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|a WAA
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|a spa
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|a Neteler, Markus
|9 21054
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| 245 |
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|a Open source gis a GRASS GIS approach /
|c Markus Neteler and Helena Mitasova
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| 250 |
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|a 3rd ed.
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| 260 |
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|a Berlin :
|b Springer,
|c 2010
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| 300 |
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|a 406 p. :
|b grafs. ;
|c 24 cm.
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| 500 |
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|a Incluye índice analítico
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| 505 |
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|a 1. Open Source software and GIS -- 1.1. Open Source concept -- 1.2. GRASS as an Open Source GIS -- 1.3. The North Carolina sample data set -- 1.4. How to read this book -- 2. GIS concepts -- 2.1. General GIS principles -- 2.1.1. Geospatial data models -- 2.1.2. Organization of GIS data and system functionality -- 2.2. Map projections and coordinate systems -- 2.2.1. Map projection principles -- 2.2.2. Common coordinate systems and datums -- 3. Getting started with GRASS -- 3.1. First steps -- 3.1.1. Download and install GRASS -- 3.1.2. Database and command structure -- 3.1.3. Graphical User Interfaces for GRASS 6: QGIS and gis.m -- 3.1.4. Starting GRASS with the North Carolina data set -- 3.1.5. GRASS data display and 3D visualization -- 3.1.6. Project data management -- 3.2. Starting GRASS with a new project -- 3.2.1. Defining the coordinate system for a new project - 3.2.2. Non-georeferenced xy coordinate system -- 3.3. Coordinate system transformations -- 3.3.1. Coordinate lists -- 3.3.2. Projection of raster and vector maps -- 3.3.3. Reprojecting with GDAL/OGR tools -- 4. GRASS data models and data exchange -- 4.1. Raster data -- 4.1.1. GRASS 2D and 3D raster data models -- 4.1.2. Managing regions, raster map resolution and boundaries -- 4.1.3. Import of georeferenced raster data -- 4.1.4. Import and geocoding of a scanned historical map -- 4.1.5. Raster data export -- 4.2. Vector data -- 4.2.1. GRASS vector data model -- 4.2.2. Import of vector data - 4.2.3. Coordinate transformation for xy CAD drawings -- 4.2.4. Export of vector data -- 5. Working with raster data -- 5.1. Viewing and managing raster maps -- 5.1.1. Displaying raster data and assigning a color table -- 5.1.2. Managing metadata of raster maps -- 5.1.3. Raster map queries and profiles -- 5.1.4. Raster map statistics -- 5.1.5. Zooming and generating subsets from raster maps -- 5.1.6. Generating simple raster maps -- 5.1.7. Reclassification and rescaling of raster maps -- 5.1.8. Recoding of raster map types and value replacements -- 5.1.9. Assigning category labels -- 5.1.10. Masking and handling of no-data values -- 5.2. Raster map algebra -- 5.2.1. Integer and floating point data -- 5.2.2. Basic calculations -- 5.2.3. Working with “if” conditions -- 5.2.4. Handling of NULL values in r.mapcalc -- 5.2.5. Creating a MASK with r.mapcalc -- 5.2.6. Special graph operators -- 5.2.7. Neighborhood operations with relative coordinates -- 5.3. Raster data transformation and interpolation -- 5.3.1. Automated vectorization of discrete raster data -- 5.3.2. Generating isolines representing continuous fields -- 5.3.3. Resampling and interpolation of raster data -- 5.3.4. Overlaying and merging raster maps -- 5.4. Spatial analysis with raster data.-- 5.4.1. Neighborhood analysis and cross-category statistics -- 5.4.2. Buffering of raster features -- 5.4.3. Cost surfaces -- 5.4.4. Terrain and watershed analysis -- 5.4.5. Landscape structure analysis -- 5.5 Landscape process modeling -- 5.5.1. Hydrologic and groundwater modeling -- 5.5.2. Erosion and deposition modeling -- 5.5.3. Final note on raster-based modeling and analysis -- 5.6. Working with voxel data -- 6. Working with vector data -- 6.1. Map viewing and metadata management -- 6.1.1. Displaying vector maps -- 6.1.2. Vector map metadata maintenance -- 6.2. Vector map attribute management and SQL support -- 6.2.1. SQL support in GRASS 6 -- 6.2.2. Sample SQL queries and attribute modifications -- 6.2.3. Map reclassification -- 6.2.4. Vector map with multiple attribute tables: layers -- 6.3. Digitizing vector data -- 6.3.1. General principles for digitizing topological data. --6.3.2. Interactive digitizing in GRASS -- 6.4. Vector map queries and statistics -- 6.4.1. Map queries -- 6.4.2. Raster map statistics based on vector objects -- 6.4.3. Point vector map statistics -- 6.5. Geometry operations -- 6.5.1. Topological operations -- 6.5.2. Buffering -- 6.5.3. Feature extraction and boundary dissolving -- 6.5.4. Patching vector maps -- 6.5.5. Intersecting and clipping vector maps -- 6.5.6 Transforming vector geometry and creating 3D vectors -- 6.5.7. Convex hull and triangulation from points -- 6.5.8. Find multiple points in same location -- 6.5.9. Length of common polygon boundaries -- 6.6. Vector network analysis -- 6.6.1. Network analysis -- 6.6.2. Linear reference system (LRS) -- 6.7. Vector data transformations to raster -- 6.8. Spatial interpolation and approximation -- 6.8.1. Selecting an interpolation method -- 6.8.2. Interpolation and approximation with RST -- 6.8.3. Tuning the RST parameters: tension and smoothing -- 6.8.4. Estimating RST accuracy -- 6.8.5. Segmented processing -- 6.8.6. Topographic analysis with RST -- 6.9. Working with lidar point cloud data -- 6.10 Volume based interpolation -- 6.10.1. Adding third variable: precipitation with elevation -- 6.10.2. Volume and volume-temporal interpolation -- 6.10.3. Geostatistics and splines -- 7. Graphical output and visualization -- 7.1. Two-dimensional display and animation -- 7.1.1. Advanced map display in the GRASS monitor -- 7.1.2. Creating a 2D shaded elevation map -- 7.1.3. Using display tools for analysis -- 7.1.4. Monitor output to PNG or PostScript files -- 7.2. Creating hardcopy maps with ps.map -- 7.3. Visualization in 3D space with NVIZ -- 7.3.1. Viewing surfaces, raster and vector maps -- 7.3.2. Querying data and analyzing multiple surfaces -- 7.3.3. Creating animations in 3D space -- 7.3.4. Visualizing volumes -- 7.4. Coupling with an external OpenGL viewer Paraview -- 8. Image processing -- 8.1. Remote sensing basics -- 8.1.1. Spectrum and remote sensing -- 8.1.2. Import of image channels -- 8.1.3. Managing channels and colors -- 8.1.4. The feature space and image groups -- 8.2. Data preprocessing -- 8.2.1. Radiometric preprocessing -- 8.2.2. Deriving a surface temperature map from thermal channel -- 8.3. Radiometric transformations and image enhancements -- 8.3.1 Image ratios -- 8.3.2. Principal Component Transformation -- 8.4. Geometric feature analysis with matrix filters -- 8.5. Image fusion -- 8.5.1. Introduction to RGB and IHS color model -- 8.5.2. Image fusion with the IHS transformation -- 8.5.3. Image fusion with Brovey transform -- 8.6. Thematic classification of satellite data -- 8.6.1. Unsupervised radiometric classification -- 8.6.2. Supervised radiometric classification -- 8.6.3. Supervised SMAP classification -- 8.7. Multitemporal analysis -- 8.8. Segmentation and pattern recognition -- 9. Notes on GRASS programming -- 9.1. GRASS programming environment -- 9.1.1. GRASS source code -- 9.1.2. Methods of GRASS programming -- 9.1.3. Level of integration -- 9.2. Script programming -- 9.3. Automated usage of GRASS -- 9.3.1. Local mode: GRASS as GIS data processor -- 9.3.2. Web based: PyWPS – Python Web Processing Service -- 9.4. Notes on programming GRASS modules in C -- 10. Using GRASS with other Open Source tools -- 10.1. Geostatistics with GRASS and gstat -- 10.2. Spatial data analysis with GRASS and R -- 10.2.1. Reading GRASS data into R -- 10.2.2. Kriging in R -- 10.2.3. Using R in batch mode -- 10.3. GPS data handling -- 10.4. WebGIS applications with UMN/MapServer and OpenLayers -- A. Appendix -- A.1. Selected equations used in GRASS modules-- A.2. Landscape process modeling -- A.3. Definition of SQLite-ODBC connection.
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| 650 |
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0 |
|a Geología
|9 1052
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| 650 |
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0 |
|a Programa de ordenador
|9 2904
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| 650 |
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0 |
|a Software de código abierto
|9 2918
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| 700 |
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|a Mitasova, Helena
|9 21055
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| 942 |
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