems so [hat [haT (hey rhey match march rhe the projection projectio n systems used in Olher mher GIS dar-abases. databases. Mismatched ns h3ve have been [he Misma(ched map projectio projections the bane b"1I1e of many sparial spa rial analys analysisis efrons effof{s and doubtlessly, do ubrless ly, [here ,here are many man y published publ ished and reported repof{ed smdy study resules res ults rh that at suffer suffe r from Ihis th is malady. The likelihood likel ihood is ,ha, ,hat there rhere will be future sr udies and wrinen prod ucts {hat many furu re sruclies wrirten products (har will also ro map map projection projecrion problems. One of rhe Ihe reabe subjec, subject to sons fo rorr projections projc rions problems is [hal thar many GIS GIS users are unaware of the intent of projections unaWate projec ti ons and filii fail co ro realize thar there are sub-componellls. that chere sub-com ponents, such as coordinate coo rdinate sysys[CI11S tems and datums. datum s, which neneed cd to bl! be considered co ns ide red when projecrion. Anmher Another conrriburor working with a projection. cont ribu tOr (Q to This rhis
:Ire are referred refe rred to as ~lS either eithe r being north or o r south somh from From rhe the baseline (e.g., 2nd parallel north basdine norrh or 71h 7th para paralld llel sou so uth lh of of a
problem is ,he the inabiliry inab ili ty or of many ma ny deskrop des ktOp GIS GIS soliware softwa re programs [0 to manipuhne manipu late spatia s pariall darabase database projections. Although projection proj ect io n .t!gorithms algo rithm s are becoming more desktop CIS ~IS software sofrware programs, programs. they are ryptypcommon co mmon in deskmp
baseline).. Guide meridi meridians baseline) ans were also established escabl ished at al 24mile intervals cast and west of the pri inrervals easr pr innciple ciple meridian. The guide meridians were established esrablished astronomically astronomica lly and are numbered relarive rel ative ro to their thei r posicion east or o r wes wesrr of rhe the principle meridian (e.g .. 4rh (e.g., 4th meridian east. e'dSt, 8th 8th meridian west of the principle princi ple meridian). The grid of meridi:ans merid ia ns
ica ically lly more robust robuS! in a full-realured fu ll -featu red GIS GIS sof,ware software program CIS user or ana analys yo u musr must be cogniz31H cognizanr of gr3ms.s. As a GIS lyst,t, you the projections that are associated wirh spatial spatia l databases. Wh \Xlhen en obtai ob tainin ningg GIS GI. databases. databases , either eith e r from wi within rhin or from ooutside U[side an organization. o rgan iza ti on, it is critical to obrain obtain as mll muchh info information rmarion as possible abou aboulr rhe the srrucrure structu re of rhe the
and pa rallels rall els creales c rea tes blocks, each eac h nominall nominallyy 24 miles
data ,. which wh ich should sho uld at leaSt leas[ minimally include info informarmarion aoour rhe map projection. projecrion. Information U[ sprlli~1 spatial lion .Jbour Informadon abo 300m databases darabases can ca n be stored s[Qred in a metadara metadata document. documenr. We'll discllss disc llss meradar3 meradara in more detail deraillarer laler in this chapter chapre r bur turn ou rurn OUtr anC'mion ane mi on now no\.... ro to GIS data s(ru st ructu turd res..
that extended e:lst east and west of rhe the principal meridian
(Ihus (thus parallel to ro the rhe baseline). Parallels are numbered, and
square. fo rming mnge range lines (run· (runTownships are created creared by forming ni ng north and soulh) south) .md and (Ownship township lines (running (ru nn ing easT east ning .and Jnd wesc) west) boch borh :H ar six-mi le imervals. Each [Qwnship township is six
48
38
Part 1 Introduc1ioo 10 Geographic Informalion Syslems, Spalial Dalabases, and Map Design
GIS Database Structures Like moS[ digilal fi les, spa(ial databases muS[ be conS[ruc(ed so rim (hey can be recognized and read by a GIS software program. Although GIS manufacmre rs have developed the ir ow n data fo rmats. there are still [wo commonly lIsed data structures for GIS clara: rasrer and vector. Many GIS manufacrurers have created their own spatial dam formats bue al most all make lise of ras ter or veeror formar principles. T he ras te r and vector data structures are as diffe renr as n ight and day, and borh have strengths and weaknesses to be conside red for use in va rious appli-
S(Qres information abom the resources or characrerisrics is associated with rhe cell. These values can be numeric (e.g. remperam re, e1evarion) o r ca n be descrip tive (e.g. fo rested, prai rie) an d are used to desc ribe all areas represented by each cell. Some common rasrer GIS databases include rhose rela red (Q satellire imagery, digiral elevation models. digi(a l orthophotographs, and digita l raSler graphics. AI(hough (he particular fo rmal {how they are sto red digirally} d iffers a mong raster databases a nd the tech niques used to creare raster databases may also diffe r, rhe raster approach co scoring spatial information is consistem: a sysrem of cells (usually sq uare) that covers a la ndsca pe.
cations. Ahhough many of the applications in this book involve vector databases, most GIS users will evenrually find themselves using a combinati o n of both raster and veem f databases.
Raster data structure Ras te r dara Strucrures are co nsrructed by whar can be considered grid cells or pixels (picture dements) that are organized and referenced by their row and column position in a darabase file. Raster data st ruct ures arremp{ (Q divide up and represenr the landsca pe duough rhe use of regu lar shapes (Wolf & Ghi lani, 2002). The shape (har is almost exclusively used is (he sq uare (Figure 2. 11), yer other shapes can also cover the Earth completely and in a regular fashion. such as {riangles and octagons. For each cell or shape in a rasrer database. an atrrib ure va lue [har
... r-
G~ n ~ric
Satellite imagery is a term used ro describe a wide array of products generated by remore sensors comained widlin satellites (Figure 2.12). Satellites are either positioned statio nary above some loca ti o n on rhe Ea rrh , o r ci rcumnavigate (he Eart h using a fixed orbit. Although satellites have been sent inro deep space, and have returned imagery co Earth. nam ral resource management is gene rally co ncerned only wirh imagery rhar provides informa[ion abo ur pla neta ry fearures. Whe n view in g sarell i[e imagery of the Ea rth. ir may seem as if [here is no relief associared with the landscape. since the images were collected from a very high e1evarion (l00+ miles), however, you ca n assoc iate elevation data (OEMs) w ith raster images, and subsequently view them in rhree dimensions.
Raster or grid cell
Columns Figure 2.11
Satellite imagery
ra.Sler dala SlrUClure.
Fig ure: 2. 12 La nds:H 7 sa te:lli l ~ image capmred using Ihe: Enhanced Thema t ic M a pp~ r Pl us Sensor that shows the Los AlamoslCe':rro Grande fire': in May 2000. This simulalcd natural colour compositc image was crealcd through a combination of Ihr« ~nsor bandwidths (3, 2, I) operaling in the visiblC' speCIrUIll . Image councsy ofWa)'llC' A. MillC'r. USGS/EROS Data Ontt"r.
49
Chapter 2 GIS Databases: Map Projections, Structures, and Scale
39
Digital elevation models Digital elevation models (OEMs) are databases that con-
scanned) th at have been registered to a coo rd ina te system.
min informacion abol![ the [O pography of a landscape.
ally corrected through the use of precise positional data, OEMs, and information about the platform sensor (e.g. camera system used). The majori ty of the US has been represented by d igital o rrho pitotograph y. created through a mapping program sponsored by the US Geological urvey (USGS). Digital orrhophotographs are generally made avai lable in porrions that match [he extent of USGS 7.5 Minute Series Quadrangle maps, and are often reFerred to as digital orrhophoro quadrangles (OOQs). Since the USGS Quadrangle maps cover large grou nd areas (7.5 minutes of lo ngitude and latitude). digital orrhophorographs have been developed w cove r portions o f Quad rangle maps as
The grid cells in these data bases cama in measurements of
elevation across a landscape (Figu re 2. 13). It is possible
(0
derive rerrai n models from OEMs char represent aspect,
gro und slope classes, and shaded relief maps. It is also possible co perform a wide va riery of rerrain-based analyses, such as landsca pe visualizario n o r wate rshed ana lysis. Elevation data can be co Heered by a variery of mean s,
including sensors located on satellite or aerial platforms. o r phorogrammercic techn iques chat use aerial phorography in co njunction with CPS dara. Elevation data may also be collected from the bonom of wate r bodies such as oceans, lakes. or screams, th ro ugh rhe use of so nar and acoustica l se nso rs operated fro m bOa£s o r submersible wate rcraft. This data can be used w create cross secrion profiles or w support enginee ring projects that involve bridges o r other infrastrucru re.
Digital ortbophotographs Digital orthophotographs are essentially digital ae ri al photographs (or ae ri al photographs that have been
Fi gu r~
2. 13 Digi tal
~ l ~vali o n
model (OEM ).
The displacement com mon w aerial phowgraphs is usu-
well. Many count ies in the US have also commissioned more derailed d igi tal o rthophorographs, as have private
co mpan ies. Digita l o rrh ophotographs provide a data so urce fo r rhose interesred in ob£a ining a relatively fine scale image of landscape o r in obtai nin g a base data layer
fo r digitizing landscape features (Figure 2.14). One of the strengths of digital orrho photographs is that each image is geo referen ced wa coo rdinate and projection syste m; the refo re. GIS users can use a deskwp GIS software programs w digitize and crea te GIS databases
Figun 2. 14
Digital onhopholO
quadrang1~
(DOQ) .
50
411
Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design
using 'heads-up' digirizing. The rerm 'heads-up' digitizing indica res that the person doing the digitizing is looking ar a co mpurer screen (i.e., (heir head is up) rather than a
digitizing table (w hich requires rhem [Q look down ) when digirizing landscape fearures. Through heads- up digirizing, you can quickly creare a GIS darabase from a di giral orrhophorograph image on a compurer screen .
Digital raster graphics Digital raster graphics (D RGs) are digirally scanned represemarions of [he USGS topographic maps (Figure 2. I 5).
These maps cover the emire United Scares, are published
at several different scales. comai n a wealth of information, and, very importantly for GIS projec[5, are avai lable as digital databases that can be lIsed by most GIS sofrwa re pro-
grams that have raster display capabilities. Within Canada. [he Nat ional Topographic D ara Base (NTDB) is maintained under the adm inistration of Natu ral Resources Canada. The NTDB co ntains vecror databases (hat are similar to rhe USGS ropographic maps and are availab le in
severa l digital formars ar scales of I :50000 and 1:250000. The most detailed of [he maps produced by [he USGS are rhe 7.5 Minute (7.5') Quadrangle maps, which have a published map scale of I :24,000. The 7.5' refers [0 [he total amounr of laritude and longirude , in degrees,
on rhe Earth's surface rim each Q uadrangle represenrs. In some cases, fearu res from the Quadrangle maps are available in a vecro r for-
mar as a digi,al line gra phic (DLG) . Si nce [he 7.5 M inute Series maps typically illustrare cul[Ural resources, such as roads, large buildings. elevation conrours, and narural features such as water bodies, similarly ro DOQs, georefe rell ced Quadrangle raster databases can be used as a base layer for digitizing orher landsca pe features ofinreresL Quadrangles also provide a great resource for rhose who wam ro learn more about a landscape .
A closer look at USGS 7.5 Minute Quadrangle map Given [he broad avai lability of [he 7.5 ' maps, rheir cartogra phic detail. and their populariry as a GIS darabase template for foresr ry and natural reso urce app lic3rions, we will closely examine one of rhese maps. and describe some of the more norewonhy components. Alrhough this close r look is focused on an example using
[he Corvallis Quadrangle from Oregon (also rhe subject of rhe previous figures demonSHaring a OEM and DOQ), the reatures
desc ribed below should be avai lable on most orher 7.5' maps. In panicular, you should look closely at rhe information (hat appears
....... - ..:..:.-='
-~...:-
Figure: 2. 15 Digi[al raslc: r graphic (ORG) image:.
Quadrangle maps also provide information primed along rhe rop margin or the map, bur rhi s is gene rall y much more limited and a subser of what YOli find along {he bonom
margi n of [he map. A[ [he time this book was being devel o ped a digi[al copy of ,his 51
Chapter 2 GIS Databases: Map Projections, Structures, and Scale
41
Quadrangle could be downloaded from page 8 of the list of quadrangles avai lable at Imp:llwww.reo.gov/gis/data/ drg.,files/indexes/orequadindex.asp.
Figure 2. 16 Corval lis Quad rangle with neatJines around map areas be described in detail.
10
Lower right corner The lower right corner of the Corvallis Quadrangle (Figure 2.17) contains a representative scale (1 :24,000) and several scale bars with units exp ressed in miles. feet. and meters. lnfo rm arion is also provided for (Wo comour inrervals: the main contour inrerval 0(20 feet (s hown by solid lines on the map's su rface) and a seco ndary contour inrervaJ of 5 (eet that is represented by dashed lines. The reason for dual conlOur intervals is that the Corvallis Quadrangle includes a mixtu re of moderately-sloped, forested areas and relatively fl ar areas where urban and agricultural development has occurred; the dashed five-foor contours are lISed for [he flarrer areas. Below the scale bar is a sraremenr about compliance with the National Map Accuracy Standards, a subjecr rhar will be examined in more derail shoerly. Below the contour inrerval descriptions. there is informat ion abom where to purchase hard-copies of [he Corvallis Quadrangle and the ava ilability of topographic map and symbol descriptions. Moving to rhe right. a graphic ca n be seen that indicates rhe location of rhe Quadrangle relative (Q rhe Oregon State border. Below [his graphic, a note clarifies (hat [he purple areas of (he map were updated with aerial photography captured 111 1982 {and hence edited in
, l
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!,
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,.
..
:
r
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r
\
i
....... ,,/ ""-'J"-'._ """_.co _ _l4M _ ...._ _. _ __
<~
.- ............
_000 5.0. . ..
Figure 2.1 7
"".~
,,"U"';' " ...1>0._ Dl.....,. 0I:Il0lA00 aont.,OIIIUU... _
. " . ~ .... "" .. .... It ... , .. . ~" ....... _
u..L&cOlOGC.OI.~"
- -- .-~.---
--- ------..... _ _--_Y,,-.. --.-. --......... .._r-, ' __ I
tot<'O"'~ ' .. 'L ..... ' :10 ,tU 1',_,,,, .., " 00,· •""'•" ,_ ...."".D _...._"'"" """oc.o..
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CORV _ _ALUS.OREO ....
'r~
" 'l) U-JJ-OU
- ,,,
,-
1'toO'
Lower right corner of the Corvall is Quadrangle.
52
42
Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design
1986). Moving ro JUSt below the bottom right corner of the map, you will fin d a legend for road ma p symbols. The name of the map is liSted (CO RVALLIS, OREG.) and the map is descr ibed as ,he bottom righr corner (SE/4) of rhe 15' Corvallis Quad rangle. The Ohio code description, sometimes refe rred
(Q
as rhe USGS M ap Reference
Code, is liSted as 44 I 23-E3. This means rim the map is locared in a block of geograph ic lar irude and longitude that begins at rhe intersection of 44° larirude and 123 0 longirude (USDI US Geological Survey. 1995). Each block oflarirude and longirude can comain up ro sixry-fou r 7.5' Quadrangle maps rhar co mprise an eigh r by eight matrix . Leners are used from A- H [0 denoce rows starr ing from rhe corner of rhe latitude and longitude inrersecdon and moving upward. umbers are lIsed from 1- 8 movi ng westwa rd [Q signify columns. Thus, rhe Corvallis Quadra ngle is located in [he matrix at the inrersecrion of rhe 5rh row and the 3rd col umn (Figu re 2.18). Many map disrri buro rs use th is Ohio code system to idenriry rhe relative location of quadrangles. T he yea r of rhe o riginal aerial ph orography (1969) used to create rhe map is liSted, as is the laSt revision yea r (1986). The bot-
coo rdinates (44°30' and 123° 15') rhat desc ri be itS locarion. JUSt ro the east and slighd y north of (his corner, a cross appears. These crosses are loeared in simila r loca-
tions relative [0 all four mapped area corners and demonstrate how the mapped su rface co rn ers can be adjusted [0 switch rhe datum of rhe ma p's projection from AD27 ro
NAD83 ( AD is short fo r North American Datum). A full Unive rsa l Transverse M e rca to r (UT M) casti ng
(479000 m) is found JUSt weSt of this corner and a full UTM northing (4928000 m) to Ihe north. Fu ll UTM coo rdinates are also given along the o pposite map co rn er.
H ash marks [hat extend o utward from {he mapped area indica te the locat ion on rhese coordinates relative ro rhe
rest of the map. T hese hash ma rks continue around rhe emire peri meter of rhe map's su rface. but the last three
digits of rhe easri ngs and notthings that accompany the marks are nOt primed in order
[0
co nserve map space.
LOlUty Itft comty The lowe r lefr co rner of the map (Figure 2. t 9) States thar [he map was produced
by
(he USGS w ith control o r rete r-
ro m corner of the mapped area has a set of geographic
,
,
~
M
~
~
45' H
G
"--":',
F
(,
,
,
E3
E
D
C B 1'oIoo<""".,00K1_
10,()()O<_ .. ..t ""...
1927 ...."' ........ U"Ie.'~~ a~ 0. ..... <_4,."•• ,.,_.
" .. 1 ~ .....
A
44' 8
7
6
5
4
3
2
I
1000 .... .. ' U"..WI • •• • ..... w "'.... tot I~C Uk>. _ 1 0_,,101_ 1"tId"'.. U """", ""-"-' _ _ "DoIurot 1M.),
._--- ......_---.01 ___ ... _--.._ __ -u. .....
T. pIocI _ _
t1-.frUoI_ .. SIt1Oo _ _ ..... .....
,.,. .....
Figu.rc 2. 18 Ohio codc loca ti on of the Corva.l lis Quadrangle.
__ _......,-....
Figure 2. 19
..
--'.... """...... "",.
w- .. .... _ , _ _ "" _ ...
...... -
Lower Itfl corner of Ihe Corvallis Quadrangle.
53
Chapter 2 GIS Databases: Oatabases: Map Projections, Structures, and Scale
USGS, US CoaS! CoaSt aand ence points eStablished established by [he rhe u. G ,U nd Geode[ic Su Survey, d [he Geodetic rvey, an and the S,ate Srare of Oregon. Mapped surFaces F.rces were taken raken from 1967 ae rial phorography photography and were fiel fieldd checked checked in 1969. Projection Projeclio n info rmacion rmar ion is rhen then listed lis red and ci,e the base s" su rflee rface is described as a polyco polyconic ni c projectio n, NAD27, usi using S,a,e Plane No nh ng [he the Oregon Srare Nonh Coordinate Coord inal< System. ystem. A line follows to (Q describe rhe the coloring of {he rhe UTM coordinmes coordinates that th iU are lisred listed around [he rhe perimeperime~ [er rer of ,he the map, and [he the UTM zo zone ne (10) [hal rim was used used.. In Instructions SHlIcrions for fo r convening converting rhe rh e mapped su surface rface from NAD27 to NAD83 are also given and provide a qquanti[auanti tarive tive assessme assessmelH nr of how these twO datums darums differ in the th t quadrangle area. This information is usefu usefull fo forr pOlcnrial porenrial coo rdinate coord ina te conversio conversions ns in a GI GIS . In addition. the [he [ot( text srates states (hat Ihat only landmark la ndmark buildings arc are shown in map areas [hat
are 3re rimed tinted red red.. Landmark buildings are [hose those dlat that serve rhe the public. have cu cuimrai ltu ra l or historical sign significance. iftc3 l1ce. or are unusually large in relation relarion ra to surrou surrounding nding buildings. rhese S[3(ements, To [he lhe righ[ righl of th ese Slatemen lS, a graph ic at of rhe rh e map's o rienrarion rientation [0 m severaJ seve raj definidons definirions at of nonh norch is shown shown and (exl text bc:low below the graphic explains that the orientation is fro trom m the map's center. The longest nonh line is lopped topped by a Sta rr symbol symbol and refers to to astro asrronomic nom ic norch. nord,. The line [Q to [he the left lefr of astronomic as trono mi c norrh nonh refers refe rs to grid norrh nonh (GN) and is ooriented riented 0°13' 0° 13' (0.22°) wesr west of ot astron astronomic omic nor[h. north. Grid north norch is (he rhe dire direcrion rion in which rhe Oregon State Stare Plane Coordinate Coo rdinare Sysrem ysrem is referenced. reterenced. The line ro to (he rhe declinarion right at ofaasrronomi st'ronomicc north shows rhe the magneric declination ((19° 19 east), nonh. [hat easr), relative relarive [0 [Q astronomic norrh, thar existed exiSled in 1986. Since ince magnetic nonh north can Auctuate AucUlate from trom yea ye-A rr (0 [Q yea r (eve n small daily shifts are also possible), ,he year the dale date of (he the measurement measuremenr is important imporranr for to r those who wish [0 (0 conCOI1vert ven [heir their data data to march match rh thee map' map's projection. at rhe sourhwesr Geographic coo rdinates appear ar sourhwest corner at of rhe mapped su surface. rtace, and an d rhe UTM coo rdinate abbrev iations abb reviati o ns are lisred li sted.. A full lisring listing of Stare Plane Coo rdin Coord inates a[es (NAD27) are liSted listed and also ma marked rked by harch lines to [he lhe norrh nonh (320,000 fee feet) r) and eaSt (1,260.000 (1,260,000 feet) leer) of ot this corner. corner. FilII Full stare srare plane coordinares coordinates are also listed the upper right co rner. Unmarked hash marks along rhe righr corner. around the rest resr of {he the map's perimerer perimeter denote denore gradations thee stare ates. Periodicall Periodically, of rh sta re plane coordin ares. y. range and township divisions appear as longer longer dashed dashed. lines oonn the {he surface. Alo Along map's surflee. ng [he the la[i[udinal latitudinal axis of Figure 2. 19, I 9, you em ca n see R. 6 W. and R. 5 W. T This his signifies [he rh e d ivision between berween Range sion Ra nge 6 and 5. west wesr of (he the reference meridmerid~ ia n (Willamcne) (Willamene) rhar [hat was used lIsed for cre-dring crearing rhe the PLSS for tor Oregon. Larger numbers ap appear pea r on the map mapped ped surF.rce surface and describe the boundaries between secrions sections and donation (DLCs). As menrioned earlier in this rion land claims (OLCs). earl ier in this chap0
43
[(r, rer, most mOSt of the western wesrern U US was was o originally riginally divided according [0 to [he PLSS. The PLS PLSS splir spli[ regio regions [he US rhe PIS. ns of lhe U in a grid of tow rownships nships (a (approxima pproximately tely six by six mile blocks created by rhe intersecrions inrersecrions of mwnsh township ip and range lines) (hat thar were created from a reference meridian with townships creared towns hips furrher divided into secrions ections measuring approximarely approximately one square mile. Section ection numbers range fro m 1-36 in almoSt almost all PLSS PI_SS srares srates rhough though you yo u might find I1nd an occasional section secrion numirregularit ies warr3IHed wa rranted bered 37 where measurement irregularities adjustments to rhe adjus[mems the PI..5S. PLSS. Some orne Slares, stares, including Oregon ., Florida, and New ew Mexico, had adopted adopred less rigorous land measurement rems [hat rhat superseded the lhe PLSS. measu rement sys systems PLSS. Through rhese these sysrems, systems, sealers serders could stake srake claims cla ims [Q (Q lands and rhese systems were ge generally nerally re!erred refe rred to as DL DLCs.. DLe DLC bo boundundaries are numbered numbered. srart srarring ing with 37. In ge neral, ner. l, green shadin shad ing g (gray in the black and while white image of Figure 2. 19) is used to represenr represen t fo rested or o r nar2.19) foresred ural lIral areas. areas, and no shading shad in g is used to represeO( represent developed areas. Roads. Roads, streams, screams, and other cultural and "arural natu ral landscape featu res are arc also visible rhroughour throu ghout [he the map. map. National Map Accuracy Standards Accord According ing (Q to information informacion illustmed illusrrared in Figure 2. 17. I 7. rhe the orvallis Quadrangle map complies wirh Corvallis with [he lhe Na[ional Natio nal Map Accuracy Standards Standa rds (NMAS). ( MAS). The U US Bureau of [he rhe Budge[ Budger ooriri ginall y developed [hese these standards in 194 1I so that guidelines wou would ,hal ld be available for [he establishment of ho rizontal and vertical horizontal verrical map accu accuracy racy a[ at multiple scales. nes were also intended [0 The guideli guidel ines (Q help protect and inform consumers abom aOOm rhe the qualiry of map map product products rhey they acqui acquired. red. The guidelines assume that thar orga organizations nizations claiming adherence ad herence ro to NMAS guidelines are responsible for fo r ensu ring compliance. lasr revised in 1947 ensuring co mpl iance. The NMAS MAS was last (Thompson , 1979) (Thompson, 1979).. The guidel ines (Figure 2.20) provide horiwnral horizontal accuracy standards for map scales larger [han I :20,000, and for scales at a[ I :20.000 :20,000 oorr smaller. For [he lhe larger map scales, no more than 10 per cem of ot rhe points verified shall be in error by 1/30th an inch. inch, as measured on rhe the map sursu rerror I130r h of o f an face smaller 0th of an F.ce.. For smal ler scale maps, this tolerance rolerance is 1/5 I150rh inch. The Co Corvallis rvallis Quadrangle Quadra ngle Falls F.rlls in Lhe the latter laner Cl catetegory. To (esr resr ror tor NMAS compliance. comp liance, locations or or elevations from map points poims are compared to their actual acrual measuremems, where locations locadons or or elevations e1evarions have been derived by highly high ly accurare accurate ground surveys. Within these comparo nl y 10 per cent cem of (he isons, only the points ca n be in error by more lhan than me Lhe tolerance. tolerance. Table 2.1 2. I describes the rhe tolerances in relation ro some of rhe the more common commo n map scales. scaJes. For [he ngle, [his ld be 40 feer, feet, the Corvallis Co rvallis Quadra Quadrangle, this [hreshold threshold wou would indicating ind icating rhat rhar not nor more {han r.han 10 per cenr cem of lested 54 rhe rested
44
Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design
Umlnl Stllt~ NfltlOlUU Map /\((1IMy SIIIIIdllfds \'oOI. __ "'doo _ _ _ .... pNI .. "''''~'''''I''' .. hIrto'''!,-ill ......... , .... 1>ro.od ..... 100 .u...wcl '" I"'ftCIf"I _"". I>uf .......... ""......"'" porunol>r - . . oI ..... idouI . ....our40, c!' «j ..,,,,";v!IlI .... blbhaI .... po..., ~ .. ~.
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2.20 US
alional Map Accur:.lIcy Slandards.
map points should diffe r from rhei r acma l locadons by more {han 40 feer. Vertical accuracy srandards are applied by using half of rhe como ur inrerval as a benchmark. For verr ical accu racy of rhe primary COntour lines (20' inrer· vals) of the Corvallis Quadrangle, the standa rd indicates char 90 per ce nr of [he elevation points checked along [he co ntour lines should nor be in erro r by more [han 10 feer. Typically on USGS 7.5' Quadrangles, 28 points are examined fo r accu racy verificatio n, represe nting a very small portion of rhe population of map poims. h is also worth noti ng thar rhese poims are not random ly selected,
TABLE 2, \
Map scales and associated National Map Accuracy Standards for horizontal accuracy Standard
bur represent locations that are read ily visible on the photograp hy from whi ch the map was created, such as road i nrersections, bridges, and other noreworthy srructures . Th us, yo u wo uld expect that the po ims represem [he mapped areas whe re the phorogramme[ric methods used ro crea te the map were [he most reliable for mapping purposes. It is also worth noring rhat 10 per cem of those po ilUS could be off by any distance, and [he resulting map would still be MAS compliant. Even though you a re assured compliance with a published standa rd , potenrial fo r errors related to the accuracy of locarion of landscape featu res can still be signif\canr . Our discussion of raster data s[rucrures and CIS databases above provided examples of so me of th e more common rypes of raster data and a few porenrial applications . Ras[er data provide a powerful means ofi nve nrory ing and analyzi ng Earrh 's featu res and no doubt new raster dara fo rma ts and applica rions will arise in future years. The usefulness of raste r CIS databases will depend on the needs and ca pabiliries of each narural resource organization. bur if is increasingly likely that raster data will be parr of every o rganization 's data holdings. parr icul arly as technological adva nces conrinue ro b ri ng economies to remmely sensed landscape data. We now {Urn our attention to the other primary data strucru re fo r sparial databases: the vecto r data structure.
Vector data structure Vector dara. as co mpared to raster data, is generally considered ' irregular' in irs construction and appearance. T his descrip ti o n is nor a co mm enr o n the quality or usefulness of vector data srrucrures. bur JUSt a characrerizarion of rhe rype of dara it represems. Vecro r data are generally grou ped inro three catego ri es: points, lin es, o r polygons. This categorization is sometimes referred (0 as rhe fearure model of GIS. Almost any landscape feature on rhe Earth ca n be des cribed usin g one of these three shapes. or a combination thereof (F igure 2.21). Points a re
f~~l
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:l
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:l
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2.21
Poine . line, and polygon v«to r shapes.
55
Chapter 2 GIS Databases: Map Projections, Structures, and Scale rhe mOSt basic of rhe shapes btl[ define the essence of all three fo rms. A line is a set o f co nn ec red paines. A polygon is a co ll ection of lines thal form a dosed loop. Poim. line, and polygon vecror fearures ca n be referenced by almos[ any coordinare system . To represent a poinr, a si ngle measure from each X (east-west) and Y (no rch- sourh) axis is needed ro describe rhe location of rhe poinr wi th in a coo rdin a te system. Wi th lines and polygons. each coordinare pair is referred [0 as either a node o r as a venex. The coo rdin ates of poinr, line, and polygon fearures allow calculations of distances berween features. and in rhe case o f lines and polygo ns, dimensions of featu res. Point features have no dim ension, or size, beca use a sin gle pair of coordinares represenrs rhem. A coord inare pair does nO{ allow for length, area, or volum e calc ulati o ns. Line feawres a re singl e dim ensio n shapes in which coo rdina[e pairs can be used ro cakula re a length. Polygon features are rwo dimensional in naru re. wirh coo rdinate pairs being used ro nor only cakulare a perimeter d isra nce a round a polygon. but also being used ro calculate an area within rhe polygon. Topology Mos t GIS software programs Sto re loca ti on informacion (X, Y coordinates that describe the position of landscape feam res) in separate GIS dacabases fo r each ropic of inrerest. For example. the coordi nates that are used ro describe a roads GIS database are separate from the coo rdinales char describe a ",earns GIS database. Although moS( of the location information thar defi nes vec[Q r feamres will be rranspa rent to users of desktop GIS software programs, it is vital in eS(abl ishing and mai nrai ning topology. Topology describes the spatial rela tions hips between (o r among) poinrs. li nes. and polygons. and is a very impo rtant co nsiderarion when conducling spatial analyses. Topology all ows you [Q delermine s uch thin gs as the distance between points. whether lines intersect, or whether a poi nr (or line) is located within the boundary of a polygo n. Topology can be defined in a number of ways but the most common defi niti ons invo lve aspects of adjace ncy, connectivity, and conrainmenr (Figure 2.22). Adjacency is used ro describe a landscape featll re's neighbo rin g features. You might use adjacency relationships to describe polygons rhat share common bo rders (e.g., in suppo rt of greenlip requi remenrs in a forest managemenr conrexr). or to identify lhe lines rhal make up a pol ygo n (a rea). Connectivity is rypically used (0 describe: linear ner\vo rks, such as a network of culverts thar m ight be connected by drainage d itches or a sr ream network. Co nnectivity would
co Adjacent polygons
Connected stream network
45
One polygon contained inside another polygon
Figure 2.22 Exam ples of adjacency, conn~ctivity, and containment.
allow you ro (race rh e fl ow of water (hrough [he strea m system. You can also incorporare direction in [heir descripti o n of co nn ectiviry. Based on the topograp hy of a la ndscape in which a culven system is siruared , you cou ld determine the ove rland fl ow paths o f warer rhrough the system, given tha[ water flows downhill. Conrainmenr allows you (0 describe which landscape features a re located w ith in . o r inrersect, the boundary of polygons. Conrai nmenr in fo rm at io n can be used ro describe (he well locations (po ints) or the power lines (lines) that are located w ithin a pro posed urban growth boundary, for example. In order for ropology (Q exist. a system of coding topology thar can be undersrood and manipulated by a computer must also exist. With GIS darabases co mai ning point features, there is li[tie need for anythi ng more than a file of coordi nare pairs (X, Y coordinates) since all points are idea ll y se para te from one anot her, and thus there a re no issues of adjacency. co nn ecti vi ty. and comainmenr ro resolve. H owever, you must be ca reful in describin g feature locations and linkages when using GIS databases containin g line and polygo n fearures. The spatial integrity of lines and polygons is mainrained by managing rhe nodes. ve rtices. and lin ks of each feature. A node is rhe starrin g a nd ending point of a li ne. and also represents rhe intersec tion of twO or more lines (Figure 2.23). A vertex is any poim rhar is nor a node but specifies a location or creares
¥
Vertex
.........Node - - - Figure 2.23 Examples of nod~$, links. and vertices.
56
46
Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design b. node coordinate file
a. Network of nodes, links, and polygons
5
C
Node
X
Y
1
0.5
2.4
2
2.1
3.1
3
3.2
1.7
4
4.7
3.3
5
5.4
5.0
6
3.6
0.5
4 0
2
2
6
Y
5 3
E
7
B
A
6
X c. topological relationship file
node
End node
Left polygon
Righi polygon
1
1
5
A
C
2
5
6
A
E
3
1
2
C
B
4
2
4
C
0
5
4
3
E
0
6
3
2
B
0
7
3
6
E
B
link
Figure! 2.24
V~Clo r
Begin
topological data. (a) Network of nodes, links. and polygons. (b) node coordin:lIc fi le, and (el topological rdationship file.
a directional cha nge in a line. A link, sometimes called an arc, is a line [har con nects points as defined by nodes and verr ices. Nodes, ve rt ices. and lin ks are usually numbe red and mainrained in a C IS database fi le [Q mainrain wpology. In a network of lines and polygons (Figu re 2.24), this woul d invo lve us ing num eric codes fo r network pieces (nodes and links) to identilY the node locations, the nodes cha r afC attached CO eac h link, a nd the polygo ns th at may fo rm o n either side of each link. Topology a lso allows yo u [Q inspecr rh e spa cial inregr iry of lines a nd polygons. Fo r instance, yo u ca n lise topo logy informario n ro determine whe rhe r any breaks o r gaps occu r in lines that are meant to rep rese nt streams. F rom a pol ygo n perspecrive, topology wou ld all ow you [0 dere rmine wherher a pol ygon fo rms a closed boundary, o r whether an ex rra neous polygon ex isrs inside, or alo ng rhe outside border, of a not her polygo n (Figure 2.25). One of the primary differences between full-featured G IS softwa re programs a nd desktop GIS software progra ms is whether they can identify and co rrect topology problems in v
and M aplnfo, may use vecto r clara form ars thar are not topo logicall y- based (C hang, 2002). Thus landsca pe features, such as adjacent polygons, may nor be represented as sha ring a common boundary line wirh orher polygo ns. Most full-featured GIS software programs such as ArcGIS may also allow lise of vecro r fo rm ars char are nor rypolog~ ically based. but will usually have tools or options to draw and ma nipulate rypo logy. I n so me cases, use rs may be
a. An un-closed polygoo
b. An extraneous polygon
Figure 2.25 Examplu or lopological urou. In (a). an undershoot has occurred and instead of a closed figure crcat ing a 1>olygon, a line has been crated. In (b), a small loop has been rormed extraneously adjacent to a pol)·gon . This might reprCUllt a digitiz.ing error or the result or a nawed o\'crlay process.
57
Chapter 2 GIS Databases: Map Projections, Structures, and Scale
What is topology? Topology, o r topological coding, provides the intelligence in (he data structure relat ive to the spatial relationships among landscape feacuces (Lillesand & Kiefer, 2000). For example, in a vector GIS database containing polygons, topological coding keeps "ack of each line [hat forms each polygon, and
able [0 define their own copological rules. or whether certain topological relationships can be ignored in a specific database. The dange r for users of desktop GIS software programs is that some programs will allow users co proceed, without warning, with spatial processes and analyses even though ropological problems exist. Th is condition may lead [Q errors in linear and area measuremenr calculations, or an inabilicy to complete certain spatial operations (hat rely on these dimensions for processing. Users of desktop GIS sofrware programs may only become aware of (apological problems after ca reful examination of GIS databases and associated analyses. or may not notice potential problems altogether. The point. line. and polygon vecmr data structure provides a method to represent irregularly-shaped Earth features. More often than not. vector GIS databases do nO[ completely cover a landscape of interest (e.g .• a vegetation GIS database may only contain the vegetation located within the ownership boundary of a natu ral reso u rce management organization. and nOt the vegetation outside of the ownership boundary-<juite different from satellite imagery or digital orthophotographs), and represent landscape features that are quite diverse (e.g .• polygons of different sizes and shapes. rather than a regu lar size and arrangement of pixels). Examples of diverse vecmr databases include road and st ream representations. Both of these rypes of databases tend to have unique geographic shapes that do not completely occupy a landscape (unless, of cou rse, the 'landscape' is the size of a pothole. or some pool in a stream). Some poim databases. such as those that describe timber inventory cruise plots. come close to a regular arrangement across a landscape. yet they usually deviate from regularity as a result of the sampling method selected for each stand. Point locations of wi ld life sightings are usually very irregularly distributed across a landscape. Polygons. whether they represent sta nds of similar trees. soils. or recreation areas. tend to be very irregular in shape. although in areas where the Public Land Survey
47
the common nodes each line shares with each other line. In addition, the polygons that are formed on either side of each line (since polygons may share a boundary defined by a line) are known. Thus with topology you can understand which forest stands, for example. are next to which other forest stands.
System or other culturally-based land delineations have been implemented. so me edges of forest stands now seem to have an aspect of regulariry built into them.
Comparing raster and vector data structures There are a number of ways in which the differences between vector and raste r GIS data can be described (T able 2.2). Since GIS users will ultimately use databases representing both structures. and since users sometimes conve rt raster to vecCQr data. and vice versa. an illustration of the differences is needed. Therefo re. an examination of a generic dara structure conversion process might be helpfu l to ill ustrate how the three main rypes of vector data (points, lines, and polygons) might be represented in a raste r GIS database. The right side of Figure 2.26 also illus trates these three features but demonstrates how they might be represented in a raste r database structure. The vecto r representation of points can yield fairly precise locations, depending on the rype of coo rdinate system used to reference the points. This means that you co uld use a GIS to determine with some degree of precision where these points could be located on a map that
TABLE 2.2
Comparison of raster and vector data structures Raster
Vector
Structure complexiry
Simple
Complex
Location specificiry
Limited
Not limited
Computational efficiency
High
Low
Data volume
High
Low
Spatial rcsolu[ion
Limited
NO(lim iu!"d
Representation of topology among feamrcs
Difficult
NO( djfficult
58
48
Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design
• •
• •
•
Figure 2.26 Point, line, and polygon fe atures represen ted in vector and ras ter data StruCtures.
includes coordinate refe rence marks on its axes. While the raSter represemation also shows these point locatio ns, there is less precision (specificity) in their map location, which is dependent upo n the raster gri d cell size assu med. You would know that the point is iocared somewhere in the grid cell. bur th e precise locatio n is elusive. You can also see some similar relationships be[Ween the data S[rucrures when describin g line and polygon features. Both the line and polygon vector features have very discrete shapes that are somecimes lost when co nverted to the raster data structure. This faCt is perhaps most noticeable when yo u examine the junctions. or nodes. of the line featu re (A), or those places where polygon features intersecr (B). The loss of specificity when co nve rting a vector feature imo a raster ca n, at least in pan, be overco me by selecting a smaller raSter grid cell size to rep resent the rasterized vecto r features. This choice comes with a price, however: a n inc reasing storage size requiremem fo r the resuhing raSter GIS database, and greate r strain on computer processing resources. The loss of specificity when converting
from vecto r to raster data structures may be acceptable if you r goal was to simply represent the rdative locatio ns of landscape features. However, if you needed to know (he precise loca tion of a well, road junction, or propeny boundary, representing these landsca pe features with a vector da ta structure may be more appropriate . In general. raster data struCtu res may be more ap propriate for representing continuous surfaces than the vector data structure. For exam ple, if yo u were inrerested in describing precipitation, temperature, or species diversity across a landscape, raster data structures may do this more efficiently because rhe dara may be more appropriately sw red and illustrated with grid cells. Because of the regularity of features (i.e., each grid cell is the same size a nd shape) the co mpu te r processing requ ireme ms are lower when usi ng raste r data structures. When performin g G IS processes wi th raste r data, generally no calculatio n of the intersection of landscape features (lines or polygons) is needed , given rhe regular shape of the cells. In contrast, analysis processes that involve vector G IS data usually must deal with the potential intersection of landscape features (e.g., overlapping polygo ns) . Unfortunately, GIS databases sto red in the raster data structure can become very large, especially when fine resolution cells are assumed . One of the hindrances to using raSter data is that every cell must have a value associated with it; even cells where no landscape featu res of interest are present (e.g., vegetation outside of an ownership). From a computer storage perspective, this means that all raster grid cells have a value (e ither a valid value or a null value) that must be s[Qred and maintai ned. In conrrast, vecto r GIS data need only have points, lines, o r polygon featu res in locations where landscape information is present.
Alternative data structures Although points, lines, and polygons represent the most common forms of vector G IS data, several other forms of vector GIS data that may be useful in representing landscape features. These other data structures include triangular irregular networks (TINs), dynamically segmenred networks, and regions. What follows is a brief discussion of each of these mysterious-soundi ng dara structures, and a po tential applicatio n that might be useful in understanding the potential uses of these ahernarive data structures.
Triangular Irregular Network A Triangular Irregular Network (TIN), like a ras rer data structure, is useful for representing a continuous sur face 59
Chapter 2 GIS Databases: Map Projections, Structures, and Scale (an entire landscape). A TIN, however, add resses some of [he problems (hat raster data structures have in accurately
representing landscape feamres , especially those problems that result when you use regularly sized raster grid cells to describe landscape feamres. If the raster grid cells are small. in comparison (Q the size of other landscape features , you will probably have success in accurately repre-
senti ng mose features. If the raster grid cells are large, in comparison co orner landscape features , you might lose
some of {he integrity of [he landscape features in [he resulcing raster GIS database. A TIN attempts to avoid (his problem by us in g. as the name implies, a set of triangles-rather than a set of squares-co represent landscape
features (DeMers, 2000). Each of the th ree sides of each triangle can, in fact, have a different length, making the rriangles irregu lar in nature. Thus a TIN is composed of irregularly shaped objects. yet covers an entire landscape. I n most applications. TINs are used to represent elevation models. An elevation is associated with each triangle co r-
ner, as illustrated in Figure 2.27. In landscapes that are highly irregu lar in te rms of elevation (as are many forested
landscapes, for example), the TIN may berrer represent topography than a raster-based data structure . Working
with TINs, however, is beyond the abili ty of many standard desktop GIS sofrware programs because of the complexity involved in storin g and processing irregularly sized triangles and represent in g three-dimensiona l surfaces . Some software developers ofTer modules associated with
desktop GIS sonware programs (at add itional cost) that will allow GIS users ro utilize TINs.
49
data structure. The dynamic segmentation data structure is designed to represent linear features. and traditional uses of this strucrure include moddling efforts related co river systems. utility distributions , and road networks. Dynamic segmentat ion allows GIS users co create rOutes that represent the movement or presence of an endty along a linear network. The routes are actually stored as information within a vector GIS database. Dynamic segmentation eliminates the need to create a separate GIS
database fo r each route and fucil itates advanced data handling and manipulation of GIS databases. Underlying the route structure are sections and event cables. Sections are the li near components o r segments that. w hen added together. form a route. Event themes are (he data sources or attribute tables (hat are connected
ro the routes. The dynamic segmentation data model has the capability to associate information with any portion or segment of a linear feature. Event themes can be associa ted w ith each line o r a single point on a line. This in formation can then be stored, queried. analyzed. and
displayed without affecting the Structure of the original vecror GIS database. Dynamic segmentation anempts to link a network of lines based on a common attribute so that the lines are grouped into catego ri es of interest. An example of this
app roach might relate ro a screams GIS database. A typical Streams GIS database uses a series of lines to represent a stream netvlork. Each of the lines would have a set of nodes, or beginning and ending points, and the nodes
wou ld be placed at all tributary junctions along the Stream netvlork. Depending on the size of the stream net-
Dynamic segmentation of linear networks
work, hundreds Ot thousands of lines might exist. A
A data structure that uses dynamic segmentation is based on a netvlork of lines. and thus is a va riation of the vector
stream ecologist interested in analyzing the stream system. for example, could associate all lines that are used to represent the main channel of a river. Any an ributes th at are used to describe the main channel, such as length, depth. or temperature, can then be summarized. The stream ecologist might use this dynamic segmentation approach for the entire stream netvlork to create a new
GIS database that groups all lines based on some attrib ute (such as the stream name) . The scream ecologist may also be interested in maintaining specific point locations along the stream database that contain features of interest. such as a smolt trap o r culvert location. Point evenr tab les of
these features could be stored within the dynamically seg-
Figure 2.27 TIN representation of an elevation surface.
mented stream, and show not only (he locations of these features. but also distances and directions from other fea[Ures within the database. Dynamic segmentation allows GIS users to organize a 60
50
Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design
GIS da[3base so that analysis and storage can be easier and
more polygons co represent the non-overlapping areas of
more efficient. Dynam ic segmemat ion can also be used to ass ist in schedul ing management operations that involve transportation or movement with in a resource area, or in planning or tracking almost any phenomenon that is associated w ith a linear netwo rk.
trees, and other polygons for each of the overlapping areas of logs. For the five F...llen trees displayed in Figure 2.28, enforcing co rrec t ropology for these landscape features
would create a total of II polygons. This would result in both a loss of information and the creation of a larger set
of database records than might be appropriate to describe Regions Another alrernarive vectOr data srrucmre is called [he region . This data srrucmce is based on polygo ns or
the trees. With the use of the region data structure, you can retain individual (fee data records. while also associating th e overlapping trees with one another.
approxim at ions of areas, slich as stand boundaries or
ownership parcels. One of the featu res of a ' topologically correct' polygon srruc(Ure is that polygon features can nor
Metadata
be represented as overlapping areas in a topo log icall y
Metadata is 'data about data'; this is a relatively recent phenomenon in working with spat ial databases. T ypically, metadara is a di gital document that acco mpanies a GIS database that describes the content and quality of th e data. With recent advancements in some of the
enforced polygon database. When two polygons meet, a new polygon is created. to represent the overlapping area.
As mentioned earl ier , some deskrop GIS software programs do not allow you ro determine whether polygons are topo logically correct. A region data st ructure will allow (he existence of overl apping polygons while also maimain ing (Opology. A forest scientist interested in capturing the locations of Fallen (fees within a steam channel
would find regions to be useful (Figure 2.28) . Polygons could be used co represent the lengths and widths of the trees, bur any trees that are s[3cked on (Op of each o ther,
like you might expec( co find in a log jam, will not be accu ratel y represented in a ropologically correct polygon structure. Two fal len trees that overlap each other might
result in multiple tOpo logically-correcc polygons: one or
full-featured GIS software programs it is now possible to have a metadata file digitally linked to a GIS database. New sofrware programs also make it easier ro create and populate meradata fields with information about the data. Metadata is an excellent place to store and retrieve informacion about the characte ristics of a database. including the map projection and coo rdin ate sys tems used. Other useful information in a metadata file might include a desc ription of the original data so urce, any editing that
has been done, a list of the attributes, the intended use of the darabase. an d info rm acion related ro the database developer. The descrip tions should allow users [Q trace
the evolution of the GIS database. In the US, most federal and state agencies are required to make mecadaca available
for any GIS dacabase that is offered for public use. The US Federal Government has developed standards for producing and repo rting meradata. Requirements fo r producing metadata are highl y variable among private natural resource management organ izations because no govern ing body enforces meradata compliance. GIS users should always ask for metadata wheneve r acqu ir ing a GIS
database.
Obtaining Spatial Data The USGS has developed the most comprehensive collection of spatial data in the world. This collection includes Figure 2 .28 Example of me region data structure used to capture the placement of downed woody debris in a stream channel. Typical polygon topology would create II polygons (Q represent the: five woody debris pieces . Regions allow for polygons ro overlap. crearing a fiveshape database. with one complete shape for each piece.
OEMs, OOQs, ORGs, di gital line graphs (OLGs), and many sou rces of raster and vector data fo r [he US. The major ity of this co ll ect ion is available via the Interner, along with associated meradata. Several US and Canadian 61
Chapter 2 GIS Databases: Map Projections, Structures, and Scale
51
federal agencies, as well as state and provincial groups, produce and maintain spacial databases for [he lands that they manage, and this information is also available to the public online. A more detailed discussion of data acquisi-
tion processes is provided in chapter 3.
Scale and Resolution of Spatial Databases GIS dacabases are often characterized in terms of their scale or reso lution . Scale or resolU[ion refers [Q [he size of
landscape features represenred in GIS databases. Issues of scale are usually associated with vector GIS databases.
while issues of reso lution are associated with raster GIS databases. Typically, the scale or resolution of a GIS data-
1:100,000 map
1:24,000 map
Figure 2 .29 Map of stream network displayed at scales of 1:100,000
and ' ,24,000.
base relates to {he source material from which the GIS database was created. Source material. as described in chapter 1, can include aerial phocographs. existing maps. sate ll ite data. o r information gatheted from survey instruments such as total stations or GPS receivers. Many sources of veccor data are derived from remote sensing
techniques, particularly from aerial photographs. The scale that is associated with vector GIS databases typically relates to photographic scale (a function of camera
height, lens length , and photo size) . Scale is often expressed as a ratio, or representative fraction, such as
I :24,000 o r 1:100,000 (Muehrcke & Muehrcke, 1998).
co in terms of relative units, such as 1 cm = 1 km, or
through the use of a scale [hat graphically illustrates approximate ground distances .
Wi[h imagery derived from satellite or aerial pla
platfo rm to delinea<e landscape features on the ground determines the resolution. A I m resolution image implies that the senso rs used [Q colleer the imagery captured a value for each square meter of the landscape. For raster
GIS databases that were developed by scanning from maps or photographs, such as a DRG or DOQ, the size of the
The ratio ex pression is unidess, and implies that 1 unit of measurement on a map or phoco represenrs 24.000 or
raster grid cell in representing landscape features deter-
100,00D unirs on the ground . Sometimes, confusion
ground distance, the raSter GIS database is said co have a
exists as to the correct use of the terms 'large scale' o r
' 30 m resolution'. This means that each raster grid cell represents 900 m' (30 m X 30 m) of ground area.
'small scale'. The ratio I :24,00D is a larger ratio than 1:100,000 (I is a larger portion of 24,000 than of 100,000) and thus, 1:24 ,000 is a larger scale than I: 100,000. If you examine both 1:24,000 and I: I 00,000 scale maps printed on the same size paper, the I :24,ODO map would show less area bur greater detail than the
1: 100,000 map (Figure 2.29). Scale can also be referred
mines the resolution. If each raster grid cell spans a 30 m
Although scales or resolutions are associated with sparial databases . some users mistakenly believe that they can
improve [he detai l of GIS databases by focusing on small land areas . Users need co be cognizant that the scale or resolution of a GIS database remains static, regardless of how closely yo u view an area of the landscape.
Summary This chapter discussed one of the fundamemal consider-
developed to provide readers with a brief description of
ations of any successful GIS program: spatial data. One of the main issues when working with GIS databa ses is
data projections and data structures-both raster and veccor-as well as a few alternatives. In some cases, exist-
knowing what projection system the database is set within, and how this coincides with other databases being used within an organization. or perhaps by other organizations with which data is shared. This chapter was
excellent source of base data from wh ich to verify other spatia l databases or CO create new spatial databases altogether. How GIS databases are stored and managed is a
ing databases such as DOQs or DRGs may provide an
62
52
Part 1 IntroductiOIl to Geographic Information Systems, Spatial Databases, and Map Design
function of the decisions natural resource managers make
regarding rhe purpose and inrenr of use for each GIS database. In many cases, however, YOli have no choice regarding rhe strucmre of GIS darabases. For example, satellite imagery uses a faSter dara Structure whereas veg-
you determine rhe characteristics of a GIS da tabase. including rhe projection system used. Finally, understanding the scale or resolution of GIS databases and thei r associated landscape fearures is important, as it relates co
Service ge nerally use rhe vec[Qr data structure. In addi-
the usefulness of a GIS database in assisting with analyses related ro management decisions. Perhaps an interesting ropic of conversation over a cup of coffee might be how
rion, most natural resource organizations use venor data
well 30 m grid cells obtained from sarellire images por-
srruc[U res
reay forest vegetadon and help you develop management recommendations.
erarion and soils darabases acquired from rhe US Foresr
[0
rep resem management units, roads . and
ocher landscape feamres . Meradara are useful in helping
Applications 2. 1. Projection param e te rs . Your supervisor. Steve Smith, h as JUSt learn ed (hat another natural resource
b) How many ac res (or square meters) would yo u expect to find in a section?
organization that you intend ro share spatial data with srores their GIS databases in a projection format that is
2.4.
different from yours. Steve is unfamiliar with projections,
ma nagement forester for a timbe r company in the south-
and asks YOll co provide some background for him. a) Whar is a projecrion? b) Why are projecrions necessary' c) Whar is an ellipsoid? d) What is a geoid' e) What are the major projection rypes, what are their assumptions, and how have they been used?
GIS data structures. You have been hired as a land
ern United States. As a recent college graduate you are expected to have the most cu rrent knowledge of forest measu rement and data acquisition techn iques. Your supervisor, John Delaney, an older forester, is interested in GIS and is cu rious about database struc tures. Describe to him the difference between a raster and vector data structure. an d give an example of a GIS database that m ight be designed with each structure.
2. 2 . C ho o sing a projection. You 've been asked ro recommend either a Lambert or Mercaror projection that best fits the dimensions of the State or province mat you live in. a) What is the largest north-south dimension of your state or province in miles and kilometers ? b) Wh at is the largest west-easr dimension of your state or province in miles and kilometers?
c) Which of rhe cwo projecrions would d) Defend your projecrion choice. 2.3.
YOll
choose?
Public land survey system. Wirh rhe exceprion of
rhe original 13 colonies, seve ral other states, and other des ig nated ownerships, much of the US has been surveyed (meas ured) using rhe PLSS. Significanr areas in Canada have also been surveyed using a similar app roach. Given the broad use of this system and its counterparts in Nonh America, it is imponant that you understand severa l key components. a) H ow many square miles (or square kilometers) would you expect to find in a township?
2.5
Quadrangle challenge. Locare rhe USGS 7.5
m inute Quadra ngle that contains your GIS classroo m or work location.
a) b) c) d) e)
Whar is rhe name of rhe quadrangle? When was me map originally compiled? If rhe map has been updared, when was ir updared? Whar is rhe Ohio code descrip rion of rhe map? When was rhe copography developed?
f) How much magnet ic declin atio n existS in the map? 2.6. Resolution and scale. A consulranr has proposed using sarellire imagery co quickly updare rhe foresr resources thar your natu ral resource management organization manages. Some peop le in your organizarion are arguing for a complete and fresh phow interpretation of rhe land base to accomplish (his goal, resulring in a vector GIS database of the vegetation condition of the la ndscape. The differences in resolution and scale are two of me hot copies when comparing these alternatives . Explain the difference belVv'een resolutio n and sca le. and how they relate [Q raster and vector data structures. 63
Chapter 2 GIS Databases: Map Projections, Structures, and Scale
2.7. Designing GIS databases. You have been hired by a natural resou rce co nsu lting agency [0 develop an d maimain a small GIS operation . While your expertise is in natural resource management, the owners of [he consulrp
53
have been inventorying timber for the past three hours, and now it is time for lunch. Your lunch. of course, is located in yo ur truck. The distance you measure on your map from your current posicion to [he Huck is 12 cm. If
ing agency we re incrigued by your GIS expertise, and have
the map scale was 1: 12000, how far are you fro m your
been interested in providing chese services (0 their clients. You, of course, (Oak the job because it is an opponuniry to pur your GIS and namral resource management skills to usc. As a stan, what data Structu re might you use to
£ruck?
describe the landscape features in the following databases? a) timber stands
g)
precipitation
b) c) d) e)
h) i) j) k)
land ownership scream buffers owl locations owl habitat
screams roads inventory plots culverts f) logs in scream
2.8. Map scale and ground distances. You are employed as a field forester for the Ministry of Natural Resou rces , and arc stati oned in northern Maniroba. You
2.9. Spatial resolution. The natural resource management agency you work for in [he imermoumain wes[ is considering [he purchase of 30 m resolmion sa[e lli [e imagery for assiscing in [he managemem of [heir natural resources. How much area, in acres, does a single 30 m
grid cell cover? How much area would be covered by a single 100 m resolu tion grid cell? 2.10. Spatial scale. Your supervisor has asked that you bring a map at the largest map scale possible to yo ur next plann ing meeting. You have you r choice of the following map scales: 1:24000 or 1: 100000. Which map do you bring with you?
References Cadle, F.W. (1991 ). Georgia land surveying history and law. Athens, GA: The University of Georgia Press. Chang, K. (2002) . Introduction to geographic information systnm. New York: McGraw-Hili. Clarke, K.c. (2001). Getting starud with geographic information systems. Englewood Cliffs, NJ: Prentice Hall, Inc. DeMers, M.N. (2000) . Fundammtals ofgeographic information systems. New York: John Wiley and Sons, Inc. Dent, B.D . (1999). Cartography thematic map design . New York: McGraw·Hili. Ladell, J.L. (1993). They kft their mark: Surveyors and th~ir role in the settlement of Ontario. Toromo, ON: Oundurn Press. Lillesand, T.M., & Kiefer, R.W. (2000). Remote smsing and image interpretation (4 th ed.). New York: John Wiley & Sons, Inc. Muehrcke, P.c., & Muehrcke, J.O. (1998). Map use: Reading, analysis, and interpretation (4th ed.). Madison, WI: JP Publications.
Robinson, A.H , Morrison , J.L. , Muehrcke, P.c. , Kimeriing, A.J., & Guptill, S.c. (1995) . Ekmmts of cartography. New York: John Wiley & Sons, Inc. Snyder, J.P. (1987). Map projectiom-a working manual. Washington,
DC: Uni[ed 5ca[es Governmem Priming
Office. Stewan, L.a. (1979). Public land surveys. New York: Arno Press.
Thompson, M.M. (1979) . Mapsfor America (3rd ed. ). Washington, DC: US Government Printing Office. USDI US Geological Survey. (1995) . Geographic names information system, data users guide 6. RestOn, VA: US Geological Survey. Retrieved January 15,2003, from from: http://mapping.usgs.gov/www/ti/GNIS/gnis_ users-suide_toc.hcrnl . Wolf, P.R., & Ghilani, C.O. (2002) . Ekmentary surveying: An introduction to geomatics (10th ed.). Englewood Cliffs, NJ: Prentice Hall, Inc.
64
Chapter 3
Acquiring, Creating, and Editing GIS Databases Objectives This chapter discusses a number of topics related co acquiring, creating, and edidng GIS databases. Readers should gain an understanding of the opportunities and challenges associated with the need [Q obtain GIS data from a variety of sou rces. At the conclusion of this chapter, readers should be able to understand and discuss issues related [0: I . {he acqu isition of GIS databases, panicularly via the Imerner, 2. [he various methods for creari ng new GIS databases, 3. the processes for editing existing GIS databases, and 4. the error types and sources [hat are poremialiy associated with GIS databases.
Acquiring, creating, and editing GIS databases to address rhe needs of natural resource management decisionmaking processes is a co ntinual process. Ideally, narural resource professionals would have a complete and robust set of GIS databases at their disposal prior to performing an analysis. However. as new and inreresting opportunities to incorporate GIS analysis in decision-making processes arise, the G IS database needs change as well. Four general cases a re common in natural resource o rganizations. as rhey relate to the availabi li ty of GIS databases: 1. GIS darabases required for a specific analysis do nor eXIst.
2. GIS databases exist. bue they were created for other
general uses and may not be quite appropriate to address [he issues rel ated to a specific analysis. 3. GIS databases exist, bue rhey were created for other specific analyses and a re not quire appropriate to address the issues rel ated to another specific analysis. 4. GIS databases exist, and they are adequate and appropriate to address the issues of a specific analysis. Ideally, you would hope that your organization is continually positioned near the fourth case noted above. However many GIS users find. evenrually, that the first three cases are real , and that time mUSt be spenr acquiring or develo ping a GIS database. Several options are available to GIS users faced with having to acquire, create, or edit GIS databases. These options include having someone else create a GIS database (e.g., a GIS contractor) based on maps and oth er input provided to them, using C PS or some other field-based data collection method to fuc ilitate the development of a new GIS database, modifYing or editing an existing C IS database. creat ing a new GIS database by digitizing maps, acq uiring a GIS database from the Internet (for example, the National Werlands In vento ry from t he US Fish and Wildlife Service [http://weriands.lWs.govl]), o r acq uiring a GIS database from mher organizations. The decision [Q pursue one of these strategies will depend on several factors inherent to a person's job. such as the budgetary resources, time constraints. a nd the skills and computing resources that are available within the natural resource organization. 65
Chapter 3 Acquiring. Creating. and Editing GIS Oatabases
Acquiring GIS Databases One of the main concerns of natu ral resource managers is locating and acquiring GIS data. Physically receiving the GIS databases is relatively easy; they can be sen< and received as an email attachment via the Internet or by way of media such as portable USB drives, compact discs (CDs). or DVD disks. GIS databases can be acquired from a variety of clearinghouses. many of which are maintained and supported by federal, provincial, or state organizations. The US federal government, in fact, is perhaps the larges< source of GIS dara in the world. A variety of federal agencies in the US provide data , and the Manual of Federal Geographic Data Products (US Geo logical Survey. Federal Geographic Data Committee. 2002) provides a wealth of informacion concern ing rhe agencies from which GIS databases can be acquired. Natural Resources Canada (Natural Resources Canada. 2007) and the Geography Network Canada (Geography Network Canada. 2007) provide access to many types of geographic data within Canada. Provincial and state agencies. such as rhe Washingcon Depanment of Natu ral Resources. also d isrribuce a large number of GIS databases related to the resou rces of each state. Acquiring GIS databases over the Internet has become a widely used practice over the past few years. The disadvantage, however, is that the fo rmat of the data is generally limited to that of the most popularly used data. and may not be directly compatible with some GIS software programs. Agencies that require GIS users {Q make requests for GIS databases may provide a wider variety of products to be acquired, as well as a wider variety of formats, depending on the provider. In the case of data requests, however, the agency may require mat a payment accompany each request. The payment covers the Cost of the media and staff time required to package and deliver the GIS databases; the rates are usually reasonable because public organizations provide the data. People requesting GIS databases are asked to provide a va ri ety of info rmation specific to their request (Table 3. I). so that the final product will meet (as closely as possible) their needs without the staff investing extra effort in formatting, reprojecting. or adjusting the GIS databases. As a n example of acquiring GIS databases via an Internet site, rhe Gifford Pinchot N ational Forest (Washington State) maintains a website where a number of GIS databases can be acquired (Gifford Pinchot National Forest. 2007). The Internet site is located at http:// www.fs.fed .us/gpnflforest-research/gis/. By accessing this
TABLE 3.1
55
Typical information associated with a GIS database reque st
• G IS database requested • Locarion (Township/Range/Section. Topographic quad indO[ Ohio code [or nameD • File format (e.g., Spatial Data Transfer Standard [SOTS] . Ardnfo export format, ESRI shaptfile. Tagged Image File Format [TIF 1. Imagine format [IMG], ESRI geodat1lbase) • Map projection. coordinate system. horizontal and vertical measurement units, and related parameters • Metadara • Contact person for questions related to the GIS database • Delivery method (USB drin. DVD. CD. e·maiI. FTP, ere.) Database compression format (Zipped. MrSID. TAR, ere.) Billing information Product license agreements
website. you will find that all of the vector GIS databases are available in Arclnfo coverage (ESRI. 2005) expOrt format. D atabases can either be downloaded directly from this Interner site, or obtained on a CD-ROM or 8 mm tape. The COst of obtaining GIS databases on media is $40 (US currency). In addition. some meradara related to each GIS database can be accessed through the Gifford Pinchot Nati<;>nal Forest website. The data related to the forest trail system, for example. indicates that the source scale for the GIS database was I :24.000. that it was last updated in 1999, that the projection system is the T ransverse Mercator using the Clarke 1866 spheroid. that the coordinate system is the UTM system. and that the datum is NAD27. In addition, the me[adata identifies the primary contact person and alerts GIS users [hat some gaps in the trai l system may exist so that users know to examine the data closely before using i[ to make management decisions. As you may have no ticed from the Gifford Pinchot example. a variety of GIS databases are available in addition to [he forest trails system, including hydrology, elevation, forest stands, roads, streams, and others. While these GIS database a re commonly used to support resource management on rhe National Forest, they are main ly vector GIS databases. Raster GIS databases. such as digital orrhophotographs, related to a particular landscape are perhaps more difficult to obtain . As mentioned in chap<er I. digital orthophotographs look like aerial photographs but {hey are srored in digital form and are registered to a coo rdinate and projection system . Raster GIS 66
56
Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design
,. t
~
l'_
~'In" Depth
~1:..1~~<..
'.~ . .
'
Reference is made in this book to a number ofInrernet sites that you can visit for additional informatio n and resources. The Internet is in a constant state of Aux, however, and organizations often change website
addresses, o r URLs. Since URLs change periodically, you might find that some examples provided in this book have become obsolete. Should you have a problem accessing the URLs we have provided,
twO
strate-
gies can be employed to reach the approptiate address. First, verify that the website address being used <xactly matches the URL listed in the book. Even the smallest of differences (one incorrect letter, or an extra space) can resuh in not reaching the app ropriate website. Second, with a li ule ingen ui ty you can locate the In ternet sites listed in this book by using an onl ine
search engine. For example, the URL to the Washington Department of Natural Resources Information Techno logy Division order form has changed several times since the first edition of th is book. To locate the most cu rrent version of the order form, you could anempr an online search similar to the one oudine below:
databases can also be acqui red via the Internet. The Minnesota Planning, Land Management Information
Center (2007), for example, maintains a clearinghouse ( http://www.lmic.state.mn.us!chouse!metalong.htmll) where GIS users can access the 'Imagery and Photographs'
• Search the Inrernet for 'Washington Departmenr of Natural Resources'
- Result: http://www.dnLwa.gov! • Select 'Publications & Data' from [he list of items on [he DNR main page. - Result: http://www.dnr.wa.gov/baselpublications. html • Select 'GIS Data' from the lise of item s under
Publications and Data sub-heading - Result: http://www.dnr.wa.gov/dataandmaps! index.html • Select the 'Available GIS Data' choice from the available links on the Data page. - Result: http://www3.wadnr.gov!dnrapp6/data web!dmmatrix.html At this point, you should be presented with a list of available databases that can be downloaded in an ESRI shapefile format (ESRl, 2005). Thus in four steps, you can reach the currenr website from wh ich spacial da[a
can be accessed from the Washington State DNR.
data category and download county-level digital aerial imagery, including orthophotographs, varying from 1 m to 10m spatial resolution. In add ition, the clearinghouse provides CIS users w ith some meradar3 related to the digital imagery. You can ascertain from viewi ng meradar3
How do YOli acqu ire data over th e Internet? Usually. clicking on an Internet link will starr the download
this process is that PCs are able to communicate w ith
process by presenting a dialog box that asks where the downloaded file should be stored. Another way is to
are automatically convened into a Windows~-compat ible format during the transfer process. FTP was once
download GIS database files to a computer is to use a FIP. FTP stands for File Transfer Protocol, and it is a widely used method of transferring data over the Internet. This method allows you to transfer computer files to other remote computers. or ro download computer files from a remote computer. One advantage of
only ava ilable on a PC th rough a DOS interface, but
UNIX-based machines, and GIS database file fo rm ats
severa l low-cost Windows@ appli cations are available
that simpli fy this process and make additional file transfer or sharing op tions available. To locate such a utility, try an Internet search using the keywords 'FfP' and 'Windows' together.
67
Chapter 3 Acquiring, Creating, and Editing GIS Databases
that much of the imagery uses coordinates referenced within the UTM system, with an GRS80 ellipsoid and NAD83 datum. The clearinghouse also provides information on the accuracy, consistency. and completeness for some of the imagery. With advances in technology, many private firms have
emerged in recent years that develop and sell GIS databases. Most GIS and land surveying trade magazines will fearure advenisements from these firms . The breadth of GIS databases and daca creation services cont inues (0 improve and all indicacions are that growth in [his area will continue. The current possibilities range from small
land surveying firms who are capable of collecting highly precise and accurate vector data from relatively small land areas using either digital tOtal stations or ground-based LiOAR, to larger organizations that offer raster imagery
captured from aerial or satellite platforms that are capable of imaging large land areas (regions. nations, continents.
the globe). Many of these organizations also advertise their services via the Internet.
Creating GIS Databases
57
GIS databases can be a time-consuming and costly endeavour. The most common methods used to create new vec-
tor-based GIS databases include traditional digitizing, heads-up digitizing, and scanning. The process of creating GIS databases, either by digitizing maps, using a GPS capture spatial coordinates that describe landscape features, or by other means, usually amounts to 70-75 per cent of the total time invested in GIS in support of a spato
tial analysis (DeMers, 2000) . As you will find in subsequent chapters. new GIS databases can be created as a
result of spatial analysis processes such as buffering, dipping, and overlay analysis. When creating new GIS databases with spatial analysis processes, concerns about the projection system, the coordinate system. me datum, and map units are lessened because the resulting GIS database:
is usually represented by the characteristics of the other GIS databases involved in the spatial analysis process. If GIS darabases do not exist, but maps of the landscape featu res of interest exist , these maps can be digitized using a manual digitizer. A series of measurement reference points (sometimes called control points or 'tics') mUSt be available to allow you to register the map to a
digitizing table. Reference points can include easily If GIS databases required for an analysis do not exist in digital form in your organization, and cannot be obtai ned through other means, such as via the Internet or by request from a state. provincial, or federal agency, you may conside r creating a new GIS database. Several factors mUSt be considered when creating a new GIS database.
including the type of information needed to adequately develop the database, the intended format of the data, the projection and coordinate system required. and (he accu-
racy desired of the resulting GIS database. Creating new
located landscape features, such as road intersections and
building corners, or less easily located landscape features, such as property corners, section corners, or a systematic
grid of points (Figure 3.1). Each of these reference points must be in a dearly definable location on the map, and the ground coordinates (coordinate system units) must be known . A common source for ground coordinates is
USGS 7.5' Quadrangle Maps since they usually feature coordinates along the map border in geographic, UTM, and State Plane Coordinate systems. It should be noted
490000 500435 510436: +----,-1----c---="--,,,--t 510436 -
Roads
D
Stands
+
Reference points (with associated XandY cOOfdinates)
490000, +--=:=...l.-JL....1-L-_c..._+ 500435 500000 5~ Figure 3.1 Measurement reference poinu for the Daniel Pickett forest to enable digitizing additional landscape features for the creation of new GIS databases.
68
58
Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design
that coordinates derived from paper Quadrangle maps
principle applies. In this example, digitizing technicians
would nor have high accuracy since the coord inates are listed at broad unit imervals, and you must incerpolare
may have rather precisely drawn landscape features to ref-
the location of landscape features. At least four registra~
cion points or rics are required to facilitate the digitizing of a map. Additional reference points, if available, will likely increase the accuracy of the spatial position oflandscape features in the resulting GIS database. At beSt, digitizing is an imperfect practice. and the quality of results can be dependent upon many factors. The accuracy of digitized GIS databases can be affecred by the experience of the person doing the digiti zing, by errors in either the location of reference marks or their associated geographic coordinates, and
by
imperfections
in digitizing equipment and software (Keefer et aI., 1988; Prisley et aI., 1989). One of the often-misunderstood accuracy issues relates co the digirizarion of the map itSelf. If the map is old, or if it had been exposed to moisture (or even humidity), it may be subject to shrinkage or expansion. The shrinkage or expansion could vary across the
map's su rEace, and thus the locatio n (as well as the shape) of any landscape features that are digitized from it may be distorted. I n addition, the methods used to delineate landscape features for digitizing cou ld cause inaccuracies
in the resulting GIS database. Regardless of how experienced a digitizing technician may be, if the map being
digirized includes poorly delineared landscape features (Figure 3.2), then perhaps the 'garbage in, garbage out'
erence (upper left image) when creating a landslide GIS database, or so me rather imprecisely drawn landscape features. When digitizi ng the upper right im age of Figu re
3.2, for example, would the landslide be defined by the outer edge of the thick line used to describe the landslide area, the inner edge of the thick line, or the center of the
thick line? When using the lower left image, the landslide area is not represented by a closed polygon, thus the technician would need to use judgment or intuition in digitiz-
ing a closed polygon from the model provided. Within some GIS software programs, landscape features can be digitized directly from a registered image that is displayed on a computer screen without havin g {O establish registration points. This process is known as
'heads-up' digitizing and has increased the usefulness and popularity of goo-referenced raster products (such as the digital raster graphics and digital orthophotographs discussed in chapter 2). By not having {O establish registration poims, the digitizing process does not require a digitizing tablet, is faster, and offers less opportunicy for error. Two advantages of heads-up digitizing are the ability {O use digital imagery as an on-screen backdrop during
the digitizing process, and the ab ility to change scales at which the digitizing takes place (by zooming in or Out). Heads-up digitizing is explored in more detail in chapter
8, when the processes of updating GIS databases are described . Scanning. as discussed in chapter 1, can also be used
[0
conven a hardcopy map to a GIS database. Scanners sense the differences in reflection of objects o n a map and
encode these differences numerically in a digital file. For instance, lines and points (if identified by dark ink) would generall y be distinguished from background areas (if [he lines and points were drawn on white paper), and raster
grid cells would be created to describe rhese features (Figure 3.3). The size of the grid cell (a rasrer data structure) will obviously be important when scann ing landscape features . With increased resolution, or a greater number of grid cells per unit area, the greater the ability of the scanni ng process [0 return discrete shapes from a
scanned surface will be. With larger grid cell sizes, less
Figure 3.2 A landslide drawn on a map with a r~gular sharpen~d left), a mark~r (upper right), a sharpened pencil, y~t in a sloppy manner- me landslide are:& is not closed (lower left), :& m2rk~r. yet in a sloppy manner-me landslide ar~a is barely closed ( Iow~r right). p~ncil ( upp~r
compute r storage space is be required. bur some landscape featu res may not be as accurately captured as desired. If needed , raster grid ce lls can then be converted to points. lines, or polygons (vector data structu res) using ras ter-tovecror conve rsion algorithms that are common to most
GIS software. 69
Chapter 3 Acquiring. Creating. and Editing GIS Databases
59
The processes tha[ you would use co find landscape features or 3nribuces requiring editing ca n be class ified as 'verification processes'. Wirh a verificadon process, [he
(al
goal is to verify [hat a parricuiar set of values within a database is appropriate (o r reasonable, or within some
s[andard). Verifica[ion processes should be devised so [hac bo[h [he landscape fea[ures and [heir amibu[e da", can be assessed for completeness an d consistency. These
processes are probably bes[ accomplished by involving mulciple personnel so [hac G IS da[abases ace checked independently. and [hac a[ lease twO da", qualicy assessmenes are performed. The cypes of error [hac can easily be recognized include [he improper loca[ion of landscape feamres, an improper projection system, and mi ss ing or inappropriate attribute data (data o utside a reasonable range of values). These data errors could arise at any stage
in the da[abase upda[e process. and regardless of [heir origin. should probably be correc[ed. If errors are loca[ed. or ocher changes need co be made. [hen [he G IS da[abases need CO be edi[ed. Figure 3.3 A limber stand (a) in vector format. from tbe Brown T ract, scanned (b) or converted to a raster format wing 25 m grid cells, then convcru~d back to vector fo rmat (e) by con necting lines to the center of each grid cell .
The following represents a shorr example illustrating a framework for verifying da ta and locati ng errors in GIS databases: Assume you work for an organizat ion that manages
Editing GIS Databases
a large area of forestland. over 200.000 ha. Wi[hin
There are numerous reasons why you would edit a GIS database, such as re-projecting a GIS database to a common proj ect ion system used by a particular natural resource organization, edge-matching GIS databases describing landscape features in adjacem areas (e.g .• (Qwn-
have responsibili [ies for upda[ing and managi ng [he GIS da",bases tha[ desc[ibe [he forestland. from
{his organization there are a variety of people who
ships. quadrangles. ece.) so tha[ they fie [oge[her seamlessly, and other generalization and transformation processes necessary to convert a GIS database to a standard format or reso lmion . In addi tio n, some GIS databases need co be continually updated and maintained, as landscape features and their attributes change over time.
inventory fores ters who collect (he data. to information systems analysts who incorporate the data into standard database formats for use within the organization. A general process of updating the inventory databases (both tabular inventory and
spa[ial landscape fea[ures) might S[a[[ wi[h the inventory forester compiling new inventory data (cimber cruise repons) and maps showing changes to {he forestland due to management acci vities over
Processes rela[ed co upd a[i ng G IS da[a bases will be
a period of [ime. perhaps [he las[ yea r (Figure 3.4).
explored further in chapter 8, bue as an example, most forest industry organizations update their timber stands
This information is passed
G IS da[abase annually co accoune for [he changes [hac
and inventory) co ntain the appropriate type and
have occurred in the forest land base over [he previous 12 months as a result of harvesting and Q[her management activities, growth of the forest reso urces, and disturbance
forma[ of da[a needed ro successfully comple[e [he upda[ing process (verifica[ion process # 1). If errors
evenes (e.g .• floods or wildfires). The da", collec[ion and repo rting associated with updating processes can be laborimcnsive and error-prone, indicating a need for standardized ve rification and editing processes.
to
the info rmation sys-
cems analyses. who verilY tha[ [he data (boch maps
are located, some of this information is passed back to the inventory forester for clarification and editing. If the inform ation is complete. and formatted co rrectly. the maps are digitized and the invemory
da", files are encoded (e.g .• da[a is keypunched ineo 70
60
Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design Inventory forester
Information systems
analysts
maps, data files
r - ------- -- - - ---- - -- - -,
~ .
I
Delineate changes to be made to
maps, data files
inventory
,
, ,,
,,
maps, data files
, ,,
GIS
Check data f(l( mistakes and omissions
I I
I I I
- J Verification process #2
! maps, data
Integrate into GIS database
,, Verification process #4
~ -;I
!
,,
Check data for mistakes and omissions
Verification process #1
Lmaps, data files Digitize changes to spatial data, encode inventory
I
,, , ,, , ,, , ,, , ,, , ,,
databases
,. ,
Check data f(l( mistakes and omissions
J
GIS databases
Check data for mistakes and omissions
files
~ I I
Verification - J process #3
Figure 3.4 A gc ncraliud process for updating a forest inventory GIS databa.sc.
a compurer file format), and a subsequem verifica-
tion process (#2) is used to check whether these processes were performed successfully. The inven[Ory data is then integrated into a standard GIS database format, and a third verification process is
used to ensure that all of the changes have been incorporated into the updared GIS database. One way to do so would be to check the resulting GIS database against the information supplied by the inventory forester. Finally, the GIS database(s) are distributed back to the field office, where the inventory forester has the opportunity [0 verify (process #4) whether the proposed changes have been incorporated into the GIS databases. It becomes obvious that editing processes, not JUSt for annual inventory updates bur also for periodic changes that sho uld be made when discrepancies are found. should be considered in time and budget estimates to develop and distribute GIS databases to field offices. Some of the more common methods used [0 edit GIS databases include:
I. Add new landscape features (points, lines, polygons) [0 an exist ing GIS database. 2. Change the shape o r position of existing landscape fe-atures. 3. Add new fields (columns) to the tabular portion of the G IS database. 4. Edit data in fields (columns and rows) in the tabular portion of a GIS database.
Editing attributes Attributes, as described in chapter 2, are values used to characterize or describe landscape fearures and, thus, the qualities of the landscape. Through verification processes you can determine whether the attributes of landscape features are appropriate by assessing whether they are outside of the range of appropriate values. or missing entirely. In addition , as GIS databases are updated. you might assume that some attributes change over time. For examp le, as trees grow the characteristics of a forest will change. In the Daniel Pickett forest stands GIS database (Ta ble 3.2), each stand is represented by a vegetation 71
Chapter 3 Acquiring, Creating, and Editing GIS Databases
the stream segments was also of imerest. [he positions of lines or areas that capture stream locations may also need to be adjusted.
Attributes of stands in the Daniel Pickell s tand. GIS databas e
TABLE 3.2
Age
O riginal MBF"
Re· inventoried MBP
200
50
21.2
23.2
C
175
40
12.9
15.3
3
A
210
55
25.8
26.8
4
A
250
65
34.2
37.0
5
C
90
20
3.1
5.6
6
A
220
55
25.7
28.2
7
C
150
35
8.7
10.5
30
C
190
45
17.3
20.3
31
C
110
25
4.1
7.7
Vegetation
BaW
"'P'
u ...•
A 2
Stand
• square h
(ttl ~r
61
acre
thousand board feet ~ r acre at [he dme of original inventory
• thousand board fect per acre afrer compiling new inventory
type, basal area, age, and volume (thousand board feet per acre, or MBF) at some panicuiar time in [he history of management of [he forest. If you had re-inventoried me forest a few years after the stands GIS database was cre~ ated, you might have found that the trees within the forest have grown, and tha< the stands GIS database may be in need of edicing (as the age, basal area, and vo lume have likely changed). Another example might include aquacic habir3r invencories that attempt ro monitor change in habitat conditions. such as fallen large woody debr is concenrcarions or pool densities within a suearn system. Aquatic habitat variables are consrantly in Aux within river systems as flow characteristics vaty both by season and annually. Large woody debris is continually introduced into some stream systems as the result of natural processes or management activities, and is cransponed through a river system as flow regimes allow. An inventory of such streams would likely determine changing patterns of woody debris and pool concemrarions on at least an annual basis. If natural resource managers wanted to track changes, new data columns could be added for each of the habitat characteristics chat are of imerest. and the updated invemory data could be entered into che new columns. If the position of
Editing spatial position Attributes of landscape featu res can also include [he spatiaJ coordinates of the feacures, and as these change, or are found to be inaccurate, chey require editing. For example, as owls disperse, the spatial location of their nests may change, and the X, Y coordinates that describe owlnesting sites require editing. Or as vegetation panerns change within a research plot, the polygon representing the plot may require splining so that all patterns are recognized. Or as GPS data are collected and incorporated into a GIS database, multi-pam error may be present, and should perhaps be either eliminated or corrected. GPS collection of data can also improve the accuracy of the location of landscape features that were previously defined and delineated with less accurate methods. For example, GPS data collection methods can improve the accuracy of a roads GIS database [hat was originally created from measurements collected from aerial photographs or with a staff compass and surveying tape. Spatial position edicing techniques vary widely across GIS software programs due, in part, to the different file formats that the different programs use. In general. however, editing the spatial position of a landscape feature requires chat GIS users make a spatial layer 'editable' . Once a GIS database is editable, edi ting tools are used to move, copy. create, or delece points. lines, and polygons. While GIS databases containing point landscape features are typically easy ro edit because a single coordinate pair represents them, databases that comain line and polygon landscape features usuaJly require more care because the topological integrity oflines and polygons must be maintained. As discussed in the last chapter, topology describes (he spacial structure oflandscape features and is essentiaJ when comparing the positions of features to other features. While some GIS software programs have fearures to auromatically examine and correct topological problems. others offer no rools for topological considerations and will consequently produce flawed analysis results when correct topology is not in place. Depending on the amoum of editing that is necessary. ahering the spatial position of landscape features can be a demanding chore. and one that requires great attention to detail. Regardless of the GIS software program used , a 72
62
Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design
good first step fo r any spacial editing process would be to ensure that a back-up copy of the database is made and securely srored. Once in the editing process, it may be imponant to remember to save the changes to GIS databases often (to avoid losing work due
[Q
data. the value should be a number that is far outside the range of acceptable data values and large enough to have a very noticeable effect in results if inadvertendy used for analysis.
power shortages
or computet failure). and to periodically document the editing progress (to avoid forgetting what work has been done. and what work needs to be done next) . Documentation may be particularly important if multiple people are editing (at various times) a single GIS database.
Checking for missing data One issue that affects the accuracy of GIS databases is the omission of cenain landscape feacuces. How would you know if one, or many, landscape feacures have been om it-
ted from a GIS database? Comparing GIS databases to reference maps or photographs is perhaps [he simplest process. Feacures could be omined because of improper map creation procedures or other blunders (for example. changes in the landscape that were not accou nted for in an update process). Omissions may also occur in the 3n ribure data assoc iated w ith landscape features. For
example. wh ile a streams GIS database may contain all of the streams associated with a watershed , it may lack certain characteristics of some streams, such as the width or
depth. Performing queries of landscape features to lea rn where attributes have been omitted will allow you to locate the landscape features that need editing. In the example illustrated in Figure 3.4, an information systems
Checking for inconsistent data You should expect some level of map error in each GIS database simply because all hard-copy paper maps contain errors and these errors are carried along inco any digital form of the map that results from digitizing or scanning processes. With app ropriate co ntrol in map creation processes, this error should be kept within desired tolerances. Map error can also result from inconsistencies in how landscape features are defined. For example some
timber stands might be very finely delineated. whereas others are more coarsely delineated (Figure 3.5). In other cases, error arises because rwo (or more) GIS data bases were created independendy, using different encoding processes. This may result in features within a GIS database being represented with different precision. For example, one database may have used one process (e.g .. digitizing) to create a fo rest stand GIS database. and anmher
process (e.g .• GPS) might have been used to independently create a roads GIS database (Figure 3.6). Upon close inspection. GIS users may find some public roads conta ined w ithin timber sta nds , when they shou ld more accu rately be represemed as being located outside of tim-
analyse could query the updated GIS database (as part of verification process #3) for stands that were altered during the update process, then examine the attributes of those
stands for missing data. In doing so. the analysts could verifY that all of the stands needing to be updated were. in fact. updated correctly (by comparing the updated GIS database against the maps and inventory data provided
by the inventory forester). Some GIS software programs are unable to explicitly handle missing data values for numeric attribute variables
and will assume that a default value of 0 if an attribu te value has not been specified. As you might imagine, this shortcom ing can result in sign ificant problems if these values are included in analysis resul ts, as most statistical summaries or testS take sa mple size imo account, and samples of '0 ' are included in the computations. One strategy for handling this problem is [Q assign a large negative val ue. such as -999 o r -9999, to missing attribme values. Regardless of what value is used to signify missing
Figure 3.5 A tim~r stand drawn more precisely (tOp) and less precisely (bottom). Note that the lines on the south and ea5[ern portion
of the figures are different.
73
Chapter 3 Acquiring. Creating. and Editing GIS Databases
Inconsistency _
Roads
o
Timber stands
Figure 3.6 Spatial inconsistency betwttn a timber stand CIS databau: created through digitizing and a roads database created through GPS meuurtments.
ber stands, and under the jurisdiction of some state, county. or municipality. Depending on the quality of the GPS receiver that was used. the likelihood is that the GPS database would more accurately represent the location of the road. Performing attribute queries of landscape fea{Ures with values that are unusual, or outside of some logical range of data. and observing the results either through tabular or graphical means. should help identify whether problems exist and where editing processes are needed. eressie (1991) provides some gu idance with identifying spatial data outliers, and suggestS verification processes such as creating histograms and distribution modelling.
Sources of Error in GIS Databases One of the fundamental facts about computers is that they follow the instructions provided by computer users (unless they are suffering from an internal hardware problem or a computer virus). Computers have no sense of right or wrong. Therefore, assuming that the hardware is functioning correcdy and you have been diligent in scanning for viruses. any errors that are found in GIS databases should be assumed to be a result of eithe r encoding (database creadon) or editing processes. Granted. some computer software programs are not perfect, and go through a number of versions to correct processing problems inherent in their computer code. However, when an error is located in a GIS database. it likely arose from a data creation or editing process. The re are three sources of error commonly found in GIS databases: systematic errors, gross errors, and random errors. Systematic errors, sometimes called instrumental errors, are propagated by problems in the processes and
63
tools used to measure spatial locations or other attribute data. Systematic errors are sometimes called cumulative errors, since they tend to accumulate during data collection. They can be corrected if you can understand how each measurement is systematically affected. For example. suppose in digitizing a map that the reference points that were used to establish the initial location of the map were all erroneously shifted the same amount slightly ofT to the east. The landscape features that are digitized after this error is introduced will also be systematically positioned the same distance to the east of their true location. You might correct fo r this displacement by adding the corresponding distance, in coordinate units, in a westerly direction to the coordinates of all of the landscape fea[Ures in a GIS database. Systematic errors in the collecting and processing of attribute data can also exist. For example, if you we re [Q compute the area of a series of watersheds using acres, and then convert the area measurements to SI units (hectares) using an inappropriate conversion factor (e.g., 2.4 hectares per acre rather than 2.471). the metric areas would all be systematically incorrect. Corrections in this case can be made by a recalculation of the SI units by using the appropriate conversion factor. Gross errors, sometimes called human errors, are blunders or other mistakes made somewhere in the data collection, map creation, or editing processes. For example, when digitizing a set of landscape features. such as landslides. a landslide that was delineated on the map being digitized might be omitted from the resulting GIS database. Or, when collecting forest stand information, an incorrect species code may be used to describe cerrain tree species. There may be no pattern in the occurrence of these errors, thus they may only be identified and corrected through verification processes. A thorough verification process will involve database checks by someone who did not participate in data collection or recording. Undoubtedly, human errors have the potencial to exist in almost every GIS database. Random errors are a by-product of how humans measure and describe landscape features. In contrast to machines, most humans are incapable of repeating a task over and over without any difference between repetitions. In the context of landscape measurements, ic is unlikely that a person will be able to use a measurement instrument and return measurements that are consistently accurate and precise. No maner how carefully data such as tree heights from a forest are collected, there wi ll be error in the representation oflandscape features and their asso74
64
Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design
cjared attributes. Chances are that recorded meas urements will be ar leasr slightly off from rhe [Cue mark due to (he limitations of vision, musculature, and instrument se[p up and application. As long as consistent procedures are followed and no blunders occur, measurements will tend to be grouped closely around the actual measurement, with differences occurring slighdy in all directions. These types of small errors are called random errors; rhey remain after all sys tem atic errors and blunders are removed. Random error occurrence does tend to follow rhe laws of probability, and rhus should be normally distributed in a statisrical sense. The statistical distribution of repeated measuremems can be estimated in order to obtain an idea of the variation expected in either the spatiallocarion of a landscape feacuee or the associated attribure dara. Through the process of least sq uares adjusrments, you can attempt to remove random error from GIS data bases when concern about accu racy issues is hi gh (Ghilani & Wolf, 2006). More ofren howeve r, in narural resource management it is assumed that data collection efforts were carried Out diligently with respect to [he accuracy and precision of measurements, and {hat random errors rend to ca ncel each other out. For this reason, random errOfS are sometimes termed compensadng errors. Two (erms are important w hen assessing the usefulness of a G IS database: logical co nsistency an d completeness. Logical co nsistency is (he term most GIS aficionados use co describe how well the relationsh ips of di ffe renr types of dara fir rogetlher within a system . In some cases (his refers to the consistency of the ropological relationships amo ng GIS databases. For exa mple, when streams are disp layed in conjunction with the contour lines of an elevation GIS database. all streams shou ld
What is error? Error can be defined as something produced by misrake or as t he difference between rhe true va lue of a feature and its observed value (Merriam-Websrer, 2007). How would you know there was an error in a GIS database? Perhaps by compa rin g rhe va lue (o r shape of a landscape fearure) in a GIS database to what is known as the correct value (or sha pe of a fearure) from a field survey, phocograph, etc. When you co nsider GIS database erro rs, the goal is to understand three issues: the type of
appear fl ow downhill (as opposed ro uphill) . In orher cases. logical consistency refers to the anribme dara of a particular GIS database. For example, the dominant tree species in o ne polygon may be labeled 'Loblolly', whi le in ano rher polygon ir may be labeled 'Loblolly Pine', and in a rhird, 'Pinus taeda'. Completeness is a term used to describe rhe types and exrent oflandseape features rhat are included in a GIS database, and conversely, those that are omitted. For example, in some cases not all types of streams (e.g .• ephemeral streams that only contain flowing water during rain events) are included in a streams GIS database. In add ition, smaHer streams may be omined because they weren't apparent in the remotely-sensed da ta that were used co create the database. If only a portion of total number of stream types were included in a streams GIS darabase, you might co nclude that rhe database is not complete.
