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Dozens of persons were employed developing new tools, building digital databases, assisting with complex applications and fielding the technology across the Department of Defense.

Tasks requiring days and months with paper and acetate overlays could be accomplished with this newly emerging geographic information technology within minutes. These installa- tions include millions of acres of lands needed for military training and testing.But even in the mid-1980s, GIS technology involved signifi- cant capital investment. Army Construction Engineering Research Eaboratory (CERE) in Champaign, Illinois has the mission of developing and infusing new technolo- gies for managing U. Other uses included wildlife management, hunting and fishing and forestry, grazing and agricultural production.OPEN SOURCE GIS A GRASS GIS Approach Second Edition Markus Neteler and Helena Mitasova Open Source GIS: A GRASS GIS Approach OPEN SOURCE GIS: A GRASS GIS APPROACH Second Edition MARKUS NETELER ITC-irst - Centro per la Ricerca Scientifica e Tecnologica, Italy HELENA MITASOVA North Carolina State University, U. Print ©2004 Kluwer Academic Publishers Boston All rights reserved No part of this e Book may be reproduced or transmitted in any form or by any means, electronic, mechanical, recording, or otherwise, without written consent from the Publisher Created in the United States of America Visit Springer's e Bookstore at: and the Springer Global Website Online at: our friends and to all GRASS developers, present and past Contents List of Figures xiii List of Tables xix Foreword xxi Preface to the First Edition xxv Preface to the Second Edition xxvii Acknowledgments xxix 1. KLUWER ACADEMIC PUBLISHERS NEW YORK, BOSTON, DORDRECHT, EONDON, MOSCOW e Book ISBN: 1-4020-8065-4 Print ISBN: 1-4020-8064-6 ©2005 Springer Science Business Media, Inc.PROCESSING OF AERIAL PHOTOS 253 10.1 Brief introduction to aerial photogrammetry 253 10.2 From aerial photo to orthophoto 257 10.3 Orthophoto generation 257 10.3.1 Aerial photo and LOCATIONS preparation 258 10.3.2 Orthophoto generation from vertical aerial photos 260 10.3.3 Generating orthophotos from oblique aerial photos 266 10.4 Segmentation and pattern recognition for aerial images 268 11.

NOTES ON GRASS PROGRAMMING 271 11.1 GRASS programming environment 271 11.1.1 GRASS source code 272 11.1.2 Methods of GRASS programming 273 11.1.3 Level of integration 273 11.2 Script programming 274 11.3 Automated usage of GRASS 280 11.4 Notes on programming GRASS modules in C 282 12.

GRAPHICAL OUTPUT AND VISUALIZATION 177 8.1 Two-dimensional display and animation 177 8.1.1 Displaying map layers using the GRASS monitor 177 8.1.2 Creating a 2D shaded elevation map 180 X OPEN SOURCE CIS 8.1.3 Monitor output to PNG and HTML files ('(|') 181 8.1.4 Animations in 2D space 183 8.2 Visualization in 3D space with NVIZ 184 8.2.1 Viewing multiple map layers 184 8.2.2 Querying and analyzing data in nviz 189 8.2.3 Creating animations in 3D space (f|") 191 8.2.4 Visualizing volumes ('ft') 195 8.3 Creating hardcopy maps 196 8.3.1 Map generation with 196 8.3.2 Map design with Xfig and Skencil 198 9.

SATELLITE IMAGE PROCESSING 201 9.1 Remote sensing basics 201 9.1.1 Spectrum and remote sensing 201 9.1.2 Satellite sensors 203 9.2 Satellite data import and export 206 9.2.1 Import of raw and geocoded satellite data 206 9.2.2 Export of multi-channel data sets 209 9.3 Understanding a satellite data set 209 9.3.1 Managing channels and colors 209 9.3.2 The feature space and image groups 213 9.4 Geometric and radiometric preprocessing 215 9.4.1 Geometric preprocessing 215 9.4.2 Radiometric preprocessing 222 9.4.3 Application: Deriving a surface temperature map from thermal channel 228 9.5 Radiometric transformations and image enhancements 231 9.5.1 Image ratios 231 9.5.2 Principal Component Transformation ('fl') 231 9.6 Geometric feature analysis 233 9.6.1 Matrix filter: Spatial convolution filtering 234 9.6.2 Edge detection 236 9.7 Image fusion 237 9.7.1 Introduction to RGB and IHS color model 237 9.7.2 RGB color composites 238 9.7.3 Image fusion with IHS transformation 239 9.7.4 Image fusion with Brovey transformation 241 9.8 Thematic reclassification of satellite data 242 9.8.1 Unsupervised radiometric reclassification 245 9.8.2 Supervised radiometric reclassification 248 9.8.3 Supervised SMAP reclassification 25 1 Contents xi 10.

