Open source gis a GRASS GIS approach /

Guardado en:
Detalles Bibliográficos
Autor principal: Neteler, Markus
Otros Autores: Mitasova, Helena
Formato: Desconocido
Lenguaje:Español
Publicado: Berlin : Springer, 2010
Edición:3rd ed.
Materias:
Geología
Programa de ordenador
Software de código abierto
Aporte de:Registro referencial: Solicitar el recurso aquí
Sistema de Bibliotecas UNRN de Universidad Nacional de Río Negro
Tabla de Contenidos:
  • 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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