Types of Error in GIS Databases Since creating, editing. and acqu iring GIS databases may involve many different processes, a variety of errors can obviously crop up. Some of the more commo n types include those relared to the locacional position of a landscape fearure, those rela ted co the tabular att ribmes of a landsca pe feature, and those resulting from compmational problems. Positional errors simply imply that a landscape feature is locared in the wrong place in a GIS darabase. These errors can arise during GIS database creation processes, such as digitizing or scann ing maps. As mentioned ea rlier, the digitizing of landscape features requires that a map be registered (Q a set of ground coordinates. How well the
error that exists, the so urce of the erro r, and the extent of the error. All of these relate CO the unce rtainty associated with the landscape features contained in a sparial database. Hopefully you have a hi gh level of co nfidence in the dara (i.e., uncertainty regarding the location oflandseape fearures and their attri butes is minim ized), so that analysis efforts can be used with confidence to positively facilitate decisions made regarding the management of natural resources. 75
Chapter 3 Acquiring. Creating. and Editing GIS Databases registration is performed and how accu rate the coordinates are represented on a map are both factOrs that contribute to errors in a resulting GIS database. Those who digitize maps also make e rrors-sometimes systematic errors (nor using a digitizing puck correctly throughout an enrire digitizing session), and sometimes gross errors (missing or displacing some objects entirely) . Estimates of positional error usually indicate thar some percentage
65
include using a minimum root mean square error
(RMSE) when registering maps for digitizing processes or for establishing a set of verification processes for elimination of gross and systematic errors. In addition , when measurements ace taken of a featu re, be it point coordinates collected by a GPS receiver, linear features such as scream o r trail lengths, or area features sllch as watershed
boundaries. a RMSE can be used to assess and repof( dif-
of landscape features should be located within some dis-
ferences between collected measurements and the true or
rance either horizontally or verticaIly from their true positi on. A statement of positional accuracy, for example, might indicate that 90 per cent of the landscape features are within 150 meters of their true, or on the ground , position .
estimated positions of featu res (FGDC. 1998). While the positional accuracy oflandscape features in
The ultimate use of a GIS database is also considered
a CIS database can be estimated, the uncertainty about what are termed 'local shapes' is more elusive (Schne ider,
2001) . For example. assume that a road has been digitized (Figure 3.7) . and that one segment of the road has
safety) when using the GIS database is high. developers of
been represented by four vertices. The actual location of the road between the vertices is unknown, and can be described in a number ways other than by a direct line between each vertices. The positional accuracy of these
GIS databases may broaden the accuracy statement to safeguard themselves against lawsuits. An example of a sit-
local shapes is therefore a function of the quality of the digital encoding process associated with the landscape fea-
uation when human safety could be affected by the accu-
ture (whether manual digitizing, heads-up digitizing, or
racy of a GIS database is in databases that are used as navigational maps for maritime app li cations. Using an appropriate set of comrol standa rds may minimize the amount of error in a CIS database. Control standards can
capture of data through GPS field techniques was applied).
when describing the positional accuracy of the landscape features rhey contain. If the risk of a GIS user making a catastrophic mistake (e.g., an event that impacts human
Root mean square error (RMSE) is a common term used in GIS. RMSE measures the error between a mapped point and its associated true ground position. Commonly used when digitizing a map, RMSE measures the positional error inherent in the registration
Errors in the attributes of landscape features arise when incorrect values are assigned to features. either
Xch«k.i
Ydm .;
RMSE=
n
Where: Xciata.i
(longitude) coordinate of each collected point i X
Y (latitude) coordinate of each collected point i y (latitude) coordinate of true or
points on the hardcopy map. RMSE calculates the squared differences between each reference point and its known or estimated position, sums these differences, than uses the square root of the sum to compute a measure of the positional accuracy. The formula for RMSE when location coordinates are of interest is:
x (longitude) coordinate of true or estimated location i
estimated location i n = number of observations (number of
collected coordinate pairs)
A perfect RMSE is 0.00 where the reference points are located in a GIS database in exacdy the same relative position as on the ground or whe n GPS collected data are exacdy the same as control points that are lIsed to test accuracy. This is rarely obtained when using realworld data. A sample RMSE calculat ion is shown in
Table 3.3. 76
66
Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design
TABLE 3.3
Example of Root Mean Square Error (RMSE) calculation for GPS coordinates
Point
GPS-X (m)
Known-X (m)
GPS-Y (m)
Known-Y (m)
Squared Error (m)'
477395.3
477397.2
4934669.5
4934671.8
8.90
2
477399.7
477398.7
4934677.2
4934675.2
5.00
3
477405.5
477405.3
4934670.8
4934675.3
20.29
4
477407.5
477406.9
4934673.9
4934671.7
5.20
5
477406.9
477405.7
4934670.4
4934668.5
5.05
Sum of squared crror (m l )!>
44.44
Average squared error sum {m l)e
8.89
Root Mean Squared Error (m)d
2.98
• Squared straight-line differences between CPS coordinates actual known positions: (X-direction error 2 + V-direction error !) I>
Su m of [he squared errors
C
Average of the sum of the squared errors: (Sum of squared errors I number of poinu)
d
RMSE: Square root of the average of squared errors: (Avt.l'Olge squared error) tI~
through editing processes or through spatial joins (which are discussed in chapter 9), or arise because the attribute data is outdated. Keyboard entry of attribute data can result in attribute data erro rs, particularly if (he people performing the anribmion processes are nO[ paying close Digitized road segment
Real-world representation #1
Real-world representation #2
Real-world representation #3
Figure 3.7 Uncertainty of thc local shape of a road scgment (after Schneider, 200 1).
anentian to the quality of their work. As we mentioned earlier, verification processes can range from an examination of primed maps and tabular databases related to the GIS database of interest, to an independent third-party examination of the landscape features a nd associated anribures comained in the resulting G IS database. Compurational processes can lead to anothe r source of error that can be rather trans parent to GIS users. Processes such as general ization. vector-to-raster conversion (or vice-versa), and interpolation cause alterations in the characterization of landscapes. I n vector-faster conversio n. fo r example. vector fearures are converted to raster grid cells. Obviously the size of the resulting grid cells, as discussed in chapter 2, will influence the quality of the landscape features contained in the converted GIS database. With large grid cells (e.g., 30 m or larger), the curvature of roads and streams could be lost. as well as small (and perhaps important) extensions of polygons. Users of the converted GIS databases wou ld also need to determine whethe r the spati al resolut ion of the grid cells tha t result after vector-faster conversion is appropriate for the landscape fearures being represented. For example. are 30 m (or larger) grid cells appropriate for representing all roads and streams? Is a 10m resolut ion more appropr iate? The drawback of using a 10 m grid cell resolutio n is [he relatively large amount of data required-nine times the amount of data contained in a 30 m resolucion database (nine 10m cells are contained withi n a single 30 m grid cell). 77
Chapter 3 Acquiring, Creating, and Editing GIS Databases
Ideally, in lieu of starements of error, the processes used to develop GIS da tabases. such as any transformations and conversions, should be documemoo and made known co allow users [Q understand the potential direction and magnitude of error associated with subsequenc GIS analyses. In addition. meradara. such as projection
67
users to consider how a GIS darabase was developed. This is, of course, rhe ideal case. For example, rhe GIS dara-
bases used extensively in (his book are void of S[3[emems of error and of meradara. They were developed as hyporhericallandscapes for students in rhe aurhors' GIS applications courses.
and coordinate systems, should be made available to allow
Summary Ultimately, most narural resource managemenc organiza-
rions will develop needs rhar will ourgrow rhe capabiliries of their current collection of GIS databases. Acquiring, cre-
to address a particular cask. acquisition. creation. and editing processes must be considered. Those who simply view
GIS as a sysrem to make maps will likely underesrimare the amount of work required to acquire. create, or edit the
ating. and ed iting GIS databases are common processes encountered by natural resource professionals when they are in need of data or databases to assist in making man-
GIS darabases necessary to make rhose maps. In addirion, regardless of how GIS darabases are generared, rhe pres-
agement decisions or evaluadng alternacive management
ence and elimination of errors must
policies. When rhe GIS darabases rhar are available to a
will likely require extensive verification processes to ensure
natural resource management organizacion are not suitable
mat the pmential errors are minimized.
be
considered, and
Applications 3.1.
Acquiring GIS data about Arizona National
Forests. Assume you are interested in o btaining information about the streams related to the Prescott National Forest in Arizona. The website related to the GIS data is
situated in Massachusetts. As such. you are interested in obtaining digital orthophorographs about an area in Massachusetts . One source of this data might be the
h rrp:llwww.fs.fed .us/r31 prescorrl gisl index.shrml (USDA Foresr Service, 2007). Based on rhe informarion rhat rhe
Massachuserrs GIS websire (http: //www.srare.ma. u.1 mgisl) (Commonwealrh of Massachuserrs, Office of Energy and Environmental Affairs, 2007) . Based on whar
GIS data s ite provides, what are the ca tego ries that
you can gather from the website:
describe rhe available GIS darabases? In reviewing rhe meradara for rhe 'Fire History' GIS darabase: a) Whar is rhe purpose of rhe Fire History darabase? b) How were the data creared? c) What datum, projection, and spheroid are used to represent fires? d) What data structure is used to represent the fires?
e) What is rhe spa rial extent of rhe fire hisrory dara-
a) Whar rypes of black and whire digiral orrhophorographic imagery are available? b) Whar spatial resolurions are available for 1:5000 color orrhophorographic imagery ' c) Whar sparial resol urions are available for 1:5000 black and whire imagery? d) What datum and coordinate system are used to represent these images?
base in longitude and latitude?
f) Who is rhe primary contact should you have further questions?
3.3.
Data acquisition (1). You have been hired by a
private consultant in Washington State to develop a GIS program . The con sultant has a small office, com prised of
Note: Please do not contact the Prescott Forest GIS coordinator for answers to these questions.
only five employees, and is interesred in developing a GIS program thar will urilize desktop GIS software (A rcGIS ArcView, MapInfo, or GeoMedia) and GIS darabases cre-
3.2. Acquiring digital orthophotographs about
ated by other organizations. To get started, you decide that acquiring base maps describing the counties, towns, secrion lines, and ownership of rhe Stare is important.
Massachusetts. Your position as a natural resource consultant has allowed you to become invo lved in a project
78
68
Part 1 Introduction Introduction to Geographic Information Information Systems, Systems. Spatial Databases. Databases, and Map Design
a) Is each of mese these databases da
timber stands map ,. and then hand-draw the additional [imber srands to (Q
be digitized by the contractor. Use the digital orthophotograph cogl"dph associated with wirh [he me Brown Tract Tracr to hand-draw the new srands. stands.
3.7. Owl locations on the tbe Daniel Pickett forest. forest. The wildlife biologist associated with the Daniel Pickett forest, forest. Kim Dennis, is co ncerned about the ty of the owl concerned rhe quali quality nest location GIS database. database. a) How could yo u determine whether the me owl nest locations locarions on the rhe Daniel Picken Picker t forest forese are rep reprerecorreCt place? sented semed as being in the correcr b) What are [he the coordinates coordin ates associated w with ith each owl locarion ? nest location?
3.4. Data acquisition (2) . Assuming you were interinter· ested in acquiring acqu iring soils GIS databases for 10 townships rownships in Was Washington hington State from the Washington Washing
3.8. Errors in landscape feature and attribute data. As a user of the Brown Tract GIS databases, databases. you are very
b) What are (he the coordinate units? contact for more information about the c) Who is [he me comact
ime inrerested rested in the quality quali ty of data that rha t each provides. What might you say about the possible errors in the stands G IS srands GIS
soils database? the soils information come from? d) Where did me
database features that are located in the fo llowing places? following a) X = = 1263950; Y Y= = 371726 b) X == 1273647; Y == 363943
3.5. Data acquisition (3). The Washington Washi ng
Development of base map for a digitizing contractor. Assume that char the standard stand ard process within your yo ur
managemem organizarion organization for updating updat ing natural resource management G GIS IS databases is ro to develop hand-drawn maps of the
des ired, to deliver these po tential tentia l changes desi red, and then [0 changes to [0 a COlHractor contractor for digitizing work. In the (he cemer of the Brown Tract is an open o pen area of land. Assume thar that the Brow Brownn T Tract rhe racr recently acquired this the owners of the: D evelop a base: base m map. the currenc sstands ap. showing [he tands land. Develop tics and the current roads. Create a GIS database of fou r [ics
ma rks) and place them on the map as well. well, (reference marks) along with their thei r assoc ia[ed iated X Xand rdinates. Print [he the alo ng wirh and Y coo rdin ates. Prine
3.9. Verification processes. As technology progresses me capabil capabilities and the ities of field personnel increase increase (with (wi th new co llege co urses in GIS, cont inuin g educa tion co urses, college conrinuing educadon courses) Ctc.), the co collection etc.), llection and transfer transfer of data dara can occur with processes that would seem seemingly ingly save effort and COSt to co an organization. invencory organizadon. For example, example. in Figure 3.4 the inventory foreSter would transfer cruise reports (hard co copies) forester pies) and (hand-drawn) informarionn system analysts <1nalysrs in an maps (hand-draw n) to informatio annual update process. Several verification verifkation processes were noted alnmed in an efforr effort to (Q maintain the rhe consistency consisrency and qu quality of data moving from the me field to [ 0 the information informatio n systems rems deparrment department and vice versa. a) H ow mighr might these processes process es in the flow chart cchange hange if the terr were to me inventory invemory fo res resre [Q provide
spatial data that was digitized in the field office, office. and cruise data that was collected coUeeted with a hand-held data dam coll coiJector? ector?
b) Could the tion responsibilities shift under me verifica verification these ccircumstances? ircumstances?
Sources 3.10. So urces of error. Your supervisor, Steve S teve Sm ith, idt , is interested in understanding that may be undersranding the types of error [hat inherent in herenr in GIS GIS databases. daraba ses. Describe fo forr Steve Sreve the differences between the foHowing following three mree types rypes of error: systematic, random, and gross error. 79
Chapter 3 Acquiring, Creating, and Editing GIS Databases
69
3.11. Types of error. Given the inventory updating
3.14 Calculating Root Mean Squared Error: Tree
process oared in Figure 3.4, describe [he sources of error (posi tional. a[[cibure. comput3rional) that could result at
location database . Assume you work as a nacu ral resource manager in Oregon. and your supervisor is con-
each step in the process.
cerned about the qualiry of data that you are colleCting wich a GPS receiver. You supervisor has asked you co cal-
3.12. Calculating Root Mean Squared Error: Woodpecker nest GIS database. Assume you are digitizing a
culate the RMSE between GPS-collected coordinates and coordinates that had been collected by a digital total sta-
map of red-cockaded woodpecker nest cree locations of a Nacional Forest in Florida. You have four reference marks that you can use [0 reference the map [Q known coordi-
cion . Boch secs of coordinates are lisred below.
GPS-X
Station-X
GPS-Y
Total Station-Y
4934688.3
4934691.9
477311.7
477309.0
2
4934693.6
4934690.8
4773 10.5
47731 1.9
Y.direction
3
4934686.9
4934687.0
4773 16. 1
4773 13.8
error (ft)
4
4934686.1
4934683.9
4773 12.7
4773 12.7
3.526
0.963
5
4934678.8
4934682. 1
4773 10.2
477309.0
2
-2 .890
-2.452
6
4934680.3
4934683.2
477307.6
477306.0
3
0. 985
-1.987
4
- 1.598
-2.850
nates. When registering the map you find that the Xdirecrion and V-direction difference berween the reference marks and the acwal known locations are as follows: Registration Point
X-direction error (ft)
Total Point
3.15 Calculating Root Mean Squared Error: Stream gauging stations. Assume chac you are collecdng daca
Whar is the RMSE, or positional error, associarcd with the
from a stream survey. A stream ecologisc has given you a
resulting woodpecke r nest GIS database?
set of GPS coordinates that had been collected from gaug-
3.13. Calculating Root Mean Squared Error: Trails GIS database. Assume you are employed by a National Park in Alberta, and are in the process of digirizing a map of trails [har were drawn by the recreadon specialist associared with ch e park. On the recreadon specialise's map chere are six reference marks chat you can use co regisrer che map [Q known coordinaces. When regiscering che map
you find that the X-direction and Y-direction difference becween the reference marks and (he accual known locadons are as follows: Registration Poine
X-direction ~rror
(m )
Y-direction ~rro r
(m)
3. 125
- 1.588
2
2.564
-1.992
3
2.548
-2.987
4
1.998
- 2.856
5
1.268
2.857
6
1.489
3.897
ing stations locared nexc co a scream. She has also provided a sec of coordinaces from the same gauging scadons
that were collected from LiDAR data. What is the RMSE of che differences between chese secs of coordinaces? GPS-X
GPS-Y
UDAR-X
LiDAR-Y
934681.0
477392.8
934676.6
477402 .2
2
934670.8
477405.5
934675 .3
477405.3
3
934673.9
477407.5
934671.7
477406.9
4
934670.4
477406.9
934668 .5
477405.7
5
934665.8
477399.2
934666.9
477402.1
6
934668. 1
477399.5
934668.6
477398.3
Point
Whar is che RM SE, or posidonal error, associ aced. wich [he resulcing cra ils GIS darabase? 80
70
Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design
References Commonweal th of Massachusetts, Office of Energy and Environmental Affairs . (2007). Massachusetts g'ographic information sysum. Retrieved April 19,2007, from http://www.state.ma.us/mgisl. Cressie, N. (1991). Statistics for spatial data. New York: John Wiley & Sons. DeMers , M .N. (2000). Fundamentals of g,ographic information systems. New Yo rk: John Wiley and Sons, Inc. Envi ronmental Systems Research Institute. (2005). GIS topology. Retrieved April 19, 2007, from http://www. esri.comllibrary/whitepapers/pdfs/gis_topology.pdf. Federal Geographic Data Comm itree (FGDC) . (1998). GeospatiaL positioning accuracy standards. Part 3: National standard fo r spatial data accuracy. Reston , VA: US Geological Survey. Geography Network Canada. (2007). Data. Retrieved April 27, 2007, from http://www.geographynetwork. ca/datalindex.html. Ghila ni , CD ., & Wolf, P.R. (2006). Adjustment computations: Spatial data analysis (4th ed.). New York: John Wiley and Sons, Inc. G ifford Pi nchot National Forest. (2007). Gifford Pinchot National Forest geographic infonnation sysums, available data uts. Retrieved April 19, 2007, from http: // www.fs.fed. us/gpn fifo rest -researchl gisl. Keefer, B.J. , Smith, J.L. , & G regoire, T.G. (1988). Simulating manual digitizing error with sradsricai models. Proceedings, GIS/LiS '88. Falls Church, VA: America n Soc iety of Photogrammerric Engineering
and Remote Sensing, American Congress on Surveying and Mapping. Merriam-Webster. (2007). M"rian- W,bsm onlin, search. Retrieved April 27, 2007, from http://www.m-w.com.
Minnesota Planning. Land Management In fo rm ati on
Center. (2007). LMIC's clearinghouse data catalog. Retrieved April 19, 2007, from http ://www.lmic. Sta te. mn . us!chouse/. Natural Resources Canada. (2007). Mapping. Retrieved April 19, 2007, from http: //www. nrcan-rncan.gc. calcom/subsujl mapcar-eng. ph p. Prisley, S.P., Gregoire, T.G., & Smith , J.L. (1989). The mean and variance of area estimates in an arc-node
geograph ic information sys[em. Photogramm~tric Enginuring and Remou S,nsing, 55, 1601- 12. Schneider, B. (2001) . On the uncertainty of local shape of lines and surfaces. Cartography and Geographic Information Sciences, 28, 237-47. USDA Forest Service. (2007). Pmcott National Form: GIS-geographic information sysums. Presco[[, AZ: USDA Forest Service. Retrieved April 20, 2007, from http://www.fs.fed.us /r3/ prescottl gisl index.sh tml. US Geological Survey, Federal Geograph ic Data Committee. (2002). Manual offtdual geographic data products. Reston, VA: US Geological Survey. WashingtOn Department of Natural Resources. (2007a). GIS data. O lympia, WA: Washington Department of Natural Resources. Retrieved April 20, 2007, from http://www.dnr.wa.gov/dataandmaps/index.html. Washington Department of Natu ral Resources. (2007b). Avai"'ble GIS data . Olympia, WA : Washington Department of Natural Resources. Retrieved April 20, 2007, from htrp://www3 .wadnr.gov/dnrapp6/dara webl dmma[rix.h[ml. Washington Department of Natural Resoutces. (2007c). Reference desk. Olymipia, WA: Washington Department of Natural Resources. Retrieved April 20, 2007, from http://www.dnr.wa.gov/nhp/refdesk/gis/index.html .
81
Chapter 4
Map Design Objectives The common features of maps are described in this chapter. and emphasis is placed on developing those that field professionals can use to present results of GIS analyses or (0 illustrate themes of interest including forest management areas, tree species maps, harvest plans. wildlife habitat, and mher narural resource management actions. At the conclusion of this chapter. readers should have acquired a firm understanding of: I. the main components. or building blocks. of a map; 2. rhe qualities of a map that are imporram in communicating information co map users; and 3. the types of maps that can be developed to visually and quickly communicate information to an audience. Within the various fields associated with natural resource management, we expect chat maps will be available to illustrate resources and areas that we manage because of the prevalence of GIS use. Maps are amazing tOols that, if constructed properly, have the ab ili ty to quickly and clearly communicate a message [Q an audience. Maps are an effective method of communicating spatial relationships among landscape features. Maps are also engaging-people are drawn to maps. Most GIS software programs provide users the capability to produce sophisticated maps and maps often represent the output produced by GIS analyses. thus most people mainly tend to associate GIS with map-making activities. Although this association may ignore many of the other analytical ca pabilities of GIS. the ability to geographically portray the results of an analysis is one of the primary distinguish-
ing characteristics that sets GIS apart from other software programs. Maps have been part of human civilization for millennia and have been used for many purposes, including data sto rage, navigation, and visualization. Maps have been used to create and sway opinion in many disciplines. including those related to the managemem of natural resources. In a manner similar to the use of statistics. maps can hold tremendous power over the message that is delivered to an audience and. when created skillfully. maps can be used to influence people's opinions (Monmonie r. 1995. 1996). One of the great dangers presented by maps is that people assume the landscape features represented on maps are accurate ponrayals of the natural resources that they claim to manage. However. maps are. at best, abstractions of the real world, and will usually possess some measure of non-conformity, be it directional or proportional or both, from a set of landscape features . A skilled map-maker will be able to choose a map projection that best preserves feature qualities (e.g.• area, shape) and that best suits a map's objective. This skill might also include applying strategies for representing data characteristics or qualities through different shapes, colors, or sizes. Understanding that maps must be created and imerpreted with a discerning eye is one of the first steps necessary to becoming a successful mapmaker or user. Maps usually are two-dimensional representations of the landscape. although th ree-dimensional maps can be used to show volume o r perspective. Symbols. colors, and text are combined to communicate information and, as with graphs, flow charts, and other diagrams. maps are graphical representations of information. Mapmakers 82
72
Part 1 Introduction to Geographic Information Systems, Systems. Spatial Databases. Databases, and Map Design
different manner than (hat attempt to ro transmit ideas in a differenr that ocher forms of communication. communica(ion. The goal of (he used by other the map-making process is co to produce a visual display (ha( that spatial pQ[encial map users. communicates spa rial information to co potential llsers. its limited capacity ro to Stofe s(Qre inforThe human brain, with irs marion, mation, may be able (Q to understand ideas more effectively when suppo supporting rting concepts are presented graphically on a map (Phillips, (Phillips. 1989). The design of a map can affect aifec( (he the abi lity to communicate spadal spatial information, information. thus a wellability designed map will wi ll likely communicate ideas co to an audience (e.g., (e.g .. co-workers and supervisors) more effectively rhan a poorly designed map, (han map. Poorly designed maps can lead to misinterpretations and costly cosdy or inappropriate
size. shape, shape. and symbology of each component componene of a The size, map should reA reAec( ect (he the likely responses (0 to (hese these promprs. promp". [Ools can be employed to (0 make a A variety of common [ools map both useful for namral natural resource resou rce management pur-
aesrhecically pleasing. The landscape features feacures poses and aesthetically to illusuared on a map should include enough landmarks co illustrated allow the users to reference rderence themselves to [he the mapped
area. and help navigare (hrough area, thro ugh a landscape. Mapmakers should keep ewo two importane important aspecrs aspects of maps in mind: (I) not evetything everything known about aboUt a landscape needs to be dismap. an andd (2) co to communicare communica(e effeceifecplayed on a single map, (ively. tively, maps should focus on displaying a limi(ed limired number
and for the bence bc(rer part parr of the [wemicrh twentieth century it was a
me
of landscape features. These concepts emphasize emphasiz.e that carca rrographers should shou ld focus on characteristics directly charac reristi cs that rhar direcdy rel relate ate ro to the map's intended message, and thus rhus they should ensure that other map components do not mask
skill developed (hrough through ex(ensive extensive experience in making la(e 1980s when G maps by hand. Since (he the late GIS IS began co to be
fea(u res primarily or cloud (he the message. The landscape features wi(h (he emphasized on a map should be (hose rhose associared with the
the vast pervasive in namral natural resource organizations. tbe majority of maps have been made by non-professional ca cartographers. rrographers. This shift is sim simply access iply because of the accessi-
main intent of the map. For example, on a map develto illustrate stream classes) classes. other landscape features. fearures. oped ro such as roads, roads) timber stands, and soils, soi ls. should be secondarily emphasized. omirred from the map ent entirely. irely. emphasized, or omitted
decisions. decisions.
Cartography is the rhe science and art arr of making maps,
biliry sohware. Some people may bility and ease of use of GIS software. argue that several of the prescriptive prescriprive aspects of making
maps are no longer necessary necessaty (Wood. (Wood, 2003). However. However, we would consider our dfoIT effort to educa.te educate readers reade.rs abom 3bom the capabilities capabili(ies of GIS less ,han than successful if we failed to (0 describe the important aspec[S aspects of maps, and suggest ways
aesrhecically appealing. make maps aesthetically When developing maps for Q[hers others to use, mapmakers should keep in mind that tha( not nor all map users will be oper(0 to
ating on the same level of competence. In fact, map users can be categorized as experienced. experienced, inexperienced, inexperienced. o orr reluc-
(ane tallt (F ranklin, ranklin. 2001). The detail de(ail and clarity c1ariry of mapped feacures afTecc how well a map coneribu conuibu tes res (0 to featu res will likely affeer natural resource management. management. Within natural resource namral ro management, maps should be clear enough for users [Q
undersrand me understand ewo twO main (hings: things: (I) (he the land area (ha( thar the map represents, and (2) the message the map intends to to the land area. communicate aboU( abom [he area . In order to meet these requirements, iremems. a mapmaker map maker needs ro to understand: understand: two requ_
• • •
(he the objec(ive(s) objective(s) of the map (the «he message), message). the people who may use ,he the map (the (he «he audience), audience). (he da(a (ha( will be displayed in (he map «he inforin forthe data that the (the mation available),
Map Components natural resource manageWhen developing a map for a natura] ment purpose, several basic components shou ld be conmem
componenrs sidered. These co mponentS include (he [he symbols being used to describe landscape features, a north arrow, [he the
scale, the legend, the quali(ies qualities (fone. (font, size. size, ere.) etc.) of the scale. (he legend. and (he addi(ion. you may find it i( (ex( rext (labels and anno(a(ion). annmation). In addition, to include other components in a map, such as necessary [Q a description of the mapmaker, the rhe filenames menames and file me loca(ions of (he the GIS databases da(abases used, used. and map qual quali(y iry locations caveats. Each of these components is briefly discussed important ro understand each e1emem elemem below, and it is imponant djscuss the common types of maps that can be before we discuss
developed using GIS software sofeware programs.
Symbology Symbology can be (hough( thought of as (he the art of expression, expression. based on symbols (Merriam-Websrer, (Merriam-Websrer. 2007). A large suire of map symbols has been developed (0 to ideneifY identifY and iUusilluscrate trate significant landscape features on maps. Some of
sohware for displaying map infor• the rhe use of graphics software
(hese I) we were na( ional rhese symbols (Figure 4. 4.1) re developed as narional
mation, and • me the final format of the printed or digital version of the
standards for srandards fo r illustrating landscape features (e.g., conrour contOur
map (the «he product). produc().
lines. hydrologic symbols) found on cenain cerrain widely used lines, maps. such as (he topographic maps, the US Geological Survey (opographic 83
Chapter 4 Map Design
Campground ......... .. ........ . .... ..... Gravel. sand. clay .
Of
borrow pit ......
lc.nqlounI!
.... Grll~t!I?iI
Mine shaft ... .. . ..... ...... ....... ... ..... Seawall. ... ....... ................... ... ..
S £AW " L~
ShoaL ......... ........... ........ . ... ......
Shoal
I!l
Spot elevation........ .. ............. .....
. mts
State or territory.... ........ ........... - - - - - Tunnel: road...... .... .... .. .... .... ... ~ _ £ ... _
Figure 4. 1 A subset of USGS topographic map symbols (USD I US Geological Survey, 2003).
maps (USDI US Geological Survey. 2003) or the National Topographic System maps of Canada (Natural Resources Canada. 2006). Symbols have also been developed to represent organizational standatds for identifying landscape features. For example. the US National Park Service has created a standard set of symbols for use on National Park Service maps (USDI National Park Service. 2003). which include typical symbols for roads and other landscape features as well as the highly recognizable pictographs (Figure 4.2). The International Orienteering Federation (2000) has also developed a set of standard map symbols fo r orienteering events. This provides a common approach to the interpretation of o rienteerin g maps, and therefore promotes a fair competition amo ng people involved in the sporL Most GIS software programs provide a standard set of map symbols for map users. H owever. developers of maps
0
Airport
II Amphitheater
e
,.
Boat la unch
I!J Boat tour Bicycle tra il
El Bus stop/Shuttle stop
g
Campfire
~
Campground
!!! Canoe access Figure: 4.2 A subset of the slanda rd National Park Service pictographs for maps (USDI National Park Service, 2003).
73
can easily misuse them, since documentarion is usually limited within the dialog boxes provided by the GIS software. Nevertheless, a variety of symbols are available in GIS software programs that allow the mapmaker to describe landscape features. It is also possible within many GIS software programs [Q create a customized symbol set. I n so me GIS software programs symbols are merely bitmap graphic files that can be edited o r created th rough graph ic software programs. And. if the existing symbology within a GIS sofrware program is nO[ adeq uate, some GIS software programs may allow the use of customized tools or products developed by third-party software developers. A variety of free symbols sees can be obtained over the Internet (e.g .• Sheahan. 2004). and you can purchase special symbols from companies such as Digital Wisdom. Inc. (2006).
Direction Mapmakers typically use cardinal di rections (north. south, east, west) (Q indicate map ori entation. The use of a north arrow thus provides map users with a systematic method to not only help locate places on the groun d. but also [Q understand where those places are in relation to ocher landscape features. While most maps are usually oriented with north at rhe tOP of the page, and south at [he bo[{om of the page. it is appropriate to remove the uncertainty assoc iated w ith orientation of a map by explicitly indicating the direc(ion through the use of a north arrow. Omitting a north arrow from a map is considered poor cartographic practice. A wide variety of north arrows has been developed to help map users understand direction and location, and the choice of which to use is usually determined by the mapmaker. Many of these forms of north arrows are available within GIS software programs, and a num be r of o(hers can be developed by hand us ing lines. arrows. and text (F igure 4.3). Some organizations. such as the US National Park Service, require the use of a standard north arrow o n their official maps (USDI National Park Service, 2003). Prospective cartographers should also realize (har single-sided north arrows are used by some organizations to represent magnetic north . The Earth's magnetic fields are in constant flux and cause compass needles [Q point in alignment. W ith in much ofNorrh America, the magnetic variation ranges berween 20° East an d 20° West declination , thus creating a large angular difference berween what many consider [rue north (which is astronomically derived) and magnetic north. 84
Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design
74
the scale) is equal to (using the equal sign) one unit of distance on the actual landscape. Each side of the scale can use a different unit of meas ure (e.g., 1 inch
=
10 chains),
which distinguishes this type of scale from the proportional scale, where each side of the scale is unirless. Proportional scales are generaIly presented using a rep-
resentative fraction. such as 1:24.000. With this type of scale, users should interpret 1 unit on the map as repre-
Figure 4.3 A variety of north arrow d~igns.
senting 24.000 of the same units on the ground (e.g .• 1 map inch represents 24.000 ground inches, or 1 map
Scale
ce ntimeter represents 24,000 ground centimeters). Proportional and equivalent scales are also interchange-
Maps are models of real landscapes. and a scale is used to
able. For example. an equivalent scale that reads 1 inch = 1 mile is the same as a proportional scale of 1:63.360 (I inch on a map represents 63.360 inches. or 1 mile, on the ground).
ind icate the ratio of (he map features [Q the actual landscape (i.e., map distance compared to actual ground disranee) [0 the map users. For this reason, scales are essencia! beca use they permit map users to reference map features to their actual size. A sca le is almost always
required on a map. and can be displayed using graphical, equivalent (verbal). or proportional scales (Figure 4.4). Graphical scales generally do not indicate the exact scale of a map (as do proponionai or equivalent scales) , but serve to visually associate the length of a map feature to
actual ground distances. Man y people relate to this form of scale more effectively than they do to the proportional or equivalem scales. Equivalent scales are those where one
unit of d istance on a map (usually the left-hand side of
Graphical scales:
---
-
500m
o
500m
1000m
1 mile
o
1 mile
2 miles
Whether graphic, equivalent, or proportional scales are used, the appropriate metries (English versus metric or
SI system. feet versus miles [meters vs. kilometers)) and appropriate font sizes should be employed to avoid distracring users from the map's main message. Puc another way, the scale is supplementary informacion on a map. and therefore it should not he so large that it auracts attention away from the map itself. Finally, the units displayed in a scale muSt make sense to the user of the map.
For example. a proportional scale of 1:23.987. an equivalent scale of 1 cm = 2.3 km. and a graphical scale which is divided inro 700-meter sections represents relativdy uneven divisions. While the units displayed in scales may he automaticaHy created in this manner in GIS, the mapmaker usually has comrol over them and can adjust them accordingly to provide a mo re logical representation of scale. In general, map users will be able to relate more
easily to rounded scale figures. such as 1:24.000 or 1: 100. rather than to more precise representations .
Equivalent scales: 1 inch = 1 mile 1 inch = 500 feet
1 inch = 10 chains 1 em = 1,000 meters 1 em = 5 kilometers
Legend All of the features displayed in a map should be described in the map legend in order fo r users to fully interpret the
map's message. Therefore. the symbology that is used to display features in a map should be replicated in the legend and associated with some text that defines the sym-
Proportional scales: 1: 12.000 1 : 24.000 1 : 250,000 Figure 4.4 Graphical. equivalent. and proportional scales.
bols (Figure 4.5) . Of course. if you wanted to intentionall y add myStery to a map . the legend may omit the description of certain landscape features. Some maps ,
such as the US Geological Survey topographic maps. may contain numerous features (and co rresponding symbols).
The legend that would be required for these maps would 85
Chapter 4 Map Oesign
75
Brown Tract Roads and Trails
Legend Streams
Roads
EEl
- - - Stand boundaries
........
Property boondary
............
~ Harvest area
o
Log decl<s I Landings
Gates
Houses
Figure 4.5 A map legend containing symbology and definitions.
overwhelm the map itself. In these cases, only a few landscape features are noted in the map's legend. and users must refer to the published standa rds (e.g., USO I US Geological Survey, 2003) for a full explanation of the remaining map symbols. Legends can rake many different forms and can use symbols. points. lines. polygons. colors. pa[(crns. and [ext co clarify what users may see. Some legends should milize a font size and font rype (hat is appropriate for the map. Symbols sizes for features may also be varied to show the differences in quamiries. The choices are nOt always obvious, but as in the case of the map's scale, [he legend should nor distract users from the message of a map. In addition , the appropriate descripro[s for each symbol should be used. Abbreviations should be avoided if interpretation of symbols might be unclear, or if a broad aud ience is targeted. When data are presenred and indicate quantities (such as length or area). the measurement units should be presented. Most GIS sofrware programs now offe r [Ools {hat allow {he automatic creation of map legends. These processes simply reference the GIS da
Legend Trails
__ ""~, e .. Roads
~"'
Figure 4.6 A map of the Brown Tract roads and trails containing a neatline, locational inset, title, legend, scale, and north arrow.
Brown Tract Roads and Streams
Locational inset The approximate locatio n of {he mapped a rea within {he conrext of a larger, more recognizable landscape feature (e.g., a basin, forest, counry, o r State or provincial boundary) can he indicated on a map using a locational inset. The locational inset may be exrremely helpful fo r the map's audience if they are not familiar with the landscape being illustrated. The locational inset might show the location of a watershed within a drainage basin, or a property within the boundary of a county. The locational inset, however, should be a minor component of a map and it must not compete with the main feature(s) of a map for the attention of an audience. Figures 4.6 and 4.7
Benton County
Lr
-....
,"-nd
-.._..
,
+ _c-.,.. ..... __ .. JfI07
Figure 4.7 A map of the Brown Tract roads and 5lreams conuining a ncacline. locational insct, title, legcnd. scale, and north arrow.
86
76
Part 1 Introduction to Geographic Information Systems, Systems, Spatial Databases, and Map Design
were developed as reference maps by Students s[Udenrs in one of our GIS rses. and ahhough not perfect in every sense. G1S cou courses, sense, each example provides a locational loeational inset in [he lower left corner. In one case, casc, the loeational inset contains the ourOut-
line of the Brown Tract. Traer. In the other case, the location was approximated by the student using a symbol (a trianand gle), with the orientation an gle). d size of the symbol implying less accurate information about rhe the lacarion. locarion.
Th ree other rypes types of insets can also be used. used, an enlargement ated area inset , and a special en largement inset, a rel related subject inser. inscr. An enlargement inset can be used on a
map to co show more detail of a speciFic specific area located within the primary map region. region . A related area inset can be used on a map [Q res [hat [0 illustrate fearu features thac are nOll nonco nriguous with wim the rh e main map figure but are still imporcontiguous tant (3m to (Q display. For example. related area insets are ofren often
Annotation
me
anno racion , or tex( (ex( applied directly to (0 the map, is Map annotation, important in further describing landscape features beyond whar can be described with wich a legend legend.. In some cases, it what
me
not be practical (or possible) to indicace indicate all of the may noc characteristics th ro ugh the intended characterist ics oflandscape features through beeause of space limitations use of a legend or symbology because features. Therefore or because of the shape and size of the features. map makers may find annotation helpful helpflll in communicatmapmakers addi tio nal messages to the [he end-users. end-users. Listed below are ing additional annotation in natural several examp les of map annorarion natural resource management.
p
included in ma ps thar rhat illustrate ited States. maps illumate rhe the 50 Un iced Scaces.
me
Here. d Hawaii are posirioned Here, Alaska an and positioned in [he insets insets..
tather locarion (which {which rather than in their actual geographic location would then rhen include ponions pordons of Canada and vast expanses of the Pacific Ocean). Ocean}. A special subject inset can
• Ownership: The owners of individual parcels may be
displayed on a map wich with words, words. such as 'GeorgiaPacific', 'Srace 'Scare of Alabama', Alabama'. or 'Province of Alberta'. Alberta'. appl ied as • Road numbers or names: These may be applied to further describe the road system. labels to maps to o r other locationa loeationall in informacion: • Surveying or formation: Township
numbers and range numbe rs (if using the Public Land Survey
be used on a map
o r the Dominion Land System), or distances System or direcdons from a metes and bounds survey may and directions be illustrated on maps with annotation, as could the
to show different themadc representarhe main map area (e.g. precipitation precipitation,, canopy tions of [he
cover). This is a popular mapping approach used in many adases.
Neatline A neadi neadine that surrounds all of the landscape ne is a border tbat
type of markings (e.g., (e.g.. pink flagging, Aagging. orange paint) used to delineate treatment area boundaries. • Areas of concern concern:: The names of the homeowners near treatment areas may be placed on a map to allow land managers to understand who they must contact conract in case
of a problem.
hue lies within the oU[side ourside edge of me rhe features on a map but
(paper).. Adding a neatline neadine ro to a map is mapping medium (paper) canographic praccice practice btl[ bur its presence is considered good cartographic
crirical than that thac of a scale bar or a legend. legend, generally less critical Nearl ines can also be placed around other map e1emenrs elements Neatlines to help distinguish disringuish them and keep them separate separare from
• Stand auribures: attributes: While stand attributes atrr ibures can ca n be used [Q {Q shade a thematic map (as we will see soon) several attribu tes of forested stands are commonly displayed attributes with annotation inside individual stands. These atuibattributes vegetation age, or Utes could include the stand or vegetar ion eype, age.
area (Figure 4.8).
(e.g.,, [0 separate the [he locadonal loeational inser inset from other objects (e.g. to separare
neadine is composed of at ac leasr least one the legend). Usually a nearline line, co provide a more draline. but multiple lines can be used ro marie effect. RegardJess Regardless of the style, nearlines can be be usematic (Oots that bring organization and disrincrion distinction (0 to ls rhat ful too
sofeware programs mapped landscape features. Some GIS software mapping that will not on only include mappi ng tools [0015 thac ly create a neadine, bur that to specifY speciFy how the area contained btl( thar will allow you co
the neacline nearline will be filled. For example. example, the backwithin [he tide, landscape fearures. features, and ground area behind [he the map tirle. amomacic neadine creadon legend, could be shaded. An amomaric nearline crearion presentatool in GIS sofcware will allow the creation of a presenration-quality map or poster, bur but nearlines neadines can aJso also be cre[ion-quaJiry
wichouc much difficulty. ated manually without
Typography The content co ntent and form for m of text [ext used to describe map features is an important the communicative ability imponam aspect of [he abiliry of a map map.. and is often used informally to differentiate
professional-looking maps from maps made by GIS GIS novices. Since colors and patterns alone might not be ab le
co to
fully explain the message of a map, map. the texc text used in
annotation, the annotatio n, labels, titles. tities, and legends plays a role in [he
appearance and aestherics ty of GIS aesthetics of a map. The abili abil ity GIS users to interpret imerprcr the written information rhey they find on maps is a function of many variables that can be described 87
Chapter 4 Map Design
n
• In one study, Phillips et al. (1977) noted that the capability of people to search and find infotmation on a map was enhanced when the text was displayed in a
normal weight (not bold), with letters all in lower case, except an initial capital. However, capitals should be 51 70.86
51 5.49
50 3.85
used for all letters in text when names are difficult to pronounce or need to be copied accurately. • Text set entirely in lower case has been shown to be harder to locate on a map than text set entirely in upper case. However when the initial letter of a piece
39 58.39
of text was slightly larger than the othet letters, locating the text was quicket (Phillips, 1979).
46 27.94
Color and contrast
54 25.07
It may be difficult to believe at first but people tend to
Figure 4.8 Map annotation: age (top, yurs) and area (bottom, hectares) of a ponion of the Brown Tract stands (vegetation) GIS
database.
associate colors oflandscape features on maps with events, emotions, and socio-economic status. Men and women respond to color with similar emotional reactions (Valdez
& Mehrabian, 1994); however, people's emotional reaction [0 colors may vary across cu ltures . Listed below are general emotional react ions to various colors by south-
under the broad heading of typography. Some of the most important rypographical elements are typeface
eastern US college students, as suggested by Kaya and Epps (2004) .
(font), weight (bold/normal), size (point size), and case (use of capitals) of text contained on the map itself. The font chosen will undoubtedly inAuence the ability of users
• Green: they felt relaxed, calm, and comforted, and associated me color with nature
to interpret maps, rhus a no rmal font (Times Roman, Aria], etc.). and a normal and consistent font size is usu-
• Blue: they felt relaxed , calm, and comforted, yet asso-
ally appropriate for most maps. Some important thoughts on map typography include the following:
• Yellow: they felt lively and energetic, and associated
• Some font types may be easier co read than orhers.
Mixing font rypes on a map may create (he impression that parts of a map arc nor clearly or logically connected. • Use larger font sizes for map fearures mat are relatively morc importam than others. However, if you we re (0 differentiate landscape features by differem sizes of labels or annotation, you must remember that only a
small number of classes are discernable by most people. In addition, small font sizes may be difficult for
ciated the color with sadness or loneliness the color with summertime • Red: they associated the color with love o r romance, yet also associated the colo r with ange r
• Purple: they felt relaxed and calm, and associated the color with childhood o r power • White: they associated the color with innocence, peace, pur ity, or emptiness. and also associated the color with snowfall or conon
• Black: they associated the color with sadness, depression, fear, and darkness, yet also with richness, power,
and wealth • Gray: they associated the color with negative emo -
tions, bad weathet, and foggy days
some people to see.
• The tirle of a map should be displayed with a larger font size (han the rest of the components of the map.
display the legend, scale,
Studies of peop le's responses to color have indicated comp lex emotional relationships. For example. in the
and Other material not contained in the mapped area.
study by Kaya and Epps (2004), the color green evoked
The font size of these items should not overwhelm the
the mos t positive response among college students because it reminded them of nacu re. Yellow was a close
Use font size judiciously
to
information contained in the mapped area.
88
78
Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design
second. Yet, students associated [he color green-yellow with feelings of sickness and disguSt. In an earlier Study of the effects of color on people's emotions, Valdez and Mehrabian (1994) also found green-yellow to be one of the leaS[ pleasant colors, YCt one of the most aro using.
In designing a map whe re fea tures will be colored, rwo general rules sho uld be followed: 1. Color certain landscape features with their
mOSt
ships so [hat the important objects of a map are separated from those that are considered ancillary. In human perception, figures are the objects that are most strongly per-
ceived and remembered by a map user, whereas data (text) are less distinc[ive and memorable (Dene, 1999). Techniques such as figure closure, comrast with other objeccs, and object grouping can be used to escablish distinctive ground-figure relationships.
obvi-
ous associated color (e.g., roads-black; screams-blue) . 2. When co loring polygons in a thema ti c map, use gradarions of one or two co lo rs to represem classes rather than a set of conrrasting principal colors. The lance may confuse the ord ering of importance of classes in the map user's mind. Visual contrast is o ne of the most important facmrs in
creating a map (Robinson et a1 ., 1995). T he extent of contraSt employed by a mapmaker affects how well one
Ancillary information In some large namcai reso urce management organiza tions
where multiple people share data developmenc [asks, placing the names of people who contribu ted to [he developmene of a map on a map is typically discouraged. However, in field offices of narurai resource m anagement o rgan izations. where field personnel are generally respo ns ible for developi ng ma ps to ass isc wich on-che-ground
set of in fo rmation is pro moted above (he other informa-
decisions (and hence not specifically [he development of GIS databases), it may be desirable to know both who cre-
rion available in the map (Figure 4.9). Feature size, shape,
aced a map and when ic was creaced. Since GIS dacabases
texture, and color can all be altered to introduce contrast into a map. Contrast will help focus the attemion of (he
may be modified frequently, knowing the date tha[ a map
of the au dien ce co determine the cla ri ty of a map.
was creaced mighc be as imponanc as knowing che map's developer. This sou rce informacion allows map users co place che conee nt of a map in perspeccive w ich che ve rsion
Mapmakers must also balance figure-ground relarion-
of the GIS database(s) used to creace the map.
audience, and wi ll playa signi ficant role in the abi li ty
(b) More extensive contrast among groupings
(a) limited contrast among groupings
Land allocations
o o
Uneven-aged stands Even-aged stands Rock pits, research areas, meadows, oak woodlands, shelterwood stands
l and allocations _ Uneven-aged stands
o D
Even-aged stands Rock pits, research areas, meadows, oak woodlands, shelterwood stands
Figwe 4 .9 An cxaInple of visual contrast. The limited contra.Sf among the groupings in the first map (a) d~s not promote the differences in stand types as stron gly as the more cxtens ive contra$l of the g roupings in the second map (b).
89
Chapter 4 Map Design
For example. suppose it is currently September 2008 and you were examining a map developed in June 2007 that represented wildlife habirar across several thousand acres of a landscape. Assume that the wildlife habitat being mapped was a funcdon of forest stand conditions, and that the GIS database describing forest stands had been updated in Dece mber 2007. If the date were displayed on the map Qune 2007). you would be able to understand that the qualiry of wildlife habitat illustrated in the map was estimated using an earlier version (i .e., not the current version) of the forest inventory data . Withom such information. you could very likely assume (incorrectly) that the estimates of wildlife habitat are current. It is relatively uncommon for mapmakers to provide the names of the files, projects. or compurcr code (e.g.. macro) used in m aking the map on the m ap itself. However, providing [his info rmacion would allow you co readily go back to the GIS darabases or the computer code. modify some aspect related co the composition of the map. and generate a new version of the map relat ively quickly. Without such guidance. you may find ir difficult [0 remember how a map was originally constructed, The map projection might also be provided if the map perspective is affected by the projecrion system used. This rype of ancillary information is usually placed in a subordinate position on a map. in relation to the other aspects of a map. and displayed with a relatively small font size.
79
A map disclaimer is a statement that embodies the legal position of the mapmaker with respect to map users. In many cases, the map maker uses a disclaimer to disrance himself or herself from any legal responsibiliry for damages that could result from use of his or her map. Caveats. similarly. warn others of certain facts in order to prevent misinterpretat ion of maps. In general. cavea ts are less sweeping than disclaimers, and may on ly address certain portions or aspects of a map. Warranties . on the other hand, are usually written guarantees of the integrity of a map. and of the mapmaker's responsibiliry for the repair or replacement of incorrect maps. In practice. disclaimers and caveats are regularly used. and warranties are rarely (if ever) used in associarion with maps and GIS databases. Quite often organizations add disclaimers or caveats to their maps in an attempt to warn users of the limitations of the map content. In some cases. disclaimers are noted directly on a map. and in other cases disclaimers are provided on websites devoted to the distribution of maps. Pima Couney, Arizona, for exam ple. provides a very thorough disclaimer abou t its products on a website (Pima Counry [Arizona] Department of Transportation. 2003). The mai n ideas found in caveats and disclaimers include:
Caveats and disclaimers
• rights reserved via copyriglu and permission requirements for modificat ion co mapSj • degree of error found on the mapSj • suitability for usej
Increasingly. natural resource management organizations are adding more information to the maps that they produce both to clarifY the accuracy of mapped landscape features and to clarifY the intended uses of the maps. This
• liability. or responsibility. for errors o r omissions (e.g.. organizations usually assume no responsibility for misuse of their maps and subsequent losses); and • contact information (e.g., addresses, phone numbers, e-mail addresses).
information is important in helping users understand the appropriate applications of mapped information and in helping avoid damages and injuries that might result from improper map use. For example, maps have long served as vital navigatio n aids to mariners and pilots. The ability to safely pilot passenge rs depends on the quality o f the mapped information used as a navigational guide. Should a landscape landmark be misplaced or unidentified. the consequences to people and veh icles that are navigating with the erroneous data could be disastrous. As you might imagine. there are other reasons why maps should contain information related to the quality of data. However. providing this information or deciding not to provide this information is, at least indirectly. a function of [he litigious nature of today's society.
Caveats and disclai mers vary in form and co ntent from organization to organization. Listed below are four examples.
• Indiana Geo logical Survey (2007) : The maps on this web site were compiled by Indiana University, Indiana Geological Survey. using data believed to be accurate; however. a degree of error is inherent in all maps. The maps are distributed "AS-IS" without warranties of any kind. eicher expressed or implied. including bur not limited to warranties of suitability to a particular purpose or use. No attempt has been made in either the design o r production of the maps co define the limits or jurisdiction of any federal , state, or local gov90
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Information Systems, Systems, Spatial Databases, Part 1 Introduction to Geographic Information Databases, and Map Design
ernment. T The ernmenr. he maps are intended for use only at the nd published scale. scale. Detailed on-rhe-ground on-the-ground surveys aand hismcical analyses of sites may dHfcr differ from the maps.' hismcica.l • USDI Bureau of Land Managemenr Management (2001), Glennallen, AK: 'Info 'In fo rm arion ation displayed on oouurr maps was derived from mulriplc multiple sources. sou rces. Our OUf maps are only for graphic disp display general plan planning lay aand nd general nin g purposes. Inquiries informacion ddisplayed Inqui ries concerning information is played on our maps, ttheir heir sources, and intended uses should be (0: .. . . ..' directed to: • Orange Counry County (Florida) Property Properry Appraiser (2002): (2002) : County Pro Property The Orange Counry perry Appraiser Appraise r makes every effon effort to current and ro produce and publish the most currem informacion possible. possibl e. No warran ties, accurate information warranties. expressed or implied. implied, are provided lor for the dara data herein. herein, lues are irs values its use, or its interpretation. The assessed va NOT cerrified certified va values lues and therefore aare re subject to tax purchange before being finalized for ad valorem rax (cadastral) maps are produced poses. OCPA's on-line (cadasrral) fo su rveys. forr properry property appraisal appraisal purposes, and are NOT surveys. warranties. expressed exp ressed or implied, are provided for No warranties, [he data therein, {he therein. its use, or o r its interpretarion.' interpretation.' ksbu rg (Virginia) (2007), on the • Town of Blac ksburg rhe Blacks burg WebGIS si te: ''DISCLAIMER: T he inforDISClAIMER: The Blacksburg site: contained on rh this [Q be conmarion comained is page is NOT co srrued strued or used as a "legal description". description". Map informaaccu rate bur but accuracy is nor tion is believed to be accurate not guaranteed. Any errors or omissions should be reponed raphic reported to rhe the Town of Blacksburg Geog Geographic Informat Systems Office. Office. In no event will the Info rm at ion SyStems T Town own of Blacksburg be liable for fo r any a ny da damages, mages, includlosr profits. profits, business imerrupcion. inrerruption. loss ing loss of data, lost of business info information rmation or orner ocher pecuniary loss [hat that might the informami ght arise from the use of this map or rhe ir comains.' contains.' tion it
Map Types type of map thar you develop should be a function The rype (1) the type rype of dara data (i.e., poim. point, line. line, polygon. polygon, raster) of: (I) that is contained in GIS GIS databases, and (2) the mess.ge(s) message(s) rhar rhar yo u wish to communicate [Q to an audience. For exam exam[har the main GIS database used to create a map conpie, ple, if rhe tains poinr ures, and you yo u wish to illustrate cfjfferences differences point feat features. between the point values. you may want wanr to show rhe the berween (di fferent sizes of points as dots or graduated symbols (different points based on rhe the paine po int anribute att ribute values) the main poinrs values).. If [he GIS database used [0 ro create creare a map conwns contai ns line feacures, featu res, you may wane wam ro to illusuare illustrate rhe the differences in the lines with p
di fferent line types. fearures of interest differem cypes. If features inreresr represent areas, GIS databases co conta ntaining ining polygons can be displayed as thematic maps, qua litative area maps. and others. qualitative others. Volumetric databases. databases, such as digital elevation elevarion models, can be displayed as gridded fishnet maps, as shaded relief maps. maps, or simply as images with different shades or colors assigned to individual pixels. inrerested in illustrating illust.rating features of ofaa landIf you were interested e, yo scape across tim time. youu can develo developp maps with multiple mulriple contain ntain a view of the panels. where each panel would co landscape during a differenr different time rime period. In some cases, cann be animated, as in a short movie. They can also maps ca ' Ay through ' ability when viewed. contain a 'fly viewed on a computer compurer sc reen. The next few sections of this chapter screen. chapler describe (he the most common types rypes of maps developed for natural resource manage management mem purposes in more derail.
Reference maps Reference maps are those (hose that ill il1ustrate ust rate a number of diffeaOlres, and mat provide users with a ferent landscape features, broad perspective perspecrive of the landscape. Road maps are one example of refere nce maps, and may contain nly reference comain not nor oonly rypes, but bm also the locations of [Owns, towns, major rivers. rivers, road types, poljtical boundaries (provinces, states, states. counries, and political counties, etc.) that are set among the road system. sysrem. Maps that display stream st ream systems or watersheds are another example of refmaps. H Here, erence maps. ere, you would be able to place a watersysrem within the co concext shed or stream system ntext of a larger geograp hic area, graphic area. and thus these maps may also comain conrain the [Owns and political polhical boundaries. Land ownerlocations of (Owns these may contain comain ship maps are a third example, and (hese streams, [Owns, and other features featu res necessary to roads, strea ms, towns, place tile the land you manages within a larger landscape con[ext, text. Reference maps are commonly made when you menr-relared acrivicy maps. ated activity maps, such as those develop manage ment-rel for tree planting or locations locatio ns for new features such as trails roads. oorr roads. The characteristics characte ristics of reference maps will vary dependthe audience. For example, some refe reference rence maps ing on [he might display unique landscape features that th at are essential for high quality I) qualiry recreational recrea tional experiences. Edwards (200 (2001) describes ntent and features of fishing rhee desirable co content descr ibes th maps developed for angle rs. It th at these rhes. anglers. Ir is suggested that rypes of reference maps include the complete road sys tem types system surrounding to su rrounding a fishing area, water depths, access points (Q water. tions of off-limit fishWater, names of local features, features, loca locations ing areas, buyy areas. locations loca tions of fishing lodges and places to bu fish ing permits, places (Q to park vehicles, and, interes in te restingly. tingly, fishing 91
Chapter 4 Map Design
local pubs. In [he case of fishing maps, Edwards (2001) suggests that they be developed in such a way that the
(b) Five classes
imporr3m information is easily accessible to the eye ,
and that they be represented with easily understandable carrography.
Thematic maps Thematic maps use colors or symbols [Q describe the spatial variadon of one or more landscape features. Map features displayed with a combinatio n of colo r and texture have been shown (0 be easier to find on maps than features displayed with variations on texture alone (Phillips & Noyes, 1982). Several types of thematic maps are common. Perhaps (he most common is the choropleth map
on which a range of appropriate values (Figure 4.10) or gradations of a color illustrate [he relative magnicucle of
attributes of landscape features . Color schemes generally range from an empty shaded fill (for lowest valued attributes) to a full shaded fill (for highest valued attributes),
Trees per hectare
with various shades of color for intermediate classes.
c::J
The legend is crit ical when developing choropleth maps because the colors related (0 the va lues muse be explicitly described. in order for map users (0 interpret [he
D D
D _
0-500 50H.000 1,OOH .500 1,501-2,000 2,001.
values effeccively. Several design aspec[s muse be
(c) Seven classes
(a) Three classes
Trees per hectare
D
Trees per hectare
D D _
0-1 ,000 1.001-2.000 2,001+
D D D
D _ _
0-20 2H20 12H60 16H90 191-220 221-250 251.
Figure 4 .1 0 A range of classes of trees per hectare on the Brown Tract illustrated in a choroplerh map.
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Part 1 Introductioo to Geographic Information Systems, Spatial Databases, and Map Design
addressed when creating a legend, The number of legend classes, or categori es. is imponanr , Too few classes may not contain enough informacion , while [00 many classes may presenr [00 much detail or result in an overly 'busy' map. Sometimes it may be necessary (Q experiment with several choices co determine which might work beSt (see
Figure 4, 10). Dent (1999) provides some guidelines related to this topic. Humans have difficulty differentiating more than 11 gray [Ones, so as a general rule. a minimum or four, and not more (han six, classifications
manual methods of creating classification ran ges, I( IS advisable for map makers to visually examine the distribution of the data (hey are mapping to bettcr understand its
character, and then to decide what legend type might be useful. Again , an important co ncept when utilizing GIS software is that the software will usually do what you ask.
The responsibility !ails on the mapmaker to determine the appropriate classification for a legend. The diStribution of data, such as the range of basal
should be used on a map. The size or ranges of the intervals for each class also
area within the stands of a property yo u manages, can take on many different shapes. but (he most common (for map-making purposes) are norma1. random. and even
has a significant impact on a map's message. MoSt GIS
distributions (Madej, 2001) . Normal distributions follow
software programs offer processes to help create legends,
a stat iscically-based representacion of va lues thar you would expect to see from most populations or population samples. As a result, a standard deviation legend classification, with its emphasis based on statistical variation from
and (hey may cake inca account (he distribution of [he data that are being mapped. An equal interval legend, for example. would take into account the range of data val ues
and create intervals (classes) that share an equal disrribution of the range (Figure 4.11) . A quantile diStribution would put an equal number of observations (e.g., polygo ns) into each interval (class). Intervals might also be creared based o n how many standard deviations an observar ion is from the mean, or be created using natural break points in the disrribmion of observations. While these 3momarcd processes can save rime when compared to
an average. works well. Random disrribmions are present in data where you cannot discern a regular panern in the occurrence of data vales. Natural breakpoints can be
established by locating sub-gro upin gs of random data, thus you wou ld manually create divisions between subgroups . Even disrribmions include data where the va lues do not appea r to change very much . For example. if you
managed 1,000 hectares ofland, yo u might expect to find
(b) Quantile interval of classes
(a) Equal interval classes
Trees per hectare
Trees per hectare
0 0-249 250-499 0500-749 _ 750-999 _ 1,000+
0 0-277 0 278-593 594-873 874-1 ,236 _ 1,237+
o
Figure 4 . 11
o
An interval cla.uification and a quantile interval classification of trees per hectare on the Brown Tract.
93
•
Chapter 4 Map Design
an equal number of hectares in each 10 m' ha- I basal area class. and thus you would expect to find a relatively small standard deviation in the data values here. A quantile clas-
83
ors. A similar effect can be generated with gray tones. as demonstrated with some of the figu res in this chapter.
Single color progressions are particularly helpful for con-
each category. Regardless of how thematic classes are created. mapmakers need to ensure that the intervals are consistent
tinuous numeric variables . For nominal data classifications. such as ownership or land use, or numeric data with only a few categories. distinctly different colors or patterns can be used to make certain categories stand out on a map.
and that they make sense from an interpretation paine of
Contour maps (Figure 4.13) are also a type of the-
view. This also includes verifYing that legend classifications do not overlap and do not in advertendy omit data ranges (Figute 4.12). If a characteristic of a landscape feature (e.g.• basal area per hectare) can be placed inro more than one class, then the classes overlap. Alternatively, if a
mad e map. and are sometimes called isoline or isarithmic maps. Here. lines or collections of similar features are used to emphasize gradients or distributions, such as elevations or precipitacion levels across a landscape (Star &
characteristic of a landscape feature cannot be placed inco any class. a data range has been omitted and the feature
adjacent contour lines. The choice of an interval is important when creating concour maps: tight intervals may resu lt in a cluttered map while wide intervals might misrepresent landscape variation. To reduce clutter on maps. not every contour interval is described with a data value. only those representing significant changes in elevation-
sification works well for even or uniform distributions because an equal number of observations are placed in
Falls into a classification level 'gap'. Either way. the potential problems with the classification must be addressed. With the increasing capabilities and affordability of color printers and plotters. the use of color to graphically ponray different class ifi cations in thematic maps has become standard. In general, tonal progressions of a sin gle color are useful for illustrating magnitudes of change, with the lighter (Ones of colo rs indicating a lesser quanc:iry
(or quality) of an attribute value than the darker tone col-
Estes. 1990). The contour interval is the distance between
usually denoted themselves by the elevation interval. For example. while the comour interval between adjacent comour lines may be 10 meters, the only comour lines represented with data values may be those that represent every 50-meter change in elevation.
Compare this
W"h~e~m.~~~~~~~-r__~
polygoo in 4.7(b)
'
(b) Omilted classes
(a) Overlapping
17"O'...J-'
classes
Compare1tiese Trees per hectare
D
D D D _
0-500 501-1 ,000 1,001-1 ,500 1,501-2.200 } 2,001 +
Trees per hectare D 0-500 D 501-1,000
2001-2200 " overIap
B ~ :~~~=~',: } _
wtth the same polygons in 4.7(a)
1501-1600omilted
2,001+
Figure 4.12 A range of criteria used for a choropleth map, with (a) overlapping classes. and (b) omitted classes.
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84
Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design
Figure 4.13 A contour map of the Brown Tract (elevation in meters above sea level) .
Tree Seedling Measurements Randle, Washington
o Until now, the discussion has focused on maps created using vector GIS databases, but raster-based maps are also important for displaying data stored in the raster GIS data structure. These maps are very similar to the choropierh maps noted above: mapmakers must classify the values held by the rasrer pixels and then disp lay them on a map using a legend. Raster-based maps, unless created by raste rizing a vector GIS database, usually have more of a hJzzy appearance than vector-based maps. and therefore generally represenr more hererogeneiry across a landscape (Figure 4 .14).
25
+II
N
50
MeIers
Prepared by: Michael Wing last Updated: November 17. 2007 Figure 4.14 A raster map of uee seedling
Seedling Diameter (cm)
Skid Roads - measu~me nt$ .
large number of landscape areas, or in maps with a high density of landscape features within a given area (e.g .• a multitude of small polygons).
Other types of maps Dot density maps (F igure 4.15) were once commonly used in nam ral resource management, although less so now. Each dor in a dot density map represems a given value of an attribute. Thus areas with a greater de nsity of dots are meam to represent areas with greater values of some particular anribme. Graduated circle maps place ci rcles on top of landscape fearures and scale their diameter proportionate to one of the feature's attr ibute values (e.g.> population of towns) to demonstrate differences among the landscape features. Carrograms (Figure 4 .16) are another type of maps in wh ich more than one an ribme of landscape fearures can be viewed. These types of maps are also fairly uncommon because of the large amount of clutter that can be generated in a map with a
Figure 4 .15 A dot density map of basal area on the Brown Tract.
95
Chapter 4 Map Design
110 .. ~
85
Develop Map
110
Get Feedback
No
EdH Map
Yes Stand Attribute
D _
Trees per acre Basal area per acre
Figure 4.16 A cartogram map illustrating two measures offorc$[ density for each stand-trees per acre and basal area per acre on the Brown T net.
Map Completed Fig-urc 4. 17 A basic design loop for making maps.
The number of iterations of a design loop will be a
The Design Loop Novice mapmakers, especially students, assume that their first attempt at a new map will be sufficient [0 effectively communicate informacion (Q their audience (or to cam-
ple[e an assignmenc). However, maps usually should go through more (han one ve rsion before they are delivered to a custome r, whether changes are needed based on the mapmaker's visua l assessment of (he map. or based on a customer's suggestions (Figure 4.1 7), Each iteration in the development of a map cou ld be considered one irera-
[ion in [he design loop. Feedback from supervisors and co-workers will allow you to fine-cune maps that are made for reports or management activity plans. Besides the aestheric concerns presented in this chapter (map type. number of classes shown, etc.), mapmakers should strive to achieve visual balance within their map products. This concern is one of {he reasons why maps may need co be edited numerous rimes. Visual balance is affected by {he
function of your abi li ty to address a range of visual concerns (visual contrasts. visual balance. legend. etc.), your abi lity to address a range of illustrative concerns (showing the correct information), and your time constraints
(the time remaining before a deadline) . I[ might be advisable co scan the map-making process by first developing some hand-writren notes that contain the main ideas about the intended map message(s) or purpose and the
rype of audience [hac is likely to view [he map. An outline format wi[h a primary objeccive and sub-headings that address other intended map purposes might be worth considering. Once these concepts have been identified . it might also be worthwhile [Q create a hand-
drawn skecch of the basic map componenes and how they fit together. At this poine, the developmene of [he map wi[hin GIS can begin. A well-developed, visuallycentered map will be a reRection of one's professionalism as a natural resource manager.
Common Map Problems
size of [ex[ (tide, legend, and ancillary informa tion) and the location of map components (north arrow, scale, and location inset) in rel at ion to the visual center of the map. One key to identifying an unbalanced map is the presence of a large. empty space in some portion of the map.
Miscakes (rypographieal errors), oversighcs (wrong color or symbology used), and omiss ions (missing information) can impair a map's ability to deliver {he imended message to an audience. Probably the most important aspect of 96
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Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design
creating a map is co focus on [he audience [hat will be viewing the map. If your co-workers (ocher foreseers, biologists, erc.) will be rhe primary audience, rhen perhaps the basic map elements (e.g., a north arrow, bar scale. or locational inset) may not be as importam ro include; [his audience may expect to see instead more of [he technical info rm acion, such as annotat ion or Q[her descriptive informadon, that is related to the landscape feacuces illustrated. They may also expect [Q see a morc extensive variacion of feature classes across the landscape. However, if rhe aud ience is composed of rhe general public-rhose nor familiar with the landscape resources displayed on a map- then it would seem imporram {Q include [he basic map e1emems (e.g.• tide. credits. north arrow, scale bar, and inset) that help the audience orient themselves to the map. Less variation (fewer mapped classes of information) wou ld reduce the confusion associated with the map. An audience composed of academics o r scientists may want to see more detail in the data values. Map problems are usually rhe resulr of leaving a key element, like a scale or another detail important to the audience. off of a map. Sometimes, these problems result from misjudging the audience, and other times it is simply a matter of oversight on the part of the mapmaker. For example, since automatic spell-checking processes are available in mOSt word processing software, many mapmakers somerimes forger thar spell checking is usually a manual operation within most GIS sofrware. In addition, some place names (names associated with cities, regions,
provinces, erc.) have unusual spellings and, if spelled incorrecdy. may nor be detected even if a spell-checker is used . This requires rhar mapmakers ca refully read all map text before presenting the final product to their customers. Excessive detail , or clutter, can also detract from a map's message and intent. Most GIS software programs now feature impressive arrays of map-making symbols and rool s rhar are capable of producing any number of cartographic symbols and orher aids. While many of rhese tools can be quite helpful-not to mention interesting to experiment with-too many objects on a map produce clutter an d hinder a map's intended message. Excessive detail, especially in maps that are intended general information displays. can also result when mapmakers insert toO much text (annotation or labels) OntO a map. It is also common that output devices (e.g., printer or plorrer) produce maps wirh colors rhar appear slightly different from what is viewed on a compute r screen . These problems can be very frustrating, especiall y after you have painsrakingly pur rogerher a colored legend scheme rhat differentiates between different colors or values. The subsequent adjustments can be frustrating. Sometimes the easiest solution to a color mismatch problem is to simply choose colors from a palette file that is com pliant with the output device. Other sol ution s include using only the addirive primary (red, blue, green) or subtracrive primary colors (magenta, cyan, yellow), or creating color schemes that take plotter translations of monitor colors closely into account.
Summary Maps are used to convey info rmation about the spatial location and cha racteristics of natural and man-made resources. When they are well developed. maps a re an effective way of communicating ideas [Q an audience. This chapter described a variety of map components that can be used to help develop effecrive maps. These included legends, scales, annotatio n, symbology, and typography. The representation of rhese components on a map may be important to inevitable customers of the map. For example, there are cerrain componentS of maps that are required when developing maps for co-workers or others internal [Q a natural resource management organization, certain components thar are required when developing maps for external clients (e.g., when submitting a
harvest plan to a stare or provincial age ncy), and certain components are required when developing a map for personal use. In addition, some organizations require that all maps produced by their employees contain a n organizational logo. use a standard layout format, and use othe r features that reflect the organization a nd the map 's intended purpose. There is no singu lar, correct format rhar firs all organizarions, and you should balance your creativity with the advice provided in this chaprer. You should, however, have an understanding of the map rype options, and develop the appropriate map for the intended audience. Keep in mind that eve ry map is potentially a reflection of your reputation as a narural resource professional. 97
Chapter 4 Map Design
87
Applications 4. 1. Age class distribution map for the Brown T ract. The manager of rhe Brown T racr, Becky Blaylock, would like you co produce a map char illustrates the age class dis-
4.7. C ulvert installation dates. The road engineer associared wirh the Daniel Picke[[ foresr, Bob Packard, is in the process of developing a culvert replacement plan.
cribucion of the forest. To complete this exercise, develop
He would like you co develop a map illusrraring rhe road
a rhemar ic map showing I I age classes: 0-10 years old, 11-20, .... ,91-100, and 100+ years old.
system, the culverts, and the culvert installacion dates for
4.2. Tree densiry map for the Brown Tract. Afrer reviewing your previous work, Becky Blaylock would like
4 .8. D isclaimers) caveats, and warranties. Imagine that yo u work for an age ncy chat has developed a statewide streams GIS database. At one of your regular staff meet-
a map thac ill ustrates the trees per hectare for vegetation stands contained within the Brown T ract; she needs the map for an annual reporr chat she is developing. Develop a thematic map chat classifies the stands by trees per
hectare. using five logical classes.
rhe Daniel Pickerr properey.
ings, rhe discussion shifrs co rhis GIS darabase and the need co add or associate some SOrt of disclaimer, caveat, or warranty with the GIS database. During this conversation you
conclude rhar rhe group is very confused abour rhe use of the terms 'disclaimer', 'caveat', and 'warranty'. What guid-
4.3. Owl locations on the Darnel Picketr forest. The wildlife biologisr associared wirh rhe Daniel Picke[[ foresr needs a map chat illustra tes the historical spotted owl (StTix occithntalis) nest locations that were known to have
ance can you provide co help rhe sraff undersrand rhe dif-
been used in the Forese. Develop a reference map illustrating rhe two nest locations, and annotate rhe map with the
4.9. M ap scales. One of your colleagues, Mike Marshall, does nor like rhe eype of scale [har you com-
dare of rhe laS[ known sigh[ing of the owls.
mon ly incorpo rate into your management maps. He prefers to use anothe r type of scale, and insists that you
4.4. Stream rypes of the Daniel Picketr forest. The hydrologiS[ associared wirh rhe Daniel Picke[[ foreS[
use ir as weU . IdenrifY, define, and describe rhe possible advanrages and disadvanrages of rhree eypes of approaches
needs a map illustrating the different stream rypes, in o rder CO direct a summer crew co the locat ions he would
for creating map scales.
like
4. 10. Map development. A sma ll consul ring firm In Brirish Columb ia has recendy hired you, and one of your
to
survey for fish species and habitat conditions.
Develop a reference map that iIlusrra rcs the different scream rypes associated with the Da ni el Pickett fo rest.
4.5.
Potential harvest unit. The land manager of the
Daniel Picken forest is considering a timber sale in un it
ferences between the terms, and how the terms might used in relation to the streams GIS database?
be
first assignments is to make management maps for various planned act ivities on the land managed by your firm.
IdenrifY five irems or objecrs rhar should be placed on almost every map.
number 13 on rhe Daniel Picken forese He would like produce a management map indicating that unit
4.11. M ap legends. The manager of rhe Brown Tracr
13 is a proposed harvesr area, and co display rhe road and
desires a map illustratin g the trees per hecta re for each stand on the property. There are different approaches for
yo u
(0
stream systems in rdarion
(0
the uniL
4.6. Brown Tract hiking map . Becky Blaylock, man· ager of rhe Brown Tracr, wou ld like you ro develop a map illumaring rhe crail sysrem, highlighring borh rhe authorized and unauthorized trai ls. Recrearionists who
visir rhe fo reS[ will likely use [his map. Develop a refer· enee map that incl udes the road system and the contour lines (wi th associated elevations) as supplementary informa ti on.
organizing and displaying sparial dara inro a map legend. a) Whar is a general guideline fo r choosing rhe number of categories in a legend thar uses gray cones?
b) Whar is an equal inrerval legend, and how does ir disp lay numeric data? c) What is a quantile distribution legend, and how does if display numeric data?
d) Whar is a srandard devia
88
Part 1 Introduction to Geographic Information Systems, Spatial Databases, and Map Design
References Dent. B.D. (1999) . Cartography thematic map design . New York: McGraw-HilI. Digital W isdom. Inc. (2006). Cartographic symbols & map symbols library. Tappahannock. VA: Digital Wisdom. Inc. Recrieved March 28. 2007. from hnp:llwww.map-symbol.com /sy m_lib.htm. Edwards. D . (2001). Maps for anglers. The Cartographic journal. 38(1).103-6. Franklin. P. (2001) . Maps for the reluctan t. The Cartographicjourna~ 38(1). 87-90. Indiana Geological Survey. (2007) . Copyright. map disclaimer. and limitation of warranti~s and liabilities. Bloomington . IN : Indiana Geological Survey. Retrieved March 3. 2007. from hnp:// igs.indiana.edul disclaimer.cfm. International Orienteering Federation. (2000). Intanational specification for orienuering maps. Radio-katu, Finland: Incernational O rienteering Federation. Kaya. N .• & Epps. H .H. (2004). Relationship between
color and emotion: A study of college students. Co/l'ge Student journal, 38. 396-405 . Madej. J. (2001) . Cartographic dlSign using Arc View GIS. Albany. NY: OnWord Press. Mecriam-Webster. (2007). Mariam-Webster online search. Retrieved March 28. 2007. from hnp://www. m-w .coml cgi-binl dictionary. Monmonier. M.S. (1995) . Drawing the line: Tales ofmaps and cartocontroversy. New York: Henry Holt and Company. Monmonier. M .S. (1996). How to Lie with maps (2nd ed.). Ch icago. IL: University of Chicago Press. Natural Resources Canada. (2006). Topographic map symbols-introduction. Ottawa, ON : Nacucal Resources Canada, Earth Sciences Seccoc, Mapping Services Branch. Recrieved March 23. 2007. from hnp:llmaps.nrcan.gc.caltOpo 10 I/symbols_e. php. Orange County (Florida) Propetry Appraiser. (2002) . OCPA map record inquiry system. Retrieved March 29. 2007. from http://www.ocpafl.orgidocs/disclaimer_ map. html. Phillips. R.J . (1979) . Why is lower case bener? Some data from a search task. Applied Ergonomics. 10.211-14. Phillips. R.J. (1989). Ase maps different from other kinds of graphic information? Cartographic journal, 26. 24-5.
Phillips. R.J .• & Noyes. L. (1982). An investigation of visual c1uner in the topographic base of a geological map. Cartographic journal. 19. 122-32. Phillips. R.J .• Noyes. L.. & Audley. R.J. (1977). The legibility of type on maps. Ergonomics. 20. 671-82. Pima County (Arizona) Department of Transponacion. (2003). Department disclaimer and me restrictions. T llcson , AZ: Pima COlincy Department of T ransporration, Geographic Information Services Division.
Retrieved March 28. 2007. from http://www.dot. co .p ima,az,lls!mapdis,hrm ,
Robinson. A.H. Morrison. J.L. . Muehrcke. P.c. . Kimerling. A.J .• & Guptill. S.c. (1995). Elements of cartography. New York: John Wiley & Sons. Inc. Sheahan. B.T. (2004). The unofficiaL Arc/Info and Arc View symbol page. Victoria. BC: Spatial Solutions. Inc. Retrieved March 28. 2007. from http://www. mapsymbols.com/. Star. J .• & Estes. J. {I 990) . Geographic information systems: An introduction. Englewood Cliffs. NJ: PrenticeHall. Inc. Town of Blacksb urg (Virginia). (2007). Blacksbu rg WebGIS site. Retrieved March 28 . 2007. from http:// arcims2 .webgis. net/blacksburgl defa ul t.asp. US DI Bureau of Land Manage ment. (2001). Map disc/aimer. Glennallen. AK: Bureau of Land Management. Retrieved March 28. 2007. from http://www. blm.gov/aklgdo/documents/map_disclaimer.doc. USD I US Geological Survey. (2003). Part 6 publication
symbols. Standards for 1:24.000- and 1:25.000-scale quadrangle maps. Reston. VA: US Geological Survey. Retrieved March 23. 2007. from http://rockyweb. cr. usgs. gov In m ps tdsl ac rodocsl q rn a ps/6psym 4 03. pdf. USDI National Park Service. (2003) . NPS map symbols: Updated March 4. 2003. Retrieved March 27. 2007. from http://www.nps.gov/carto/PDF/symbolsmapl . pdf. Valdez. P .• & Mehrabian. A. (1994). Effects of color on emotions. journal ofExperimentaL Psychology: Genera~ 123. 394-409. Wood. D. (2003). Cartography is dead (thank God!). Cartographic Pmpectives. 45.4-7.
99
Part 2
Applying GIS to Natural Resource Management
act 2 of rhis book focuses on the GIS applications common to namra) resou rce managemen t organ izatio ns. The majority of data supporting daily GIS use in namral resource manage ment is stored in vectO r GIS databases. although rhe in tegratio n of vector and raster GIS databases is becoming increasingly com mon. For example. the development of resource managemem plans may require rhe use of GIS databases representing fo res t stands (polygons), roads and srreams (lines), wate r sou rces and wildlife observat io ns (points). and topography (raster). T he chapte rs included in this part of the book present a number of GIS processes such as querying, buffering. clipping. raste r analysis. and simultaneous integratio n of vector an d raster GIS databases in an analysis. The ropics increase in complexity wi th each passing chapter. and rhe applications associated with each wpic integrate processes presented in previous chapters. Some advanced wpies are also presented. such as delineating the land classifications of a landscape and delineating the recreation opponunity spectrum classes of a landscape. In panicular, [he raster-oriented chapeers provide examples of adva nced a nalys is techn iques and possibilities. A va riety of processing pathways can be used within GIS co address a si ngl e management concern and it is imponant [Q note th at there may be more than one processing solution for a part icular challenge. While we provide direction for the mOSt straightforward processes. students should feel free to be crea tive in their approaches co addressing each appli cation. Finally, so me of the applications require students CO think more broadly (beyond GIS) abom how the results of an analysis may affect natural resource management opportunities within a given landscape.
P
100
Chapter 5
Selecting Landscape Features Objectives A vari ety of methods can be used [Q locate and select landscape features based on either thei r attribures or their proximiry to o th er feaw ces. Spatia l and attribute reference queries ca n be used [Q select features based on the informacion scored in attribute rabies (layer database) o r (he spatial locatio n of landscape feacuces. At the conclu-
sion of this chap ter readers sho uld be /am iliar with, and have a working understanding of:
1. the variety of methods that can be used ro select landscape feawces fro m a GIS database; 2. the meaning of the term 'query', when applied spatiall y o r referentially; and 3. the methods you can use to develop a description of the resources located on a landscape. In a reeem annual report to their stockholders. one of the largesr timber co mpanies in the United States said the
foll owi ng abo ut the use of GIS: 'Our foresters use advanced Geographic Information System (GIS) models to get a picture of timber species. sizes, and age c1assesalong with a mul titude of environmental detai ls. from
streams and fis h to wildlife pop ul at io ns and habitat' (Plum C reek Timber Co., 2001). This statement recognizes the extent
to
that are important in the management of their property). These o rganizations include public agencies such as the US Forest Serv ice. the Canadian Forest Service, the US Park Service, th e US Bureau of Land Management. State and provincial orga nizatio ns, private industrialla ndowners, and those organiza tions that manage smaller tracts, such as forestry consu ltants, and university research forests. Each organization has a different mission, ye t each relies on sim ilar methods fo r storin g and organ izi ng the ir geographic databases . The methods used to extract information from these databases are also very similar, and help provide a picture of the namral resources that they manage. The chapter emphas izes one of the most common methods of data extraction from a database: the use of queries. Besides asking questions like 'what is here?' o r 'what
type of feature is that?'-the rypes of questions wh ich hold our auenrion in the first part of th is chapter-there are at least four types of information acquisi tio n processes you can use when asking questions of a GIS database . The four processes are outlined briefly below, and when we ar rive at the section entitled 'Selecting features based on some database criteria', we will become engaged in one or more of these processes. The ap plicatio ns provided at the
end of the chapter will further rein fo rce the need for these acquis ition processes in natural resource management.
which GIS is viewed as a val uable man-
agement too l and acknowledges the power of GIS to assist in the management of resources. Other natural resou rce management organizations also rel y on GIS to sto re location information about the natural reso urces that they manage (as well as those that they might not manage but
1. Obtaining sp"ific focts Is there spo tted owl habi tat w ithin the property that is
being managed ? Are there any steep hiking trails within the property being managed? 101
Chapter 5 Selecting Landscape Features
Is ther< any old-growth forest within the property being managed?
2. Obtaining extended information Besides spotted owl hab itat, what other ty pes of important wildlife habitat are found within the property bei ng managed? Besides steep hiking trails, what other types of trails are contained within the property being managed? Besides old-growth forests. what other dassificacions of forest are conta ined within the property being managed?
3. Obtaining broader information blUed on complrx queries Are there any areas that meet all of the habitat requirements for all of the species of interest within the properey being managed? Are there any trails that, when combined . provide a
relacively easy hiking experience? Are there any forest stands th at can be managed co provide both wildlife habitat and timbe r resources?
4. Obtaining information on resources in limited sup;" Are [here any areas of habitat [hat appea r in small quantities across the area being managed? Are there any trails that provide experiences that occur rarely ac ross the area being managed? Are there any forest stands that represent unique forest types within the area being managed?
Selecting Landscape Features from a GIS Database As we noted in the introduction to cha pter I, nat ural resource managers are consistently called upon to describe the condition of a landscape using GIS softwa re programs and GIS databases. Generally speaking, natural resource managers are co ncerned with understanding where la ndscape features are located and what characteristic(s) they might have now, have had in the past, or will have in the future. There are at least eight processes you can use to select landscape features from GIS databases: I. 2. 3. 4.
Select Select Select Select
one feature {manually}. many features {manually}. all features (ma nually or a uto matically). no features {manually or automat ically} .
91
5. Select features based on some criteria . 6. Select features from a previously selected set of features. 7. Switching (inverting) a se t of selected features so that all unselected items become selected. 8. Select features within so me proximity of other features.
Selecting one feature manually The abi lity to select landscape features using a computer mouse or digitizing puck is an essential tool for examining or editing individual landscape features in GIS, and for editing tabular data contained in an attribute table. As we have JUS[ implied, within GIS software programs landsca pe features can be selected either from the window that presents the spatial display of landscape features, or from the window that presents the associated a[[ ribute table. GIS software programs are either designed to allow users to select landscape features by default {e.g., Maplnfo} or to provide a well-positioned, easily accessible function (i.e., the 'select features' tool in ArcMap or the 'select features ' button in ArcYiew 3.3) [hat allows users to manually select individual landscape features. A careful positioning of the computer mouse's cursor over a landscape feature {either in the display of spatial landscape features or in the tabular database} and a simple click of the mouse will usually do the job. Selected landscape features will normally be colored or shaded differently {in both the spatial display of landscape features and tabular database} from other landscape features in the GIS database of interest, allowing users (0 visually verify what has been selected. GIS software programs use standard characteristics for displaying selected features, such as the light blue color used by ArcMap or the yellow colo r used by ArcYiew 3.3 for displaying a selected spatial feature or its attribute record. Some GIS software programs allow users to define the characteristics (i.e., color) of selected spatial features or a[[ributes. and most GIS softwa re will allow users to specify which GIS layers ca n be 'selectable'. As a resulr, either those layers that are visible in a window are selectable. or those that have been chosen from a list are selectable. Th is ability can make feature selections more efficient and also prevent analysis errors.
Selecting many features manually There are times when selecting many landscape:: features manually will also be of value in ass isting the develop102
92
Part 2 Applying GIS to Natural Resource Management
menr of inform mem information arion (map or data) dara) to co fac facilitate il irate a natural resoll rce managemeor resource management analysis. The process fo forr selecdng selecting multiple landscape feamces mulriple features is sim similar ilar (0 ( 0 the process for selecting selectin g single sin gle landscape feamres m manually, an ually, wi with th the compucer computer mouse playing {he the ce nrral ntral role. The selection of multiple landscape feamres features can generally be accompl accomplished ished rwo ways: in one of twO
1. Using the seJect select tool associated wi with th a GIS GIS softwa re program, draw a rectangular reccangular box in the rhe window that tilar fearures. Here, you would displays the spatial features. wou ld specifY specilY the location (by clicking and holding down a mouse button) for one corne cornerr of the box. Then Then,, drag the rhe mouse to [0 specify the diago diagonal nal corner, and release the button. mouse burton. 2. Using [he the seiect sdect [001 tool associated with wirh a GIS GIS software soltware program, select sdccr features individually ind ivid ually while depressing a computer puter keyboard (e.g. , the 's 'shift' hilt' key key on the com when using ArcMap). This process can be used in the rhe window w indow that displays the [he spatial fearuces fearu ces or in the rhe attribute arrribu r. table. Each GIS software program may involve invo lve a variation va riation (or [Wo) rhese techniques. rec hniques. M More ore than likely li kdy an alternat alternative ive two) oonn these exisrs that exists th at allows users (Q to more efficiendy efficiently select multiple landscape la ndsca pe features fearu res manually. Fo Forr example, assume ass ume you yo u polygons thatt conwere interested in selecting selectin g vegetation vegecarion po lygons tha tained (rees that were, on average, over 100 years of age. age. Within aan co uld so rt the vegetarian vegetation n attribute table you could sore records polygon reco rds by their age, either eicher using an ascending (youngest (yo ungest to ooldest) ldest) or descending (o ldest to youngest) so rr. This wi sort. willll re·posi re-positio rionn the [he vegetation vege tation polygon polygo n records reco rds such that the ones th that at comain contain th thee oldest trees are grouped together. rogether, th us facilitating rncilita ting a more efficient manmu lriple features. Had H ad you not sorted the ual selec[ion selection of multiple records, you yo u would wo uld have needed to ro scroll th through rough [he the attribute [able table to locate loca te records wi th average tree age vallies this is likely would wou ld have lead {Q to errors of om omisisues over ove r 100; [h sion sio n (missed records) and it would nO[ not have been an efficient use of yo youurr time. time.
