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Open Source Geospatial Tools: Applications in Earth Observation 2015 ed. [Kõva köide]

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  • Formaat: Hardback, 358 pages, kõrgus x laius: 235x155 mm, kaal: 7037 g, 50 Illustrations, color; 46 Illustrations, black and white; XXVII, 358 p. 96 illus., 50 illus. in color., 1 Hardback
  • Sari: Earth Systems Data and Models 3
  • Ilmumisaeg: 09-Dec-2014
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 331901823X
  • ISBN-13: 9783319018232
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Open Source Geospatial Tools: Applications in Earth Observation 2015 ed.Suurem pilt
  • Formaat: Hardback, 358 pages, kõrgus x laius: 235x155 mm, kaal: 7037 g, 50 Illustrations, color; 46 Illustrations, black and white; XXVII, 358 p. 96 illus., 50 illus. in color., 1 Hardback
  • Sari: Earth Systems Data and Models 3
  • Ilmumisaeg: 09-Dec-2014
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 331901823X
  • ISBN-13: 9783319018232
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319018232.html
Märksõnad:
Our book focuses on the use of open source software for geospatial analysis, provides concise solutions for analyzing and visualizing spatial data, and demonstrates the effectiveness of the command line interface for handling geospatial data.

Each chapter includes a well structured solution to an in-depth geospatial data analysis problem for raster, vector and 3D point data. Throughout the chapters, we introduce the respective appropriate open-source tool for data processing and clearly explain how it should be used.

Finally, a hands-on guide to the Python and C++ Application Programming Interfaces of GDAL/OGR is provided for those readers who want to improve on existing tools or develop their own software.

Our target audience includes remote sensing scientists, GIS analysts, researchers, teachers and students. The book will serve as an introduction to novice users of geospatial data as well as a reference text for advanced software developers.

