E-Book, Englisch, 370 Seiten
McInerney / Kempeneers Open Source Geospatial Tools
2015
ISBN: 978-3-319-01824-9
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
Applications in Earth Observation
E-Book, Englisch, 370 Seiten
Reihe: Earth Systems Data and Models
ISBN: 978-3-319-01824-9
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
This book focuses on the use of open source software for geospatial analysis. It demonstrates the effectiveness of the command line interface for handling both vector, raster and 3D geospatial data. Appropriate open-source tools for data processing are clearly explained and discusses how they can be used to solve everyday tasks.A series of fully worked case studies are presented including vector spatial analysis, remote sensing data analysis, landcover classification and LiDAR processing. A hands-on introduction to the application programming interface (API) of GDAL/OGR in Python/C++ is provided for readers who want to extend existing tools and/or develop their own software.
Autoren/Hrsg.
Weitere Infos & Material
1;Foreword;8
2;Preface;10
3;Acknowledgments;14
4;Contents;16
5;Acronyms;21
6;Command Line Utilities;23
7;Part I Geospatial Data Processing withGDAL/OGR;26
8;1 Introduction;27
8.1;1.1 Introduction to Geospatial Data;29
8.2;1.2 Projections and Coordinate Reference Systems;30
8.3;1.3 Spatial Data Models;30
8.4;1.4 Earth Observation Data;31
8.5;1.5 Software Tools Covered in the Book;34
8.5.1;1.5.1 Geospatial Visualization Tools;35
8.6;1.6 Structure of the Book;39
9;2 Vector Data Processing;43
9.1;2.1 Vector Data Model;43
9.2;2.2 OGR Simple Features Library;45
9.3;2.3 ogrinfo;46
9.4;2.4 ogr2ogr;51
9.4.1;2.4.1 Manipulating Data;56
9.5;2.5 ogrtindex;61
9.6;2.6 OGR Virtual Format;63
9.7;2.7 Spatial Databases;69
9.7.1;2.7.1 PostGIS;70
9.7.2;2.7.2 Spatialite;70
9.7.3;2.7.3 ogr2ogr with Spatialite;73
10;3 Raster Data Explained;75
10.1;3.1 Coordinate Reference Systems;76
10.2;3.2 Single and Multi-band Images;80
10.3;3.3 Complex Datasets;81
10.4;3.4 Raster Data Types;82
10.5;3.5 Raster Data Encoding;84
11;4 Introduction to GDAL Utilities;85
12;5 Manipulating Raster Data;87
12.1;5.1 gdalinfo;87
12.2;5.2 gdalmanage;92
12.3;5.3 gdalcompare.py;93
12.4;5.4 gdal_edit.py;94
12.5;5.5 gdal_translate;96
12.5.1;5.5.1 Convert and Scale Rasters;99
12.5.2;5.5.2 Subset Rasters;100
12.5.3;5.5.3 Change Raster Attributes and Encoding;101
12.5.4;5.5.4 Compress Rasters;103
13;6 Indexed Color Images;105
13.1;6.1 rgb2pct.py;106
13.2;6.2 pct2rgb.py;107
14;7 Image Overviews, Tiling and Pyramids;109
14.1;7.1 gdaltindex;110
14.2;7.2 gdaladdo;113
14.3;7.3 gdal_retile.py;115
14.4;7.4 gdal2tiles.py;118
15;8 Image (Re-)projections and Merging;122
15.1;8.1 Introduction on Projection and Image Merging;122
15.2;8.2 Resampling;123
15.3;8.3 gdalwarp;127
15.3.1;8.3.1 Reproject Images;132
15.3.2;8.3.2 Warp Images;133
15.3.3;8.3.3 Mosaic Images;134
15.3.4;8.3.4 Clip Images;135
15.4;8.4 gdal_merge.py;137
15.5;8.5 nearblack;141
15.6;8.6 gdaltransform;142
15.7;8.7 gdalsrsinfo;145
15.8;8.8 gdalmove.py;147
16;9 Raster Meets Vector Data;151
16.1;9.1 gdal_sieve.py;151
16.2;9.2 gdal_polygonize.py;152
16.3;9.3 gdal_rasterize;155
16.4;9.4 gdal_contour;159
17;10 Raster Meets Point Data;162
17.1;10.1 gdal_grid;162
17.1.1;10.1.1 Interpolation Methods;166
17.1.2;10.1.2 Data Metrics;166
17.2;10.2 gdallocationinfo;168
17.3;10.3 gdal2xyz.py;170
17.4;10.4 gdal_fillnodata.py;171
17.5;10.5 gdal_proximity.py;175
17.6;10.6 gdaldem;177
17.6.1;10.6.1 Hillshade;178
17.6.2;10.6.2 Slope;179
17.6.3;10.6.3 Aspect;180
17.6.4;10.6.4 Color-Relief;181