Selecting all of the features in a GIS database The ab abiliry sdecl all la ndscape features from a GIS ility to seleer G IS database is a standard stan dard process among GIS software programs. In addition, most GIS software programs generally gene rally have specific functions funct io ns to allow users to select all land-
seape features with JUSt scape jUst one (or (o r a few) click(s) c1ick(s) of a mouse rather ra ther chan than having to ro select selecl all the th. landscape features featu res abiliry is useful when summ summaarizing rizing data manually. This ability about abo ur all of the landsca landscape pe featu features res in a dambase database or o r when you are co considering nsidering spatial spacial analysis processes. Some users G IS prefer to the landscape features to which the of GIS ro select (he landsca pe fearures rhe GIS processes would be applied appl ied (all landsca landscape pe features fearures in 0 11 the rhe co common mmo n default (if no case), rarher rather than rely on this case). landscape features fearures are selected, selec<ed, an analysis applies ap plies to all landscape features). features) . Another opportuniry opportunity to selecr select all all the features fearures of a GIS GIS database darabase is if you are calculati calculating ng values for fields fi elds in an attribute tab le. Calculations are usually performed on selected records reco rds in a tabular database, daraba se. so, {Q to perform a calculacion records, such as the calcalcu latio n on all of the me records. culation of some portion portio n of a habita habitatt suitability suitabili ty score for fo r polygons, vegetation po lygons, you may need to selecr select all of the reco rds as a preliminary prelimina_ry step in the process. records
Selecting none of the features in a GIS database T he ability abili ty to select none of the landscape features in a GIS database (or to ' un-select' un-sdec[ o orr 'clear' the rhe selection) selectio n) is iated also a standard standa td process assoc associa ted with GtS GIS software programs. Most GIS soltware software programs have specific functions-either menu items or buttons-that allow users co to un-sdecr all landscape features with wirh JUSt jUst one click of a un-select rhan having co to un-select un-selecr all landcomputer mouse (rather than scape features featu res manually). manually) . There are many reasons reaso ns for to perform th is ac wanring (Q this anion; tion; one o ne of the most common co mmo n reasons [Q clear previollsly previously selected selecred landscape features featu res reaso ns is to from a GIS GIS database darabase before performing a spa spatial rial process such as bu buffering. ffering. In a buffering process, when no spa spatial rial fearures features are selected) selected, generally genera lly all of the features are used to develop buffers. If one o orr more mo re features are selected, selec ted, features are buffered. h It is usually after oonly nly the selected feamres alter viewing the results of ofaa spatial process that you realize real ize you have forgonen to un-select landscape feacures features before pe perrforming (he th e ooperation. perati o n.
Selecting features based on some database criteria criteria By now it may be evident that there musr must be a faster way [he entire tures to select a subset of the enrire se sett of landscape fea features mher rather than having to selec[ sdeer [hem rhem manually. Within all GIS software programs, lity CO programs. users have the abi ability to ask questions, questions. that CO develop queries, q ueries. about aho m the landscape la ndscape feacures features [har is, {Q 103
Chapter 5 Selecting Landscape Features
What is an attribute? It is defi ned as a characte ristic or quality of an o bject, or in our case, a characreriscic o r quality of some namral resource. Within GIS. you are usually imeresred in a characteristic or quality of some feature found on the landscape. such as the vegecarion . the so il , the water, or the land . More specifically, a
contained in a GIS database. A query is simply a question. or set of questions. used co request in fo rm ation abom some resource contai ned (or described) within a database.
forester might
93
be inte rested in the basal area, timber vol-
ume. or habitat qua/iry of [he srands [hat are delineated on a property. An amibute of a landscape feature can be extended, howeve r, co incl ude its spatial position. size,
perimecer (or length). and even the cype of data it represents (e.g.• point. line. polygon. raster grid cell).
the selection of resources of imerest across a landscape, and help a managemem team focus o n the more suitable areas.
Imagine a co-worker aski ng. 'Please help me find all of the
In developing GIS queries, you must build a set of
fo resred areas that might require a pre-commercial thinning rrearmem.' To locate the potencial pre-commercial th in ning areas , the request should be refined , then redirected by asking questions abour the info rmacion contained in one or more GIS databases, in this case, perhaps
criteria to en able a search of data base, and subsequently to enable the selection of app ropriate landscape features . Fo r those mo re attu ned to vis ualizing processes from a computer programming perspective, queries are similar to the developmem of stattments. As the auribures of each landsca pe are examined. ifsome conditions (criteria) of the landscape features are true (or conform to the criteria).
a forest stand GIS database. You might ask the GIS database 'Where are all of the young. overstocked. conifer stands?' This wo uld seem to be a good start. yet a GIS software program wou ld need more specific. quancitative instructions, detailin g the an ribuces co use in [he search for the ap propriate landscape feacu res, and detailing the
bounds of the val ues of [he amibutes. For example, to find
If
the landscape feature will be placed into a 'selected' set. The following examples of queries rel ate to the data presented in Table 5.1. Answers are provided to allow students to work through the queries o n their own , and
to
understand how (and why) the results were obtained.
the yo ung stands in a forest stands GIS database. you might search an 'age' anrib uce field for those forest sta nd
polygons [hat have values between 10 and 20 (IO and 20 years old). The more refined query then becomes 'find all of the forest stands where age ~ 10 and age S 20.' You might ask. 'Why would we need to perform que ries?' One reaso n was illuscrated wit h the need [Q idencify the po temial pre-co mmercial th inning areasthere are times when natu ral resource managers need to know where certai n resources are located to help facili tate making managemem decisions regarding the resources. Queries can range from the rather simplistic (findi ng the mOSt appropriate huming area) to the rather complex (finding the most appropriate areas to commercially thin trees over {he next (WO yea rs). Suppose a management decision needed to be made rega rding locat ing the most appropriate area to develop a new hiking trail. You might first locate and describe all of the cha racter istics of a landscape (current hiking crails. timbe r stands of var ious charac teristics, etc.) that would influence the placemem of a new crail. A query of these GIS databases could facili tate
TABLE S,l Stand
A limber stand database
Acra
Hectares
MBP
Ag'
TPA'
TPW
100
40.5
12
25
200
494
2
70
28.3
20
45
150
371
3
250
101.2
13
26
200
494
4
80
32.4
6
18
300
74 1
5
60
24.3
2
12
575
1,421
6
120
48.6
10
23
200
494
7
40
16.2
7
20
400
988
8
60
24.3
14
28
150
37 1
9
75
30.4
3
15
550
1.359
10
95
38.4
10
600
1,483
• Thousand board ft et ptr acre h
Trees per acre
<
T rets per hectare 104
94
Part 2 Applying GIS to to Natural Resource Management
Single criterion queries Si Single ngle criterion queries examine exami ne a single attribute (also
called a variable. variable, or field) of each landscape la ndscape featu fearure. re, use a single operator. and include a single [hreshold threshold si ngle relational operaror, value.
Relational Rela tional operator: ~ (gn:a (grearer rer than rhan or equal to) Threshold value: value: 950 Query: TPH ~ 950 Answer: 4 stands sta nds (5,7,9. (5.7.9. and 10)
g) How many tim timber ber stands are a re larger than 100 a) How many timber stands are 20 years old? A[cciburc: Attribute: age operator: = (eq (equals) Relational operaror: uals) Threshold value: val ue: 20 Query: age = 20 Answer: I srand stand (7) b) How many timber s[ands stands are greater than or equal eq ual
to 25 years old? old? Attriburc: age Aucibme: Relational Relationa l oper.ro operator:r:
[0
c) How many limber timber stands are less than rhan or equal [Q ro 20 years old? old? Attribute: Attri bute: age Relarional operator: S; Rela[ional S (less than rhan or equal to) [0) Threshold value: 20 Query: age S; S 20 Answer: 5 stands (4,5,7.9. Answer: (4.5.7.9. and 10) d) How many limber timber "ands stands cama conta in at ar least 15 thou-
sand board feel feet (MBF) per ac acre re of timber volume' volume? Amibure: Amibute: MBF Relational Rela[ional operaro operamc: r:
per hectare (TP (TPH) H)?' Amibute: TPH Relational operawc: operator: >> (greater than) than) Threshold value: value: 700 Query: TPH > 700 Answer: 5 stands (4.5.7.9, (4.5.7.9. and 10)
f) How many dmber timber srands stands have at least 950 95 0 trees per hectare? per Anribure: Anribule: TPH
hectares in size? AttriblHe: hectares Attribute: Relational operator: >> (greater than) Threshold value: 100 Query: hectares > 100 Answer: I stand (3)
Multiple criteria queries criteriaa queries are com combinations Multiple criteri binations of single criterion queries, queries. held together rogether by logical operators (and. or,
1/ot). not). They allow you to ro develop a complex query wi without thout havi ng [0 having to perform several single cr criterio iterion n queries in mulriple criteri criteria a queries mat that sequence. Below are several multiple
relate to the rhe dara data fou found nd in Table 5.1. 5. !. a) How many c.imber timber stands are less than or equal to 20 years yea rs of age, and contain more than rhan 950 trees
per hectare (TPH)? Attribu(es: Attributes: age. TPH Relational operarors: operators: Age: S; S (less than or equal to)
TPH : > (grea (greater TPH: ter than) Threshold values: Age: 20 TPH : 950 TPH: Logical operator: and Query: (age S; Query: S 20) and (TPH > 950) Answer: Answer: 4 srands stands (5.7,9, (5.7.9. and 10) b) How many ti mber stands are ar old, at least 25 years old. co ntain at least 10 thousand board feer or contain feet (10 MBF) per ac re of timber rimber volume? volume? Attributes: age, age. MBF Relational operators: Age: ~ (greater [han Age: than or equal to) MBF: ~ (greater than or equal to) MBF: Threshold values: val ues: Age: 25 MBF: 10 Logical ooperator: peraror: or Query: (age
Chapter 5 Selecting landscape Features c) How many timber stands are at least 20 years old, and are no older than 30 years old, and contain more than 500 trees per hectare? Amibutes: age, age, TPH Relational operators: Age: ;" (greater than or equal to) Age: S (less than or equal to) TPH : > (greater than) T hreshold values: Age: 20 Age: 30 TPH: 500 Logical operators: and, and Query: (age;" 20) and (age S 30) and (TI'H > 500) Answer: I srand (7) To illustrate the use of a complex query, we wi ll ask a few questions regarding the polygons contained in the Brown Tract stands GIS database. First, assume that the managers of the Brown Tract are interested in managing the forest for timber production, and maximizing the growth potential of (he [fees in the forest. One way ro achieve this goal may be to use precommercial thinning. As a result, (hey need to understand whether any potencial commercial thinning opportunities exist. Assume that the criteria developed by the managers of the Brown Tract [Q assist in (he analysis was based on four ideas:
be between 30 and 40 years old, the land allocation should include only the even-aged Stands, and the timber volume prior to thinning must be above 9 MBF per acre. The criteria, placed within the structure of a query then becomes: (age;" 30) and (age S 40) and (MBF ;" 9) and (land allocation = 'even-aged') The resulting eight stands (42 hecta res) on the Brown Tract that conform to this query are illusuated in Figure 5.1. These areas can be considered. the poremial commercial thinn ing opportun ities for the fo rest in the near future.
Selecting features from a previously selected set of features Rather than develop a long, complex q uery containing multiple criteria. you can design a set of less complex quer ies that are hierarchical in nature and that reduce the landscape features contained in the set of selected landscape features with each additional query. This process may help you stay organized and prevent the occurren ce of mistakes that may be difficult to understand when usi ng a long and complex query. To selec, landscape feacures from a previously selected set of landscape features, a number of single criterion queries are assembled .
l. Thinning should occur about 10 to 15 years prior to the fin al harvest age assumed by the organization
(45-50 years). 2. Enough crop crees should remain un-cut in the thinned stands so that they (the residual trees) sufficiently respond (within increased growth rates) to the increased ava ilabili ty of light, water, and nutrients for the remaining 10-15 years prior to final harvest. 3 . Commercial thinning will only be applied to evenaged forested stands. 4 . Commercial thinning operations should remove, at a mInimUm, 10 MBF per hectare (abou, 4 MBF per acre). Because the managers have specified a minimum residual volume level the dmber volume per unit area prior to thinning should be substantially greater. The criteria for the query that the managers of the forest decide to use includes the age of the stands that could be thinned muSt
95
Figure 5.1 Stands on the Brown T net that meet the following criteria: age 2: 30 and age :5 40 and MBF 2: 9 and land allocation . 'even-aged'.
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Part 2 Applying GIS to Natural Resource Management
Example c, presemed ea rlier. involved the following multiple criteria query,
(age ~ 20) and (age
s: 30) and (TPH > 500)
which could be subdivided intO three single criterion quenes: age ~ 20 age s: 30 TPH > 500 Each of these can be performed in sequence; the first from the full set of stands GIS database landscape features, age
~
20
(6 stands [1,2,3,6,7 and 8])
the second from the set of 6 landsca pe features that were selected (sta nds 1,2,3,6,7 and 8), age s: 30
(5 stands [1,3,6,7 and 8])
and the third from the remaining 5 landscape features (stands 1,3,6,7 and 8),
TPH > 500
(I stand [7])
resulting in the same landscape feature selected as when
the multiple criteria query was used. The preference for a particular technique (selecting landscape features from a previously selected set or selecting landscape features using a multiple criteria query) wi ll vary from user [0 user, depending on each user's confidence and experience. If you were co try this hierarchical process of selecting landscape features on the Brown Tract thinning example from above, w here rhe criteria was,
Figure 5.2 Stands on {he Brown Tract that meet the following criterion: age' ~ 30.
Breaking down a complex query into smaller, single criterion queries may not work when the logical operawr
involved is 'or'. In the following example, the complex query cannor be broken down into rhree single criterion queries.
(age
= 29) or (age = 30) and (TPH
> 500)
The set of stands that might comprise TPH > 500 can be su bdivided into those that are 29 years old. However, the resulring set cannot further be subdivided into sra nds (har
(age ~ 30) and (age s: 40) and (MBF ~ 9) and (land allocation = 'even-aged') you could subdivide the querying process into four steps. Ste p I: Select from rhe entire set of stands those
stands where age ~ 30 (result: 212 stands shown in Figure 5.2). Step 2: Select from the 212 previously selected stands, those stands where age s: 40 (result: 23 stands shown in Figure 5.3). Step 3: Select from the 23 previously selected stands, those stands where MBF ~ 9 (result: 9 stands shown in Figure 5.4). Step 4: Select from the 9 previously selected stands, those scands where the land allocation is even-aged (resu le: 8 stands shown in Figure
5.1).
Figure 5.3 Stands from the previously sciecte'd set (age 2: 30) on the Brown Tract that meet the fo llowing criterion: age S 40.
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Chapter 5 Selecting Landscape Features
(age
97
= 29) or (soil_rype = 'PR') and (TPH > 500)
Again, we can locate the stands where TPH > 500, and from those we can locate the stands that have an age of29 years. However. there may be many other stands beyond those in the resulting set that have TPH > 500 and a so il
rype of 'PR' (yee an age that is nOt 29 years). However, the following multiple criteria query could be broken down into three single criterion queries:
(age> 28) or (age < 31) and (TPH > 500) Here, the set of stands that might comprise TPH > 500 can be subdivided into those that are greater than 28 years
old. The resulting Set can furrher be subdivided into stands that are less than 31 years old. Figure 5.4 Stands &om the previously ~Iected set (age O!: 30 and age :5 40) on the Brown Tract that meet the following criterion: MBF O!: 9]
are 30 years old (they are 29 years old) . Similarly, in the following example, the complex query cannot be broken down into three single criterion queries.
One of the most common mistakes made when asking questions of databases is that results are often accepted as 'truth' without considering whether results are reasonable. For example, the Brown Tract timber scands
GIS database contains a number of polygons th at, when summed, describe a 2,123 hectare area. Within the Brown Tract a variety of ages of forests, ranging from recent c1earcuts (age = 0) to older stands. are present. To describe the current structure of the
Inverting a selection Occasionally, you may find yourself in a situation where you need to understand {wo aspects of the spatial features contained in a GIS database: what is the condition (state or characteristic) of one set of features, and what is the
forest, as in this case, the sum of area represented by
the multiple queries should equal the sum of the resources in the original GIS database. If the sum of the area in the age classes is greater than the size of the Brown Tract, some areas were double-counted, perhaps using queries such as these,
Age class I: Age class 2:
(age (age
~
0) and (age" (0) ~ 10) and (age" 20)
Brown Tract, you could develop an age class distribution that indicates the area within. say. la-year age classes. After performing queries of the various forest age classes, the sum of the area queried should nor
result in more or less than 2,123 hectares (the size of the properry). You should always ask yourself whether the results obtained seem reasonab le, given the resources being queried. Whenever possible , if a method of verifying results is available, it is advisable to check yo ur work or have a colleague check your work. If multiple queries are performed that are designed to completely describe the resources of the
where the area of I O-year-old stands is included in both classes. If the sum of the area in the age classes is less than the size of the Brown Tract. some areas were nor counted. perhaps using queries such as these.
Age class I: Age class 2:
(age> 0) and (age < 10) (age> 10) and (age < 20)
where the area of O-year-old stands (c1earcurs) is not
included in age class I , and the area of IO-yea r-old srands is nor included in either age class.
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Part 2 Applying GIS to Natural Resource Management
condition of everyth ing else. Two sets of queries can be
developed to identifY these two sets of features; howeve r, if rhe second set contains 'everything else', simply inverting rhe selected fearures after rhe first query will produce rhe second set. For example, if you were in terested in
available for the Pheasant Hill planning area of the QU'Appdle River Valley in central Saskatchewan. Here, we have created a GIS database that contains so il s, topography, and land classification information . There are 168
polygons with in the GIS database. Assume that we, as nat-
understanding how much land area was considered 'reserved' on the Brown Tract, and rhen how much land area remained 'un-reserved', you could first develop rhe
ural resource managers, are interested in understanding the land areas that contain clayey soils, have steep or
query for rhe reserved areas,
having no significant lim ita cion as they pertain to agricul-
undulating topography, and that have been categorized as tural practices. Initially, we could develop a multiple cri-
(land allocarion = 'meadow') or (land allocatio n = 'research') or (land allocation = 'oak woodland') or (land allocation = 'rock pit') and find that it contains 42 stands covering about 229 hectares. By then inverting the selection, you will find that what rema ins is a ser of 241 stands covering abom 1,893 hectares. A second query for even-aged. unevenaged. and shelrerwood stands was not necessary. The inven selection technique simply switches the GIS database selections, so that featu res previously selected are no longer selected. and vice versa. Some G IS software will
teria query to select from the larger set of features only those that have clayey soil types. There are a number of
soil types in the Pheasant Hill planning area, thus the query would be designed something like this: (Soil_type = ' Indian Head Clay') or (Soil_type = 'Indian Head Clay Loam') or (Soil_type = ' Indian Head Heavy Clay') or (Soil_ type = 'Oxbow Clay Loam') or (Soil_type = 'Rocanville Clay Loam')
make this capability ava ilable through a menu choice in
Given that a polygon is assigned on ly one soil type. we needed to use the relational operator 'or' in the query rathe r than 'and'. As a result of this multiple criteria
the cabular database window while ocher programs may
query, we find that only 69 of the original 168 polygons
make this capability available through a menu or button
have a clay component in their assoc iated soil type. In order to locate those areas within this sub-set of the landscape features that are also located on undulating or steep slopes. we can perform a second multiple criteria query.
in the spatial database viewing window. Some GIS software programs include borh capabi licies.
Example 1: Find the landscape features in one GIS database by using single and multiple criteria queries and by selecting features from a previously selected set of features A co mbination of query processes can be used if you believe that they are necessary to accurately arrive at the desired set of GIS database features. In this example, we
use a GIS database created from the set of GIS databases
_
(Topography = 'STEEP') or (Topography = 'UN DULATING') and here only select features from the previously selected sub-set oflandscape features (not from the larger, o riginal set of landscape features) . In this case, we find that 28 of the polygons have both the soil characteristics and topO-
Areas that meet the query specifications
C=:J Other areas that do not meet the query specificatioos Figure 5.5 The result of a query for areas with dayey soils, located on steep or undulating topography, and with no limitations for agriculrural practices using GIS databases developed for the Pheasant Hill planning area of the Qu 'Appelle River Valley, Saskatchewan ( 1980).
109
Chapter 5 Selecting Landscape Features graphic characreriscics characteriscics of imerest (0 to us. Finally. Finally, we perform a single criteria query to determine how many of the remaining remain ing 28 polygons also have no sign significant ificant limitalimica dons tions for agri agricultural cultural praccices: practices:
me w
' No Significam Significant Limitations) (Land Class = 'No Again, this query is made by selecting from the previously selected set of28 selecred of 28 polygons. As a result of this final query, query. we find lind that thar 18 of the ooriginal riginal 168 polygons have soil, topographic, topographic. and land classification characteristics suitable for our original original na(Unatural resource management managemem analysis a nal ysis (Figure (Figu re 5.5). We could have arrived at the same answer by performing one long, long. mulciple multiple criteria query. Alternatively. we could have arrived at the same answer by using single criteria queries to build up the selected so soilil rypes (adding to the selected selecred ser set each time rime addirional additional polygons that thar have soil rype imeres[ (Q us) attributes of interest us)., then selecting from the previously selected set that have the desired topographic se r those rhose thac ropographic and land classification amibmes. attributes.
Selecting features within some proximity of other features In addition to selecting selecring landscape features fearures based on the set of attributes available within the tabular portion of a GIS database, you can select landscape features based on their spatial relationsh ip to other landscape features. fearures. This ask, for example. example, whidh fearures allows you to ask. which landscape features are within a threshold distance of. adjacenr (0, roo o orr in close proximity proximiry of other landscape features. feamres. For example. you may wam want {Q to know which research resea rch plots are in older forest stands. srands. what forest stands are next (0 to research areas, or which water sources are wirhin within a certain cerrain distance of a road. The abil abiliry ity [Q (0 ask questions quesdons in spatial spadaJ terms rerms is but bur one indication of the power of GIS. The following three [hree exa examples mples provide a description of three common fo forms rms of spatial spadal queries. Example 1: Find the landscape features in one GIS database that are inside landscape features datahase (polygons) contained contamed in another GIS database rhis example, a3 narural interIn this natural resource manager may be ineerested in examining [wo (wo GIS databases: databases; one has landscape features that he oorr she is ineerested imerested in knowing something orner has landscape features that about; the other thar represent areas within which he or she is only concerned. Obviously the second GIS database suggests sugges" that it consists of polygons
99
fe-drures, fearures. since line or point poine feat features ures do nO( not describe areas. Alternatively. A1ternarively. the first firsr GIS database could comain conrain point. point, line. or polygon features. feamres. In this example. example, assume that the first GIS G1S database, the one containing features feamres the manager wishes to know something about. contains points. From a natural resource perspective. managers of the Tracr may be interested in understanding [he Brown Tract the habitat conditions within which certain cerrain wildlife species reside. In order (Q CO collect coUee[ habitat information ir it may be necessary to locate and install forest inventory plots. and Permanent forest sample the characteristics of the forests. Pcrmanem inventory plms. plots. those rhose that have already been installed and are periodically re-measured. re-measured, can also be used for this purpose. From a review of the natu natural ral history hi story of the rhe wildlife interest, you may decide that species of interest. thar only [hose those research plots that resea rch plotS thar are contained within wirhin older forest sstands rands (those at ac least leaSt 100 years old) require measurement. measuremem. Thus the problem becomes one of selecting the plots that reside within w ith in older Stands. stands. T The he GIS database that thai contains co ntains the rhe landscape features fearures of interest inte rest is the research plot GIS database. and the th e GIS GIS database that represents rep resents the older fo foreSt rest areas is the forest stands GIS database. In order ro to complete complere the spatial sparial query. query , you would first firSt select the older o lder stands in the forest stands GIS database of the Brown Tract, using a single criterion query: age ~ 100. The focus of the analysis is then sh ifted ifred to the research plot GIS database. darabase. where the question is posed: how many plots are located within the selected selecred landscape features of the srands stands GIS database (rhe (the older Stands)? stands)? The entire sparial query process, in generic terms, terms. can be described as this two-step two-Step process: (I) select the older stands from the forest stands GIS G1S database using a single si ngle crieerion criterion query, and (2) develop a spa spatial rial query on the rhe researdh research plm plot GIS database daeabase where [he the selection seleceion is performed using the spatial location of selected landscape features from within the fo rest reSt stands GIS database. This spatial selection abiliry may be described as 'selection by location' or some other similarly named menu choice or burton, bu([on. depending on the GIS softwa software re being used. Landscape featu fearures res in the inrersect the research plot GIS database are selected if they intersect fearures in the space covered by the selected landscape features stands GIS database. darabase. The result of this spatial query process should yield 40 research plotS plots that full within the rh e boundaries of older forest stands interested in seands (Figure (Figu re 55.6). .6) . Similarly. Similarly, if you were imeresred locared in you yo ung knowing how many research plots were located ng 110
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Part 2 Applying GIS to Natural Resource Management
Figure 5.6 Permanent plot point locations within older stands on the Brown Tract.
stands. you would first query the stands database for young srands (perhaps age!> 30). then perform the spatial query simila r [0 the process noted above. The result should yield 1 research plot.
Example 2: Find the landscape features in one GIS database that are close to the landscape features contained within another GIS database In this example. the imerest is again in examining two GIS databases: one has landscape feacures of interest; (he other contains landscape features [hat represent those a reas around which (nor JUSt within which) there is concern. The seco nd GIS dambase suggestS that it consists of polygon features. but here it could also consist of point o r line features. since [he area of concern is the area represented by a zone of proximity arou nd landsca pe features. The first GIS database could also contain point. line, or polygon features. Assume that the GIS database of interest contains point featu res, and that the GIS database that will represenr the area of interest contains line features. The managers of the Brown Tract may be inrerested in developing a fire management plan for the fores t, an d thus would need to understand the types of water resources that are in close proximity to roads. Therefore, the problem becomes one of selecting the water sou rces rh at are within some distance of a road. The GIS database thar contains the landscape features of interest is the water sources GIS database (beca use of the need to know where the approp riate water sources are located). The GIS database that rep resents landscape features around which one
can define an area of concern is the roads GIS database. In order to perform (his spatial query. you muse first determine the distance from the roads that is cri tical for meering the needs of the fire management plan. Assume here th at it is 30 merers, suggesting that water sources within 30 meters of a road may be of benefit to forest fire fighting efforts. This assumes that fi re-fighting vehicles can draw water from these sources and transport the water to the fire area. In the development of the fire managemenr plan, you may have also assumed that only certain rypes of roads can support fire-fighting vehicles. although th is example will proceed under the assumption that aU roads on the Brown Tract can suppOrt these vehicles. A generic description of the spatial query process might then include the following two steps: (1) select all of the landscape features in the water sources GIS database, a nd (2) develop a spatial query on the water sources GIS database where the selection is performed usi ng the spatial location of landscape features con tained in the roads GIS database. Landscape features in the water sources GIS database are selected if they are located within 30 mete rs of any road contained in the roads GIS database. The result of this spatial query process yields 5 water so urces th at lie within 30 meters of a road (Figure 5.7). As you might imagine, this example, as well as the previous example. would also be helpful to those concerned with the proximity of certain resources (water sources, home sites) to potential management activities (herbicide or fertilization applications) , or even to potential sites for wildlife or fisheries studies.
o Figure 5.7 Water source point locations within 30 meters of roads on the Brown T tact.
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Chapter 5 Selecting Landscape Features
Example 3: Find landscape features from one GIS database tbat are adjacent to otber landscape features in tbe same GIS database In this example, the inrerest is in performing a spatial query that uses landscape features within a single GIS database. Adjacency issues in natural resource management usually concern the placemenr of harvests or the location of habitat) and imply that activities may be prohibited from being implemented next to (or nearby) other receody implemented activities. In the case of habitat development. natural resource managers may desire co develop habitat next to (or nearby) other good wildlife habitat areas. Alternativdy. an invesrmem in research may need co be protected by limiting activity in nearby or su rrounding areas. Since this example concerns adjacency issues, the GIS database used also suggests that it conrains polygon features. This example will assume that a natural resource manager is interested in understanding the extenr and number of stands that are adjacent to research areas. Since the Brown Tract is a working forest that contains some research areas, coo rdin ation of both research and harvesting act ivities is paramount. particularly if the harvestin g activities affect a resource being smdied in the research areas (for example, species of wildlife or hydrologic conditions under canopy). A generic description of the spatial query process might then include the following steps: (1) select the stands in the stands G IS database that are designated a 'research' land allocation, and (2) develop a spatial query on the stands GIS database where the selection is perfo rmed based on how far away other stands are from the research areas. In this case. you can assume that the stands to be queried are 0 meters away from the research areas, and essentially touch a research polygon. Depending on the GIS softwa re program used, the resul , of this spatial
Structured Query Language, or SQL, is the most popular com puter language for querying and manipulating data contained in relational databases. Sometimes simply called 'sequel ', the language allows you to develop quer ies similar to those presented here to access data from large data bases. Although IBM , Oracle. and Microsoft have led the recent developments of SQL. and many other organizations have
101
query process may yield 44 stands, including the research a reas . To remove the research areas from this set of selected landscape featu res. you can perform a single criterion query from the previously selected set of landscape feamres. such as, Land allocation <> ' Research ' where attribute: land all ocation relatio nal operator: <> (not equal to) threshold value: research areas By performing this single criterion query on the previously selected set oflandscape featu res. you can select and identify JUSt those stands adjacent to the research areas. Figure 5.8 illustrates the spatial location of the 37 stands that are adjacent to resea rch areas on the Brown Tract.
Figure 5.8 Stands adjacenllo research areas on the Brown Trace .
tailored the SQL language for various applications. the American Nationa l Standards Institute (ANSi) and the International Organization for Standardization (ISO) have developed standard versions and offer them for sale. Some GIS software programs supPOrt the use of the SQL language, and extend it to the management and manipulation of spatial data features. 112
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Part 2 Applying GIS to Natural Resource Management
Advanced Query Applications Advanced app lications of GIS-related database queries have concentrated on limiting the focus of queries only
discourage some users of GIS. MoS[ of these problems occur because brackets or parentheses are missing from a query; this results in an incomplete query. such as in the two cases shown below from ArcMap que ries.
to rhe features inside a spatial or temporal window
defined by the user. In addition
to
simply providing a
summary of rhe resources contained within a specific area, as the user-defined window (location or time frame) sl ides is expa nded. or is contracted, the query is updated (Q reflect those features that have left rhe window and those [hat have entered rhe window. Queries can be completely re-eva luated as a window slides (or
otherwise changes). or iteratively evaluated by updating the query by considering only the changes that have occurred (Edelsbrunner & Overmars. 1987; Ghanem et al.. 2007). Dynamic queries can be designed that allow users to adjust questions asked of GIS databases by incorporating slider objec[S in a window rather than ask-
in g the user
to
redefine (by typing) the adj ustmentS
needed. For example. a graphical user interface can be
designed to allow users to easily adjust the upper and lower bounds of a quantitative query along a scale, using
a computer mouse (Domingue et al.. 2003). and vide those results quickly.
to
pro-
Syntax Errors Syntax errors that occur when developing queries can
[Height] >= 50} and ([Age] >= 25
(beginning and ending
( [Age] >= 25) and 30 )
{the attribuce 'age' is missing from the second
( <=
parentheses missing)
part of the criteria} Most software programs require [hat every bracket or parentheses be balanced, so, if you begin with an opening bracket or parentheses. you must end with one as well. These parentheses and brackets can also be used to control the order of operations in queries that include mathemarical operations. The dialog boxes that are generall y
used to help develop queries will assist with the placement of parentheses and brackets as long as the fields. operarors, and values are selected in a logical manner using the computer mouse. Syntax errors usually arise when a user erases part of a query. then continues with its development (i.e .. through keyboard intervention). Queries with syntax e rrors usually need manual keyboard intervention to correct the resulting problems. One of the eas iest methods (0 allev iate syntax errors during queries may be to simply close the query dialog box and begin anew.
Summary There are a variery of methods you can use (0 select landscape features from a GIS darabase. from using a mouse (0 manually pick the landscape features (0 selecting them with queries. The most common requests of natural resource managers that involve GIS quer ies include: (1) a map illustrating the landscape features that conform to some criteria. (2) a GIS database containing only the landscape features that conform to some criteria, and (3) summary statistics of the landscape features that conform to some criteria. Many of the applications described later in th is book require YOll to develop a map showing some landscape feat ures of interest. and to describe some characteristics of those landscape features. Queries will enable YOli to
acco mplish these tasks quickly and efficiently. An
important habit
to
develop is to ask yourself whether the
results of an analysis (here. a query) are reasonable. It has become very easy in modern GIS software to create spatial and anribute reference queries. and to instantly receive the results. This ease of use may lead you to believe that the results should not be questioned. Most GIS programs are designed to perform comp lex queries provided that the query syntax is correct. H owever, rhey sim ply provide you with results based on your instructions. and do not have the ab ili ty to question whether a query was correctly designed. It is therefore important to be critical of the ourpUt from a query. and to examine whethe r the resu lts are wit hin the bounds of reason. This will help you to detect errors either in the GIS databases being analyzed or in the methods being used to perform an analysis. 113
Chapter 5 Selecting Landscape Features
103
Applications 5.1.
Daniel Pickett Forest Annual Report. For the
AnnuaJ Repof[ ohhe Daniel Pickett forest. you have been
asked by Hugh Davenport (DiStrict Forester) to provide some in formation related [Q rhe forest's resources. Me Davenport poses his request as a series of questions:
5.2. Information for a new s upervisor of Brown Tract. A new supervisor (Sharon G ill man) was recendy selected [Q manage the Brown T mct, and she wanes to get familiar with the natural resources located mere. She has asked you to provide some information about the T ract and j[5 resources:
From the stands GIS database: a) How much area ofland contai ns forests:S; 20 years
of age? b) How much area of land contains forests > 20 years of age and:> 40 years of age? c) How much area of land co ntains forests> 40 years
of age?
From the streams GIS database: a) How many miles or kilometers of fish-bearing, large streams are in the database? b) How many miles or kilometers of fish-bearing, medium streams are in the database?
c) How many miles or kilom
d) How much area otland contains vegetation type A? e) How much area ofland contains vegetation type B? f) How much area of land contains vegetation type C? g) How much area of land contains ave rage timber
e) How many miles or kilom
vo lumes ~ 49.4 MBF (tho usand board feet) per hectare (20 MBF per acre)?
J) How many milts or kilom
h) How much area of land co ntai ns ave rage timber
volumes ~ 74.1 MBF per hectare (30 MBF per acre)? i) How much area of land contains average timber volumes ~ 98.8 MBF per hectare (40 MBF per acre)? From the soils GIS database: a) How much area ofland might have a high response [0
fenilization?
b) How much area of land might have a medium response to fertilization ? c) How much area ofland might have a low response to ferti lization? From rhe streams GIS database (map units are feet): a) How many miles or kilometers of Class 1 streams are in the database?
b) How many miles or kilometers of Class 2 streams are in the database? c) How many miles or kilometers of Class 3 streams are in the database? d) How many miles or kilometers of C lass 4 streams are in the database?
d) How many miles or kilom
From the roads GIS database: a) How many miles or kilometers of road on (or near) the Brown T mCt are rock roads?
b) How many milts or kilom
From the soils GIS database: a) How much area ofland of 'PR' soil type is there o n the Brown Tract?
b) How much area of land of 'DN' soil type is there on the Brown Tract?
c) How much area of land of 'WL' soil type is there on the Brown Tract? From the water so urces GIS database: a) How many beaver pond water sou rces are on the Brown Tract? b) How many hyd rant so urces are on (o r near) the Brown Tract? c) How many water tank sources are on (o r near) the Brown Tract?
e) Why might these values be misleading, and what caveat might you provide to Mr Davenpon? G raphics for the repo n:
a) Develop a histogram of 10-year stand age classes, showi ng the amount ofland in each age class.
From the trails GIS database: a) How many miles or kilometers of authorized trails are there on the: Brown Tract? b) How many miles or kilometers of unauthorized tra ils are there on the Brown Tract? 114
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Part 2 Applying GIS to Nalural Natural Resource Management
c) How many miks mius or kilom, kilom.tm ,,n of proposed trails rra ils are
grou ps of stands ~ 150 years old and ~ 25 hecrares .8 groups hectares (61 (6 1.8
there o n rhe the Brown Tract? T ract?
acres) in size?' What will yo your ur response be?
add itio n to the Brown Tract T ract Annual Report. In addition
some of [he rhe resources located within and around rhe the
5.6. Wildlife habitat. habitat. A note was placed on your computer's keyboard, from Will W ill Edwards. Edwards, the Brown Tract puterS wildlife biologiSt, biologist, which read: 'Could yo youu perform some
Tract. Brown T racr, she asks you to supply the following infor-
q ueries of the Brown Tract stands database for me? I am queries
5.3.
requests
by by Sharon
Gi ll man to get her acquainted Gillman acq uain ted with
mation macion for the rhe Annual Repon:
working on some wildlife habitat suitability models. models, and
a) How much area of land lan d is in in even-aged forests forests?? forests? b) How much area of land is in uneven-aged forestS?
c) How much area ofland is assigned to the 'research' category? ow much area of land contains board foot vold) H How umes ~ 74. 74 . 1 MBF MBF per hectare hecta re (30 MBF per acre)? acre)' e) How much area ofland has a density of trees ~ 988 per hectare (400 per acre)?
am interested incerested in the (he following: followin g:
of land containa) Sharp-sh inned hawk habitat: area ofland ing ~ 25 and S s:; 50 year old Stands, stands, tat: area ofland containing ~ 30 b) Cooper's hawk habi tat: and S srands, s:; 70 year old Stands. c) Goshawk hab habitat: itat: area of land containing ~ 150 year old stands, Stands, and d) Red tree vole habitat: area ofland containi containing ng ~ 195 year Stands. yea r old stands.
5.4. Annual operating plan, Brown Tract. In order to develop a budget for fo r management man agement activities next year) year, rhe rh e
staff of rhe the Brown Tract T raet needs informacion information regarding regardin g of
Also. Also, could yo youu make me a map of the sharp-shinned hawk habitat?'
the amount amo unt of land area that can be treated w with ith va rious ri ous
silvicuicurai treatme nts . Through conversations co nve rsations with s il viculrura.l treatments. them. rhe the criteria criter ia have been bee n na.rrowed narrowed down to [he rhe following: followi ng: Pre-commercial rhinning Pre-commercial thinnin g candidate cand idate stands: stands: ofland concains even-aged stands a) H ow much area of land comains
that are S s:; 20 yea years rs old and have hectare (400 trees per acre)?
~
5.7.. 5.7
preparation for Fire management plan. In preparacion fo r the devel-
opment fi re management o pment of a fire managemem plan the district manager of the Brown Tract T ract is interested in knowing the followi following: ng: a) How many water sou rces (all types) are within
30 meters (98.4 feet) of rocked or paved roads? b) How many pond water sources sou rces are within 30 meters merers
(98.4 feet) of rocked or paved roads?
988 trees per 5.8. 5 .8.
Commercial thinning candidate ca ndidate stands: sta nds: a) How H ow much area of land contai contains ns even-aged stands
s:; 40 years old? old? that are ~ 30 and S b) How H ow much area of ofland land concains contains even-aged stands rhat are ~ 30 and S that s:; 40 years old. old, with ~ 494 uees trees per hectare (200 trees uees per acre)? Final harvest candidate stands: a) How much area ofland contains concain s even-aged stands that are ~ 45 years, years. S s:; 100 years, years. and have a board foot volume ~ 74.1 74. 1 MBF per hectare (30 MBF per acre)?
Research plots. One of the resea research rch scientistS sciencists associa ted w with racr is interested ciated ith (he the Brown T Tract inte rested in measuring a few of the research plotS plots on the forest, to Study stud y the growth of certain ce rtain stand types. They wanr want to unde understand rstand the
following: a) How many research plots are located in research plots on srands stands with ages ranging from 30 to 50 years?
b) If the quety query were expanded to include Stands stands with ages ranging ra nging from 25 to 50 years. years, how many resea rch plots plotS would (his rnis query contain?
5.9. Potential Fertilization Project. The managers of (he the Brown T Tract ract are cons considering idering fen fertilizing il izing aU all stands that [hat
are aged berween 25 and 40 years old. The managers are 5 .5. Proposed recreation area. You received an e-mail 5.5. read : forester, which read: a few days ago from Erica Eri ca Douglas, forester, 'We are considering developing a campground or o r trail th e Brown T Tract. nds oorr system fa Ct. Are there th ere any sta s mnds syste m oonn the
not the proximity nor only concerned about rhe proxim ity of fertilization rces . o peratio ns to {he to water sou sources. operations the stream system, bur also (0 ow many wate H How waterr sources sou rces are within 60 mecers meters (I96.9 (196.9
feet) of the stands that could potentially be fertilized? fertilized'
115
Chapter 5 Selecting Landscape Features
105
References Domingue. j., Stun, A" Manins, M. , Tan, ] ., Pcrursson, H .• & Morea. E. (2003). Supporting online shopping
Ghanem. T.M .• Hammad. M.A .• Mokbel. M.F .• Aref. W.G .• & Elmagarmid. A.K. (2007). Incremental eval-
(hrough a combination of ontologies and interface metaphors. IntenzationalJournal of Human-Computer Studirs. 59. 699-723. Edelsbrunner. H .• & Overmars. M.H . (1987) . Zooming by repeated range detection. Information ProctIsing
uation of sliding-window queries over dara Streams .
Lmers. 24. 413-1 7.
IEEE Transactions on Knowledge and Data Engineering, 19.57-72Plum Creek Timber Company. (2001). Plum Creek annual report 2000. Searde. WA: Plum Creek Timber Com pany.
116
Chapter 6
Obtaining Information about a Specific Geographic Region Objectives This chapter is designed co provide readers with examples and app li catio ns from namral resource managemem (hat will allow you to analyze the resources contained within specific geographic regions. Specific geographic regions
can be defined in a number of ways, for example, (a) by using query processes (the subject of chapter 5), (b) by using buffer processes (the subject of chapter 7), or (c) through GIS overl ay processes (the su bject of chapter II). From a land managemen t perspective. there is a wide range of reasons why we would want to understand what is contained within these regions, and we will illustrate a few of these. At the co nclusion of this chapter. readers
should have acquired knowledge of: 1. how a clipping process works. and what products shou ld be expected when it is used; 2. how an erasing process works . and what products
should be expected when it is used; and 3. how to use both clipping and erasing processes CO obtain information about specific geographic regions. and obtain in format ion that is relevant to natural resou rce management planning.
As natural resource managers, we are often interested in understanding the characteristics of the land resources we manage within . or perhaps outside of, specific geographic areas. For example, if you were to manage riparian areas, where limited amountS of activity can be pre-
scribed. you might be interested in [he type and quantiry of resources within these areas, as well as the type and quant ity of resources outside these areas-subject to a w ider range of managemenr. Understanding the soil conditions within a property is another example. since many soils GIS databases are acquired from governmental organizations. where the coverage of data extends well
beyond the boundary of the land you might manage. The main GIS processes that can be used to obtain infor-
twO
matio n about a spec ific geographic region. and hence the focus of this chapter, are the clipping and erasing processes. To some people. the te rm 'cl ipp ing' conjures up
thoughts of American football athletes using an illegal blocki ng maneuver on their opponents; to other people it conjures up thoughts of snipping pieces of vegetation from a plant. It is also used invariably as both a noun and
a verb, such as (a) a way [Q hold something in a tight grip, (b) a device to hold cartridges for a riAe, (c) a single instance or occasion. or (d) the act of cutting (Merriam-
Webster, 2007). In a GIS context, a cli pp ing process implies something similar to cutting cookies from a sheet
of dough, when baking holiday-related cookies, although nothing is actually baked here. Imagine a landscape rolled our fl at, like a map on a table. If you were to cut our a ponion of the landscape w ith a pair of scissors, you would
have essentially 'clipped' it from the landscape. There are a number of reasons why you would do such a thin g. and we will explore some of the more common applications in this chapter. 117
Chapter 6 Obtaining Information about a Specffic Geographic Region The term 'erase' also has several meanings. and is mainly used a verb in (he English language. in manners such as (a) (Q rub ou( or scrape away. (b) to remove written marks, (c) co remove recorded data from a magnetic medium, and (d) to nullifY an effeC( (Merriam-Webste r, 2007). When the term 'erasing' is used in a GIS context, it is much more closely aligned with the notion most people have about rubbing or scraping rhings away: some feacures are be ing removed (e rased) from the landscape. Imagine two GIS databases, a fo resr stand GIS database and a stream buffer GIS database. If you wanted to visualize all of (he forest stands areas oU[side of the stream buffers, you could use the polygons that describe the
stream buffer area co erase all of those areas from the tim-
GIS database
Tabular attributes
Input GIS databases
Stand attributes: basal area, volume per acre, etc.
Database to be clipped: stands
Database
use~dO
do the clipping:
150·foot (45.7 m)
Buffer attributes: buffer distance, etc.
stream buffers
ber stand GIS database. As you can see you can use clipping and erasing tools co obtain resource information about specific geographic regions.
The Process of Clipping Landscape Features
Spatial features
107
Output GIS database
Resulting
database: stands within the
Stand attributes: basal area, volume per acre, etc.
150·foot (45.7 m)
One of the assumptions behind rhe use of a clipping process is that you are interested in creating a new GIS database that comains only those features within a specific geographic region. A clipping process involves the use of twO GIS databases (Figure 6. I), and results in one new ourpur GIS database. The process involves the location of the intersection of lines (in the case of line and polygon features being clipped) and the loca tio n of features wholly contained within an area (in the case of all rypes of GIS features), The location of line imersection points is an essential part of GIS (Clarke, 1995). Cl ipping processes can be manually called upon wirhin GIS, and in some cases are aurornatic and transparent to GIS users, as in the case of websites that are designed to allow users to specify an area within which data will be extracted. When using vector G IS databases, one of the input GIS databases needs [Q coma in polygon features «(he cookie cutter); the other (rhe GIS darabase to be clipped) can camain eith er poin t, line, or polygon features. The cookie-cuHer GIS database is overla id on the GIS database to be clipped, and only those features within the boundaries of the polygon(s) in the cookie-cutter GIS database are retained in (he output GIS database. Thus, (he output GIS database contains the same rype of spatial features as the GIS database being clipped. In addi tion , rhe size of features (lines or polygons. but not points) may be spatially ahered in the output database. as they may have been cur a{ (he edges of the polygons contained in the
stream buffers
Figure 6.1 Clipping the $lands within the 150·foof (45.7 m) sueam buffers on the Daniel Pickett forest.
cookie-curter GIS database. This implies thar lines could be shorrened and the shape of polygo ns altered. The output GIS database contains all of the attributes of the GIS database that was clipped, but generally none of the attributes of the GIS database that was used to do the clipping. The spatial extent of the output GIS database is limited to the boundary of rhe polygons conta ined in rhe cookie-curter GIS database. For example. in Figure 6 .2. a road and a fire area overlap the boundary of a land ownership polygon. The land ownership polygon can be used as a cookie-cutter to clip the portions of the road and the £ire area rhat actually res ide within the ownership boundary. In doing this, the road (initially described by line 2), is clipped at the intersection with lines 11 and 104 of the properry boundary polygon (creating line 2\), and the fire area is cl ipped with lines I , and I , . Line 3\ of the fire area is shortened , original line 3, discarded, and lines 3, and 3, creared based on the location of lines 13 and 1 ~ of rhe properry ownership boundary. Clipping a GIS database with a very large extent (such as a national soils database) to the boundary of a managed properry is one common application of this type of 118
108
Part 2 Applying GIS to Natural Resource Management a. GIS databases prior to a clip process. +----- Road
j __ '1I
/ ( I
•
C.
.......- "I
Property boundary
Property
1, boundary
I,
(cookie cutter)
J'-' Fire area
,
GIS databases after a clip process.
b. GIS databases during a clip process. (0 = node) 1,
Road belore
RoadJ after clipping process
2
clipping
process
21
I,
1,
Properly (0
= node)
i--ll.:l---- Clipped road
1, boundary I, (cookie cutter)
Rre area 3 before , " clipping,'
1... ......
I,
I
process:
Property boundary
....'tY-,...
1
. I
I
I
Fire area after 3!.'''3 clipping ~ __ ~ 4 process 33
./ 3l
Clipped fire area Figure 6.2 Clipping a road and a fire area to tilt: pro~rty boundary of a land ownership.
A clipping process is essenrially the same as makin g
cookies from a sheet of cookie dough. It is called 'erasing outside' by some GIS software programs, which is simply another way to describe how ir works: a map of a landscape is laid flat on a table, a solid object is placed on tOP of it {perhaps a book}, and everything that is visible outside of the boundary of the book {except what is under the book} is erased. The process of 'erasing out-
MuniCipality boundaries in the Pheasant Hill planning area of the Qu'Appelle River Valley in central Saskatchewan
Floodplains, wetlands, and areas with trees in the Pheasant Hill planning area
side' (or clipping) is the inverse of anorher process we
describe sho rdy, called the 'erase' process. We expect some level of confusion due to the similar terminology,
but are co nfident that with study and hands-on practice with GIS, readers will be able to grasp the differences. In an erasing process, you seek to remove from one
Floodplains, wetlands, and areas with trees for each municipality (municipalities clipped using floodplains, wetlands, and treed areas)
GIS database everything that is spatially located under the features conrained in another GIS database (which
contains polygons). Using the example of the map and the book that lies upo n it, everything on the map that is under the book would be removed in an erasing process. Thus an erasi ng process is me inverse of a clip-
ping {or erase outside} process. The example provided
Areas that are not floodplains, wetlands, nor areas with trees for each municipality (municipalities erased using floodplains, wetlands, and treed areas)
using the municipali ty boundaries and floodplain , wetland. and treed areas GIS databases illustrates how a sin-
gle GIS database {the municipalities} can be divided into [wo com pletely separate, non -overlapping databases
usi ng the clipping and erasing processes {Figure 6.3}.
Figure 6.3 Application of clippi ng and erasing processes.
119
Chapter 6 Obtaining Intormation Information about a Specmc Spec~ic Geographic Region process. There are a number of other reasons why you want [0 would wanr to clip a set of spatial features fearures co (Q the boundary of a properry. property. One of them relates to the accuracy and consistency of an organization's o rganization's GIS databases. GIS databases can be digitized in-house or by comracrocs, contractors, created through other spatial operations, developed with GPS technology, obtained from oorganizations rganizations that sell dacatechnology. seU databases, downloaded for free off of the Internet, or simply passed from one person [0 to anomer. another. Given me the wide variva ri ety o rga nizacions can acquire GIS databases. it is ery of ways organizations to imagine [hat that the extent of the covernot unreasonable (0 age of the GIS G IS data wil wi lll likely not perfectly fit the extent exrenr of an ownership's boundary. Some o rganizations require fit perfectly with the that the extent of each GIS database lit tbey ensure this boundaries of their land ownership, and they by clipping each GIS database to their ownership boundary GIS database. Granted, there are some GIS G IS da tabases, such as roads and streams, that you may nO[ not want dipped clipped screams, mat [Q an ownership boundary (interest may ceorer center on where [0 [he the roads and screams streams come from and where they go beyond a property boundary), but there are GIS databases, such as soils and land cover, where the argument may hold (interest may not cemer center on the soils of other land owners). Other rypes types of polygon features could be used in a dipp ing be interested dippi ng process. For example, example. you yo u may he ince rcsted in contained w ith in a riparian zone, the resources contajned with zone. or within Further, when narural natural resource management a watershed. Further. rhey may decide to organizations share GIS databases, they limit what is shared. For example, in Washington State, Stare, where watershed analysis analys is has been an important aspect of foresr coordinated dfon effort among organizaforese management, a coorciinared tions leads to the identification idemification of the limits of appropriwithin watersheds. In ate management activities wirhin Ln many watershed analyses. analyses, a single organization will perform rhe the G IS analysis tasks, and thus rhus acquire acqu ire all of the GIS G IS dataGIS particular watershed wate rshed regardless of bases related to a pan.icular landowner. Usually private namral natural resource organizations landowner. organizadons GIS are very hesitant to share their G IS databases with their GI S databases are considered propricompetitors-some GIS etary and may contain sensitive information. In addicion. addition, organizations may be hesitant to ro release GIS GIS databases that are dated or chat coma in unverified information . that may conta However,, since watershed analysis may benefit all However landowners in the long run, some GIS dacabases databases arc are usuorganizadons. In ally shared among land management organizations. ro sha re as cases such as these, organizadons organizations may decide to share little lirrle GIS data as possible to meet [he the goals goa ls of the watershed anaJysis. analysis. and thus avo id revealing any other informarhus avoid
me
109
thar they tjon starus of the natural resources that tion regarding the status manage. Therefore, a clipping process is used to ro limit the ro that mat concerning specific amount of information shared to geographic regions, such as individual watersheds.
Obtaining information about vegetation resources within riparian zones using As a first fi rst example of us ing a clipping process, let's assume that we are interested iD(~res(ed in the [h~ vegetation resources conriparia n zones tained entirely enti rely within some pre-defined riparian within a fo rest. In chapter 7. 7, we will describe the rhe process forest. creating of taking spatial spat iaJ features, such as streams, Streams, and crea ting physical zones (buffers) around aroun d them. them . For now, let's ler's GIS database containing polygons that assume that thar a GIS describe 50-meter riparian wnes (stream buffers) for the Brown Tract already exists (Figure (F igu re 6.4) . The type of information informarion we may be interested in knowing includes the amount of land and the volume of timber within the generalJy limi ted-use manageriparian zones, which are generally limited-use ment areas. A clipping process wou would ld allow us to develop GIS database contajning containing only chose those umber timber stand areas a GIS that are within the boundaries of the polygons that describe the riparian zones (Figure 6.4). The original timber stand polygo polygons ns that intersect the stream buffer polygons would be redesigned such that their boundaries now the riparian zones. Those timcoincide with the edges of rhe with in the ber stand polygon boundaries that fall entirely within riparian zones would be left intact, intact . those timber stand polygons that fall fitJI entirely outside of the riparian zones would be e1imina[ed eliminated.. What you should find in the tabular database (attributes) of the clipped GIS database is the same set (number Utes) and type) that can be found in the original rype) of attributes thar However, the values rhe vegetarion GIS GIS database. However. valu es of (he an ributcs associated with 'area' (acres and hectares) may attributes need to be adjusted to reAect only the area of the th e poly[n addition, some gons within the clipped GIS database. In polygon GIS GIS databases may also comain contain a perimeter measuremem that describes the [he linear distance distanc~ around each polygon border. The perimeter distance may also need to be recalculated in order to accu rately represent perimerers accurately perimeters that were affected by a clipping operation. Some GIS softadjustments automaricaIly auromatically ware programs perform these adjustmems depending on the type of spatial database; other GIS softrecalculate the areas with ware programs require users to recaJculate automated) process. Arcributes Arrribures mar that (albeit automared) a second (albeir of land (e.g., tree describe something other than an 'area' 'a rea' ofland 120
110
Part 2 Applying GIS to Natural Resource Management (a) so· meter stream buffers.
age or stand volume). are not adjusted during the dipping process. With the lise of a dipping process. you should be able ro understa nd how much area is comained within the riparian zones, 446 ha in the case of the Brown Tract (Table 6.1). The resulting dipped GIS database also provides information necessa ry for subsequem analyses, such as those that involve understandi ng the average age of the riparian zone vegetation. or the timber vo lumes contained within the riparian zones.
Obtaining information about soil resources within an ownership
(b) Vegetation polygons.
Soil resources for North America have been mapped at va rious scales for each Canadian province and US St3te. While special soils surveys have been conducted for individual landowners. the most widely used soils databases we re developed by governmental agencies. For example. in (he United Scates, t he USDA Natural Resources Conservation Service (2007) provides an online inreractive process that allows you to acquire soils (and other) GIS databases for a specific ponion of the country. One
A subset of the tabular data contained in the GIS database that resulted from clipping Brown Tract stands within SO-meter
TABLE 6.1
stream buffers Stand number
Acres
Hectares
Age
Volume'
0.63
0.2;
;2
12.7
3.06
1.24
;2
12.7
2
12.37
;.01
46
13.3
2
2. 16
0.87
46
13.3
2
0.06
0.02
46
13.3
2
1.80
0.73
46
13.3
2
053
0.21
46
13.3
3
4.47
1.81
;1
16.6
3
3.24
i.31
;1
16.6
270
0.14
0.06
2
0.0
283
4.03
1.63
43
I.;
1,101.39
44552
(c) Vegetation polygons within 50-meter stream buffers.
Figure 6.4 Clipping the stands within 50-meter stream buffers on the Brown Tract.
To,.]
• thousand board reet per acre
121
Chapter 6 Obtaining Information about a Spec~ic Geographic Region
product that can be obtained is the soil survey geogtaphic database (SSURGO) for most counties in the US. The majority of SSURGO data were mapped at a 1:20,000 scale. and the minimum mapping area is 1-2 hectares. This level of detail in the mapping of soils was designed for use by Farmers. landowners. and other natural resource organizacions. The STATSGO soils database is another national-level soils GIS database for the United States (USDA Natural Resources Conservation Service. 2006). However, the STATSGO data is a general soil map, and not as spatially refined as the SSURGO data. STATSGO was mapped at a 1:250,000 scale, and the minimum atea mapped is about 600 hectares. This level of detail in mapping soils was designed for broad natural resource planning and management uses.
The National Soil Database of Canada (Agriculture and Agri-Food Canada, 2006) contains databases on soils, landscape features, and climatic data for each Canadia n province. and is the national archive for land resource information collected by federal and provincial field surveys. The GIS databases contained in the National Soil Database range from the more general, mapped at a 1: 1,000,000 scale or smaller (like STATSGO), to the more detailed, mapped at a 1:20,000 scale or larger (like SSURGO).
111
Assume.you were to acquire the SSURGO data for the county within which the Brown Tract is located (Figure 6.5). If the managers of the Brown Tract were interested in the eype of soils that the Nacucal Resources Conservation Service has delineated for the land that they manage. a dipping process can be used CO obtain that information. In this case, you would use the boundary of the Brown Tract as the polygon theme to perform the clip, and the SSURGO soils database as the theme on which the clip would be performed. The resulting GIS database provides an indication of (he major types of the SSURGO soils that are being managed on the Brown Tract (Figure 6.6).
Obtaining iruormation about roads within a forest In the previous examples provided in this chapter, interest was placed on obtaining information aboU[ polygon features (timber stands and soils) that were located within some geographic region . This type of information is informative for land managers, yet it is a1so possible ro
~ Dixonville-Gellatly, steep slopes
t::::I Price-MacOunn-RItner complex, steep slopes •
o
Figure 6.5 Soil polygons in me Brown Tract and surrounding at(~a.
D D
Dbconville-Gellatly, moderats slopes Joey silty clay 1oam, Iow slopes Jory-Gelderman complex, moderate slopes
Other soil types
Figw~ 6.6 Major soil
types within the boundary of th~ Brown Tract.
122
112
Part 2 Applying GIS to Natural Resource Management
obtain information abour other types of landscape feacures (lines or points) within certain geographic regions. Most forest road GIS databases, for example, contain a system of roads that extend well beyond the boundary of the property that is being managed by a nacural resource management organization. Th is allows these organizations and their managers to see how the road system they manage is integrated with the road systems managed by states, counties, or other nacural resource management organizations. In addition, it allows organizations [Q weigh their options: If you were to harvest timber stand X, which route cou ld be used to deliver the timber to the mill? If you were to fertilize timber stands Y and Z. how would you get the fertilizer to those stands? If you were to perform an owl survey in watershed A, how could you get to (a nd get around) watershed A? There are times, however, when an organization might need to understand only the characteristics of the road resources within the boundaries of the land that they manage. Each year. for examp le, a land manager may need to develop a budget for road maimenance expend icures, and over a longer period of time. a plan for the continued maintenance of the road system. When developing a long-cerm plan for maintaining rock surface roads (for example), you may first want to understand the extent of rock-surfaced roads within the land being managed. As an example of a clipping process, the roads GIS database of the Brown Tract can be dipped to the property boundary. If you were to open the roads GIS database in a GIS software program, you would find that the database contains over 79 kilometers of paved. rock. and native surface roads (Table 6.2). These roads extend well beyond the boundary of the Brown Tract in some cases. and allow the forest managers to view the landscape that they manage in a larger context. However, after clipping the roads GIS database to the ownership GIS database. and making sure that road lengths were updated in the output database. you would
TABLE 6.2
Kilo meters
Paved
5.7
9.3
Rock
41.3
66.4
2.3
3.6
49.3
79.3
Native Surface Total
Length and type of road within the boundary of the Brown Tract Miles
Kilometers
Paved
2.0
3.3
Rock
37.5
60.3
1.5
2.5
41.0
66. 1
Roadrype
Native Surface Total
find that only about 66 kilometers of road (Table 6.3) actually reside within the boundary of the Brown Tract (Figure 6.7). This exercise also produces an example of a potential problem related to this type of information extraction process. If you were to look closely at the resulting clipped GIS database. the eastern edge of the forest (Figure 6.8) comains a discontinuous piece of a road. Upon inspection, you might find thac chis road is a rocked road and that one of four situarions has occurred: I. The road was incorrectly digitized into the GIS database (which could be verified by viewing the digital orrhophotograph associated with the Brown Tract). 2. The road was incorrectly laid our in the field. and
Length and type of road within the roads GIS database developed for the Brown Tract Mil..
Road ty~
TABLE 6.3
_ Paved _ Gravel _ _ . Dirt Figure 6.7 Roads within the boundary of the Brown Tract.
123
Chapter 6 Obtaining Information about a Specnic Geographic Region
TABLE 6.4
Length and type of streams within the streams GIS database used by the Brown Tract Miles
Kilometers
Fish-Ixaring 1 large
0.9
1.4
Fish-Ixaring I medium
3. 1
5.0
Fish-Ixari ng I small
4.9
7.8
Non-fish-bC'aring 1 large
0.0
0.0
Non-fish -bearing J medium
0.0
0.0
Non-fish-bearing I small
25.7
41.4
Total
34.6
55.6
Stream type
Adiscontinuous piece of road
/ Figure 6.8 A potential error in the clipped roads GIS
113
datab~.
actually does reside within the boundary of the Brown tracr.
3. The boundary of the Brown Tract was incorrectly digitized into the boundary GIS database. 4. The road and boundary are correctly located and the road may be (he result of an use easemem, a remnanc from a previous road network. or a potencial ingress by an adjacent property owner. In any event, developing a ma inrena nce plan that includes rhis. and ocher, small p ieces of road may not make sense from an operational perspective.
the extent of certain landscape features within a specific geographic region. and a clipping process can be used to extract those resources from other GIS databases. Using the Brown Tract as an example. prior to clipping the streams to the boundary of the fo rest. approximately 56 kilometers (35 miles) of streams are represented in the streams GIS database {Table 6.4}. The resulting GIS database. after clipping the streams GIS database to the forest boundary GIS database and updating the stream lengths (Figure 6 .9). contains only about 45 kilometers (28 mi les) of streams that are actually located w ith in the bounda ry of the forest (Table 6.5). You find with this analysis that (here are no large, fish.bearing streams
Obtaining information about streams within a forest Most sc reams GIS databases managed by natural reso urce organizations are designed in a fash ion similar to roads GIS databases: they co ntain a system oflines or links that extend well beyond the property boundary of the organizat ion so that the man agers can understand how their activities are integrated within a larger watershed system. For example, yo u m ay be interested in knowing where water flows, in the event that the need to monitor forest operations (after logging, fertilization. or herbic ide operations) is important. There are times. however, when you may need to quan ti fy the stream characteristics only within the boundary of the land that you r organization manages. For example. in order to develop a budget for stream surveys, you may need to know how many and what rype (along with their length) of streams are located within the boundary of land that you r organization manage. This implies that you are interested in understanding
Figure 6.9 Streams within the boundary of the Brown Tract .
124
114
Part 2 Applying GIS to Natural Resource Management
TABLE 6.5
Length and type of streams within the boundary of the Brown Tract
Stream typ<
Mil"
Kilometers
Fish-bearing I large
0.0
0 .0
Fish-bearin g I medium
2.1
3.4
Fish-beari ng I small
2.8
4.5
Non-fish-bearingJ large:
0.0
0.0
Non-fish-lxaring I medium
0.0
0.0
Non-Fis h-bearing I small
23. 3
37.5
Tmal
28.2
45.4
within the Brown Tract, although some are present in the more extensive streams G IS database. In addition, while only about one·quartet of the streams (by length) in the more extensive Streams GIS database are fish -bea ring, almost hal f of the streams (again by length) removed as a result of the clipping process are fish-bearing. Since the Brown Tract contains the headwaters of several stream systems. it is not unreasonable to assume that (he fishbearing portions of these systems are located in the lower reaches (i.e .• off of the Brown Tract).
The Process of Erasing Landscape Features Thus far. our imerest has been centered on understanding the extent of resources that are located within certain geographic regions. Now. our focus will sh ift to obtai ning informatio n abour the resources located oZltsid~ ofcertain geographic regions. The erasing process is well suited to this tas k. and is essentially the opposite of the clipping process. When using an erasing process. you a re interested in creating a new GIS database that contains landsca pe features located outside of a speci fic geographic region. Just as with a clipping process, an erasing process involves using two GIS databases as input d atabases (Figure 6.10). and the process results in one output GIS database. When using vector GIS databases. one of the input GIS databases (the eraser) needs to comain polygon features; the other GIS database (the database in which landscape fearures will be erased) can contain point. line, or polygon features. The eraser GIS database is ove rlaid on the GIS database containing the fearures of interest. and only t hose landscape features located outside of the GIS database
Spatial features
Tabular Attributes
Input GIS databases
Stand attributes: basal area, volume per acre, etc.
Database to be erased : stands
One note of caution about clipping processes: prior to performing other rypes of spatial analyses. such as the buffering processes, you must consider whether or nO[ a clipping process is appropriate. For example, if you wanted to understand the extem of riparian areas on the Brown Tract, you might nor wan t to clip the st reams to the boundary of rhe Brown Tract as an initial step in the analysis. By doing this, streams outside of rhe forest boundary are igno red. to which you probably would reply '50 what?'. Well, those streams may have a riparian area about them that extends inside the boundary of rhe Brown Tract. Put another way. just because a panicular stream resides outside the boundary of the forest you manage does not necessarily imply it ca n be igno red: part of its area of inAuence (rhe riparian area). and an assessmem of activities m ar YOll might be co nsidering within this area. may need to be included in your management plan.
Dalabase
used~O
do the erasing
(Ihe eraser):
Buffer attributes: buffer distance, etc.
150-loot (45.7 m) stream buffers
- - --
------ ------ , Output GIS database
Resulling database: stands outside the 150-foot (45.7 m) stream buffers
~
Stand attributes: basal area, volume per acre, etc.
Figure 6.10 Erasing the stands within I 50· foot (45 .7 m) stream buffers from tht Danid Picktn stands GIS databast.
125
Chapter 6 Obtaining Information about a Specffic Geograph ic Region
bou ndaries of the polygons contained within the erase r GIS database are rerained. Thus the output GIS database contains the same type of features as the database being emsed. In addition, the size of featu res (lines o r polygons, but not points) may be spatially altered where they overlap with the edges of the polygons conta ined within the ems« GIS database. Figure 6.1 1 illustrates a small example and utilizes a fire (the eraser) and a timber stand (the polygon to be erased). After the erasing process has been performed, yo u can see that [WO of the o riginal lines that defined th e boundary of a timber stand (l , a nd I ,) were sho rtened [Q the point of intersection with a fi re area, and a third line was created (J ,) to describe the edge of the fire area tha t is common wi th the timbe r stand. The resuhing erased timber stand GIS database has all of the attributes of the original timber stand GIS database, yee the spacial extent is eq ual [0 the original cimber stand database minus [he overl ap with [he fi re database. As w ith [he clipping process, feature measurements in the OU(3.
0
GIS databases prior to an erase process.
/"
, "
Timber stand
, I
,, I
......... _ / '
'
,
b. GIS databases during an erase process. (0 = node) 1,
1,
Erased
12 Timber stand 13 (to be erased) Fire area 31........ ~
(eraser),/ '
,,,
1,
12
Timber stand
:,
, ,,
, '"..._"'3 2
c. GIS databases after an erase process. (0 = node) Erased timber stand
, :...- Fire area , ,,
,~
,,
''
' "...- "
Figure 6.11
put dara base fro m an erasi ng process should be updated (Q reRect cha nges in area or perimeter of polygons. or length of li nks in line data bases. If yo ur GIS softwa re does not make these updates automaticall y. you shou ld ensu re th at yo u use softwa re or ot her app roaches (Q update the measu rem ents. There are a number of reasons why you would consider using an erasing process. One reason involves the need to understand the characteristics of a landscape Outside of areas that are co nsidered Ttstn·cttd. Alternatively. the goal would be to define the unrestricttd areas of a lan dsca pe with regard to m anage ment act ivities. For exa mple. ea rlier in this cha pter a clipping process was used to develop a GIS database that allowed us to summarize the resou rces located within ripari an zones. Management within riparian zo nes is gene rally restricted in some fo rm or fashion. Areas outside the riparian zones could then he considered unrestricted, assum ing there are no other co nstrai nts on land ma nageme nt, such as those related to owl nest loca tions, research areas, and so on . Understanding the extent of the landscape where man agement is not restricted, may be important when considering decisions related [Q harvesring operations, the use of herbicides or fertilizer, or other types of management practices.
Obtaining information about vegetation resources outside of riparian zones
~ Fire area
,
115
Erasing a fire area from a timber 5t:lnd.
1, 1,
13
To build upon the examples previously provided in this chapter, let's obtain information about some landscape features (vegetation polygons) loca ted outside of th e 50meter riparian zones th at were developed for the Brown Tract. An erasing process allows us to develop a GIS database containing o nly those features (in this case vegetat ion polygo ns) located outs ide the boundaries of the polygons that describe the riparian zo nes. T he origi nal vegetation polygons are aga in redesigned such that their boundaries coincide with th e edges of the polygons that desc ribe the ripa rian wnes. Those vegetation polygons that were entirely located outside of t he ripa rian zo nes are left intact, and those vegetation polygons (hat were ent irely contai ned within the boundaries of ripa ri an zones are eliminated (F igure 6.12). The tabular database related to the resulting erased GIS database should contain the same set of attributes that were contained in the original vege tat io n (sta nds) GIS database, with only the area (a nd pe rhaps the perimeter) values adjusted to 126
116
Part 2 Applying GIS to Natural Resource Management
reAeer the sizes of the redesigned polygons. When performing a check of the data. you should find that the size of the resulting erased GIS database. about 1.677 hectares (4.143 acres). is-and shou ld be--less than the size of the original stands GIS database (which was about 2. 123 hectares or 5.245 acres) . Erasing processes can be used for other purposes as well. For example. if an area of the Brown Tract were designaH~d for sale, the managers of the Brown T Tact may be imerested in knowing what resources would remain after the pending sale. Alternatively, if you were co develop a land classification for the Brown Tract which involved buffering streams and classifying uplands from riparian zones, you may erase (h e buffer woes from a stands data-
base (for example) as a intermediate stage of classifying the landscape and identifying upland areas. These types of analyses may prove useful in (he applications described in Figure 6.12 Vegetation polygons (stands) outside of 50· mcler buffers on the Brown T race.
subsequent chapters of this book.
Summary Clipping processes are spatial operations that allow users of GIS to obmin information about landsca pe features within cercai n geographic regions. When using vec[Qr GIS databases, polygon features are used to clip the feacures from a second GIS database comaining poines, lines. or polygons. The feacures that are comained in the OUCput GIS database are those comained within the boundaries of the polygons
of [he clipping GIS database (the cookie cu[(er). Erasing processes can be viewed as the inve rse of clipping processes. With an erasing process, you can obtain informadon about spat ial features located outs ide of certain geographic
regions. In fIct. you could use a single GIS database to clip landscape features (used as the cookie cu[(er). and subsequently to erase landscape features (used as the eraser). If
both processes were applied to [he same GIS database (e.g.• stands, roads, or streams), the twO output GIS databases, when combined , should cover the same landscape area as
the original GIS databases that were clipped or erased. For example. a GIS database was created, using a clipping process, to represem those vegetation polygons on the Brown Tract that were located within 50-meter riparian zones. A second GIS database was also created, using an erasing process. to represem those vegetation polygons located outside of the 50-meter riparian zones. If co m-
bined. these twO GIS databases (the clipped and erased GIS databases) should equal [he land area and vegetation resources thar can
be found
within the original vegetation
(stands) GIS database--no more. no less.
Applications 6.1. Obtaining information about features within a watershed. Suppose that [he hydrologist associated with the Daniel Picke[( forest. Michelle Rice. has been working for some time on a watershed ana lysis with a few other natu ral resource managemem organizatio ns. The
watershed being analyzed is the Dogwood Creek Watershed. The warershed analys is team is now ar the
point where they need to obtain as much GIS data as possible to describe the currenr condition of rhe water-
shed. Michelle asks you to provide her with the following informacion: a) a summary of the area of timber stands located ~ithin the Dogwood Creek watershed. by vege .. (Ion type; 127
Chapter 6 Obtaining Information about a Spec~ic Geographic Region
b) a summary of the length of roads located with in the Dogwood Creek watershed. by road type; and c) a summary of the length of streams located within the Dogwood Creek watershed. by stream type. In
Vegetation type A
21-40
by stream
41-60
type.
c
8
0-20
addition, summarize these values in terms of scream miles per square mile of land.
117
61 - 80 80.
Develop a shon memo add ressed the resulrs of you r analyses.
6.2.
(Q
Ms Rice that details
Summarizing resources within a management
area. Jane Hayes is developing an annual reporr on [he management of the Daniel Picken forest and has become very interested in certain as pects of the forest resou rces. Since she knows that you are becoming proficient with GIS, she has asked you ro provide some information she feels is necessary for her report: a) amount of land in each watershed.
b) length of rock road in each wate rshed. c) length of d irt road in each watershed. and d) length of stream classes 1- 3 in each wate rshed. In addition, she is imerested in knowing something about the resources that are wit hin 50 merers of the streams.
Using the 50-foot stream buffer provided by the GIS
6 .4. Potential sale of watershed for conservation reserve. The managers of the Daniel Pickett forest have been approached by a non-profi t o rganization specializing in developing and managing conservation reserves. The non-profit organization is specifically interested in acquir-
ing a specific portion of the Daniel Pickett forest-all of the land located in the Trout Creek Watershed. It seems that the non-profit group has been acdve in developing a larger reserve system in the Trou t C reek area, and the Daniel Picken forest just happens to contain the headwaters of the watershed . The region manager of the Daniel Picken forest, Becky Blaylock, is interested in understanding the effect this land sale may have on the management plan for the area. She asks you to provide her a before- an d after-sale description of the resources, in the
fo llowing format: a) Before-sale conditions (area) of the Daniel Pickett forest.
Departmenr, provide the followi ng:
Vegetation type
a) area of land within 50 meters of the streams,
b) area of older forest (age ~ 60) with in the 50-meter stream buffers,
c) length of paved road within the 50-meter stream buffers, and
d) length of rock road within the 50-meter stream buffers. Provide a memo addressed (Q Ms Hayes that details the results of you r analyses. Keep in mind the appropriate
Age class
A
0-20 21- 40 41-60 61 - 80 80.
b) Alier-sale conditions (area) of the Daniel Pickett forest.
units (perhaps kilometers fo r length rather than meters). and the appropriate precision (to the nearesr 0.1 hecta re
or 0.1 kilometer) for this type of report. 6.3.
Fertilization possibilities. Within the so ils GIS
Vegetation type
Age class
21-40
rresp, which was meant to ind icate the probabi lity of tree response to fertilization as a functio n of the underlying
41-60
'high response' areas using (he following format :
A
c
8
0-20
darab~ of the Daniel Pickerr foresr is an attribute, fer-
soil type. Describe the types of vegetation found in the
c
8
61 - 80 80. 128
118
Part 2 Applying GIS to Natural Resource Management
In add ition, she has also requested rhe following: c) a map showing the after-sale arrangement of vege-
rario n classes (rypes) on rhe Daniel Pickett forest, and
d) a brief summary of yo ur opinion of rhe effects of the sale o n the management of the forest,
References Ag riculture and Agri-Food Canada. (2006). The national soil database (NSDB). Ottawa, ON: The Nationa l Land and Weather In formation Se rvi ce,
Agriculture and Agri-Food Canada. Retrieved February II , 2007, from http://sis.agr.gc.ca/cansis/ nsdb/intro.html. Clarke, K.c. (I 995) . Analytical and computl!T cartography. Upper Saddle River, NJ: Prentice-Hall. Merriam-Webster. (2007). Merriam- Webster online search. Rerrieved February 4, 2007, from http://www. m-w.com/cgi-bin/diccionary.
USDA Natural Resou rces Conservar ion Service. (2006) . US general soil map (STATSGO). Washingwn, DC: Narional Cartography and Geosparial Cemer, USDA Natural Resou rces Conservation Service. Retrieved
February II, 2007, from http://www.ncgc.nrcs.usda. gov Ip rod uctsl da rasetslsta tsgo/. USDA Narural Resources Conservation Service. (2007). Geospatial data gateway. Washington, DC: USDA Natural Resources Conservation Service. Retrieved
February 9. 2007. from http: //datagateway.nrcs.usda. gov/NexrPage.as px? H itTab= I.
129
Chapter 7
Buffering Landscape Features Objectives
have a proximity crite ria (Association for Geographic
Chapter 7 is an introduction and examination of GIS
Informarion, 1999). The GIS process of buffering usually infers that a boundary is about to be drawn around some selecred fea-
buffering processes. A number of examples and applications are presented in this chapter co provide readers with experience in several of the common GIS-related tasks in namral resource management that require the use of a buffering process. At the conclusion of this chapter. readers should understand. and be able co discuss, the peninent aspects of:
I. whar bujf
3. how buffering can be applied
tures. There are numerous reasons why you would want to draw boundaries around selected landscape features in natural resource management . For instance, one of the guiding management policies for an organization may suggest that some management activities may be prohib ited within a certa in distance of a stream, a road, a trail. or a home. Therefore. as a namral resource manager. you may be interested in the approp riate limits of allowable management activity. As another example, managemenr activiti es within a certain distance of nesting, roosting. or for-
assess alrernarive man-
aging sires of a wildlife species of concern may be curtailed
agement policies and to ass ist in making natural resource managemenr decisions
during certain times of the year. Therefore. delineating these 'home ranges' or 'critical habitats' may be an impor-
To someone unfamiliar with [his GIS process. the term
you might develop sparial buffers within GIS
(Q
tant aspect of namral resource managemem. In these cases,
buffering may lead to some confusion. Technically, the noun 'buffer' refers to (a) a device fo r reducing shock
to
identifY
resources located wit hin certain distances of important
landscape features (poinrs, lines, or polygons). You mighr
when co ntacted, (b) a means fo r cushioning fluccuations in busi ness activities. (c) a protective barrier, (d) a substance capable of neutralizing both acids and bases. and (e) a temporary sto rage unit on a computer (Merriam-
also be incerested in examining the potential impacts of rhe policies that suggest the use of buffers. in order to understand how the objectives of namral reso urce manage-
Web"er, 2007). In GIS appl ications in natural resource
widely used in natural resource managemem to identify riparian managemenr areas (streamside management zone) and {Q define areas where management may be restricted for one reason or another. Yet buffers have also been used to track and assess the site impacts of logging equ ipment on soil resources and residual stand conditions (Beninger
management. we generally refer to the buffering process as a method for creating a buffer zone. which is defined as a land area that delineates separate management activ-
ities or emphases. Wirhin GIS, a buffer rone is a polygon that encloses an area within a spec ifi c distance from a point. line. or polygon, and is useful in analyses that
ment be affected. As you may gather, buffering has been
er al., 1994; McDonald er .1., 2002) . 130
120
Part 2 Applying GIS to Natural Resource Management
Fortunately, GIS software programs provide the ability co easily identify features that are within some proximity of other features (Star & Estes, 1990). Developi ng the boundaries of a region within a specific distance from a landscape feature, or set of selected landscape features, is often called a 'proximity analysis', or buffering process.
Therefore the subject of [his chapter involves the identificarion and delineation of natural resources w ithin cerrain distances of other landscape features.
How a Buffer Process Works
features can all be buffered but the buffer creation process depends on {he feamre rype. To visualize how a buffer is created around a point feature. imagine a point on a piece of paper; with a pencil and compass set, a circle is drawn
around that point (Figure l .la). GIS software programs can perform this type of operation on thousands of poims in a few seco nds. Delineating buffers around lines and polygons requires a simil ar process bur involves some addition al process ing. With lines. a buffer is created around each venex (Figu re 7 .1 b)' then tangents are ereared between each of these buffers, and on ly the outside
edge is kept, fo rming a closed polygon. W ith polygons, Buffer processes work by using marhemacical algorithms to delineate the space around selected landscape features. When using vector data, one or mo re features of interest are selected. the desi red buffer di stance is specified, and a line is drawn in all directions around the features until a
solid polygo n has been formed . Point, line, and polygon
you may have the choice of creating buffers that represent
only the area outside of [he polygo n being buffered, the area outside of the polygon pi us the entire area of the
polygon, the buffered area both inside and outside of the edge that define [he polygon, or the area buffered inside the polygon (Figure l.ic) . The type of proximity analysis
(e)
(a)
Buffer
- - -..I around
C D
.. oD ····· . ,., .. "':.·............ ···r. ", ' .
·
the point
,
Original polygon Outside buffer Inside buffer
[ ==: Tangents around vertices
-----Result 1: Buffered area represents only the area ootside of the polygon that was buffered.
Result 2: Buffered area represents the area outside of the polygon that was buffered, as welt as the area of the polygoo itself.
(b)
,, "
Result 3: Buffered area represents the area outSide of the polygon that was buffered, and the area inside of the polygon that might have been buffered.
Buffer around the line "" (Tangent 1)
,,
,
\
Vertex 1
\
,, ,
,
,,
,,
I
'..
, , "
,,
Vertex 2
\
(Tangent 2)
, "
,,
~
Result 4: Buffered area represents just the area inside of the polygon that might have been buffered.
Figure 7 . 1 Developing a buffer around (a) a point lUing, (b) a line with three vertices, and (c) a polygon .
131
Chapter 7 Buffering Landscape Features
that is required will suggest the appropriate 'Ype of buffering process required when polygo ns are concerned. For example. if you were interested in the type of vegetation within 1.000 meters of a set of polygons chac define crucial owl habitat, yo u would create a buffer outside the sec of polygons. On the other hand, if yo u were interested in understanding rhe amount ofland chat is associated with a policy chat prevents management activity within 100 meters of the edge of a managed property (to avoid conAict with homes a nd o ther developed a reas oU[side rhe propenyL you would create a buffer inside rhe boundary of the managed proper'Y. Buffering processes performed on vecto r GIS d ata may require so me ra ther complex geometrical calculations. with lines and tangents CO compute, and overlapping areas perhaps merged together. To remove overlapping areas, imersecrion and dissolving processes are used. Buffering processes performed on raster GIS data involve couming the number of pixels away from selecred or specified pixels. Buffers used in natural resource managemem can rake on many forms, bur rhe [wo most commonly employed are a conS[(lnt buffer widrh and a variable buffer width. Constant width buffers are the most commonly used form in natural reso urce managemem (Bren, 2003), a nd assume a symmetrical distance around each buffered landscape feature (same distance buffered on each side of a srream, for example) . Variable width buffers assu me that fearures are buffe red differently based on some inherem or assumed characteristic, such as stream size. Other ry pes of buffers include [hose based on (a) environmentalloading values and (b) othe r outside influences. Stream buffers based on loading values, for exa mple, might take imo accoum the amoum of area comriburing to an impact. For example. strea m reaches that have larger water contributing areas might be buffered using a wider buffer wi dth than stream reac hes with smaller co mribucing areas. These are differem than variable width buffers in thar each section of a stream may be buffered a differem distance based on the size of the watershed that contributes water (Q that secrion of the stream. Buffers based on other outside influences may include stream buffers that take inro accou m the amou m of sunligh t that reaches the stream irself. In these cases, buffers on the so utherly sides of streams may be wider (han buffers on the northerly sides of streams (Bren, 2003). As we mentioned, com mo n buffer distances can be co nstam (fixed) distances. o r they can vary for each feature in a GIS database based o n an att ribute of th ose
121
fearures . Most GIS sofrware programs can accom modate both buffe ring approaches. To ill ustrate these differences, suppose yo u have a GIS database that includes 10 streams, each of a different stream class (T able 7. I) . With this hyporhetical data, it is ass umed that the lower the stream class number, the la rger (wider) the stream. One task in planning natural resource management activities may be to delin eate ripa ria n buffe rs a round these streams. If a constant buffer distance of, for example. 30 meters is assumed. each stream would be buffered the sa me distance (30 m). H oweve r, many regulations pertaining to the proximity of manage ment activities arou nd ripari an areas require wider buffe rs around larger Streams, a nd narrower buffers arou nd smaller streams. Therefore, distances specific to each landsca pe feature (each srream reach in rh is example) can be used in the buffer process, to allow the development of variable distance buffers. For example, for each of the 10 streams you could have developed an attribute to describe the appropriate buffer distance (Table 7.2), and use that attribute to guide the buffering process. Readers will examine borh of these buffering assu mptions (constant di stance and variable width) in the forthcoming examples as well as in the 'Applications' section at th e end of this chapter. When buffering mult iple landscape features, a buffer is created around each feature independent of the other fea tures. One option available with most GIS sofrware concerns the handling of the ove rl apping areas. The choices are [0 retain individual buffer polygons for all features to be buffered (caJl ed uncomiguous, or nonTen hypothetical streams and
TABLE 7.1
their stream claBs, length,
and width Su....
Su.... da>.
2
Streun
Stream length (m)
width (m)
1000
50
750
45
3
2
500
10
4
2
450
10
5
3
375
3
6
3
450
3
7
3
400
2
8
4
300
9
4
250
10
5
275
o 132
122
Part 2 Applying GIS to Natural Resource Management
TABLE 1.2
Ten hypothetical streams and their stream class, length, width, and buHe r distance
Sueam S.......
d,,,
2
Strum
Stream
Buff.,
Ie.ngth (m)
width (m)
distance: (m)
1000
50
30
750
45
30
3
2
500
10
20
4
2
450
10
20
5
3
375
3
10
6
3
450
3
10
7
3
400
2
10
8
4
300
10
9
4
250
10
10
5
275
0
0
contiguous polygons), or co eliminate the overlapping areas, creating contiguous polygons. The advanrage of retaining ind ividual buffer polygons is th a t once (he buffers are created, [he buffer perta ining (Q each individual landscape feature can be accessed, which may allow you [0 determine which fatuus are within what distanu of other fearures. This allows individual analys is for each point. line, or polygon fearu re that was buffered. The disadvantage is that there is a high likelihood thar some of the retained individual buffers overlap; rhus the overlapping area can porentially be counted more than once in any subsequent area summary calculations. The creation of a single buffer from overlapping buffe red areas avo ids this problem. However. the ability CO understand the buffer required for each individual landscape feature is [hen obscured. The goals of each analysis should direct users to the choice of one buffering method or the other.
Generally when buffers are delineated with a GIS process. they are saved in a new GIS database that is separate from rhe one containing the landscape features that were buffered. To further enhance the power of buffer processes, most GIS software programs only buffer the features that are selected (manua lly or through a query) . If no landscape features are selected, generally all of the landscape features are
but users shou ld understand how the two methods differ in their approaches and results.
Buffering Streams and Creating Riparian Areas Ripar ian a reas can be defined as land areas that are in close proximity ro a stream. lake. swamp. or other water body, and those that are often are occupied by plants that are dependent on their roOtS reaching the wate r rable (Society of American Foresters. 1983). Alternatively, they a re areas where vegetation and microclimate are inAuenced by seasonal or year-round water. high water ta bles. and soils exhibir some wetness characrerisrics (Oregon Department of Forestry, 1994a). The first definition includes administrative and ecological aspects. wh ile the second is based mainly on ecological and physical aspects. When we work with riparian a reas in natural resource management. they are more commonly defined administratively rathe r than ecologica ll y. Generally. riparian management area widths are designated by federal . stare. provincial. or organization policies, and are designed ro provide adequate areas ro retain the physical components. to maintain the functions necessary to meet protection objectives and goals for fish. water quality, and other wildlife (Oregon Department of Forestry, 1994b). While some policies suggest that riparian areas should be protected from logging, grazing. and other types of exploitation. ocher policies allow a set of limited activities within certain distances from ce rtain rypes of streams. Thus it is important for land managers to know where riparian areas are on a landscape. and to understand whar resources are affected by riparian area designation s. In the following examples, streams will be buffered first with fixed (constant) buffer widths. then with variable
buffered but an examination of buffer output can confirm whether your GIS software follows this assumption. One processing step commonly forgotten is either [Q remember to select the features that need to be buffered (perhaps just all C lass 1 strea ms), another is [Q clear all previously selected features (if some features were selected for a reason unrelated to the buffer process) . 133
Chapter 7 Buffering Landscape Features
123
buffer widths (according to a set of stream buffer gu idelines) to del ineate the riparian managemenc areas.
Fixed-width buffers In this first example of a buffering process, the streams GIS database of the Brown T ract (Figure 7.2) wi ll be used to ge nerace fiXed-width buffers, o r buffers that do not vary based on some 3cuiburc of the landscape features being buffered. In this case, assume thar an organizational policy exis(5 chat directs the managers of the Brown Tract to delineate a fixed )50-foot buffer aro und all of the streams. During the buffer process, a buffer polygon will be created arou nd all stream lines in the streams GIS database. As noted earl ier, mOSt GIS sofrware programs provide the options of either leaving the buffers as individual polygons around all stream lines or el iminating the overlapping areas. H ere, we will illustrate the overlap being eliminated, so that buffer area estimates will not be overstated. At the conclusion of the buffer process, areas 150 feer on either side of (he Brown Tract streams (Figure 7.3) are delineated; no other land areas are represemed by the buffer polygon(s) contained in the new buffer GIS database.
Variable-width buffers Ra[her [han a single, fixed-width buffer, some managemem objectives may require a buffer [hat varies based on an auribuce of a landscape feamre. For example, in some S[ates or provinces rules exist which indicate that the size of riparian areas should be a function of the type of
D D
Stream buffer Forest boundary
Figure 7.3 Fixed-width (I 50-foot} riparian management areas, generated by buffering the streams GIS database of the Brown Tract.
stream with which they correspond. In some cases, these riparian designarions allow no activity within a cerrain distance from the stream system; in other cases, li mited acrivity. The general norian is that, with wide r streams, Streams with year-round warer flow, o r streams with known fish populations, wider buffers are required. Once [he set of buffers is based on the characteristics of the streams, they are consid ered variable-width buffers because [hey vary according to the characteristics of eac h srream. In Oregon, for example, the ri parian guidelines require a ) DO-foot buffer aro und 'large' fish-bearing or domestic wate r use streams, and lesse r buffer widths around smaller screams (Table 7.3) and streams that are nO( curren tly fish-bearing or used as sou rces of domesric wate r (Oregon State Legislature, 2005) . Since the Brown Tract is fictio nal, the buffer widths are assumed for the various stream classes in rhe streams GIS database. Stream class 1 is the largest srream, inro which all of the other streams flow, so a larger buffer (100 feet) is
D " -
Stream
D Forest boundary
When you wanr co create riparian management areas by buffering a set of streams, you usual ly do so by ind icatin g how wide the buffer should be on either side of a stream, rather than by indicaring the rotal w idth of the buffer (from o ne edge of the buffer, across the stream, to the other side of rhe buffer).
Figure 7.2 The stream system within and arollnd the Brown Tract.