1 GRASS Development Model 4 2.1 Data models in GIS: raster, vector, point data and attributes 8 2.2 Data dimensions in a GIS 11 2.3 Earth’s surface representation in map projections and coordinate systems 1 5 2.4 Example for Gauss-Kriiger Grid System 20 3.1 Organization of GRASS DATABASE, EOCATIONs and MAPSETs 26 3.2 Graphical startup of GRASS 29 3.3 GRASS used in the KDE environment on GNU/Einux 30 3.4 Spearfish soil raster map with overlayed vector streams and archeological sites 31 3.5 GRASS text-based startup screen 34 3.6 Definition of a xy and a projected EOCATION 36 3.7 Definition of a region for xy EOCATION suitable for importing an image or scanned map 45 4.1 Types of raster data 54 4.2 Sample workflow to import GIS data and to geocode scanned maps 63 4.3 Geocoding of a scanned map 65 4.4 Vector types in GIS: vector line and vector area 68 5.1 “Moving window” method for neighborhood opera- tions in raster map algebra 101 5.2 Modules for transformation of different types of raster data to vector representation 106 5.3 Difference between resampling and interpolation 109 XIV OPEN SOURCE CIS 5.4 Map composite of roads, land use map and elevation model 116 5.5 Raster data merging 117 5.6 Spearfish noise impact map from interstate (simple noise buffer model) 119 5.7 Visibility impact analysis of sample windpower plant east of Spearfish 128 5.8 Simplified planning procedure to find a location for a windpower plant 129 6.1 Digitizing common area boundaries in a topological GIS 136 6.2 The node snapping function in GIS 137 6.3 “Overshoots” and “undershoots” in vector maps 138 6.4 Correction of “spaghetti digitizing” 139 6.5 Possible results of intersecting vector data 144 6.6 Methods for transforming and interpolating vector data to raster and site data 146 6.7 Interpolation of raster map layer from vector data (contours) 148 7.1 Selecting subsets of site data 155 7.2 Conversion of site data to raster for discrete and con- tinuous phenomenon 158 7.3 Interpolation methods available in GRASS and the re- sulting surfaces 159 7.4 Tuning the character of interpolated surface by tension parameter 162 7.5 RST interpolation with anisotropy 163 7.6 Impact of constant and spatially variable smoothing 164 7.7 Segmented processing of large data sets 167 7.8 Surface created from raw LIDAR data 168 7.9 LIDAR data interpolated at 1 m resolution 170 7.10 Interpolation of a surface with fault representing an edge of a gully 172 7.11 Interpolation of precipitation with influence of topography 173 8.1 Map display with d.