17.6.5;10.6.5 Terrain Ruggedness Index;182
17.6.6;10.6.6 Topographic Position Index;182
17.6.7;10.6.7 Roughness;182
18;11 Virtual Rasters and Raster Calculations;183
18.1;11.1 Virtual Raster Format Description;184
18.2;11.2 gdalbuildvrt;184
18.3;11.3 Virtual Processing;187
18.4;11.4 gdal_calc.py;188
19;Part II Third Party Open Source GeospatialUtilities;191
20;12 Pktools;193
20.1;12.1 Basic Usage;193
20.2;12.2 pkcomposite;194
20.3;12.3 pkextract;199
20.4;12.4 pkstatogr;204
20.5;12.5 pksvm;206
20.5.1;12.5.1 The SVM Classifier;210
20.5.2;12.5.2 Class Labels;211
20.5.3;12.5.3 No-Data Values;212
20.5.4;12.5.4 Optimizing the SVM Parameters;212
20.5.5;12.5.5 Feature Selection;213
20.6;12.6 pkdiff;215
21;13 Orfeo Toolbox;218
21.1;13.1 Atmospheric Corrections;219
21.2;13.2 Download SRTM;223
21.3;13.3 Image Segmentation;225
21.4;13.4 Edge Detection;230
21.5;13.5 Texture Features;233
22;14 Write Your Own Geospatial Utilities;237
22.1;14.1 Introduction to API Programming;237
22.2;14.2 OGR API;239
22.2.1;14.2.1 OGR API Using Python;240
22.2.2;14.2.2 The OGR Data Model;240
22.2.3;14.2.3 Visualizing Vectors with OGR;240
22.2.4;14.2.4 Buffering with OGR;246
22.2.5;14.2.5 X-Y CSV to OGR Format;248
22.2.6;14.2.6 Point-Based Sampling Frames;252
22.3;14.3 GDAL API;257
22.3.1;14.3.1 GDAL API Using C++;257
22.3.2;14.3.2 The GDAL Raster Data Model;258
22.3.3;14.3.3 Read Raster Files;259
22.3.4;14.3.4 Create and Write Raster Files;262
22.3.5;14.3.5 Parse Options from the Command Line;264
22.3.6;14.3.6 Add Color Tables via the GDAL API;265
22.3.7;14.3.7 Create Cloud Mask Based on Landsat QA;273
22.3.8;14.3.8 The GDAL Algorithms API;279
23;15 3D Point Cloud Data Processing;280
23.1;15.1 Introduction to LiDAR Data;280
23.2;15.2 LiDAR Data Formats and APIs;282
23.3;15.3 LiDAR Data Utilities;285
23.3.1;15.3.1 LibLAS;285
23.3.2;15.3.2 PDAL Utilities;288
23.3.3;15.3.3 LAStools;290
23.3.4;15.3.4 PulseTools;291
23.3.5;15.3.5 SPDLib;292
23.4;15.4 LiDAR Data Derived Products and Applications;293
23.4.1;15.4.1 Digital Elevation Models;293
23.4.2;15.4.2 Canopy Models;296
23.4.3;15.4.3 Point Density;296
23.4.4;15.4.4 LiDAR Intensity;298
24;Part III Case Studies;300
25;16 Case Study on Vector Spatial Analysis;301
25.1;16.1 Digitizing in Google Earth;301
25.2;16.2 Preprocessing Data;305
26;17 Case Study on Multispectral Land Cover Classification;310
26.1;17.1 Create Input Data;312
26.1.1;17.1.1 Create Cloud Mask;313
26.1.2;17.1.2 Create NDVI Mask;316
26.2;17.2 Create Training Data;319
26.2.1;17.2.1 Get Training Data;319
26.2.2;17.2.2 Add Label Attributes;321
26.2.3;17.2.3 Add Band Attributes;324
26.3;17.3 Image Classification;325
26.3.1;17.3.1 Unsupervised Classification;325
26.3.2;17.3.2 Supervised Classification;327
26.3.3;17.3.3 Post-processing;328
26.3.4;17.3.4 Accuracy Assessment;329
27;18 Case Study on Point Data;338
27.1;18.1 Convert Data to SPD Format;338
27.2;18.2 Classify Ground Returns;339
27.3;18.3 Interpolate Points to Raster Format;341
27.4;18.4 Calculate Canopy Metrics;343
28;19 Conclusions and Future Outlook;345
28.1;19.1 Outlook on Geospatial Processing;346
28.1.1;19.1.1 Developments in GDAL/OGR;346
28.1.2;19.1.2 Other Emerging Developments;347
28.2;19.2 Anticipated EO Data and Related Software Requirements;348
29;20 Erratum to: Open Source Geospatial Tools;350
29.1;Erratum to: D. McInerney and P. Kempeneers, Open Source Geospatial Tools, Earth Systems Data and Models 3, DOI 10.1007/978-3-319-01824-9;350
30;Appendix AData Covered in the Book;351
31;Appendix BInstallation of Software;356
32;Glossary;362
33; References;363
34;Index;365