134
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Part 2 Applying GIS to Natural Resource Management
TABLE 7.3
State 01 Oregon riparian management area policy Riparian management area width (feet) Domestic water we or fish.bearing
Non·domestic water we and non-fish-bearing
100
70
Medium"
70
50
Small<
50
20
• Average annual flow of 2: 10 cubic feet per second. Average annual flow of ~ 2 cubic feet per second and < 10 cubic feet per second.
h
~
Average annual flow of < 2 cubic fec t per second, or drainage area
s: 200 acres. Source: Oregon State Legislatu re. 2005
Buffering Owl Nest Locations
assumed around this st ream class, and smaller buffers are assumed [Q be required around the other Stream classes according to the direction provided in Table 7.3. Fortunately. most GIS software programs allow users to des ignate a field (also called column, attribute, or variable) in a GIS database to use as the refe rence for the desired buffe r width fo r each landscape feacure. Since each row in the tabular porrion of the streams GIS database represents a Stream line or reach (Table 7.4), the values located in the ' buffer width' field can be used to represent the appropriate buffer width for each stream . During the buffering
Sample stream reaches represented in the Brown Tract streams GIS database, their characteristics, and resulting buller width-
TABLE 7.4
process, each stream line will then be buffered according the appropriate buffer width, based on each stream's class. The buffering process will read the buffer distance from an attribute table, one record (row) at a time. to create the buffer. Most GIS programs will do this very quickly, such that only a few seconds or less are required. Following the creation of all buffers, subsequent processing will eliminate all overlapping buffer areas, even though [he size of the buffer may change in the overlapping area. A map of the variable buffer widths associated with the Brown Tract streams (Figure 7.4) shows that the amount of land area in the riparian areas will vary by stream class, and that no other land area (o utside the variable width buffer) is represented in the buffers. to
Up to this point ou r concentration has been placed on one of the more typical GIS buffering operations performed in supporr of natural resource managementbuffering streams. However, any type of landsca pe feature (owl nest location , road , wetland, etc.) ca n be buffered. For example, in the western United States it is important to protect an area a round spotted owl (Strix occidtntalis) nests. The area within these buffers may eithe r totally prohibit management activities or may limit management activities by duration and extent. Thus. it may be imporrant to understand the amounr of resources located within owl buffers. As an example, assume that an owl nest is located in the central portion of the Brown Tract, and assume that federal regu lations requi re land
Buffer
Length
Fish
(r.
width
bearing~
Stream size
362
no
small
20
2
176
no
small
20
3
992
no
small
20
4
384
no
small
20
174
1953
no
small
20
175
2143
Y"
mcdiwn
70
176
3159
Y"
small
50
Stream
,each
(feet)
• Stream reaches arc no t necessarily or:clusivcly located within the bou ndaries of rhe Brown T racr.
o o
Stream buffer Forest boundary
Figure 7 .4 Variable.width riparian management areas, generated by buffering the streams GIS daubue of m e Brown Tract.
135
Chapter 7 Buffering Landscape Features
Users should bear in mind that the buffer distances and map coordinate units of GIS data layers that are being buffered must be considered. A buffer operation will typically assume that buffer distances specified either through fixed-width or through variable-width processes are in the same units as the map coordina te system of the G IS layer. If the buffer distances and map coordinate unics are in differem measuremenc systems (51 versus USeS) or are in the same system bur at different scales (e .g. meters versus kilometers) the appropriate conversion should be applied to the buffer distances prior to beginning the buffer process. Some GIS software will prompt you for the buffer or G IS layer units, and will even do any conversions necessary during the buffer process. In th is case, as long as the correct conversions were specified by th e user, the buffer output should be coerceL In other cases, however, the user must verifY that all buffer-dependent measurem ent units are in agreement.
125
•
Owl nest location
o
Forest boundary
o
Owl nest buffer
Figure: 7.5 Owl nest location and associated 1,000 Brown Tract.
fOOl
buffer on the
managers to manage the area within 1,000 feet of these nests much differently than the areas beyond 1,000 feet of an owl nest. A buffer process can be performed using the owl nest locarion as the selected landscape features, and a 1ODD-foot radius as a fixed (constant) distance around the nest locarions. The result of the buffer process is a new GIS database that delineates the areas within 1,000 feet of the nest location (Figure 7.5). If more than one nest were located on the Brown Tract, and the resulting buffered areas overlapped, you could elect to eliminate the overlapping area (uncontiguous result) or allow the overlapping areas to remain (cont iguous result). If your goal was to determine the amount of area and land resources associated with each individual nest then the unconriguous resulr would be best. Conversely, if you r goal was to determine the toral area and land resources encompassed by all owl nest buffers then the contiguo us result would be preferred.
as well. Of course, when using vector GIS databases this is only possible with polygon features. To illustrate this process. let's assume that the managers of the Brown Tract are concerned about the impact of management activities on nearby homeowners. In some cases, homes are very close to the edge of me forest. In orner cases, homeowners' ya rds and personal belongings (sheds, etc.) are on the edge of the property. To avoid any potential instance of damage to adjacent homes or property, or any potential physical harm CO nearby landowners. the managers have decided that they will allow only limited activity within 200 feet of the boundary of the property. To understand how much of the property will be allocated to a limited activity land classification, the inside of the boundaty of the forest (a single polygon) can be buffered (Figure 7 .6) , and the resulting area (397 acres) can be compared with the total area of the forest (5,245 acres) to determine the im pact of the policy (7.6 per cent of the forest shifted to limited use) . As an alternative co this policy. the managers of me Brown Tract could delineate this limited activity zone by buffering the homes a cenain distance. although locating these areas on the ground would be much more difficult than using a policy such as 200 feet .from the boundary, since the buffer around each home is circular.
Buffering the Inside of Landscape Features
Buffering Concentric Rings around Landscape Features
In addition to delineating buffers outside oflandseape features, you ca n develop buffers inside of landscape features
If you needed to generate multiple buffers around the same landscape fearure(s) , you co uld perform each buffer136
126
Part 2 Applying GIS to Natural Resource Management
o 0
"-I
o o
D D
CJ
1)\)
0
X
D
Lake Land Eagle nest Zone'
[)]] Zone 2
\J
Buffer Forest boundary
Figure 7.6 A 200-foot buffer inside the boundary of the Brown Tract.
ing operation independently using different fixed-width buffer distances. However, the resulting buffers would contain so me areas [hat overlap . Ahernariveiy. if you wanted to avoid the overlap that wou ld ultimately OCCUf with this method. most GIS sofcware programs contain a process that enables users to easily develo p co ncentric, non-overlapping rin gs. One caveat for this process is that
Figure 7.7 Concentric 330·foot
buff~rs
around
two
agle nests.
illustrates the development of the concentric ring buffers. whe re Zone 1 extends 330 feet outward from the nest tree.
an example of the use of concentric rin gs of buffers. [he
and Zone 2 extends another 330 feet from rhe edge of Zone J. Upon closer inspection, you would find thar the buffers describing rhe rwo Zones do not overlap; therefore,
Maryland Deparrmenr of Naru ral Resou rces (2005) recentl y esrablished guidelines for rhe managemenr of land near bald eagle (Haliaeetus lrococephalus) nesr reees.
(he area underneath is nor double-counted. While these examples describe the use of concentri c ri ng buffering around point features. the process can also
the buffer interval for each ring needs
to
be constanL As
The guidelines require managers [0 acknowledge th ree buffer zones around each nest tree.
be used to develop rings of buffers around line or polygon features. if management objectives suggest they are necessary. For example. ripa rian buffers for forested areas on
• Zone I extends from a nest tree ourward to a radius of
Prince Edward Island need to be 20-30 merers wide, ye t
330 feer. o Zone 2 exrends from rhe edge of Zone 1 (330 feer) ourward to 660 feer in radius. o Zone 3 exrends from rhe edge of Zone 2 (660 feer) ourward to 1,320 feet.
a 15-meter undisturbed area must be maintained
Zone 1 prohibirs land use changes, such as those relared (Q
development or dmber harves ting. Zone 2 proh ibits
(Legislative Counsel Office, 2006) . If you were managing land on Prince Edward Island. and assumed that the max-
imum fo rested ri parian buffer width would be 30 meters wide, you cou ld develop I5-meter concentric ring buffers around the line features th at describe [he streams. This would enab le the mandacory 15-meter area to be represented on a map. as well as the larger 30-meter buffer
development (clearing, grading, building, etc.) but allows
boundary.
selective timber harvesting. Zone 3 prohibits any activity during the eagle nesting season. If you were interested in
Buffering Shorelines
quickly developing buffers to represent Zones 1 and 2, the buffer interval for the concentric rings would
be 330 feet.
Zone 3 is not 330 feet from the edge of Zone 2 and thus would need to be developed separately. Figure 7.7
The actual or planned management of areas near shorelin es of lakes may be of inte rest to natural resource managers. 10caJ land use planners. and citizen stakeholders. 137
Chapter 7 Buffering Landscape Features
Buffer output can take different shapes around the linear features that are being buffered depending on your GIS software. Some GIS software will allow you to specifY which side (left or right) of a line to create a buffer, rather than buffering both sides. The trick
in this case is
[0
determine which side of your line is
left and which is right, a co nditio n which will often he determined by (he direccion in which your lines
As a result, information regarding the extent of actual or planned land uses near lakes may help info rm land planning processes. JUSt as an example. assume that the area of
interest near the shorelines of lakes in the Pheasam Hill planning area of the Qu'Appelle River Valley in cemral Saskatchewan is 300 meters from the edge of each lake. A buffer process can be performed to create polygons that represem areas 300 meters outside of the edge of each lake (avoiding buffering the inside of each lake feature). The result is a new GIS database (hat contains areas within 300 meters of the shorelines. A clipping process can men be employed to clip the zoni ng GIS database associated with the Pheasam Hill planning area of the Qu'Appelle River Valley, and obtain the land use classes that are designated within 300 meters of the shorel ine (Figu re 7. B) . A quantitative assessment can then be made of the amount of area of eac h actual or planned land use within a close proximiry to the lake, as well as visual assessment of the juxtaposition ofland uses near the shoreline.
127
were originally digitized or created. Individual lines can also have their ends buffered through a circular
shape, which is the usual default, or through a flat shape. If you chose the flat shape the output result of buffering a straight line would be a rectangle. Lines that change direction and are buffered through this process will produce output polygons wit h flat (noncurved) ends.
Othe r Reasons for Using Buffering Processes The exam ples provided in this chapter have focused on common natural resource management concerns. The delineation of riparian mana gement areas and zones
around wildlife nest locations are twO typical examples of using a buffering process in namral resource management planning. There are, of course, a number of Q[her reasons why you would use bu ffe ring operations in natural resource management. including:
me
1. Buffering stream systems [0 delineate zone that herbicide operations must keep oU{ of. due co [he proximity to water systems. For herbicide operation planning, local buildings (particularly houses), roads, agricultural fields, and orchards may also requ ire buffering. 2. Buffering research areas (plots or sta nds)
CO
prevent
the planning and implementation of logging operadons within them . Generally speak ing, research plots require extra protection because trees rhat are treared
(given a researchable app lication) should nOt also be considered edge trees (rrees adjacent to a cleared area).
therefore the plots need to be buffered fro m any nearby harvest activities. 3. Buffering trail systems or roads to delineate areas of visual sensitivity within which logging operations may be limited. In many forested areas, degrad atio n of
r=;:] lake _
Agriculture priority 2
~;, ;~~j Recreation ~ Urban [=:J National area
[=:J Indian reserve [=:J Other areas beyond 300 m of the shoreline
Figure 7 .8 Planned or actuaJ land uses within the Pheasant Hill planning aua of the Qu'Ap~l1e River VaJley. Saskatchewan (1980).
recreacion opporrunides is a co nce rn, whether the recreational activities involve humans walking (hiking)
or driving. The ability to quickly delineate and visualize these areas of concern is a valuable asset in land management planni ng. 4. Buffering property boundaries to recognize mat development codes, which may limit st ructu res o r other 138
128
Part 2 Applying GIS to Natural Resource Management
landscape alternations from occurring within a threshold dis,.nce from property lines. are observed. This may also app ly to easements or utility co rridors. which are often subject [0 municipal. county. or provincial development regulations. In addition. buffer processes may assist namra! resource managers in evaluating the potential impacts of local forest regulations. Local forest regulations are generally concerned with protecting envi ronmental quality and aesthetics, and with safeguarding local government investments in roads and other infrastructure (Martus et al.. 1995). They are mainly developed as a result of the conflicts that occur with the continuing shift of the human population
from urban to rural settings (Cubbage & Raney. 1987). The types of co ntrols include requiring the development of forest plans. buffering specific landscape features. and placing restrictions on certain silvicultural practices. For example. timber harvesting ordinances developed at the local level are intended to limit site degradation and environmental qualiry in association wich logging activit ies. Harvesting operations may be restricted within certain distances of public roads or ocher public resources. therefore requiring a buffer around these resources. Natural reso urce managers may need to identify these buffers in a harvest plan. and may also be concerned about the cumulative impact of local regulations on the profitability and feasibility of their management operations.
Summary GIS buffering processes are powerful tools that allow you ro investigate the nearness of landscape elements ro your feacures ofinteresr. These features of interest can be represented by points. lines. or polygons (as demonstrated here) or by raste r grid cells. Buffering processes are spatial operations that allow users of GIS software programs to identifY areas within some proximity of selected landscape feacures . After landscape features are selected. a zone (a polygon) is delineated around them to represent the buffered area. By default. if no landscape features are selected. all features are buffered. but you should confirm whether your GIS software uses this approach. Buffer OUtpUt approaches. such as contiguous or uncontiguous results. can be customized to meet analysis objectives. In add ition , some GIS software
enables users to specify whether buffers created around lines have a round or flat shape at the beginning and ending of the line. The resulting buffer GIS databases allow users co visually understand the area that lies within a certain distance from some landscape feacure(s) of interest, and to quantify the resources contained within the buffered area. Buffering streams and other resources of importance (e.g.• owl and red-cockaded woodpecker nest locations) to create limited management zones is a common management objective included in natural resource management plans. Estimating the impact of these types of restrictions on natural resource management is important, and helps landowners investigate the effect of current and proposed policies.
Applications 7.1.
Current Riparian Policy for the Brown Tract. The currell[ organizational policy for the Brown Tract indicates that the following riparian area buffer widths should be used in conjunction with management activities: Class I: 100 feet C lass 2: 75 feet Class 3: 50 feet C lass 4: 40 feet
a) How much area (ac res) is located inside the riparian areas? b) How much area (acres) of each vegetation type is located inside the riparian areas? c) How much area (acres) of each vegetation type is loca(ed outside the riparian areas? d) How much timber volume is located in the riparian areas?
Becky Blaylock. the Manager of the Brown Tract. wants to know (he 'current situation' with regard to riparian areas:
Ms Blaylock also wants you to develop a map that illustrates the stream buffers and includes the roads. streams. and timber stands. 139
Chapter 7 Buffering Landscape Landscape Features
77.2. .2.
Proposed Organizational Policy. Becky Blaylock recently attended 3([cnded a meering meedng of policy makers, where she heard char rhar rhe the riparian rules might mighr change. She now needs [Q to understand undersrand the rhe paremial potential impact impaer of a proposed 125foot no-harvest no~harves[ buffer around all screams streams on [he the Brown T racr. She requestS Tract. requem rhe following: a) How much area (acres) is located locared inside the rhe riparian areas? b) How much area (ac (acres) res) of each vegerarion vegetation 'Ype type is located inside tbe the riparian areas? c) How much area (acres) of each vegerarion vegetation 'Ype type is located Qucside oU[sicle the riparian riparia n areas? d) How much timber amber volume is located in the riparian areas?
129
roads, streams, and timber stands. In addition. additio n, she wanrs wa nts roads. you [0 to develop a memo that (hat describes rhe the differenc.es differences berween the between rhe policies noted nored in Applications Appl icarions 7. 7.1. 1, 7.2, 7.2. and the potential policy pol icy nored noted here. rhe poremial
7.4. National Forest Riparian Policy. A local National Narional Forest uses me the following riparian ripa rian area guidelines guidel ines in conjunction with harvesting act activities: ivi ties: Large Streams srreams 250 feet feer Med ium Streams Medium srreams 150 I SO feer Small srreams 100 feet feer SmaU streams
me
Again, Ms Blaylock also wants Again. wams yo youu [Q to develop a map rhar that illustrates these rhese stream buffers and that rhat includes the roads. streams, and timber stands. In addition, addition. she wants you to co develop ;:Ia memo th that at describes the lhe differences d iffe rences becween berween me rhe policy noted nared in Applicarion Application 7. 1 and the rhe paremial potential policy noted here. 77.3. .3. Ballot Ba Uot Initiative. A proposed bailor ballot iniriarive. initiative, developed by a local locaJ conservation group, suggests suggestS tha thart [he the following riparian ripa ri an buffer widths may soon be required for the region within wh ich the Brown Tract is Si (U3red: which situated: Large. fish-bearing srreams Large, streams 150 ISO feet feer Medium. fish-bearing fish-bear ing srreams stteams 100 feet feer srreams feer Small. fish-bearing streams 75 feet Large. La rge, non-fIsh-bearing Streams srreams 125 feet feer Medium, non-fIsh-bearing stteams streams 75 feet Small, Small. non-fish-bea non-fish-bearing ring streams srreams 50 feet feer
As a result, resulr. Ms Blaylock wants wams to understand undersrand rhe following: a) Ifthese If rhese rules were applied to the Brown TraCt, Traer. how much area (acres) would be located locared inside the rhe riripa parrian areas? b) If rhese these rules were applied to rhe Brown Traer. TraCt, how much area (acres) of each vegerarion vegetation type 'Ype wo would uld be located ins ide the riparian areas? c) If rhese these rules were applied to the Brown Traer. T raCI, how much area (acres) of each vegetation type wo would ul d be located oU[side outside rhe the riparian areas? areas? d) If these rules were applied to the rhe Brown Traer. Tract, how much timber rimber volume would be locared located in rhe the riparian areas? are-.lS?
As before, before. Ms Blaylock also wants you to develop a map that illus(rates illust rates these Stream stream buffers and that includes the
As wim the arher other pol policies, wirh rhe icies. Ms Blaylock wants wams to understand srand the rhe following: a) If I f these rhese rules wefe were ap plied (0 to the Brown T Traer. ract, how much area (acres) would be locared located inside the rhe riparripa rian ia n areas? b) If mese rhese rules were applied ap plied to the rhe Brown Traer. Tract, how vegetation 'Ype type wou would much area (acres) of each vegerarion ld be ins ide (he areas? located inside rhe riparian areas? c) If these rhese rules were applied to the rhe Brown Traer. T ract. how much area (acres) of each vege vegetation tation type would be located outside [he the riparian areas? d) If ,.hese d) these rules were applied to rhe the Brown Traer. Tract, how umber volume vo lume would be located in (he the riparmuch timber ian areas? And again, again. Ms Blaylock also wants wams you to develop a map that illustrates these stream buffers bu ffers and [hat that inducles includes the roads, Streams, and timber stands. In addition, she wants \Yams you co memoo char that describes [he the differences to develop a mem between noted in Applicarions Applications 7.1 7.1,. 7.2. 7.3. 7.3, berween the rhe policies nored and rhe the porenrial potential policy nared noted here.
7.5. Current Owl Buffer Policy. Suppose that rhar the rhe currem rent policy pol icy regarding owl nest locations is ro to maintain a ~O-acre no-harvest buffer around owl nest 1IOO-acre nesl site locations. Develop a memo an d a map for Ms Blaylock that rhar describes rhe amoum amount of land (acres) of me rhe Brown Tract Traer that the single owl buffer pertaining rhar would be covered by rhe pertain ing ro to this property. 7.6. Protection of of Research PlotsThousands PlolS.Thousands of research plots have been established across North America ro to facilfac ilitate the estimation of the response of forests and wildlife co a variecy variety of silvicullUral silvicultural uearmems. treatments. Unfortunately. Unfortunately, har[0 vesting operations are usually implemented independently of research programs. and tOO often the research plots are harvested because the loggers were unaware of 140
130
Part 2 Applying GIS to Natural Resource Management
their locarion, and withom the researchers knowing that
the plots were in jeopardy of being destroyed. The resulting loss of the research plot investment (layout, tagging of trees, etc.) may be considerable, and the loss of the opportunity for one final measuremem may be equally as
impo rtant. In many organizations, a communication sys[em norifying researchers of potencial impacts ro research plms has been developed. allowing researchers to either measure (he plots onc last time , o r CO delineate the research areas as off-limits to harvesting activities. Assume that rhe permanenr inventory plots on the Brown T faCt were designed [0 evaluate {he long-term
growth and yield of the forest properry. Assume further that these plms were designed {Q remain untouched, even though rhe surrounding forest may be harvested (clearclH
or thinned) . Finally, assume that the plots need to be maintained with a sufficient buffer of trees around them so that rhe effects (win dthrow, increased sun light, etc.) of harvesting trees outside the plot (o n the plot's trees) are min im ized . These plots are circular l/5-acre plots, an d
require an additional 100-foot buffer from the edge of the plot boundary to be considered 'protected'. Develop a memo for Becky Blaylock that describes how much land area would be off-limits from harvesting operations as a resuh of protecting the research plots. Develop a map of the permanent inventory plots and their buffers. Include the roads. streams, and timber stands on the map. In addition. for added protection of the investment in
research , a second 100-foot buffer (fo r a total of200 feet) could be delineated around the research plots. In this supplemental area we can also assume that the trees in the buffer are designed to remain untouched, even though the surro undin g forest may be harvested (clearcut or thinned) . Based on the timber volume contained in the
buffers associated with the first (I OO-foot buffer) and second (200-foot buffer) cases, and a price of$400 per MBF, what is the cost of each case, and therefore what is the additional cost to require the added protection aro und
each plot'
References Association for Geographic Information. (I999). GIS dictionary. Retrieved February 4, 2007, from http: //www. agi.org.uklbfora /systems/xm lviewerldefau lt.asp'a rg= DS_AGI_TRAINART_701_firsttide.xsIl90. Bettinger, P., Armlovich, D., & Kellogg, L.D. (1994). Evaluating area in logging trails with a geographic infor-
mation system. Transactions ofthe A.s:.1E, 37(4) , 1327-30. Bren , L.J. (2003). A review of buffer strip design algorithms. In E.G. Mason and c.J. Perley (Eds .), Procudings of the 2003 ANZ/F Uoint Australia and N ew Zealand Institute of Forestry) Conference (pp. 32~35) . Retrieved September 5, 2007, from http:// www.forestry.org.nz/articles/ conf2003/Bren. pdf. Cubbage, F., & Raney, K. (1987). Counry logging and tree protection o rdin ances in Geo rgi a. Sottthern
Joumal ofApplied Forestry, 11, 7~82. Legislative Coun sel Office . (2006). Chapter £-9, En vironmental Protection Act. Charlottetown, PE: Gove rnmem of Prince Edward Island . Retrieved
February 4, 2007, from http: //www.gov.pe.callaw/ statu tesl pd fl e-09 .pdf.
Martus, CE., Haney, Jr. , H.L., & Siegel, W.C (1995) . Local forest regulatory ordinances: [rends in the east-
ern United States. Journal ofForestry, 93(6), 27-3 1. Maryland Department of Natural Resources. (2005). Sustainable forest management plan for Chesapeake forests lands, Chapter 8, Wildlife habitat protection and management. Annapolis, MD: Maryland D epartment of Natural Resources, Forest Service. Retrieved
February 4, 2007, from http: //www.dnr.state.md.us/ forests/download/sCmgt_plan_chapters/chapter8cA. pdf. McDonald, T .P., Carter, E.A., & Taylor, S.E. (2002). Using the global positioning system to map disturbance pa((erns of forest harvesting machinery. Canadian Journal of Forest Research, 32, 310-19. Merriam-Webster. (2007). Merriam- Webster online search. Retrieved February 4,2007, from http: //www. rn-w .coml cgi-bi nldictionary. Oregon Department of Forestry. (1994a). Oregon forest practius rules and statutes. Salem. OR: Oregon Department of Forestry.
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Chapter 7 Buffering landscape Features
Oregon Deparrmenr Departmem of Forestry. (I994b.) (l994 b.) Forest Fomt practi« practict water prouction roks; ruks; Divisions 24 and 57. Salem, Salem. OR: O regon Departmem Deparrmenr of Foresrry. Oregon Forestry. Oregon State Legislarure. Legislatu re. (2005). Chapur Chapter 527-iIlS«1 527-imect and disease control; forest form practius. practices. Re Rerrieved 5, diJ~as~ controL; trieved February S, 2007, from htrp:llwww.leg.srate.or.us/ors/527.html. hrtp:llwww.leg.srare.or.us/ors/527 .hrml. 2007.
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Sociery of American Foresters. Foresrers. (I983) (1983) . Terminology offorest scitnct scienct technology practice praChct and products. Be(hesda, Berhesda, M D: Sociery of Ame American rican Foresrers. Srar, Star. J., J.. & Estes. Esres, J. J. (1990) (1990).. Geographical information systems: Urns: An introduction. Englewood Cl Cliffs, iffs. NJ: Prentice Prenrice Hall.
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Chapter 8
Combining and Splitting Landscape Features, and Merging GIS Databases Objectives The objecdves of this chapte r are to provide readers with a n understanding of rhe opportunities related to, and potential pitfalls associated with, using a GIS CO combine and split landscape features . In addition. since merging twO or more G IS databases rogcrher is similar to co mbining landscape featu res. another objective is (Q describe rhe pros and cons associated with this GIS process. After completion of this chapter, readers should have the knowledge and ability to understand: I . why. when . a nd how YOli might wane to com bine landscape feacures; 2. the reasons for splitting landscape feacures. and the situations whe re th is process might be appropriate; and 3. why two or more GIS databases might be merged, and what yo u would expect co find within a merged database. In rhe previous chapte rs an emphasis was placed on understanding how much of a resource (fo r example. the length of road or an area of land) was located within a certain geographi c region. such as with in a set of stream buffers. Queries were used. along with clipping. erasing. and buffering processes, to determine the size of the resources in question. In performing chese analyses, imerest was placed only in the end result of the set of GIS processes performed, a result that most likely included some very precise and accu rate, yet pe rh aps unexpected,
landscape features . For example. as a result of pe rforming a clipping process, numerous spu rious polygons might have been createdj polygons so small that it would seem un reasonable (0 manage the land they represem in a COSteffective manner (let alone find them on the ground). In this chapter GIS processes a re imroduced to help accomp lish two goa ls: (1) clea n up GIS databases and (2) facilitate more efficiem spacial analysis processes. The GIS processes emphasized relate to combining, spli rting, and merging landscape features. In introducing these GIS processes, examples rang ing from facilitating wildlife habitat analysis to estimating unrestricted (from a forest managemem perspective) areas in a landscape are used to help readers understand rhe usefulness of these procedures within a natural resource managemem context.
Combining Landscape Features Multiple landscape features within a single GIS database can be combined to produce a single landscape feature. The combine process ge nerally begins by assessing what the landscape features of interest have in common . To make the management of GIS data more efficiem, similar landscape features could be combined so chat a smaller number of features are contained in a spatial database. These similarities could be associated with spatial position (fearu res touch one another), or attribute valu es (they have the same characteristics-age, type, ere.). In Figure 8.1, for example, twO polygons are to be combined based on rheir current spatial position-they share an edge. 143
Chapter 8 Combining and Splitting Landscape Features, and Merging GIS Databases
After the combine process is completed, we find that the edge that was shared was eliminated. because the line that
defined the shared edge was not needed to define the boundary of the new polygon. In a spatial database, the initial two polygons represented in Figure 8.1 would each be stored separately. In other words, twO polygons and twO polygon records would be represented. After combining the polygons, only one polygon record would be contained in the resulting database. Although the reducrion in database complexity is modest in this example, it
may be substantial when hundreds or thousands of spatial features are involved. In addition, although this example featured twO adjoining polygons, the landscape feamres being combined may physically overlap, or may be physically separated by a gap. In the case of overlapping landscape feacuces, the overlap is eliminated when the polygons are combined through the creation of a single polygon represeming the overlap area in the oucpuc data-
base. In the case of physically separated landscape features, the combined landscape feature is composed of two or more distinct pieces. In some GIS software programs, a combine process is known as a dissolve process. During the management of GIS databases, or as a result of an analytical need, it might be necessary to com-
133
bine landscape features. Landscape features should be of the same feature 'Ype to be combined. In general, polygons are combined with other polygons, and lines with other lines. The reasons for combining landscape features ace numerous, but are generally based on [he fact mar it is easier [Q manage a sma ller database (one with fewer
records) than a larger one. In addition. spacial analysis and data storage considerations are typically more efficient with a smaller database. There are at least six reasons why you might want (Q combine landscape feamces:
l. You may wish to eliminate unintended small landscape features that were created through digitizing or
some other GIS process (e.g., clipping, erasing) that affected the geometry of a line or polygon spatial database. Combining landscape features can then be effectively used to reduce [he number of feamces being managed and can correct unintended feam ces. For
example, after a GIS process, spurious polygons may be present. Spurious polygons are fractions of polygons broken or created as a result of a GIS process. and you may wanr
[0
combine them with other neighboring
polygons to reduce the number of polygons being managed. 2. Changes in organizational policies may suggest that some spatial features need to be combined. For example. an organizatio n may redefine the minimum map-
a. Prior to combining the polygons
ping unit (hat it manages. The minimum mapping
unit defines the smallest sized unit that should be present in certain types of GIS databases . A change in an organizational policy, say to increase the size of the minimum mapping unit, m ay require eliminating some small polygons or lines. Making the minimum
Shared edge
r
Polygon 2
resolution of landscape features recognized (a COSt). Making the minimum mapping unit smaller has the inverse effecc: a higher spacial resolution is recognized, and more data needs to be managed. 3 . The acquisition of GIS databases from mher sources
Polygon 1
b. AHer combining 100 polygons
I
,---------,I
mapping unit larger not only reduces the volume of data to be managed (a benefit), but also reduces the
(ot her than developed internally within a natural
r Polygon 1
Figure 8.1 Combining twO polygons. by diminating a shared edge. to produce a single polygon.
resource management o rgan ization) may prompt the use of a combine process. You may find within an acquired GIS database, that the mapping unit standards are inconsistent with the standards used by their organizat io n. In add ition, existing adminisrra d ve
boundaries, be they socio-political (ownership) or natlIral (watershed areas), may nO[ be desired in a spatial database. For example, a private company may acqu ire 144
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Part 2 Applying GIS to Natural Resource Management
a GIS database from the US Forest Service. The acquired database may include polygons smaller than what the private co mpany typically manages, suggesr-
ing that some landscape features need to be combined adhere to the minimum mapping unie standard chat
[Q
gesr using a combine process. For example. the condition of a road may change due co imp rovemen(S made (Q ir over rime. A rocked road may become a paved road, a native surface road may become a rocked road. o r a road of any type may become a decommissioned
the company uses. A polygon layer (hat contains
(obliterated) road. If cond itions of landscape features
watershed boundaries at a sub-regional scale may provide unneeded detail if a regional watershed boundary wi ll suffice. Sub-regional watershed boundaries could be aggregated through a combine process. 4. Comb ining landscape fearures may be necessary because it simply makes sense from a management perspeccive. For example. in a stands GIS database there may reside two polygons side-by-s ide that describe fo rested areas with trees of similar ages, similar structural conditions. similar sire classes. and similar growth rates. The field personnel responsible for
change, it may seem reasonable to combine th ose landsca pe features with other adjacent landscape features of the same stature or condition. A similar example might involve stream network measurements such as what occurs du ring a standard watershed ana lysis project. Field stream crews will visit selected streams and segregate Stream networks into simil ar hydrologic or geomorphic categories. or ca tego ries related to the surrounding land cover. These groupings are stored in a spat ial database as separate line fearu res with associated dara records containing descriptive information. Some co mbi ning of streams in a GIS database might be necessary to manage the stream network more
managing these stands might also suggest (hat the two stands would be tceared with similar treatments, at
about (he same time. and with simila r equipmenc. Thus from a management perspective. combining the
two polygons into a single polygon within a GIS database might make sense.
5. Since landscape features can change in shape and characteristic over rime, managing these changes may sug-
efficien tly. 6. It may be appropriate to combine landscape features to facilita te a spatial ana lysis. For example. co delineate one category of the recreational opportunity spectrum, we may need co identify the size (area) of contiguous timber stands where rhe average age of rrees is 50 years
Combining processes must be used in a thoughtful manner. Once landscape features are combined. the
database, the altered database is no longer considered the official database of the organization . For example,
topology that describes the resulting landscape feature
John Goheen, a GIS analyst, may be the 'owner' of a
is altered in the Output database. Should a user decide ro delete the input databases (a common thought once a new database has been created) the orig inal descrip-
roads GIS database within an o rganization. John may then give a copy of this database to Paul Chapman, a
tion of the landscape topology is lost. Given this risk, larger natural resource organizations have generally
placed the decision to edit and manage GIS databases (w here combining landscape features may be necessary) to the person who has been given 'ownership' of
the GIS database. All decisio ns rega rding the development and maintenance of a given GIS database are
then the responsibility of the database owner. Other individual users of the GIS database. however. can perfo rm GIS processes. such as combining features, on copies of the original GIS database. However, once performed by someone other than the owner of the
field forester. If Paul were to edit the database by combining or splitting roads, the copy of the GIS database that Paul uses will not be considered the official roads GIS database of the organization. even if the database helps Paul make better management decisions. Can
Paul simply provide the edited roads GIS database to John? Certainl y. However, John will likely need to ensure that the changes Paul made conform to organizat iona l standa rds related to data maintenance, and
then subsequently verify that the database does not conta in any errors. If John can do these things. the data edited by Paul can be incorporated into the orig-
inal (and official) roads GIS database. 145
Chapter 8 Combining and Splitting landscape Features, and Merging GIS Databases
135
or greater. Using a query, we can identifY these stands, but to determine how large the contiguous area might
these stands together (such as those illustrated in Figure 8.3), co reduce the number of managemem units tracked
be would requ ire (a) either combining the queried Stands, o r (b) sum by hand the area of all adjacent
in a database. There is o ne imponam issue thar must
polygo ns meeting the size req uirement. The lance technique may lead [Q error, rhus co mb ining the queried polygons may be more appropri ate. A com-
bine or dissolve process (dependin g on the GIS software being used) would help faci litate this analysis.
be
kept in mind before combining landscape features: you should make a note of the attributes of each landscape feature before combini ng them. The combine GIS process, depending on the GIS software program being used. w ill either (a) comain the a((ribute data related co
o ne of the landscape featutes, (b) comain the attrib ute data related co the other landscape feature. (c) comain an average. o r some other stat istical summary . of numeric data associated with all com bined features. or (d) not contain any anribure data of either landscape feature. Users
Some balance must be struck between the appropriate number and size of landscape features being managed. and the amo unt of real-world general ization that occu rs w hen fewer features are used [0 represent a landscape.
should take heed of the options available when ru nning a
The decision to combine landscape fearu res should be
combine process so that the intended output res ults.
made after a serious contemplation of these issues. The
Additionally, in some GIS software programs the re may
following th ree examples describe the end-result of combining landscape features.
scape fearures, and the resulting combined landscape fea-
be more than one process that effectively combines landture may contain either no attribute data , o r the attrib ute
Contiguous, similar landscape features
data of the firSt landscape feature selected fo r combining
Suppose you queried the Brown Tract Stands GIS database fo r even-aged srands berween the ages of 40 and 45, using the fo llowing query: (La nd allocation = ' Eve n-Aged') and (Age ~ 40) and (Age S; 45) From this query we find that several stands touch each other, or are contiguous (F igu re 8.2). From a managemem perspective. you may decide to combine some of
c:::J c:::J
Forest boondary Even-aged stands between the ages
r ___...::Of..:,4o:,;and 45
Figure 8.2 Stands on the Brown Tract that au even-aged. and between the ages of 40 and 45.
Figure 8.3 Two similar-aged stands on the Brown T rac{ that share a common border. Both are even-aged. and between the ages of 40 and 45 .
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TABLE 8.1 Stand
Results of combining two stands
Ag.
Acres
Site
Trees per
Height
Board feet
index
hectare
(m)
per hectare
Both stands before using a 'combine features' process
First wmd ulut~d 75 7.5
3.0
44
100
250
23
15.325
&COlli stand u l«ud 88 9.9
4.0
45
11 7
492
28
39.388
Combined stand after using a 'combine features' process 0 0 0 0 0
0
0
0
Combined stand after using a 'union features' process 75 7.5 3.0 44 100
250
23
15.625
(Table 8. I). In either case. the attribute data of the combined landscape feature may need to be edited if the com-
2 . Split the stand into two separate parts, creating twO separate stands. Separate database records wou ld then
bined landscape feature will represent a weighted average of the conditions of (he original twO landscape featu res.
may decide that there is a sufficient amount of time
represent each stand. The owner of the G IS database and budget to comb through the database, locate
Multiple spatial representations within a single landscape feature or record At first glance, you might expect that each landscape fea-
inconsiste ncies such as this, and correct them.
Spl itting stand 283 into
twO
separate stands might be
considered a logical response; spli tting landscape fea(Ures will be discussed in more detail shordy.
ruee, such as a forest stand, would be considered a si ngle record in a GIS database. However, this is not necessarily true. Discontinuous landscape featu res can be combined
co produce a single landscape feature describing a portion of the landscape (Figure 8.4). Thus, another consideration in (he development and management of GIS databases is whether sparialJy disconrinuous landscape featu res
should be represented with one database record or with multiple records. In the case of nand 283 on the Brown Tract, for better or for worse, a single database record represents two areas (regions) . Perhaps prior to the development of the rock pi t that now separates these two areas, the stand was represented by a single contiguous polygon . T he Brown Tract databases are, as mentioned earlier, nOt perfect. However, they do allow an examination of
some rather typical problems users of GIS databases must consider, such as this one. There are three options related
to the GIS database management of stand 283: 1. Leave the stand as it is-represented by twO spatially discontinuous regions, yet a single database record. This may be consistent with the standards used by the managers of the Brown Tract. This option would require no additional effort co manage the GIS database.
Figure 8.4 Two polygons (regions) represen le
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Chapter 8 Combining and Splitting Landscape Features, and Merging GIS Databases
3. Combine the small discontinuous piece of the stand into another adjacent stand. Combining the small portion of stand 283 to another scand thar is adjacent to [he small pardon would require chat (he adjacent stand have similar characteristics (age. volume. density, ere.) approp riate for the management of (he potential combined area.
137
how to use snapping tools correctly. Practicing on a test
GIS layet before editing an actual database can help reduce snapping errors.
A discussion of buffering point, line, and polygon features was provided in chapter 7. The polygons that are created as a result of a buffer process can, perhaps. over-
lap. Usually you have the choice, at the time of buffering, of maintaining the overlapping areas or directing the GIS
Overlapping polygons
software program to remove them (Figure 8.6) . In the case where the overlapping areas of the buffer remain ,
Although it may not be your intent to create landscape fearures that overlap when developing or maintaining a
these polygons can subsequently be combined, removing the overlap and reducing the number of polygons and
GIS database, overlapping features may resulc as [he OU[pm of a GIS process. There are numerous reasons why you may find overlapping features in a GIS databasei edit,
database records that describe the buffer.
buffer, and merge processes can all lead to the development of overlapping landscape feacures, especially with
gons . When a merge process is used, the overlapping areas
GIS software that does nOt enforce ropological rules.
meaning that no new nodes are created at the intersection
When editing polygon feacures within GIS databases, for
of lines, and that overlapping polygon areas are not removed. Quite simply, when GIS databases are merged,
instance. you can easily affect (he shape of polygons such
Merging polygon GIS databases, as will be described later in this chapter, can also result in overlapping polyamong polygons and lines ate genetally not affected,
that they either overlap or not touch at all (Figure 8.5). When editing GIS databases. it is wise co understand the process available within GIS sofrware programs to 'snap' the verrices of one landscape feature to those of another. With the abiIicy [Q snap vertices together. you can edit the
shape or position of polygons and allow a precise match of the boundary of one to that of another. The challenge for most GIS users is to remember to acriva.te the snapping ability. It also requires practice. once activated, to learn Overlapping areas of the buffer are removed
Gap
Overlap
Overlapping areas of the txJffer remain
Figure 8.5 boundarj~s.
Ov~rlap
and gap
r~maining aft~r ~diting polygon
Figure 8.6 Th~ results of twO buff~ring operations, on~ wh~r~ th~ oY~rlapping ar~as of th~ buff~r around ~ach str~am ar~ r~moy~d, and th~ oth~r wh~r~ th~ ov~rlapping areas r~majn.
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How would you know whether a polygon GIS database contains overlapping polygons' One method might be to examine very closely the boundaries of each polygon-a tedious process. Another method may be co compare the sum of the area of polygons in
the suspect GIS database with another polygon GIS database of the exact same spadal extent. For example. if you were concerned about overlapping polygons in the Daniel Pickett forest stands GIS database, you could compare the sum of the area of the stands to [he
area of the boundary GIS database. The extent of the two databases should be exacdy the same: the stands
one set of landscape features is simp ly laid on top of another sec.
GIS database should contain polygons that are a subdivision of the ownership defined by the ownership boundary. The stands GIS database has many polygo ns, and the boundary GIS database has only one polygon, yet the sum of the areas should match. You could also choose to combine all polygons within a copy of the stands layer. The area of the resulting combined stands database should also match. If the sum of the areas does not match. eith er one or more overlapping polygons exist in the stands GIS database, or some
gaps exISts between polygons in the stands GIS database.
gons should probably be broken down (split) into smaller management unitS to enable land managers ro plan har-
vests mote app ropriately (Figure 8.7). In terms of linear
Splitting Landscape Features The decision
(0
from some need
use a splitting process general ly arises {Q
redefine the topology of spatial data.
3. l60-acre (64.8 ha) polygon and a stream that will be used to split it Into two smaller polygons
L
The word 'split' is defined as a process ro divide or sepa~ rate an item into pares or ponions (Merriam-Webster,
-
2007) . Within GIS, we use a splitting process to divide polygons and lines (but not points) into smaller pam or portions. Points do not describe areas; however, buffers
around points can be spli t because they are polygons.
To illuscrate several reasons for using splining processes. cons ider the su b-dividing of land ownersh ips that regularly occurs in many rural and urban areas throughout Nonh America. Many property owners subdivide their lands in order to earn revenue or bequeath property to land stewardship organizations or heirs. Land ownership records and GIS database are kept by county. provincial. or metropolitan organizations and mUSt be updated to accura tely represent new parcels that result from subdivisions. Spliuing processes are used to separate parcels from one another and lead to the creation of addi-
•
Node of line defining the polygon
~ Stream line used to split ttle polygon
/
_ _ Polygon to be split
/ b. Resulting two smaller focest management untts after splitting ttle 160·acre (64.8 hal polygon
Polygon 1
tional polygons in a GIS property boundary database. As another example involving polygons. imagine a state or province where cleareut size limits are imposed. For organizations that plan c1earcur activities in these regions. the forest management units represented in their stands
GIS database should probably be smaller in size than the maximum clearcut area allowed. Therefore. these poly-
Polygon 2
Figure 8.7 A 160-acre (64.8 ha) polygon split along a stream. forming two smaller forest mamlgement units.
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Chapter 8 Combining and Splitting landscape Features. and Merging GIS Databases
139
fea[l1res, assume mar you are involved with [he planning
streams may need to be split to be[(et delineate the capac-
and maimenance of a road system for a namral resource management organization . Over some period of time, the scams of a porrion of a woods road may have changed. perhaps from a rocked surface [0 a paved surface (or alternatively, parr of the road was obliterated in a restoration
ity to suppOrt (or not support) fish populations. The way you go abour splitting polygons or lines (yet
process). Within the roads GIS database. splitting the line that represents the road at the location of the stams change would seem appropriate, since each resulting piece of the road should be described by different attribute data. The same argument for a sp!iHing process can be used for stream data managemenr. In this case, assume a recent stream survey identified some differences becween (he GIS data and the acruai stream system. Some stream reaches, fo r example. may have been found [Q be ab le [Q support fish populations. yet the GIS data indicates otherwise. In these cases. the various lines that represent
nor points) varies according to the GIS software program being used. In some GIS software programs. splitting is as
easy as drawing a line through a landscape fearure (Figu re 8.7). This holds true for polygons or lines that are solid or continuous. However. where a landscape fearure is repre-
sented by multiple regions (or objects). and a gap separates these regions (such as that illustrated in Figure 8.4). drawing a line through the gap. without touch in g either
of the pieces of the landscape feature . may not result in (he fearure being spli t inro twO separate pieces. In these cases. a more com plex process using combining. clipping. editing. or pasting ptocesses may be more app licable
(Figure 8.8). Alrernativeiy. some GIS software programs have processes for convening multipart features to single
Stands GIS database
Draw a polygon around one of the nl.lltiple representations
Select the Ofiginal po/ygoo
Save the original polygon In a GIS database
Clip
One portion of the original polygon
Erase Stands GIS database
Remove original polygon from the stands GIS database
portion of the original polygon
Copy into the stands GIS database the two portions of the original stand
Figure 8.8 A complex process that can be used to split a polygon, initially represented by regions, into twO separate landscape features.
twO
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Part 2 Applying Applying GIS to Natural Resource Management
pieces. These types rypes of automated processes make the rhe [ask task of separaring separadng non-adjacent pieces of spada! spacial data more efficient.
Merging GIS Databases Merging. for rhe the purpose of managing GIS databases. is defined as the rhe process of combining multiple GIS GIS databases inco Therefore, refo re, when you use a into a single database. The merge process, a new GIS database darabase is created from a set of two or more previously developed GIS databases. Point. polygo n databases can be merged together. roge
merge process for this ou r co ncern is not placed u,is purpose, our concern on ,he the likel likelyy overlapping overl a pping polygons in lhe the resul'ing resulring m erged GIS merged GIS database. da tabase. bur on making makin g the ,he analytical a nalyt ical th at process more efficient. With a merged GIS database ,ha, represents restricted areas, a single erasing process ca cann be lIsed used to arrive at the unrestricted areas (full property Without developing developi ng restricted areas ~= unrestricted areas) . Witham database. three rh,ee erasing processes would the merged GIS da",base. (1) full fu ll property - stream srream buffers = have been needed: (I) ,emporary temporaty da,abase database I; (2) ,emporary temporary database da",base I - endangered species habirar habitat = temporary da",base database 2; (3) ,empetem podacabase 2 - research areas = unrestricred unrestricted areas. rary database faci litares a In the second seco nd case, where a merge process facilitates mapping process, ou r co concern ncern is not placed o n the likely overlapping polygons in rhe datathe resul'ing resulting merged GIS dararhe message that we communicate commu ni ca te base, but rather on the printed map. Us Usiing ng the previa LIS liS example, exa mple, the with the primed custo mers message we want to communicate to the map customers would ' Here rest ricted and unrestricted areas.' wouJd be 'He re are the restricted databases together rogether and creating crea,ing a By merging mergin g seve ral GIS da,abases GIS database d atabase of a common th eme (,restricted ('res tr icted single GIS areas'), you could make m ake the carrographic canograph ic process mo more re an needing efficient. For example, rather th than need ing to specify the rhe rhe three rhree GIS da,abases color scheme for the databases [hat that ,epresent represent endangered ngered species the restricted areas (stream buffers, enda habi,a, hab itat buffers. and research resea rch areas). areas), ,he the color scheme schem e of a si ngle GIS dalabase database (rhe (th e merged database rep representing resenting ,he the single resrric,ed tricted areas) needs oonly nly to ro be specified. In addition. add ition, res deskcop GIS software programs now contain automany desktop mated fu ncri nctions ons co to assist users in developing thei r maps. Withour rhe restricted areas a reas into inca a single G GIS IS W ithout merging the database, we may find severa) seve ral GIS G IS databases da tabases listed in the map 's legend-each representing portions of the map's rhe restricted area. In this rhis case, management of the rhe map's map 's legprint it and present il it to end may be required before we prinr oour ur Cllstomers. customers.
Determining how much land area is unrestricted de monstrate using a merge process, the following To demonstrate example d iscussion of developing a exa mple will expand on the discussion the conrepresentation of unrestricted areas by applying [he cept to the D aniel Pickert Pickett forest. fo rest. Knowing what the opercep' ro ,he Daniel wha, ,he able 'decision space' is on the forest may be important when decisions regarding regard in g harvesting, herbicide operaope ramade, As with many of the tasks pre(ions, etc., must be made. tions, several pathways you ca n take rake sented in this text, there are severa) within GIS '0 to comple,e complete an analysis of ,his th is type. The focus wirhin GIS 151
Chapter 8 Combining and Splitting Landscape Features, and Merging GIS Databases
Streams GIS database
Roads GIS database
Owl nest localion GIS database
Buffer 100 feel
Buffered streams GIS database
Buffer 100 feet
Buffered roads GIS database
Buffer tOD~ feel
Owl nest location GIS database
141
Merge
Merged GIS dalabase
Stands GIS database
Figur~
Unrestricted areas GIS database
Erase
8.9 A process that an ~ used to delineate unreuricted areas in a forested landscape.
here will be on developing several GIS databases that
870.9 hectares (2, I 52 acres) are thus unrestricted. The
describe
unrestricted areas, given the crireria noced above, are open to (he full suite of management activities appropriate for
me types of restricted areas,
then on merging
them together as a single GIS database. The polygons contained in the restricted area GIS database can then be erased from (he boundary or stand GIS databases, leaving unrestricted areas as a resulr. The process can be described
with a flow chart (Figure 8.9) to help visualize the steps necessary [0 complete the task. In the delineation of unresrricccd areas of the Daniel Pickett forest, some criteria area needed to describe (he restricted areas:
the forest types and landscape conditions of the Daniel Pickett forest.
D
D
Unrestricted areas Restricted areas
• 30.48 m (J 00 feet) around all stroams, • 30.48 m (J 00 feet) around all paved roads, and • 304.79 m (J ,000 feet) around tho owl nest locations. Using the process illustrated in Figure 8.9, you will find that 140.8 hectares (348 acres) of tho Danid Pickett are resrricted in some form or fashion (Figure 8.10), and
Figure 8.10 A description of the restricted and unrestricted areas on the Daniel Pickett forest.
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Part 2 Applying GIS to Natural Resource Management
Summary The need to combine and splir la ndscape fearu res and the need to merge GIS darabases together are influenced by th e progression of GIS processes you choose in orde r ro address a management issue. If there is a need co eliminate small landscape features c reated by some o th er GIS process (e.g., clipping, erasing), co mbining rhese wirh othe r landscape fearu ces would seem approp riate. If there is a need (Q combine multiple feacures within a single GIS darabase (regardless of rheir size) to fac ilirare further analysis. combining them wo uld again seem prudent. If rhere is a need ro physically separare pieces of a
polygon or line, rhen a splirring process would be warranted. If there was a need ro combine features conrained in separate GIS databases. a merge process would be appropriate. The decis ion to combine or spli t landscape feacuces. o r [0 merge GIS databases must not be made lighrly. While rhere may be a vari ery oflogical reasons for us ing these GIS processes, there may be anocher reaso n (such as the contem of the resuhing anribuce table) that suggests an alternative process should be used. Documeming the workAow when using these processes is therefore important.
Applications 8.1. Characterizing unrestricted areas. The Region Manager associated with the Brown Tract, Becky Blaylock. is very interested in the managemenr oppOrtunities associated with this land, given its proximity ro an a rea of suburban growth. Since she knows you know so mething about GIS, she has again come to you for some information. Specifically, she is interested in understanding the extent of the forest resources that are outside of areas where fo rest ma nagement is restricted for one or more reasons (either by regulation or by an organizational policy). She defines rhe zones where fo rest management is restricted as: • Areas wirhin 152.4 m (500 feer) of a urhor ized trails; • Areas wirh in 152.4 m (500 feer) of homes; • Areas wirhin 2.4 km (1 .5 miles) of owl nest locations; • Areas within the riparian zones: - 30.5 m (100 feer) around large fish-bear ing streams, 2l.3 m (70 feer) around medium fish-bea ring streams, 15 .2 m (50 feer) around small fish-bearing streams, - 21.3 m (70 feer) around large non-fIsh-bearing streams, - 15.2 m (50 feer) aro und medium non-fishbearing streams, and 6.1 m (20 feer) around small non-fIsh-bearing streams; and • Stands with the following land allocations: Meadow, Oak Woodland, Research, and Rock pir.
a) How much area ofland is umestricted? b) How much area of unrestricted land is included in rhe following land allocarions' a. Even-aged b. Shelrerwood c. Uneven-aged c) Develop a map of rhe Brown T racr, illustraring rhe unrestricted areas. Include on the map the road and stream systems. 8.2. GIS processing. H ow would you have add ressed problem 8.1 if you incorporared a process rhar merged the GIS databases that represented unrestricted areas? H ow would you have add ressed problem 8 .1 if you incorporated a process that co mbined all landscape features representing unrestricted areas into a single polygon? Provide a Aow chart for each alternative process. 8.3. Combining landscape features . Your co-worker, Ka rl Douglas, has suggeSted during one of your momh ly inventory meetings that all management un its in the stands GIS database smaller than four hectares should be co mbined with another adjacenr managemenr unie. His a rgum ent is that [his will make the process of managing the forest more efficient. Besides the faCt thar some small polygons may represent sign ifica nt landsca pe fearures (rock p its, wildlife habirar, erc.), and rhus should be disrincrly represenred in a GIS darabase, whar argument might you provide against this potential change in GIS database management policy. particularly from th e GIS processing and invento ry management perspectives? 153
Chapter 8 Combining and Splitting Landscape Features, and Merging GIS Databases
References Merriam-Websrer. (2007). Mtrriam-W,bsur on/in, search. Retrieved April 28, 2007, from http ://www. m-w .coml cgi-binl dic[ionary.
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Chapter 9
Associating Spatial and Non-spatial Databases Objectives This chapter provides an inrrodu ction to techniques that will allow yo u to associate fea mres in spati ally- referenced GIS databases with data from other sources (other types of databases) which may not have a n explicir spacial refere nce. Once this c ha pter has been completed. readers should have a firm underscanciing of: l. how (Wo or more databases ca n be remporarily com-
bined withom creating a new database. modifying a database rabie, or modifying landscape feacuresi 2. what types of GIS processes are available when there is a need to associate data from different sources; 3 . how non-spacial data can be associated with spatial databases. and how data from one spa tial database can be associated with data of a nother spatial database; and 4 . what it means to relate (link) two tables, and how thi s process is different than joining darabases. In the last few cha pters a concentra rion was placed on taking (WO (or mo re ) G IS databases an d . with a process such as erasi ng o r clipping, creating a third (new) GIS database that featured differenr. o r modified, landsca pe features. The GIS databases were, in essence, combined in a permanent fashio n in the new database. In th is chaprer the databases will be combi ned in a way that is temporary bur that still retains each database's o ri ginal structure (both the spa tial structure and attribute table
structu re) . Si nce new landsca pe features are nOt being created in th is process. the re is no need to create a new GIS data base; howeve r, this is also possible and we will discuss this process later in the chapter. To present additional methods of combini ng databases, two processes of associa tion will be introduced: the jo in and the rel ate (link) processes. The first section of this cha pter examines joining data in non-spacial databases with landscape features in spati al d atabases. and desc ribes several of the common join processes yo u might encounter. The relationship between the non-spatial data and rhe landscape fea tures is based on a common attribute value found in both da rabases. The second section of the chapter examines joi ning landscape features from [wo differen t GIS databases. and how the relationship between the landscape features in each GIS database is a fun ction of the location and type (poi nt. line, or polygon) of the features on the landscape. The third sectio n of the chapter discusses how you can make the temporary joined assoc iations among databases permanenr. The final section of the chapter exam ines relaring {linking} features or data from one database to that of another. With a relate process. you can view one of th e related databases. selecr a landscape featu re. then view the associated related data in the ocher database even rhough the database will not appea r to be physically associated. Join processes do not follow this ap proach. but. instead, result in a single database that contains dara from borh joined databases. 155
Chapter 9 Associating Spatial and Non-spatial Databases
145
Joining Non-spatial Databases with GIS Databases When you wane [Q join twO databases together the objective is to associate both databases such chat a single database resul rs. Therefore, dara from one darabase (in rhis case a non-spatial database) is transferred to rhe 3ncibucc rable of rhe other darabase (rhe sparial darabase) . There are several types of possible associarions when joining non-spa ri al darabases wirh spa rial GI S darabases. The [wo most common join associations are one-co-one and oneto-many joins. A non-spatial database is one chat lacks associated landscape feacures and their geographic reference. Perhaps the simplest example is a text file chat you can create in a word processor or text editor (Table 9. I). You mighr logically ask why, if a GIS darabase of landsca pe feacures exists, would you store ocher data associated with those features in a separate. non-spacial database? Perhaps there are instances where it is moce efficient for analyses (o r foresters. biologises. etc.) to develop data (such as wildlife hab irar su irabi liry scores) separare from GIS, knowing m at a process exists [Q quickly associate the dara developed back ro rhe appropriare landscape fearures. Perhaps an an alyst may wam a separate tabular da[3base that they can then import into a statistical software program. In addition, there are othe r software packages. such as hydrologic simularo rs, growrh and yield models, and landscape analysis models. that can take GIS output. process the output so that additional information is added, and feed rhe new resulrs back imo a GIS . There are other reasons as well, including the comfort some people
TABLE 9,1
A non.spatial database in AScn text file format illustrating comma-dellmited data
·S r.md ', 'HS12010', 'HSI 20 1;', 'HSI 2020' 1,0.2%, 0.3 12,0.325 2.0.4;8, 0.49; , 0.516 3,0.333, 0.36;, 0.372 4,0 .87;, 0.88;, 0.889 ;,0. 12;, 0.215 ,0.23; 6, 0.468, 0.476, 0.48; 7,0.906,0.908,0.9 11 8,0.648,0.74;,0.7;3 9, 0.378, 0.42;, 0.431 10,0.096,0.102,0.118
In performing join processes, three terms are imporram: rhe ,ollrce tabu, the targ't or tk,tination tabu, and rhe join it<m (or field) . The source rable comains rhe dara rhar will be moved ro the rarger rable and associated with some particula r landscape fearu res srored rhere. The rarger rable comains rhe landscape fearures with which the source tab le's data will be associated. After the join process is complete, the source table's data will be transferred [Q the target rable so rhar when you view rhe rarger rabie, all amibures from boch darabases should be presem. The join item is the attribure or field that is common berween the source and ta rget rabies, and is the irem thar bri ngs rhe [wo rab ies rogether. If no common amibute ex.isrs berween the so urce and target tables. there is no basis fo r a join. Some examples of common attributes are stand numbers, road names, stream numbers, culvert numbers, and watershed names, yet any ami bu re specified by a GIS analysr can be used.
have with performing calculations in a spreadsheer rather than in GIS. As you will see with one-to-many joins. joining is an efficient way of associating (temporarily) nonspa rial dara wirh landscape feawres.
One-to-one join processes A one-to-one join process assumes mat mere is exactly the same number of records in the source cable as there is in the target table, and that each of the records in the source table is associated with exactly one record in me target rable. For example, suppose you have a G IS darabase of permanent growth and yield measurement plots (Figure 9.1), and you want to join to this database to a file containing the installation dates of each plot. The join item in this simple example is obvio usly ' Plot' in the source table and the ' Plot' attribute in the target table. A5 you can see, the re are exacdy six records in both the source and target tables. and each installation date record from the source table is associated with only one unique record in the GIS database containing the permanem plo[s. The original assumption behind one-to-one join processes can be relaxed. and [he one- to-one join process can also be made with fewer records in the source [able [han in the target [able. For example, assume thar record 156
146
Part 2 Applying GIS to Natural Resource Management
Database
Table structure
Database
Table structure
Soon;e
Soon;e
Plot , Installation dale
Comma-delimited text file containing plot number and Installation date
r-
t---
Plot, Installation date .---- 1, 1998 2,1997 3,1999 4, 1998 5, 2000
Comma·delimited text file containing plot number and installation date
1 1998
2. 1997 3, 1999 4, 1998 5,2000 6, 1999
Target (Destination)
GIS Database representing permanent
L...-.
plots
Plol 1 2 3
Target (Destination)
Vegetation Type
OF
WH
4
OF OF
5 6
GIS Oatabase representing permanent plots
L..,.
Figure 9.1
Vegetation Type
1
OF
2 3
WH
4
OF OF
WH
5
WH
OF
6
OF
Joined database Resulting database : The original permanent plot GIS database with the temporary field Mlnstallation date"
Plol
Joined database
Plot
Vegetation Type
Installation date
1
OF
2
WH
1998 1997
3 4
OF OF
1999
5
WH
2000
6
OF
1999
1998
Performing a one-to-one join usi ng a fi le of installation
dates as the source table, and the Danid Pickett permanent piau GIS
Resulting database : The original permanent plOt GIS database with the terTij)Orary field -Installation date-
Plol
Vegetation Type
Installation dale
1
OF
1998
2 3
WH
1997 1999
4
OF OF
5
WH
2000
6
OF
1998
Figure 9.2 Performing a one-to-one join with one r«ord missing from the source table.
database as the target table.
6 (Plor 6, insrallarion year 1999) was missing from the source rable. If YOli were to perform a join process using the source and target rabies. a one-to-one join wou ld still occur, yet Plot 6 in the GIS database would not be joined with any data from the sou tee tab le (Figu re 9.2). To work through a one~to~one join. assume you have an ASCII text file (HSl.rxt) containing habitat suitability ind ices (HSls) for salamanders related to every dmber stand on the Daniel Pickett forest. HSls range from 0.0 (poo r habitat) to 1.0 (optimal habitat) and can be quite complex ro calculate; therefore. it is nO[ unreasonable ro assume that they were generared outside of GIS. perhaps in a sp readsheet or stacisdcal sofrware package. The challenge. once HSI values have been calculated. is [0 bring them back into a GIS environment [0 allow the creari on of a thematic map. To perform a join process of the nonspatial HSI database (HSl.rxt) and a GIS database (Daniel Pickett stands) the following general steps can be taken when using ArcG IS 9.x:
I. Open the Daniel Pickett stands GIS database (t his is the target table). 2. In the table of contentS, right-click the target table (stands GIS database). seiect 'joins and relates', then seiect [he join option. 3. IdentifY the join item from the target table in option 1. 4. Choose the sou rce table (HSl.rxt) for option 2. 5. IdentifY the join item from the source table in option 3. G. Perform the join process (press OK). In ArcYiew 3.x these general steps can be taken to per~ form the join: I. Open the Daniel Pickett stands GIS database. 2. Open the Daniel Pickett stands attribute table (this is the target table) . 3. Open the HSl.rxt database (th is is the source tab le) . 4. I n the source table. use the mouse to seieer the join item (stand). 157
Chapter 9 AssoCiating Spatial and Non-spatial Databases
5. In the target table, use the mouse item (stand) . 6. Click the table join button.
[Q
select the join
Once the one-to-one join process is complete, the HSI values from the HSI.txt database should be temporarily srored inside of the stands GIS database attribute table. To confirm this, you muSt open and visuaUy examine the stands GIS database attribute table. A thematic map can be created (Figure 9.3) using the HSI values, to illustrate the spatial arrangement of habitat on the Daniel Pickett forest. Areas with in each habitat grouping can also be calculated to enable the development of a report concerning the amount of suitable habitat on the forest for a particular wi ldlife species.
t47
process, the likelihood of errors is minimized, and the process of associaring different buffer width data with each stream is fitst and efficient. For example, Figure 9.4 illustrates a one-to-many join process where the source table has four records and the targer table has seven records. The join item is 'Stream type' in the source table, and the 'Type' attribure in the target table. Two of the source table records (,Perennial - large' and 'lnterminenr') are associated with more than one record in the ta rget table (hence 'many' records). The sou rce table could have included four (or more, or less) items and me target table could have included 1,000 or more items-it makes no difference to the one-to-many join process. However. similar to me oneto-one join processes, should a record be omitted from the source table, the affecred records in the target table would be represented by null or missing values in the joined field .
One-to-many joins Many-to-one (or many-to-many) joins In COntrast to one-to-one join processes, the assumption behind one-to-many join processes is that there are more records in the target table than in me source table, and that each record in the source table may be associated with more than one record in the target table. Assume that you have a streams GIS database that contains 1,000 stream reaches. Assume also that you desi re to buffer the Streams to create a map of diffe rent riparian management zone policies, and that each stream needs to be buffered a disrance that is based on its size. If you were interested in developing variable-width buffers
In certain cases, the source table can contain errors a nd lead to results that are different than what was originally Database
Comma·delimited texl file containing stream type and buffer distance
Table structure
Stream Type, Buffer
· Perennlal-large", loo -Perennial-small". 75
"Intermittent", 50 "Ephemeral ",25 Target (Destination)
GIS Oatabase representing permanent
plots
Stream
Type
1
Perennial-large
2
Intermittent
3
4
Perennial-large
5 6 7
Intermittent Ephemeral Intermittent
Habitat SUitability Index (HSI) value
Joined database
CJ 0.000-0.200 CJ 0.201 - 0.400 CJ 0.401-0.600 c:::::El!I 0.601 - 0.800 _
Fi.gure 9.3 Habitat Suitability Index (HSI) valu~s for the Daniel Pick~tt forest.
0.80H.000
salamand~rs
on
Resulting database: The original
streams GIS database with the temporary field "Buffer"
Type
Stream 1
Perennial
2 3
Perennial
5
Perennial Intermittent
6
Ephemeral
7
Intermittent
•
large
Intermittent small large
Buffer 100
50 75 100
50 25 50
Figure 9.4 P~rformjng a one-to-many join wing a fiI~ of buff~r distances as th~ source tabl~, and streams GIS databas~ as th~ targ~t tabl~.
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Part 2 Applying GIS to Natural Resource Management
intended. These are cases where twO o r more records in the source table (and one or more instances of such) are associated with a single reco rd in rhe ta rget rable. These can result in a many-tO-o ne join. For examp le. the manyto-one join process illustrated in Figure 9.5 shows that rhe fourth and fifth records of the source cable have exacuy the same the join item value (,Stream type' = 'Ephemeral') .
Oatabase
Table structure
Soun:. Comma-delimited text file containing stream type and buffer distance
Stream Type, Buller "Perennial-large", l00 "Pereooial-small ", 75 "Intermittent ",50 "Ephemeral", 25 "Perennial- large", 125
When rhe join process is performed only one 'Buffer' value from the cwo source table reco rds can be associated with the ephemeral stream in the target table (the first value in ArcGlS 9.x, the last value in ArcView 3.x). The example in Figure 9.5 shows that the first value (25) was present in the joined database, not the last value (35).
Target (Destination) Stream GIS Database representing permanent plots
Which value is presem in the target table (the first instance or subsequent instances in rhe source table) depends on the GIS software program being used. Many-to- many join processes behave in a similar fashion (Figure 9.6). The following two examples bring together some concepts that wefe introduced in (his and earl ier chapters. In each exa mple. a non-spatial database is joined co a GIS
Database
Table structure
Sou",. Comma-delimited text file containing stream type and buller distance
Stream Type, Buller "Perennial-large", l00 "Perennial-small ", 75 "Intermittent", 50
-====::::::::-
Type Perennial-large
2 3
Intermittent Perennial-small
4
Perennial-large
5
6
Intermittent Ephemeral
7
Intermittent
Joined database
Resulting database : The original streams GIS database with the temporary field "Buller"
Stream 1
2 3
Tyll" Perennial
Butler
large
5
Intennlttent Perennial small Perennial large Intermittent
6 7
Intermittent
4
Ephemeral
125 50 75 125 50 35 50
Figure 9.6 Performing a many- to-many join using a Ale ofbufTer distances as me source table and a streams GIS database as the target table.
"Ephemeral ",25 "Ephemeral ", 35 -
database. Then, a spatial query is performed to determine which of the point fearures is contained within an area represented by a polygon feature. Ultimately, some info rmarion that was originally contained in [he non-spada I database is summa ri zed based o n its assoc iated spatial locat ion.
Target (Destination)
GIS Oatabase representing permanent plots
Stream 1 2 3 4
5 6 7
Type Perennial-large Intermiltent Perennial-small Perennial-large Intermittent Ephemeral Intermittent
Example 1: Determining the number of hardwood sawmills in a state
Joined database Resulting database : The original streams GIS database with the temporary field "Buffer"
Stream 1
2 3 4
5 6
7
Tyll"
Buffer
Perennial large Intermittent Perennial .maD Perennial large Intermittent Ephemeral Intermittent
100 50 75 100 50 25 50
Figure 9.S Performing a ma ny-to-one join using a file of buffer distances as the source table, and a streams GIS database as the target table.
Assume (hat you currently work for Dunn and Herndon , Inc. in cent ra] Tennessee, and are considering building a new hardwood sawmill somewhere in (he state. Initia lly, what you might find helpful is an estimate of the number of hardwood sawmills in the state of Tennessee. Another piece of information tha t would be valuable is an estimate of the number of people that they employ. To develop rhis info rmation we will use a CIS database of the southeastern US states, a GIS database of mill locations, and a non-spa rial database of mill attributes. Mill locations and their associated non-spatial attributes were acquired from 159
Chapter 9 Associating Spatial and Non-spatial Databases
149
(Figure 9.7) . As a result, you might find that there are 329 hardwood sawm ills in the State of Tennessee that employ 3,959 people, assuming that the data are current.
rhe USDA Foresr Service (2006). The process includes rhe fo llowing sreps: 1. Jo in the non-spatial mill attribute data to the GIS database of southeastern US mills. The non-sparial database comains a field called 'MILL_lD'. This is rhe join item from rhe sou rce rable. The mill GIS database contains a field called 'MILL-lD' . This is the join irem from rhe rarger table. Once the two tables are joined, the spatial features (poims) in the mills GIS database will have associated wirh them rhe attributes of mills
Example 2: Determining sawmill employment in a county As a second, similar example. assume mat you are a consultant based in Mississippi. working for Saunders Geomacics, LLC. Assume also that you are doing some CQncract work for the Nuxubee County Chamber of Commerce. They want ( 0 know how many sawmills are in the county, and chey want an estimate of how many
from [he non-spatial database.
2. Query rhe southeasrern US srates GIS database for the scate of Tennessee. This is a query that simply uses a field (srate) of the southeastern US "ares GIS darabase to locate the appropriate actcibuce of the scares (State = Tennessee). 3. Perform a spatial query of rhe mills GIS database to determine (by location) which mills are completely
people rhese mills employ. To develop rhis information
me
we will use a GIS database of southeastern US counfies, a GIS database of mill locations, and a non-spacial database of mill attributes. Once again . milliocacio ns and thei r associated non-spacial attributes were acqui red from
the USDA Forest Service (2006). The process includes the following steps:
contained within the Scate of Tennessee (the current
selected feature in the southeastern US states GIS dacabase).
1. Join rhe non-spatial mill attribure data
(0
the G IS data-
base of southeastern US mills. as was described in
Once these steps have been performed, we will have
example 1 above. 2. Query the southeastern US counties GIS database for
selected those mills mac are within the Scare of Tennessee. The question now is whether they mainly accept hard-
simply uses a field (county) of the sourheastern US
Nuxubee County, Mississippi . This is a query that
me appropriate attrib-
wood [fee species, and whether they are cons idered a sawmill. A final query using the attribures of the mills GIS
counties C IS database
database is therefore needed
be careful here if there are muldple counties in the southeastern scares with me same name {e.g .. 'Floyd' is (he name of a county in more than one s[a[e).
[0
[0
locate
ute of rhe co unties (County = Nuxubee). You need to
seiect from (he currently
selected features (all mills in Tennessee) those that are sawmills. and mainly accept hardwood reee species
KY
.
.. . .
. ,
'
. .. '
.: ..
.
..
'
.
.. ".
MS
:
Al
Figun 9.7 Hardwood sawmills in Tennessee.
160
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Part 2 Applying GIS to Nalural Natural Resource Management
3. Perform a spacial spatial query of the [he mills GIS GIS database dalabase [Q to determine de<ermine (by location) locarion) which mills are completely contained within Nuxubee County (the current selecred selected feature in the me southeastern SQUmeasrern US counties coumies GIS database). Once these Steps steps have been performed, we will have selected those mills that are within rhe (he county. The question now is whether they are sawmills. An examination of the mills tab table le finds finds that there are three sawmills in [he the county (al[hough (although only two are seemingly visib visible le on the an d that they employ 23 231I people, assuming assum ing that map), and the database is currenL currene.
Joining Two Spatial GIS Databases With a spatial join process. our intent imenr is to learn aboul abour the qualities of some landscape features that are near other landscape features featu res of imeresr. interest. In joining non-spadaJ non-spacial dataro spatial databases, da[abases, a common field (the ([he join item) bases [Q was used [Q associate the data data in the non-spatial database [Q the landscape features in the to [he rhe GIS database. The associalion ation itself was non-spatia non-spatial-the I-the location locatio n of landscape features GIS database (the target table) fearures in the [he GIS rable) was not used fa spadallocation of to assist in making the association association.. The spatia!locarion landscape fearures features can, however. however, be used (Q associate landscape sca pe fearures features from one database with those in anothe anotherr database. This is a powerful feature feam re that often goes unused by GIS users. users. Both databases, however. however, must mUSt co conncain in terested in tain spatial data. For example. example, if you were interested knowing the rype torest stands that sources of water type of forest (ponds, sp rin rings, fan within, withi n, you might join a GIS GIS gs, etc.) fall database water data base containing a set of points (representing wa ter data base containing a set se t of polygons sources) with a GIS database (dm ber stands) [0 to understand the type of forest that su surr(timber rounds each water wa ter source. source. Thus rhe the anribute attribute data within associated iated to the arnibarr ribthe timber timber stand database can be assoc me ute dara data wirhin within the water sources database based on the sparial spatial locarion location of the landscape fea features rures within each GIS darabase. database. so me softwa re packages. such as ArcGIS ArcelS 9.x, a In some spatial join results in the creation of a new layer that contains the joined info rmation . Check C heck you r GIS GIS softwa re CO whetherr new databases are 3re created ware to determine whethe during spatial spatia l joins. You should a150 also be aware that sparequire tial joins will usually requi re that a map projection be associated w with layers . A key component co mponent of ith all invo lved layers. every map projection is the definition of a map unit that
describes the coord coordina in ate te division intervals. inrerva'!s, typically feer. international feet, fec t, or meters. Spatial Spatia l expressed in feet, joi ns are based on the comparison of feamre feature (point. (poin t, line, joins or polygon) locatio locadons ns to other feature feamre locations. locarions. The There re that inherently recognize the are many GIS processes thar coordinate system of a layer without needing to know the real-world definition of the map un unit. [he it. Without the map unit n. however. spatial joins will usually unir definitio definition, however, sparial nO notI be possible. non-spatial join processes, a sou rce database As with non-s patial jo in processes. source an and d rarget target database are required. required, yet the join item wi willll tly different-it d ifferem-it is the spatial position of each be sligh slightly GIS da database. most landscape feature in each GIS tabase. The [wo twO mosr common types of that are com mon rypes o f spatial spat ial join processes are those fhar defined by which features are closest (sometimes called th thee neareSt nearest neighbor) and those [hose thar that are evaluated by whether a feature is intersecred inte rsected by another another feature or is completely inside ano another lher feature. feature . A th third ird type of spatial join invo involves lves linear features and is evaluated by determining whether one linear feature feawre is located along or inside the extent of another linear feature feature.. The outpur properties an and d options of the spatial join process will depend upon Ihe the fearure feature type (point, (poim, line, or polygon) of th thee source and targe targett databases and are a re summarized in Table 9.2 . The nea resr neighbo neighborr spatial join works fo nearest forr almost all feature feamre type comparisons excepr except for line li ne on line. Other neighborr join process than this exception, except ion . the nearest neighbo allows the characrerisrics (poin rs. lines, lines. or polycharacteristics of features (points, gons) in a source table to be associa ted with the closest associated use spatial feature in a destination table. Thus you can usc ro not oonly nly iden idemifY tify the rhe nearest ne-dtest point (e.g. join processes to uee), line (e.g. road) oorr polygon (e.g. warershed) tree), watershed) fearure feature GIS database, but from all point (e.g., house) features in a GIS you can ca n also determ determine ine [he the distance to the neareSt point feature feature.. Typ ically. ical ly. the distance [Q to the nearest feature and the anribmes anribures of me the nearest feature feacure are included in the Output database database.. of join process is rather A point-in-polygon or 'inside or straightforward: straightforwa rd: determine determ ine the polygon (from a source so urce rable) within wirhin which each point (in a desrinario destinationn table) is table) localed, located, then join the attributes of each polygon with each point. For For exam example, associated paine ple. you may be interested in understand ing the rhe characteristics characterist ics of t(imber imber stands sta nds co conntaining [he the owl nest locations mat that exist on [he the Daniel Pickert forest (Figu re 9.8). In the Daniel Pickerr (Figure Pickett owl GIS database [here there are twO owl nest location points. Associated with wirh eac eachh po point int is information, based on ow owll surveys, regarding the number of adult and juvenile owls surveys. 161
Chapter 9 Associating Spatial and Non-spatial Databases
Spatial join options by target and source feature type (Italics indicate output products for each option)
TABLE 9_2
Polygons
Target
Poinu
Points
I. Nearest points Artribuu summary H ow ma,,}
Lilla
a,~ IUllrrSI
1. Intersecting li nes Attrihuu summary
I. Falls within AttrihuUl
How mall) are IU(lreSl
2. Nearest point Attributn &diJlllllU
2. Nearest line AttribuUl &diltflllU
2. Nearest polygon AttribuU! 6- diJtllfirt
1. Nearest points o r points intersected
I . In tersecting lines Attribute ru", mary
I. In tersects polygons Aurihult' summary
Attribu/~ summal]
How N/ally II" lIea"st
Attrihum &- dimmu 2. Nearest poi n t A ttributn &distallu
Polygons
151
I . Poin ts that rail inside Attri hutr f u mmfl,]
2. W ithin o ther lines
AltrihuiN I. Imersecring lines Artribuu sum",ary
How mall) are imitk
2. NarcS[ polygon or intersecting polygon Attribuus d- diSlI.m" I. ImersectS polygons Attn'buu summary H()w mall] imfl'S«t
How many imm«r
2. Nearest point d- distanu
AllTibutn
•
2. Compk[dy inside A uribum
fou nd there (Figure 9.9), along with the first and last sighting of the owls. What is not known by simply viewing the owl GIS dacabase are the characteristics of the forest surrounding each owl nest. With just two points. this can be determined by visual inspection: owl poim 1 is located within stand 25, a rathe r densely stocked l O-year old stand; owl point 2 is located within stand 29, a 5D-year old stand of trees. However, when a large number of points are presem in me destination table, or the source table comains a num ber of relatively small features (making visual inspection difficult)' an automated process may be preferred. such as the general process noted below chat can be used in ArcGIS 9.x:
Owl Point #1
•
2. Nearest line A ttribum ¢ diua u«
Owl Poinl#2
• •
Stand #25 Stand #29
Figure 9.8 Associating owl nest locations with the: timber stands withi n which they are located.
1. Open the Daniel Pickett owl GIS database (target table) and stands GIS database (source table). 2. In the table of contents, right-click the target table (stands GIS database), and seleCt joins. 3. Make sure the first option in the dialog box that opens is set to 'Join data from anothe r layer based on spatial location' (the default setting is 'Join attributes from a table)_ 4. Select the stands GIS database as the layer to join to this layer (use ArcCatalog to add spatial reference information if necessary)_ The Join Data d ialog box 162
152
Part 2 Applying GIS 10 Natural Resource Management Database
Table structure
Sourr:e GIS database representing timber stands
. 25
A
: 29
.
I
260
I
200
I
:
:
I
A
I 37.7
70
I
50
I 21.1
"
Target (Destination)
Ad"1s 2
Fledglings
Firstsight
Lastslght
1
1
19950618
20070821
2
1
0
19980623
20070901 !-
Point
GIS Database representing
owl locations
Joined database Resulting database: the owl pOints database with the appropriate stand conditions that StXround eacfl point
Point
Adults
Fledglings
Firstslght
laslsight
SIan<1
Veo_type
Basal_area
Age
Mbl
1
2
1
19950618
20070821
25
A
260
70
37.7
2
1
0
19980623
20070901
29
A
200
50
21.1
Figure 9.9 Spatially joining the Danid Pickett stanch GIS database with the owl GIS database.
will update to show you the types of feature classes that yo u are joining, in this case Polygons CO Points. 5. Use the radio or option burron and choose that you want each point to have the 3([cibures of the polygons [hat 'it fulls inside' ramer (han 'is closest [0 it'. 6. Specify an outpUC location and name for the resulcing joined database. 7. Choose OK to initiate the spatial join. In ArcView 3.x these general steps can be taken form the join:
{Q
per-
1. Open the D an iel Pickett owl GIS database (targer table) and stands GIS dat.abase (source table). 2. Open the amibute tables of both GIS databases. 3. Clear all selecced landscape features in both amibute tables (to ensure no landscape fearures are selected). 4. With the mouse, click on the join item in the so urce table (e.g .• the 'shape' field in ArcYiew) . 5. With the mouse, click on the join item in the target table (e.g.• the 'shape' field in ArcYiew) . 6. Click the join table bunon to initiate the spatial join process.
While this example is relatively straightforward, imagine a case where you have several hundred poines. such as research plots. and the goal is to quickly and accurately determine what rype of stand each research plot is located within. A poine-in-polygon join process would seem to be a logical and efficient option to accomplish this goal, and would likely result in fewer errors than a manually driven process. The spatial join process can also be used to identify the number of features within another point. line. or polygon database that are closest to features within a target database. For example. suppose you had four possible ttail· heads withi n a watershed where you co uld park YOUT vehicle in order to visit six rain gauges from which YOli need {Q collect precipitation measurements. Using the trailhead locations (poi nts) as rhe target database. you could spatially join the six rain gauge locations (points) and determine wh ich of the nailheads was closest to the largest number of the six gauges. This wou ld help you at least (Q pick a trai lhead locacion in which to begin your sampling. Another example might include examining a number of watersheds (polygons) and wanting to deter163
Chapter 9 Associating Spatial and Non-spatial Oatabases
153
mine wh ich was the most densely forested according to (he number of uees, assuming that you had coordinates
both databases. With this process, the goal is to have the abi lity to display twO GIS databases, and to be able to
(points) of all trees throughout your watersheds. Using
select a landscape feature from one and view the associ-
the watersheds as your target laye r, you could spatially join the trees laye r and return the number of trees, complete with an aruibute summary (average tree height for
ated landscape feature{s) in the other. For example,
example) for each wate rshed.
Making Joined Data a Permanent Part of the Target (Destination) Table After a join process has been completed, you might decide that the joi ned data should permanently reside within the target table. One strategy to accomplish this goal would be to add the appropriate number of empty fields to the target tab le that will ultimately contain the joined data, and declare that the data type of the empty fields be the same as the data type of the joined data. T he va lu es of the
empty fields can be calculated to equal the values of the joined data, thus filling the empty (yet permanent) fields with the joined (yet temporary) data. Saving the target table at this point results in a permanent change to the database, with the data from the source table now a permanent pan of the target table. In addirion, removing or changing the source table will not result in a correspon-
assume you have a source table represented by a GIS database that contains multiple records related to landscape featu res in another GIS database (the target table). In Figure 9. 10, the tables of twO GIS database tables are illustrated; one represents a road system GIS database and the other represents a culvert GIS database. As yo u may notice, there are multiple culvertS associated with each
road (e.g., culvens 4 and 5 both are associated with road 602) . Joining these twO databases together would result in a manY-CQ-one joi n process or o ne-to-many join process, depending on what is chosen as the source and target tables. With a manY-(Q-one join process (culverts as source, roads as targetL some of the records in the source table will not be present in the target table after the join process has been completed . With a one-to-many join process, (roads as source, cu lvens as target) you can identify which road is associated w ith each cu lve rt, however, you will not be able to seiect a culvert and automatically view the associated road .
Table structure
Database
ding change to data in the new fields in the target table. Another suategy may be to perform a join process, and tI,en save (or export) the spatial GIS database that represents the target table to a new database. In some GIS software programs the newly copied and saved GIS database will contain all previously joined data as a permanent pan of the GIS database rather than as a temporary association .
Unked Table 11
been performed) may rn.cilitate some natural resource management processes, such as report gene ration, metadata creation, or other subsequent non-spatial analyses.
602
I
•
I
1006 1007
Rock
•
I
Rock Oirt
I
Linked Table 12 GIS Oatabase representing culvertlocatlons
Culvert
Type
Road
1 2 3
Aluminum
544
SIee1 Cedar Polyethylene Polyethylene Polyethylene Aluminum
544
5
6 7
On occasion, you may want to simply link or relate twO GIS databases together, allowing you to view both the source and target tab les as separate ent ities, and to view landscape features that are assoc iated with each other in
Rock Rock
2 3
4
Linking or Relating Tables
Type
1
I
Finally, exporting the target table (the tabular portion of the GIS database) after a join process was performed wi ll generally create a new fil e that includes all data. Unfortunately. this process does not preserve the landscape features of the target table, only the underlying attribute data. An exported target table (after join processes have
-
Road
GIS database representing a road system with 1007 road segments
544
602 602 714
714
-
Figure 9. 10 Linking a roads GIS database with a cuJvcru GIS database. 164
154
Part 2 Applying GIS to Natural Resource Management
Which process-Joining or linking-is more dynamic? Perhaps linking. With linking. you can
some data are not linked (in either the source or tar-
visualize both the source and target tables. select landscape feamces from either table. and view the associated links from wither perspective. Further, if
ana lysis . When a join process is used, source table data not joined with target table data are unavailable for funher spatial analysis.
To further illustrate the link or relate process, a general
1. Open the Brown Tract roads G IS database. 2. Open the roads GIS database attribute ,able (this is the target table). 3. Open the Culverts. txt file (this is the source table) . 4. Select the link item in the source table (road). 5. Select the link item in the target table (road). 6. Choose Link from ,he Table menu.
set of steps can be followed in ArcGIS 9.x to associate two GIS databases related (Q the Brown T fact: a culverts data-
base (Culverts. txt) and a roads GIS database: 1. Open the Brown Tract roads GIS database and culvens text file.
2. In ,he ,able of contents. right-click the target table (roads GIS database). and select Joins and Relates. then select Relates. 3. Selec, the relate field in the ,arget table. 4. Identify the source table. 5. Select the relate field in the sou rce table. 6. After relating the two databases. perform an identify process. selecting one of (he roads that are noted in the culvert database. You should be presented with informarion for both the road and the associated culverts
along tha, road. In ArcView 3.x, the following general set of instructions may work to create a link between the roads and the culven databases:
get ,able) they are still visible and ava ilable for further
At this paim a one-way link becween the roads and culverts
databases has been created. The process may need to be repeated with ,he roles reversed (roads GIS database as the source. Culverts.cxt as the target) to create a cwo-way link.
By linking the (wo tables together. you ca n select records in one database (or features in either database), and the landscape features they are associated. with may be
selected and highlighted in the other database. For example. after linking together the culvert database with the roads GIS database, you can select one or more cu lvens. and the roads associated with those culverts may be simul-
taneously selected and highlighted in the roads GIS da,abase. In ArcGIS you can view the related data by using the identify tool.
Summary Join and link processes provide GIS users with a way to temporary assoc ia te two or more databases. allowing an expans ion of the mapping and ana lysis opportunities within GIS without making permanent changes to the databases. Before utilizing one of these processes you
should consider the types of databases ava ilab le. and the type of association desired (e.g .. one-(O-one, one-to many. and so o n). In addition. the purpose of the
process should be understood. which mighr provide guidance in the choice of a join or link process. For example. you may decide on different courses of action if the purpose is to:
A. Bring together spatial and non-spatial databases to make thematic maps. B. Funher the ab ili ty to perform other spatial operations, such as assoc iating non-spatia l stream buffer data with a streams GIS database in order to facilitate a buffer process. c. Understand the spatial relationship between a sec of points and other landscape features (through a point-
in-polygon process) . D . Understand the association among multiple landscape features in one database and their counterparts in
another database (through a li nk process). 165
Chapter 9 Associating Spatial and Non-spatial Databases
155
tables (with a relate) . GIS users sho uld rec-
to this vinuai [esuh is with a spatial join, in which results are usually stored in a new database that conrains all sparially jo ined info rmation. You shou ld check your software package to dete rmine wherher it follows these OutpUt resu lts in the same way. Regardless of
ognize mat this temporary condition exiS(5 and that further acr ian is necessary in order fo r a permanent record of rhe joined or linked info rmation to be created. The
associate two databases. users should track thei r procedures and manage the ourpur acco rdingly.
Joining and relating processes often produce databases char are only 'viccually' combined. meaning that no new databases have been crea ted. but char ex ist ing informarion appears ro be associated either in one [able (with a
jo in) or in
twO
exception
which process (tabular-based or spatial join) is used to
Applications 9.1. Salamander habitat suitability index. Bob Evans, the Brown Tract's wildlife biologist/hydrologist, developed some habitat suitabili ty index (HSi) values for a salamander. He suggests joining rhe data provided in
Brown Tract stands GIS database, please add ress his needs noted below. a) Determine how much land area of higher quality habitat (0.6-1.0) is contained within the Brown
'SAL_HSl.txt' with the Brown T ract stands GIS database. He then wants you ro answer several questions abour the extenr and spadal distribut ion of these areas, which are summarized below. a) How much land area is contained in the 0 .8 [Q 1.0
HSI range? b) How much land area is contained in the 0.6 0.79 HS I range?
to
c) How much land area is contained in the 0.4
[Q
Tract.
b) Prod uce a single map of the sharp-shinned hawk habita r showing HSI values for the years 2010, 2020, 2030, and 2040. 9.4.
0.59 HSI range? 9.2. Newt habitat suitability index. Bob Evans has also developed some habitat suitability index (HSi) values for a newt. He provided data in the file 'NEWf_HS l. rxr'. After joining the dara with the Brown Tract stands GIS
database, please address his needs, which are noted below. a) Determine how much land area of high quali ty newt habitat (HSI ~ 0.65) is contained with in 100
Water sources and land allocations. John Frewer,
a forester associated with the Brown Tract, is interested in knowi ng what types of land allocations the water sou rces were located w ithin. To accomplish this task, perform a poine- in-polygon operation (or selection by locar ion query), using the water sources as the source table and the
stands GIS databases as the target table. a) How many water sources are located in 'Even-aged' stands on the Brown T ract? b) What types of water sources are loca ted in unevenaged stands (lis r the water source types)? c) How many water sources are located in research areas?
m of the road system.
b) Determine how much land area of high quality newt habitat (HSI ~ 0.65) is contained wit hi n 1,000 m of the owl nest site. c) Determine how much land area of high quality newt habitat (HSI ~ 0.65) is contained in even-aged stands over 50 years of age. 9.3.
9.5.
Sawmills in a woodshed. You have recently been
hired as a procu rement forester for Chupp and Daughters
Sawmill in Floyd County, GA. Yo u need
to
understand
the competit ion for wood in the area. To perform rhis task, use the southeastern counties GIS database, the
southeastern mills GIS database, and the mill data DBF fi le. Jo in the mill data DBF file to the southeastern mills
Sharp-shinned hawk habitat suitability index. In
GIS database and determine how many sawmills are
addition to his previously discussed developments, Bob Evans has also developed some habitat suitability index
within 100 miles of Floyd County, GA. The mill loca-
(HSi) estimates for the sharp-shinned hawk covering the years 2010, 2020, 2030, and 2040. His rendeney to pro-
the book's website o r the USDA Forest Service (2006).
vide this data in a texr file continues, and you can fin d it
9.6.
in 'SSHAWK_HSl.txt'. Afte r joining the data with the
and Housron Lumbe r Company, a company that is
tions and non-spatial mill a[cribures can be acquired from
New mill location. You work for Walker, Avery, 166
Part 2 Applying GIS to Natural Resource Management
156
considering building a new hardwood sawmill mill in Coffee County, AL. They need to understand how much hardwood volume currently exists in (he area around the county. To perform this task, use rhe southeastern counties GIS database and rhe county volume
DBF file.