This book focuses on the use of open source software for geospatial analysis. It provides concise solutions for analyzing and visualizing spatial data as well as demonstrates the effectiveness of the command line interface for handling geospatial data.
Part I Geospatial Data Processing with GDAL/OGR
1 Introduction
3(16)
1.1 Introduction to Geospatial Data
5(1)
1.2 Projections and Coordinate Reference Systems
6(1)
1.3 Spatial Data Models
6(1)
1.4 Earth Observation Data
7(3)
1.5 Software Tools Covered in the Book
10(5)
1.5.1 Geospatial Visualization Tools
11(4)
1.6 Structure of the Book
15(4)
2 Vector Data Processing
19(32)
2.1 Vector Data Model
19(2)
2.2 OGR Simple Features Library
21(1)
2.3 ogrinfo
22(5)
2.4 ogr2ogr
27(10)
2.4.1 Manipulating Data
32(5)
2.5 ogrtindex
37(2)
2.6 OGR Virtual Format
39(6)
2.7 Spatial Databases
45(6)
2.7.1 PostGIS
46(1)
2.7.2 Spatialite
46(3)
2.7.3 ogr2ogr with Spatialite
49(2)
3 Raster Data Explained
51(10)
3.1 Coordinate Reference Systems
52(4)
3.2 Single and Multi-band Images
56(1)
3.3 Complex Datasets
57(1)
3.4 Raster Data Types
58(2)
3.5 Raster Data Encoding
60(1)
4 Introduction to GDAL Utilities
61(2)
5 Manipulating Raster Data
63(18)
5.1 gdalinfo
63(5)
5.2 gdalmanage
68(1)
5.3 gdalcompare.py
69(1)
5.4 gdal_edit.py
70(2)
5.5 gdal_translate
72(9)
5.5.1 Convert and Scale Rasters
75(1)
5.5.2 Subset Rasters
76(1)
5.5.3 Change Raster Attributes and Encoding
77(2)
5.5.4 Compress Rasters
79(2)
6 Indexed Color Images
81(4)
6.1 rgb2pct.py
82(1)
6.2 pct2rgb.py
83(2)
7 Image Overviews, Tiling and Pyramids
85(14)
7.1 gdaltindex
86(3)
7.2 gdaladdo
89(2)
7.3 gdal_retile.py
91(3)
7.4 gdal2tiles.py
94(5)
8 Image (Re-)projections and Merging
99(30)
8.1 Introduction on Projection and Image Merging
99(1)
8.2 Resampling
100(4)
8.3 gdalwarp
104(10)
8.3.1 Reproject Images
109(1)
8.3.2 Warp Images
110(1)
8.3.3 Mosaic Images
111(1)
8.3.4 Clip Images
112(2)
8.4 gdal_merge.py
114(4)
8.5 nearblack
118(1)
8.6 gdaltransform
119(3)
8.7 gdalsrsinfo
122(2)
8.8 gdalmove.py
124(5)
9 Raster Meets Vector Data
129(12)
9.1 gdal_sieve.py
129(1)
9.2 gdal_polygonize.py
130(3)
9.3 gdal_rasterize
133(4)
9.4 gdal_contour
137(4)
10 Raster Meets Point Data
141(22)
10.1 gdal_grid
141(6)
10.1.1 Interpolation Methods
145(1)
10.1.2 Data Metrics
145(2)
10.2 gdallocationinfo
147(2)
10.3 gdal2xyz.py
149(1)
10.4 gdal_fillnodata.py
150(4)
10.5 gdal_proximity.py
154(2)
10.6 gdaldem
156(7)
10.6.1 Hillshade
157(1)
10.6.2 Slope
158(1)
10.6.3 Aspect
159(1)
10.6.4 Color-Relief
160(1)
10.6.5 Terrain Ruggedness Index
161(1)
10.6.6 Topographic Position Index
161(1)
10.6.7 Roughness
161(2)
11 Virtual Rasters and Raster Calculations
163(10)
11.1 Virtual Raster Format Description
164(1)
11.2 gdalbuildvrt
164(3)
11.3 Virtual Processing
167(1)
11.4 gdal_calc.py
168(5)
Part II Third Party Open Source Geospatial Utilities
12 Pktools
173(26)
12.1 Basic Usage
173(1)
12.2 pkcomposite
174(5)
12.3 pkextract
179(5)
12.4 pkstatogr
184(2)
12.5 pksvm
186(9)
12.5.1 The SVM Classifier
190(1)
12.5.2 Class Labels
191(1)
12.5.3 No-Data Values
192(1)
12.5.4 Optimizing the SVM Parameters
192(1)
12.5.5 Feature Selection
193(2)
12.6 pkdiff
195(4)
13 Orfeo Toolbox
199(20)
13.1 Atmospheric Corrections
200(4)
13.2 Download SRTM
204(2)
13.3 Image Segmentation
206(5)
13.4 Edge Detection
211(3)
13.5 Texture Features
214(5)
14 Write Your Own Geospatial Utilities
219(44)
14.1 Introduction to API Programming
219(2)
14.2 OGR API
221(18)
14.2.1 OGR API Using Python
222(1)
14.2.2 The OGR Data Model
222(1)
14.2.3 Visualizing Vectors with OGR
222(6)
14.2.4 Buffering with OGR
228(2)
14.2.5 X-Y CSV to OGR Format
230(4)
14.2.6 Point-Based Sampling Frames
234(5)
14.3 GDAL API
239(24)
14.3.1 GDAL API Using C++
239(1)
14.3.2 The GDAL Raster Data Model
240(1)
14.3.3 Read Raster Files
241(3)
14.3.4 Create and Write Raster Files
244(2)
14.3.5 Parse Options from the Command Line
246(1)
14.3.6 Add Color Tables via the GDAL API
247(8)
14.3.7 Create Cloud Mask Based on Landsat QA
255(6)
14.3.8 The GDAL Algorithms API
261(2)
15 3D Point Cloud Data Processing
263(22)
15.1 Introduction to LiDAR Data
263(2)
15.2 LiDAR Data Formats and APIs
265(3)
15.3 LiDAR Data Utilities
268(8)
15.3.1 LibLAS
268(3)
15.3.2 PDAL Utilities
271(2)
15.3.3 LAStools
273(1)
15.3.4 PulseTools
274(1)
15.3.5 SPDLib
275(1)
15.4 LiDAR Data Derived Products and Applications
276(9)
15.4.1 Digital Elevation Models
276(3)
15.4.2 Canopy Models
279(1)
15.4.3 Point Density
279(2)
15.4.4 LiDAR Intensity
281(4)
Part III Case Studies
16 Case Study on Vector Spatial Analysis
285(10)
16.1 Digitizing in Google Earth
285(4)
16.2 Preprocessing Data
289(6)
17 Case Study on Multispectral Land Cover Classification
295(28)
17.1 Create Input Data
297(7)
17.1.1 Create Cloud Mask
298(3)
17.1.2 Create NDVI Mask
301(3)
17.2 Create Training Data
304(6)
17.2.1 Get Training Data
304(2)
17.2.2 Add Label Attributes
306(3)
17.2.3 Add Band Attributes
309(1)
17.3 Image Classification
310(13)
17.3.1 Unsupervised Classification
310(2)
17.3.2 Supervised Classification
312(1)
17.3.3 Post-processing
313(1)
17.3.4 Accuracy Assessment
314(9)
18 Case Study on Point Data
323(8)
18.1 Convert Data to SPD Format
323(1)
18.2 Classify Ground Returns
324(2)
18.3 Interpolate Points to Raster Format
326(2)
18.4 Calculate Canopy Metrics
328(3)
19 Conclusions and Future Outlook
331(6)
19.1 Outlook on Geospatial Processing
332(2)
19.1.1 Developments in GDAL/OGR
332(1)
19.1.2 Other Emerging Developments
333(1)
19.2 Anticipated EO Data and Related Software Requirements
334(3)
Appendix A Data Covered in the Book 337(6)
Appendix B Installation of Software 343(6)
Glossary 349(2)
References 351(2)
Index 353