frame: three frames with shaded DEM, soils and geology map 178 8.2 Shaded elevation maps with different sun azimuth angles 180 8.3 Spearfish geology map draped over a DEM with over- layed streams and roads as vector data, and archaeolog- ical and insect collection sites as point symbols (pyra- mids and spheres respectively) 186 List of Figures XV 8.4 Displaying topography at multiple resolutions con- trolled in the upper part of the Surface menu, using multiple, masked-out surfaces 187 8.5 Interactive control of light aided by a sphere 189 8.6 Interactive 3D query of elevation surface with slope map draped as color 190 8.7 Viewing multiple surfaces next to each other or in their relative position with a cutting plane (elevation surface before and after construction) 191 8.8 Fly-by animation menu in nviz 192 8.9 Volume (3D grid) visualization integrated in nviz 195 8.10 3D p H values displayed in Vis5D visualization tool 196 9.1 Distribution of solar radiation (reflective portion of the spectrum) on upper boundary of atmosphere and at earth’s surface with gaseous absorption 203 9.2 Idealized reflection curves of green vegetation, sandy soil and water with LANDS AT-TM5 channel filter functions 204 9.3 Color functions for density slicing of grey scale images 211 9.4 Pixel in a three-dimensional feature space 213 9.5 Spectrum showing typical spectral response of com- mon objects with LANDSAT-TM5 channels and distri- bution of pixel brightness levels in a two-dimensional feature space 214 9.6 Geocoding of a satellite image to raster/vector refer- ence maps 218 9.7 Pattern-overlay to verify the geocoding accuracy of a satellite image to a raster reference map 221 9.8 Incident angle geometry related to direct solar irradia- tion onto a tilted surface 225 9.9 Example for cosine correction of terrain effects with uncorrected and illumination corrected SPOT-1 PAN image 227 9.10 Multispectral pixel values shown as standardized data vectors with related first and second orthogonal princi- pal component vectors in polar and coordinates view 232 9.11 Principal Component Transformation applied to chan- nels tm3 and tm4 of a LANDSAT-TM5 data set 233 9.12 RGB (red, green, blue) cubic color space and IHS (in- tensity, hue, saturation) hexcone color space 237 XVI OPEN SOURCE CIS 9.13 Exo-atmospheric solar radiation and relative spectral sensitivity of LANDS AT-TM5 channel filter fnnctions 238 9.14 Geometric resolntion improvement of LANDS AT- TM7 data (IHS image fnsion method) 240 9.15 Standard RGB composite of SPOT-1 HRV channels and image fnsion of SPOT-1 HRV channels (20 m) with SPOT-1 PAN (10 m) with Brovey transformation 241 9.16 Unsnpervised and snpervised classification procednres for mnltispectral data 243 9.17 Sample screen of interactive training area identification 249 10.1 Aerial photo terminology 254 10.2 Terrain mapping to a map plane and an aerial photo plane 255 10.3 Zoomed fidncial mark in an aerial photo 259 10.4 Lidncial marks in aerial photos 263 10.5 Attitnde angles of an aircraft 267 12.1 LS factor for Spearfish area compnted at 30 m, 15 m and 10 m resolntions 291 12.2 Sample from the USGS seamless National Elevation Data set 304 12.3 Interpolating DEM from contonrs: profile cnrvatnre displayed with inpnt contonrs 309 12.4 High resolntion DEM interpolated from 2 ft contonrs with bnildings 310 12.5 Elow accnmnlation maps based on D8, vector-grid (D- infinite), and mnltiple directions algorithms 3 14 12.6 Proposed grassways 321 13.1 gstat/GRASS: Semivariogram of zinc contaminations of the Maas river bank soil samples 331 13.2 gstat/GRASS: Ordinary kriging prediction of zinc con- taminations on the Maas river bank 332 13.3 R/GRASS: Cnbic trend snrface of p H valnes in Spearfish region 338 13.4 R/GRASS: Boxplot of soil type distribution against el- evation in Spearfish region 340 13.5 R/GRASS: Empirical cnmnlativedistribntionfnnction (ECDE) plot, integer elevation model 341 13.6 R/GRASS: Empirical cnmnlative distribntion fnnction (ECDE) plot, reclassified elevation model 342 13.7 R/GRASS: Density plot of Spearfish elevation data 343 List of Figures xvii 13.8 R/GRASS: Maas river bank soil data: plots of zinc con- tamination 345 13.9 R/GRASS: Maas river bank soil data: zinc contam- ination - contamination severeness, flood frequency classes, histograms, histograms of logarithmic trans- formed data, QQ plots 347 13.10 R/GRASS: Maas river bank soil data: zinc contamina- tion 2D kernel density 350 13.11 R/GRASS: Maas river bank soil data: Distribution of power-transformed zinc contamination data (various exponents) 351 13.12 Screenshot of GRASS / UMN/Map Server demonstra- tional Web site 355 13.13 Sample UMN/Map Server implementation model 357 List of Tables 2.1 Standard ellipsoids as used in various countries 14 2.2 Selected projections used in various countries 17 3.1 GRASS GIS functionality 24 3.2 GRASS module function classes 27 9.1 Classification methods in GRASS 252 Foreword William D.

At the time, this focus set GRASS apart from marketplace capabilities, which were primarily based on vector data and tools and did not include image analysis.