Join the county volume DBF file to the somheastern counties GIS database and determine how much hardwood (soft and hard) volume is conrai ned in the counties
that surround, and include, Coffee County. The countylevel can be obtained from the book's website or the USDA Forest Service (2007) .
References USDA Forest Service. (2006). US wood-using mill locat;0",-2005. Research Triangle Park, NC: USDA Forest Service, Sourhern Research Stadon. Retrieved
April 16, 2007, from http: //www.s rs.fs.usda.gov/econ/ data/mills/miIl2005 .htm .
USDA Forest Service. (2007) . FIA data mart: Download files. St Paul , MN: USDA Fo rest Service, North Central Research Station. Retrieved April 16, 2007, from http ://www.nc rs2 .fs.fed.us/FIADatamart/ fiaclaramarc.aspx/fiadaraman.aspx.
167
Chapter 10
Updating GIS Databases Objectives
ated, and some stream characteristics {pools. sediment, fish
provide readers with a discus-
abundance} may change as woody debris moves through the system. Although you may have developed or acquired
sion of GIS processes that should be considered when updacing GIS dacabases. Database updates are necessary
GIS databases at o ne po int in time. as management needs or direction change. or as the resources you manage
when landscapes and associated character isti cs. such as
change, GIS darabases used to describe landscapes must be
ownership, change. There are a variety of methods you
updated. Table 10. 1 illustrates a number of events that
few are pre-
could occur and affect the landscape being managed, suggesting that the GIS databases used to describe rhe landscape being managed must be updared to reflect changes.
This chapter is designed
(Q
can use to update a GIS database, yet only a
semed here. The objective of this chapter. therefore. is
to
provide an introduction to the poremiai applicarions in (his area. More specifi cally, at the conclusion of this chap-
ter, readers should be able to understand:
Most natural resource management organizations (as well as data development o rganizatio ns) have created a set of
I. why GIS databases need to be periodically updated and
bases. Finding and illustrating a standard protocol is.
processes and protocols to guide the updating of GIS datamaintained,
2. what issues might be associated with an update process, and
3. whar GIS processes could be used to physically update a database. To accomplish these objectives, a discussion of the reasons for updating GIS databases is firsr presented. Two rypes of update processes are then examined, one where new landscape features are added to an existing GIS database, and another where me landscape features and attributes in an
exisring GIS database are modified. These cwo examples likely address rhe [WO most common fo rms of GIS darabase updares. This chapter relies heavily on the GIS
therefore. difficult because each organization generally will develop the steps they feel are necessary to integrate new dara within their system of natural resource information. For examp le. assume a tract ofland was recendy purchased by a land management organization. Integrating the forest stand component of this new tract into a forest stand GIS database can be accomplished in a number of ways, such
as the three processes described in Figure 10. 1. As database protocols and organization strategies vary from one organization to anomer. there is no one update approach thar will work for every organization . The users of GIS databases are the ultimate customers of groups (GIS departments. consultants, agencies) that
processes associated with edicing GIS databases. For a
produce the sparial data. As GIS databases become available, and users begin to explore the usefulness of rhe data
review of these editing processes. please refer to chapter 3 . GI S databases are rarely considered stacic entities: vegetation conditions change due to human manipulation and namral disturbances. roads are constructed and obi iter-
for assisting in natural resource management processes. the limitations of [he databases will become evident. The period of rime from initial GI S database availability to serious consideration of updates tQ the databases may last 168
158
Part 2 Applying GIS to Natural Resource Management
TABLE 10.1
A sampling 01 reasons lor updating GIS databases
Events
Update spatial data
Examples
01'
Update tabular data 01'
Stochastic disturbances
hurricanes. fires. insect outbreaks
T ransiriOlls of (orcslS
growth and yield
Management activi ties
harvesting. road consuucrion, insrallalion I removal of culverts. creation of trails. thinnings. NC .
01'
01'
T ransacrions
land acquisitions. donations. sales
01'
01'
Regulations
riparian management areas, owl
01'
01'
01'
habitat areas, woodpecker habitat areas
Organizational policies
special areas. personal reservations
01'
01'
Improvemcnu in technology
digital onhophotographs, GPS captu re: of road data, ownership boundaries, crc.
01'
01'
Organizational initiatives
periodic I annual cruises. photo interpretation of harvested areas not normally recorded via normal processes
01'
01'
01'
New data availabiliry
databases developed by other organizations
01'
C hanging map projections
conform ing to new o rganizational standards
01'
Collaborative projectS
watershed analyses. Ia.ndscape planning efforts
01'
01'
Periodic maintenance
cleaning up databases after spatial operations. digitizing, or anributing processes
01'
01'
Process A
Process B
Process C
Digitize an
Digitize an area
Collect GPS data lor an area
area
1
1
Erase overlaps
using corporate database
1
1
Attribute the new spatial features
1 Update COij)Orate
database
Erase over131>S using corporale database
Select and copy newly digitized features
t Paste new features Inlo corporate database
1 Attribute the new spatialfealures
Figure 10.1 acquisition.
1 Edit spatial features (e.g., remove muttipath)
1
Select and delete features to be updated (il necessary)
1 AddGPS features Into corporale database
1 Attribute the new spatial features
Three examples of update processes related to land
from a few hours to a few months. Users of GIS databases will ultimately suggest a variety of enhancements to the databases that would facilitate further ana lyses. For example, the Brown T ract vegetation GIS database could be modified to show more explicitly (he riparian areas, or could include more anributes that describe forest stand structure. A roads GIS database might also be enhanced to show the type of road surfacing or all of the trails (unauthorized roads) that weave through the property. Updating these GIS databases ro include all of the informarion (hat is necessary to make natural resource management decisions may be, however, limited by the time and budget available ro make the changes, the quality of (he information available to make the changes. and other organizational data standards. The needs of namral resource managers. with regard to GIS databases. must eventually be considered along with (he costs of data development.
The Need for Keeping GIS Databases Updated Natural resource managers generally base managemem decisions on the best ava ilable data. The qua lity of data 169
Chapter 10 Updating GIS Databases
can range from very precise and accurate (collected with a high q ual ity GPS receiver) to somewhat imprecise and inaccurate {drawn by hand from memory}. Keeping the data used for making decisions accurate and updated is therefore imponant, and thus the interval between updates becomes important. For example, the update interval that a resource managemem organization uses to refresh the spatial extent {history} of their management activities and the growth of their forest inventory is imponant, since subsequent management decis ions might be affected by previously implemented managemem decisions. The imerval chosen can range from six momhs. to a year, or even (WO years between updates, depending on the GIS database considered. The interval chosen depends on the organization's perception of the usefulness and cost-effectiveness of such an update on a GIS database. For example, if the goal of an organization (e.g., a southern US forest management organization) were to generate revenue for its stockholders, the need for updating the data related to its primary resource (pine forest stands) may be more important, and updated more frequently than data related to secondary resources (hiking trails). Other resources, such as roads, streams, culvens, water resources, and wildlife may be more or less imporram, depending on the goals of the organization, thus the frequency with which these GIS databases are updated may vary according to the organization's perceived need to do so. At the extreme end of the spectrum. every GIS database could be updated continuously; however, the cost of doing so may be qu ite high and the task would require employees {or consultants} dedicated to the task. Two ofche more important questions an organization must address, beyond determining when a GIS database should be updated, are how the update process will be accomplished, and who will do the work. As mentioned earlier, the methods by which a GIS database could be updated vary considerably; the fo rm of input cou ld range from hand-drawn maps to LiDAR-derived measurements, and the GIS processes could involve scanning, digitizing. attributing, and other methods {Table 10.2} . As you may have gathered from chapter 3, when a GIS database is being updated, rhe database is being edited. In some form or fashion , the imem is to change something about a GIS database-either the landscape features or their underlying attributes, or both. Two examples of GIS update processes are now presemed, one related to a forest stands GIS database maimained by a forest industry organization in Florida, and the other related to a streams GIS database
TABLE 10.2
159
Inputs and processes that can be used to a ssist a GIS database update
Input Hand-drawn maps GPS features T2bu lar datab2Ses Field nOles A person's memory GIS Features developed by field personnel Digital orthophotographs and subsequent interpretation GIS processes Scanning Digitiring Updating Joining Linking Copying J p2Sting Attributing Importing Querying and verification
mai ntained and distributed by the Washington State Department of Natural Resou rces.
Example 1: Updating a forest stand GIS database managed by a forest management company A typ ical forest management company in Florida might update their forest stand GIS database on an annual basis. Their field personnel collect information related {Q changes in irs forest land ownership throughout a calendar year, and the forest stand GIS database is updated near the end of the calendar year. Why would they update the forest stand GIS database once a yea r? The forest stand GIS database is arguably the most important GIS database for assisting industrial forest management activiries, and field-level managers require high quality data (maps and inventory data) to make managemem decisions. In addition, most corporations require an annual estimate of the value and volume of resources, for planning and tax reporting purposes. A less frequent updating interval may not be appropriate given the short rotacions typical of southern industrial forestry operations. For example, waiting two years between updates of a timber stand GIS database may represent 8- 10 per cent of the lengrh of a forest harvest rotation. A more frequent imerval, say six months, may provide field personnel with higher qualiry information with which to make management decisions, particularly in cases where a large amount of activity takes place over a six-month period. Some have argued (hat continuously updating GIS databases may be appropriate. but the: time and cost required to update a GIS database may make a nearly continuous update process impractical. Further, field personnel could easily become confused when faced with a cominuously changing set of GIS data170
160
Part 2 Applying GIS to Natural Resource Management
bases. thus updating databases and leaving a window of time {a year. perhaps} between changes may be perceived as more des irable. The changes to the forest stand GIS database that are recommended by field foreSters and other natural resource managers may be indicated o n hard-copy maps and timber cruise forms. or they may be comai ned in d igital databases created in GIS or with GPS. Field foresters. timber procurement managers. or other profess ionals responsible for managing la nd will typically indicate {draw} on maps the changes {e.g .• harvest and regeneration activi ti es} that have occurred on [he forest land base as these act ivit ies have been completed. This informacion is usually sent to a central office {Figure 10.2}. which takes ownership of the timber Stand GIS database. The cemral office checks rhe new data for mistakes and omissions according to a set of organizational standards, and may ask for clarification from the field staff. The information is rhen digitized, either in-house or by an external con n actor. The resuhing digitized GIS database is checked again for mistakes and omissions, and then imegrated into the official {sometimes called 'corporate} GIS
Field office
Central office
.r------ ---- ------- ----,, Delineate changes to be made to a database
,~ , , ,, , ,, , ,, , Make management ,, , decisions using ,, the database , ,, , ,, , ,, , ,, , , ,, Check data for mistakes and omissions
Check data for mistakes and omissions
L Digitize changes
~
~ Check data for mistakes and omissions
r-
,, ,, ,
,, ,
J
L Integrate into corporate database
~ -,
~ Check data for mistakes and omissions
Figurc 10.2 A gcnerali"lcd proces.s for updating a forest stand C IS database.
,
-J
database. and again checked for mistakes and omissions. Finally. the forest stand GIS database is distr ibuted back to the field office. either as a GIS database. or as hard-copy maps and tables. The field office may then have itS own verification procedures for checking the updated database (or maps) fo r m istakes and omissio ns that may have arisen during the update process. Processes such as these. with a systematic method for data collection. entry, and verification, are designed to ensure that high-qual icy data will be developed and avai lable for use in natural resource decision-making contexts.
Example 2: Updating a streams GIS database managed by a state agency In rhe State of Washington, all fo rest harvest plans must be subm itted to t he Department of Natu ral Resources {DNR} for review and approval. A map must accompany each plan, and illustrate the juxtaposition of proposed activities in relation to, among other landscape features, the stream system. To ensure a consistent definition of the 'stream system', the DNR provides (at a minimal COSt, as was illustrated in chapter 3) a streams GIS database for the entire state. This database is conti nuously updated by the DNR as new info rmat ion is co llected. However, processes and protocols exist that are related [Q each potential change to the GIS database. For example, assume a pri vate landowner surveyed a stream reach and noted that the type of stream {and perhaps location of the stream} on the landscape is different than the type of stream illustrated in rhe DNR streams GIS database. The landowner has the option to submit ce rtain documentation [Q the DNR in suppOrt of a request to change the DNR streams GIS database. The DNR d irects each request through a review process, and based on the outcome of the reviews, decides [0 ei ther accept or reject the proposed changes suggested by the landowner. The amount of time required to make a change in the streams GIS database, from initial submission by the la ndowne r to official incorporation in the streams GIS database, may require several months. The process is considered a continuous one since approved changes to [he streams GIS database can be made at any time during a calendar year. Therefore, landowne rs may need to continuously review the statuS of the DNR streams GIS database in the areas where they own or manage land, and acquire updated data as they deem necessary to reflect the latest stream info rmation. 171
Chapter 10 Updating GIS Databases
Updating an Existing GIS Database by Adding New Landscape Features
Updating a stands GIS database
me
Assume that (he owners of Daniel Pickett forest have pu rchased 80 acres (32.38 hectares) of land adjacent to the southwest corner of the original forest boundary (Figure 10.3). Following process B illuStrated in Figure 10. 1. the Stand bounda ri es of this area have been digitized inco a new GIS database chat is separate from the o riginal stands GIS database. and these features have been attributed wim data fields similar to that in the original stands GIS database {Table 10.3}. The edge between the newly digitized stands and original stands is seamless, implying that there is no gap between the polygons of the two GIS databases. and no overlap if the two sets of poly-
D D
Smnd
2
Dani~ 1 Pick~[( for~$[
Vegetation Basal type area"
Hectares
Acus
17.24
42.6
A
15.t4
37.4
8
Age
MBP
190
55
21.3
15
7
0.8
• squar~ f«t per acr~ h
thousand board fttt per
ac r~
gons were placed together. By simply copying the landscape features from the land purchase GIS database into the stands GIS darabase. it is possible to bring the newly digitized land purchase polygons into the stands GIS database, however. the attributes of the new stands may not be present. depending on the GIS software program being used (Figure 10.4) . The new fo rest stand polygons would then need to be attributed a second dme, after they have been pasted intO (he original stands GIS database. To avoid duplication of effort in update processes, three options are clear: (l) digitize the new landscape features directly into the original stands GIS database. (2) use a merge process to com bine the newly digitized stands with the origi nal stands GIS database. or (3) if available in
D
Forest stands
Original stands
Stand
VegType
Basal Area
Age
MBF
Stands in land purchase area
1
A
200
50
21.2
2
C
175
40
12.9
30
c c
190
45
17.3
110
25
4.1
o
o
o
o
o o
o
31
Figure 10.3
Attributes of stands in a 32.38 hectare (80 acre) land purchase adjacent to the Daniel Pickett forest
TABLE 10.3
GIS databases can be updated with new landscape features (points. lines. or polygons) by either adding the new landscape features (Q an existing GIS database, or by editing the existing landscape features. or boch . Two examples are provided below to illuStrate updating a GIS database by adding new landscape features . The firSt example involves a land purchase and subsequenr addition of twO forest stands co a stands GIS database. The second example involves the addition of new trails to a trails GIS database. In each case, assume that the new landscape features were either digitized or collected with a GPS SYStem and are available in a GIS format. Prior to [he iniciarion of the update process, you should assume that the new data are comained in GIS databases that are separate from the GIS databases that need updating. Refer back to chapter 3 for a review of methods and tools for development of a new GIS database.
stands and land purchase area.
161
0
Figur~ 10.4 Daniel Pick~n forest stands and land pu rchas~ a r~a aft~r copying and pasting landscape fntures from th~ land purch3# GIS datah3# to th~ stands GIS database.
172
162
Part 2 Applying GIS to Natural Resource Management
rhe GIS software program being used, use an 'updare' funcrion . ArcMap and ArcView 3.x, for example, both have (he ability to use an update function made available
rhrough the XTools extension (Data Easr, 2007; Oregon Department of Foresrry, 2003). When using a merge process or an update funC[ion, the stands GIS database
will be updared with rhe new polygon dara contained in the land purchase GIS darabase. If rhe land purchase GIS database includes fields named and formarred exactly as
purchase polygons beyond che excent needed in the new land purchase GIS database, creating an area of overlap
with the polygons in [he "ands GIS database (Figure 10.6). Then, erase from [he land purchase GIS da[abase the area of overlap with the stands GIS database, creating
a second (new) land purchase GIS database. In [his new land purchase GIS database, [he edges of the new polygons seamlessly match the edges of [he associated polygons in the stands GIS database (Figu re 10.7).
those in the forest stands GIS database, the 3ttrihme data
wi[hin [he land purchase GIS da[abase will be moved (along wi[h rhe associa red purchased polygons) to rhe updared srands GIS darabase (Figure 10.5). The assumprion was made rhac [he polygons in che land purchase GIS darabase seamlessly marched [he edges of polygons in rhe Daniel Picke" foresr scands GIS da[abase. How is this possible? Matching rhe spacial juxtaposition of the new landscape features
to
the landscape fea-
cures in [he GIS da[abase being updaced (the stands GIS da[abase) can be accomplished using one of at leasc cwo mechods, depending on what rype of process is available within the GIS software program being used: (I) copy [he new po lygons into the original stands GIS database and use snapping tools [0 properly match rhe new polygons
wi[h the original scands polygons, or (2) use a process as described in the fi rst cwo sceps of processes A and B of Figure 10.1. Here, you might firsc digi[ize the new land
D
Updating a trails GIS database The existing trails system for the Brown Tract was digi-
tized several years ago using hard-copy maps provided by the forest recreation planner (Figure 10.9). While suitab le for recreation planning and the development of recreation maps to guide visitors around the area, the trail
syscem described in the [rails GIS database co uld very well be considered out-of-da[e. The [rail system, like ocher fea[Ures of a landscape, evolves as the managers of the forest
develop new trails, or as people find different hiking or mountain biking rouces through the landscape. The latter Overlap
D
Or~inal stands
Stands in land acquisition area
Timber stands
Figure 10.6 Overlap of new land purchase polygons with a GIS database that will be updated.
D
Stand
VegType
Basal Area
Age
MBF
1 2
A
200
50
C
175
40
21.2 12.9
31
c c
45 25
o o
A
190 110 190
B
15
7
30
Figu~
10.5
55
o
Original stands Stands in land acquisition area
17.3 4.1 21.3 0.8
Daniel Pickett forest stands and land purchase ana after
updlUing the stands GIS database wing the land purchase GIS database.
Figure 10.7 Land purchase GIS dat:lbasc after erasi ng the overlap with the stands GIS database.
173
Chapter 10 Updating GIS Databases
The term digitizing, as described in chapter I. means to cooven a hand-drawn (or other rype 00 map CO a digical image of a map. Normally. digitizing is performed using a digidzing table and a digitizing puck. A map is laid on the table, taped down to ensure that it doesn'[ move dur-
ing [he digitizing process. and at least four control points on the map. for which on-the-ground coordinates are known, are emceed imo the compurcr system using the digitizing puck (similar co a computer mouse) . The puck is
163
ally based on
Figure 10.8 Two delineations of a young fores l, one using twice many vertices when digitizing (above) than the other (below).
;u
174
164
Part 2 Applying GIS to Natural Resource Management ---- . Trails - - - Proposed trail
-
Roads
CJ Forest boundary
\.
./
.•..••..
1
I
'. <"
i..
.......
.......~ . \
....•.
Figure 10.10 Proposed new trail on the Brown Tract.
For example. the new trail may not end with a node that allows direct connection to a vertex of a trail in the o rigi-
nal trail system (Figu re 10.1 1). This creates a gap in the --- - Trails - - Roads [:=J Forest boundary Figure 10.9 Trail system of the Brown T r:lCL
linear extent of the trail at the intersection point and will require some post-merging editing to co rrect.
To update the original trails GIS database, the original trails GIS database could be merged with the proposed trails GIS database. When doing so, only the fields (attributes) with in the proposed trail GIS database that match exactly w ith the attributes within the original tra ils GIS
case usually involves the development of unauthorized tra ils. which the fo rest managers mayor may not decide to more fully deve lop and mainta in (or they may decide to develop measures to hinder the use of those trails) . In addition, as resources become more popular and visitations increase (as is the case with many urban-proximate recreation destinations), there may be a need CO identify additional resources and trails with which to decrease the density of use. In some cases, closing trails eith er seasonally or permanently is necessary to prevem resource degradation such as soi l compaction and erosion. The forest recreation planner decided that a new authorized (rail would be of value (0 the recreation pro-
database (i n both attribute name and type) will be moved into the new merged GIS database. The spatial position of the proposed trails , as mentioned above and desc ribed in
Figure 10.1 I. may then need to be edited. In addition, a verification of the attribu te data (Figure 10.12) may suggest some alterations as well (e.g.• the trail number of the proposed new trail is the same as another existing (rail in the original trails GIS database) . An understanding of the
update process (Figure 10.13) will be of value in the planning of projects that involve alterations or updates to GIS
databases.
gram (Figure 10. 10). Mor the new trail was developed, the spatial coordinates of the trail's location were collected using GPS, brought into a GIS software program as a new GIS database comaining line features, attributed, and saved
as the 'proposed trail' GIS database. Since both the origin al trails GIS database and the proposed trails GIS database are composed of line features, and not polygons, they can be brought together without the worry of creating landscape
featu res that overlap (and hence, in the case of polygons, leading (Q a double-counting of some areas in area calculatio ns). However, careful anention must be paid to the connectivity of the network of lines that desc ribe the trails .
---- . Trails - - Proposed trail
...., .""',.,.," ",
....... "',
Figure 10.11 Proposed new trail and iu relation to another trail from the origina1 trails GIS d21abase.
175
Chapter 10 Updating GIS Databases
165
Collect GPS data IOf an
area
c;. . . <. ., ,0 ----'.'.
---- Traits
c::::::J
~
!
; :~
.: .'
Fores. boondary
~ Edit spatial leatures (e.g., remove multipalh)
~:r,
Attribute spatial features
..
><"'::~:;: "'~('
<./
~ Merge new features into original database
~
AuthOfized
Source Trails Trails
Edit spatial position of new features
704.1
Unauthorized
Trails
1261.5
Unauthorized
Trails
Verify I edit allribtJte data In updated database
Trail
Length
Conation
410.5
AuthOfized
2
1183.2
42 43
Figure 10. 12 Updated trail system of the Brown Tract.
Large GIS database updating projectS require careful consideration of the time and COst requ ired to successfully complete the project. For example, a roads GIS database for a 100,000 hectare foresr may have been originally digitized from hard-copy maps (drawn by hand) and, therefore, might contain some spatial position errors. If you were to consider updating many of the roads in the GIS database by collecting new data with a GPS, the following should probably be considered: I. The development of a sta nda rd protocol fo r GPS
data collection (e.g., maxi mum PDOP), to ensure an acceptable and consistent level of accuracy in the data coUected. 2. The need to drive (or walk) all roads that need updating. 3. The need to differentially correct and manage the GPS-collected databases. 4. The identification and elimination of erro r in the GPS data (such as multi-path erro r).
~
Figun 10. 13 The process used to update the trails G IS database.
5. The removal of the old roads from the updated roads GIS database. 6. The connectivity of new roads [0 old roads that were not updated. 7. The developmen( of a verification process to ensure that (he attributes of (he new roads are correct. Determining the number of person-days required to accomplish each step will depend on the people, equipment, and technology available. The alternative [0 a large, single process fo r updating a GIS database is to perform it in small phases. However, while the COS( of using multiple smaller phases may be lower than a single large project (assuming it requires several years to complete all of the phases), the total cost of the update process will likely be lower if the entire project were completed as a single project due to the economies of scale (fewer stanup and clean-up operations) . In addition, fewer errors might arise since the same people will be working with a protocol that is clearly stated and understood. 176
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Part 2 Applying GIS to Natural Resource Management
Updating an Existing GIS Database by Modifying Existing Landscape Features and Attributes An alternative to updating GIS databases with new landsca pe features contained in other GIS databases is [0 modify existing landscape features using [he editing functions described earlie r in this book. While this alternative may see m mo rc logical than what was previously described in this chapter. the ri sk of damaging the GIS database being updated is grearer. For example. the processes described earlier have a relatively low risk of damaging the original GIS features (the ones not requirin g updating). These processes invo lved creating and modifying landscape fearures in a G IS database se parare from the original GIS database, then moving the new features into the original GIS database only when it was appropriate ro do so. Here, editing the original GIS database may pose a higher risk of damaging landscape features thar did not requi re updating due [0 human error. And it is possible that these errors can occur withom realizing a mistake had been made. In addi rio n. unless the steps taken in editing a GIS database were carefully documented, when errors are located it may prove difficult to understand wh ich landscape fearures had been verified as correct, and which may require furthe r editing. Two processes are next briefly described to illustrate rhe update of GIS databases by modifying existing landscape features. The first process requires editing the location of landscape features with the assistance of digital orthophotographs. The second process illustrates updating attribute data in a GIS database with the assistance of a JOIl1 process.
orthophotograph to examine how well rhe landscape fearures are being rep resented. Us ing the boundary GIS database of the Brown Tract as an example, you will find that a particular line may either incorrectly identify the forest boundary, or that a management operation on an adjacent landowner's property may have been incorrectly located (Figure 10.1 4). If this is. in fact. an incorrect boundary line specification within the boundary GIS database, then editing the appropriate vertices that define the boundary while using the digital orthophotograph as a guide can easily modify the spatial position of the boundary. If. however. yo u needed to be very precise regarding the location of me forest boundary, info rma-
Editing the spatial position of landscape features using digital orthophotographs As described earlier. heads-up digitizing may be used to assist in the GIS database update process. Digital orthophotogtaphs may be of benefit in updating the position oflandscape features if the orthophotogtaphs are registered appropriately [Q the correct landscape position, and if they have been stored in the coordinate and projection sys tem consisrent w irh the GIS databases to be updated. Point. line. and polygon GIS databases ca n be displayed in a GIS software program on top of a digiral
Figure 10.14 Boundary line issue on the Brown T raC l.
177
Chapter 10 Updating GIS Databases tion from a land surveyor survey-grade GPS measuremems of properey corners would be more appropriate in
Stand-level forest
updating the spatial position of the boundary. When updating the spacial position of landscape featu res in a GIS database, you must also be aware of the potential issues that may arise in other associated GIS databases. Here, for example, the imem may be to simply
167
Stands GIS database
Forest growth and yield model
update the position of the boundary of the Brown T race. However, by doing so, the spatial extem of the land ownership is no longer consisrem with other polygon databases used to represem the Brown T racti the stands and
soils GIS databases being cwo good examples. Thus, afcer
Summarize stand -level data
updating the boundary of the Brown Tract. a correspon-
ding update of the affecced polygons in the stands and soils GIS databases may also be requi red.
Updating the tabular attributes using a join process Replace old summaries with new summaries
In some circumstances only the attribute data of a GIS database may require periodic maimenance. For example. if over a given yea r no activities have been implememed on part of an ownership then perhaps only the a[[ributes that describe the structural condition of the forest(s) need [0 be
Remove the join
updated. In this process {Figure 1O.IS}, the update might be accomplished by passing the stand-level forest inven[Ory data through a growth and yield model, summarizing the resulting forest structural conditions. saving this data in a non-spatial database. then joining the non-spatia l
database to the stands GIS database. A unique stand identifier. such as the stand number. could be used
records in the non-spatial database
[0
to
connect
the forest G IS layer.
Using the Daniel Picken forest as an example. a non-
spatial database that represents the updated growth of stands {Update. txt} can be joined [0 the stands GIS data-
Figure 10.15 A process to update attribute data in
the Daniel Pickett
stands GIS d:lt.abase.
anributes that represem the updated basal area. age. and
base attribute table. using the stand identification number as the join item. Then. the anributes within the original
quently be removed from the original stands GIS data-
stands G IS database can be replaced with the joined
base, and the updated stands GIS database can be saved.
volume {MBF} . The joined non-spatial table can subse-
Summary There are a variery of methods yo u can employ [0 update a GIS database. Several of the approaches were described with the examples provided in this chapter. GIS databases are rarely static. and in only in a few situations, such as in the delineation of national. provincial. sta te. or couney boundaries. can you be con fidem that changes to a GIS database will rarely occur. Landscape features can change
quite often: watershed boundaries change as the processes
used to define watershed change, public land survey {PLS} section lin es may change as corners are reestablished. stream locarions change as they are better mapped using
GPS or digital orthophotographs, and of course, vegetation stand boundaries change wi[h management of natural resources or with nacural disturbances. Attribute data 178
Part 2 Applying GIS to Natural Resource Management
168
associated with GIS databases can change just as often, and may need periodic updati ng. In addition, given continued improvemenrs in measurement technology, it is a certainty that the effort and COSt requ ired for collecting update informacion for natural resou rces will conrinue CO decrease in the future . The redu ced resou rces required fo r
database will likely be different from one o rganization
collecting update information will likely lead to an
ultimate customers (field perso nnel) will help determine
increase in update frequencies for many natural resource organizations. At the very leas t, update COSts will play less of a role in dererm ining how often to update a spatial or non-spacial database.
the appropriate update process for each GIS database. In any update process, copies should be made of the databases that are subject to updates. These copies should be
The processes used to perform an update of a GIS
[Q
the next, and from one 'Ype of GIS database to the next. Careful cons ideration of the components of a system (the people involved. the databases cons idered. the data acqu isition options. the softwa re and hardware techn ology
available, the budgetary constraintS), and the needs of the
kept until such time that the updates are veri fied, and that no furthe r need exists for the updates.
Applications 10.1. Land purchase. In the middle of the Brown Tract
polygons representing the land putchase and the polygons
you may have noticed a piece ofland that is managed or owned by someone else. Let's assume that the owners of the Brown T ract purchased this piece ofland , in an effort to consolidate the ownership area. You have been asked to develop stand boundaries for this area, and incorporate
in rhe original stands GIS database was assumed to be seamless.
them into the stands GIS database. Specificall y, you need to
a) What GIS processes co uld be used to ens ure that the edge would be seamless? b) Draw a Row chart of a process that could be used to ensure a seamless edge.
delineate the stands into logical age or structural cate-
gories, although the only attribute you are asked to add to the GIS database is one for 'land allocations' (even-aged or
database. Assume locations of a new set of roads have
uneven-aged). The forest staff will evemu ally develop an inventory for these stands and produce age, timbe r volume, and other statistics. To accom pl ish [his updaring
been captured with GPS equipment. The GPS-captured features are stored in the GIS database called 'New_roads'. Update the Brown Tract roads GIS database by incorpo-
task, you decide to use the digital orthophotograph asso-
rating these new road features . Use your best judgment to att ribute them in a consistent manner with the attributes
ciated w ith the Brown Tract GIS databases as a backdrop, and to use heads-u p digicizing techniques to delineate the new stands.
The forest planning staff needs the following information :
a) How much area ofland in eve n-aged sra nds will be added to
10.3. Adding new roads to the Brown Tract roads GIS
in the roads GIS database. a) How many kilometers (or miles) of dirt or native surface roads were in the original roads GIS database?
b) How many kilometers (or miles) of dirt o r native su rface roads are comained in the updated roads
GIS database? c) How many kilometers (or miles) of rocked roads were in the original roads GIS database?
d) How many kilometers (or miles) of rocked roads are contained in (he updated roads GIS database?
tograph. 10.4. Update process for a streams GIS database. A 10.2. Pondering the update process. In one example in this chap ter an update of (he Daniel Picken forest stands
GIS database was described, where a 32.4 hectare (80 ac re) parcel of land was purchased. The edge between (he
field forester working on the Brown Tract has compared mapped locations of streams to actual stream locations.
and has discovered some differences. She has proposed that the mapped meams be updated and has asked for 179
Chapter 10 Updating GIS Databases
your guidance in how this process might be accomplished. Describe three options fo r gathering the data necessary to fuci lirate an update of the streams GIS database. and the merits of each approach.
10.5. Update intervals and approaches. A recreation manager. who has learned of you r geospatial data skills. has asked your advice abou t updating the I O-year-old GIS (fails system. Two trails have been added [0 the system
169
10.6. Update tools and approaches. You've been asked to create a s pacial data laye r representing scructures
(b uildings). streams. roads. and watershed boundaries within a relatively small 259-hectare (640 acres) experimental fores( (hat is home to studies on hydrological observarion and testing. At present, a polygon identifies th e adm inistrative boundary of the experimental forest and only primary roads and perennial screams are
during (he past five years . The recreat ion manager is new to the area and does not know much about GIS and rel ated techno logies. The trails system is near an urban cenrer and indications are that use demity is increasing.
included in exisring spatial databases. The exisring data was created from hard copy maps at a scale of 1:24.000. You have at your disposal a 1:24.000 digital topographic quadrangle. a 30 m digital elevation model. a I m' resolution digiral orrhophotoquad. a consumer grade GPS.
Evidence of increasing use density includes a growing
and a tOtal station. To use the rotal station, cwo reference
by horseback riders.
benchmarks are located within 2.0 km (I.2 miles) of the
dog owners. and hikers throughout the forest. Additional evidence includes an unauthorized trail that is now clearly visible on the landscape. and which shows signs of signif-
forest boundaries. a) What data source andlor inscrumenr would you
number of conflicts being repocted
use to capture the new spatial information relared
ieanr use at one of the primary trailheads. a) What is an approp riate update interval for trail information? Defe nd yo ur cho ice .
(0 :
b) How wou ld you recommend that any new spatial data be collected?
111.
I.
ll .
structures? secondary roads? smaller st reams, including ephemeral streams?
c) How does any new spat ial and attribute info rma-
watershed bounda ries? b) Which of the new spatial information sources
tion collected be integrated into the exisdng GIS trails database?
described in pan (a) would you incorporate into existing GIS databases to update them?
d) What arrribures of trails would you encourage the
c) For the data you identified to be updated into exisring GIS database (described in parr (b». how
recreation manager to collect during field visits co the trails? Defend you recommendations.
IV.
would you incorporate the new information into existing GIS databases?
References Data Easr. (2007). XTools pro. Retrieved April 21, 2007. from http://www.xtoolspro.com.
Oregon Deparrmenr of Forestry. (2003). Guide to Xtools extension. Salem, OR: Oregon Department of Foresny.
180
Chapter 11 Overlay Processes Objectives
and identify spatial relationships becween
twO
o r mo re
databases. Overlay processes are powerfu l GIS tools and grate landscape features and attribute info rmation from
they represent what many consider ro be the essence of GIS: the ability to integrate and organize information
more [han one GIS database into a single GIS database. This capability is usefu l because it allows you to actively
from multiple spatial data layers into a single database. Overlay processes can be thought of as modelling tech-
investigate and determ in e relatio nshi ps between landscape features. Upo n co mpletion of this chapter. readers should have a suffic ient amount of cools in their GIS [001-
niques that allow us to consider what might occur from (he inregration of info rmation from multip le sources.
box to perfo rm many of the common vector GIS overlay
(ma nual) overlay analysis is that demonstrared in Ian McHarg's boo k Design with Nature (I 969). As me ntioned earlier in chapter I, this book provided examples of manual map overlays that were used ro idenrify areas that were su itable for certa in activities. Design with
One of [he grea< strengths of GIS is the capability to inte-
analyses required of field personnel working in natural resource organizat ions. w hethe r they are employed in
forestty, wildlife, soils, fisheries, or hydrology fields. The objectives of this chapter to provide readers with an introduction to three primary rypes of overlay analysis: the intersect, identity, and union processes. In addition, the ability of these overlay processes [Q accommodate different vector feature rypes (point. line, and polygon) is con-
sidered. When this chapre r is completed, readers should have obtained knowledge and understanding of: 1. the Outcomes from using an overl ay process to accompli sh one or more analytical tasks w ith in GIS;
2. the circumstances that help you decide when each of the three overlay processes might be used (0 suppOrt an analysis or research objective; and 3. the differences among the th ree ove rlay processes, and between them and other similar GIS processes. The topics discussed in this chapter relate to spatial overlay processes. Overlay processes are spatial analysis techniques that involve two or more GIS databases. More specifically, overlay processes can be used to investigate
Perhaps the most widely recognized example of pre-G IS
Nature inspired many people to apply manual overlay techniques for their own analysis needs and to also con-
sider developing digital tools (GIS) that might be used to make ove rl ay analysis more precise and efficient. The topics discussed in rhis chapter relate to spatial analysis processes that are similar ro merging (c hapter 8), and to obtai ning information about specific geographic regions (chapter 7) . There are, however, some very distinct differences between the merge process and (he overlay processes presented in this cha pter. Whe n twO (or
more) polygon GIS databases are merged, for example, the resulti ng GIS database may consist of overlapping polygons. The processes presented in this chapter-the intersect, identity, and union processes-all result in new output GIS databases where the landscape features do not overlap. Overlapping areas and attributes are combined. in some form or fashion, rhus the potential exists for polygons to be splir and combined and their topology reassessed in the result ing GIS database. Calculating the area 181
Chapter 11 Overlay Processes
covered by polygons in a merged GIS da",base may therefore be misleading, due to the presence of overlapp ing polygons. Funher. you may be imerested in the characteristics of rhe actual areas that overlap, such as the overlapping soils and forest stands. and how this information can
GIS database
Spatial features
171
Tabular attributes
Input GIS databases
attributes: basal area, volume per acre, vegetation type, age
Database to be intersected : stands
help you make berrer (or more informed) management decisions. To accomplish this with a merged GIS database is difficu lt, as the overlapping polygons are independent and share no informadon mher than that which the user can obtain visually on a map or computer mon iroc.
Intersect Processes
Database used to perform the intersectioo: fire
attributes: day, month, year
When performing an in tersect process. you hope to acquire information about rhe overlapping areas of [wo
GIS databases. The term 'inrersecc' implies (hat features meet or cross at cenain points. and (in the case of polygons) share common areas (Merriam-Webster, 2007). In using an imersect process. a third. new GIS database will be created [hat consists of only those areas where the two original GIS databases ove rl ap, no more , no less. For example, Figure 11.1 shows rwo GIS databases , one that represems vegetat ion (a stands C IS database), and the other representing a burned area from a fire. If you used an inte rsect process on the two GIS databases, the result-
ing GIS database wou ld conta in only the geographically overlapping landscape features. Spatially, the extent of the output will be defined by the boundaries of the polygons in the original two GIS databases: stands outside the burned area are excluded and the burned area outside of the vegetarion stands is excluded. The extent of the attribute data that will be conta ined in the resulcing GIS database can include attribute data from one data-
-----------------Output GIS database
Resulting database: stands, defined along original stand boundaries yet only within the boundary of the fire
attributes: basal area, volume per acre, vegetation type, age, day, month, year
Figure ) ).) Intersecting the stands GIS database with the fire GIS database on the Daniel Pickett forest.
stand and fire attributes. you shou ld consider using an Intersect process.
The intersect process works by locating the arcs (links) that are present in one GIS database that overlap some area in the complementary database. For example, GIS
databases. By comparison, a similar result can be obtained using a clipp in g process. Here, you might clip the vegetation
database # 1 in Figure I 1.2 contains a single polygon defined by two arcs. GIS database #2 contains twO polygons and 7 arcs. The polygon contained in GIS database #1 does overlap the polygons in GIS database #2, as arc I , runs from the middle of arc 2, to the middle of arc 2"
GIS database using the fire GIS database. This alternative,
crossing over arc 2<\ in the process. Therefore, arc 11 over-
while providing the same geographic representation of the vegetat ion burned by the fi re. provides no attributes of
#2, while arc
base or all arrribute data from both of the original GIS
the fire in the resulting output GIS data base. Although the example includes only one fire, suppose you had a fire
GIS database that contained the geographic boundaries of several fires that may have occurred over a summer. If
each fire polygon was attributed with the day, month, and year of its inception, this information would not be contained in rhe output GIS database if a clipp ing process was used . To generate a GI S database that not only included the stands within the burned areas, but also the
laps an area of represented by the polygons in darabase 12
does not. Further, portions of arcs 2 4 • 2 6 •
and 2, overlap some area represented by the polygon in database #1, yet arcs 2 " 2" 2" and 2, do not. The resulting output GIS database will conta in two polygons, with one side bounded. by portions of arc I I (split at the intersection with arc 2<\), and the other sides by portions of arcs 2<\. 2 6 , and 2 7 , To further ill ustrate the power of the imersect process, the next example illustrates how you can use the results [Q assist in developing information relevanr to natural 182
172
Part 2 Applying GIS to Natural Resource Management
~ ~,,
Input GIS Database #1
,
Input GIS Database #2
2, 2,
2. . 2,
Input GIS Database #1
,:, 22 ,, 2, ,, ,
. 2,
_~I.o'---
~
In#l and #2
Inl1 , outside 01112
Input GIS
+-- --1-
Database #2
In #2, outside of #1
area. In addition , the ability to accurately and precisely meas ure and map changes in soil types is limited by the tremendous effort that is required to sample and del ineate soils. Nonetheless, by overlaying the twO GIS databases using an intersect process (Figure II.3), a GIS database is created where polygon boundaries are now defined by those lines that were present in both the stands and soils GIS databases. The outside boundaty of both the soils GIS database and the stands GIS database was exacdy the same, therefore no areas covered in either database are excluded in the resu lting stands/soils GIS database. The resulting sta nds/soils GIS database co ntains more polygons (47 polygons) than what was fo und in th e original stands GIS database (31 polygons) and soils GIS database (7 polygons) . Upon inspection, you may find that may of these polygons are actually spurious (small and irrelevant) polygons crea ted simply due [Q the happenstance location of the bounda ries of polygons from the original twO GIS databases (Figu re 11.4). The tabular data co ntained in the resulting intersected stands/soils G IS database contains all of the anribures of
AP~r,on Output GIS Oatabase
ola~ olaret, . Aportion of arc 26
Aportion of arc 2,
GIS database
Aportion of arc 2 ~
Figure 11.2 An example of the processing of landscape features during an inH:rsect process.
resource management planning. In this exam ple. ass ume YO ll are interested in developing information for a potential forest fertilization project, and an examination of the inre rsecrion of [he D aniel Pic ke[[ stands and so ils GIS databases would be helpful, since the decision to fertilize may be based on both fo rest srru crural co nditions and soil conditions. Separately. each of these [\.'10 GIS databases comains a theme: th e stands GIS database describes the forest structural conditions of the Daniel Pickett forest and the so ils G IS database describes the underlying soil types and their characteris tics. The stand polygon boundari es are defined by tra nsitions in forest structural conditions (e.g., a change from young fores t to older forest defines a sta nd boundary), roads, and perhaps st reams. The soil polygo n boundaries are defin ed by changes in soil characteristics, although so me would argue that these boundaries shou ld be co nsidered 'fuzzy' because soils, generally speaki ng, do not change as abrupdy as stand characteristics might, bm change gradually over a larger
Spatial features
Tabular attributes
Input GIS databases Database to be Intersected: stands
Database used to perform the intersection: soils
~ ~
attributes : basal area, volume per acre. vegetation type, age
attributes: soil type, response to fertilization
------ ------ -----Output GIS database Resulting database : stands, defined along original stand boundaries as well as soils polygon boundaries
§i
attributes : basal area, volume per acre, vegetation type, age, soil type, response to fertilizati on
Figure 1l.3 Intersecting the stands GIS database with the soils GIS database on the Daniel Pickett fo rest.
183
Chapter 11 Overlay Processes
Spurious polygon
173
to ferti lize. To find these areas in the Stands/soils GIS database, you might develop a query such as the following (for a review of queries, please refer back to chapter 5): [Age;" 20) and [Age S 30) and [Fertresp = 'high')
-;/jLy--J Height at this end: about 1.5 meters
Width: about 20 meters
where: Age
=
the stand age attribute in the Daniel Picke[[
Stands GIS database, and Fenresp
=
the fertilizatio n response a[[ribute in the
Daniel Pickett soils GIS database Figun 11.4 A spuriow polygoo that was created during the intersect process.
each of the original stand polygons, and all of the attributes of each of the original soil polygons. Thus for each of the 47 polygons in the stands/soils GIS database, you now know the stand conditions above ground as well as the
With th is rype of query GIS will provide the locations of areas where the imersecrion of stand age and so il type meeting the criteria. As you can see in the case of the
Daniel Pickett forest (Figu re 11.5), the potential fertilization areas may not correspond directly with the original stand boundaries. Natural resource managers using an
soil conditions below ground level. With this GIS data-
analysis such as this will subsequently need to decide whether to fertilize whole Stands (not JUSt the part of a
base you can perform queries that rdate to both stand and soil conditions. For example. based on a discussion
nand whe re me stand and soil condi tions are appropriate. since stand boundaries are usuaIly easy to locate) or pans
with the foreSters associated with the Daniel Pickett foreSt, it may be decided that 20-30 year old Stands on soils
of Stands. The advantage of fertiliz ing a whole Stand,
that we re ame nable [Q a high forest growth response [Q a fenilization treatment would be the most preferable areas
when only a portion seems appropriate. is that you do nO{ need to waSte time trying [Q identify a vague transi-
tion in soil types. On the other hand, fertilizer, and the
What is a spurious polygon? When something is
included with other larger, adjoining polygons. In
labeled as 'spurious'. it is meant [Q indicate that it is counterfeit. false. fictitious. or not legitimate (Merriam-WebSter, 2007). Within GIS, spurious polygons are certain ly genuine. and can be somewhat trou-
some cases. the spurious polygon could become a part
bling to eliminate. These polygons arise simply because the com pmer and GIS software are taking two GIS databases and combining them according to the
instructions provided by the user. They are only doing what they were told to do: break polygons along intersecting lines and create new polygons using the newly formed intersections. Spurious polygons might nO{ be considered legitimate. however. given the managemem needs of an organization. Most organizations. in fact. have what they term 'minimum mapping units'.
and polygons below this size are eliminated by being
of an adjoining polygon with which it shares the longest edge. The shared edge is essentially removed, and the difference between the spuriolls polygon and its adjacent neighbor is lost. When using GIS processes such as me intersect, clipping. and buffering processes.
spu rious polygons will undoubtedly be created. How you manage them (e .g .• ignoring them, elim inat ing them. etc.) once they have been created is a matter of personal preference. or perhaps a reaction to organization 5[andards. Most natural resou rce management
decisions will not be affected by the presence of spu rious polygons, however the presence of spuriolls polygo ns may become a database management problem and may detract from a message presented in a map. 184
174
Part 2 Applying GIS to Natural Resource Management
CJ
Stand boundaries
[-":.......J Potential fertilization areas
~ ~ ,, ,,
Input GIS Database #1
,
Input GIS Database #2
,,: 21 2,
,,, ,
2,
,
--------- , -------,,,
Input GIS Database #1
hel icopter system to disrribme the fertilizer, which is a co mmo n pract ice when fertilizing forested stands, it is
possible that G PS technology may be employed to assist in locating the desired fertilization areas. The GPS data may facilitate the devel opme nt of a digital map. which can help guide the pilo t or can be co upled with an internal guidance system to autOmatically apply fertil izer.
/+--
In #t and #2
__ __ __ .J ___ _ _
'--_ _ -./-'1-- -
applicado n of fertilizer. costs mon ey. and o rgan izatio ns may want to operate as efficiently as possib le. Therefore, rhe goal would be [Q apply treatments only where necessary. Ie is becoming increasi ngly co mmon [Q provide a fenilizar ion co ntraccor with rhe exact geograp hic coordinates that define rhe fertilizatio n area, either with maps or acrual GIS databases. If a fenilizarion contrac[Qr uses a
2,
t 27
. 2, Figure 11.5 Potential fertilization areas on the Daniel Pickett forest that consiSt of forest stands 20-30 years of age located on soils that provide a high response to a fertilil.3tion applic3[ion.
, ,,: 22 ,,, ,
Input GIS Database #2
In #1, outside 01#2
+---t- In #2 , outside
:-- ...... _,
01#1
, .........
,/; "
2,
:
.~:+--+ In #1 and #2
I
I
2,
Output GIS Database
Figun~ 11.6 An example of the processing of landscape fea lures during an idenucy process.
Identity Processes ing GIS database has a geogtaphic extent representing the When performing an identi ty process, you hop e to incorporate information about the overlapping area of
one GIS darabase into a second GIS database. While the term 'identiry' refers to a distinguishing character of a per-
sonality (Merriam-Webster. 2007). this process is more likely similar ro the term 'identi ry element', w here pares of the o rig inal data are left unchanged when combined with other data. This is partially true-some portion of the
original GIS data may be left unchanged {and present in the o utput)-w here a second set of GIS data does not co incide spatially . Si milar to the intersect process, o ne GIS da tabase is physica ll y laid onto another, yet there is usu ally a d isti nct d ifferen ce in the resul ting Ol1[Pur when compared w ith th e in te rsect process . The resulting GIS database is
same area as database #2, yet the arcs that defi ned the
polygo n in database # 1 are present where the polygo n from database #1 overlapped the polygons in database #2. The resul ti ng GIS darabase now includes 4 polygons. 12 arcs, and 9 nodes , as arcs 2 4 • 2 6 • and 27 were split into twO pieces (a and b) based on their intersection with arc 11> and arc 11 was split into two pieces based on its intersectio n w ith arc 2 4, To extend the idenri ry process exam ple (() the D an iel Picken fores t, aga in examine the case of the stands GIS
database and the fire GIS database. Suppose the intent was [Q
develop a GIS database that contained the entire stands
data (geographic an d tabular). but with the fire bo undaries being integrated into the stands database. Using an ident ity process you ca n see that some stand pol ygons
defined by the boundary of o ne of the input GIS data-
have been split along the fire po lygon boundary (Figure
bases, not by the boundary of the ove rlap between the two GIS databases . Figu re 11 .6 illustrates that the resulr-
11.7), thu s the fire GIS database has an influence on the structure of the resultin g polygons. In addi tio n, 3nribure 185
Chapter 11 Overlay Processes GIS database
Spatial features
Tabular attributes
175
o
Stand bouooaries
D
Firearea
Input GIS databases attributes: basal area, volume per acre, vegetatiOil type, age
Oatabase to perform the identity process Oil : stands
Oatabase used 10 perform the identity process: fire
o
------
attributes: day, month, year
------ -----Output GIS database
Resulting database: stands, defined along original stand boundaries and along the booodaries of the fire
~
attributes: basal area, volume per acre, vegetation type, age, day, month,
,ear
Figure 11.7 Performing an identity proass on the stands GIS database using the fire GIS database.
fields are present in the resulting GIS database [0 represenr those that were presenc in the fire GIS database. However,
only the polygons within the fire boundary actually contain data related [0 the fire. The amibute fields from the fire GIS database related [0 polygons outside the fire area are empry and contain '0' values (Figure 11 .8). A key concept when performing the identiry process is obviously determining the spatial extent of the [wo GIS databases that is [0 be retained. In the above example, the fire GIS database was overlaid on the stands GIS database, and the incem was [0 rerain the spacial extent of the stands GIS database. If you were to reverse the order and overlay
m e stands GIS database onto the fire GIS database, the resulting GIS database would have quite a different look [0 it (Figure 11 .9), as the spatial extent of the resulting GIS database is defined by the spatial extent of the fire GIS database. Here, only the stand boundaries within the fire remain. While the stand-level data anributes associated with the stands in [he fire area are presenr in the resulting GIS database, no stand-level data attributes are available
for the polygons outside of the area represented by the original stands GIS database (Figure 1l.l0).
Stand V StandY StandW
Stand
VegType
Basal Area
Age
MBF
Month
Day
Year
T
C
120
5S
19.5
7
2
2002
U
A
260
70
37.7
7
2
2002
V
A
260
70
31.7
0
0
0
W
C
190
45
17.3
0
0
0
X
B
20
10
1.8
7
2
2002
y
B
20
10
1.8
0
0
0
Figure 11.8 A more detailed examination of the results of the identity process of the fire GIS databue overlaid on the stands G IS database.
Union Processes In a union process, the intent is to overlay one GIS data-
base on top of another GIS database, and re tain all of the spatial within [wo or Figure
boundaries of the landscape features contained both GIS databases, A 'union' is the act of joining more features into one (Merriam-Webster, 2007). 11.11 illustrates that when using a union process.
the resulting GIS database has a geographic extent representing the same area as both database # I and database
#2, yet the arcs that defined the polygon in database # I are present where the polygon from database #1 overlapped the polygons in database #2. The resulting GIS database now includes 5 polygons. 13 arcs, and 9 nodes, as arcs 2... 26 • and 27 were split into two pieces (11 and b) 186
176
Part 2 Applying GIS to Natural Resource Management Tabular attributes
Spatial features
GIS database
~ ~ ,, ,,
Input GIS
Input GIS databases
Database " Database to perform the identity process on: fire
attributes: day, month, year
o
Input GIS
Database .2 2,
,
,
: 21
,:, 22 ,, ,,
,, ,
,, ,
2,
... 27
... 26
8:. ---
Database used to
attributes:
perform the identity p(ocess: stands
basal area, volume
-------------
per acre, vegetation type, age
Input GIS
Database"
2,
In# 1 and #2
_____ J____ _
~
In ", outside 01112
Input GIS
output GIS database
+---t-
Database '2
Resulting
database:
" "
attributes: basal area, volume per acre, vegetation type, age, day, month, year
fire, defined along original fire boundaries and along the boundaries of the stands
./
,,'
;--- .... : I
In '2, outside
oil'
\~f--+-
In 111 aOO'2
2, Output GIS
Database Figure 11.9 Perfo rming an ide ntity process on the fire G IS database using the n ands GIS database.
/'
o
Fire area outside of Daniel Pickett forest
D
Fire area inside 01 Daniel Pickett forest
Stand M
\
Stand N
Stand
VegType
Basal Area
Age
MBF
Month
Oay
Vear
L
C
120
30
5.6
7
2
2002
0
7
2
2002
C
0 190
0
N
45
17.3
2
2002
U
A
260
37.7
2
X
B
20
70 10
7 7
1.8
7
2
2002 2002
M
Figure 11.11 An example of the processing of landscape fe atures during an union process.
Figure 11 . 10 A more detailed examination of th(' results of the identity process of the standt G IS database overlaid on the fi re G IS database.
based o n the ir intersection with arc 11. and arc II was split into twO pieces based on its imersection with arc 2 4, To ill ustrate a union process with a more realistic natural reso urce management problem. suppose a union process were to be performed on the Daniel Picket[ forest fire and stands GIS darabases. The res ul ting GIS database has the combined geograp hic extent of rhe (wo GIS databases, and co ntain s similar landscape fea tu res as we re fo und in rhe origi nal fire and stands GIS darabases (Figure I 1.12). This iliumares o ne adva ntage of using a union process: rhe sparial delinearion of rhe polygons in rhe resul ring GIS database is a fu nction of both of the origi nal GIS databases. thus polygon boundaries fro m both original GIS databases are reta ined. However, it also suggests a disadvantage of the process: rhe resulting GIS data base may comain landscape features outside of [he boundary
187
Chapter 11 Overlay Processes GIS database
Spatial features
1n
Tabular attributes
Input GIS databases
Database to perform the union process on: fire
attributes: day, month, year
Database used to perform the union process : stands
attributes: basal area, volume per acre, vegetation type, age
o o
o
Output GIS database
Resulting database: fire and stands, defined along original fire boundaries and along the boundaries of the stands
attributes : basal area, volume per acre, vegetation type, age, day, month, year
Stand attributes, no fire attributes Stand attributes, fire attributes No stand attributes, fire attributes
Figure 11.13 Illustration of completeness of a tabular database after a union process of the fire and stands GIS database.
ment analysis. In locating [he su itab le areas, you decide that the criteria should include locating a certain type of
soil (loamy soils), on a ce rtain slope condition (flat
sented in ,he OUtput G IS database. The attribute fields contained in both of the original GIS databases may also
slopes), where the area is currently wned for agricultural use, and where there are few limi tadons for using the land to grow agricultural produces. In this assessment, there are four distinct anribures about the land: so il type, slope condidon, zoning code, and land classification . These four attributes are comain ed within four different GIS databases associaeed with the Pheasant Hill planning area, and are delineated using polygons that do noc necessary
be present in the resulting GIS database, however some
coincide (spatially) from one G IS database to the next.
data cells will likely be empty in the attribute table (Figure
One way {Q accomp li sh this overlay analysis is {Q use an iterative union process to bri ng all fou r databases together so chat all of the anribures are available in a single, com-
Figure: 11.12 Performing a union process using the fire GIS database and the stands GIS database.
of interest, and thus perhaps may include unnecessary
landscape features. Although some of the fire polygon lies outside the Daniel Pickett forest, this area will be repre-
11.13). The union process is useful for those situations in
which you want to preserve all of the spatial and non-spatial data that is present in twO input GIS databases. More complex analyses can be performed using the intersect, identity, or union processes than simply bringing together the characteristics of twO GIS databases. For example, suppose you were interested in locating areas suitable for a certain type of agricultural praccice in the
Pheasant Hill planning area of the Qu'Appelle River Valley in central Saskatchewan. While the databases we will use in th is example are dated (\980), they are rich with information and they allow us to examine (he usefulness of the union process for a natural resource manage-
bined GIS database. Initially, two of the GIS databases would be unioned, then a third would be unioned to the union result of the first two. Finally, the fourth database would be unioned to the union result of the first three GIS databases. Using a query thac involved the criteria listed below, the areas suitable for the praccice you had
in mind could be identified (Figure 11.14), since the union of the four GIS databases would contain the attrib-
utes of al l of the original GIS databases, and since the polygons would be split along the boundaries of the original polygons.
188
178
Part 2 Applying GIS to Natural Resource Management
.
- ..-
-= ,pg."
.IiOi'_
_
Areas suitable for an agricultural practice
c=J
Other areas that do not meet the criteria for an agricultural practice
Figwe 11.1 4 The result of a query on the union of soils. topography, land classification, and zoning G IS databases developed for the Pheasant Hill planning area of the Qu'Appclle River Valley. Saskatchewan (1980).
Query criteria
Original database
Soil_type ", Canora Loam or Soil_type ", Indian Head Clay Loam or SoiLrype ,. Oxbow C lay Loam or Soil_ rype ,. Oxbow Loam or Soil_type" Rocanvillc C lay Loam or So il_type II: Whiresand Gravelly Loam
soils soils soils soils soi ls
Topography
topography
=
FLAT
soils
Class" No Significant Limitations or Class", Moderate Limitations
land classification (eLi)
Zoning = Agriculture Priority I
zoning
As an example. if you use a GIS database containi ng lines as an input GIS database. and then intersect it wi th a GIS database containing polygons (Figure 11. 15), the resulting GIS database wi ll be composed of line features _ The line features will be split at all intersections with the boundaries of the polygons in the pol ygon GIS database,
land classification (eLI )
Incorporating Point and Line GIS Databases into an Overlay Analysis Although the overlay examples thus fa r have focused on the analysis and manipulation of polygon GIS databases, it is also possible to inco rporate other types of features (points and lines) into overlay processes. For example. point and line GIS databases can be used in association with polygon databases when performing the intersect and idemity processes. The union process. however, requires that all GIS databases of interest be composed of polygon features. When using the imersect and identity overlays. the input GIS database can be composed of poin ts, li nes, or polygo ns but the overlay GIS database must be composed of polygons with one exception. With in some GIS software. the intersection of two line databases is possible. with the result being a new point database that has ca ptured all intersection locations. When point or line databases are involved in an overlay process with a polygon. the resulting Output GIS database will be of the same feature type as the first input GIS database (point or line).
,
,,, ,, ,,
Input GIS Database '1
.
Input GIS Database In
,
,,
2,
, ,,
,,, ,,
2,
,, , ,,, ,, ,
• • • • 2,
2,
2,
2,
2.
- - -- - -
- -- -
--- -
1, Overlay of Database #2 on Database.1
2,
2, 2,
1, 1,
2,
I,
2, 2.
2,
--- ---- -- - -
- -- -
Output GIS Database
1,
Figure 11.15 An example of the manipulation oflandsc.1pe features during an intersect overlay of line and polygon databases.
189
Chapter 11 Overlay Processes
179
and only lines that full within the extent of the polygons will be retained in the outpUt GIS database. While this represents a process similar
[0
the clip process, the resul t-
ing lines contain the information (attrib utes) of the polygons within which they fell. In the example in Figu re 11.15, a line (I) that represents a road (perhaps) is being overlaid by the two polygons from previous examples in
.
<, 1.'1.
this chapter. The line initially comains two nodes, but when the overlay process occurs, it is broken into four
", 1.'. '..-:
arcs (II' I" 1" and 1. ) with 5 nodes. The pieces of the original road that fall outside the area covered by the polygons (I I and 1, ) are subsequently eliminated. The resulting GIS database comains a ponico of the original line (sections 12 and 13), and each port ion of the original
".~>
~ Research Plots [:=:J Forest Stands Figure 11. 16
Research plots and forest stands on the Brown T ract.
li ne contains the anributes of the associated polygon within which it was contained. An idemity process using point or line GIS databases as the input database results in a GIS database that concains all of the original point or line features, yet the landscape features would contain the 3ucibure information of the polygons within which they fell. Li nes would also be split at polygon boundaries as in (he previous example. The primary purpose of the identi ty process wou ld be (Q distribute anribute data from an overlaid polygon GIS database (Q a poim or line. T he idenrity process with a point GIS database as the input GIS database is similar, in fact, to a point-in-polygon query, although the resu lts here are not temporary.
Applying Overlay Techniques to Point and Line Databases We presenr he re some examples of app lying overlay processes (Q po int and line databases (Q demonsuate potential overlay applications. Our examples make use of the Brown Tract databases described earlier in the texc. In the first example, cons ider the distribution of research plots as they relate to the Brown Tract forest stand
ual stand boundaries in wh ich they were located. A statis-
tical frequency could be generated from the new database and could demonstrate the distribution of land allocations conta.ined in the resea rch plot locations (Table
11.1). In this case, the majo ri ry (48 of 57) of research plots are located in even-aged stands. Given rhe geometry of the research plots and stand boundar ies, an identity
overlay process berween these flies should produce the same results as the inrersect process, As mentioned earlier, overlay operations involving line da tabases are also possible, As an example consider the arrangement of streams relative (Q stands in the Brown
Tract (Figure 11.17). The re are portions of the Brown Tract whe re the stream network extends beyond the forest boundaries. In this case, the intersect and identity overlay commands would lead (Q diffe rent output databases. The intersect overlay between these layers would result in an output stream system that would be reduced in extenr from the original; o nly those stream segments that overlapped the stands would be retained in the out-
put database. In addition, all data fields in both databases would be populated in the output database. The identiry
boundaries (Figu re 11.16). It might be of interest to dete rmine rhe distribmion of land allocation categories (described in the forest stand layer) that are associated with each research plot. A point in polygon intersect overlay is one approach an analyst could use to determine this information . The results of the imersecr overlay in this case create a new point layer that comains all plot loca-
tions and an expanded list of data fields. Each of the resea rch plot records wou ld contain both the or iginal data
fields and the same data fields and values of the individ-
TABLE 11.1
Land allocation
Frequency distribution of land allocation categories in research plot locations within the Brown Tract Number of research plots
48 Research
6
Uneven-aged
3
190
180
Part 2 Applying GIS to Natural Resource Management
the omput product of overlay analysis in these cases. In order to assimi late information from point and line databases into a polygon database, other approaches such as tabular or spatial joins must be considered.
Additional Overlay Considerations
- - Streams Forest Stands
Figure 11.17 Streams and forest stands on the Brown Tract.
overlay would retain all the Stream segments, bur we would only find stand data in a resulting database for a ponioo of [he stream segments. The selection of which overlay to use-intersect or identity-will depend on the analysis goals. Let'S assume that the analysis goals suppon retaining aU st ream segments in a final streams database. An identity ourpm database between streams and stands contains 398 stream records since geometric intersections are created at all coincident locations in the input databases. The distribution of land allocation values fo r these streams is rep resented in Table 11.2. Note that there are 40 stream segments that do not have a land allocation value. These missing values represent streams omside the Brown Tract boundaries. Although point and line spatial databases can be used with overlay processes to derive info rmation from polygon databases, polygon databases are unable to serve as
TABLE 11.2
Land allocation Even-aged
Frequency distribution of .. land allocation categories ill relation to stream segments within the Brown Tract Number of research plots
281
Meadow
2
Oak Woodland
3
Research
7
Unevcn·agcd
65
40
Each of the three overlay processes discussed in this chapter are designed for different end products (Figure 11.18). Although different outputs will usually result depending on whic h overlay is chosen, there are situations where the ompm of two overlay processes will be the same, regardless of which process is used. For example. if two polygon databases have the same spatial extent, it will not matter whethe r an intersect, identify, or union process is used. In addition, jf one spatial database is contained completely within the extent of another database and is used as the primary overlay database, an identity or intersect command should produce the same resulr.
Input GIS dalabase 1
~ ~ 0
~
Input GIS database 2
Output GIS database
0
bJ
0
~
Intersect Output
Identity Output
~ 0
LlSJ
Identity Reverse Order Output
~ Union Output
Figure 11.18 Summary of results for intersect, identity, identity reverse order. and union overlays for the stands and fire GIS databases.
191
Chapter 11 Overlay Processes The order in which GIS databases are selected for an overlay process may also have significance on the ompur database, depending on your GIS software. The identity
overlay will take into accoum (he spatial extent of one of your databases, and reduce other layers (Q the same spatial extent. For this reason it is imponam (Q recognize how you r soft\vare selects (he inpur database that is used to determine the idenriry spatial exrenL The union and intersect overlays. on the other hand. are less discriminating as to the order in which spatial exrencs are considered. The union output wi ll include the emire spatial extent of all layers while the intersect OUtput w ill only include the common areas. Some GIS sofrware. however, may only include by default the attributes of the initial input layer in the outpm database unless the user signifies otherwise. Depending on you r GIS software, you may also be able co involve more than [Wo layers in an overl ay process. Nthough this ability may be particularly useful for some applications, the complexity of the Output product will increase with the number of layers that are involved. Overlay ompm databases cake into account the topology
181
and attr ibutes of all input layers depending on operaror choices. Bear in mind that overlay processes are complex operations, particularly when many features are present in input databases. As such, and depending on computing resources, some overlay omput databases may take more time [Q complete than oilie r, less intensive. GIS processes. Users may have [Q be patient in awaiting overlay ompm results. Finally. many GIS sofrw-are programs allow users to select the attribute fields from both the input GIS database and the polygon overlay GIS database that are ro be carried into the resulting output GIS database. Often you will find that selecting a subset of attribute fields for the desired analysis will reduce the time requirements related to interpreting and analyzing the results of overlay processes. Given the considerations expressed above, you should always carefu ll y consider the objectives of your a nal ysis to dete rmine which overlay process [Q use. In some cases, knowing whether input vec[Qr databases represent point, line, or polygon spatial features, or some combination thereof, will help lead ro an appropriate ove rl ay command choice.
Summary With the conclusion of this chapter you have examined most of the common vectOr processes available within GIS software programs. As we have shown in this and previous chapters, to address natural resource management problems a number of different courses of action can be used, each utilizing different techniques or different sequences of GIS processes. For example, the following three processes might be employed ro develop a summary of forest resources within owl buffer areas: I. Buffer owl nest locations. Clip the owl buffer areas
from a stands GIS database. Summarize appropriate statlS[1cs. 2 . Buffer owl nes( locations. Intersect a stands G IS database with the owl buffer GIS database. Summarize appropriate statistics. 3. Buffer owl nest locations. Overlay a stands GIS database on the owl buffer GIS database using an identity process. Summarize appropriate statisdcs. The GIS toolbox available to readers of this text, as it relates [Q vector GIS processes, should now contain a
number of useful rools. The challenge lies in deciding which tool(s) to use to address each natural resource management issue. The intersect, identity, and union processes each result in different outcomes, and you need to match the process to the type of information you desire. While the union process is restricted to polygon input and outpm databases. point and line vec[Q r databases ca n be used in intersect and identity process. The intersect process only provides information for features that overlap. An identity process breaks the featu res contained in one GIS database along the lines provided in a second GIS database, yet retains the full extent of features contained in the first database. In chis case. some of the fearures in rhe first database will not contain information from the second where the features in the second are absent. The union process also breaks the features contained in one GIS database along the lin es provided in a second G IS database, yet retains the full extent of features contained in both databases. Where features overlap, rhe attributes of both databases will be present in the attribUte rable. Where features did not overlap, only arrributes from one of the GIS databases will be present. 192
182
Part 2 Applying GIS to Natural Resource Management
Applications 11.1. Fertilization Plan. You have been asked to help develop a fertilization plan for the Brown Traer. In next year's plan of action, the managers of the forest may consider fert ilizing some forest stands. However, the managers need [Q know how much area of forest could be ferr ilized, and what casc co anticipate. After a short di scuss ion with the Brown Tract managers, the follow~ iog criteria for ident ifying potential ferrilization was determined:
have taken advantage of the intersect process, and obviously used a buffer process at some point. T o illustrate the steps raken to accomplish the analysis associated with
Application 11.1 , develop a flow chart similar to the one below (Figure 11 . 19) that describes the process you used . 11.3. GIS processing (2). Use a flow chart to describe (WO other (different) processes that could also be used to gene rate the same results as those ge nerated for
• Only stands;:>: 25 years old and $; 35 yea rs old should be considered. • Only stands on soil types leading to a high fertil ization response should be considered (soil types 'PR' and ' ON').
Application I\,\, You may want to actually perform the process and check your results to see wherher the results are identical to those obtained in Application I \.\.
• Only stands oU[side of a 50~me[er stream buffer
class, burned on the Daniel Picke(( forest during the July 2, 2002 fire? Use the '070202_fire' GIS database to describe the boundary of the fire.
(a round all types of streams) sho uld be considered.
11.4 Fire losses. How much forest area, by vegetation
The forest managers wam co know the following:
a} How much area of land could be ferti lized, given the criteria noted above?
b} Assuming the forest will develop a contract for the fertilization project, and assuming the contractor will lise a hel icopter to spread the fenilizer, how much area of land would you recommend, and why? Consider the following in your recommenda-
11.5 Operations around research plots. Assume that the managers of the Daniel Picke(( forest have decided to clearcU( sta nd 28. They noticed, JUSt prior to allow ing Vegetation GIS database
tion: (I) Are some of the stands of the appropriate age bisected by (WO or more soil types? (2) Would it be possible fo r the helicopter pilot to recognize these changes from the air? (3) Are some of the
Buffer streams 50 meters
areas suggested by the GIS analysis for fertilization
tOO small to be worth rhe effort? (4) Is there a minimum size assumption would you make regarding pocenrial fenilization areas?
c) If the COSt of fertilization was $250 per hectare, what might be the total COS t of the project? d) How much fenilizer is needed if you expect
[0
use
450 kilograms of fert il izer per hectare (about 400 pounds per acre)?
e} Please provide the staff with a map of the potential
Ouery process
areas that yo u are recommending to be fenilized
(from part b above). Include rhe Brown Tracr roads and streams on the map.
11.2. GIS processing (1) . There are a number of processes that can be used to arrive at the info rmation requested in Application 11.1. For examp le, you may
Figure 11 . 19 Hierarchy of intermediate and final GIS databases created in the development of a GIS database describing the older fo rest vegetation within 50 meters of all streams.
193
Chapter 11 Overlay Processes logge rs
to
begin operatio ns, that a research plm (number
3) was locared in me srand. If a 100 merer buffe r was left
183
on the map (either with annotation or with a shad-
ing scheme) the basal area of all of me stands.
around the research plot,
a) How much fo rese area in srand 28 will acwally be clearcur?
b) If rim ber values we re $400 per rhousa nd board fee< (MBF). whar is the value of rimber thar will remain around rhe research plor [h int: arrribure 'Mbf' represents thousand board feet of timber volume per acre)? c) What is the djfference in dmber volume and value
11.6. Integrating streams and vegetation data. Develop a strea ms GIS database fo r (he Brown T ract where each stream contains the data related to the vegerarion polygon within which it is located. Using this data, develop a thematic map that illustrates one of the vegetation characterisrics of the forest around each stream. In this exercise, change the appearance of the Streams to illustrate the condit io n of [he forest.
between leaving the buffer around the research plor and not leaving me buffer?
d) Develop a map thar describes the vegetation condi-
11. 7. Fish bearing streams.
rions on the forest after the harvesting operation
a) How many stands in the Brown Tract contain fish bearing streams?
has comple<ed. Assume rhar rhe 100-merer buffer
b) How many srands are within 100 merers of fish
was maintained around the research plot. I1Jus[rare
bearing streams?
References McHarg. l. (I 969). Design with nature. Garden Ciry. NY: Natura l Hiseory Press.
Merriam- Webseer. (2007). Mtrriam- Webster online uarch. Rerrieved April 29. 2007. from hrrp :/Iwww. m-w .coml cgi-binl dictionary.
194
Chapter 12
Synthesis of Techniques Applied to Advanced Topics Objectives As you may have found during your vario us interactions with GIS, there are a number of spatial processes (o r mix(Ures and arrangements of methods) that can be used to find the appropriate solution to a namral resource management problem. As we near the end of Parr II of this book, this chapter seeks (Q integrate and synthesize the GIS processes introduced in previous chapters. and apply them (Q more complex namral resource management problems. At the co nclusio n of (his chapter, yo u should be familiar with: 1. how a set of complex management ru les or assumptions can be synthesized into quantitative information [hat can be used in a GIS analysis; 2. how a number of GIS processes can be integrated to allow you to develop a spatial representation of a value (ecological, econom ic, or social) rhar represems some aspect of a landscape; and 3. how ecological , economic, or social descriptions of a landscape can be developed [Q provide managers and other decision-makers with informat ion regarding the cur rem sratus of a landscape. Many of the GIS processes presented in previous chapters can be inregrated to allow users of GIS [Q perform com plex landscape analyses. As we progressed through chapters 5 [Q 11, we buih upon GIS processes to show how t hey may be complementary. For example, query
processes allowed you to qu amitatively examllle the results of analyses that req uired buffering processes (chapter 7). In anorher example, clipping and erasi ng processes were used in tandem to create a new database of select features (chapter 8). C hapter 12 presents some examples of advanced topics in natural resource management fo r yo u to consider, and each requires an imegrarion of the GIS techniques presented in ea rli er cha pters. The roo Is acq uired by wo rking through the previous chapre rs should be more than adequa te to address the problems introduced in th is chapte r; rhree rypes of advanced management pro ble ms are prese nted and the 'Applications ' at the end of the chapter provide an opporru niry for readers to perform similar analyses themselves. Once again, there are a num ber of paths you can take to ap proach each management problem presented in the app lications section. You should approaeh each problem acco rding to you r preference of methods and techniques. H owever, only one se r of answers to each narural resource management problem exists, and no maner what process you select, you should ultimately locate the ap propriate answers. We begin with land classification, where separate and disrincr categories of land are delineated, each suggesting a d ifferent level of natural resource management acriviry wi ll be allowed. Land classifications are, in fact. common starr ing points fo r [he development of land ma nagemenr plans. You may need to use the querying, buffering, erasing, and ot her GIS processes to parse a land ownership into distinct non-overlapping classes that co mpl etely 195
Chapter 12 Synthesis of Techniques Applied to Advanced Topics
185
cover a landscape. A similar problem is then addressed.
perspeccive where srracificacion of land is based on eco-
where a landscape is delineated into Recreacion Oppor~
tunity Spectrum (ROS) classes. ROS classes are designed to
nomic and managemenr- relaced variables (roads. screams. erc.) . The example we provide below caprures (he essence
describe and emphasize the potencial recreation opportunities across a landscape, ranging from primicive recre-
of this type of land classification. Alternatively. land classifications can be made purely from an ecological perspec-
ation opporcunicies with few visitors to chose where
tive, using vegecation, soils. wacer, climare. and ocher
momrized vehicles and many visirors may be prevalenc.
physica l variables. The fou r ecological classifications used in Canada are a good example of these. They are designed
Again. the querying. buffering. erasing. and other GIS processes may
be
used to delineate the non-overlapping
in a h.ierarchy and range from broader ecozones co smaller
ROS classes. Finally. wildlife habitat suitability index
ecodiscriccs. Each of rhe c1assificacions have ecosysrems rhar are predominantly woody vegeracion, and are delineaced wichouc regard co commercial va lue (Canadian
measures across a landscape are examined. Here, suitability is a function of [he condition of the landscape, and
how far each [}'pe of vegetation is from the road system. A hypothetical habitat suitability index is presented to provide you with a challenging spatial analysis. A number of GIS techniques can be used to develop the habitat suitability index values. including complex mathematical calculations within the attribute table of GIS databases. In conjunction with these advanced narural resource management problems. we emphasize the use of Row charts to mai nrain order in (he analysis process. The flow-
Forest Service. 2007). Mosr narural resource managemenr organizarions
establish their management plans with in a land c1assificacion framework. Therefore. one of che inicial sceps in che development of a managemenc plan is co describe che
resources ([he land) that are managed. and the type of managemenc appropriace
[0
each portion of me land base.
After a land classification has been performed. the goals. scracegies, and implemenration of management can be
charting process may be useful for your own GIS analyses,
planned and implememed accordingly. For example. afrer
as a means of developing a logical approach to addressing
classifying the land base. an organ ization may decide co
management issues. Since many intermediace (cemporary)
exclude ce rcain managemenr acrivicies in some areas, limic
GIS databases are created and used. tracking the process of
the types of activities allowed in other areas. or allow full
an analysis wich a flow chart may prove co be very useful , rhus several examples are provided.
consideration of silviculrural o r o peracional activities in
Land Classification
socio-economic, ecological land classificacion syscem mar
other areas (Table 12. I). For example. the Washington State Parks and Recreation Commission (2006) uses a inregraces physical land feacures with porential human
Land can be classified by vegetation. soils. range. habitat. landform (physiography). and other measurable physical or soc io-economic characceriscics. Any map chac delineaces unique pieces of land can be considered a land classificarion. Land classifications have many pu rposes in nacural resource managemenc. from serving as a basis for assessing che scams ofland resources [0 serving as a framework for assessing me local management opportunities
(Frayer et al.. 1978). Land classifications are necessary for providing both policy direction (knowing what types of resources are availa ble) and for assiscing wich policy implemenracion (knowing where che resources are
located) . Land classification systems are generally based o n landscape characreristics char can be seen and meas-
ured. and they ideally would be based on flexible. logical. general. and professionally credible concepts. and th us would be described with a quamifiable set of ru les (Frayer et al.. 1978).
TABLE 12.1
An e xample of a management-related land classification sys tem
Class I ( R~servcd) Administratively withdrawn areas (offices and orh~r facili ties related to resource managemem) Wilderness ar~as Areas of special concern Rock pits Ponds or lakes Viewsheds Other areas where managem~nt activities are precluded Class 2 ( Limit~d management) Riparian ar~as Visual qualiry corridors around trails Areas designated as buffers around wi ldlife habitat Other areas where management is limi(~d Class 3 (G~nera1 managemelll) Areas not classi fied as Class I or 2
Land classifications can be made from a managemenc 196
186
Part 2 Applying GIS to Natural Resource Management
uses of the land. Associated w with ith each land class is a description of the philosophy of each dass, class. the appropriare ate physical featu res used to delineate the land class. and a matrix of allowed and prohibieed prohibited activities within each land class area. A land classificario dassification, n, in addition to guiding the development of a management man agement plan. may a]50 also be a requirement participation volunta n[ary ry stewards srewardship hip prome nt for partic ipation in volu example. organizalions organizations [hat ro comp comply ly grams. For example, thar need to with the Sustainable Forestry Initiative'" In itiative@(SFI) (SFJ) (Amer ican (American Forest & & Paper Paper Association Association,, 2002) are required to to classify dassify thei r land acco rding rdin g [0 to the SF! SFI land classification system.
This requiremem requirement may be in addidon addirion ro to {and (and different (han) {he th e land c1assificacion classification a narura! nacura i resource management organizadon organization may develop during their th eir normal norm al menr course co urse of management planning. The number of classes withi withinn a land classification may vary. For example, example. the Washington Wash ington State Parks and system Recreation Commission (2006) land classification syste m us
able for long-term cultivated cultiva ted crop c rop management) are a re their porential potential for production of grouped according to their permanent vegetarian rd ing to [0 vegetation cover. cover, and grouped acco according
the potential risk of soil damage (Klingebiel & Montgomery, 1973). Montgomery. 1973) . The Canada Land Inventory Inventoty provides broad examples ofland classificarions classifications related to ro foresny, forestry. agriculture. agriculture, land use. wildl ife. The development developmenr of land use, recreation, and wildlife. capabiliry capa bility classes related [0 to forestry uses is based on a rated acco rdnational classificatio classificationn syste m where land is fated irs capability [0 to grow rrees trees for fo r commercial uses uses.. ing to its The raring system considers cons iders land that th at has nor not undergone T he rating improvements (such as fertilization fertil ization or dminage drainage accivicies), acrivicies), and focuses on seven tree productivity classes: 1. Land with no limitations Limitations on [he the growth of com commercial mercial forests, where soils are deep. have good water-hold water-holding ing capaciry. capacity, and are high in fertiliry. fertility. Productivity of tree growth is greater rhan grow[h than 77.77 .7 7 m' m3 per hectare per year (J (1 11 11 ft' fr' /aclyea /aelyear). r). 2. Land with slight limitarions limitations on the growth of commercial forests. forests, where soils are again deep :tnd and have so me limitagood water-holding capaciry. capacity, yet there is some tion on growth (dim (dimate. ate, rooting depth. depth, low fertility fertility., for fo r example). Productiviry Productivity of tree growth is between 6.37 and 7.76 m m'J per hec hectare tare per year (91-1 (9 1-110 10 ft'l ft' l aclyear). aelyear). 3. Land with moderate limitat limitations ions on the growth of comme commercial rcial forests, foresrs. where soils soiJs are shallow co ro deep and have good water-holding capacity, capaciry, yet they may be slighrly ferti~ry. and have periodic water imbalslightly low in fertility, ances. Productivity Productiviry of tree growth is between 44.97 .97 and 6.3601' 6.36 m' per hectare per year (71-90 fr3/aclyear). ft' /ac/year). 44.. La Land nd with wirh moderate to seve severe re limitat limitations ions on the forests. rests, where soils are shallow growth of commercial fo to deep and have other highly variable variab le charactetiStics. characteristics. The main limitations limitarions are [00 roo much or roo too lilittle ttle mo moisis(Ure, depth, and low fertility, ture, restricted roming depch. feniliry, Productivity of tree growth is between 3.57 and 4.96 01' tiviry m' per hectare per year (5 1-70 ft'/aelyear). fr' /aclyear). ith severe limitat limi tations ions oonn [he the growrh growth of co m5. Land w with mercial forests forests,, where soils soi ls are sha shallow ll ow and poorly drained. The main limitations limitatio ns are a re roo much or roo litmo isture, restricred restricted rom rootin ing g depth. depth , low fertility, tle moisture, content, aand nd high levels of carbonates. excessive rock coment, Productiviry of tree growth is berween between 2.1 7 and 3.56 m m'J Productivity per hecta hectare re per year (31-50 (3 1-50 ft' fr' /aclyea /aelyea r). 6 . Land with severe limitations on the growth of commercia merciall fores[s, forests. where soils are shallow, excessively 197
Chapter 12 SyntheSiS of Techniques Applied to Advanced Topics
drained, and are low in fertility. The main limitadons are resuicted rooting depth. low fertility, excessive rock content, excessive soils moisture. and high levels of soluble sa lts and exposu re. Productivity of tree growth is between 0.77 and 2.16 m' per hectare per year (11-30 f" /adyea r). 7. Land with severe lim itatio ns on the growth of commercial forests, where soils are shallow and may conta in tox ic levels of soluble salts. A large portion of these areas include poorly drained organic soils. The main limitations are restricted rooting depth, low fertility, excess ive rock content, excessive so ils moisture. and high levels of solu ble salts, and exposure. Productivity of tree growth is less than 0.77 m' per hectare per year (1 1 ft 3/adyea r). The land classification is based on potential natu ral tree growth an d soil characteristics. The proximity of some areas of land to the ocean may also affect the class ification of forest land. These seven main classes can be fU rther subdivided into subclasses that are based on climate, soil moisture, rooring depth. and orher soils characteristics. An example of the broad-based lan d classification for a portion of southern Ontario (using data obtained from Natu ral Reso urces Ca nada, 2000) is provided in Figure 12. 1. An impo rtant aspect to consider in any land classification process is that the sum of the area in the va rio us classes should equal the sum of the area in the landscape being managed. For exam ple, using the State of Oregon system, the sum of rhe area in the special, focused, and general stewardship land classes should equal the total area of the landscape being classified. If not, one or both
of the following situations exist: (1) there is some overlap among the landscape features (polygons) in one or more of the land class GIS dacabases, o r (2) some area of che landscape is not being represented by any of the stewardship classes. As an example of developing a management-related land c1assificacion for a managed property, we will illustrare the appli cation of the general classes represented in T able 12.1 to the Brown T ract. First, some quantitative assumptions about the three land classes need to be made to allow us to delinea te rhe areas on a map . We will assume that class 1 (reserved) areas will contain meadows. research areas, rock pits, and oak woodlands. C lass 2 (limited management) will be those areas of land that are within 50 m of streams, 100 m of hiking trails, 100 m of homes, and 300 m from any owl nest locations. Finally, class 3 (general managemenr) areas are assumed to contain the land that remains after class 1 and class 2 management areas have been delineated. To delineate these three classes, a series of GIS techniques such as query ing, buffering, clipping, and erasing processes may be requi red (Figure 12.2) . However, other arra ngements of GIS processes could have also resulted in the same solution. The polygons rep resented in the resulting three land classes (Figure 12.3) should not overlap, which means that no single un it of land will be co unted twice. Put another way, each unit of land can o nl y belo ng to a single land class. In this example of a management-related land classification, class I consists of 229 hectares (567 acres), class 2 consists of671 hecrares (1 ,657 acres) , and class 3 consists of 1,222 hecta res (3,020 acres). When all three of the land classes are added togecher, chey equal che size of the Brown Tract.
Port Rowan Classes 1-2 Class 3 Classes 4-5 Classes 6·8 Figun: l2..
187
t Nor1h
Land d usification cxamplt: for a portion of soum t:rn Ontario.
198
188
Part 2 Applying GIS to Natural Resource Management
(a) Class 1 stewardship areas
0""
process
(b) Class 2 stewardship areas
Buller
process
Buffered
trails GIS database
Merge
process
Buffer process
Clip process
Erase process
Buffer process
process
Erase process
process
(e) Class 3 stewardship areas
Eras.
Buller
Figure 12.2 Hierarchy of intermediate and final GIS databases created in one process that facilitates Tract land classification.
Recreation Opportunity Spectrum A number of classification processes for outdoor recrea rion have been developed, including t he Recreacion Opponunity Spectrum (ROS), ca rry ing capacity, limits of accep table change. and th e Tourism Opportunity Spectrum (Bu rler & Waldbrook, 1991 ). The latter system is based on (he Rec reation Opportunity Spectrum, and includes aspects of accessibility (i.e., trans portation systems) , murism infrastructure, social interaction, and other non-adventure uses. The ROS was developed by the USDA Fo res t Service and the US DI Bureau of Land Management as a tool for managing recrea tion and
me development of the Brown
[Quflsm o n fede ral la nd. and for integrating recreation and tourism with other lan d uses (Clark & Stankey, 1979). ROS is used to describe and identi fy recreational serrings. and to illustrate the likelihood of recreational oppo rtunities along a spectrum that is divided inca several classes. This system combines the physical landscape characteristics of location and access (Q allow you [Q delineate areas of land that may be used for differenc recreational purposes. For exam ple, in the most recent forest plan for the Hi awa th a National Forest in norrhern Michigan (US DA Forest Service, 2006) it is suggesred that recreation-rela ted development, activities, management practices, and access will be consistenc with the delineated 199
Chapter 12 SyntheSiS of Techniques Applied to Advanced Topics
189
scape settings. As a result. there is a need to delineate those areas spatially, so that management activities related to recreational activities (and other management objecrives) can be planned acco rdin gly. The original version of rhe ROS classificarion (Clark & Stankey. 1979) divided land areas into six classes: I. Wilderness (now called primitive) 2. Semi-primitive non-motorized 3. Semi-primitive motorized 4. Roaded narural 5. Rural 6. Urban
Category 4 has since been expanded to two classes,
roaded natural and rooded modified. alrhough me exacr
Land classification _ Class 1
c:::::::J Class 2 c:::::::J Class 3 Figw~
12.3 A land dusification of th~ Brown traCt.
ROS class for each area. Thus. the recreation oppo rtun ities will be provided in a manner consistent w ith the ROS designation for each management area. The ROS classes represent a wide range of recreational experiences. from rhose rhar include a high likelihood of self-reliance. solitude. challenge. and risk. ro rhose rhar include a relarively high degree of resource development and interaction with orner people. A recreation opportunity class. therefore. is an a rea of land that may yield certain experiences for recreationists in a specific landscape setting. Consider an activiry such as cross-country skiing. a popular recreational activ iry in western North America. Cross-country skiing experiences in and a round cities. such as Bend. Oregon. are likely to result in experiences that are exercise-oriented yet include a high frequency of interaction with other people and developed resources. However. cross-country ski ing experiences in the backcountry. such as the nearby Deschutes National Forest, while also exercise-oriented. are more likely to include elements of solitude. risk. personal challenge. and will likely have a lower frequency of interaction with people. Therefo re. the same activity. cross-country skiing. can be associated with different experiences in d ifferent land-
classes used seems to vary from one management situation to the next. In the most recent forest plan for the Hiawarha Narional Forest (USDA Forest Service. 2006). the o riginal six classes are used. The ROS classes suggesr rhar specific kinds of recreation activities and experiences owing to certain physical (e.g.• size). social (e.g.• encounters with orher people). and managerial (e.g.• legally designared wilderness area) characteristics can be supported. The rules that define the ROS classes can include spatial relationships. For example. to be considered a primitive area, land must be more [han 1.5 miles from any road (Table 12.2). Further. some spaTABLE 12-2
A subset of rules with spatial consideratioDs for delineating recreational opportunity spectrum (ROS) classes
ROS class
RuI,
Primitiv~
Areas ofland great~r than 1. 5 miles fro m a road.
(P)
Areas of land that ar~ greater than 0.25 mi les from a road , hav~ forest stands ~ ;0 years of ag~. and ar~ ~ 202.3 hectares (500 acres) in
Semi-primitive. non-momriud (S PNM)
aggrega t~
Areas of land that ar~ grear~r than 0.25 miles from a pawd road. hav~ forest stands
S~mi - primi cive,
motorized (S PM)
Roaded natural (RN)
Road~d, man ag~d
siu.
Areas of land with stand ages ~ 50 years. and 2: 16.2 hectares (40 acres) in aggregat~ siu.
(RM ) Ar~as that do nor fit imo any of rh ~ other
classes.
200
190
Part 2 Applying GIS to Natural Resource Management
[ial aggrega[ion of polygons may be necessary. The semiprimitive, non-motorized ROS class suggestS mat there muse be a[ leas[ 500 comiguous acres (202.3 hec[ares) of land of certain forested conditions before land can be classed as such. Here, you may have co add cogecher [he area of several contiguous polygons to determine how much area rhey represent in aggregate. As with rhe land classification example described eacIier, [he sum of [he landscape area in [he ROS GIS da[abase(s) (Figure 12.4) should equal [he co[al area of [he landscape being classified. If no[, one or borh of [he following sicu3rions exist: (1) there is some overlap among the landscape fearures in onc or more ROS classes, or (2) some area of rhe landscape is not being represented by a ny of rhe ROS classes. A process you might use co create the primitive and semi-primitive portions of an ROS map migh[ resemble [he flow chan illusera[ed in Figure 12.5. As you may gather from this set of processing steps,
ROS classes
I:J!!!It!!I Primitive (none) rz::::iiiI Semi-primitive, non-motorized (none) _
Semi-primitive, motorized
c::J Roaded, natural c:::J Roaded, mCYlaged Figure 12.4 Recreation OpponunitySpecuum (ROS) classes for the Brown Tract.
Buffer roads
Buller
1.5 miles
0.25 miles
m.ds
Erase
Erase
process
process
Query
process (age ~ 50)
Erase process
Query
Aggregate or combine
process
process
(size :
Figure 12.5 Hieruchy of intermediate and final C IS databases created in the development of the primitive and semi-primitive portions of a Recreation Oppo rtunity S~ctrum map.
201
Chapter 12 Synthesis ofTechniques Applied to Advanced Topics
when you consider the development of the full range of ROS classes, rhe sec of processing steps may become cumbersome and confusing. Developing a flow chan to describe the processing steps you used, and co identify the intermediate and final GIS darabases, will alleviare some of this confusion.
191
1.2,-----------------, 1.0 0.8 Basal area
sco<,
0.6 0.'
Habitat Suitability Model with a Road Edge Effect
0.2
Habitat suitability models provide natural resource man-
agers with a glimpse inca rhe potencial of a landscape co suppOrt habitat for a specific species of wildlife, or group of wildlife species (Mo rrison et ai., 1992) . These models generally describe habitat suitability as the geometric mean of two or more variables that represent (o r inAuence) the occurrence and ab undance of a particular wildlife species. A geomerric mean is calculated by raking rhe nth root of the product of a group of numbers, where n is eq ual to rhe number of observations. Rempel and Kaufmann (2003) define habitat as rhe set of foresr struc(ural conditions that provide some means (e.g. , nesting. reproduction. foraging) for a species of wildlife during its life history. Each forest stand in a vegetarion database is assigned a single habitat value based on rhe structural conditions thar exisr in (and perhaps around) the srand during some period of time. conditions which can cha nge as man agement activicies are implemented . The suirabiliry of habirat is generally scaled becween 0 and I, and wildlife managers are called upon to determine what levels are appropriate (0 describe optimal habitat. While there is considerable debate concerning the usefulness and accuracy of habitat suitabiliry models (see Brooks, 1997), when well-developed and validated, they do allow natu ral resource managers (Q examine rhe relative quality of one area versus another with respect to some species of
Basal area per acre (sQuare feet)
Figure 12.6 BanI area scores for a range of stand basal areas.
geometric mean of the scores of each variable. The purpose of rhis exercise is to obtain a graphical descriprion of the landscape features that are important in describing
rhe habirar of the vole, given an undemanding of rhe vole's habitat requirements.
HSI calcularion
=
(basal area score X stand age score X
distance from road score) 113 In order [Q calculate each of the individual parameter sco res, a ser of quantirative rules is needed. These rules are generally developed rhrough research , lirerature reviews, or perhaps are based on the advice of biologists
who are experts on rhe life history of voles. Since rhe vole assumed here is a fictional species, the set of rules have
been developed by the authors of rhis book, and are hyporhetical. The basal area score, for example, is a funcrion of rhe square of srand basal area (fr2 per acre) mulriplied by a consrant, ro indicate a non-linear posirive response of vole abundance to more heavily s[Qcked tim-
ber stands (Figure 12.6).
inceresc. To illustrate the developmenr and display of a habirar suita biliry map, a hypothetical habirat suitabiliry index (HSI) is developed for a fictional species of vole. The model will allow you CO evaluate habitat suitability as a function of foresr basal area, age, and the distance of habi-
(Figure 12.7), where age is multiplied by a co nsranr.
tat from roads.
When combined with the basal area score, me [wo por-
Basal area score = 0.0000 I 15 X (basal area) 2 If Basal area score> 1.0, then Basal area score = 1.0 The stand age score is a lin ear funccion of stand age
tions of rhe HSI favor older srands that are well-stocked HSI = j(basal area, age, disrance from roads) The HSI incorporates rhese three paramerers into a single non-linear model that is used ro calculate rhe
(where both rhe basal area sco re and stand age score are high), over older srands rhat are not well-stocked, or younger srands rhat are over-stocked (where one score is
high and the other low) . 202
Part 2 Applying GIS to Natural Resource Management
192 1.2 1.0
Habitat scores _ 0.80H.000 _ 0.601 - 0.800 c::J 0.401-0.600 c::J 0.201-0.400 c::J 0.000-0.200
0.8 Stand
a,.
0.6
score 0.4 0.2 0.0 1----,--,----,--,----,--,----,--,----r----1 10 W M ~ W ~ ro W 00 @ Stand age (years)
Figwe 12.9 Habitat suitability scores for a vole on the Brown Tract.
Figure 12.7 Stand age scores for a range of stand ages.
Scand age score = 0.01 X (scand age) If Scand age score> 1.0, chen Scand age score = 1.0 While the basal area and stand age parameters may more likely describe the abundance of rhe vole, the distance from road parameter describes rhe potential occurrence of the vole species. In other words , the distance to road factor is used {Q represent the assumption that areas near roads will represent lower habitat quality than simi-
lar areas of land fa[(her away from roads (Figure 12.8) . Distance from road score
0.25 if wichin 15.24 m (50 feec) of any road, or
0.50 if 15.25 (0 30.48 m (50.1 100 feec) of any road, or
(0
0.75 if 30.49 CO 45.72 m (100.1 150 feec) of any road, or I .00 everywhere else
(0
G raphically displayed, che map of HSI for che vole indicates the relative quality of vole habitat across the land-
scape (Figure 12.9). A score of 1.0 represen" opcimal habitat , a score of 0.0 represents the poorest quality
habicar. Somewhere along che 0.0-1.0 range, biologisrs will need to determine the threshold levels th at separate good habitat from poor habitat. One process that can be used to arrive at these scores is presented in Figure 12.10 .
Here, che roads are buffered chree cimes (15.24, 30.48, and 45 .72 m). Two of rhe buffer GIS databases are chen subjected
to
an erase process. resulting in a buffer band
around each road (a 15.25-30.48 mecer band and a 30.49-45.72 mecer band). The 0--15.24 mecer buffer is 1.2
then combined with these MO buffer bands (Q create a GIS database that represents three of the buffer distances
1.0
by polygons, wich no overlapping polygons presem. The
0.8
Distance from road score
0.6
I
D••
0.2
I
I
nor included in chese chree buffer discances. The buffers are then overlaid on the vegetacion GIS database. breaking
vegecacion polygons ac che buffer boundaries. The basal area, stand age. and distance from road scores can then be
calculaced in che cabular ponion of che resulcing GIS dacabase. The final HSI score can be calculaced as a funcrion
0.0 0
fourth buffer distance, as you can imagine. is everything
~
N
Distance from road (feet)
Figure 12.8 Distance from roads scores for a range of distances from the road network.
of the basal area, stand age, and distance from road scores. and a thematic map can be developed to illustrate the distribution of vole habitat across the landscape. In addition ,
che final GIS dacabase can be queried co develop a cable of area by habicar class. 203
Chapter 12 Synthesis of Techniques Applied to Advanced Topics
Iv~m~ GIS da!abase
I L
GIS R_ / database
I Roa~ I I I GIS
da!abase
Roads GIS database
i
~
~
~
Calculate basal area
Buller roads
Buffer
score
Buffer roads 15.24m
30.48 m
45.72 m
~
~
~
~
I(
Calculate stand age
BUllere,,';
(o-~~m)
score
193
r_
I( II I( III Buffered
(o-~~ m)
roads Buffered
(0-45.72 ml
~ Erase process Erase process
~
I I Bullered roads
I I ~
(3Q.48-
45.7Srn)
Buffered r_ (15.24-
30.48 m)
Combine process
~ Overlay process
I
/
~ HSI GIS database
IL II
/
Road buffers
I
Calculate
road score
calculate HSI
score
Develop mapaf HSI scores
Figure 12.10 Hierarchy of intermediate and final G IS d:ltabue$ created in the devdopmcnt of an analysis of potential w ildlife habitat suitability (HSI) areas for a vole on the Brown Tract.
Summary This chapter illustrates juSt a few of [he more com plex
spatial analyses that may be performed (or requested) by natural resource managers. The number and arrangemem
of GIS processes could vary in addressing analyses such as
these, and may include buffering. clipping, erasing, and
need fo r quantirative rules and a log ical set of GIS processes [0 separate one set of landscape featu res from another in an analysis of ROS classes is important because a single un it of land must be assigned only o ne ROS class,
querying of landscape features. Therefore. the chapter
and all units ofland must be assigned a class. The graphical display of the result of a complex GIS analysis. such as
represents a synthesis of the tools readers have acquired
the ones illustrated in this chapter. is also important
from previous chapters in this book. It should be appar-
because land managers rypical ly use these products to
ent by now that it is important to explicitly define the quantitative rules and the GIS processes th at might be used to address complex spatial analyses. For example, the
help them visualize and make decisions regarding the management of natural resources. 204
194
Part 2 Applying GIS to Natural Resource Management
Applications 12. 1 Land classificat ion. Becky Blaylock, manager of the Brown Tract, wants you to develop a managementrelated land classification for the forest. She asks you to develop GIS databases for each of three classes using the following rules: 1. Special stewardship areas will consist of the following landscape features: Oak woodlands, Meadows, and Rock pits. 2. Focused stewardship areas will consist of the following landscape feamces: a. streams buffered according co the Oregon State Forest Practices Act (30.48 meters [100 feet] around large fish-bearing st reams [Size = 'Large' and Fishbearing = 'Yes], 21.34 meters [70 feet] arou nd medium fish-bearing streams, 15.24 meters [50 feet] aro und small fi sh-bea rin g streams, 2 1.34 meters [70 feet] around large non fish-bearing streams, 15.24 meters [50 feet] around medium non fish -bearing streams, and 6.10 meters [20 feet] around small non fishbea ring streams); b. a buffer of 100 meters around all water sources tha t are not culvert spills or water towers; c. a buffer of 100 meters around all authorized (rails; and d. research areas. 3. General stewardship areas will consist of whatever land remains. Do the following: a) Develop and illustrate a process (How chart) for accomplishing the task of defining the land classificat ions of the Brown Tract forest according to the rules listed above. b) D ete rmine how much land area is contained in the special stewardship land classification . c) Determine how much land area is contained in the focused stewardship land classification. d) Determine how much land area is contained in the general stewardship land classification . e) Produce a map of the entire Brown Tract, ill ustrating the three land classifications.
As a general strategy, yo u may want to follow [his process: • Develop a special stewardship GIS database. • Develop a focused stewa rdship GIS database by performin g the appropriate buffer and query
processes, and then intersecting these GIS databases. (Why would you nOt use a merge process here?) • Erase the special stewardship GIS database features from the focused stewardship GIS database features. • Erase both the special stewardship GIS database features and [he focused stewardship GIS database features from the stands GIS da tabase, creating the general stewardship GIS database. 12.2 Recreat ion O p portunity Sp ectru m. The Dimict Manager associated with the Brown Tracr (Becky Blaylock) would like you to determine how much area might be classified in the five recreation opportunity spectrum (ROS) classes (see T able 12.2) . Based on this subset of rhe ROS criteria, a) How much land area is contained in the primitive class? b) How much lan d area is contained in the semiprimitive, non-mororized class? c) How mu ch land area is contained in the semiprimitive, motorized class? d) How much land area is co ntained in the roaded natu ral class? e) How mu ch land area is contained in the roaded managed class? f) Develop a thematic map illustrating the five ROS classes on the Brown Tract. g) Draw a flow chart [Q describe the p rocesses lIsed [Q develop the ROS classes, including the GIS operations and all GIS databases used (original, intermediate, a nd final GIS databases). 12.3. Visual quality bu ffers. You have been asked by the manager of the Daniel Picken forest [Q evaluate the potential impact of two proposed organizational policies for the forest resou rces found there. It seems that the owners of the property are becomin g very concerned with the public perception of management on the forest, thus they are interested in the trade-off's assoc iated with alternative management pol icies. Policy #1: Buffers next to neighboring landow ners. Assume for this example that even-aged forest management is practiced across the property. This potential pol icy suggests that c1earcllt harvesting activities adjacent to neighboring landowners of the Daniel Pickett forest will be restricted. 205
Chapter 12 Synthesis ofTechniques Applied to Advanced Topics
a} If a 50-meter uncut buffer were to be left adjacent co all other property owners, how much land would this require as a volumary contribution [0 rhe pCQ[ecrion of adjacent landowners resources?
b} How much timber volume of vegetation class A would be found in the buffer (vegetation class A is the older timber class. perhaps that which can be harvested in the near- term). and what perce mage of rhe [Ocal volume in this vegetation class would
be affected? Policy #2: Buffers next to paved public roads. This potential policy suggeSts that visual quality buffers may be maintained along paved roads within rhe Daniel Pickett forest. These buffers will not be managed. bur rather treated as reserved areas, where har-
vesting is precluded. a} If a 50-meter buffer were required around all paved roads, how much land area would this involve, and
how much timber vo lume in vegetarion class A
would it affect? b} If the State decided to convert the North-South paved road on the Daniel Pickett foreSt to a highway, and required a 1DO-meter wide corridor [Q he transferred to State ownership, how much land area would be affected?
c} If bare land values were assumed to be $200 per hecta re. and timber volumes $400 per thousand
195
board feet (MBF). how much would you ask the Scare
[Q
compensate [he owners of the Daniel
Pickert forest for the loss of this land? d} If a 30-meter visual quality (i .e.• uncurl buffer was then proposed around the I OO-meter highway corridor, what is the [mal effect [Q the forest resource base, in terms of land area now affected in each vegetation type?
12.4. Habitat suitability index fo r a vole. The biologist associated w ith the Daniel Picken forest, Will Edwards. has recently become aware of a vole habi tat su itability model, and is interested in understanding the extenr of
vole habitat on the forest. Will asks you to apply the model described in the 'Habitat Suitability Model with a Road Edge Effect' section of this chapter to the Daniel Pickett forest, and m: a) Calculate the amount of land area on the Daniel Pickett forest in the following habitat suitabil ity
classes: 0.000-0 .200 (low quality). 0.201 -0.400 (low/moderate quality). 0.401-0.600 (mode rate qual ity). 0.601-0.800 (moderate/high quality). 0.801-\.000 (high quality). b} Develop a map illustrating the habitat quality for the vole by suitability class. c) Draw a flow chart of rhe process yo u used to
develop the habitat suitability classes.
References American Farmland Trust. (2006). Land classification
sysum. Washington. DC: American Farmland Trust. Retrieved February 17. 2007. from http://www. farmland.org/resources/furureisnowllanddassification system.asp. American Forest & Paper Association . (2002) . Sustainable Fomtry Initiative (SFI)"'. Washington. DC: Amer ican Forest & Paper Association. Retrieved
December 10.2007. from http: //www.afandpa.org/ ContentfNavigation Menu/ Environment and Recycling!
SFIISFl.htm. Brooks. R.P. (l997). Improving habitat suitabil ity index models. Wildlifi Society Bulletin. 25. 163-7. Butler. R.W .• & Waldbrook. L.A. (l991). A new planning tool: The tou rism opportunity spectrum. JournaL of Tourism Studies. 2(1). 2-14.
Canadian Forest Service. (2007). Ecological land classifications. Onawa. ON: Canadian Forest Service, Natural Resources Canada. Retrieved February 17. 2007. from hup:11ecosys.d1.scf. rnea n.gc.cal dassifl i n rro_strat_e. asp. Clark. R.N .• & Stankey. G.H . (I 979}. The recreation opportunity spectrum: A framework for planning, management, and research. GeneraL Technical Report PNW98. Portland . OR: Pacifi c Northwest Forest and Range Experiment Sta tion, USDA Forest Service.
Frayer. W.E .• Davis. L.S .. & Risser. P.G. (I 978}. Uses of land classification. Journal ofForestry. 76. 647-9. Klingebiel. A.A .. & Montgomety. P.H . (I 973}. Land capability classification. USDA agricultural handbook 210. Washington. DC: US Government Printing
Office. Retrieved February 17. 2007. from http:// 206
196
Part 2 Applying Applying GIS to Natural Resource M Management anagement
soils. soils.usda.gov/tech usda.govltochnicaUhandbookicontents/part622p2. nical/handbookicontents/partG22p2. html#ex2. Morrison Marcot, B.G B.G..•, & Mannan, Morrison., M.L. ., Marcot. Mannan. R.W. 92). Wildlife-habitat relationships: Conupts and ((19 1992). applications. Madison. Madison, WI : University Universiry of Wisco nsin Press. Natural Natu ral Resources Canada. (2000). OverviewofclassificaOverview o[classificadeurmining land capability for tion methodology for determining forestry foustry.. Onawa, Ottawa, ON: GeoGrads GeoG racis Cli C li enc ent Services. Services, Nacural Narurai Resources Reso urces Canada. Rerrieved Retri eved Augus[ Au gust 10, 2007, from http: //geogratis.cgdi.gc.ca/C LI /frames. 2007. hrtp:lIgeogratis.cgdi.gc.ca/CLl html. htm!' Depareme", of Forestry. Fo rest ry. (2007). Oregon adminOregon Department iJtrah'vt! ruiLs, nlus, department offorestry. istrative o/forestry, Division 35. manngeuullt agement of o[ statt state forest lands. Salem, Salem , OR: Oregon Depart ment of Fore5[ry. Depanme", Forestry. Retrieved March 12. 12, 2007, 2007. from http://a rcweb.sos.sta te.or.us/ te.or.us/rules/OARS_6001 rules/OARS_GOO/ h((p:llarcweb.sos.sta OAR_G29/629_035.html. OAR_629/629_035.html.
Rempel. R.S R.S.,.. & Kaufmann Kaufmann., C.K. G.K. (2003) (2003).. Spatial modelmoddRempel, ing of harvest co nsrra nsuainrs inrs on wood supp supply ly ve rsus wildlife wildli fe habi habicat tat objectives. objecrives. £Ilvironmeruo Environmental/ Management, 32. 32, 646-59. ment. US Bureau of Reclamatio n. (195 1). Land classification. Bureau of reclamation man manual ull~ vol. V, irrigated land use, Denver, CO: US Bureau of Reclamation. Reclamati on. use. part 2. Denver. forest, USDA Forest Service. (2006). Hiawatha national form. 2006 forest plan. Milwaukee, WI: USDA Forest fomt Milwaukee. Service, Eastern Region. Retrieved February 18.2007. 18,2007, Service. from http://www.fs.fed.us/r9/hiawarhalrevision/2006/ from http://www.fs.fed.us/r9/hiawatha/revision/2006/ ForPlan .pdf. Washington State Scate Parks and Recrearion Rec reatio n Com Commission. mission. (2006) (2006).. WAC 352-16-020 land classification. Olympia. Olympia,
Recrea(ion ComWA: Washington Srate State Parks and Recreation Reuieved February mission. Retrieved Feb rua.ry 17. 17,2007, 2007. from http://www. htrp:/iwww. parks.wa.gov/ plansllowerhoodcanaI/State%20Pa rks% parks.wa.gov/plans/lowerhoodcanaI/Srare%20Parks% 20Land%20C 20 Land%20Classifications. lassificat ions.pdf. pdf.
207
Chapter 13
Raster GIS Database Analysis Objectives
the next chapter to other raster database applications. the primary raster GIS database that is considered in th is
The skills and techniques you'll learn in this chapter
chapter is a digital elevation model (OEM) . Many difTer-
should provide insight inco the examination and applica-
em types of landscape information can be cultivated from
don of raster GIS databases for natural resou rces research,
a single OEM database.
and how raster GIS databases might be included in suppaning natural resource management decision-making.
Digital Elevation Models (OEMs)
At the conclusion of this chapter, you should have an understanding of:
As their name implies. OEMs comain information related
1. how landscape comour GIS databases are created from a OEM; 2. how landscape shaded relief GIS databases are created
to the elevation of a landscape above sea level or relative to some other datum point. T hey are different than the typical USGS Quadrangle maps discussed in chapter 4, in that they are in digital form. As with other raster GIS databases,
from a OEM; 3. how slope GIS databases are created from a OEM; 4. how to calculate slope gradients for a linear landscape feature, such as a road, trail, or stream;
5. how
(0
conduct a viewshed analysis for a parrion of a
landscape; and 6. how to create a watershed boundary based on digital elevation data.
each unit on the landscape is typically represented by a landscape-related value (or set to a null or 'no data' value), and each unit is exactly the same s ize and shape as the
other units (F igure 13. 1). The most prevalent OEM databases available in the US are the USGS 30 meter OEMs (US Department of Interior, US Geological Survey, 2007). Within Canada, Natural Resources Canada (2007) provides access to digital topographic data. Raster databases are onen described in terms of their spatial resolu-
As mentioned in chapters 1 and 2, the re are {Voto general types of data structures used in GIS coday: vector and raster. Unci l now, we have focused on vec[Qr GIS data-
bases and the GIS operations related to the typical kind of applications performed in natural resource organizat ion field offices. This chapter now delves into rhe use of raster GIS databases for namral resource applications. and a few of the GIS operations that can be performed using them. An emphasis is placed on how raster GIS databases mi ght be used in field offices to support natural resource management decisions . Although we will rurn our attemion in
tion, as in the phrase '30 m OEM'. This infers that each grid cell in the OEM database is 30 m by 30 m in size in terms of on-the-ground area that it represenrs . Many
regions in the US also have 10m OEMs available for areas within federal and state agency administrative boundaries. In some cases OEMs for states. provinces. or other large regions can be purchased from commercial entities. DEMs can be used for a variety of analytical purposes, bur the most general of these purposes is simp ly ro view
the rel ief of a landscape. OEMs can use shades of color or gray rones co illustrate differences in e1evadon through a 208
198
Part 2 Applying GIS to Natural Resource Management One notable category rep resented in the lege nd in
Figure 13.2 is the 'No Data' category. This category is necessary because raster GIS data must be stored as a set of grid cells that combine ro form a rectangular or square shape-
the width and heigh t of the image is defined by the number of grid cells. Therefore, when landscape fearures of interest do not match a rectangular shape (e.g., the shape of the Brown Tract), the grid cells that are nOt associated with [he landscape features of interest are given a nuil, or
No Data, value. For example, all of the grid cells that represent areas outside the boundary of the Brown T ract contain no data. For mapping purposes, the symbo lization
used to display cells with a null value can be assigned a transparent shade. Almost all raster GIS software programs allow the recognition of a null value, yet are designed to ignore this va lue in analytical computations. When performing a multiple GIS database overlay ana lysis, some raster GIS software programs are also designed ro ignore Figure 13. 1 Ruler grid cells from a digital elevation mode (OEM).
separation and c1assificarion of elevation values . Figure
13.2 illuscrates a gray tone color-shading scheme app lied a 10 m OEM of the Brown Tract, and uses a twelve-
to
category equal-inrerval classification scheme CO highl ight (he cha nges in elevation. The equal-imerval classificat ion takes the disrriburion of elevadon data values found
within the 10m OEM and divides it equally into twelve sub-sets of elevat ion ranges. Most GIS software will allow users to define [he number of elevation range categories shown visually. and offer choices for color and gray tone schemes [0 illusrrate distinctions between elevation C3rcgones. Brown Tract OEM Values (feet)
D
136-150
0
151 - 200
D
201-25O
D
251 - 300
~ 30 1- J50
IH;11 351 -"OO
111 401_ 450 _ _
451 -500 501- 550
_ 5 5 1-600 _ 8 0 1-850 _651-700 _ No Data
Figure 13.2
OEM.
Elevation ca t~ori ~ for the Brown Tract using a 10 m
any cells that overlap null cells in any other GIS database being analyzed. For example, if twO spatially coincident raster databases were overlaid on each orher and any portion of either dacabase comained cells with no darn, any database resuhing from an overlay or compara tive analys is that involved borh databases would also comain no data cells at rhe same locations. This result would occur even if actual values were present in a portion of one of the raster databases. Whi le th is functiona lity may or may not be appropriate for particular G IS analyses, users should be
cognizant of how null values assigned to grid cells will be handled within their selected GIS software program{s).
Elevation Contours OEMs ca n be used to create devation contours, or lines that indicate a constant or nearly constam elevation across a landscape . Contour lines are created adjacent ro each other such that elevation represented in even in crements, such as every 30, 50, or 100 m. An elevation contour GIS database is usually represemed through a vector dara srruCture based on a user-defined elevation interva l. Contour lines allow YOll to examine the relarive relief of a landscape and to make inferences about landscape ropography ro support managemem decisions. For example. co ntour intervals can be used ro delineate likely hydrologic drainage patterns and watershed boundaries. When
designing road systems, engineers rypically need to keep the slope of each road below some maximum gradient. since slopes too steep will either prevem the movement of
certain rypes of veh icles (if the vehicle is travelling uphill), 209
Chapter 13 Raster GIS Database Analysis
A contour. [Q most people. represents the ou dine of some figure or body. Contour intervals. as used on maps. represent the oudine of all areas thar have the same elevation. It would be as if you were to slice (horizontally) the landscape every 100 feet (or whatever interval was chosen) in verrical elevation. Contour plowing is a common practice in agriculture. where
or will be too dangerous to travel (if the vehicle is travelling downhill) . With many raster GIS software programs, users have the ability [Q choose a contour interval and the starting elevation value at which contours will be created. To creare contour lines a process such as rhar described in Figure 13.3 can be used. In cases where e1e-
Select contour interval
Setect base elevation value to start contours
199
plow lines are laid parallel to the contour of the landscape [Q reduce the erosion potential of rhe agricultural practice. This also reduces strain on farm machinery; this practice encourages moving laterally rather than perpendicularly through elevation gradients. Each section of a plow line is. theoretically. at about the same elevation as every other section of the line.
vatlon umts (i.e. meters. feet) do not match horizontal mapping units. a unit conversion factor may be needed in order to bring the units into agreement. Within ArcGIS a OEM must be opened as a layer, the Spatial Analyst Extension must be activated. and the Spatial Analyst menu must be opened. From the Spatial Analyst menu. choose Surface Analysis. then choose the Contour option. This should open the Contour dialog box, which will prompt you for the Input Surface. conrour dimensions, and an output database name. Using the Brown Tracr 10m DEM, a COntour interval of 50 feet with a base contour elevation of 100 feet was chosen (Figure 13.4). Using (he 100-foot base elevation should result in comour lines that o ri ginate from 100 feet and incremem in 50-foot steps. The contour line GIS database that is created is a vector GIS database, and each line contains an att ribute describing the elevation . Users can then modify this vector GIS database to display different color shades or line thickness for differenr contour lines of interest.
Brown Tract ContoIS Interval
- 50'''' OEM Value. (feet)
Verify elevation units
0
o
100 - 150 1S1-200
W
2{)1 -25O
_
2S1-lOO 301 - 350
_
351--400
_4()1 -~
_ . S l -5OO
Figure 13.3 A general process for the development of a contour line GIS database from a OEM.
_
501-S50
_
551-600
_
601- MO
_
6S1 -700
Figure 13.4 A contour line GIS database for the Brown Tract displayed on top of the Brown Tract 10 m OEM.
210
200
Part 2 Applying GIS to Natural Resource Management
Whenever boch ho rizontal coo rdinate positions and ve rtical elevations are processed simultaneously. as is {he case in the creation of contour lines discussed above . it shou ld be ascenained. [hat both cypes of measurements use rhe same units. TypicaUy coordinates and elevations will he recorded using meters, international feet, US survey feet, or some combination of these units. It is not uncommon within rhe US, however, to discover OEMs chat have coordi nate values in meters bur (hat srore elevation val ues in survey feec. A GIS analyst might mistake rhe resulring co ntour lin e va lu es (a represent meters, rhus over-rep rese nting (he elevat ions alo ng co ntou rs. The Spac ial Analyst Contour option dialog box conta ins an input box w here users can specify whe ther elevation units differ from coordinate units within a OEM.
Shaded Relief Maps Another product that can be derived from a OEM is a
Selecl azimuth that represents the sun's location
Select altitude of the sun in the sky
(raslerl Figure: 13.5 A gene:ral process for the: devdopme:nt of a shade:d re:lie:f GIS database: &om a OEM .
shaded relief map. Shaded relief maps are intended ro simulate the su n-lit and shaded areas of a landsca pe when assuming thar the sun is positioned at some location in
tal map unit (Z factor), an ompur cell size. and an output database nam e. Usi ng the Brown T ract 10m OEM, an
rhe sky. La ndscape fearures that face roward the sun will appear more brightly lit than objects facing away. For
azimu rh of 210·, and an altitude of 45·, a shaded rel ief map is created (Figu re 13.6) that shows (rel atively speaking) how much sunl ight reaches each parr of the landsca pe
raster GIS software program s that provide the abiliry co create a shaded reli ef map . the resulr of performing a shaded reli ef map process is a raster GIS database, and
in th e late afternoon (the su n azimurh of 21 0° indicates [hat the sun is located directly co the southwes t of the
each grid cell typically contains an attribute value describing a gray cone rangi ng from light (facing cowards rhe
sun) ro dark (f..ci ng away from the sun). The shaded relief map is useful for illustraring rhe ropography and provides a th ree-dimensional perspect ive of the landscape.
Shaded relief maps can be created with the genera l process described in Figure 13.5. The azimuth selected specifies the d irection from which rhe sun is sh in ing. An azimuth of 90°, for examp le, indicates that the su n is positioned in rhe eaSt, and an azimuth of 1800 ind icates that rhe sun is positi oned in the so uth. The altitude defines the
angle of the sun above the landscape. An alti tude of O· typicall y indicates that the s un is located directly over-
head, whereas an altitude of 90· wou ld indicare thar the sun is at the hori zo n. With in ArcGIS a OEM must be opened as a laye r, the Spatial Analyst Extension must be act ivated, and the Spatial Analyst menu must be o pened. From [he Spatial Analyst menu, choose Surface Anal ys is.
the n choose rhe Hillshade option. This should o pen rhe Hillshade dialog box, which will prompt you for th e Input Surface. Azimuth, and Altitude amOunts. Options are also provided for vertical unit conversion to horizon-
FigllR 13.6 Shade:d relief map of the: Brown Tract using a 10m OEM, an illumination azimuth of 210°, and an illumination altitude of 45°.
211
Chapter 13 Raster GIS Database Analysis
The oriemacion and presentation of 'direction ' has not been discussed co great extent in this book, however, it is important for readers to know the difference between an azimuth and a bearing. Why? Because compasses used in fieldwork either represent di recdon as azimuths or bearings . In some cases, both types of measurements will be represented. Azim uths are degrees of a circle, with No rth being 0° (or 360°), East being 90°, South being 180°, and West being 270°. A compass line indicating an azimuth of 353°, therefore, indicates a d irect ion of almost due North. A bearing is represented as any angle of 90 0 or less from eithet the North or South
landscape, and the 45° altitude indicates a sun position halfway between 'direcdy overhead' and 'setting'). With this shaded relief analys is, you can obtain a sense of the varied ropography of the Brown T ract. Other landscape features. such as study areas, roads, and streams, might [hen be displayed on tOp of the shaded rel ief GIS database to allow an exam ination of how chese resources might be infl uenced by landsca pe tOpography.
Slope Class Maps A third product that can be derived from an analysis of DEMs are GIS databases that represent the slope class, or gradient, of each portion of a landscape. Slope class values are measurements chat indicate me steepness of a landscape, and provide insight into the rate at which other resources, such as water, vehicles. or people. are likely to travel over [hose portions of [he landscape. Since each grid cell in a DEM contains both ho riwntal (e.g., latitude and longitude) and vertical (elevat ion) measurements, the slope of each grid cell can be computed based on the position and he ight of the ne ighboring grid cells. MoSt raster GIS software programs have the abi li ty to compute slope classes. and are able to express slope class as an angle (degrees) or as a percentage of the difference in elevat ion of each grid cell as compared to the neighboring grid cells. lr is important to understand that there are a number of different methods used in choosing the values for slope class calculations. In a raster GIS database. each raster grid cell will have eight neighbors that share a portion (a side or a point) of its boundary: fou r neighboring cells will share
201
(and directed towards the East or West). Thus an azimuth of 353° represents a bearing of N7°W. since the angle would arise from the North half of a compass, and is directed towards the West Similarly, an azimuth of 89° represents a bearing ofN89°E (the angle arises from the North half of the compass and is directed towards the East 89°). and an azimuth of 190° rep resents a bearing of S I OOW (t he angle arises from the South half of the compass and is d irected towards the West 10°). Property deeds, commonly used within North America co legally state ownership of a land area, often use bearings to describe the land boundary locations.
r.
a corner point and four neighbo ring cells will share a side. Theoretically, eight possible grid cell values can be used to calculate the slope class. Many raster GIS software programs lise a formula that takes into account me values of these neighbo ri ng grid cells in calculating the average slope elass of a single grid cell (Burrough & McDonnell, 1998, pp. 190-3). In Figure 13.7, the weighted average slope gradient between the cell of interest (the center cell with a 293 m elevation) and the eight neighbors can be calculated to determine the slope class change by computing the elevation change among the cells. You can imagine. however. that perhaps only the cells that share a side might be used to calculate the slope class for the cell of inte rest, or a broader window can be used. (e.g .. one more ring of cells around the cell of interest, or 24 neighboring cells) . To create a layer representing slopes within ArcGIS a DEM must be available, the Spatial AnalySt Extension muSt be activated, and the Spatial Analyst menu mUSt be opened. From the Spatial Analyst menu, choose Surface Analysis, then choose the Slope option. T his should open the Slope d ialog box, which will prompt you for the Input Surface
1 2 3 302 m 300m 298 m
5 293 m 290 m 295m 4
7 6 8 287 m 288m 290 m
Legend
12~m l
Neighboring cell (3) and elevation (298 m) Cell lor which slope
1293 m1 class will be computed
Figure 13.7 Slope class computation within a raster GIS environment.
212
202
Part 2 Applying GIS to Natural Resource Management
and whether degrees or percent slope is desired. Additional options include vertical unit conversion unit
{Q
Tree height =
horiwnral map
tan (30') - 50 feet
(Z faccoc), Output cell size, and OUtput database name.
or 28.9 feet
G iven [hat slope is a d irect function of distance and elevation comparisons, it is imperative with the slope process rhat users know whether (he measurement units of coordi-
nates and elevations are [he same. If the measurement units ate nO[ the same (e.g. meters for coordinates and feet for ele-
50 feet
vations), the Z factor input can be used co reconcile differ-
ences. T he slope class GIS database created from the Brown Tract 10 m DEM (Figu re 13.8) shows that slopes are repom::d in degrees, and are divided into nine categories.
Angle (degrees) = 30' Angle (percent) = (28.9 feet /50 feet) = 57.7%
The darker-shaded slope class categories represent areas
where slopes are steep, and the lighter-shaded slope class categories represent areas where slopes are gentle. Many natural resource management organizations prefe r CO work with slope classes expressed as a percentage, and thus it may be important for CO know how to perform th is conversio n:
tan (30°) • 100 = 57.7, providing a Quick conversion from degrees to percent slope Figure 13.9 A simple example of the' con'lo'ersion process from degrttS to percent slope.
T o prove th is rather simp le conversion fro m degrees co percent slope, assume that a person was standing on flat
slope class (percent) = tan (a) X 100
ground (Figure 13.9) and needed to determine the pe rcent slope from their location to the cop of a tree. By knowing
where
the angle (30') from their position to the top of the tree, tan = tangent tr igonometric function 0: = slope in degrees
and the distance from their location to the tree, the person
can calculate the height of the tree (28 .9 feet). The slope from the person to the top of the tree, as expressed in percentage terms, is then the rise (the height of the tree, or
28.9 feet) divided by the run (the distance the person is from the tree, or 50 feet), or 57.7 per cent. And, by simply inserting the angle into the equation noted above,
slope class (percent) = tan(30') X 100 yo u can arrive at the same conclusion, D epending on (he GIS sofrware program being used, you may need to conven between degrees and rad ians, since the angle reponed after a slope class calculation may be reported as a radian. An examination of the software documentation will reveal whether this consideration is necessary,
Interaction with Vector GIS Databases
--- ---
Brown Tract slope (degrees) 0
0-2.3
0
2.4-3.8 3.9-5.2
5.3-6.5
9.2-10.8
6.6-7.8
10.9- 13.1
7.9-9.1
13.2- 21.8
Figure 13.8 Brown Tract slope class G IS database' created from aIOmDEM.
There are a number of methods by which you can perfo rm a GIS analysis using both vector and raster GIS databases simultaneously. This ability has traditionally been uncommon in many desktop CIS software programs, bur as technology progresses you will see the expansion of these capab ili ties. and field personnel (those with access
primarily to desktop GIS software programs) will be able to
perform more complex analyses. Two types of analyses 213
Chapter 13 Raster GIS Database Analysis
203
that combine vector and raster GIS databases will now be explored: an examination of the slope class characteristics of land management units, and an examination of the slope class characteristics of streams.
Suppose you were ass igned rhe rask of developing a
Selecl attribute that uniquely identifies
management plan for an area the size of the Brown Tract, and one where there was significant amount of rel ief associated with the landscape. The set of management activities appropriate to each management unit defined on the
stands
landscape may vary based on rhe slope class wirhin each Summarize
un ir. For example, if you were to consider planning a forest thinning operation on the Brown T ract, it would be useful to know the locations of areas where thinning oper-
slope conditions
arions should use a ground-based logging sysrem (e.g., fell-bunchers skidders, harvesrers, forwarders, ere.) and
Slope class
the locations of areas where the thi nning operations
report
should use a cable-based logging sysrem. Since groundbased logging sysrems are appropriare fo r rhe gender slopes, slope class measuremenrs will help identifY those
FigUJ"~ 13.10 A g~n~ra1 prouss fo r tb~ developm~nt of a sJop~ class condition information for each stand ( manag~m~nt unit) on a landscape.
management units that have the steepe r slopes more
appropriare for cable logging sysrems. The slope class condition of a management unit ca n be measured in the field with clinometers or other surveying instruments or hypsometers, or the slope class cond ition can be computed using a OEM in conjunction with the vecto r GIS database that describes the management units. In the case of the Brown Tract, rather than having field crews spend several days collecting slope meas urements, the average slope class of each management uni t can be calculated with GIS
TABLE 13.1
using a process similar to that described in Figu re 13 .10.
A rabular reporr is generared by the process described in Figure 13.10, and provides a summary of the slope class condition for each of the management units. An annotated vers ion of the output, showing information for the first ten stands of the Brown Tract, is provided in Table 13. 1. The first variable in the table represents the a((ribure that
was selecred ro uniquely identifY each srand (rhe srand
Output of percent slope values for management units
Count
A=-
M;"
Max
Range
Mean
Std
Sum
3 19
343603
0.11
15.44
15.33
5.3 1
3.81
1692.78
2
2186
2354595
0.34
23.55
23.21
9.41
3.76
20564.20
3
770
829386
0.44
22.46
22.02
10.22
4.15
7866.61
4
2884
3 106428
0.28
23.01
22.73
9.54
3.66
27521.07
5
533
574107
1.71
19.80
18.09
8.34
3.14
4446.68
6
1195
1287164
0.44
23.72
23.28
8.51
4.24
10168.51
7
338
364068
0.20
15.15
14.95
6.20
3.52
2096.76
8
2494
2686349
0.15
26. 11
25.95
13.65
4.27
34040.15
9
337
362991
3.20
25.4 1
22 .21
15.03
3.9 1
5066.74
2395
2579714
1.55
24.2 5
22.70
11.52
3.90
27591.07
S.... d
10
number units (10 m grid cells) in the database Area .. squar~ feet Min. minimum valu~ in [h ~ da[abas~ Max .. maximum valu~ in me darabas~ CoUnt:o
Range ,. (maximum value - minimum val u~) Mean z averag~ slope Srd '" standard deviation o f values in [he database Sum = sum of the slope for aU units
214
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Part 2 Applying GIS to Natural Resource Management
number}. With this value, you could join the tabular data co the stands GIS database. using a one-to-one join process (see chapter 9 for a review of join processes), and facilitate a graphical display of the slope class for each managemenr unit. The variables 'Co unr' and 'Area' list the number of grid cells from the slope class GIS database that F...l1 within each management unit, and the area mat the grid cells represent. The 'M in', 'Max', and 'Range' provide the minimum slope class value within each management unit, the maximum slope class value, and the difference becween these [wo values for each management unit. The 'Mean' is the average percent slope (what was hoped to be obtained for the thinning opportunity analysis) and the 'Std' vari-
able is
me standard deviation of the slope class values of rhe
grid cells located within each managemem unic. The stan-
dard deviation provides information on rhe disrcibucion and variat ion of slopes classes within each management unit, Large standard deviations indicate a wide variado n of slope class values whereas small standard deviations indicate a narrow varia don. In the managemenr of namral resources, the condition of a stream system may also be imporram [0 know from
both a hydro logic and fisheries perspective. For example, you might need [0 understand the abilicy of Streams to support fish populations, o r to understand the potential water runoff im plicar ions from extreme rainfall evems. Stream slope class (gradient) is one common measure of the condition of a stream system. Stream slope class can be calculated by field personnel using clinometers or other surveying instruments or hypsometers, yet this requires a visit to each stream to provide measures for the e ntire landscape , a very costly and time co nsum ing proposition. The slope class conditions of streams across a landscape can, alternatively, be estim ated rather quickly if a OEM and a streams GIS database is available for the landscape. The straightforward approach to calculating slope values for streams would be to fo llow the previous example of supporting a thinning operation. and use the slope class GIS database for the entire Brown Tract. However. si nce the slope class GIS database was created for the entire landscape and only a small portion of the landscape is of interest (the streams), a different approach might be approp riate (F igure 13 .1 1). One solution would be to create a raster GIS database of the streams . A raster database of
Conversion to raster database
Overlay
analysis
Select attribute that uniquely identifies streams
Summarize slope
conditions
Slope class
report Figun: 13.11 A general proces.s for the development of slope inform:uion for each individual stream on a landscape.
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Chapter 13 Raster GIS Database Analysis
Intervisibility is a term used to describe the number of viewpoints with a view of each unit of land. For example, a number of homes may
be within the viewshed of
205
areas of the viewshed may be visible to a single home. From a management perspective, this information may impartanr. and allow natural resource managers to
be
a property being managed by a natural resource organ-
focus thei r public relarions efforts to rhe directly
ization. These homes could be considered viewpoims.
affected homeowners. If you we re to develop a GIS
By performing the viewshed analysis described above.
database that describes the intervisibility of a landscape.
however, you do not necessarily understand the imer-
the phrase 'cumu la tive viewshed map' (rather than
visibility of the landscape. or the number of homes that
'viewshed map') mighr be used. to illustrate the num-
acmally have a view of each unit of land. In fact, SOffie
ber of homes viewable from each land unit.
streams that matches the spatial extent and resolution of
adonal visitors are influenced by the visual appearance of
other raster GIS databases previously developed for this landscape wou ld fucilitate future overlay analyses. The grid cells th at contain ac cuai values (other than 'no data') should only be those that overlap a stream in the vector screams GIS database. An overlay ana lys is is then performed in conjunction with a OEM CO enable the creation of a raster GIS database thar describes the elevation of each
the landscape. and thus can be negatively affected by manage mem activities that leave visible impactS (Ribe. 1989) . A key to reducing (or heading off) potential public relarions
grid cell represent ing a stream. Now that only those raste r
grid cells that touch a stream have been identified. and the elevation of each is avai lable. you can calculate the average slope class values for each of the streams . Si milar to the previous analysis, the res ult of this analysis is also a tabular database. By using the attribme that uniquely identifies each stream. yo u could join this tabular data with the streams GIS database to facilitate the
display of the slope classes for each stream . A similar processing approach could be used to identifY slope or elevation characteristics for any vector GIS database that con-
problems in natural resource management is to ascertain what pardons of a landscape are visible to recrearional visitors, and to adjus t the management plans associated with
those areas accordingly (Wing & Johnson. 2001). Vicwshed analysis can facilitate an understanding of the portions of a landscape that are visible from specific landscape features of interest. For example. observation or viewing si tes (overlooks) may be represented in a GIS database by poinrs. and the location and elevation of the points would be used to determine which other parts of
the landscape would be visible. If rhe viewing sites were described by lines, the vert ices (points where the line
direction changes) of each line would be used for the viewshed analysis. Fo r example. lines that represent a road
or trail through a landscape. could be used to determine
tains lines (e.g .. roads, trails, facility corridors, etc.) .
which other parts of the landscape can be seen from those featu res.
Viewshed Analysis
The landscape in a viewshed analysis is represemed by a OEM or a TIN (see chapter 2 for a description of a TIN). The objective of a viewshed analysis is to calculare rhe line
The maintenance or enhancement of aesthetic values is becoming increasingly important in natural resource management. Research has shown that the experiences of recre-
of sight berween the viewing sites (e.g., observation points,
homes) and other landscape features (F igure 13. 12) .
Figure 13.12 Line of sight from a viewing site to the surrounding landscape.
216
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Part 2 Applying GIS to Natural Resource Management
Features [hat are identified as being in [he line of sight of
To illumate [he development of a viewshed analysis, a
the viewing sites are considered visible, whereas all other
GIS database rep resenti ng home locat ions surrounding the Brown Tract will be used as the viewing sites. A cur-
landscape feamces are not considered visible. A number of considerations must be taken into account when conducting a viewshed analys is . Ahhough a OEM assoc iated with a landscape may have been
sory in vestigation of [he homes GIS database reveals [hat [here are multiple homes along rhe eastern half of [he Brown Tracr (Figure 13.13). A viewshed analysis will
acquired (perhaps from rhe US Geological Survey), an
allow yo u to determine what portions of the Brown Tract
assessmem of its fitness
represent the landscape in a
are visible by residents of [he n... rby homes. One of [he
viewshed analysis should be performed. I n heavily
first steps in the viewshed analys is is to define the observer height. You might assume that the average person has a
(Q
forested landscapes. the OEM may nor represent the effect of tree heights on your view, as [ree canopies extend above the elevation surface (t he ground. as represented in the
OEM) . The current management of rhe landscape will also affect a viewshed analysis because different aged stands have different heights, and therefore [he (Op of rhe current canopy (which is what is usually seen in a scenic view) may be misrepresenced. One solution to this problem is co acquire a vegetation GIS database that contains tree height information , and [Q incorporate these measurements inco the DEM elevations. Another consideration is to make sure that the DEM surface cove rs the entire landscape area between a viewing site and the landscape bein g analyzed. In some applicat ions. a managec might be interested in determin ing the visibility of a resoucce from surrounding viewing sites (homes oc roadways) but only has access to a OEM that describes the area managed (such as the OEM for the Brown Tract, as shown in Figure
view height (above ground level) of 5.5 fee<, and [hat owners of [he homes around [he Brown Tract could likely view rhe foreS[ from [heir second Aoor windows (add 10 feet), resulring in an adjusted observer heighr of 15.5 fee<. To perform the viewshed analys is. you might pursue a process similar to that described in Figure 13.14. Since
[he original OEM for rhe Brown T racr was clipped
(0
the
ownership boundary of the tract, you could acqu ire USGS
OEMs and creare a new OEM GIS database rhat includes rhe Brown Tract and the areas that cover the homes. As mentioned earlier, addressing vegetarian height in the viewshed analysis might be app ropriate. since much of
[he Brown Tract is foresred . The stands GIS database contains a vari ab le named 'H eight' that provides the average height of each of the management units. By co nvert ing this vector GIS database to a raste r GIS database format. height information can be added to the Brown Tract
13.2). To perform a viewshed analys is, i[ will be necessary acquire a OEM that covers not only the landscape of interest ([he land being managed), but also contains [he
•
to
•
areas whece the viewing sites are located. One consideration in viewshed analyses often ovec-
looked is [hat G IS users can generally modify rhe height of
........, .
•••• •• • • ••••• • • •
the viewing site. For example. the height of the viewing site is genera lly considered to be the acrual ground-level elevation at the location of the viewing site. If the viewing si tes were meant to represent people who were standing
and viewing [he landscape, then an observation height of five or six feet higher than the ground elevation at the viewing s ite might be more realistic than assuming a
•
ground level view of 0 feeL This relatively modest change
•• •
in viewing elevation can have significant impacts on viewshed resules w hen large land areas are involved. As viewing elevation rises, the amount ofland returned by a viewshed ana lys is will typicall y also increase. Orher considerat ions in performing a viewshed analysis might include sen ing limits as to how far viewing sites are allowed to 'see' ac ross the landscape. and limits on view-
ing angles.
D •
Brown tract tocation
• •
•••
Homes
Figure 13.13 Locations ofhome.s near the Brown Tr:lci.
217
Chapter 13 Raster GIS Database Analysis
OEM
(raster)
Create observer height attribute
207
Stands GIS database
Conversion to raster
database
Overlay analysis
GIS database
(raster)
Viewshed analysiS
(raster) Figure 13.14 A general process for the development of a vicwshed analysis for the Brown Tract based on a set of vicwshed sites (nearby homes).
OEM values co get an adjusted elevacion that includes the average tree heights for each management unit contained in the stands GIS database (Figure 13.15). The Spatial Analyst extension within ArcGIS contains a convert menu that offers a 'feamres to raster' utility for this purpose. The dialog box for this option will prompt for the database to be converted and a variable co be represented in the raster (height in this case). Once converted the stand height raster can be added to the DEM ro create a newelevation surface that contains updated elevation data for the Brown Tract and surrounding areas (Figure 13.16). After performing the preliminary database development steps, the acmal viewshed analysis can be performed by computing the areas of the landscape that are visible from each home. With in ArcGIS, this can be accomplished by using the Viewshed command. under the Surface Analysis roo Is within the Spatial Analyst extension . A dialog box will open and offer prompts for the inpuc surface (OEM or other modified elevation surface) and observer points (locations from which the input sur-
, StnI..,{tI)
0-"
..... "... "...,
81-100 101-120 lZ1-140 141-180 ISHel
"''''' Figure 13.15 Average tree heights for managemcO( units contained in the Brown Trace stands GIS database.
218
208
Part 2 Applying GIS to Natural Resource Management the nearby homes are located mainly along [he trac[ borders, howeve r a significam amount of land within [he tract boundary is also visible from one or more homes (a lthough you cannot, from chis analys is determine how many homes have a view of each piece ofland).
Watershed Delineation
Figure 13.16 OEM G IS databuc fo r the Brown Tract and. surrounding areas that includes me :average tree hcighu of the management uniu contained in the stands G IS database.
face will be analyzed). Depending on the size of the DEM, this processing dme may be lengthy, however, afrer processing is complete. a raster GIS database is created (hat illustrates the areas with in and around the Brown Tracr that are visible from the nearby homes (Figure 13.17). The areas within [he Brown Tract that are vis ible from
Figwc 13.17 Arc:as (dark shade) within and surrounding the Brown Tract that are visible from the ncarby homes.
GIS has come to be a standard tool for hydrologic research and wate r resource modelling. GIS applications for water resources are commo n and vary from simply mapping water resources (Q sophis ticated ana lyses t hat co nsider water qua lity infl uences and [umce water ava ilabi lity (Wilson et aI., 2000; Marcin e< al., 2005). A primary use of GIS for water resource managemenr is that of watershed delineation. A watershed can be defined as a landsca pe area [hat shares a common drainage. This definition assumes that if water were able [Q flow freely over a landscape area, jt wou ld follow a downhill trajectory and exit (he landscape a rea at a common painr. W atershed boundaries are commonly used by federal . St3te, and provincial organizations ro separate a landscape inro smaller managemenr areas. In addition, watershed councils-groups that help (Q determine management acdv ities within a specific watershed-are becoming mo re common in North America. Some refer {Q watersheds as 'catchmems' but the intent is rhe same: {Q describe an area according to the fl ow of water ove r its su rface. The delineation of a watershed boundary is a func[ion of topography and changes in landscape relief. You must decide on the scale of the wate rshed to be created and have access to topOgraphic information in order to begin delineation. Previous to digi[al GIS and the availabili
Chapter 13 Raster GIS Database Analysis
~ .. ~ ............ ~ .. ::::------::::: a
209
... E ) . b
c
Figure 13.18 Watershed boundary location and Stream flow patterns derived from contour inu:rvai shapes: (a) watcrsh~ bound2.ry parallels contour saddles and (b) splits peaks as indicated by a dosed contour homes, and (e) waler Rows from the bonom of conlour funnels through
the tOp.
end. Closed conrour shapes were ro be spl ir rhrough rhe middle as [hey indicared a peak. and boundaries should
split
CQmour
saddles in a parallel orientarion. This
required those involved in the delineating watersheds co continually ask themselves 'which way would water flow if a bucket of water were dumped at a parcicular location?' The delinearion process could be drawn direcdy on rhe coorou r map o r accompl ished rhrough heads-up digirizing on a monico[ with backgro und databases of streams and contours being displayed . Nonetheless, manual watershed delineation is a tedious process with significant opportunities for human e rror. If a common drainage point. line. or area can be digitaUy represented in a GIS and a OEM exists of the area. many GIS can automatically generate a watershed area boundary given some basic data manipulations. In addition to a feature that represems the drainage locarion(s) and OEM. a darabase represeming flow directions is usually required for watershed delineation. A flow direction database is represemed through a raster database structure and assigns an expected direction of water flow to each raster cell. Fortunately, a flow direction layer can usually be creared from a OEM rhrough an a1gorirhm rhar evaluares rhe e1evarions of each rasrer cell and all neighboring cells. The possible direcrions include rhe parh co each poreorial neighboring raseer cell (eighr direcrions) as derermined by rhe lowese devarion among rhe neighbor cells. In addition, a 'no direction ' choice is possible and is referred to as a 'sink'. A sink occurs when a raster cdt has an elevation value mat is less than all surrounding cells. Any ponion of a landscape can be analyzed for irs watershed boundary given that a OEM is ava ilable of the emire pmential watershed extent. If the OEM does not cover the entire potemial wa[(~:rshed area, an incomplete watershed boundary may resulr without any warning from rhe supporting GIS software as (Q its incompleteness. As an example of GIS-based warershed delinearion. a portion of the stream network within (he Brown Tract that falls within the smaller incerior private ownership area is considered (F igure 13. I 9). A logical sequence of
steps for the watershed creation process is represented in Figure 13.20. A separare darabase muse firsr be creared that represents on ly this stream network portion, or at least the lowest elevation point within the porrion. This lowest elevation point in this database will serve as the watershed source, sometimes referred to as a pour point. Rather than risking a misidentification of the lowest point, it's probably more expedient and reliable to clip, or otherwise separate, the ent ire portion of the stream network into a new GIS database. Within ArcGIS, you can use the Select Features tool to select all of the stream features and then ex port (he selected feawres into a new da
'
...
.'
:
\~.j 1.,;"-
;
""
..,.'
··~····'J·····,··.>m \· J
.'
Figure 13. 19 The portion (in dark bold) of the stream network within the Brown Trace for which a watershed area is to be created .
220
210
Part 2 Applying GIS to Natural Resource Management
separate Stream
sections of interest
Create Flow direction
database
(raster)
Cooversion to raster
database
Watershed (raster)
analysis
Regardless, it may be necessary to detect and eliminate sinks within the OEM before flow direcrions can be calculared. Wirhin ArcGIS, rhe fill command can be used to manipulate porential sinks bur must be run through the Sparial Analysr Hydrology rools in rho ArcToolbox or rhrough rhe command line inrerface. The fill command will create a new raster with elevation values for sink locations raised to the minimum elevation found in surrounding cells. The modified ('filled') rasrer ca n rhen be used to calculate flow directions. Once the flow direction raste r is complete, th e watershed area can be derived. Within ArcGIS the raster calculator can be used with rhe Watershed com mand to create watershed boundaries. The syntax is 'watershed ([flowraster, streamsecrion])' whe re flowraster is the name of the f1owdirection raster and srreamsection is the name of the raster version of the portion of the Brown Tract Stream network for which a warershed will be creared. The warorshed croa red should not be ve ry large in co mparison to the Brown Tract boundary (Figure 13.21 ).
Figure 13.20 A general process for the ddineation of a watershed area for a portion of the stream network in the Brown Tract.
The next step is to create a flow direction raster based on the Brown Tract OEM. This process can be accomplished wirhin ArcGIS by using rhe rasrer calcularor and flowclirec rion com mand. Assuming that (he extended
OEM for [he Brown Tract is used. (he raster calculator syntax would be ' f]owdirecrion ([brow ndemex])'. The result of this command should be a temporary raster file with cells ass igned to one of nine possible values: one for each direction to the eight neighboring cells and a sink or 'no lower elevation' value . A si nk can be caused by a natural fearure. such as a lake or mher water body with no surface o utl et, o r ca n be the result of a OEM erro r.
.~.
Figure 13.21 Th~ watersh~d (i n gray) fo r a portion of the Brown T net stre2m network.
Summary The examp les in this chapter have demon strated several GIS operations [hat are possible when using raster GIS databases, and have concentrated on [he wealth of opportunities associated with using a OEM. From the basic elevatio n information contained within a DEM, you can gen -
erate contou r lines. a shaded relief image. and a slope classification map. Rasrer GIS databases can be integrated in spatia l analyses to calculate slope classes for management units as well as gradiems of streams. and to create a database of me viewshed relared to nearby landscape feamres, 221
Chapter 13 Raster GIS Database Analysis such as the homes located around the Brown Tract. In addir ion. DEMs can be used to analyze landscape topography and can create addition al raster databases to support watershed delineation . These examples are but a small
211
sample of the types of applications that are possible with a single raster GIS database in terms of examining landscape (Opography. We turn our anemion in rhe following chapter to exploring orner poremiai raster data applications.
Applications 13.1 Shaded relief map. You have been asked by the
engineer associated with the Brown Tract has asked you
manager of rhe Brown Tract ro create a shaded relief
to
map that simulates rhe sun 's lighr on rhe landscape in early morning.
roads in the Brown Tract using two GIS processing
a) What values would
YOli
use
to
approximate the
sun's azimuth and altitude? b) Develop a map that illustrates the shaded relief.
13.2 Slope map of the Brown Tract. During one of your monthly planning meetings. it was suggested that a
slope map would be of value to the foresters who plan timbe r sales on the Brown Tract. This map would be of equal value [0 (he recreadon manager, who is interested in potendal trail systems throughom the forest.
a) Create a slope GIS database of the Brown Tract that has 80 ft con [Our lines that originate from a 300 fr base elevation . b) Develop a map illuStrating the slope classes.
calculare road slope gradients (percent slope) for the
methods. For the first method. the slopes should be calculared using [he entire DEM available for the landscape. For the second method. the slopes should be calculated using only those raster grid cells that overlap the road network. The engineer wants to understand the difference, if any, (hat would be observed in the average slopes gradients when using these two methods. Prepare a short report to convey this information (0 the forest engIneer.
13.6 Brown Tract viewshed analysis. The manager of the Brown Tract would like you to develop a viewshed analysis using only those homes located on the north side of the Brown T racr. Use the assumptions and processes described in the example provided earlier in this chapter.
How do the viewshed results differ from [hose of the
13.3 Viewshed analysis planning. Your supervisor has approached you and asked that you assist in a viewshed analysis for part of your resource management area . She is interested in knowing what port ions of your resource management area are visible from a nearby road. She
needs the info rmation quickly and has asked you to scope our the project with regard to how this mighr be accomplished in GIS (e.g .• what GIS databases are necessary.
example presented earlier in this chapter?
13.7 Brown Tract watershed analysis. The hydrologiSt for the Brown Tract, Samantha Wasser, has asked for
your help in identifying a watershed for a portion of the
potential pitF.Uls of conducting the visibility analysis.
Brown Tract stream network. The lowest elevation stream segment in the portion is identified through a numeric value of 123 in the stream variable within the streams database. Develop a watershed for this stream network portion and report the resulting watershed area in both acres and hectares.
13.4 Road gradient analysis planning. Your supervisor
13.8 Brown Tract beaver watershed analysis. T he
is interested in knowing the average slope gradients of a set of roads within you r organization's management area.
Brown Tract manager has asked you to delineate the watershed area that Aows into a large beaver pond. The beaver pond location is described in the water sources database as a point location. Create a warershed for this point and repOf[ the resulting watershed area in both acres and hectares.
what processes are necessary, etc.). Provide a brief descriprion that lists required GIS databases, techniques, and
She has asked you for a one-page description of how this might be accomplished in GIS. Prepare a reporr for her that describes the necessary GIS databases, potential techniques, and conside rat ions that would be involved in
using GIS for this purpose.
13.9 Brown Tract stand watershed analysis. The 13.5 Road gradients on the Brown Tract. The forest
Brown Tract manager has asked you to delineare rhe 222
212
Part 2 Applying GIS to Natural Resource Management
watershed area that flows into a stand inside rhe Forese.
13.11 Combining raster GIS databases. Wha t is the
The stand is identified as number 36 with the stand field
result when two raster GIS databases are combined. yet
in rhe Brown T f aCt stands database. Create a wate rshed for this stand and repon rhe resulting watershed area in bmh acres and hectares .
'no dam' values are present in some portions of rhe landsca pe in one of the [wo raster GIS databases?
13.10 Basic characteristics of the Brown Tract. Becky
in the Brown Tract is located at (he lowest average elevatio n? Which stand is located at the highest average elevatio n?
13.12 Highest and lowest elevation stands. Which stand Blaylock. the manager of rhe Brown T ract, is interested in
some basic knowledge of the landscape. Specifically. she wants to know rhe following: a) What is rhe m inim um, maximum , average. and
standard deviation of slope (degrees) fo r the Brown Tract? b) What is rhe m inimum , maximum, ave rage, and standard deviation of slope (percent) for rhe Brown
T racr?
13.13 Elevation information for a single stand. What are the ave rage, minimum , and maxim um elevations of
the stand described in question 13.9 (stand 36)? 13. 14 Water source elevations. What is the average elevation for each of the points described in the water sou rces database?
References Burrough. P.A.• & McDonnell. R.A. (1998). Principles of geographical information systems. Oxford: Oxford University Press.
Martin. P.H .• LeBoeuf. E.J .• Dobbins. J.P .• Daniel. E.B .• & Abkowirz. M.D. (2005). Interfacing GIS with water resource models: A state-of-the-art review. Journal of the American Water Rtsourus Association, 41 , 1471-87. Natural Resources Canada. (2007). Mapping. Retrieved May 26. 2007. from hrrp:llwww.nrcan-rncan.gc.cal com/subsuj/mapcar-eng.php. Ribe. R.G. (1989). The aesthetics of forestry: What has
emp ir ical preferen ce resea rch ta ught us? Environ-
mental Managemen t. 13.55-74 . US Depart ment of Interior. US Geological Survey. (2007) . USGS Geographic data download. Retrieved May 26. 2007. from hrrp:lledc2. usgs.gov/geodatal index.php. Wilson. J.P .• Mitasova. H .• & W right. D.J. (2000). Water resource applications of geographical informa-
tion systems. URlSAjouma!, 12(2).61 -79. Wing. M.G .• & Joh nson. R. (200 1). Quanti /Ying forest visibility with spatial data. Environmental Management. 27. 4 11-20.
223
Chapter 14
Raster GIS Database Analysis II Objectives Chapter 14 builds on the previous chapter and further explores raste r data for spatial analysis. Like chapter 13, this chapter also involves [Opograp hic applications of raster data bue broadens the (reatment of raster data applications. In addition, more technical detail is provided for raster data processing techniques and analysis. Ar the conclusion of this chapter, readers should understand and be able (0 converse aOOm: 1. the potential applications of caster data for natural resource problem analysis. 2. how distance functions can be applied to raSter data. 3. the types of statistical summ ary search functions for raster data,
4. the capabilities and applications of density operations, 5. raster data reclassification and map algebra processes, and 6. data structure conversion considerations.
Raster Data Analysis We further develop raster data analysis in [his chapter by describing some general considerations for raster analysis working parameters. examples of raster analysis functions. and applications of processi ng commands for manipulating raster data. We then describe some application examples that make use of select raster funct ions. The raster ana lysis functions we discuss include distance, statistical sum mary search, and density functions. In some cases, rhe functions can accommodate both vecror and rasrer databases as input. General processing commands cov-
ered in this chapter involve raster reclassification. rasrer map algebra. and data structure conversions. We also provide more detailed information abour the procedures within the world's most popular GIS softwa re-ArcGISthar can be used for raster analysis. An assumption for rhese procedures is that the Spatia l An alyst software extension for ArcGIS is available. This extension is specifically designed for raster data analysis and provides a set of menu choices and commands that make some of primary raster capabilities more readily available. If readers are using different raster software (i.e. , Imagine, GRASS. Idrisi , etc.) for their app lications, the procedures described in this chapter should provide a template for applying the functions and analyses that we present.
Raster Analysis Software Parameters Some raster software will allow you to set working environment parameters that the softwa re will observe du ring use. These conditions establish resolurion, analysis, ourpur. and other conventions that affect Output raster databases that are created during processing. One of the distinguishing characteristics of raster databases in comparison to vector data is that they are cypically larger in size in terms of digital storage space. Raster working environment parameters can help constrain raster databases to specific study areas and resolutions so thar Output files do not become overly large in size. Some raster software will only create temporary raster databases unless otherwise specified by a user. These remporary raster databases will be removed from the hard drive once the GIS software is closed, which can cause some distress for unsuspecting 224
214
Part 2 Applying GIS to Natural Resource Management
users the nex( time [he software is resrarred. Depending on the raster software, work ing environment parameters may need co be reestablished ar rhe beginning of every analysis session. An important rasrer parameter (Q consider is an anaJy~ sis mask. An analysis mask, as esrablished by rhe dimensions of anQ[her GIS layer. will restrict processing to on ly those areas coincident within the layer. The extent oprions allow you co further resrricr the areas that will be considered when new raste r output is created . Depe nding on you r software, possible choices include existing layers. sparial combinarions of exiscing laye rs. or a bounding ser of coordinates. In addition to raster databases. vector databases can usually also be used (Q create an analysis mask. Another critical choice for processing out pm raster databases is cell resolution. Typically, working pa ramete r choices will allow users CO set a designated raster cell resolution for Output databases. This can typically be set [Q match orher analysis layers. combinations of other layers. user-specified cell resoiurions, or a user-specified set of columns and rows. Sening a cell size and analys is extem [Q match an existing layer will ensure that our pur raster cells are registered to the existing layer. and that output raster cells from both layers will be coincident and nor offset from one anot her. These potential parameters described here, and orhers rhar may be available depending on software, can help make raster analys is more efficiem. These choices can help establish a common resolution, analysis area, and output location for raster processing results. The appropriate options should be set prior [Q any raster analysis session.
closest feature. The function can be appl ied to either vecror or raster databases. As an example, consider the water sources poim locations within and around the Brown T racr (Figure 14.1) . Ir mighr be beneficial for emergency response units combating wildfire to know how far away the closest water sou rces are located, parriculariy if helicopters will be used ( 0 carry water from sources to a fo rest fire and stra ight line distances were of imerest. A distance function cou ld demonstrate relative distances [Q the nearest water source (F igure 14.2). An allocat ion distance functio n assigns areas [Q the closest feature. In this case, raSter cells will be assigned a pixel value that recognizes the nearest wne of influence, similar to the vector representation of a Thiessen polygon. A Thiessen polygon is created for each feature in a spatial database a nd represents the area that eac h feature is nea res(. In the case of the nine potential water sou rces, nine allocation zones are identified with each water source having irs own zo ne of influence (F igure 14.3) . A cost weighted distance function allows for ass igning different weighting to raster cells that take into account what is required for a pathway to cross through the cell (Chrisman, 1997). This fill1crion mighr be used ro assess the amount of time needed for water to flow through one end of a srream nerwork to anorher, the relative costs of materials [Q co nstruct or resurface a trail, or resources necessary to develop access ro areas in mountainous te rrain. The COSt weighted function requires that in add ition to
I
Distance Functions Distance functions calculate the measured distance [Q features of interes(. There are va rious ways of computing distances, ranging from a straight line measurement to more involved approaches (i .e., shorrest paths) that use constraims of moving across a landscape due [Q cha nges in relief or other impediments. Distance functions can be used to help determine the next closest feature, areas th at might be ecologically sensitive because of their proximity to namral features. or the most expedient route to take from one feature to another. Typ ical dista nce functions include the straight line, allocation distance, cost weigh red disrance, and shorresr parh (Theobald, 2003). The stra ight line distance function will create a raster output wim direct (also called Euclidean) distances ro rhe
D ... Water sources Figure 14. 1 Water source5 in and around the Brown Tract.
225
Chapter 14 Raster GIS Database Analysis II
215
... Water sources Cost weighted slope values
U ... Waler sources
.' 3
W,t.r source distance (ft) 0
LIZJ
0- 1.000 2.000-3.000
.. _ _ _
4.000-4.000 5.000-5.000 6.000-7.000 8.000-8.000
_
9.000-10.000
1 low slope aco..mu/ation
!2iJ 2 _
4 medium Slope accumulation
•
7h~" _
"",mula""
Figure 14.2 Straight line distance categories to the nearest water source in the Brown T raet.
Figw e 14.4 Cost weighted slope values to water sources in the Brown Tract.
the raster database representing desired destinations, the cells contain values representing ilie weights. The weigh ts represent the cost inherent in accessing the cell, be it symbolic of steepness. required constructi o n materials, or some other value th at has significance. The COStS are the n added togecher. starting at the destination(s) and moving outward. to form an output raster that sym bolizes the entire COStS o r resources from each cell to the desired des[ination(s) . A cost weighted distance surface wi th the water sources as destinations and the Brown Tract slope values as weights is shown in Figure 14.4 .
The shortest path function is intended to do what the name implies: identify the path of least distance or resistance to a desired destination. The shortest path function requires supporting databases representing cost weighted distances and least cost directions. The least cost direction raste r is ge nerated for each raster cell and is used to designate which of the eight neighboring cells is upon the least COSt path.
,---------- ----- ------- ----- ------A------ ------------- - -- ----,
Srariscical summaries can be generated from individual raster databases. multiple raster databases. and combinatio ns of raste r and vecto r databases using raste r- based search funccions . These fu nctions search within a database and recurn summary values based on search criteria provided by the user_The search crireria may be based on areas within a second coincident database, a search area within a given pattern and size, or values comained within multiple raster fi les_ Three general types of raster statistical summary search functions include cell. neighborhood. and zonal statistics opti ons (DeMers. 2002) _ Cell and neighborhood search functions are sometimes called local and focal searches, respectively. Each search function requires user input to direct the search extent and the con[em of statistical summary information that is returned . Cell, or cellular, statistics allow multiple raster databases to be evaluated in the creation of a new raster database. Each coincidem raster cell in all input databases is considered and a statistical summary of cell values is selected fo r output into the new database. Stads[ical summaries can include average. summary, minimum. maximum. standard deviadon. most common val ue. or other
//
r-''''-o!''/
L= _ _ _=_ _ _ __=____=___ =_ _ _ =_____=__
...
±: ~
_:b ___=______= ____...L.L ... ~
Water sources
......J Allocation areas
Figure 14.3 AJlocation areas for water sources in the Brown Tract.
I i !
Statistical Summary Search Functions
226
216
Part 2 Applying GIS to to Natural Resource Management
possibilities. Values Values within input darabases databases are arc also restricted to to numerical formats formats.. The cel cellular lular srariscics statistics capability capabili~ represents aa type cype of raster overlay operation but bur all values va lues in in all input inp"' databases are treated "eated equally acco accordrd[he statistical su mmary selected. Potential ap plicaing CO to the summary applica tions of cellular slatisrics statistics involving multiple raster datations and structure srructure elevations e1evarions co (0 bases include adding ground and create creare aa surface elevacion elevation layer, layer. developing a composite ind<x by summarizing poremial potential fuel and an d landfire risk index qualities , and developing an average temperature scape qualities. con[ain annual temperature given a set of databases that contain measurements. measurements. Neighborhood statistics functions are for single s ingle raster vecmr files and allow for statistical summaries based on oorr vector aarea rea sea searches rches within a sin single gle database. database. The sea search rch will co nsiderr the emire entire extent ex;,rem of (he the database bur will consider conside (point, line, line. or polygon} polygon) or raster rasrer cell and each feature {point. apply the [he search area parameters parame«rs for each. Regardless of raster or vec(Qr vecco r input. input, a new Output ou tput raner raster is created that contains conrains rhe the results of area a rea evaluations evaluadons in a summary value within each output outp ut raster raste r cell. cell. Neighborhood search functions funcdons ca cann have several differenr ferent shapes including rectangles, rectangles. circles, circles. and wedges. In addi[ion, [he funcaddition , an annulus shape is possible which has the tional donut, with an inside area (donut rional appeara nce of a donut. ho le) (hat that is ignored ig no red in searches and an outside area a rea hole) (donut) that is evalua<ed. (donu,) evaluated. The user designates the size of ,he the neighborhood search area shapes. Within each area, a rea, a statisrical summary is possible of any si single ngle numeric field summa ries inpur database. 5tadsdcal Statistical numeric summaries in the input include me rhe average. average, summa sum mary. ry. minimum minimum,. maximu m aximum, m. deviation, mOSl mOSt common value. value, and other frestandard deviation. quencyevalua
zones wnes can be designated designa<ed by an attribute value in either either numeri c or ca tegorical numeric catego rical format, format , such as counry county names, land cover categories, Qurpuc includes catego ries, or road numbers. Output aa statistical summary in tabular format for each idenrified identified zone zone and contains the che number of of coincident raster cells and associated rea. In addition, addition. numerical summaries summa ries associa ted aarea. include the me minimum, maximum, range, average. standard deviation, deviation ) sum, variety, va rie ty. majority. majority, minoriry, minority , and median val ues found within each zone. Zonal values Zona] statistics offer off'er the ilie advanrage advantage of allowing raster ...ster databases to ro be summarized in relation (Q to zones described with w ithiinn a vector veccor or raster database in a single analysis. The vector veccor database raSter can be point, line. line. or polygon format. Examples in the previous chapter chapcer included calculating slopes s lo pes for forest stands and streams. Other srands Oilier potential applications include dete rmining maximum elevations of precipitation gauges. determining scream, and the me average elevation In in average aspect of a srream. the home range of a wildlife speeies. species.
Density Functions The intensity or frequency with which something occu occurs rs dscape or portion of a landscape can be across a lan landscape demonstrated through a density function . Implicit in the lation is the tabulation of requirements requiremenrs of density calcu calculation so me resource in magnitude or location some locat ion relative to so me Within many raster-based area quantity (Chang. (Chang, 2002). Wiiliin
GIS programs. density can be calculated for point and line ourput results being wrinen written to a ras rer vector with output vecror layers wirh densiry surfaces is database. The creation of smoothed density a lso possible for poinr point and a nd line fearures featu res (Silverman, (S ilverman, also 1986). 1986). Density estimates are useful for describing rhe the road assess ing the relative quality qu ali ty or syste m within a forest. assessing system suitabi lity ofhabirat of habitat areas given ilie the number of wildlife wildl ife or suitability other features fC'dtures present in an area, and for demonstrating ' hot spars' spots' of an acriviry activity or resource conglomerations or 'hot database. condition when many features are present in a darabase. a re ap apparent stro nger Hot SpOts are parent locations or areas where stronger concentrations co nditio obse rved. When io n are observed. concentratio ns of some condir of locations are present presenr in a database, irit may be thousands oflocarions ro map and determine locations rhat that have challenging to concentratio ns than others o thers (Wing (W ing & Tynon. Tynon , ncentrations heavier co particularly true when point features are of 2006). This is panicularly a nd many occur in the same location loca tion.. A single si ngle interest and point in [his this sicuacion situation can obscure other poims points when point pioned on top of one another on a map or computer compurer ploned monitor. In I n rhese these siruations, situations, density function functionss can ca n be monimr. that demonstrate concentration applied to create shades that app lied (0 227
Chapter 14 Raster GIS Database Analysis II
217
SPOtS
can also create a hotspot. In other words, a subset of
are locadons where some feamre of interest is occur-
att ribu te values might differ markedly from other a([ribure values. The locations of the features would
What is a hot spot? In terms of GIS analysis. hot ring with greater frequency. A hot SpOt can
be indi-
polygon features in a vector database. Groupings of
then be used to identify the hot spot's location. Hot spots are frequently mentioned in spatially-based
raster cells can also mark hot spots. Beyond the den-
crime research and a number of analyt ical techniques
sity of spatial features, an 3nribuce field that contains
one or more features with increased or heightened
have been developed to identify hot SpOt locations. These techniques can also be app lied to natural
an ribure values in comparison
resource applications.
cared through increased density of point, line , or
(Q
mher feature values
intensities and ca n more quickly draw ones attemion
[0
likely hot Spots. Densicy functions within GIS cypically allow a user to select an attribute within a layer
(0
serve as the population
quamiry for density calculation. If no attribute is selected,
it is then the number of points or length of line within a search distance that is quantified. Two types of densities
are usually available: simple and smoothed. For each cype, a search radius and area unit must be specified. and Outpur results are wrinen to a raster database. The search radius determines how far from each raster cell in the output database in which CO search for features. The area unit will be the size of the landscape unit area in which frequencies are assessed: per square meter, square kilometer, or other area unit. The simple density method will result in a raster Output in which each cell shows the number of features per unit area. If the user chooses a populat ion
field, the field quanticy is used as the number of times in which
CO
Raster Reclassification Raster cell values can represent almost any numerical or ca tegorical value, ranging from reAected electromagnetic energy co descriptions ofland cover categories. It may be necessary to recode raster values so that they rep resent a modified range or more representative range of values, given an analysis objective. It may also be necessary that raster values need co be aggregated to form a smaller set of values. The reasons for needing to reclassify raster values
vacy and include: I. Values within a raster may have been updated through additional data collection . 2. Numerical values are needed instead of current values that are described using categorical or nominal values . 3. A more detailed description of categorical raster values
may be desired.
count each feature in the density summation.
The smoothed densicy also uses the same approaches but stretches or 'smoomes' the results such that density will be
highest at each feature location but reduced gradually to D at the outside radius of the designated search distance. The output of the smoothed densicy is cypically more aesthetically pleasing than the simple densicy output, bur it is less precise in its demonstrat ion of density. Some refer
the smoothed dens icy approach as the kernel dens icy method.
Simple road density surface low
to
An example of a simple density raster is shown in
Figure 14.5. In this example, the roads in the Brown Tract have been selected as the source to create for the density raster. A 2,000 fe radius was selected as the search distance and areas with greater road density are displayed
using darker shades.
dens.,
-
moderate density
_
high density
Figure 14.5 Simple density surface for roads in the Brown Trac[ wing a 2,000 ft search radius.
228
218
Part 2 Applying GIS to Natural Resource Management
4. Rascer Raster values may need to [ 0 be rescaled in order [0 to supsup· P pore O f[ a single raster raster analysis. lues may need co o rder [Q ro sup5. Raster va values to be rescaled in order SUpporr port a mulciple multiple raster analysis.
Reclassification Reclassification is differem differenc from altering the symbology or legend of a raseer raster database fo forr display purposes in that it ic goes beyond simply altering the rhe appearance of a raster raseer dacabase. database. Regardless of the reasons for reclass reclassifYing ifYing a raster database, the rhe reclassificat.ion recl ass ification process leads to a new raster data base with pixel values reflecting ceRecting the rhe recoded values. val ues. The new raster database and its reclassified values can then be used for mapping and analysis purposes. Within the ArcGIS Spatial Analyst, Analyst. a reclassifY comreclassifY command will mand is available. Selecting the reclassify [hat prompts the selectionn of a raster open a dialog box that rhe selecdo database. field., and input inp ut cells for fo r reclassidatabase, reclassification field fication values. By default, default. this interface will use the existex istto display the raster. For example. ing symbolization used m should sho uld current raster values be displayed using five ranges or categories. or categories, these same five c1assificacions classifications would be displayed on the reclassifY cel celll options. o ptio ns. To select different classifications from which to begin the recoding. recoding, the 'clasbutton can be chosen. sifY' butmn
Raster Map Algebra T The he abilicy ability to m access and evaluate values within multiple raste lts to [Q a new raster datarasterr databases and Qurpm output resu results Ras ter map base presents powerful analysis oppormnities. o ppo nunities. Raster evaluation algebra involves a mathematical eval uation that is applied more rasterr databases [Q to create a new database to one or mo re raste (DeMers. (DeMers, 2002). Mathematical Mathema, ical evaluations for fo r single raster databases may include add adding ing or multiplying raster cells by a constant val value, ue, or app lying trigonometric. trigo no metric, logarithmic. mathematicaJ marica1 transformarions transformations co to raster rithmic, and other mathe values. A common example of single raster map algebra might be takin rakingg an elevation-based raster and multiplying th nversio factorr ro conve rsionn facto to move from thee elevation values by a co to anome anothe r, such as converting eleone measurement unit co tical evalu avation values from meters to feet. Mathema Mathematical evaluations for multiple rascer raster databases data bases might involve addin addingg rions compa rin g muhiple mulriple raster or multiplying raster values, valu es, com parin databases and returning remrning the largest value for fo r all coincidemal dental cells, or using raster database values within a forfo r mula that cap tures landscape processes such as thar capmres that for ware velocicy and discharge disch arge rates. waterr velocity
Whether orr more raster databases are used, ras raster Whe ther one o rer map algebra results resulrs in a new raste rasterr database that coma contains ins the resul" results of the mathematical evaluations specified by fo rms of map algebra can be accom accomplished plished the user. Some forms earlier. such as cellular stathrough techniques discussed earlier, that at aHow a llow for statistical sracis ri cal summaries to to be calcu lated tistics th ltiple raster databases. Operations th at require from mu multiple raste r mathematical manipulations of single or multiple raster direct irect approaches approaches.. databases,, however. however, requi re more d databases Within With in the Spat Spatiial al Analyst function of ArcGIS. ArcGIS, the raster algeb ra, calculator calcu latO r is provided in support suppo rt of raster map algebra. and also provides access to additiona additionall raster raste r related funcdons. functions.
Database Structure Conversions It may be necessary fO to convert a spatial database from fro m a to vector vec(Or data structure structu re for vector to raster or from a raster to a variety of reasons. Potencial Potential reasons include: incl ude: 1. I . sup supporting porting a GIS GIS process or analysis ana lysis that only on ly acco accommvector data. modates veccor 2.. supporting a GIS 2 G IS process or analys analysis is that only accommodates raster data. data, modares only 3. sharing data with a colleague who can o nl y access one data structu structure re type. irements of a client or funding 44.. meeting the data requ requirements oorganization, rgan ization. and 55.. database storage srorage size considerations.
As your work with GIS conti nues. nues, it is likely that you will have to convert spatial spatiaJ data from one srrucmre structure to 3re co converting nvening anothe r. Regardless of which Structure you are to. to, there are decis decisions ions that mUSt must be made fo r eithe e itherr (Canstra nsformat ion . The good news newS is that a seco nd database is formation. typically cypically created through throu gh the conversion conversio n process. This usually ensures that if the rransformadon transformation is unsuccessful unsuccessfut. subsequent untilil a satisfactory su bseq uent attemp ts can be made unc created . product is created. The creadon creation of point. point , line, or polygon po lygo n features is of interest imeresr in conven converting ing vector Vec[or databases [Q to raste raster. r. Usually attribute bure field is selected as the information (0 to be caran arrri
ried into the new raster database. Depending on the soli:softoutpUt ras raster use, [he the OutpUt ware you use. ter format might be integer imeger or Roaring point. Aoat in g poi floating poine. Integer and floating point nt are {he the two twO prisupported within typ ical mary rypes rasterr dara data formats supporred typical types of raste GIS softwa re. A key distinction between these twO types rypes is software. that integer inreger raster raster da databases tabases will usually accommodate 229
Chapter 14 Raster GIS D Database atabase Analysis II mulriple fields or variables, and can acco accommodate mmodate non non-data. Floating point raster databases darabases are rypically typically numeric dara. used for numeric data clara char includes that incl udes precision. precis ion . as evi-
denced by decimal values. Floating point raster raSter databases are usually restricted [0 [Q one arui 3nriburc bure value. rather than lhan mulriple. mulriple, being associated associared with wim each raster rasrer cell. In some cases, it may be possible [Q {Q ccnven co nve rt a floarin fl oating g point poin[ raster rasrer darabase to an integer foemar versa, but fo rmat database. or vice versa. bur database you must consider co nsider what types an and d formats formars of dara data are
intended for fo r the rhe final producr. product. In addition addi tion [Q to the arrribure attribute field [0 ro carry into inca the new raste r database. a raSter cell rasrer cel l reso resolution imion mus mustt also be selecred. selected. This T his is a critical choice and it ir will have a large influence on the specificiry specificity of me the represemarion representation of vecroc vector features feacures in rhe the raste rasterr database and the scale at which sub-
219
occu occurrin rringg value in cel cells ls that mat will wi ll be combined in the me outourput pur product, producr, might be chosen chosen.. In tn moving to a vecror vecto r structure from a raster database. database, raster value will need co to be selected for {he (h e transformaa raster tion producr product if [he rhe raster suppOrtS supportS multiple fields. A key choice will be the vecror vector fearure featu re rype type for the outpur output daradatabase. Users will usually have (Q ro choose from point, line, or rypes. The choice of one feature type polygon fearure feature types. rype over another wi ll be a function of the in input pur rasrer raster database and analysis objectives. RasreriZ3tion • Rasterizatio n is the process of crearing creating a rasre r da[3base database from a vector vector database. darabase. Vectorization is the process of creating a vector data• Vecwrizacion base from a raster raste r database .
sequenr sequent analysis analys is can occur (Mirchell, (Mitchell , 2005). Equally important is (har that this choice will also impact impacr rhe the amount amounc of digital digiral storage space requ required ired by me the resul resulting ting raster raster database. A smaller cell resolution resolur ion will w ill represent vecmr darabase. vec[Q r features more precisely. but ill req uire more sro rage bur w will require morc storage space. If a study aJea area is large in size, and analysis goals are oriented towards a landscape or regional scale. or iented more rowards scale, men then a larger cell resoiurion choice resolution may be an appropriate app ropriate cho ice [Q co consider. If If a study area is small in size. or o r if more detail is desired in the representation of oflandscape landscape features, fea tures, then a smaller cell resolution should shou ld be chosen . Increasing a
The sspat pacial ial resolution of the raster raste r darabase database in eilber either
process may influence the ri,e quality of the resulting GIS database (Bettinger et aI., a1 ., 1996).
Getting Started with the ArcGIS Spatial Analyst The Spatia.! Spati al Analyst extension sofrwa software re for use wit with h
cell resolution resolurion by ha half, lf, however, resul results ts in a four-fold
ArcGIS must be purchased in addition ro the rhe base sonsoftadditio n to ware, and will not work independently of the sonwi ll nor rhe base softSparial Analyst exrension extension must be enabled ware. The Spatial
increase in crease in the number of raster cells. Keep in mind that eit her require requ ire or many raster analysis processes will either
within an open ArcGIS session in order for rhe exrension extension software to work. As wit withh all al l ArcGIS ArcG lS extensio extensions, ns. the
depend on raster rasrer databases having the same cell resoludon resolution
Spatial Analysr AnalySt is enabled by selecting [he the Tools menu,
in oorder rde r to produce reliable ompur outpur results. resulrs. Raster resamresam~ pIing piing involves changi changing ng the rhe resoluc resolut ion of an exisring existing raster layer and is a commonly used process among rasterraster based GIS users. However. However, decisions abou aboutt how cell values va lues are to be [realed treated in the resampled product must be made. made. When the me resampling of a raster database darabase increases the rhe spatial resol resolut utiion. on, such as moving from fro m a 30 m [Q (Q a 10 10m m
selecdng selecting the Extensions option. option, and selecting the check box next to ro th thee extension . The Spa Spacial rial Analyst roolbar roolbar
p
resu lting cell can be spatial resolution, resolution. the rhe value for each resulring taken from fro m the 'parent' 'parenr' cell cell directly. direcrly. When the resampling of a raSter raster database decreases spatial resolution. resolurion, such as moving from a 30 m to a I km resolut reso lution. ion. the value val ue for requi re some s(3tistical summarion pur each cdl cell will wi ll require statisdcal summa tion of in input sta tistical raster ce lls that contain numeric values. The ras re r cells T he stat istical summation migbt the average or or highest value of input might be me cells. For For raster databases with catego rical values. valu es. such as ca tegorical the choices will be more lim ited land cover or tree species, lhe limited in how values are resampled. resam pled. The value in the nearesr nearest or mosr completely coincident cell, most cell , oorr the most common ly
must then be enabled through eirher either the rhe T oolb.r oolbar choice under the View menu, or by right" right clicking on an open location on o n one of the menus, then selecting the Spatial Spatial
Analyst. Once ,he the Spatial Analyst toolbar is available, availab le, session parameters pa rame ters ca can n be establi estab lished shed by accessing rhe the ice at a[ the (he bottom borrom of the th e Spatial Ana Analyst lyst menu menu.. options cho choice The general options allow you to set your wo rking rkin g direccory. tory. where w here newly created raSter raSter databases will be saved. and also the me parameters paramerers of an analysis mask. The analysis mask can be set [0 to the spada! spat ial dimens dimensions ions of another GIS GI S laye r and processing will only occur occur in coincident areas. The extent options, options. avai available lable in the second tab of (he the oprions me oprio ns dialog box, allow you to furrher furthe r customize the designation of areas co considered nsidered fo forr raster raster aourput. m pul. C Choices hoices include existing exiSting laye layers, rs. spatial combinauons combinations of existing layers, or a bounding set o f coordinates. Both vector vector and laye rs. of 230
220
Part 2 Applying GIS to Natural Resource Management
raster databases can
be used. The cell size options are pro-
vided in the third tab of the options dialog box. and can
be used CO establ ish the resolution of Output raSte r databases. Settings include other analysis layers. minimum or maximum area covered by all input databases. and userspecified resolutions. The number of columns and rows in [he raSter can also be chosen . Setting a cell size to match an existing layer will lead to spatial agreement berween the layer and any output raster databases. This is a key choice that can help ensure consistent spatial registration of raster databases, an attribute that helps support reliable analysis output. The default ra ster format with in the Spatial Analyst extension is an ESRl grid. The grid data structure bears similarities to that of ESRI coverages in terms of transpon, storage, referencing, and naming conventions. These convemions are nor very forgiving but are manageable given a few ground rules. ESRI grids should be named using no more than 13 characters and should not stan with a number , should not contain spaces, and shou ld not use unusual characters such as an ampersand (&) or dollar symbol. These same rules should be considered for the naming conventions of folders under wh ich grids are smred. An underscore can be used in place of a space. JUSt as with an ESRJ coverage, an ESRl grid is actually composed of two folders and all information in both folders must be stored under the same directOry. Fo r th is reason. an Ardnfo interchange file with an .eOo file extension is often used to transport ESRJ grids and coverages, much as a common zip file format performs when it is used to compress and transport multiple files. The majority of the raster analysis and processing functions described are available under the primary Spatial Analyst menu or within sub-menus. The previous chapter explored the majority of functions available in the surface analysis sub-group. We turn our attention to (WO examples that involve raster data processing and analysis functions.
Determining the Most Efficient Route to a Destination Let's assume that the Brown Tract staff would like [Q take rocky fill material placed in one of the rock pits (described in rhe Brown T racr srands GIS database as Stand 282) and transpoC[ it to anot her location near the southeast entrance of the Brown T racr boundary (Figure 14.6). The material will be used to create a new trailhead ro accom-
Brown Tract • Rock pit
•
Figure 14.6 Rock pit, Brown Tract.
south~ast ~ ntr.tnc~.
and road
Southeast entrance
Roads
5yst~m
in
th~
modate the growing use within the forest. Mulriple trips with a dump truck will be required and the staff would like m minimize the impact on the forest road system during the fill hau ling process. A potential logical sequence for the GIS operations to su pport the shortest path creation is represented in Figure 14.7. The roads database is in a vector data struCtu re and describes the road surface rype according to one of three possible values: paved. rocked. or dirt (unpaved). These values will be used to ass ign a cost to each of the Brown Tract roads. The idem ificarion of the most efficient route will assign costs of I. 5. and 10 to the three road rypes. respectively. The slopes of the Brown Tract roads will also be considered in the analysis. Due to the relatively coarse reso lution of the Brown Tract OEM (10 m). road slopes will be divided into three broad categories with COSt values of 1 for mild slopes (< 5 per cent). 5 for moderate slopes (5-1 0 per cent). and 10 for greater slopes (> 10 per cent) . Although the choice ofa range between 1 a nd 10 is somewhat arbitrary. choosing values from a larger range will help differentiate possible routes more distinctly than will values from a smaller range . The cwo layers are then added to each other through raster map algebra to form a si ngle database with coSt values for each cell. COst weighting and direction are then calculated for the Brown Tract road ne(Work in order to reach the rock pit. After the supporting databases have been developed a shortesr path function can be used to identify a preferred transportation route for the fi ll material (Figure 14.8) . Although th e road system represented in the Brown Trace database is not substantially large in extent, many forest and other natural systems have exrensive transportation ne[Works. 231
Chapter 14 Raster GIS Database Analysis II
Slope GIS database
Conversion to raster database
slope categories
Reclassify road type
Combine values
221
Rock pit GIS database (vector)
Reclassify
Develop cost path & cost direction (raster)
Best path algorithm
(vector) Figure 14.7 A general process
lO
identify the shortest path between two locations on the
Brown Tract.
In these simations, the number of potenrial
fOUCes
can
surpass (he ability of transportation planners to sysremar· ically evaluate and select from a full range of options.
Shorrest path algorithms can assist planners and managers
in making sound decisions.
Creating a Density Surface for the Number of Trees Per Acre
Brown Tract • Rock pit •
Sootheast entrance
-
Shortest path
-
Roads
Figure 14.8 Shortest path between rock pit and southC:l$t entrance of the Brown Tract, given cost weights for road surfaa and road slope.
Density functions can be used to demonstrate [he relative abundance or strength of the locations of features an d anributes, The stands database for the Brown Tract contains an attribute named 'trees_acre', This attribute has a relative weighting of the trees that you would ex pect to find in each stand within the Brown Tract on a per ac re basis. It may be of interest to determine the areas in the forest where this field is strongest, indicating where higher numbers o f trees are more likely to be fou nd. Deter-
mining this info rmation could be done through plotting the polygons and using shaded symbols to demonsttate intensity values of individual polygons. This approach, however, would neglect the influence of neighboring 232
222
Part 2 Applying GIS to Natural Resource Management
What is a centroid? A centroid is a coordinate pair that is intended co represent mid-point of a feature or group of fe-drures. A centroid could be creatai co represent {he center of a group of points by taking the average of all the longirude and latitude coordinates. In terms of a line feature, a centroid position is easily determined. by dividing the tQ[al length of the line in half and using a coordinate pair [Q represent the half-way point. A polygon centroid can be more difficult to determine if the polygon shape is
me
polygons in irs representation . A more helpful approach might be
to
create a densi ty surface which would search
surrounding areas and determine inrensicies that take into account (he nllmber of trees for each stand while considering the number of trees in neighboring stands. A density surface must be created from a point or line
feature type. In order
[Q
irregular or non-homogenous (is not round, square, rec-
tangular, triangular, etc.), patricularly if some or all of the boundaty that make up a polygon contains curves, as is orren the case when describing narural features. The cen-
troid of such a polygon is determined through mathematical integration (calculus) with the goal of determining where the center of gravity of the polygon is located. The center of gravity can be thought of as the point at which the polygon would balance if set flat upon a pole.
strate the detected densities. Figure 14. 10 shows the Output that results for a smoothed density surface using the same stand centroids and a 1,000 ft search radius for trees per acre.
apply the density function to the
stands layer in the Brown Tract, we'll need ro conven the
stands polygon feamce rype. A point representation is probably preferred over a line feature type for the con-
Simple density surface
--
Trees per Icre
o
vened stands. A common method for representing polygons as poims is {Q calculate the cemroid, or middle of a
polygon's extent, which is determined geometrically if the shape is basic, such as ,hat described by round, square, or rectangular fearures. For irregular polygon shapes, centroid determinat ion must be accomplished with more rigorous mathematical techniques. Most GIS software systems will offer routines for cemroid determination and can quickly create a cemroid represemation of a polygon or line feature with one oUCput point created for each
lOw density
CJ _
moderate density
_
hIgh density
Figure 14.9 Simple density surface for num~r of trees per acrc based on a 1,000 ft search rndius.
input feature. In addition, all of the attribute values will be carried into the poim anribuce table. Within [he
ArcGIS software, ,he ArcToolbox has a 'Featu re to Point' conversion command that will accomplish this cransformation. The XTools extension software, a popular lowCOSt ArcGIS extension program, has commands that also support centroid creation. After the stand polygons have been converted to poims, the density surface can be created. Figure 14 .9 shows the resuh of a simple density surface based on the
Smoothed density surface
Trees per Icre
•• i ..
number of trees per acre. The darker shaded areas highlight the areas where greater numbers of crees would be expected. The search rad ius was set to 1,000 ft and density circles were created for each stand cencroid to demon-
Figure 14.10 Smoothed dcns ity sUrhce for based on a 1,000 ft search radius.
o
Iowdenslty
CJ
II!llI _
moderate density
_
high denslty
num~ r
of trees per acre
233
Chapter 14 Raster GIS Database Analysis II
223
Summary We demonstrated in (his chapter how raster databases can be manipulated and analyzed (0 solve questions related [0
bases. The procedures facilitate spatial analys is and su p-
natural resource app licado ns. A host of functions are
are su ited for specific analytical purposes. In add itio n, we presented [Wo potential applications in which some of the
available ro supporc analysis including distance, sracisrica1 search summary. and density functions . The functions
differ in their application and in the types of database structures that can be used for analysis. Omput may be tabu lar, veC(Of, or rast er depending on the function . Several common procedures within most raster-based software include raster reclass ifi cation, raster map algebra.
and conversion routines between vector and raster dar3-
POrt
,he ability
[Q
prepare spa,ial daubases so ,har ,hey
functions and procedures discussed earlier in the chapter were applied. The raster analysis p rocesses and examp les we presented by no means represent the extent of the porential of raster analysis. Rather, these processes an d functions describe so me of the more usefu l and co mmo n commands and processes for namra1 resource analys is with raster data.
Applications 14.1 Straight line discance function for points. A!; parr of your position as a natural resource manager. you manage the research areas on the Brown Tract. Concerned abom
their dimibu
' PLOT'
to
describe) has rhe highesr e1evarion and
what is the elevation?
c) Which research plor (use ,he numbers in ,he field ' PLOT' ro describe) has rhe lowest e1evarion and what is the elevation?
arrangement. Create a straight line dismnce raster database for research plot pointS co nrained in the Brown Tract.
14.6 Neighborhood statistics. Using ,he elevation laye r
14.2 Straight line distance function for lines. The
for the Brown Tract as an analys is extent and a template for Output cell reso lution , what is the longitude and lati-
density of stream syste ms can be used to define their cha racter. Given the st ream s GIS database for rhe Brown Tract , creare a srraight line distance rasrer database for streams contained in the Brown Trace.
14.3 Straight line dinance function for polygons. A!; another analysis related
{Q
the distribution of research
areas, create a straight line distance raster database fo r the
"and polygons in ,he Brown Traer where ,he LANDALLOC field is designared as 'Research '. 14.4 Allocation distance. As we mentioned earlier, in devel oping an allocation distance, ras te r cells are ass igned a pixel value that recognize the nearest zone of in Auence, si mil ar to the vector representation of a Th iessen polygon. Create an allocation raSter database for resea rc h plot po ints contained in the Brown Tracr.
rude of ,he cenrer of rhe ,hree by rhree grid cell neighborhood wir h rhe highest e1evarion' 14.7 Zonal sta,inics. Assume ,har a fire has s<arced in srand 140 of ,he Brown Trace. Ifhand crews canno[Control the fire, water must be acquired from nearby sou rces to help extinguish the flames. What is the average distance to the nearest water so urce for stand 140? 14.8 Zonal statistics. What is the average distance {Q the nearest water source for so ils polygon 166? Use the SOILS_ field to determ in e where this stand is located wirhin th e so ils database in the Brown Tracr?
14.9 Density surface for basal area. C rea,e borh a simple and smoorhed density surface for [he Brown Trace stands. Base rhe den sity upon (he basal area field contained with (he stands attribute table.
14.5 Ceu statistics. To furrher you r undersranding of the spatial distribution of research plots on the Brown Tract, you decide (Q embark on a series of raste r analyses to determine their venical distribution . a) What is the average elevation of the resea rch plots?
b) Which research plo< (use rhe numbers in rhe field
14. 10 Density surface for research plots. Creace borh a simpl e and smoothed dens ity surface fo r rhe Brown Tract
research plors. Base rhe density upon [he field named 'sl' which co ntains a numeric value for the site index of eac h
ploe. 234
224
Part 2 Applying GIS to Natural Resource Management
References Bettinger, P., G.A. Brndshaw, & Weaver, G.W. (I 996). Effects of geograph ic information system veC[Q f- rasrervector data conversion on landscape indices. Canadian journal ofFomt Restarch, 26,1416--25. Chang, K. (2002) . Introduction to geographic information systems. New York: McGrnw-Hill. Chrisma n, N . (1997). Exploring geographic information systems. New York: John Wiley & Sons, Inc. DeMers, M.N. (2002). GIS modeling in raster. New York: John Wi ley & Sons, Inc.
Mitchell, A. (2005). The ERSI guide to GIS analysis. Volume 2: Spatial 1nf!asurements and stah'sties. Redlands, CA: ERSI Press. Silverman, B.W. (I986). Density estimation for statistics and data analysis. New York: Chapman and Hall. Theobald, D.M. (2003). GIS conupts and ArcGlS methods. Fort Collins, CO: Conservation Planning Technologies. Wing, M.G., & Tynon , J.F. (2006). C rime mappi ng and spatial analys is in National Forests. Journal of Forestry, 104(6),293- 8.
235
Part 3
Contemporary Issues in GIS
t!ographic Information Systems: Applications in Natural Rt!sourus Managemt!llt focused on the background and development of GIS in rart I and delved into GIS applications in Pan 2. In Parr 3. we try [Q provide a glimpse of where GIS use may be heading in the nea r future. Trying to look ahead and predict what may happen is a difficult task because both GIS-related technology and the society that su rrounds it are changing rapidly. Nonetheless. in chapter 15 we disc uss some trends that 3re associated with GIS in
G
namra1 resource management. These trends are related
CO
technological developments. rhe
handling and sha ring of spatial data, and the legal issues that may impact organizations that use GIS. C hapter 16 makes note of how the increased ava ilability of GIS has transformed rhe delivery and struc(Ure of GIS operations in many organi zations. Also important in chapter 16 is rhe discussion of possible barriers ro successful GIS impiemenrarion and how implementation effectiveness can be assessed. We also consider some of the current challenges within the GIS communicy in Parr 3 . T he final chapter, chapter 17, examines the on-going and sometimes contentious discus-
sion of how the GIS profession should be defined and recognized. There are a number of other established professions that are also involved with measuring and mapping features and there has been friction at times in agreeing on me capacity in which certain professions
should apply GIS. Such discussions are evidence that GIS and GIS professionals have an important and necessary ro le in today's society. While many other professions have welldefined activities, competency standards. and governi ng bodies that describe and guide irs members. the GIS comm unity has only recently developed some initial pathways for certifying GIS competency. Criticism has been leveled toward these initial effo rts on the grounds mat sufficiently rigorous processes to establish competency have not been developed. The discussion ofeIS competency arose initially from concerns voiced. from me land surveyi ng and engineering communities about the potential for GIS users to perform traditional surveying and measuremenr activities while not actually having professional license as a surveyor or engineer. The final chapter probes the issue of whether GIS users shou ld be licensed. a hot ropic of conversacion particularly when you try to define what a 'professional' GIS user is and what sorts of activicies (hat person is qualified ro perform.
236
Chapter 15
Trends in GIS Technology Objectives GIS technology is constantly evo lving, ada pting. and changing according to the needs and capabilities of GIS users, panicularly (hose within the field of namral resou rce management. While namral reso urce man age rs may only represent a portion of the tOtal population of GIS users, natural resource management also benefits from the influence other fields (t ransportatio n, utility management, public plan ning, etc.) have on [he evolucion of GIS technology. This chapter provides a discussion of so me of the current trends associated with GIS technology and use. When we initially developed th is chapter in 2004 (Bettinger & Wi ng, 2004), we co ncluded that forecasring the direction and success of trends was challenging, but allowed you [0 consider what potential ap plications might ex ist within the area of natural resource management. As you will see, some of the trends in GIS tech nology have remained ove r the past duee yea rs, while others have recently appeared. After co nsidering the topics presented in this chapter, readers should have a reasonab ly firm understanding of a number of issues related to the trends in GIS technology and lise. As a result, readers should be able to describe and debate the assoc iated strengths and weaknesses of: I. the common trends related ro GIS technology, and how these might be app lied in natural resource management, 2. the oppo rrunities for strengthe nin g GIS technology and app lications within natura l resource management o rganizatio ns. and
3. the current an d potential technological developme nts that might promote or hinder the adva ncement of GIS as an effective problem-solving roo l.
Integrated Raster/Vector Software For many years, GIS and other spatial software systems have been defined by their ability to wo rk with either raster or vecror data. In fact, many GIS software programs either rest ri cted lIsers ro working with one data structure o r the other, or aiiowed users to conduct analyses with one data strucru re and limited the use of the other data Stru cture to rudimentary purposes (e.g., viewing only) . Recently, however, almost all traditiona lly raster-based GIS software programs have begun to include algorithms and techniques to allow the capability of managing vector GIS databases, and similarly, al most all traditi onally vecto rbased GIS softwa re programs have begun to include algorithms and techniques to allow the capability of managing raSter GIS databases (Fa ust, 1998). The primaty hindrances to providing the capability to use both d ata srrucrures for spacial ana lyses were {he marked differences between the twO data structures and in part icular, how they were stored. To further complicate marters, software manufactUrers created t heir own proprietary formats for raster a nd vector data structures that were best suited to their product. In addit ion, each data structure could also be described by more than one format. The di ffe rent stru ctu res (a nd formats of srruc~ cures) overwhelmed the computing capabi li ties and software design efforts of earlier GIS softwa re com pan ies. As 237
Chapter 15 Trends in GIS Technology
computer technology and software programming languages evolve. a (Orally imegrated system, one that would be able to incorporate both vecrof and raster GIS data structures simultaneously in the spatial analysis of natural resource issues, is a trend in softwa re development that will continue [0 drive the direction offurure GIS software
227
Linkage of GIS Databases with Auxiliary Digital Data While we think of GIS as a system for displaying and manipulating geo-referenced maps and images. we have
programs. For example. such a system would allow the use of vecmr GIS databases (0 assist in image classification,
[he ability in some GIS software programs to associate spatial data with other non-spatial data. Of course, data in an atuibme [able, or data from a non-spatial joined table
whereas previously only raster-based GIS databases would
can full into this category as well, but what we refer to
allow a system to perform GIS operarions such as buffer-
here is the association of an im age that is nO( georefer-
ing. overlays. and proximity operations. with hoth raster
enced with some spatially-referenced data. For example.
and vector processes in a seamless and efficient manner. In a totally integrated GIS, processes such as vecrof-toraster or raster-[Q-vector conversion (as described in chap-
ter 3) and analyses that use both raster and vector data simultaneously (as described in chapter 13) would therefore be transparent to users of GIS (Faust, 1998). Software such as ENVI (ITT Corporation, 2007) and Erdas Imagine (Leiea Ceosystems, LLC, 2007) not on ly provide a vast suite of raster-based analytical tools (e.g. , image classificacion, terrain analysis), bm also allow you to integrate vec[Qr data with raster data and perform the buffering, digicizing, and edicing funcrions that were dis-
cussed earlier in this book. Erdas Imagine also allows yo u to clean and build the tOpology of vector GIS databases, which is useful when editing vectOr GIS data. Coogle Earth (Coogle, Inc., 2007) is a similar system but it may be more appropriately co nsidered as a geospatial exploration program at this point in rime. The Google Earth system has the ability to integrate vector and raster data to a limited
extent, but its real value lies in all owing users
to easily visualize landscapes through an Internet browser.
in the field of urban forestry, you might capture the spatial position of (rees within a ciry as a set of vector points. These points may be attributed with tree characteristics
(species, height, etc.) and other local landscape variables. The points that represent the trees can also. in some CIS software programs, be atuibuted with a link [Q a picture and, when you select a point representing (he tree, the
picture of the tree is presented (Figure 15.1). The linkage of GIS databases to this type of auxiliary data is generally made using a hyperlink. A hyperlink is a navigation element that allows. when selected, [he viewing of the referenced information associated with the link. Hyperlinks are used widely on the Internet for navigation purposes, but they are nO( limited to Internet usage. obvi-
ously. They were designed as a way for you to link to specific portions of related documents without having to open each new document at its beginning and search for
the desired page. Hyperlinking is a useful way to associate pictures. documents, videos, or any other relevanr data to a mapped feature. thus allowing for a more comprehensive use of information systems. The urban tree example
is bur one of many logical and valuable uses ofhyperlink-
Figure 15.1 A GIS database of urban treC$, and an associated hyperlinked picture of a trcc (Courtesy of Andrew Saunders).
238
228
Part 3 Contemporary Issues in GIS
ing non-spatial data to GIS databases. Having the abi li ty to view (with actual photos) oblique perspectives of land-
sions during periods when an increased number of CPS
scapes from various vistas or overlooks represents another
satelli tes will be available.
valuable use of hype r/inking data for natu ral resource management purposes.
Spadal data collect ion technology conti nues to evolve. and natura l resource managers are likely to see new techniques that im prove upon present CPS, satellite im agery,
High Resolution GIS Databases
and LiDAR data collection methods in the upco ming
New areas of resea rch and develo pment, call ed 'precision
data collection technology is related to high spatial resolu tion GIS data. GIS databases developed from the rasterization of color ae rial photography and developed from satelli tes such as IKONOSTM (GeoEye. 2007) are becom-
the tree canopy is least dense. or by scheduling GI'S mis-
yea rs. One of the mos t promising secrors o f improved
fo restry' or 'p recision agriculture', have recently been introduced in natura l resource management. These areas
of research and development seek co use digital technologies for improving, and making more efficient. namral resource management activities. 'Precisio n tech niques'
ing available at 1 m to 4 m spatial resolutions (Figu re
15.2). While geo- registered color aerial photography of
might include using GPS as a navigational aid for farm or
large land areas can now be collected, processed . and
forestry equipment. capturing remotely-sensed imagery co describe the status of soil properties (e.g .• the need for fertilizer or pesticides), or using digital aerial photography to
satell ite imagery at 1 m resolution requires a longe r time
record crop planri ngs and outcomes. Precision agriculture techniques have been actively used and recognized as a
made avai lable to clients the following day. acquiri ng period (generally 10 days o r more) and depends o n the area and time frame of inte rest. Another promising area of improvement in data is high spec cral resolution raste r
discipline for at least a decade. In contrast. the first fo rmal
databases. Normally. aerial photographs that are con-
recognition of precision forestry occurred in June 2001 at the University of W as hin gw n's Precis ion Forescry
verted into digital o rrh op ho tographs cove r a 0.4 to 0.9 micromete r range of the electromagnetic spectrum. Some satellites systems captu re energy in longer wave-
Symposium. High among the list of goals of precision forestty is the identification of methods fo r the collection. analys is. and use of hi ghly accurate and precise data from the Earth's surface w facilitate bener management of natural resou rces. Examples of precision fo restry techniques might include using electronic distance measuring (EDM) (Ools ro capture the precise spatial position of forest land-
lengths. but usually within 10 distinct bands (ranges of energy) or less . Higher spectral resolut ion data implies
scape features, capturin g precise and timely sa tellire imagery (0 ass ist in monitoring threats (0 forest health
(e.g .• fire or disease). or developing precise. fine-scale DEMs to identify steep forested areas susce pti ble ro potentiallandslide activ ity. The main o bstacle ro implementing precision forestry or agriculture techniques in natu ral reso urce management remains that of obtain in g accu rate. prec ise. an d timely spatial data of landscapes . Some applicatio ns of precision technology may be implemented more easily in differing land uses, however. For example. in COntrast ro many agricu ltu ral applicatio ns, forests are characterized by a dense ca nopy cove r and so meti mes by mountainous terrain, which can limit the types of technologies that can be used {Q coll eCt precise spatial data. A thi ck canopy cover, for instance, often hinders CPS receptio n. and hillsides can prevent satel lite signals from reaching a CPS receiver. There are ways to avoid some of these problems, such as by co llect ing CPS data durin g rhe winrer months when
FigllR 15.2 IKONOS satellite image at 4 m resolution of Copper Mountain located in the Colorado Rocky Mountains (Imagc.s courtesy of GeoEye).
239
Chapter 15 Trends in GIS Technology that many mOte bands of enetgy have been captured, caprured, and can be used co to monitor mo nito r and evaluate the Earth's Earm's surface. AVIRIS AV1RIS (National (National Aeronautics and Space Administration, o ne such high spectral resolution 2007) is an example of one al most 15 years. years. system, although it has been available for almost With this system, 224 bands of data can be captured for o ne point in time. time, aUowing aIlowing scientists a single landscape at one to use the appropriate spectral reflecrances re£lecrances and managers [0 for analyzing various nacurai namrai resource reso urce management issues. Still among [he ncerns of most natural nam ral Sdll the primary co concerns resource resou rce management organizacions o rganizations is (he the COSt related [0 (Q the acquisition acquisi tion of high-resolution spatial spatial data. While initially rel atively high , as new technologies and data relat ively high, sources become available avai lable the cost and ava availability ilability of highresolu resolution tion G IS databases might be as low as $0.03 $ 0.03 per acre. Anomer Anorher issue of concern is the dara data scocage s[Qrage re'luirerequirements. H igh- resoluti o n images can require massive mass ive menrs. High-resolution amounts of computer hard drive dtive space. space. Although large hard drives (I (100 00 GB and above) have become the norm, gathered storage space can be filled quickly as images are ga
Distribution of GIS Capabilities to Field Offices There are a number of reasons why me the use of GIS has, has. (0. spread from a cemraliz.ed centralized organizational and cominues conrinues ro, to field offices: more and more people are becoming office to universities arc: are educomfortable using GIS. colleges and universiries cadng appli cation of GIS in in natu nacu cating srudc:ms students in the use and application raj resources, ral reso urces, and natural namral resource organ o rganiizarions za rions are rec-
229
ognizing that more timely analysis and map products can be obtained if the wo work rk is more closely situated co to the end Bettinger, 2003). 2003) . [n In addiadd iuser (Bettinger, 1999; Wing & Berringer, tion, rion , rhe the increased power of com compurer puter [echnologies technologies (speed and memory) and the [he advancements made in the operaring operating systems of perso personal nal compurers compute rs have borh bo th allowed a wider user base to use GIS technology (Faust, (Faus[, com puter systems are relatively inexpensive 1998). Since computer (a GIS workstation worksrarion can now be purchased for unde underr $2,500) , and GIS $2.500), GIS software has been developed with end toward a users (e.g., field personnel) in mind, the trend is [Oward GIS sysrem system where, in larger organizacio organizations, ns, data dara developmem cemral ment and maintenance maimenance casks tasks are performed at a cemeal clara analysis and map production productio n [asks tasks are office, and data performed at remote sma ller organizarem ore field offices. In smaller tions, the d istribution of processes may be less clear. distriburion dear. rions, because the becween a central office and field [he distinction distincrion between offices may be blurred (or (o r non-existent). In some organizations. o rganizatio ns, approved person personnel nel who work in the field perform th thee data maintenance tasks tasks.. Changes C hanges to G GIS IS databases can be made at the field office, sent electronically co [he the central cemral office for verification and integration, and eventually passed back to field offices. In systems such as these, only o nly one person can be mak making ing time. As a result, dara data being changes at each point in time. edited is 'checked out' (like a library book) until umil [he the ed edititing has been completed. The transfer of updated information to ro [he the field offices would wo uld ideally be instantaneous, but it is not. not. A delay of 15-30 minutes is required for centraljzed centralized systems co to complete [heir the ir rasks. tasks. The benefits of a distributed GIS GIS system are aimed at o r field office productivity productivi ty and decisionenhancing local or making. Two T wo of me the main benefits include a more timely to analysis and map production productio n needs of field response co offices, and a decreased work load on a centralized GIS office (allowing (al lowing more time and effort to be devoted to GIS database quality and maintenance). Within a distributed GIS system. GIS sys tem, dearer clea rer channels of communication should shou ld (those exist, since generally speaking, (he the customers (rhose requesting maps or analysis) and suppliers (those performing the analysis oorr making maps) are in the same office (or (o r perhaps are the same person). This fuce-to-fuce face-eo-face communication is often effecdve in meering meecing rhe the goals of a map o ften more effective or analysis request than chan comm communication unication processes mat that rely ca lls. [n on e-mail or phone calls. In addition addition,, field personnel involved in GIS analysis and map productio productionn are likely (0 to they have a greate feel as if rhey gre-arerr investmenc investment in [he (he GIS program. program, and perhaps will develop a greater sense of responsibility responsibiliry for maintaining accurate accurare GIS databases (Bettinger, (Berringer, 1999) 1999).. 240
230
Part 3 Contemporary Issues in GIS
Given the ongoing technological {software and hard-
The benefits of the system include reduced paper-related
ware} advancements related co GI S. and the proliferation
processes {e.g., transfer of maps and data to a centralized office} , and an empowerment of people to adopt and use
of GIS training, it is high ly likely that the distributed GIS model of capabilities wiH continue to grow, and become
new technology at the field level. Users in field offices can update GIS databases using heads-up digitizing, and sub-
more prevalent than the centralized model. At some point, the distributed model may replace the centralized model
mit data and repons on progress in managing the state's
completely in many organizations. The challenge in man-
forests. Future endeavors include extending the real-dme
aging this paradigm shift will be to ensure that organizational prococols and monitoring are in place to protect distributed users from using spatial data improperly.
capability to hand-held data recorders equipped with GPS
Web-based Geographic Information Systems The widespread use of Google Earth has suggested to many that GIS can be both affordable and easy to use
technology to all ow an immediate data capture and update to occur, which can increase the efficiency of map-
ping and reporting wildfires, water quality problems, and insect and disease outbreaks. Although the intent of the system is to make administrative functions of state land managers more efficient, as with the previous discussion,
across the Internee. Some organizations have recognized the need to provide GIS data management services over
there may arise some data quality issues related to remotely-performed modifications of databases that are not consistent with organizational standards. Only time will tell whether these advances will result in COSt- and
the Internet as a way [0 more rapidly update databases and provide information to users in the field. Ideally, a natural
stantial reduction in data quality.
time-savings and in increased productivity with no sub-
resource organization would maintain a system where data
being updated by an employee in a field office can be
Data Retrieval via the Internet
'checked our' over the Internet and managed (updated or
modified) . While the data is being modified remotely,
As we discussed in chapter 3, the Internet is becoming a
other users would have the abilicy to view the data, but not checked back in, orner users in the organization can then
common source for acquiring GIS databases, GIS metadata, and other information regarding the acquisition of GIS databases. In fact, the current popularity and preva-
use the modified data. Overall, [his type of process has the
lence of GIS can at least be parely attributed to the
potential for substantially reducing the time required to
traditionally update GIS databases (see chapter 10).
Internet. As public agencies began to produce and make GIS databases available, customers who wanted the data
However, there may be some data qualicy issues related to remotely-performed modifications of databases that are not consistent with organizational standards.
were often required to pay for the storage medium (tape, co, etc.) and for the time required to place the GIS database{s) on the medium . Since the media had to be mailed
As an example of a system such as this, the Virginia Department of Forestry has recently implemented the
to the customer, this process also required a period of sev-
eral days (to weeks) before the user could actually use the
Integrated Forest Resource Information System (IFRJS).
GIS databases. Presently, most public agencies offer their
simultaneously modify the data. Once the data has been
Many organizations have developed their own Intranet. These are private, networked computer environments
tion information) to the sophisticated {those that offer graphical interfaces for access to data, software, or links
in which only members within me organization have access. Imranets look and act like the Internet, and can
provided through an internal (co the organization) web-
range in complexity from the vety simple (a set of folders or subdirectories containing data or other organiza-
to other organizational services}. Each of these services is site. Inrraners are a method that can facilitate a disrribured GIS system within an organization.
241
Chapter 15 Trends in GIS Technology
non-sensitive non-sensidve GIS databases over the Imernet, Internet, allowing allowin g GIS users the opportunity [0 to qu quickly ickly acquire acqu ire the data at no COSt. In cases where GIS GIS databases are very large and therefore nO( nO{ practicaJ Imernet practical for Imern et transfer (for (for example,
as wi rh OOQs DOQs or orher resol urion raster rasrer imagery). imagery) . with other highhigh-resolution public age agencies ncies may still require that co consumers nsumers pay for data cransfer transfer costs. COSts. However, software However. data compression sofrware conrinues [0 to improve. im prove, which reduces the technology condnues me need Private organ organizations izatio ns that marfor non-Inrernet non-Internet transfers. Privare ket ker and sell sel l GIS databases darabases also allow cusromers customers ro to download the databases from the lnrernet. Internet. In these cases, cases) custo be regisrered registered w ith rhe pri private vate tomers [Omers usually need ro organization because access ro to the data is restricted.
Portable Devices to Capture, Display, and Update GIS Data
231
man mon GIS GIS databases darabases that rhar can also be used to ro visually check measurements, as well wel l as to faci faciJir3re li tate a uaverse traverse of {he rhe
landscape. dara collectors collecrors are moderately moderarely expensive Hand-held data ($1.000 to ro $5,000) $5.000).. depending on the rhe quality of the rhe instrument and the functions they allow. POAs are less expensive (around $500). $500) , and can be used as data dara collectors. to rs, but they are generally less rugged and more prone to factors (e .g.,, rain) and damage from environmental facto rs (e.g. human error (e.g., (e.g.• dropping droppin g the rhe device). Some GIS GIS con-
sultants sulranrs have developed sof1ware software that rhar will run on POAs. PDAs. ArcPad (Environmemal (Environmental Systems Research InstitU[c, Inst itute, Inc., Inc. ,
2006) is perhaps the rhe mosr widely known produce. product. A growing list IiSl of accessories can also be purchased to make POAs more durable and useful in inclemen, inclement weather and under conditions. under other conditions.
[n rhe use of hand-held data dara collectors collecrors I n rhe the pasr past decade the and personal di digital giral assistants assisranrs (POAs) (PDAs) has become quire quite
Standards for the Exchange of GIS Databases
common for collecting co llecti ng forest inventory in ventory dara data and other attributes anribures of oflandsca landscape pe fearures. features. G GPS PS receivers, rece ivers, in faCt, use
The deve10pmenr development and use of srandards standards for exchanging
hand-held data dara collectors collecro rs [0 ro allow you [0 ro capture caprure the rhe spatial anributes attributes of oflandscape landscape features. features.lntegradon Inregration of the rwo two
GIS databases may seem like a trivial exerc exercise ise for governployees and university researchers, since meotheomental mcnral em employees
philosophies, allowing you ro philosophies. to collecr collect spatial sparial locarional loeational infortflalion collect anriba([fibinfo rmation aboullandscape about landscape features and to coliect
oorganizations rganizatio ns (for (fo r example) must adhere to federal data
ute data, results in posirive positive benefits to a natural namral resource management organ organiz.ation. iza tion. Traditionally, T radicionaHy. data collected coUected natural resource inventories wou ld be recorded in a for narural field notebook or on a map, map. and would requ require ire man manual ual data enrry entry inro into a sp dara spreadsh readsheet eer or GIS GIS database darabase (through (rhrough an attributing ributing spatial spatia l landscape fea(Ures). features). This manual process is time consuming and presents several severaJ opportuniopporcunities for human error co to be introduced incroduced into a GIS GIS database. The integration of digital technologies allows information info rm ation to be recorded in a computer database while a person is in
the rhe field. Hand-held dara da,a collecrors collectors and POAs PDAs are able co to conneCt to wired or wireless computer systems, allowing ,he rhe data dara co to be rransferred transferred to ro GIS. GI . This greatly grearly exped expedites ires the rransfer transfer of field-collected rhe field-collecred dara data to ro a GIS GIS database darabase where
the da,a can be analyzed and mapped. Th rhe dara This is process also
me
removes some of the error opportunities op portunities char that might occur
through ,he rhe manual coding and inpu inputting tting of data. clara. Dara Da,a coHeetors ro examine maps and collectors also offer offer users the th e abil iry ity to
images of oflandscape measured. landscape features fearures as they rhey are being measu red. Field personnel person nel can use this heads-up display d isplay to ro visually determine whether their measurements are in agreement with the landscape landsca pe features bei being measu red. Digital ng measured. graph ics are two comorthophotoquads or o r digital raster graphics onhophotoquads
rerica1ly dara retically data ((ansferred transferred among US fede federal ral governmenr government
standards (J1[[p:llwww.fgdc.gov/standards) . These sranstansra ndards (hrrp:llwww.fgdc.gov/srandards) dards specifY dara data formars formats that rhar are intended inrended co to facilirare facilitate the sharing of spatial data among organizations. organizations. Many university universiry researchers also urilize utilize this th is protocol (or (o r something very similar) sim ilar) in some cases because they imeract interact duringg the course of with federal granting agencies durin research. However, most private namral natural resource managemenr ment organizations are not no t bound by these daca data standards. Thus acqu acquisition isition and modification of GIS datanatura l resource management bases by private natural managemenr orga nizations undocumented; transformations organ izations proceeds undocumenced: to allow an integration and re-projections regularly occur CO of the rhe acquired GIS databases darabases into inro rhe organization's organizarion's system, since the rype type and format form at of data exchanged can vary considerably (Figure 15.3) . Moving co to a standard srandard dara data excha nge formac exchange fo rmat usually suggescs suggests that thac oone ne of cwo twO organiorgan izational policies will be used: (1) zational ( I) organizarions organiza tions co conven nvert all of [he dy in use to [Q a srandard the GIS darabases databases curren currently standard format. format, thus rhus avoiding rhe the need to conven convert GIS GIS databases when data exchange processes occur, or or (2) organizations organizarions convert GIS databases ro to a standard exchange format only on ly occur. There is a cost assodata exchange processes occur. when dara i, is a function of how often ciated with both policies, and it 242
232
Part 3 Contemporary Issues in GIS
,,
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Siale organizations
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....... Federal data standards implied when transferring data - - .... Other data standards generally used when transferring data Fi~
15.3 Datab~ transfu and implied standards among organiutions.
an organizacion perceives th at it might exchange data with orner organizacions. If an o rganization positions irs
internal data standard closely
to
particularly those (hat are used by the most common GIS software programs.
that of the data exchange
standard, the cost w ill be minimized when acqu iring GIS
databases from. or sharing GIS databases with. federal agencies. If an organization does not plan (Q acquire GIS databases from, or share them with, federal agencies. the cost of deviating from the federal standard may be minimal. Even if a natural reso urce management organization avo ids imp lementing a data exchange standard a few co mmon data excha nge formats are used by other organizations. For example. it is not uncommon for public
agencies to make GIS databases available in both ArcInfo export file format (eOO) and ArcView shapefile format. Among com purer-assisted design (CAD) software programs, the DXF (drawing exchange format) format is commonly used to exchange files. Most GIS software manufacturers recognize that users will need to accommodate data formats designed by other software vendors . For this reason, it is not unusual for a GIS software program [Q make ava ilable conversion and integration processes that make it possible [Q view other GIS database formats.
Legal Issues Related to GIS Legal issues confront (he GIS commu nity on several fronts including issues related to privacy. liability. accessibility, and licensing. Some of these issues are relatively new, while others have been associated with GIS since its inception . In either case, the issues will continue to evolve as GIS sofrware becomes more widely used. Licensing and certification of GIS professionals is an issue of cur rene concern to many GIS users and other professionals, and
will be discussed in more detail in chapter 17. Therefore issues related to privacy. liability. and accessibility are presented here. GIS data is being co llected at an ever-increasing pace, and used in novel ways as people begin to understand the power of connecting information to spatial position. For example. some o rganizations now rely on the ability co relate data abom purchasing decisions with demographic
and location information (Cla rke. 2001). This information is used by businesses to direct mass mailings, 243
CO
sug-
Chapter 15 Trends in GIS Technology
gest the rhe location of new facilities, facilities. and to place phone caUs calls co the evening [a to porenrial porenriai customers in [he (Q inquire whether they may be interested a purchasing a product or service. Although federa l and State state legislation exiSts exists to protect the rhe privacy of information collected from individuals by public organizations. organizations, very little legislation currently exists [0 to prevem prevent non-public organizations from sell selling ing or sha sharing ring (he informacion rmation that is gathered during regular consumer the info transactions. GIS has mus thus enabled organizacions organizations to [0 cubcultivate business using spatial analyses. In this way. GIS has if or nor, an effective eff'eccive become a bl1siness business rool tool., and like it one. As private organizations co nrinu ntinu e (0 to forge new ground in the collection. collection, sale. and exchange of spatial spacial the economic and social behavior of data that describes rhe individuals. society individuals, sociery will be challenged in establishing the rhe to privacy srandards. smnclards. laws and regulations that relate ro US, tbe the federal government and a nd state agencies In (he the US. have spent millions of dollars of public funds collecting and processing spatial spacial data. The Freedom of Information Informacion authorized in 1966 to grant taxpayers the Act (FOIA) was aurhorized right (Q to access informacion information relared related to the functioning of the government (Korte. 1997). Certain types rypes of information, such as mar that related to security and law enforcement tion. investigations, investigacions. among others, are exempt from the FOIA. Other types of information, informacion , however, must be provided [0 to the person making the request, usually at some minito cover the cost of processing the data and promal cost (Q viding the media upon which the rhe data is exchanged. Most states in the rhe US have developed laws based on the FOIA that also require state S[3.te governmental agencies (Q to make government information informadon avai lable. Unfortunately, new threats to public safety and national security have years. and have necessitated a closer emerged in recent years, informacion made scrutiny of the types of government information to the public. More than likely, access [Q to cerrain certain available (Q GIS databases describing such landscape features as water supplies and power facilities will be curtailed in the future. and access to orher other GIS databases will be delayed due to new security protocols. Legal liability issues are associated with circumstances where a service or product provided by a producer is not satisfactory ro sadsfactory to the customer receiving the service or product. Onsrud (1999) uCt. (I999) identifies twO tWO types rypes of liability liabiliry that are pertinent to GIS: contractual cO IHraCtUaJ and [Ort liability. tort liability. Contractuall iab iliry issues ar ise when a comracr Contractual liability arise contract berween be[Ween twO [WO parries parties has been breached. For a private oorganization rganization that provides GIS .IS products or services, this might involve (hat a software product produCt not behaving as advertised or a GIS database that mat does not adhere [0 to a data accuracy standard.
233
Tort liability issues arise when a party (person or organizacion) zation) becomes injured (sustains a physical injury, loses business. business, etc.) as the result of another party's actions or products. An example might be an accident at sea as a navigation. igation. result of using an inaccurate GIS database for nav Private organizations that provide GIS products and services are responsible for adheri ng [0, ro, and demonstrating, a level of competency associated with their disc discipline. iplin e. When others are injured as the result of incompetence. incompetence, the organization providing the service or product may be liable for damages.ln damages. In determining incompetence or negligence, a private organization may be responsible for probue this alone does ducing inaccurate or insufficient data, hue courts have sought not prove incompetence. Rather, the courtS to establish incompetence by comparing services or products to those that would be produced from an (Onsrud. stud, 1999). organization ,hat that is acting 'reasonably' (On Governmem agencies have typically been immune to litGovernment igarion or responsibility for providing inaccurate spatial igation to sovereign immunity. An exception exceprion is made data due (0 amo ng agencies that produce goods or services that are among considered discretionary. Discretionary services or prodUCtS have resulted in government agencies being held (Korre. 1997). liable for damages (Korte. Both publicc organizations organizarions that are Bo(h private and publi G IS products and services can act to invo lved in providing GIS involved limit rheir liability risk. One method for fo r lim limiting iring risk is to limi t their include information or disclaimers [hat that accompany a its intended use, data accuproduct in order o rder to describe irs racy. ty, and wa rning that the there re may be racy, data reliabili reliability, a nd a warning erro rs in the rhe data (as described desc ri bed in chapter 4) . Organerrors izations can further hlfdler protect themselves by ensuring that thae all relevant parties parries have signed a clearly defined contraer contract for products and services, and [hat that rhe the organization performs the specifics of the contract competently. compeeently. If project requiremenrs example, the develrequirementS necessitate actions (for example. opment opmen [ of other products or services) other than what is con[3.ined cOntract, the organization organ ization procontained in the original contract, contact the other viding the products productS oorr services should comact panies in volved immediately immed iately to reach agreement on the rhe parties additional products produces and services (costS, specifics of the additionaJ time frame, frame. etc.) before beginning to develop rhose those products and services (Beardslee. 2002). Licensing of GIS data products is another legal issue. issue, (Cary, and is tied directly to digital rights management (Cary. 2006) . Many organizations require payment for use of 2006). data and software sof[Ware that they produced and, dara and. without payment, organizations using [he the data risk violating [he mem, the terms of agreements that may have been implicit when the 244
234
Part 3 Contemporary Issues in GIS
information info rmation was shared. While the appropriate model for licensing GIS dara data is currencly currently being debated, the problem lies with the ease of copying digital data and sharing it wi th o[hers with o thers in me the absence of an agreemenr agreement (s imilar imi lar (0 to sharing music files). files) . New types of data sharing sharin g arrangements wiU wi ll likely be formulated that are based oonn limited dara data shari sharing ng licenses. Cary (2006) suggests a system where Licenses. Caty users of GIS are gramed granted access to certain GIS GIS data based on (he the locations of acma acruallandscape llandscape feacures fearures or proximity CO to other fearu featu res res.. This rype type of proximal dara data sharing sha rin g could balance the need for openness (as desired by the with the rhe user) wirh rhe need for confidentiality (as desired by the producer).
GIS Interoperability and Open Internet Access Interoperability in terms rerms of GIS refers to ability of different en t geosparial geospa dal insrrumenrs, inSlrumenrs, databases. data bases, and techn techniques iques ro to work together on applications. Interoperability Inreroperabil iry invo involves lves creati crearing ng standard terminology. termin ology. data formats, formats. and sofrware softwa re inrerfaces that are borh organ izainterfaces both recognized and used by byorganizations involved in geospariaJ geospatial applicatio applicarions. ns. The T he need fo forr interoperability inrerope rabili ry should shou ld not be surp surprising rising for any discipline that becomes popular popu lar amo ng a wide number of potencial users, such as has been wi potenrial witnessed tnessed by by the rhe rapid growth of GIS growrh G IS applications app lications over the past two decades. decades. The co a number of GIS software rapid growth of GIS gave rise to interfaces and data formats that we inrerfaces were re proprietary proprierary and thatt others could easily and therefore not designed so tha freely access and exchange data with the proprietary proprietaty formars. This inab inabiility lity led co to frustration amo among ng GIS GIS users mats. and gave rise co to the rh e need fo forr GIS interoperability. The Open Geospatial Consorrium Co nsortium was founded in 1994 (Open Geosparial Geospatial Consorrium, Consortium, Inc., 2007) and has 341 34 1 member organizarions o rganizations as of2007. The aGe aG e represents represems a coalition of both private privare and public organizations. The goals of the aGe are to promore promote public accessibility to geoprocess ing tools th er location-based location -based services. geoprocessing (Ools and oother Sign ificant accomplishments of the aGe include the Significant rhe standardizatio standa rdizatio n of terms rerms for GIS features (poin (po inrs, rs, polylines, polygons), the creation of the rhe Geography Markup Ma rkup Language (GML) thar that provides an open source language for describing spatial spat ial data. data, and the developmem of stanforr how geographic data can be requested and dards fo accessed from Internet Interner servers (Longley et er aI., 2005). 2005) . T he aGe has had a profound influence inA ue nce on making geosparial geospatial rools tools and services available co to IInternet mernet users and continues co nti nues
ro to work on promoting promoring geospatial geosparial software accessib accessibility. il ity. T he aGe will likely continue to develop in The innovations novarions rha[ thar Increase publi to locatio n-based rm at ion inc rease pub li c access co n- based info inform serv ices. services.
GIS Education Auention is likely to increase around the methods and Attention approaches instructors use [Q teach [each geospatial geosparial ski lls [0 to srustu-
at the university level, but also in high dents, not only ar wirhi n the rhe professional workforce. wotkforce. GIS capaschool and within biliries are now essential for natural reso urce organizabilities o rganizations, ti ons, as well as for fo r other di disc sc iplines throughour throughout soc iety (Wing & Sessions, 2007). The primary training tra ining ground for GIS GIS skills is cu currently rrently with within in the university sYStem system but geosparial geospatial skills are also being taught taugh[ to students srudenrs during e1ementaty school years. years. In addir addition. ion, train training in g opportunie1emenrary ties for in-caree in-careerr professionals appear [Q to be growing. growing. Many disciplines. disciplines, such as fores[ty, fo restry. engineering, and surveyi ng have accreditation acc reditat ion bodies that rhat rev review iew and survey ing thatt appraise the curriculums of universities and co lleges tha offer rel ated degrees. degrees . The accreditation bodies eirher eithe r offer related approve the curriculum curri culum or o r repon why if it cannor cannot be accredi ted and wha to gain accred acc red itaaccredited whatt steps are needed (Q rion. The accreditation process helps ensu re that instrucinsrruction.
[ion suppo rring the rhe necessary necessaty knowledge and skills ski lls to ti on supporting eente nrerr in to tO professional profess ional d disciplines isciplines is being delivered
appropriarely. T he accreruta[ion app ropriately. The accreditation process helps educarional educational programs become recognized and can be a significant incemive incentive for drawing sruden{s. srudents. No such accredjtation accred itation fo r geospatial geospa[ial [echnology spec ifically for technology process exists specifically although instruction per se, alth ough some engineering engin eering and surheavily ly on o n measurements measuremems and have veying programs focus heavi accrediitation rarion [hrough accred through the American Board of Engineering Technology (ABET) (ABET).. Currendy, Currently, a wide variety of methods
and approaches are used to ro [each teach geos geospatial patial skills. Th This is results in in considerable variation in achieved learning
Out-
comes (Longley er et aI., al., 2005). 2005) . A A recent publication titled
Th, Geographic Science and Technology Body The Ceographic Information Infomlation Sci",,, of Knowledge (DiBiase (Di Biase er et aJ. aI. , 2006) has arremp[ed attempted to [0 critical concep" concepts and skills ski lls that reiare relate co to geographic define critical information science and technology. Written through extensive collaboration among amo ng GISc GlScience ience researchers resea rchers and educato rs, this work represen ts an initial attempt educators, represents attem pt to protopics ies important imponant for estabestab· vide a unified description of cop
lishing geospatial geospatial skill compereney. competency. A second edition is plann ed char planned that will wi ll provide add additio itional nal detail detail and instructio inst ructio n
that su pports pporrs key concepts and skills. 245
Chapter 15 Trends in GIS Technology
235
Summary GIS technology is evolving almost as quickly as general
has c reated an efficient avenue
co mputer systems (hardware and software) evolve. People are thinking about natural resource managemem issues in
available in a timely manner. These developmems have
ways unimagined juSt a few yea rs ago, and spatial data is facilitating these effons. Natu ral resource management o rganizations are actively engaged in testin g and implementing new [Ools for collecting and analyzing spatia l data. Organizations are also making GIS technology available to a large portion ohheir workforce. and Internet
encourage furthe r G IS technology development. As G IS
me
[Q
make GIS databases
had a posicive effect on the acceptance and use
of C IS. and
technology and use evolves. however, other issues (p rivacy . access ibil ity. li ab ility. etc.) arise that mu st be addressed. These issues may require a close exam ination of policies and practices related to the use of GIS in narural resou rce managemenr.
Applications 14.1 Local regulations regarding GIS distribution. Select an agency in yo ur area (state. province. city , coumy, townsh ip, etc .) tha t uses and m ighr distribute GIS
14.4 Precision forestry and agriculture. You work for the Bureau of Land Management (BLM) in central Colorado, and your supervisor, Mark Miller, has recently
databases. a) What types of GIS databases are publicly available? b) Are the GIS databases available for down load over
learned of precision forestry and agriculture. He has asked yo u how precision techniques might benefit the managemenr of BLM land in Colorado. Write a brief memo that out lines the GIS databases, hardware , and sofrware related to precision techniques. and how rhey might benefit the
the Imerner? c) What data exchange form ats are availa ble? d) Is
e) Are there specific laws that relate
to GIS
management of BLM land in Colorado.
database
distribution , and how do they affect your abi li ty (Q acqu ire GIS data from rhis age ncy?
14.5 Distributed GIS. A timber company in the southeastern United States has JUSt hired you as a field forester. You are eager to use the GIS skills you have learned in col-
14.2 Product liability (1) . Yo u work fo r a private
lege
organizat ion that makes GIS databases available to cus-
managemenr decisions. The timber company has a cen-
tome rs. How might you help prQ[ec( your organization from liab il ity that could arise from CUS(Qmers that purchase yo ur serv ices and products?
rraIized GIS deparrmem and five remote field offices and
to
help yo urself (and others) make informed forest
they are in the midst of developing a system whereby personnel (foresters, biologists, hydrologists, etc.) in field offices (where you are located) can use desktop G IS soft-
agency that makes spatia l data available to customers.
ware to make their own maps and pe rform their own analyses. What can you do to ensure that the dist ribution
How might you help protect your organization from liability thar could arise from customers that use you r serv-
of respons ibilities (map development and analysis) to your field office will be successful?
14.3 Product liability (2). You work for a public
ices and products?
References Beardslee, D.E. (2002). Do it! Or, call them before they call you. Proftssional Surveyor, 22(2), 20. Bettinger, P. (I999). Distributing GIS capabil ities to forestty field offices: Benefits and challenges. Journal ofForestry, 97(6), 22--6.
Bettinger, P., & Wing, M.G. (2004). Geographic information sysums: Appfications in forestry and natural resources management. New York: McGraw-Hill, Inc.
Cary, T. (2006). Geospa[ial digita l ri ghts manage ment. Geospatial Solutions, /6(3), 18-2 1. 246
236
Part 3 Contemporary Issues in GIS
Clarke, K.c. (200 I). Getting started with geographic information systems (3rd Ord ed.). Englewood Cliffs, Cl iffs, NJ: Premice-Hall, Inc.
DiBiase, D., DeMers, M. M.,, Johnson, A., Kemp, K., Luck, A., Plewe, B. B.,, & Wenrz, Wentz, E. E. (Eds.). (2006). The geographic information science and technology body of
knowledge. Washing[Qn, Washingwn . DC: University Consortium for Geographic Information Science. Science, Association of American Geographers. Geographers .
Environmental Environmenral Systems Sysrems Research Institute, Inc. (2006). ArcPad - Mobile GIS software for field mapping applications. Redlands, CA: Environmental Environ menraJ Systems Research Instirute, Inc. Retrieved May 8, 2007, from http:// Institute, http: // www.esri.com www.es ri.comlsofrware/arcgis/arcpadlindex.html. /sofrware/arcgis/arcpadlindex.html. Faust, N. (1998). (1998) . Raster based GIS. In T.W. Foresman
(Ed.),, The history of geographic information systems: (Ed.) Perspectives ftom from the pioneers (pp. 59-72). Upper Saddle River, NJ: Prenrice-Hall, Prentice-Hall, Inc. GeoEye. (2007). GeoEye imagery prodncts: products: IKONOS. Dulles, VA: GeoEye. Retrieved May 9, 2007 2007,, from http://www.geoeye.com/prod uctsl imagery/ikonos/ magery/ikonosl http: //www.geoeye. com /p roducts/ default.htm. deFaulr.htm. Inc. (2007). Google, Inc. (2007) . Google Earth. Mountain Mountai n View, CA: CA: Google, Inc. Retrieved May 9, 2007, from http:// http: // earth.google.com. earrh.google.com. ITT Corporation. (2007). ENVI-The ENVI- The remote sensing exploitation platform. Boulder, CO: ITT Corporation. Retrieved May 9, 2007. 2007, from http://www.ittvis. http: //www .ittvis. com/envil. com/envi/.
Korte, Korte. G.B. (1997). The GIS book (4th ed ed.) .).. Santa Fe. Fe, NM:: OnWord NM On Word Press. Leica Geosystems, Geosystems. LLC. (2007). Erdos Erdas Imagine. Norcross, Norcross. GA: Leica Geosystems LLC. Retrieved May 9, GA. 9. 2007, 2007. from http://gi .leica-geosystems.com/ LG (Sub I x33xO. http: //gi.leica-geosystems.com/LGISubIx33xO. aspx. aspx. Longley, Goodchild. ld, M M.F .F.,.• Maguire, D.J., D.J. , & Longley , P.A. P.A .•, Goodchi Rhind, D.W. (2005). Geographic information systems ,ystems and science (2nd ed.). ed .). Chichester. Chichester, England: John Wiley & Sons, Inc. National Aeronautics and Space AdminiStration. (2007). A VlRfS: infrared imaging spectrometer. VIRIS: Airborne visible / inftared Pasadena, CA: Narional Pasadena. National Aeronautics Aeronautics and Space
Administration, Jet Propulsion Laboratory, Laboratory. California California Institute of Technology. Retrieved Inscitute Rerrieved May 9, 2007, from //aviris.jpl. http: //aviris. j pI. nasa.gov. Onsrud, H.J. (I (1999). 999) . Liability in the use of GIS and geographical graphical datasets. In P.A. Longley, M. F. Goodchild, D.J. Maguire, Maguire. and D.W. D.W. Rhind (Eds.), (Eds.). Geographical G
Wing, M.G., & Bettinger, P. (2003). GIS: An updated primer on a powerful management managemenr [001. (001. Journal of Forestry. 101(4),4-8 Forestry, 101(4).4-8.. Wing, Wing. M.G., & Sessions. Sessions, J. (2007). (2007) . GeospaciaJ Geospatial technology education. Journal journal of Forestry, Fomtry, 105(4), 105(4). 173-8 .
247
Chapter 16
Institutional Challenges and Opportunities Related to GIS Objectives This chapter offers some thoughts on rhe imegration and use of GIS within narural resource management organizadons. There are a number of issues related to rhe successful development of a GIS program with in an o rganization, and programs in many organ izadons are continuously evolving as people. techno logy, and organizational struc[Ures change. At conclusion of th is chapter, readers should have an undemanding of a number of issues that relate co rhe challenges facing the implemenracion and use of GIS in nacural resource managemenr, including:
me
t. an understanding of rhe potencial challenges mat are ahead for successfu l and efficient GIS applications within an d among namra l resource management organIZatiOns; 2. an understanding of the challenges that exist for organizations thinking of distributing GIS capabilities to field offices. a move becoming more prevalent as recent natural resource graduates likely will have GIS experience in cou rsework. and exposure or uaining in the field; and 3. an understanding of how to assess the benefit of using GIS. a measurement process that will likely be necessary to develop more efficiem business ope racio ns. higher qualiry products. and more timely managemenr decisions. The development and use of GIS in natural resource
management has evolved considerably in the past 20 years as field foresters. biologists. hydrologists. and other natural resource professionals have become empowered with this technology. and are able to perform many of their ow n spatial analyses and map production tasks (Wing & Bettinger. 2003). In addition. it is increasingly likely that natura l resource professionals wi ll have cou rsework involving GIS and related geospatial technologies during undergraduate or graduate studies (Wing & Sessions. 2007) . While GIS technology continues to evolve. natural resource management organizations are faced with several lingering issues related to the GIS use. and several nC\v challenges have arisen with the availability of GIS technology to field offices. Natoli et aJ. (2001) describe a set of challenges for GIS implementation a nd use within (mainly) municipal organizations. and Bettinger (J 999) describes some of the challenges associated with implementing a disuibuted GIS system in forestry organiza{ions. This chapter ex{ends the discussion presented by these twO sets of wo rk. a nd adds to it some additional points that are relevant to forestry and natural resource management.
Sharing GIS Databases with Other Natural Resource Organizations As mentioned in chapter 15. one aspect of the use of GIS in natural resource management is the notion mat some GIS databases can be shared among other natural resource 248
238
Part P art 3 Contemporary Issues in GIS
organizations. The potencial potential to collaborate with other organizations. organizations. and rhus GI S data. data, rep repreorganizations, thus the need (0 (Q share GIS reinteresting dynamic that is evolving in natural narural sents an inrerescing resource management. Public organizations. such as rhe the
USDA Forest Service and the USDI Bureau of Land Management. provide a wide variety of GIS databases ro to rhe the public at no COSt; as we discussed in chapter 33.. many ma ny rhese databases can be accessed over rhe lorefncr. Inrerner. of these State-level natu ral resource management orga.nizarions namral organizations GI S databases generally provide a more narrow range of GI to
the public, if they provide any at all. State-level GIS
dacabase clearinghouses. clearinghouses, on the ocher database other hand. hand . generally
variety GI S databases (DEMs, (O EMs, digital provide a wide vari ery of GIS orthophotographs, land cover, etc.) to the public at no orthophorogrdphs, (orr low) cose. cosc, Private natural (o namral resource management manage ment o rganizations generally trear their GIS databases as propriorganizations general ly [rear their GIS erary-that is, rhey etary-that they are not freely ava ilable [Q to rhe the general
hile the public (anyone outside of the organization) . W While
developed using standards and protocols developed by the WADNR, to ma intain the quality qualiry of data in the WADNRdistributed GIS databases, and to [Q minimize the costs asso-
ciated with updating updati ng and managing the state-level GIS databases. There are also a number of cases where priva te natural private lly have shared data with one actually resou rce organ organizations resource iza tio ns actua another example, a number of watershed analyses another.. For example. were conducted in Washington State in the late 1990s. In performing a watershed analysis, analys is. one of the major generallllyy coo rdinates landowners in the watershed genera rdi nates the comp ation of the GIS G IS databases associated with w ith the compiillation wate rshed. All other landowners watershed. lan downers interested in helping contribute GIS datadevelop the watershed analysis can comribure
' lead' organization. Mo bases to this 'lead' More re than likely, the amo unt of GIS data shared amongst different amount d ifferent private oorganizations rganizations will be limited to a level that ient to th at is suffic sufficienr rhe analysis. For example, complete [he example. the spatial extent exrent of
tthe he GIS databases shared will likely be limited to the boundaty of the watershed being analyzed, and the attribboundary
original source of the data dara for many of the private privare organization databases may have been a public organizaizatio n GIS darabases the private investmenc investment has been made in maintion, once me taining and updating (h thee data, concern arises ari ses about abom how the cost recouped., and whether competirors competitors may me cOSt can be recouped gain insight into the management man agement practices being used (e.g COSts. productive capaciry), capacity), thus gaingain (e.g.,.. management managemem COStS,
will need to be changed to conform co nform to the system sYStem used by
ing a competitive edge in the marketplace. Although
the lead organization. organizarion.
ute data associa ted wit withh landscape featu res may be the roral rotal set of attributes prior reduced to a small subset of rhe
data. In addition, it is likely that the projecto sharing the data. tion [ion and coordinate systems for each shared GIS database
o rganizations joining An example of multiple public organizations
some may argue that organiz.arions, organizations, public and private. private, dara so that more informed decisions can be should share data
tOgether to creare spatial database is the together ro create a significant spadal
made for a landscape, the goals and objectives of each oorganization rgan ization wi ll guide ,he the development develo p ment of policies polic ies
Puget Sound LiDAR consortium (PSLC, (PS LC, 2007). The PSLC formed metropol itan, acafo rmed in 1999 and now includes metropolitan,
to data related ro da ra sharing. As a resu lt, many private natural resource oorganizaresult, rganizations do nor nO( sha share with [hose those outside of re GIS databases wim organizarion , unless they can place a value on doing rheir organizadon, so. There are a number of cases, for example, example. of private organizations organizarions sharing GIS databases through coope cooperarive rative effons. For example. example, in planning and an d management efforts. Depanment Natural Resources Washington State, the Depart ment of ofNarural
couney, state, and federal o rganizat ions. The demic, county, rganizations.
(WADNR) (WADN R) has taken an ownership role in the development of several state-level databases: roads, Streams, streams, and severn I stare-level
the public land survey to name a few. The WADNR has whereby developed protocols whe reby both public and private natural lIral resource organizations can share their GIS data with rh effo rt to rhe state-level GIS [Q improve im prove [he thee WADNR in an effort Whilee rhe cont ri butions of databases. Whil the process is open for contributions directly information and knowledge from those more mo re di recdy tied agemenr of particular areas of land ., ir to the man management it comains cOlHains fairly Fairly rigid guidelines for contributors. co nrributors. The GIS GI databases shared sha red with the WADNR, for example. must have been
group's original goal was to use LiDAR to develop public domain high-resolution topographic data initially for sigWashin gtOn. More recenrly recendy,, public nificant parts pans of WashingtOn. organizations regon have also joined the PSLC, organizarions withi withinn O Oregon PSLC. and large portions of ofland land in Oregon are also included in data acquisition plans. Fund ing has come from the operFunding
ating budgets of group members and also from grants that membe rs have received co members ro suppOrt landscape hazard mitigation efforts. efForrs. This unique collaborative effort w ill resulr result collabo rative effon
forr most population in LiDAR data being available fo moSt of the po pulatio n centers in Washington and Oregon , and surrounding lands. The joint efforts efforrs of these organizations have undoubtedly resuhed resulted in significant cosr COSt savings for LiDAR
data, particularly in light ligh' of the fragmented ownership patterns represented by all groups. Aerial data acquisition inuou s swaths of ofland is more efficient efFicienr when cont continuous land can be im aged rather than disjoinred sections, imaged sections. which reqUire
add itional ght lines. addit ional fli fl ight 249
Chapter 16 Institutional Challenges and Opportunities Related to GIS
Sharing GIS Databases within a Natural Resource Organization The issues of GIS database ownership, ma intenance, di stribution, and data quality within an o rga nization a re problematic, and partly a result of how a GIS database developme nt and disuihurion sys tem may have been designed. For exa mple, a road engineer may be the most appropriate person (0 own and maimain a culverr GIS database, and a wildlife biologist may be most appropriate person to own and maintain a GIS database related to locations of threatened or endangered species. However, the readiness of each person to perform these tasks within GIS, and the time they have ava ilable to do so may be limited. Therefore ass igning the ownership, maintenance, and mher tasks to either a GIS technician, GIS manage r, or a GIS contracto r or consuhant may be more logical. More than likely, GIS databases will be shared at some stage to facilitate GIS database management, as a verificacion step in the maintenance stages of GIS database management, or [Q facilitate namral resource managemem (after the GIS databases have been updated). If you were co assume that a namral resource managemem organizadon was structured in a tradidonal manner, with a cemral office and a set of field offices, the GIS da tabase sharing process should be viewed as more than a o ne-way transaction between a cemralized GIS departmem and everyone else (Figure 16. 1), because the roles related to ownership,
maintenance, d istribution, and d ata quality may be shared. Defining the ap propriate roles for each pe rson in an organ ization can result in a difficult negotiation process, part icularl y whe n the roles are considered for change. Establishing a process fo r sha ring GIS databases at all levels within an orga nization is becoming more important as both field personnel and upper-level managers are becoming interested in using GIS technology. Data sharing can be as simple as routing a com purer d isk from perso n-to- person or office-to-office, or as sophisticated as placing all GIS databases o n an organization's Internet (or Intranet) site or FTPserver. In the laner cases, the ab ili cy to access the Interne t sites and FTPservers may be limited to authorized person nel. Technology and processes for sha ring GIS data are advan cing, however, leading to the potential for two-way transac tions of data within a nam raJ resou rce organization and nearly real-rime updates of the informa tion. For example, some organizations allow field ma nagers [Q update GIS data directly in the corporate databases, a ro le tradirionally held by a centralized office. These updates are sent electronically m the centralized office, and after data ve rification and consistency filrers have been ap plied, redi rected to other field offices. In an efficient system, the othe r field offices can acquire these updaces in a matter of minutes. The importance of using up-m-dare informacion is perhaps most imponant when dealing with rime-
Rotes : OwnerShip Maintenance Quality User
Role:
Use, Field office 1
Field office 1
Updates needed for database
Updated datab
as,
Centralized GIS office
Upd, I,d datab
Upd ated database
Roles : Ownership Maintenance Distribution Ouality Updates needed lorda tabas
as,
Distributed database Roles: Coordination Distribution
Centralized GIS office
Updal,d d,tab
Olstribuled database
as,
Role: User Field office 2
Fjeld office 2
Roles : Ownership Maintenance Quality
User a) Forest vegetation (stands) GIS database
239
b) Road culvert GIS database
Figun: 16. 1 Example pathWll}'S of actions when sharing data within an orga.niu tion. for (a) a typ ical forest v~getation (stands) GIS database and (b) a road culvert G IS database.
250
240
Part 3 Contemporary Issues in GIS
The following story indicates nor only rhe motivation of field office personnel !O use GIS [
sensitive decisions. such as those related
[0
wildfire con-
trol and suppression. With GIS capabilities now available in hand-held computers, manage rs in the field can make more timely and informed decisions when necessary.
Distribution of GIS Capabilities to Field Offices With rhe development of desktop GIS software the ability
tion that shou ld have been incorporated imo the corporate GIS databases. As the d iscussion grew related to
sharing the modified corpo rate GIS databases wir h the co rporate office, it was determ ined that th ere were over 1,000 variations of the corporate GIS databases stored across the organization's computer systems.
Each GIS database required Storage space on hard drive and it was likely that hu ndreds of megabytes of space we re dedicated for th is effort. I n some cases, field personnel believed that their modified version of the cor-
porate GIS database was of higher qual ity than other user's versions (eve n better than the corporate version
of the GIS darabase) . As expected, a nu mber of d iscussions regarding the quality of these mod ified GIS darabases arose, and the process of changing the GIS darabase maintenance policy for the co rporate databases was begun.
Where GIS databases are concerned • Clarify GIS database ownership and mall1tenance Issues . • Ou tl ine data acquisition protocols. Develop data distribution systems. • Develop quality control measures.
Where the technology is concerned
of every employee in an organization to use GIS is now
• Facilitate the acquisition of appropriate hardware and software.
possible. Bettinger (I 999) has noted that the dep loyment of software and GIS databases to field offices is a signifi-
• Control the purchase, update. and maintenance of software.
cant challenge, and, as nmed in chapter 1S. that is a trend that will likely continue. Some of the challenges to narural resource management organizations interested in
Where the leadership of the organization is concerned
imp lemenring a distributed GIS system, as noted by Bettinger (I 999) incl ude:
• Demonstrate st rong suppOrt for the program. • Make a long-term budgetary commitment to the
program. • Adjust reward systems for added responsibilities of
Wh ere people are concerned • Provide the approp riate training to reduce computerrelated anxiety. • Provide a statemem of purpose behind imp lementi ng a distributed system. • Implement methods to reduce the resistance to change. • Provide mmivation ro use the system .
field managers. • Document the direction and strategic goals for the system. Most young profess ionals in natural resource managemem o rganizations will likely be called upon to provide assistance in the development of maps and GIS databases to facilitate the day-to-day management of a landscape. 251
Chapter 16 Instrtutiooal Inst~utional Challenges and Opportunrties Opportun~les Related to GIS
Thls This is a relatively new job expectation (hat that may not ordinarily be placed on [he more seasoned foresters and natural resource professionals. At the onset of a program such as [his, this. you should acknowledge [hac that it i[ adds responsibility [0 to [he the field Reid manager for fo r [he the development of his or her own management-related man agement-related maps. Tasks tradicionally traditionally performed formed. by specialists specialises in a cemralized centralized office will be transferred [0 to field Reid offices. offices. This [ransfer transfer of responsibili respons ibility ty could lead [0 to anxiety on [he the parr part of field managers, managers. however [he the main advantage of the program is in reducing {he the amount of time rime required [0 to produce [he the produces products (maps and analyses) needed (Q CO make managemem management decisions. Once field personnel are sufficiently sufficiendy rrained trained and become confident in their abilities. time savings across {he the organizacion organization should be realized. (he most impona.nr important conuibutions Perhaps one of [he contributions young professionals can make is [Q ro ask pertinent peninem quesdons [hey work: tions about the organiz.arion organization within which they What role do I play in each process [hac thar involves GIS databases? How are databases? 3.f e [he the GIS GIS databases created or acquired? Who claims ownership over ovec the maintenance and disuidisrri bu[ion of each GIS bution GIS database? What [echnology technology (hardware) p
is available avai lable [0 to display [he the results resul" of an analysis? What [echnology technology (software) is available [0 to perform an analysis? rhe organization value GIS How and when does the GIS analysis in supporcing management decisions? By asking these questions, yo youu indicate your willingness [0 to understand how GIS has been implemented implemenred within with in an organ organization. izarion, and imply [hac that you understand [hac that GIS is a valuable [001 (001 in natural reso urce management. narural resource
Technical and Institutional Challenges One of the most expensive and time-consuming aspects effore (hat related [0 to using GIS is the effon that is required to creatc create a GIS database. darabase. Dupl Duplicating icating previous data co collection llection effores in [he efrorts rhe creation crearion of a GIS database should always be avoided, avoided. thus a lack of awareness of existing GIS databases is a serious challenge chaJlenge [0 to confront confrom.. To prevem prevent duplica duplicating ting GIS datab.se database development efrons. efforts, GIS GIS users within an the types of GIS organization should be made aware of [he databases that (he the organization can easily eas il y access. This might include GIS products [hac that we re developed GIS database produces imernaJly within an oorganizadon. rganization, GIS databases develinternally oped by GIS contractors or land surveyors, surveyors. or GIS databases that are availab available le through agreements or relationships with wiTh other nacural natural resource managemem management organizations. avai lable GIS dataorganizarions. Information regarding available
241
bases could be stored seo red in a searchable database or it might ca[alogued in a less formalized manner. Regardless of be catalogued how ffiis this information informadon is g:nhered gathered. and stored, stored. personnel in an organization who use GIS G IS should be able co to easily identifY and loca[e locate exiseing existing GIS da databases tabases [hac that might facilita[e lacilitate [ify the tasks that [heir their jobs require. [he rasks [hac Meradara, or information Metadata, informadon documenting documeming [he the specifica[ions database, tions and quality oflandscape feacures features in a GIS database. have become an important aspect of GIS databases in the pase decade (Dobson & Durfee. Durfee, 1999). In order co de[erpast to derermine the fitness of a GIS database for a particular use, use. the me[ada[a related co to the me GIS database should be considmetadata ered. In panicular, particular. when a GIS database is acquired from anomer organization, another organization. me the me[ada[a metadata should be relied upon co verify [hac condition of [he to verifY that [he the condirion the GIS database is what was expected when acquired. acqui red. In many instances, instances. however, Iirde mecadata GIS datalittle metadata exises ex ists to describe the qualities of GIS bases. The twO [wo hypothetical foresrs forests used extensively in examp les. In faCt, lact. more often than this book are prime examples. not you may find [hac that G GIS IS databases developed or maintained by non-federal organizations lack. [ained lack, or have insufficient, mecad:Ha. cient. metadata . Thus, Thus. nacural natural resource professionals musr must be careful when basing decisions on GIS databases where the level of qualiry is uncertain. uncenain. For organ izarions that are involved in producing produci ng and organizations distributing GIS databases [0 to orner other namral natural resource management organizations, guidelines or protocols should be in place that address all aspects aspecrs related to the distribution disrriburion of the GIS databases. WithoUt Without guidel ines. organizations guidelines. organizarions inef-hciem distribution are likely ro to be working with an inefficient syseem , and may be prone co system, to liability liab ility problems. struccure for all availGuidelines should include a pricing Structure ab le GIS databases; databasesi (his chis structure will need [0 to reflect reAecr [he the oorganization's rganization's views on cost COSt recovery. In me the case of publie organizations. rganizations, {here there may be no need or desire to co lic o recover more than than me the delivery costs costs.. Some public organizations, however. do utilize contractors conuacmrs m to collecr collect and izarions. develop GIS darabases, databases. and may need co to recoup some of the coses COSts of doing so. For most private natural resource management organizat ions, the ing srrucrure struccure will w ill managemem orga niza tions. rhe pric pricing likely need co to [eflecr reflect [he the actual accual coses COstS of collecting [he the dara. data. O rganizations [hat that distribUte Organizations distribute GIS databases will also need co comprehensive to develop a comp rehensive liability policy [0 to pro[ecr protect themselves from li[iga[ion. t11emselves litigation. A liability policy will likely need co to be [ailored tailored [0 to each particular GIS database because the content. accuracy. and uses of databases will vary. A [he conrent, method of providing GIS GIS darabases (Q to cuStomers will also need [0 [Q be identified. As discussed in chap[er chapter 15 IS,, organ organiizations zacions [hac that provide GIS data [0 to [he the publ public ic should make 252
242
Part 3 Contemporary Issues in GIS
GIS databases avai lable in an expedient manner and use cu rrent Internet technologies such as creating new wehsites chat allow users (0 browse darabase offerings and download both data and metadata. In addition. file transfer protocol (FrP) can be used to make data available. but this data-sharing system is less user-friendly. For organizations that are nor involved in producing and developing GIS databases for other namrai resource management organizations. the high COSt assoc iated with crearing GIS databases can result in a reluctance [0 share databases with other organizations. For example. certain GIS databases may contai n sensitive information. an d might reveal the location of landscape features (such as endange red species nesting locations or a rcheological sites) that might be disturbed or destroyed should the locations become public information. Two examples of these GIS databases include the location of endangered species nest sites, and the locacion of genetically-modified [fee field trials. The GIS databases may also comain information about the status oflandscape resources that would be of value to another organization with which it competes, providing the other organization an advantage in the market place. This is clearly importam tOday as the number of land sales has skyrocketed. and potential (a nd perhaps hostile) investors desire complete information regarding a land asset. A reluctance on the part of federal public organizations in the US to share or publicize data holdings is that all federal agencies are subject to the Freedom of Information Act (FOIA). FOIA was signed into law in 1966 and later ame nded in 2002. FOIA makes it possible for individuals (Q request access to federal agency records,
To facilitate a recem landscape analysis research project, a private natural resource managemem organization agreed to provide a highly detailed GIS database that desc ribed the management units within their ownership boundaries. The GIS database had been assembled at great COSt and effort, and revealed considerable information abom the natural resources that the private organization managed. The private organization required that a confidentiality ag reem ent be signed prior to making the GIS database available (Q the resea rch project. As noted in the confidemialicy
exce pt in cases where records are protected from disclosure. These requestS can also be made for spatial databases [hat were produced by federal agencies. In so me cases, spatial databases may comain information that the agency considers to be sensicive or potenrially damaging (Q the resources it manages. One example of sensitive information might be spatial records of vandalism that occur in public recreation areas. Evidence of high vandalism occurrence might deter visitors from staying in affected a reas, and potemialiy reduce revenue that is gathered from day-use permits. In addition, there may be hesitation to draw additional arremion to 'hot-spotS' of criminal activiry in case the arrenrion may encourage others to visit these locations our of curiosiry or to cause additional vandalism. Other hesitations may involve the presence of unusual features, such as special habitat areas, or archeological sites, and the potencial damage that too many visimrs may bring to these areas. For various reasons, federal agenc ies may not openly advertise the types of spatial databases that they have produced. A primary hesitation to do so is likely because of the uncertainty of what will happe n with the information within the databases should rhey become widely circulated. Understanding that there are factors that may hinder an organization's willingness to share a GIS database is important prior m requesti ng the database. For data of a sensitive nature, it may be possible to enter into a confidentiality agreement to gain access (Q the GIS database. Ultimately, it may be necessa ry (Q pay a large sum of money ($5.000 to 10.000) for a GIS database. and for some organizations, it may be difficult m locate the necessary budget resources appropriate for this type of purchase.
agreement, access to the GIS database was limited to the primary scientists of the research project, the sha ring of the GIS database with others was prohibited. and protocols for distribming information drawn from the GIS database were outlined. Without the confidentiality agreement, which facilitated the sharing of the GIS database, knowledge of [he status of resources located within the private natural resource management organization 's ownership would likely have been less accurate, reduci ng the confidence placed on the landscape analysis results. 253
Chapter 16 Instrtutional Challenges and Opportunities Related to GIS
Benefits of Implementing a GIS Program The decision to implement a GIS program (the entire suite of hardware, software, and personnel related CO the use of GIS within an organization) can be intimidating for natural resou rce management organizations. There are many factors to consider, including investments in software, hardware, personnel, a nd CIS databases. Since natural resource management organizations cypically rely on maps or mapped data to assist in making decisions, GIS ca n allow an efficiem storage of maps, and can fac ilitate the generation of multiple versions of maps in a timely manner. In addition, GIS allows landscape features to be measured, analyzed, and integrated with other GIS databases in an expedient manner. New technology has provided [he pQ[entiai to convey information [Q field managers very quickly. These capabilities, if managed properly, can allow natural resource management organizat ions to make bener management decisions, more accurately gauge the effore and cost of potencial namral resource managemem projec(S, and increase the efficiency of tasks performed by their employees.
Successful GIS Implementation Saving money, reducing the amo unt of time spenr in the office analyzing options. and thus saving resources for Q[her [aSks and management activities are common goals of namral resource managers. Distributing GIS capabilities to field offices has been suggested as one way to address some of these issues. Successfully implememing and managing a GIS program can be difficult, as the costs of implementation and ma nagement vary from one organization to the next. Perhaps the strongest ingredient for success in implementing a GIS program is in esrablishing an organizational commitmenr within the upper levels of managemenr of an organization . This commitmenr needs to view the GIS program as more than JUSt a shortterm experimenr that can be discarded after early, d isappoinring results, since initial GIS products and experiences are likely to identify implementation problems. Unfortunately, upper-level managers tend to be less
243
fumiliar with GIS technology than the actual GIS users. For this reason, GIS users should communicate their supporr of GIS in terms that are comp rehensible to the upper-level managers and help th em understand that when technical difficulties do arise, program implemenracion plans must be adjusted. Upper-level managers, in turn, should promote the G IS program as a way of making more efficient the tasks required of natural resource management. Organizations must also be realistic about the time, effort, and budgetary resources that individual GIS projects or analyses wi ll require. Proper planning requ ires that project objectives be clearly defined. Objectives provide a project mission a nd ca n help keep personnel focused, should setbacks occu r. Project objectives a re also important for establishing standards that allow you to gauge the success of a GIS project or analys is. Achievement benchmarks are also critical in justifying the continued and expanded use of GIS within an organization. Allowing users of GIS to become involved in the planning and implementation of GIS projects is also importanr, since they may be among the best qualified to assess whether GIS can accomplish the approp riate project tasks, which may lead to an improved level of efficiency in the managemenr of natural resources. Finally, GIS user training is an importanr co nsideration for the success of a GIS program within a natural resource managemenr organization. Most recenr college graduates from natural resource programs will likely have a rudimenrary knowledge of how GIS can ass ist in natural resou rce management, but will likely lack the level of experience you gain from using GIS periodically for actual, on-the-ground, management purposes. To accelerate the development of personnel, organizations can provide GIS training inrernaIly, or can allow personnel to auend cominuing education courses or pursue other training opportunities. Geospatiai training courses and opportunities appear to be increasing within natural resource disciplines (Wing & Sessions, 2007). This investment in conti nuing education increases the knowl edge level of personnel an d demonstrates an organization 's commitment to its personnel, which hopefully leads to increases in work efficiency and productiviry.
Summary What would the management of natural resources be like without a few challenges? With the imroduction of
new computer-related measurement technology and accompanying GIS databases, natural resource manage254
244
Part 3 Contemporary Issues in GIS
menr o rganizat ions are facing numerous c hallen ges
come, eve n as new and va ried issues arise. As we have
related to GIS data manageme nt. Given that GIS-related
noted, many of [he challenges facing the use of GIS
technology continues (Q evo lve to meet (he increasing needs of society. an optimistic person might expect that
within natural resource manage ment organizations are
the challenges described in this chapter can be over-
zational issues .
related to GIS databases, technology, people, and organi-
Applications 16.1 Sharing GIS databases with people outside your organization. What issues sho uld a natural resou rce manage me nt o rga nization address when cons idering making G IS databases ava ilable to other organizations. including perhaps competitors?
16.2 Managing GIS technology. A seaso ned professional (Mary Swarthmore) who manages ano ther department (Acco unting) in yo ur natural resource o rga nization is considering a career cha nge. This change will result in Mary managing your organization's GIS department. Mary has never managed a GIS department before, nor been involved in the creation. acq uisition. o r use of GIS
databases. She has approached you fo r some advice regarding the to ugh issues G IS managers face when concerned abom successful implementadon of a GIS pro-
resource management organization has developed a GIS database that may conta in in fo rmation that could assist you in some part of your job. Under w hat conditi ons should you expect you r co lleague to provide you access to
the GIS database?
16.4 Distributed GIS program. You work for a large integrated forest products co mpany th at has a ce ntral office and five district offices. The company has been
attempting to shift GIS capabil ities to the field offices by pu rchas in g the appropriate hardware and software reso urces. and insist in g that field perso nnel (foresters. biologists. managers. and others) use it to make maps associated with their management activities. Afte r twO
years, only one of the five offices has successfully implemented the program.
gram. What might yo u advise Mary?
a) Why do yo u th ink the o ther four offices have been
16.3 Sharing GIS databases within an organization.
b) Why might the o ne office have been successfu l?
less than successfu l? You've heard that a colleague in anothe r private natural
References Bettinger, P. (I999). Distributing GIS capabi lities to fo restry field offices: Benefits and challenges. journal ofFomtry, 97(6), 22-6. Dobson, J.E., & Durfee, R.C. (I 998). A quarter century of GIS at Oak Ridge National Laboratory. In T.W. Foresman (Ed.), The history ofgeographic information systems: Perspectives from the pioneers (pp. 231-63). Upper Saddle River, NJ: Prentice-Hall. Natoli, J.G ., Pelgrin, W.H., Oswald, B. , & Montie, K. (200 1). Geographic Information Systems: The wave
Puger Sound LiDAR Consortium (PSLC). (2007). Puget
Sound Lidar Consortium: Public-domain high-resolution topography for Western Washingron. Retrieved April 23, 2007, from http://pugetsoundlidar.ess.washington. edu/ index.htm. Wing, M.G., & Bettinger, P. (2003). GIS: An updated prime r on a powerful management roo l. jou rnal of
Forestry, 101(4),4-8. Wing, M.G., & Sessions, J. (2007). Geospatial technology education. journal ofForestry 105(4), 173-8.
of the fmure for information analysis. Public Works,
May, 22-9.
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Chapter 17
Certification and Licensing of GIS Users Objectives The progressio n o f GIS use in natural reso urce managemem has been evolving from development and imple-
mentation of systems. to (he distribution of analytical capabilities to field offices, to porting spatial technology into the resource setting. The evo lu tio n of GIS can be viewed from (he perspective of a single organization or from [he perspective of nationa l o r worldwide GIS communities. From a global perspective. GIS is facing one of its greatest challenges: mat of implementing certification and licensing processes to define and recognize 'professio nal' GIS users. In recem yea rs, co nce rn s have been voiced from the land surveying and engineering commu-
niry regarding the definition of surveying activities and
2. what organ izations might be relevant in certification and lice nsing discussions. an d 3. how cenification and licensing issues might affect the typical GIS user in a natural resource organizacion .
During the last 10 years, one of the primary goals of the GIS comm unity has been to educate other profession-
als and the general public about the power and usefulness of GIS beyond its map production capabilities. Many people in natural resource o rganizatio ns (as wel l as academia)
view GIS only as a map-making tool and may have li mited understanding of its analytical power. The American
Society for Pho togrammetry and Remote Sensi ng (ASPRS) and the University Consortium for Geographic Information Science (UCGIS) are perhaps the most active
whether GIS practitioners impede upon traditional surveying accivities when colleccin g o r map ping spatial data
groups in educating those who use GIS, as well as the pub-
(Gibson, 1999). These concerns have foStered debate
Nacional Convemion in Tampa, o ne AS PRS member was
among surveyors. engineers. and GIS users regarding the types of measuremem and analytical activi cies that migh t be required to compete ntly perfo rm spec ific accivit ies in co njunction with the analysis. Thus, in an effon to gain credibi lity and recognicion among other professions. the GIS commu ni ty has pondered the iss ue of cenificatio n and licensing.
heard to remark ' If you claim that GIS technology is vital
After reading th is chapter and exploring the questions posed in the applications sect ion. students sho uld have an awareness o f: 1. why certification and licensing of GIS users is being
debated,
lic, about the ca pabi lities of GIS. At the ASPRS 2007
to society yo u should also promote the need for certifica-
tion and licensing among GIS users.' The UCG IS cond ucts nadonal meetings rwice a year to identify research and other act ivi ties that wi ll idencify and promote rhe use of GIS as a problem-solving tool for society. At a narionai
meeting in June 2000, a member of the UCGIS asked the other delegates. 'Now that everyone seems co know about GIS , what are we going to do about it ?' These seemingly innocent observations speak positively about society's growing awareness of GIS while also indicati ng a potent ial pitfall within natural resou rce managemenr: GIS has been embraced by natural reso urce organizations in a way th at 256
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P art 3 Contemporal'( Contemporary Issues in GIS Part
faci li[a[es open use by any professional who might be facilitates inreresred rhe <echnology. technology. Only recently has [he the GIS GIS interested in the commu community nity begun [Q co discuss in depth whether professional standards musr must puc put in place (0 to ensure professional professionaJ analysis. competency for data development. anal ysis. and other (asks. tasks. Given Gi ven that ocher o ther professions profess ions initiated these discus-
me
sio ns, leaders and champions of o f rhe the lise of GIS in natsions, ural uraJ resource management managemenr have found themselves at rimes times
The more esrablished established and rigo rigorous rous of the me oprions options is that made by [he the American Sociery Society for Photogrammetty Phorogrammerry & Remote Sensing (2006). The ASPRS has created Remore crea<ed a Mapping Scientis Scientistt certification for GIS GIS users and has also certification ifi catio n programs for remote sensing and created cen photogrammetry. photogram merry. The M Mapping apping Scientisr Scientist cerrification certification requi res app licants to develop a sratement statement of accomplishrequires ments, mems, which wh ich are peer-reviewed. peer- reviewed, and to (Q pass a written
exam . Currenrly, Currently, only abou aboutt 50 people are cerrified certified as Mapping Sciemisrs Scientists rhrough through rhe the ASPRS. The APSRS has
in a defensive position when addressing issues rdated related (0 CO the rhe call for licensing and certification cerrificarion of GIS users. Throughout the Throughour rhe US, some professional land surveyors
also creared crea red technologist-certifications for GIS, remote
and engineers have perceived th that at GIS users use rs we re vio lating
sensing, and phowgrammerry. photogrammetty. These GIS GIS technologist rechnologisr
scate state surveying laws when usi ng CPS GPS [0 to collecr coliect spacial spatial data, and when reponing reporting positional accuracies accu racies of coldata. lecred measurements. Some of these GPS data dara collection collecrion
certifications require less work experience than the
acdviries have actually acrually led to activities ro legal dispures, disputes, parricularly particularly when the [he collection collec rion and mapping of sparial spatial data dara refercoees land ownership locations. ences locado ns. In California, state legisaccivities involving spalation was developed to clarify the activities
tial rial data dara collection collecrion (Korte, 1999). These acriviries activities full fuJI inro into [wo caregories: (I) (wo categories: (1) those that thar constitute consrim<e 'land surveying', and hence require professional licensing, and (2) all other
Mapping Scienrisr Scientist and other full certifications (th (three ree years as opposed [0 co six). six) . There are currently currendy s ix certified
G GIS/LIS IS/LIS Technologists. The Urban and Regional Regional Informar Info rmation ion Systems Sysrems Association Associarion (U (URlSA) RISA) iniriared initiated a GIS professional certificarion tion program in 2004 2004.. The GIS Certification Institute Innirure (GISel, (G ISel, 2007) manages the rhe cerrification certification program, which resulrs in qual results qualified ified app ap plicants licanrs becoming recognized as a certified geographic informarion information sysrems systems specialisr spec ial ist (GISP).
activities, ich do not activ iti es. wh which nor require professional professional licensing. Most states stares have registration boards mat (hat license and regsurveyors engineers. ulate land surveyo rs and engi neers. These boards often have rhe the abili ability interpret exis existing ting starutes statutes and laws that ty to interprer
Three categories of experience must be ~ demonstrated in order o rder to qualify. The primary experience necessary in ga ining GISP cert ification is a documented work history gaining
rcpresemarion of spacial spatial data data,. govern the collection and representation and may also initia initiate. te, support. suppocc, or approve legislation legi slat ion regarding collection llection activities. In addition addition., regard ing spatial data co state-level professional land surveying sociecies societies are active modifying laws regarding spatial data dara in promoting or modifYing
ence category is an education background that (hat can be sat-
collection, and they may occasionally engage political lobcollecrion, byists to assist in influencing the legislative legislative: process. the la land nd surveying and engineering In contrast to rhe fields. the GIS community is not directly fields, direcdy re reppresented resented or controlled nally- or state-recognized licensing contro lled by a natio narionallyboard in most mosr cases. cases. There are GIS-related GIS-relared professional state oorr province level but these societies societies at the srate long. and are typically have nOt been in existence for very long, gene rally nor very acrive active in influencing legislation rdared related generally to spa spatial rial data co collection. llection. The main objective of stare-level state-level iona.l-Ievel GIS socieries societies has been to communicate and nat narional-Ievel information related to the collection. collection, maimenance. maintenance, and to interested inrerested users. analysis ana lysis of GIS databases (Q
Current Certification Programs In terms of nationally-recognized GIS certification cerr.ification prothe US. US, there are primar primarily il y twO (wo current options. grams in rhe
involving GIS GIS and orher other spa spatial rial tools. rools. T The he second experiisfied by arrending attending conferences and workshops, as well as completing formal education ed ucatio n programs or o r earning cenificertificates. The third category is desc ribed as 'contributions' 'conuiburio ns' GIS publications. pub lica tions , conference planning or and includes GIS presentations. presentations, and volunteer vo luntee r activities related to GIS.
As Ai; of October 2007, there rhere were 1,709 certified GISPs, giving the GISel GISCI program visibility visibiliry beyond [he the certification rion programs of the rhe ASPRS. Airhough Although the rhe creation of the GISP certification is noteworthy. the experienced-based
portfolio approach to ro qualifYing as a GISP lends itSelf irself to ro criricism (Lo ngley et er aI. aI.,, 2005). It I[ remains to ro be seen criticism (Longley whether a certification approach rhar that does not include a writte n examinarion examination w will recognitio itio n written ill receive respect and recogn disc iplines. IIn profess ions and disciplines. n addition. addition, there from other professions is no clear merhod. method for addressing unprofessional unprofess io nal activicies activities
related to GIS, should [hey reJa<ed they occur. Given the emphasis on self-reporti ng of experience, ex perience, another issue of discussion is se lf-reporting status. whether any applicants have been denied GISP status. Many co lleges and universities now offer certification IS, as well as other spatial data degrees mar rhat are related ro to G GIS. co collecrion llectio n and analysi analysiss rechnologies. technologies. however no Stanstandards exist ex ist for what should be offered in those curricu257
Chapter 17 Certification and licensing of GIS Users
Besides the current course that you are taking (that
fishe ri es, wildlife, oceanography, forestry , soi ls,
hopefully uses this book), what other GIS courses
rangeland resources, and others. More than likely, GIS courses offered in departments other than geography will provide a different perspective on wha t is important (Q smdents pursu in g namral resource
does yo ur uni versity, college, or community coll ege offer? Although there are many educational instimtions that offer coursework or curricu lum related to GIS, these programs can look quite different from one instimtion to [he next. At most educacional instim[io ns, for exam ple, GIS courses are located within the
247
degrees. If a university or college does not offer courses related co GIS, students can still learn about
geogra phy department. H owever, special ized GIS
the capabilities of GIS through Internet courses, selfstudy of GIS texts, and volunteer wotk wit h local
cou rses may be found within departments such as
agenc ies or gove rnment offices.
lums. The National Center for Geograph ic In formation & Analysis (2000) has produced, and suggested for use, a core curriculum to serve as a foundation fo r studies in
The fi rst section of the Model Law clarifies the necessity for guidelines by stating that the practices ofland surveying and engineering are a matter of pubic interest. The
GIS. Typically, GIS users can earn a GIS certificate after
decisions made (or recommended) by people employed in these professions can potentially affect the life, health, and property of the general public. Sectio n 2 defines the tasks
tWO yea rs of part-time study. While this option does include organized coursework (and perhaps exams to evaluate competency), programs that offer cert ifi cat ion degrees lack a recognized set of certification gu idel ines
and are ge nerally not accredited by a professional GIS or remote sensing society. As mentioned in chapter 15, the GIS&T Body of Knowledge (DiBiase et aI., 2006) was recently published in o rder to define critical concepts and skills that relate to geographic information science and
that are associated with surveying and engineering and no longer refers directly to GIS , as it did in an earl ier version
of the Model Law. Section 2 does state that mapping involves the configuration of the Earth's feamres. the subdivision ofland, the location of survey control points, reference points, or property boundaries, and thus people performing 'mapping' are performing the 'Practice of
technology (G IST) . This document was created through
Surveying'. The Model Law suggests that these people
the joim efforts of many GIScience researchers and educators, and is an initial attempt to define the skills that you can use to describe geospatial com petency. A second edition is intended that will provide detail fo r instructional activities that suppOrt important geospatial concepts and
should be reg istered as professional surveyors before engaging in those act ivit ies. The most d irect pathway co becoming a professional surveyor is to fi rst graduate from an accredited fou r-year college program in engi neering or surveying. Then yo u must successfully pass an e ight-hour written exam covering survey in g fundamentals. Once the fundamentals
skills.
The NCEES Model Law
exam has been successfully passed, four years of surveying
The Nationa l Council of Examiners for Engineering and Surveying (N e EES) has developed a set of guidelines
experience under the supervision of a licensed surveyor must be accumulated before admittance is allowed to an eight-hour written comprehensive exam covering surveyin g principles and practice:. Once this comprehensive
described in a Model Law document
to
help states with
licensing issues related co land surveying and engineering (National Council of Examiners for Engineering and
Surveyi ng, 2006). The Model Law contai ns reference to GIS activities assoc iated with spacial data collection and use. The Model Law contains 29 sections that are
exam has been successfully passed, and all other state-level requirements are satisfied, you are qualified to become a professiona l land surveyor. Those who grad uate from four-yea r surveying cu rriculums that are not accredited must spend an addirional rwo to four years working in
designed to help state boards and other legislative bodies
the land survey profession before they can be admitted
create or amend laws for land surveying and engineering.
to the fundamentals exam. T his process of attai ning
258
248
Part 3 Contemporary Issues in GIS
licensing is daunting, and requires a long-term commitcommi tmenr ment on [he rhe part pare of potential poten tial su surveyors rveyors or others who w wish ish to to comply with the Model Law. For many cu current rrent act ive engagenatural resource professionals who use GIS. active ment in a career combined with family and communiry comm un iry commitments offer few realisdc realistic opportuniries opportunities [0 to pursue the guidance of a an engineering degree or to work under rhe land surveyor. Earl Earlier ier versions of the Model Law definition of surveying thar that contained comained direct mention of CIS GIS were criticized as being [00 (00 strin srringenc ge nt and expansive in its description descriptio n of afG GIS IS applications within thin surveying acrivities. activities. Several promiap plica tions wi nent organizations related [0 (0 surveyin survey ing g and an d G1S G IS coauthored a repon repan that suggested modifications [0 the Model Law (Ame (American rican Congress on Surveying and Mapping [ACSM] et aI., aI. , 200 2001). I). The report urged lhe the NeEES to co drop dro p exp explicit licit reference [Q to GIS as a data maniprefine the definit definition ion of sura nd mapping tool. to refme ulation and veying it was less broad, and [0 ro specifically include veyi ng so that i[ and exclude certain activities in its defini definition tion certai n GIS-related accivities of surveying. The or T he NCEES appears to ro have acted a[ a( leas leaS[t in pan recommendations. mmendatio ns. Previous to the Model parr to these reco Law changes, changes. the broader definitions of surveying su rveyi ng created creared difficulties states that char tried to incorporate the difficulries for some stares into their own ow n state Model Law's definitions of surveying inco starutes regulations ations (Thurow & Frank, 2001). statutes and regul
me
The Need for GIS Certification and Licensing Chief among amo ng the t he argumenr argum enr for GIS G IS certification certifi ca ti o n is an assessment ment of how GIS activities might impact society's sociery's assess
welfare and safery safety (Gibson, 1999). The surveying and engineering engineerin g professions professio ns have long been involved in deterdete rmining how best to accllrarcly accurately and precisely collect and ana lyze Earth Earrh aand nd structural strucwral measurements. Since Si nce land va lues in North Ame America rica w will ili likely likdy concinue conrinue to increase as values population also increases. increases, land areas that are the human popularion resulr in large monetary losses or inco rrectly measured incorrectly measmoo can result
gai ns for land ow gains owners. ners. Knowing the reliabi reliabiliry lity of land measurements (exp ressed through uncertainty unce rtai nty estimates) es tima tes) ro make better bener decisions. While will allow land managers to you may argue that a distinct parr of natural natu ral resource data quantifying collection and analytical processes involves quamifying and a nd expressin expressing g the th e uncertainty that is associated with those measurements, measurements. this quanrification quantification is rarely used to rate the reliab ility reli ab ili ty of measurements measu rements collected. collected. Thus the surveying and enginee ring professions may be better bette r posi-
tion tioned ed to provide informa information tion regarding r~garding the inherent inherem uncertainty uncertainry in land meas measurements. urements. Land surveyors are also charged with locating estab~ Jocaring or estab-
lishing roads and other utilities such as power lines and fire hydrants. hydranrs. Surveyors have argued that [hat it is inappropriate for un licensed surveyo surveyors GPS equipment for unlicensed rs to operate CPS this pu purpose rpose since locational errors can potentially potenriaIly affect addition, rveyo rs public safety. safery. In add ition, since professional su surveyors must absorb rhe th e on-going costs associated wi with th maintainma i ntain~ ing licensing and liability liabiliry insurance, they are a re likely likel y to charge more mo re fo rr thei theirr services than unlicensed GPS CPS operath at it is unfair unfai r for unl unliitors. Thus, surveyors have stated that censed o perators rs to compere compete with professional land ce nsed GPS operaro g these types typ es of data d ata collection surveyo rs in offerin su rveyors offering servICes. servIces. Licensing varies [he world and Licensin g va ries throughout throu ghout the a nd may also scate o r province. In addition, rs that the vary by stare add itio n. it appea appears numbe r of licensed professions is in transition transicion . In number Ca nada. professions mat that require requ_ire licenses are referred to as Canada, regulated regula red occ occuparions. upations. There are approxima[e1y approximately 50 di diffferenr ferent regu lated professions professio ns in Canada (Government (Govern ment in Canada, Ca nada, 2007). Within [he the US, the th e number of profesrequ ire a lice license nse fo forr participation participario n has been bee n sions thar require gradually increasing. reasing. Doyle (2007) found that lha[ about abo u( graduall y inc 20 per cem cent of all professions in the rhe US requi require re licensing, up from abo abour ut 5 per ce cent nt duri during ng the early 1960s. Abou Aboutl 50 professions have a regist registration ration process (hat that is recogstares. Some people criticize (he the licensin licensingg nized in all 50 srates. thal it resulrs results in higher prices for servprocess and claim thar a n equivalent gain gai n in the qua quality lity of the rhe services without an ice or good provided. In additio addition, n. some peo people ple see enrry entry into the profession as being bein g prohibitively prohibi tively limited once licensing is in place. While Kleine Kle inerr (2000) found (hat thar incomes from licensed occu occupations pations were we re higher for those training. occupations that [hat required more education and training, growrh was evidenced in many faste e mpl oy ment growth fasterr employment licensed professions, such as engineering and law. law, when co mpared to no compared non-licensed n-licensed professions. professions. Kleiner Kle iner (2000) also reports that empirical empirica l evidence address addressing ing whethe whetherr licensing results in greater soc societa ieta l good s. s, such suc h as increased inc reased safety, safety. is currently lacking. nforcement are additional additiona l Iss ues of conrrol and eenforcement stares have aspecrs of licensing and certification . Most states aspects clearr defi definitio ns of what constitutes acceptab le surveying clea n itions pracrices. rules les typically rypica lly cover what clients should practices. These ru expect ional land surveyor's services, services. in ex pecr from a profess professional ethical considerations. terms of products. producrs. as well as erhical consideratio ns. Ethical co conside nsiderations rarions nor nOt only on ly address add ress the surveyor259
Chapter 17 Certification Certffication and Licensing of GIS Users Users
client relationsh relationship ip but also offer advice on the professional relationships cha thatt should exist berween becween surveyors. Most Sca tes also have developed [0 fac facilitate il itate (he the subsu bstates develo ped a process [0 mirral of complaincs minal complaints against land surveyors. surveyors. All state to revoke a surveyi surveying ng licensing boards have the power ro disciplinary inary infractions occur as a resulr result of comlicense if discipl plaines. plainrs. This Th is power power encourages most land surveyors [0 to become familiar with board rules for professiona1 professional conduct they engage in survey act iviand [0 (Q adhere to the rules as [hey activinor exist in general general GIS use in natural namral ties. Such rules do not resource management. Criticism icism has also aJso been weighed agai aga inst nst un unlicensed licensed Crit GIS GIS and GPS users due to [he the lack of an acc redited redi ted educadonal tional curriculum. curriculum . Most professions, including forestry and wildlife. wildlife, have h.ve identified an education. educa[ionalI and professional backgro background und [hat that is necessary for accred acc rediitat ta tion ion or within their fields. The accred accreditation itation process usulicensing IlJithin suggestS me the coursework, minimum competency sranstanally suggests dards, professional standards, and imegration with other disciplines [hat that should be provided to students pursuing degrees in [hose those fields (Huxhold. (Huxhold, 2002). An accreditation process for GIS G IS could be developed (and perhaps is currently under developmenr). development). However. However, me the dilemma is rhat thac srudy, [he the students in [hose those if you view GIS as a field of study. curricula cu rricula will graduate with a geography degree, degree. and be considered professional profess ion al geogra geographers. phers. Most "amrai natural foresters, soils sciresource organizations hire biologists, foresters. entists, or other associated professionals. professionals, as well as geogranatura l resources. phers, to assis assistt in the management of natural orga nizations increasingly expect all of thei theirr perThese organizations sonnel ucili1.e ize GIS, GlS. not nor just JUSt those who have obtained a so nnel to util degree from an accredited accredired geography program.
GIS Community Response to Certification and Licensing As you might expect, expect. some members of the GIS commuto the sugges suggestion tion that thar certa cerrain in nity have voiced opposition co ies be included in the lis t of funcrions functions only co to GIS activit activi ries be performed by land surveyors. C Criticism riticism has been directed toward the sometimes-broad definitions of land surveying, surveying. and whether GIS databases deve. developed loped and distributed by public agencies should require management by a licensed surveyo Uoffe, 200 1). In addition, surveyorr GofFe. addition. some rtifica tionn and lilicens ing proposals for ce certificatio censin g have also been co uld viewed d prevenr natural \'iewed as exclusionary. and coul resource professionals from performing ~rforming the GIS GIS activities acr iviries thatt they tha rhey historically performed. performed. Thus the opposition opposirion to
249
certification cert ifi cation and licensing from the natural resource GIS GI communiry, community. largely composed of natural narural resource manage rs. bio logists. foresters, others. is to be expected . agers, foreseers, and others, Defining those areas of spatial data co llection and collection
mapping that porentially potentially afFecr .ffect public welfare welfure and safery safety is easy in some cases, cases. and challenging in others. orhers. Clearly, Clearly. GIS databases used for acdviries activ ities such as navigatio n, n. locatwhe n excavating. excavating, or ensuring ing underground facilities Facilities when that property boundaries have been accurately located and used in calculating calcularing land areas, areas. should fall full under [he the purview of a professio professional nal lan land d surveyor o r enginee engineer. r. Acriviries ities (hat that involve displaying display in g data for illustrative purActiv activities ies [hat that may only poses, recreational recreatio nal gu ides, or in activit .ffect perso n{s) or agency responsible for the associaffect the person{s) ated ared decision, decision. could perhaps fall ourside outside [he the purview of a surveyor or engineer. engineer. Although these professional land surveyor illusrra te distinctions di stinct ions between becween acti activvici it ies es [hat chat examples illustrate clearly affect afFecr public welfare and safety safery and those rhose that [har do not, nor, there are many orher other examples rhat that are less clear and will require furthe lUrrherr discussion before agreements between the rhe surveying and GIS commun ities are reached. In cases where mapping products mighr might affect public welfare and safety, severa severall scenarios fo r making map consumers aware of pocenriallimitations poremial limitations have been suggested. Suggestions include requiring requ iring that (hat maps explicicly explicitly iden identify ci fy me the source so urce documents, associated assoc iared meradara, metadata, appropriate approp riate use of map content, and any positional comenr. positional adj adjustments usrmenrs Goffe. Uoffe, 2001). Examples Examp les of these caveats caveatS and disclaimers were intro intro-duced in chapter chapee r 4. Whether these caveats will continue to ro fall shon short of requiring requirin g a certified or licensed GIS G IS professional sional to ro develop the mapping products wi ll likely be an area of discussion. Finally. Finally, and inreresringly. interestingly, some people have argued that the surveying profession has traditionally not required training tra ining in GIS in order to obtain professional surveyi ng cert ification (American (American Congress on Survey Surveying in g surveying and Mapping, Mapping. 1998). For th [h is reason. reason, they argue. argue, it i[ may not nor be app appropriate ropriate fo forr land surveyors su_rveyors to manage rhe the developmenr. maintenance. maimenance. and use of GIS databases. development. syllabi the national However, the current exam syll abi for fOr rhe narional land surveying exams (borh (both the rhe fundamental fundamemal an andd professional principles include I.nd informainfo rmapri nciples and practice) do incl ude GIS and land tion systems as potential poremial exam topics. topics.
MAPPS Lawsuit In June 2006, rhe the Managemenr Management Associarion for Private Phorogrammerric Photogrammetric Surveyors (MAPPS) and rhree three other 260
250
Part 3 Contemporary Issues in GIS
engineering lawsu it professional enginee rin g associations filed a lawsuit against government. The lawsuit was fi led on aga inst the US governmenr. W<1S filed
su rveying and mapping activicies. form surveying activicies, the lawsuit con-
co uncil should di direct tends that the rhe FAR council rect that licensed professionals be selected for government contracts cOntracts invo involvlv-
Acquisition Regulation behalf of the Federal Acquisirion Regul ation (FAR) referred Council and is refe rred to as the MAPPS lawsuit. The actuall tide at. v. United States actua title for the tbe lawsuit is MAPPS et al. of Am(ricft, America. The lawsuit requests changes in interpretation of the 1972 Brooks Act (US Public Law 92-582)
MArrs MAPPS
which is intended to (0 direct federal government policy in selecting selecti ng providers prov iders [0 to perform architectural. archi tectural. engineering, services. laws uitt seeks and related serv ices. More specifically. specifically, the lawsui co modify how the [0 rhe selection process for government cootivities. traccors is evaluated as it relates to mapping ac riviries.
The American Association of Geographers (MG). (AAG). Geospatial Informatio GISCI. Geosparial Informationn & Technology Association (GITA). VCGIS. and VRISA jointly submitted su bmitted Associarion
resulrs of the lawswr lawsuit may impact The resultS impacr how some US fedgovernmenr contractS. contracts. The MAPrs MAPPS eral agencies award government lawsuit has been a considerable concern for many GJSthee oriented organizations and has once again fueled rh activities ities that can be condebate over the definition definicion of acdv activides influsidered surveying and engineering, which acriviries inRusafety, and the appropriate ence public welfare and safery. geospatial certification cerrificarion and licensing requirements. requiremems. The participation of prominent prominenr oorganizations rganizations on both sides of rhe the laws uit is evidence that the MAPPS lawsui lawsu itt is not lawsuit thal rhe nor trivial legal exercise. a rriviallegal exerc.ise. MAPPS lawsuit included the Plaintiffs of the MAl'l'S rhe American Sociery of Civil Engineers (ASCE). National Sociery of (NSrE). and Council on Federal Professional Engineers (NSPE). Procurement of Architectural and Engineering Services (COFPAES). The Brooks Act established that price alone the awarding of government should nO( be used in [he governmenr contracts to individual Instead. Qualificarionsrracts ind ividualss oorr firms. In stead, QualificadonsSelection (QBS) is to be used. which involves evalBased Selecrion qualifications and expe experience. uating professional qualjficadons ri ence. in addition to seeking a 'fair and reasonable' cosr COSt [0 co rhe the govaddirion ernment,, in awa rding Negotiations for an ernment rdin g contracts. Negotiarions acce ptable price should begin with the most qualified ions fail, firm. If negodat negotiations fail , then negociations negoriarions should proceed with the second most qualified firm ., and so on . The COllncil applies the rules and laws rel ated to FAR Council related ro the ensu re mar that the intentions Brooks Act, and is supposed co ro ensure of the rhe act acr are upheld. Although the rhe Brooks Act Acr includes 'surveying 'surveyi ng and mapping' among the list of architectural and engineering services that to cover, fedthar it is intended CO eral agencies have not cons istent in their interpretainte rpretanor been consistent cion rion and adherence to stated protocols. The MAPPS MAl'l'S lawsu it seeks [0 nci l ro to compel the FAR cou ncil to more rigorously inre rpre[ and apply rhe interpret the Brooks Act in the selecrion selection of contractors for surveying and mapping services. In states where licensed surveyors surveyo rs or engi neers are required ro to per-
me
im plications of the ing surveying and mapping. The implicarions rhe [hen potentially significanr significant given that lawsuit are afe then thar many nOt draw a clear distinction berween between surma ny states do nO[
in g in their th eir laws and rules that govern veying and mapp mapping surveymg. surveYing.
to the US District Court in Virginia a briefing (0
(Alexandria Division) that thaL opposed the MAPrs MAPl'S lawsuit (Association of American Geographers. 2007). The brief stated that a successful lawsuit could cause serious constared cern nO( nm only for the rhe GIS community commu niry but also fo r other mher activ ities and professions. Other related activit ies related act ivities activities included GPS data collection, Interne lnrernett mapping, geospaanalysis , remote sensing, academic aca demic research that tial analysis, involved mapping, and the broad activities encompassed
withi n cartography. The briefing that the lawwirhi briefi ng claimed thar would ssuit's uit's impact woul d greatly affect many activities and industries that involve or rely on mapping activities and information. inform acion .
The US Districr District Court ruled againsr against MAPl'S MAPPS in June favorr of the US 2007 and issued a summary su mmary judgment in favo MArps Government. The judge in the case stated srated that the MAPPS plaintiffs failed to 'establish 'establ ish that an injury in fact plainrifFs faCt was suffered by the rhe individual surveyors or their firms'. In order for a case to be tried. a plaintiff (those hling filing the lawsuit) indicated by sufferi suffering must establish standing. standing. Standing is indicared ng a loss of some sort, be it monetary or otherw ise. The
judge found that MAPPS and the other plaintiffS had nor nOt rhar MAPl'S lawsuir. established sufficienr standing to suppOrt the lawsuit.
ate opportunities to appeal the Alrhough there rhere are rhe judge's deci sion, the decision, rhe judge's ruling appears to
strong be st rong enough
that tllar an appeal was considered unlikely. unlikely. uncertainty in determining how a sucThere was great uncenainry
MAPPS lawsuir lawsuit wou would cessful MAPl'S ld have impacted the GIS community mun iry and many community members expressed relief
the lawsuir lawsuit came to end. Doubtlessly. there will be that ti,e ro an end. ilar dispures disputes and uncertainty in the future regarding ssim imilar licen sing th that court at involves (he the co un system. Although (his this licensing represents a US examp example ar isen le of the conAict conRicr that has arisen with the widesp read use of GIS. GIS, you cou ld reasonably could reaso nably countries with an es(abenvision this happening in other counrries estabrela(ed to lished land records system and regulations relared ro engineering and land surveying practices. 261
Chapter 17 Certification and Licensing of GIS Users
251
Summary In some circumstances, certification and licensing may be necessary for those involved in (he developmem and management of GIS databases. to ensure that minimum stan-
study. Guidelines should be developed for determining the experience. educarionaJ background. professional con-
G IS databases. Although many GIS activities related to
duct, and cominuing education that defines (he qual ifica[ions appropriate for those disciplines. Guidelines sho uld also be developed to define the extent [Q which persons who are qualified within disciplines can appropriately
namral resource managemem may have no bearing on public welfare and safery, some GIS acriviries result in maps or mapped data being sold or made available to the
develop, manage, and use certain GIS databases. Until a national certification or lice nsing program for GIS gains credibility and respect within society, GIS users in namra!
dards of competency exist and (hat standards 3re being mer in the development. maintenance. and applicacion of
public. and thus may have direct or indirect implicacions
resource fields will find themselves struggling with local
on public welfare and safety. GIS has evolved into its own discipline, and is being integrated with other fields of
or national regularory groups. the legal system, and other professions for contro l of GIS activities.
Applications 17.1. Licensing. Assume that the state or province in which YOll work is considering the development of a licensing board to oversee the licensing of GIS users. and to regulate the use and managemenr of GIS databases related to your field of namral resource management.
a) What benefits might a licensing board provide for professionals engaged in GIS activiti es? b) What disadvantages for GIS users might result from the developmenr of a licensing board? c) Describe three key dimensions of the licensing
process that the board should develop. 17.2. GIS certification programs. The owners of the Brown Tract have recently become aware that GIS is used extensively in the management of the fo rese. They are concerned abo ut the credibility of their land management team, and have suggested that all employees obtain GIS certification . a) What are the potential benefits of GIS certification for GIS users? b) What are the potential drawbacks of GIS certifica-
c) What elements would be necessary in order for a GIS certification to be more widely recognized and respected within society?
17.3. NeEES Mood Law. Does the NeEES Model Law process seem like a reasonable or rational approach to clarifYing the issue of G IS licensing? Why or why not? 17.4. The need for licensing or certification. IdentifY three GIS or GIS-related activities that could affect public welfare. and that might suggest that those developing. maintaining. or using the s upportin g data sho uld be licensed or certified. 17.5. GIS community response to certification and licensing issues. Many professional (foresters. wildlife biologists. hydrologists. engineers. etc.) working in natural resource management currendy use GIS to ass ist in making management decisions. Why might they be concerned about the issue of GIS certification and licensing?
tion for GIS users?
References Association of American Geographers (MG). (2007) . Amicus Curiae Brief of the Association of American Geographus, GIS Cutification InstituU, Geospatial
Information 6- Technology Association, University Consortium for Geographic Information Science, and Urban and Regionallnfonnation Systems Association in 262
252
Part 3 Contemporal)' Contemporary Issues in G GIS IS
notion for mmmary summary jJldgm~llt. judgmt nt. opposition to plaintiffs 11otioll Retrieved on June 6, 6. 2007, 2007. from Imp:llwww.aag.orgl hnp: llwww.aag.org/ Donatellinks.html. American Congress on Surveying and Mapping (ACSM). (I998). Should surveyors superv supervise A CSM (t998). ise GIS? ACSM Bull,tin, Bulletin. NovnnberID,wnber, NovemberiDeumber. 26--3 1. I. American Congress on Surveying and Mapping (ACSM), (ACSM). American Society of Civil Engineers-Geomatics Division. American Sociery Division, Society for Photogrammcrry Photogrammeuy and Remote Remme Sensing, Management Managemem Association for Private Phocogrammeuic Phorogrammecric Surveyors, National Narional Sociery of Professional Profess ional Surveyors. Na National tional States Geog raph ic Information Council, Council. & Urban and Regional Information System Association. (200 (20011). ). GIS/LIS addendum CO to rhe the repan report of the task force Force on the NCEES NC EES Model Law for surveying. Surveying Suroeying and Land Information Syswm. 61(1), 61(1). 24-34. Infonnation Sysm"" American Society for Phorogrammerry PhotOgrammerry & Remote Sensing (ASPRS). 2006. Certification and reunification recertification guid,lines guidelines for the Ihe ASPRS certification program. Bethesda, MD: American Sociery Society for Photogrammerry Photogrammetry & Remote Sensing. Retrieved June 7, 7. 2007, 2007. from h http://www rtp:l l www.asprs.org .aspes.o rg l/ membership/cenific3rion mem beesh i pi cen i ftcat ion lI certiifica cert ficatt io ion_gu n_gu iidel de lines. ines. h tm I#Certi I#Certi f1ed fied_M _M a p pin l\&Scientist_GIS-LIS. Scientist_G IS-LIS. DiBiase, D., Johnson, A. Kemp, K., Luck, DiBiase. D .• DeMers, DeMers. M. M .•, Johnson. A.•, Kemp. K.• Luck. A., A.• Plewe, Plewe. B B., .• & Wentz, Wentz. E. (Eels.). (Eds.). (2006). TIlt The geogtographic information sciena science and technology body of knowledge. Washington. WashingtOn , DC: University Consonium Consortium for Geographic Information Informatio n Science. Science, Associarion Associadon of American Geographers. Doyle, 007). By [he work. Doyle. R. (2 (2007). the numbers: license to work. Scientific American. American, February hbruary 9, 9. 28. Gibson ., D.W. (1999) (1999).. Conversion is OUt, out. measurement is in-are we beginning the surveying and mapping
era of GIS? Proftrsional Professional SlIrlltyor, SlIroeyor. 19(7), 19(7). 14-18. 14- 18. GIS Certification Insritute. GIS Institute. (2007). (2007) . Are A" JOu you a GIS practitioner or a GIS professional? Retrieved October 5, 5. 2007, 2007. from Imp:llwww.gisci.org/. Imp:llwww.gisci.orgl. Government in ill Canada. (2007). Types of work. Retrieved October 5, 5. 2007, 2007. from Imp:llwww.goingrocanada.gc. hnp:llwww.goingtocanada.gc. carr calT ypes_oC work_in_Canada-en.htm. Huxhold. W.E. (2002). GIS professionals-get a profession! Geospatial Ceospatial Solutions, Solutions. 12(2). 12(2),58. 58. Joffe. Joffe, B.A. (200 (2001). I). Surveyors and GIS professional reach accord. Surveying Suroeying and Land Information Systems, Systems. 6/(1), 61(1). 35-6. Kleiner. M Kleiner, M.. (2000). Occupational Licensing. journal of Economic Perspectives. Perspectives, 14(4), 14(4). 189-202. Korte. G.B. G .B. (1999). Korte, (1999) . The current controversy: GIS GIS and land surveying legislation, legislation. Part 1. I . Point of Beginning. Btginning, 24(6), 24(6). 70-3. Longley, P.A P.A., Longley. .• Goodchild, Goodch ild. M.F ., .• Maguire, Magui re. D.J. D.J .•, & Rhind, Rhind. D.W. D .W . (2005). Geographic Ceographic information systems sysmm scienet (2 and science (2nd nd ed.). Chichester, Ch ichester. England: England : John Jo h n Wiley & Sons. National Council of Examine Examiners rs for Engineering and Surveying (NCEES). (2006). Model Mod,l law. Clemson, Clemson. SC: NCEES. NCEES. National Center for Geographic Information & Analysis (NCGIA). (2000). The NCGIA core curriwlum curriculum in GIScience. GIScitl1U. Santa Sama Barbara. Barbara, CA: National Nationa l Center for Geographic Information & Analysis. Retrieved June 5. 2007. 5, 2007, from htrp:llwww.ncgia.ucsb.eclu/educacion/ http://www.ncgia.ucsb.edu/education/ curr icula/gisccl. curricu la/gisccl. Thurow, ., & Frank, I) . Coming to [0 terms with Thurow. G .• Frank. S. (200 I). the Model Law: The search for a definition of surveying in in New Mexico. Survtyillg Surveying and Land Information Infonnation Systems, Systems. 61(1),39-43. 61(1).39-43.
263
Appendix A
GIS Related Terminology
The following represents a brief treatment of [he termi-
nology common to all eyp.. of GIS sofcwa re programs and processes. An attem pt has been made to avoid [he variatio ns of definitions that rend [0 be more descriptive of [he processes related to one (or more) particular GIS software programs. It is important that natural resou rce professiona ls gain an understanding of a common, generi c lan-
change. Many eypes of text fi les are commonly referred to as ASCII files; some co main co mma-delimited data
(e.g., I , 12, 3.45, 65 .2, 0.45), ochers coneain spacedelimi[ed data (e.g., e.g., I 12 3.45 65.2 0.45), and still others use other delimiters
(0
separate individual
pieces of data. See Comma-dtlimited text filt and Space-dtlimited text file.
guage. The lines of communica[ion becween [hose highly versed in GIS and [hose wi[h a cursoey knowledge of GIS
Attribute: A characteristic of a landscape feacure. A((rihutes can be represented by characters or num -
need to be clear when making natural reso urce management dec isions.
bers (o r a combination ofbo[h), and [hey describe var-
Accuracy: The ability of a measuremenr to describe a landscape feacuce 's [rue locatio n, size, or conditio n.
ious characteristics oflandscape feamres. For example, the attributes of a timber sta nd co uld incl ude the following: stand number (o r code). basal area, trees per acre, vo lum e per acre, dominam tree species, and so
Adjacency: A spadal relacio nship indicadng which land-
on. The amibmes of a research plo[ (a poine) could
scape features share boundari es. or are w ith in cenain discance of other landscape fearures. For example, adjacency relationships for timber stands may indicare which stands (Ouch other stands, allowing one to model green -up requiremems. Annotation: Text o r strings of characters and numbers used co describe landscape feamres on a map .
include: smdy eype, insta llacio n date, date last remeasured , dare of nexr remeasure, etc. See Fi~ld.
Arc: A single s[[ing of X,Y coordinates (vercices), [hac when connected, form a line. A singl e lin e may co nrain many arcs, bur a single arc may on ly represem one line. or parr of a line. Arcs have staning and ending nodes (Q allow analys is of directional travel , such as in stream systems, where water emers one end of the arc (the sta rr-node, or 'From-node') an d leaves the other
end of [he arc (the end node, o r 'To-node). ASCII: Ame rican Scandard Code for Informacion lneer-
Azimuth: A degree of a circle, wi[h North being 0° (or 360°), Easc being 90°, South being 180°, and West being 270°. Azjmuthal project jon: A projeccion sys tem where the direct ion s fro m a centra l point of origin are all preserved. Bear ing: An angle of 90° or less originating from either
[he North or Somh (and direc[ed towards [he Easc or West). Thus an azimuth of 353 0 represems a bearing
ofN7°W. Boolean expression : A rype of expression used in a query o r co mputer code that requi res a yes o r no respo nse. AND, OR, and NOT are the three most common Boo lean exp ressions. For example, a query us in g a 264
254
IS Related Terminology Terminology Appendix A G GIS hab itat suitab ili ty index va lues fo forr spec ific timber habitat specific
Boolean expression could take this fo form rm::
stand polygons, with the [he first firsr item of each line identifying the polygon polygon,, and the second item listing the habitat suitab ilility ity index:
llocation stand_age ~ 50 AND land_a land_allocation = 'Even-Aged' BASIC computer code using a Boolean expression exp ression
would th is: wou ld look like this: If (stand_age AND If (sta nd_age ~ 50) AN D (land_allocation = ''Even_aged') Even_aged') Then
Completeness: A description desc ri ption of the eypes types and extent of landscape landsca pe features fearu res included in a GIS GIS database, and
End if Buffering (or buffer): A type of spatial spat ial analysis analys is of proxim i[}',, where zones gene rated imity zo nes of a given distance are genera ted around selected lan dscape feat ures . The result of a landscape features buffering operation is one o r more polygo po lygo ns [hat that reprep~ area within a specific distance (fixed. or or variresem (he the area v'dri-
able, as defined through an amibute attribute field) around landscape features. la ndscape featu res. polygons represent Buffer rone: woe: A set of one oorr more polygo ns that represenT [he rhe area within a specific distance around landscape
feam ces. features. system: A system [hat that allows one Cartesian coordinate system: (Q [Q
locate any point on a planar sur surface divid ed face divided
1,0.657 2,0.433 3,0.298
by a set
of grid lines. Cen: see Grid cell. Cell: a ll. C haracter: A single attri bute describing a landscape feaCharacter: acrribure rure, l, or ru re, such as a le[[cr, letter, number, numbe r, o r special spec ial symbo symbol. or a
type of data that indicates the amibute attribute should be con(evenn though numbers and spesidered a piece of o f text (eve
val id attributes) attri butes) . cial symbols may be valid Clipping process: process: The process of extracting from one GIS database only those landscape features within the GIS anothe r GIS database (wh (which ich could contain bounds of another
a single polygon or a set of polygons) . This is an action act ion that essenrialJy essentially acts like a cookie-currer. thal Column: A A set of cells in a spreadsheet or database that gned aare re vertica align ed , usually representing represeming a ssingle ingl e verr ica ll y ali anribure fearure (reco rd) in the database. database. anr ibure of o f every featu re (record) Columns co could conta in col column that uld co main umn headers, oorr terms rhat describe co lumn,, oorr in spreadsheets, spreadsheers, descr ibe the data in the column ssimp impllyy be represeIHed represented by characte rs such as A, B, C,
etc. Often called ' Fields'; see Field. Related to 'Records'; see Rtcord. Ruord. 'Records'; Combine process: process: A process of eliminating the shared intersections between rwo two landscape features. edges or inrersections Comma-delimited text file: A A text file me created in a word processing system, a {ext r, spreadsheet, spreadsheer, or o r datada tatext ediro editor, in a format base, and saved ina formar where commas separate sepa.rare
items. The fo llowing, for example, could indicate irems.
co nversely, those that are oomirred. m itted . conversely, Conformal projection: A projectio n system whe re angles projecrion on proxio n the th e Earth's surface are rep resented by ap approximap, thus the angles mately the same angles on a map. rel ated to map features related fearures are preserved. Therefore. the po inr on a map us ing this proscale around any single poinr using sa me in every direction . jection system is the same Conic projection: A projection system where a cone is positioned so that it cuts cutS through the Eanh's Earth's surface at ll y the one lat irude, itude, and comes oOut ut at another (usua (usually OntO the equator) equator),, and mapped features are projected OntO surface. based on o n lines radiating outward ourward from fro m cone's surface, the cencer center of the Earrh Earth.. ven ica ista nce that di stinguishes sti ngu ishes Contour interval: The verri call d distance conto ur lines neigh bori ng co nrour lines.. neighboring that are connected, represent Contour lines: Lines (hat connecred. and rep resent locat ionnss of o f equal elevation. el evat io n. loca tio io n system where Cylindrical pro jection C y lindrical projection : A project features cylinder. then mapped featu res are projected onto onco a cylinder, the cylinder is unrolled and the map becomes a planar surface. information Database: A collection of info rmat ion that is managed Database: enticy. A spatial database inand stored as a single entity. cludes information info rm ation regarding the spatial coordinates coo rdinates
res in the database, as well of all of the landscape featu features information attributes of each landas informatio n regarding the atuibutes
usua lly in a tabular form . scape feature, fea ture, usually tabu lar (spreadsheet) (spreadsheer) form. Database conversion: The translario translationn of a database darabase from conven one her. Forr example exa mple,, to co nvert o ne format forma t to anot ano th e r. Fo
Maplnfo wo uld requi re a ArcView shapefiles to Mapln fa tables would require database conversion. conversion. Datum: A mathemat ical represenration Datum: rep rese nration of the Earth's su rrmi ng a contro ich an elli psoid face fo forming controll surface upon upo n wh which ellipsoid location nced.. referenced and oother ther locatio n data are refere Destination table: table: One of twO tables used in a Join operthe information resides after che the atio n, [he ation, the one where [he join operarion. operat ion. In (he linking database the case of lin kin g {WO twO darabase table [hat that is active JUSt just before ~e rable rabies together. it is the tables together,
they are linked. li nked. 265
Appendix A GIS Related Related Terminology
255
D igital elevation model (DEM): A GIS database formed Digital of offeamres fea m res (typically (rypically regularly-spaced as in a grid) grid) [hac that
bounds of anomer another GIS darabase da[abase (which could contain comain a single polygon or a ser ser of polygo polygons). ns). This T his is an action
X.Y X.Y as well as Z (elevation) coordinates.
rhat that essentially essemiaJly acts like a cookie-cutter cookie-curter with objects oucsicle outside the cookie-cutter being retained.
contain
Usually i[ phic da[abase. it represents a [opogra topographic database. Digital orthophotograph: An image. usually a scanned phocograph. [hat [hac has had removed some of [he photograph. me displacemem placemenc normally caused during (he the aerial photophoco-
graphic process (tilt. (tilt, terrain terra in relief) . Digital raster graphics (DRGs): (DRGs) : Digitally Digi[ally scanned represen[ations of USGS 7.5 Minute Minu<e Series Quadrangle sentations maps.
coordina[os of Digitize (digitizing): To record the X.Y coordinates landscape feamres features in a compmer compucer file or system as digital data. Digitizing can occur using a digitizing tablet. tablet, where maps are raped down, down. reference points are
me
Extent: The limits of the locations of all landscape featu res in a GIS database. Coordinates are used [0 tures to define the lower-left and upper-right uppe r-right corners of a rectangular
area tha[ mat would include all of the landscape fearures. feamres. Field: As in ami bure field. a column in a tabular da[abase database a[[[ibme that represents some characteristic characteri stic of the landscape features in that mat database. For example. in a polygon database representing e. rep resenting timber stands. stands, stand volum volume, trees per acre, basal area, area , and dominant dominam species type
could all be fields in a [abular tabular database. In an owllocaowl locadatabase, a field could be developed to tion da[abase. co represent represenc [he firs< sigh[ing. lasc sighting. sigh[ing. a.nd and number of owls ar the first sighting. last
noted, and feacu featu res (points. (paines, lines, or polygons) are then traced traced,. and theif their locations in space are known rhen with some level of precision and accuracy. A looser looser form of digitizing can occur when using the 'heads-up' techn ique, where you use technique. lise your compmcr's compuccr's mouse [Q co delineare delineate landscape feat features ures on a computer com purer screen,
File T Transfer ransfer Protocol (ITP): (FTP): A widely used memod method of transferring data over [he: the Internct, Internet, allowing one [0 to
perhaps wi[h with a digital ormophotograph orthophocograph as a backdrop. lar digiThe heads-up cechnique technique is quicker [han rhan regu regular
Fixed-width buffers: buffers: Buffers tha[ [hat do no[ nOI vary in size and are applied uniformly co to all landscape fea[ures fearures..
tizing, tizing. but usually comes with a COSt COSt reflected in lower precision or accuracy, both.. accuracy. or both Dissolve: me boundaries (a rc. lines) between Dissolve: To remove the (arc,
Flattening ratio: A ratio used to describe [he the difference
adjacenr the fact [hat adjacenc polygons. keying on [he that some of the polygons po lygons have the same value for some attribme, 3nrihure. thus rhus
[hey they should be logically combined. Dynamic segmentation: vector data analysis segmentation: A vec[Qr ana.lysis process that centers cencers on 011 the use of lin linee segments, segments. and anempts atte mpts to link a nerwork necwork of oflines. lines, based on a common attdbattribute,, so mat 3re grouped. grouped or joined into catethat the lines are ute gories of inrerest. interest. measure re of distance east of a coordinate sysEasting: A measu tem rem 's 's origin.
modifying either [he the spatial Editing: The process of modilYing spatial shape or locarion location of landscape fearures. features, oorr the me tabular data that feamre. ma[ describes the attributes of each landscape fearure. Elevation contours: Lines that indicate vertical elevation distances. or changes in elevation, across a landsca landscape. pe. Ellipsoid: A spheroidal figure used co ro describe me the shape
of [he me Earth. Ea"h. Equal area projection: A projection system where the Earth's features are represented on a map using a constant the area of land features is scant scaling factor. thus (he preserved. preserved. p rocess: The process of exrracting extracting from one GIS GIS Erasing process: database only those landscape features outs ide [he the
certain certai n po points ints on the landscape.
transfer compmer computer files to orner transfer other remote computers, or to download com puter fi les fro [0 from m a remore computer.
between the shape of the berween [he Earth and a perfect perfecc sphere. described by the relationship (a-b) /a. where a is the rhe (a-b)/a. equatorial (or semi-major) radius and b is the polar (or semi-minor) radius of the [he Earm. Earth. Font: The rype of <ext Font: text style sryle being used. such as Times New Roman, Arial. Arial, or Cour Co urie ier. r . lr l[ may also represem rypes of symbols commonly used in word prosent programs. cessing or graphics programs. From-node: One of the twO end nodes of a line From-node: Itne or arc. arc, the first one of the (wo twO (hat that was digitized. The mher other is the to-node. Geodesy: An area of mathematics that thar involves determindeterm ining the precise shape and size of Earth features, feat ures. as well as posidons positions of features on the Earth's surface.
Geographic Information Scien Science ce (GIScience) : The identification and study of issues that are related to GIS use, affect its implementat GIS implementation. ion. and that arise from
ics irs application applica[ion (Goodchild. 1992). Geographic Geograpb ic Information System (GIS): A system syscem designed to captu re, store, edit, man ipulate, analyze. capture, manipulate. ana lyze. display. and export data related reiated to geographic geograph ic featu res. display, features, and includes not nor only the hardware and software sofTWare nectasks, but also the dataaccomplish these tasks. essary to accomplish
bases acquired or developed. and the people performing the casks. tasks. 266
256
Appendix A GIS Related Terminology
Geoid: An irregular shape mar that approximates Earth's to the forces of gravicy. gravity. mean sea level perpendicular co Global Positioning System (GPS): A system allowi allowing ng one to locate their position on the Earth's surface su rface by rece receiving iving signals from satellites. satell ites. T The he signals are used (Q to calculate a posicion rrjlarerarion. and perhaps position based on triiarcration. differential differemial correction, processes. Graticule: Graticule: The collecrion collection of meridians and parallels superimposed on the rhe Earth's Ea"h's surface. Grid: A geographic database made up of rasrer raster features, commonly called grid cells. Can also refer to ro a graticule. Grid cell: The smallest unit in a raster ras ter darabasc. database. or pixel. Usually these are represented represemed by squaresi however. however, any regular form fo rm that fully covers an area (square. rectangle, "iangle, triangle, hexagon, polygon, erc.) etc.) could be considered a grid cell. Gross error: Sometimes referred [0 to as human error, it is a blunder or mher other mistake made somewhere in {he the dara data collcedon. collection, map creation, or editing processes. Heads-up digitizing: A A merhod method of developing vector veccor GIS databases darabases (of poines, points, lines, or polygons) where a user creates landscape features using the com purer's puter's mouse, SO as to generally with a raster rasre r image as a backdrop so draw (or point poim out) our) those rhose landscape fear featuures res that rhar are important. For example, displaying a digital orrhophoimporram. orthophotograph as a backdrop, backdrop , one might use the computer's com purer's mouse to draw roads, or timber stands. T This his method of dar daraa development is quicker than rradirionaJ uaditional d. digitizigitiziog, bur usually less accu ing, accurate, rate, and does not nor require a digitizing table. rable. Identity process: The acquisition of information within represe nted by a GIS database concern ing the area represented another GIS GIS database. Here, like with an intersect incersecc onto process, one GIS database is physically laid onco another, yer yet rhe the resulting third anorher, ulird (new) GIS database is defined by the rhe area of coverage of one of (he the inpm input GIS databases. darabases. Intersect process: co nprocess: The acquisition of information concerning the rhe overlapping areas of two rwo GIS darabases. databases. In p rocess, a thi third, GIS database uti lizing mil iz ing an intersect process, rd. new GIS wi where willll be created chat that consists co nsists of only those areas wbere the twO riginal GI GIS databases overlap, no more, no two o original less. Intervisibility: A descriprion description (from (fro m each unir unit of land's perspective) of the number of viewpoints viewpo ints that can be seen from a land unit. Intranet: A network of compucers Intranet: computers similar similar ro to the Internet, [nterner, except access is limited to a set of authorized 3urhoriz.ed people to an organi organ ization). (usually internal inrernal ro zation).
Join process: A process designed co to eirher ei ther (1) ( I) associare associate an th no landscape features) exrernal table rable (wi (with feamres) to a table rable of external a spatial database using a join ite item, m, or (2) to associate features fearu res from one spatial spatial database to another another spada) spatial database, usi using point-in-polygon or nearest neighbor database. ng poinr-in-polygon rechniques. When joining ,ables, tables, the 'Ype type of join can (echniques. be a one-repone one-to-one join, join. a one-tQ-many one-to-many join, a many-tomanY-(Qmany-to-few join join.. o ne join, join) or a many-ro-few Join item: A field (column) in a tabular database darabase that rhar contains similar informatio in formationn as a field (column) in a second database. example, a field in o seco nd tabular tabu lar data base. For example) one ne database may be called 's 'stand_number' tand_number' and contain co ntain timber stand numbers, while a field in a second database may be called 'srand', rimber 'stand', and again contain timber srand stand numbers. While ,he the field names do not have to be [he jnformation within each field must be the same, the informacion similar, such as in this example. where both contain co ntain rimbe r stand srand numbers. numbers. The join would then rhen link the rhe timber two databases using timber stand number twO number as the rhe common element. so data for sta stand nd 1234 from one daradatabase will be matched marched wirh with dara data for srand stand 1234 in the rhe second database. annotation, which is text or strings of charLabels: Like annotation) acters and numbers, these are used [Q to describe landscape features karu res on a map. They us usually ually arise from some anribme field. or column, in the anribure field, rhe spatial da database's tabase's rabtabular database. where for each landscape feature, feature. a description of that arrr attr ibu ibute te (such as srand stand number, ac re, trail rype, volume per acre, eype, road eype, etc.) exists. exiscs. Legend: TIl.r That parr part of a physical map that thar contains con rains the rhe reference informatio informationn necessary to inrerprer interpret [he the colors of polygons, and the rhe sryles styles aand nd colo colors rs of lines and poims. pOlms. Line: A A String string ofX,Y coordinares (vertices) that rhat make up a cominuous cont in uous linear feature. feawre. A single si ngle line feature (a road) can contain comain many small arcs that thar are to ropologipologically linked at ar their rheir nodes (end-points), (end-pointS), since each arc stans starts and ends with a node. Linlc A line thar Link that connects co nnects poims, as defined by nodes and vertices (some (somerimes rimes called caIled an arc). arc) . to ei ther assoc iate Link process: process: A process designed [0 eimer iare landscape features from one database to another database. This process allows the selection and view of landscape features database, and the inspection of associfeawres from database. ared dara in the rhe other database. Sometimes Somerimes ated {linked} (linked) data relare. called a relate. Logical consistency: A desc descrip rip tio rio n of how well the relatrionships ionships of different difFerenr 'Ypes types of dara data fit cogerher together with wir;,in in aa system. 267
Appendix A GIS Related Appendix Related Terminology Logical operators: operators: The operarors 'and', 'or', and 'not'
that rhar allowing one to ro develop a complex query wirhour without having ro to perform several single ccriterion riterion queries in sequence.
Map M ap scale: scale: T he ratio of map distance
[0 £0
ground distance disrance
represented rep resemed on the map. For example, I :250,000 map scale indicates that I un unit it (inch, perhaps) on a map represents 250,000 units (inches) on the ground represented semed by the rhe map. Merge process: prOeeM: A process that rhat creates a single G IS database from a set (or subset) of one or or more morc previouslydeveloped GIS G IS databases. Poinr, Point, liline, ne, and polygon together, however, database databases can be merged rogerhe r. however. types rypes are generally mixed. The resulting merged GIS dacabase may co database comain ntain landscape features feacures (hat that overlap. M etadata: Data that summarize the characteristics of Metadata:
databases (or 'data about data). dara). Network: A co collection ll ection oflines of lines con connected necred via their nodes, representing possible paths from one location loca tion to and represeming another. For example, a stream ne[Work network would include anomer. all of the streams, where where: the smaller headwater Streams streams connect co to wider streams screams with more water water flow. Row, and so the oceans. A road system is on to rivers, and perhaps rhe 3.nQ[her anothe r network char that is more complex in nacu re. re, because there rhere may not be a logical flow Row of traffic from (Q me de termine the next. Yet to £0 determ ine optimum paths one road £0 routes, you would need to £0 know which or alternative roUtes, roads connect (Q ro which other orner roads. roads, what types rypes of roads they are, and what whar restrictions may be placed on them .
Node: One (of two) end-points end-pointS of a line. Northing: Northing: A measure of distance diStance nonh norrh of a coordinate system's origin.
Overlay analysis: The process of analyzing or combining multiple layers of inform information ad on at one time.
Pan: To slide the rhe viewable image to to one side (left, right,
257
Point: Po int: A single X,Y X,V coordinate that represenrs represents a feature feamre on the landscape (such as a research plot, culvert, owl neSt, or feamres are usually deemed tOO roo nest, or spring). These features small (by an organization) co £0 represent as a line or
polygon. Polygon: that has an Polygon: A multi-sided, closed spatial object thar area. Polygons are formed by connecting lines (arcs) uuntil mil a closed area is formed. They may define the boundaries of timber stands, so il s types, riparian
buffers, wildlife habitat, habitar, and so on, using a set of logic consistent across the landscape. Squares. triangles, triangles. rectangles, and hexagons can be considered polygons. polygons, yet ye r
the term rerm usually applies to irregularly shaped objects. Precision: The degree of specificiry specificity £0 to which a measureconsis rency. ment is described. Can also refer to consistency. Public Land Survey System (PLSS): Established in 1785
by the US Congress as a na
established at ar 24-mile imervals intervals east and west of the principle meridian. The grid of meridians and parallels creates blocks, each nominally 24 miles square. Townships are creared created wirhin within each block by forming range lines (running (run ning north and sour south) h) and township townsh ip lines (ru (running nning easr east and west) both at sLx-mi six-mi le interinrer~
vals. Each township £Ownship is six miles square. square. Each township is divided into sections, with each section measuring one square mile; there are 36 secdons sections within a town£Ownship. Sections ship. Secrions are numbered
1-36 sraning starting at the
upper right hand corner of a township. tOwnship. Query: To selecr select a subset of landscape features from a la rger set using some selection criteria. c ri teria. These state-
view of some portion of the landscape not nOt viewable wirh with
ments can include a single piece of logic (stand_age mentS $ S 40), or multiple pieces of of/ogic logic connected by Boolean operators. For examp example, le. suppose you have 10,000 10.000 tim-
the previous arrangement. Actively called caHed 'panning,' or or this action. acrion. 'grabb ing' when users are performing (his The mC'.suremenrs of Photogrammetry: T he act of collecting measuremems
ber stands, ber stands. and you are interested in those chat that could be commercially thinned. rhinned. You could query the rhe GIS database for those stands of a cenain certa in age, with a query age.
landscape fearures features from a image or photograph. Pixel: The smallest unit in a raSter raster database, or o r a grid cell.
such as (stand_age
up. down, up, down. or some combination of these). these), allowing a
Formed Fo rmed by combining the twO fWO terms 'picture' and 'element'. £0. or are Planar coordinates: X,V coordinates that relate co, positioned on on,, a planar or or horizomal ho rizonral surface. Plane coordinate system: sys tem: Recta Rectangular ngular coordinates coo rd inates that
displayed reference landscape features fearures as disp layed on a flat map of rhe the Earth's surface. surfilce.
~
30 AND stand_age S $ 40). 40) .
quite complex. Queries can be rather ramer simple, or quite.
Random error: A narural by-product of how one measures and describes landscape featu res.. No matter marter how fearures will well maps are developed oorr data are collected, error wiU usual ly exist in the representation usually rep resentation oflandscape features. o rganized in rows Raste r: A data structure based on cells organized Raster: This his is a grid-based structure where an and columns. T rep resented by aa cell, an and entire area is represented d a single land268
258
Appendix A GIS R Appendix Related elated Terminology Terminology
scape seape fearure feature (rimber (timber stand, stand. road, road. research plOl) plor) can be represcmed by one or more cell. represented conve nverting rti ng veccor veccoc data to (Q Rasterization: Rasterization.: The process of co raster daca. data, usually by scann ing.
Record Record:: Each fearure feature (poin<. (point, line. line, or polygo polygon) n) in a GIS GIS database is represented represenred by a record in a tabular tahu lar datatabu bular lar darabase, database, thus base. A record is a row in the ta each row represents represems a feamce feacure.. Associated with each record are arc one or morc more fields (columns), which conrain tain (he the character characrc riscics istics of each feature. Region:: A data da(3 structure mat consists Region that consis ts of vector features
(lines or polygons), polygons). yet allowing al lowi ng overlapping areas. Remotely sensed data: Raster dara data acqui acquired red by a sensor se nsor (camera, satellite) that is some d distance istance from the landscape fearures features being sensed. Root mean square error (RMSE): A measure of the error between berwee n a mapped point and its associated nue true grou nd position. Common ground Com mo nly ly used when assess in ingg spacial tial accuracy o r digitizing digitizi ng a map. map , RMSE measu res d1C the posicional error inherent in the registrati registration on po ints ims on the hardcopy map. Satellite SateUite imagery: Dara Da ta eaprured capru red by a remoteremote sensing device housed in a satell satellite ite that is positioned above me Earth's surfuce. surface. Generally, the data consists of values representing represeming the relative degree of reAecrance reflectance of electromagnetic energy in cenain cereain wavelength categories (bands). T he imagery is srored sto red as rasrer raster data dara with a spatia resoludon that can range from 1I meter to 1I spaciall resolution kilometer. Scale: The relarionship relatio nsh ip between a map displayed on a computer screen or primed computer printed on a type rype of media (paper, mylar, etc.), mylar. ere.). and the rhe actual physical dimensions of tbe rhe same area. area . For example, a townsh tow nsh ip (36 sq square uare miles, m iles , or 23.040 23,040 acres) drawn on a map where I inch on the represems 1I mile on the ground, grou nd, is displayed at paper represents represenrs 63.360 a scale where I inch represems 63,360 inches. inches. This scale can then rhen be expressed as a fraction (I :63360) or a equ equivalence ivalence (i" (l" = I mile). excraccing features from a map Scanning: The process of extracting or phorograph photograph and an d sroring storing reAeered reA ccted values generally as a raster database. Line fo follo llowing wing and text recognitio recognition n processes are promising methods melhods for fo r convening converting analog maps to vec(Qr vector GIS databases. Secant projection: A projection system where whe re the Earth's surface intersec ts a map surface in mo re (han intersects marc than one locat ion. Selection Selection:: A set of one oorr more landscape features fearures (poinrs. (poims, lines. lines, or polygo polygons) ns) from fro m a single GIS GIS darabase. database, cchosen hosen based on a Query or by manual methods
(pointing and clicking wirh with a com compu ter mouse). The (poin
forest). fores t). Sbaded Shaded relief map: A map intended to simulare sim ulate the rhe sunsllnlir and shaded areas of a landscape when assuming thaT rhar li t aod the sun itioned at some location in the sky. su n is pos idoned
Slivers: Slivers: Very small polygons thar that result res ul r during du ring overlay operations (union, (u nion , ide identity, nti ty. incersecdon) intersectio n) or during clipping or erasing processes. processes. Sometimes these occur when common borders are represented differendy, differently, from sepa separate dig itizing ing processes, and sometimes rate digitiz simply these occur sim ply as a result ooff the spatial process that was anempred. attem pted.
Slope class class:: The gradien< gradienr of a portion porrion of a landscape. landsca pe, as ddescr esc ribed ibed by a distinct disriner class (e.g (e.g..•, 0-10 per een<. ce n t, 11 - 20 pe perr een<. cent, etc.). ere.). Spaee-delimited Space-delimited text file: A A text rexr file created creared in a word processing sys system, tem , a text ediror, edirar, spreadsheet spreadsheet,, or datadarabase, and saved in a format where items are separated sepa rated
by blank spaces. The following. fo llowing, for example. exampl e, could co uld indicate hab habitat irat su suitability i(abili ry index in dex vaJ values ues fo forr speci specific fic timber timbe r stand polygons, with the first hem item of each eac h line
identifying identifYing the rhe polygon, polygon. and the rhe second irem item listing lisring rhe habitat habitar suitability index: the
10.657 20.433 30.298 Spatial database (or data) data):: A A database containing co ntaining some information about an area or landscape, the relationrelacion-
ships among amo ng the features in the landscape, landscape. and perhaps some tabular or attribute (non-spatial) (non-spacial) data about abom each feature fea mre.. These T hese databases are usually stored in some known coo rdi nate system, thus each la ndscape ates [hat define feature has one or more mo re spat ial coo rdin rdinates that defi ne exists. where it exists.
Splitting process: process: A process of erearing creating mulriple multiple landscape features from a single landscape landsca pe feature.
Spurious polygons: Small fractions of polygons ereared created GIS process. as a result of a GIS State plane coordinate system: coo rdina te sys tem system : A coordinate system developed in the rhe 1930s by rhe the US Coast and Geoderie Geodetic Survey
to
create a unique set creare sel of planar planar coo rdi rdinates nates for
each of the rhe 50 United States. Sures. 269
Appendix A GIS Related Terminology
Systematic error: Sometimes referred [0 as instcumencal error, it is propagated by problems in the processes and [Ools used (0 measure spatial locations or other attribute data. Tabular data: The data in a GIS database that describe the
attributes of each landscape feature. Usually displayed as rows (records) and colu mns (fields), where each row represents a landscape fearure, and each column represents an attribute of that feature. When displayed, a
tabula r database often looks as if it is in a spreadsheet. Tangent projection: A projection system where the Earth's surface (O uches a map surface at one location
(a tangent). To-node: One of the [wo end nodes of a line o r are, the
last one of the twO that was digiti zed. The other is the from-node.
Topology: An exp ression of the spatial relationshi ps among landscape features in a GIS database.
Triangular irregular network (TIN) : A vecto r data model that descri bes the landscape using triangles.
Each corner of each triangle is described by a set of values, such as elevation, aspect, and coordinates. Unj on process: The acquisition of information within twO GIS databases concerni ng the area represenced by both GIS databases. H ere, like with an incersect
process, one GIS database is physically laid onto another, yet the resulting th ird (new) GIS database is defined by the area represented by both of the input GIS databases. Universal Transverse Mercator (lJfM) : The most common coordinate system used in the US, which divides the Eanh into 60 venical zones, each w ne covering 6° oflongitude. The zones are numbered 1- 60 stanin g at
180· longitude (the international date line) and proceeding eastward.
Update interval: The period of time betwee n the performance of subsequenc update processes on a GIS
database. Update process: T he methods used to maintain the current Sta tus and descrip tion of landscape features contained in GIS databases.
Variable-widtb buffers: Bu ffe rs that vary in size based on some anribu te of the landscape features being
buffered. Vector: A data structure common ly used to represent points, lin es, or polygons. This is a coordinate-based stru cture (nor a regular grid), which may not enrirely fill an area, and each landscape featu re is represented by X,Y coord inate pairs. Attributes can be assoc iated
with each feature (point, line, or polygon). Vectorization: The process of converting rasrer data to vectO r data. Verification process: The processes that one wo uld use to find landscape features or attri butes requi ring ed iting. With a verificat ion process, the goal is to ensure that a particular set of data is ap propriate (o r reaso nable, or within some standard)
Vertex (Vertices pl.): One of a set ofX,Y coordinates that delineate an arc o r line.
Viewshed analysis: The process of understanding the pan ions of a landscape visible from specific landscape features of interest. X, Y coordinates: A set of values [hat represent a pain[ in space, relative to the coord in ate system be ing employed. A single X,Y coordinate could represent a point featu re. Lines (arcs) are characterized by a series of X,Y coordinates. Polygo ns are formed by a collecti on ofl ines (a rcs), thus have many X,Y coordinates to define their shape and posi tion on a landscape. Z coordinate: A th ird value associated with an X,Y coordinate usually indicating the elevat ion of the paine in space above some reference (such as mean sea level) . X,Y coordinates do not necessarily need to have a Z coo rdinate to be usefu l, whereas Z coordinates need their X,Y associates to be of use. Zoom: To focus more closely on a smaHer or larger area of a spatial database, or to enlarge o r make smaller an area of a spatial database, showing more or less detail. Zoom-in refers to focus ing mo re closely on a portion of a spatial database. Zoom-out refers to focusing less closely on a portion of a spatial database.
References Goodchild, M.F. 1992. Geographical information science. Internationaljournal o/Geographical information
Spurns. 6(1):31-45. 270
,
259
Appendix B
GIS Related Professional Organizations and Journals Compi/.d by Rongxia (Tiffany) Li
The following is a list of GIS-related professional organization and peer-reviewed journals. We apologize in advance for any om issions. and will gladly add any other o rganizations or journals to the list as they are brought
(0
our attention.
Organizations American Association for Geodecic Surveying. 6 Mont-
gomery Village Avenue . Suire #403 Gaithersburg. MD 20879 USA. (h ttp://www.aags mo.org/) American Association of Geographers. 1710 Sixteenth
Street NW. Washington. DC 20009-3 198 USA. (http: //www.aag.org) American Congress on Surveying and Mapping. 6
Montgomery Village Aven ue. Suite #403 Gaithersburg. MD 20879 USA. (http://www.acs m.net/) American Geophysical Union. 2000 Florida Avenue NW. Washington. DC 20009- 1277 USA. (http:// www.agu.orgl) American Plan ning Association. 122 S. Michigan Ave.,
Suite 1600 Chicago. IL 60603 I 1776 Massachuserrs Ave .• NW. Washington. DC 20036-1904 USA. (http://www.planning.orgl) American Society of Landscape Architects. 636 Eye Sueet. NW. Washington. DC 20001-3736 USA . (http: //www.asla.orgl)
American Sociery for Phomgrammercy & Remote
Sensing. 5410 Grosvenor Lane. Suite 210. Bethesda. MD 20814-2160 USA. (http://www.asprs.orgl) Association of American Geographers. 17 10 16th Street. NW. Washington. DC 20009-3198 USA (http:// www.aag.org/) British Cartographic Society. BCS Administration. 12 Elworthy Drive. Wellington. Somerset. T A21 9AT. England. UK. (http://www.cartography.org.ukl) Canadian Association of Geographers. McGill University. Burnside Hall 805 Sherbrooke Sr. West. Room 425. Montreal. Quebec. Canada H3A 2K6 (http://www. cag-acg.ca/en/) Cartography and Geographic Information Society. 6 Montgomery Village Avenue. Suite #403 Gaithersburg. MD 20879 USA. (http://www.carrogis.orgl) Geog raphic and Land Information Society. 6 Montgomery Village Avenue. Suite #403 Gaithersburg. MD 20879 USA. (http://www.glismo.orgl) Geographical Society of New South Wales. PO Box 162 Ryde NSW 1680. Australia. (http://www.gsnsw.org.aul) Geosparial Information and Technology Association.
14456 East Evans Avenue. Au rora. CO 80014 USA. (http://www.gita.orgl) Management Association for Private Photogrammetric Surveyors. 1760 Reston Parkway. Suite 515. Reston. VA 20190 USA. (http://www. mapps.orgl) 271
Appendix B GIS Related Professional Organizations and Journals Narional Council of Examiners for Enginee ring and
Surveying. 280 Seneca Road. Clemson. SC 296331686 USA. (Imp://www.ncees.org) National Society of Professional Surveyors . 6 Montgomery Village Avenue. Suite #403 Gaithersburg. MD 20879 USA. (http://www.nspsmo.orgi) National States Geographic Information Council. 2105 Laurel Bush Road . Suite 200 Bel Air. MD 21015 USA. (http: //www. nsgic.orgl) New Zealand Geographical Sociery. Department of Geography. The Universiry of Waikaro. Private Bag 3105. Hamilton. New Zealand. (http ://www.nzgs. co.nzl) Remore Sensing and Phocogrammerry Sociery. c/o
Department of Geography. The Universiry of Nottingham . University Park. Nottingham NG7 2RD. United Ki ngdom. (http://static.rspsoc.orgi) Society of Cartographers. Mr Brian Rogers. Membership Secretary. Canographic Resources Unit, Depr of
Geographical Sciences. University of Plymouth. Drake Circus. Plymouth PL4 8AA. Uni ted Kingdom (http: //www.soc.org.uki) University Consonium for Geographic Informacion
Science. PO Box 15079. Alexandria. VA 22309 USA (http://www.ucgis.org) Urban and Regional Information Systems Associacion
(URlSA) . 1460 Renaissance Dr .• Suite 305 Park Ridge. IL 60068 USA. (http://www.urisa.orgi)
Journals Annals of th. Association ofAm"ican Ceographers (http:// www.blackwellpublishing.com/journaLasp?ref=00045608) Asian Ceograph" (http: //geog.hku.hklag/default.htm) Asian Journal of Ceoinformatics (http ://www.a-a-r-s. org/ajglindex.htm) Australian C.ographer (http://www.tandf.co.ukljournals/ carfax/00049182.html) Cartographica (http://www.utpjournals.com/carto/carto. htm1) Cartography and C.ography Information Science (http:// www.cartogis.org/publications) Computational Ceosciences (http://www.springerlink.com/ content/ 1573- 1499) Computm and Electronics in Agricultur< (http ://www. e1sevier.comlloC3te/compag) Computers 6- GloscilllUS (h [{p: //www.e1sevier.com/loca(e/ cageo)
261
Cybergeo. European Journal of Ceography (http: //www. cybergeo.presse.fr/) Environmental Modeling and Softwar< (http ://www. e1sevier.com/loC3(e/envsoft) Geocarto Interna tional (hnp:llwww .geocarro.com/ geocarto.html) Ceographical Analysis (Imp://www.blackwellpublishing. com/journaLasp?ref=OO 16-7363) Ceographical and Environmental Modelling (http://www. tandf.co.uk/jo urn als/carfax/13615939.html) Ceographical Research (Imp://www.blackwellpublishing. com/journaJ.asp?ref= 1745-5863&Site= 1) Ceographical Systems (http://link.springer-ny.comllink/ service/journals/l 0 109Irocs.htm) Ceography Compass (http://www.blackwellpublishing. com/journaLasp?ref= 1749-8198&site= I) Ceol nformatica (http ://www.springer.com/west/home? SGWID=4-40 109-70-35704166-0) CIScimce 6- Remote Sensing (http://www.bellpub.com/mses) IEEE Transactions on Ceoscience and Remote Sensing (http:// ieeexplo re. ieee.org/xpIiRecenrlssue.jsp?punumber=36) International Journal of GeographicaL Information Science
(http: //www.tandf.co.ukljournalsIrf/13658816.html) International Journal of R.mot( Sensing (http: //www. tandf.co.ukljournalslrf/O 1431 161.html) ISPRS Journal of Photogrammetry 6- Remote Sensing (http: Ilwww.itc.nl/isprsjournall) Journal of C.ographic Information and Decision Sci",ce (http://www .geodec.org/) Journal of Ceographical Systems (http://link.sprin ger.de/ linklservice/journals/l 0 109/index.htm) New Zealand C.ographer (http://www.nzgs.co.nzlJournals Online.aspx) Norwegian Journal of CfOgraphy (htt p: //www.tandf. no/ngeog) Photogrammetric Engineering 6- Remote Sensing (http:// www.asprs.org/publications/pers/index.html) Remote Slnsing ofEnvironment (hnp://Wr.vw.clsevier.com/ locate/rse) Spatial Cognition and Computation (http://www.wkap.nl/ journalhome.htm/1387-5868) Surveying and Land Information Systems (http://www. acsm.net/salisjr.html) The Bulktin of the Soci.ty of Cartographm (http: //www. soc.org.uklbulletin /bulletin.html) Th. Canadian Ceographer (http://www.blackwellpublishing. com/CG) The Cartographic Journal (http: //www.maney.co .uk/ journals/carm) 272
262
Appendix B GIS Related Professional Organizations and Journals Appendix
Ceographa (http://www.blackwell The Proftssional Proftssiollal Geographtr Omp: ll www.b lackwell publishing.com/PG) Transactions in GIS CIS (hcrp:llwww.blackwellpublishing. (http://www.blackwellpublish ing. com/journalsltgis) com/jou rnalsitgisl
fnstitllU of BritiJh British Geographm Ceographers (hnp: Transactions of the Imti"''' (h((p: Ilwww .blackwellpliblishing.com/journaLasp? .blackwellpliblishing.com/journal. asp? ref= 0020-2754&si[<= I) 0020-2754&si,e= Il URISA lo.mal Journal (http://www.urisa.org/ (http://www.urisa.org/urisajournal) urisajournal)
273
Appendix C
GIS Software Developers Compiled by Rongxia (Tiffany) Li
The following is a list of organizacions-governmemal. university, and private-that develop and distribute GISrelated software programs. Included are many of the common GIS software programs as well as contact informarion (e.g .• website addresses) for each, however. the list is not exhaustive. In most cases, GIS software programs
must be purchased either from the developers, or from
software diStributOrs, who are nor listed below. Sales representatives associated with (he developers may be able to direct you to a loeru software distributor. We apologize in
advanee for any omissions, and wiil gladly add any other products
to
[he list as (hey are brought
to
our atremian.
GIS Software Program, Distributor, and Website ArcGIS(Environmenta l Systems Research Institute, Inc.
[ESRI], 380 New York Street, Redlands, CA 923738100 USA) http: //www.esri.eom Arclnfo (Environmental Systems Research Institute, Inc.
[ESRI1 , 380 New York Street, Redlands, CA 923738 100 USA) http://www.esri .com ArcYiew (Environmental Systems Research insricuce, Inc. [ESRI], 380 New York Street, Redlands, CA 923738100 USA) http://www.esri.com ATLAS (Environmental Systems Research Insricute, Inc.
[ESRI], 380 New York Sueet, Redlands, CA 923738100 USA) http://www.esri.com Auro CAD (Aurodesk Media & Emertainmem, Mumbai, 400052, India) http://www.aurodesk.com
ERDAS Imagine (Leica Geosystems Geosparial Imaging, 5051 Peachtree Corners Circle Norcross, GA 300922500 USA) http://gi.leica-geosystems.com/LGISubl x33xO.aspx Geomatica (PC! Geomatics, 50 West Wi lmot Street, Richmond Hill, Omario, Canada, L4B IM5) http: //
.
.
www.pclgeomancs.com
GeoMedia (lntergraph Corporation, Huntsville, AL 35894 USA) http://www.intergraph.com GRASS (Geographic Resources Analysis Supporr System) http://grass.itc.i t IORISI (Clark Labs, Clark University, 950 Main Street, Worcester, MA 0 I GI 0-1477 USA) hrrp:/Iwww. c1arklabs.org ILWIS (lmernarional Institute for Geo-Information Science and Earrh Observation, [lTC], 7500 AA Enschede, The Netherlands) http://www.itc.nllilwis/ Manifold System GIS (Manifold Net Ltd., 1945 North Carson Street, Suite 700, Carson City, NY 89701 USA) http://www.manifold.net Map Info (MapInfo Corporation, One Global View, T roy, NY 12 180-8399 USA) http: //www.map info. com MGE Products (lmergraph Corporation, 170 Graphics Drive, Madison , AL 35758 USA) hrrp:/Iwww . inrergraph.com
SuperMap GIS (SuperMap GIS Technologies, Inc., 7th Floor, Tower B, Technology Fonune Center, Xueqing Road, Haidian District, Beijing, China, 100085 ) h[tp: llwww.supermap.com 274
Index accred itation, 234, 249 accuracy. 9-10; relative, 9 adjacency, 45 Albers' equal area projection, 34 allocation distance function, 214 American Society for Phomgrammerry and Remote
Sensing (ASPRS), 245--{j analysis mask, 214 annotat ion , map. 76 arc: overlay process and, 171; vecroc
data and, 46 ArcGIS, 6--7, 8, 213, 219-20 Arisrode, 28 assoc iation: databases and. 144-56
attribure, 38, 49; definition of, 93; editing, 60-1; errors in, 65--{j; selecti ng, 90-9; updating, 167 AVIRlS,229 axis, 30-1 azimuth, 200-1 azimuchal map projection. 32-4
baseline, 37 bearing, 20 I Bernard, A.M., and Prisley, S.P., 23 Berthier, Louis-Alexandre, 5 Bettinger, P., 237, 240 Boisrad, P., et al., 19 bounds, 36--7 Brooks Act, 250 buffering, 92, 106, 119-31; concentric rings and, 125-6;
constam/fixed width, 121, 123, 125; individual , 121-2; out pur of, 127; overlapping, 121-2,124,126,137; uncontinguous/ non-
concinguous, 121-2; variable width, 121, 123-4, 125 buffer zone, 119
cameras, 11 , 13-14 Canada Geographic Information Sysrem (CGIS), 6 Canada Land Inventory, 186--7 Cartesian coordinate system, 30-1 canograms, 84
cartography, 3-4, 5, 72; com purer, 3-4, 5; see also maps Cary, T., 234 catchmems,208-10 caveats: maps and, 79-80 cells; see grid cells
Census Bureau (US), 5--{j Cemral Imelligence Agency (CIA), 5 centroid, 222 certification, 245-52
Clarke Ellipsoid, 30 classification, land, 185-8 clipping process, 106--14 color: DEMs and, 197-8; maps and 77-8,81-2,83 combine process, 132-8, 144 compact discs (CD), 19 completeness, 64 computer aided drafting (CAD) sohware, 3-4 compurers, personal, 7, 19-20,22-3 conformal map projection, 33-4 conic map projection, 32-4 connectivity,45 containment, 45 COntou r intervals, 4 1, 298-9 COntou r lines, 198-200; watershed
delineation and, 208-9
contrasr: maps and, 77-8 control: datum and, 29-30 coo rdinate pair. 45 coordinate systems: geographical,
30-2; negative values in, 34-5; planar, 34-7; rectangular, 34-7; vector data and, 45 coSt weighted distance function,
214-15 Cressie, N ., 63 cylindrical map projection, 32-4 Dangermond, J., 6 data: auxiliary, 227-8; collection of, 8-20, 228-9, 231; elevation, 30, 38-9; field collection of, 16--1 9; in co nsistent, 62-3;
manipulation and display of, 19-20; missing, 62; output devices and, 20-2; sensitive,
242; data, soils, 186; spatial, 3, 9-10; storage technology and, 19 databases: acquiring, 54-7, 241-2; associating, 144-56; auxiliary
data and, 227-8; clipping, 106--14; combining, 144; conversion of, 218-29;
creating, 10-20,57-9; editing, 59--{j3; erasing and, I 14-16; format of, 55; high resolution, 228-9; joining, 144-53, 154; linking, 144, 153-4; merging, 132,137-8,140-2; official, 134; overlay with point and line, 178-80; ownership of, 239; permanent, 153; proprietary, 109, 238; raster, 275
Index 89. 197-224; relating. 144. 153-4; resolution of. 51. 228-9; scale of. 51; sharing. 237-40; soils. 110-11; sparial. 27-53; temporary. 144; terminology for. 3; updating. 59-63.157-69.229-31. 239-40; vector. 89. 202-5; see also database struc[Ure database managemem, 3-4 database struc[Ures, 38-50; alternate. 48-50; conversions
and . 218-19; raster. 38-44. 47-8.49; vector. 38. 44--8. 49 datum. 29-30. 42; vertical. 30 degrees. 31-2 density functions. 216-17. 221 - 2 density surface. 221-2 Oem. B.D .• 82 Descartes. Rene. 30-1 de Steiguer. J.E.• and Giles. R.H .• 2 desrination rable. 145 differential correction, 17
digital elevation model (OEM). 13. 16. 38-9. 197-211 digital line graph (OLG). 6 digital orthophotographs. 15-16. 39-40; updating and. 166-7 digital orthophoto quadrangle (OOQ). 39. 231 d igiral raster graphics (ORGs). 40-3.231 digital versatile discs (OVO). 19 digitizing. 57-8. 163; 'heads-up'. 40.57.58.163; manual. 10. 57- 8 OIME (Dual Independent Map Encoding). 6 direction, 201; map. 73; flow,
209-10 disclaimers. 233; maps and. 79-80 dissolve process. 133; see also
265
Earth: shape and size of. 27-8 eastings. 34--5 education. GIS. 234. 246-7, 249 Edwards. D .• 80-1
Galileo satellite sYStem. 18 geodesy. 28 Geodetic Reference System of 1980 (GRS80). 29. 30
electronic distance measuring
geographic information science and
dev ices (EOMs). 16 elevation, viewing. 206 elevation camours, 198-200 ell ipsoid. 29-30 engineer ing: v. GIS users, 246. 247-50 ENVI.227 Environmental Systems Research
Institute (ESRI). 6-7. 8 equal area map projection, 33--4 'erasing outside', 108
erasi ng process. 107. 114--16; merging and. 140 Eratosthenes, 28
Erdas Imagine. 227 errors: a[(cibure, 65-6; co mpensating. 64; cumulative,
63; database. 59-60. 62-7; definition of. 64; gross. 63; human, 63; instrumental,
63; multipath. 17-18; positional. 64--5; random. 63-4; roer mean square, 65. 66; syntax. 102; sysrema ric, 63 ERSI grid. 220 event themes, 49 Federal Acquisit ion Regulation
(FAR). 250 fiducial marks. 14 field offices: GIS capabilities and. 229-30.239-40 File Transfer Protocol (FTP). 56 flattening ratio, 29
technology (G IST). 247 geographic informacion systems
(GIS) : applicarions of. 2. 7-8 ; definition of. 3-4; hiStory of, 4-7; natural resource management and, 2, 7-8;
technology of. 8-23. 226-36 geographic information systems
specialiSt (G ISP). 246 geographical coordinate system,
30-2 Geography Network Canada. 55 geoid. 29. 30 global posirion ing sYStem (G PS) , 11-13. 16-19; errors in. 17-18; receivers fo r. 18- 19 GLONASS (Global Navigation Satellite SYStem). 18 Google Earth. 7. 227 graticule. 3 1. 32 gray tone: OEMs and. 197-8 grid cells. 38. 48. 217-18; OEMs and. 197-8; null. 198; size of. 58. 66; slope class and. 201.205 grid cell resolution. 214. 219 grid cell search functions. 215-16 habitat: definition of. 191 ; wildlife. 185, 19 1-2 habitat suitability index (HSI). 185. 191-2 high resolution databases. 228-9 'hot spots'. 216-1 7 hyperlinks. 227-8
flight line, 14--15
distance functions. 214--15, 220-1 dimibutions. 82-3
2 19 Aow direction. 209-10 focal searches. 2 15 font: maps and. 77
identity process. 170. 174-5. 178-8 1 images. graphic. 22 input devices. 10- 20 insets. map. 75-6
Dominion Land Survey. 37
Freedom of Information Act
Internet: databases and, 55-7; GIS
Doyle. R.• 248
(FOIA). 233. 242 fu nctions. 214-17. 220-2
and. 7. 230-1; open. 234 interoperab ility. 234
combine process
dynamic segmentation. 49-50
float ing point raster databases,
276
266
Index
imersect intersect processes, 170, 171-4.
178-8 178- 811 intervals: imerva ls: contour, 41,198-9; 41, 198-9: maps and, 82-3, 82-3; update, 159 intervisibility, 205 inrervisibiliry.205 Imranet,229 Intranet, 229 inven invert selecdon selection technique, 97-9
join itemlfield, item/field, 145 join jo in process, 144-53, 154, 154; nearest neighbor, 150; ISO, non-spatial and spatial databases and and,, 145- 56; 56, point-in-polygon, 150-1; 150-1, tabu lar atrributes tabular attributes and, 167 journals, GIS, 7, 261-2 Kaya, N ..., and Epps, H.H., H .H ., 77-8 77- 8 Kleiner, Kieine r, M., 248 Lambert Lamberr conformal conic projection.
33--4,35-6 33-4,35-6 landmark bui buildings, ldings, 43 Landsat Thematic Mapper, II landscape features: features: contiguous. contiguous, similar, 135-6: discontiguous discontiguous,l sim ilar, 135-6;
136-7; overlapping, 137- 8; 136-7, 8, selecting, 90-105 90- 105 latitude, 31 , 32, 35 legal issues, 232--4 232-4 legend: interval, 82-3, 82- 3; map, 74-5 liability, liabiliry, 232-3, 241 - 2 licensing: data dara products and,
233--4, professionals and 233-4; and,, 246, 247-9 LiDAR (light detecrion detection and ranging), 11 -l3, -13, 238 line, 44-5, 47 link,46 link, 46 link process, 144, 153-4, 153--4, 171 local searches, 2 15 'local shapes', 65 consistency, 64 logical consisrency,
logical operators, 94-5 longitude, 3 1, 32, 35 longirude,
l., 5, 170 McHarg, I., Manageme nt Association for fo r Private Privare Management Ph orogrammetric Surveyors Phorogrammetric
(MAPPS),249- 50 (MArrS),249-50
Manual of Federal Geographic Geogtaphic Data Products, 55 maps: anci ancillary information and. maps: llary informacion 78-9, 78-9; ch loropeth, loroperh, 8 1-2; 85-6; common problems of, 85-6, components of, 72-80, 72- 80; conrour, contour, 83; 83, design of, 71-88; design loop and, 85, 85; digitizing of, 10; dot-density, dot-densiry, 84; 84, graduated circle, 84; projeccion projection and, 27, 28, 32-4, 32--4, 37; ras<erraSterbased, 84; reference, 80-1, 80-1; scale of, 15, 17; shaded relief, 200-1; slope ciass, class, 201-2; rhematic, 81-4; thematic, 8 1--4; types rypes of, 80-4 80--4 Mapping Scientist, ScientiSt, 246 I SO, mapping unit: joining and, 150; minimum, minimum , 133, 173 MapQuest,77 MapQuest, Mercaco[ projeccion, Ivterc3co[ projec tion , 33--4 33-4 merge process, 132, 137-8, 137- 8, 140-2 meridian meridian:: prime, 31; principal, 37 Merry, K.L., K.L. , er et aI. ai.,, 8 meradata, 49, 55, 56-7, 67, 241 metadata, meres, metes, 36-7 military mili tary grid system, sYStem, 35 minutes. minutes, 31-2 31 - 2 Model Law, 247-8 Naesser, E., and JonmeiSter, jonmeisrcr, T T..,, 19 National Na tional Council of Exami Examiners ners for Engineering and Surveying (NCEES), 247-8 (NeEES),247-8 National Geodetic National Geodet ic Verrical Venical Datum of 1929 (NGVD29), 30 National Map Accuracy Standards (NMAS), 41, 43--4 43-4 National Soil Database of Canada, Canada, III Natio National nal T Topographic opographic Data Base (NTDB),40 (NTDB), 40 Natoli, J.G al., 237 J. G..,, et ai., Natural Resources Canada. Canada, 55 NAVSTAR (Navigation Satellite Tracking and Ranging), 18 neadine, map. map , 76
neighborhood search func funcrions, tions)
215- 16 Newton, Isaac, 28-9 Newmn.
categoty, 198 'No Data' category, node, 45-6 node,45-6 North American Datum of 1927 (NAD27),30 North American Datum of 1983 (NAD83), 30, 42 Nonh North Ame American rican Vertical Datum Darum of
1988 (NAVD88), 30 northings, 34-5 Odyssey GIS, 6 Ohio code system, 42 Onsrud, H.J., H.J ., 233 Open O pen Geospatial Geosparial Consortium, 234 organizations: GIS use and, 237-44; professional GIS, 260-1 260- 1 ooutput utpU[ devices, 20-2 overlay analysis, 3, 106, 170- 83; manual, 4-5, 170; null 111111 cells and, 198 parallels: parallels: PLSS and, 37 personal digiral digital assistam assistantss
(PDAs), 231 (rDAs),231 Peucker, T .K., .K., and Chrisman, Peucker. N .,6 Phillips, Phill ips, R.J., et ai., ai. , 77 photogrammetry, 13-16; photogrammerry, 1316; analytical, 15 IS phorographs, photographs, 13-16; 13- 16; scale of, 15; IS, vertical/oblique, 14; 14, see also digital onhophotographs orthophotographs pixels: rasrer raster data and. and, 38 planar coo coordinate rdinate systems. systems, 3434-7 7
plotters, plorrers, 20-1 20- 1 poinrs, 44-5 points, 44-5,, 47 polygo ns: overlapping, 137-8; polygons: regions and , 50; spurious, spurious. 133, 173; Thiessen, 214; u ncon ti nguousl uncontin guous/nonno nconringuo us. 121-2; vecto comi nguous. vec[Qrr data and, 44-5, 47 Position Posit ion D Dilurion ilution of Precision
(PDOP), (PDO r ), 17 poim, 209 pour point, precise point paine positioning. 18 precis io n. 9-10; relative. 9 precision. 9-1 OJ relative, iques'., 228-9 'precision techn techniques'
printers, primers, 20- 1 277
Index
privacy: as legal issue. 232-3 projections. map. 27. 2B. 32-4. 37
proximiry analysis, 120; su also buffering Public Land Survey System (PLSS). 37.43 public relations. 205 Pythagoras. 2B
satellite data collection. 11-12. 16--17. lB. 3B. 22B-9
scale: map. 74; photOgraphs and maps. 15 scann ing. 10-11.57. 5B screen displays. 21-2 secant map projection. 33
seconds. 31-2 sections: dynamic segmentation and .
Quadrangle maps; see USGS Quadrangle maps Qualifications-Based Selection (QBS).250 queries. 90-1. 92-102.106; advanced. 102; combinations of. 9B-9; complex. 95; dynamic. 102; hierarchical. 95-7; multiple criteria. 94-5; single criterion. 94. 95-7; spatial. 99- 101 ranges: PLSS and. 37 raster database analysis. 197-224; software parameters for.
213-14 raster database structure, 38-44, 47-B. 49; vectO r databases and.
202-5 raster map algebra. 21B raster reclassification, 217-18 raster resampling. 219 receivers. GPS. IB-19 Recreational Opponuniry Spectrum (ROS). IB5. IBB-91 reference points. 57-B reflectance intensity. 12-13 region data system. 49 regions. specific geographic. 106--IB relate process; see link process femme sensing. 3-4,11-13.38; see
49; PLSS and. 37
267
statistics, 3-4; zo nal. 216
STATSGO soils database. III stewardship classes. IB6. IB7 straight line distance function. 214
Structured Query Language (SQL). 101 surveying: v. GIS users. 246. 247-50 Sustainable Forestry Initiative (SFI). I B6
symbology. map. 72-3
selection : landscape fearuces and,
symax errors: queries and, 102
90-105 selective avai lability (SA). IB shaded relief maps. 200-1 shortest path distance function, 215. 220- 1 Sigrist. P.• et al.. 19 sink. 209. 210 slope class. 201-5 snapping. 137 Snow. Dr John. 5 software: computer aided drafting. 3-4; computer mapping. 5; desktOp. 7. 22-3; developers of. 263; GIS. B. 22-3;
synthesis: GIS processes and. IB4-96
integrated vectO r/raster. 226-7; maimenance charges and . 23; map projecrions and. 37; raster
ana lysis and. 213-14; vector data and. 46-7j workstation.
22-3
tables: joining and. 145. 153 tabular output. 22 tangem map projecdon. 33
target table. 145. 153 TerraServer. 7
Thiessen polygon. 214 TIGER (Topologically Integrated Geographic Encoding and Referencing System). 6 wpo logy. 6; edit ing. 61-2; vector
data and. 45-7 Tourism Opportunity Spectrum, IBB
townships. 37 Triangular Irregular Network
(TIN). 48-9 typography. map. 76--7 Tyrwhi((. Jacqueline. 5
soil survey geographic database
(SSURGO). III source table. 145 Space Based Augmemation Systems (SBAS) . IB
Spatial Analyst. 2 13. 219-20 spatial data. 3; quality of. 9-10 specific geographic regions:
definition of. 106; obtaining information about. 106--IB
union processes. 170. 175-B. lBO- I universal polar sterographic (ups) system. 35 Universal Transverse Mercawr
(UTM).33 Universiry Consortium for Geographic Information
Science (UCGIS). 245 'un-select'. 92 update process, 157-69; reasons
also satellite data collection Rempel. R.S .• and Kaufmann. e.K.. 191
spectrum. e1ectromagnecic. 13 spli((ing process. 132. 136. 13B-40
standards: data exchange and. 231 - 2
Urban and Regional information
restricted/unrestricted areas, 115.
standard deviacions. 204 state plane coordinate system (Spe ).
Systems Association (URlSA). 246 US Geological Survey (USGS). 6. 39-42.49- 50; 30 meter OEMs. 197; 7.5 Minute Series
119.140--1 riparian areas: definicion of, 122 root mean square error (RMSE),
65. 66
35-6 statist ical summary search functions,
215-16
for. 158
278
268
Index
Quadrangle maps, 39-43, 57-8 57-B Valdez, Va ldez, P., and Mehrabian, A., 78 7B vecto r databases, 89, B9, 202-5 vecto r database struccure, vector st ruccu re, 38,
44-B,49 44-8,49 verification processes. processes, 59-60
ve vertex, rtex, 45-6 viewshed ana analysis, lysis, 205-8 205-B wa rranries: maps and, warramies: and . 79 watershed delineation warershed delineat ion,, 20820B-IO 10 Wide Area Augmentar Augmenration ion System Sysrem (WMS) (WAAS) , 18 IB Wing, M.G., and Karsky, R., 19
Wing, M.G., M.G. , er et aI., 19 works ratio ns, 19-20, 22-3 workstations, 'World Data Bank', 5 World Geodetic System of 1984 (W DSB4), 29, 30 (WDS84), zonal sratisrics, zonal stat istics, 216
279
