RasterAnalysen

RasterAnalysen refer to methods for analyzing raster data, which are gridded datasets where each cell contains a value representing a feature or measurement. They are used to extract spatial patterns, quantify phenomena, and build models that describe continuous variables such as elevation, temperature, vegetation indices, or soil moisture. Analyses are conducted at a defined cell size and rely on how data are aligned in space, with attention to resampling and projection issues.

Common techniques include local statistics (focal or neighbourhood operations, such as mean, median, or standard deviation within a moving window), global statistics, and zonal statistics that summarize values within polygonal zones. Raster algebra or map algebra combines raster layers through arithmetic operations, logical/Boolean tests, and reclassification. Other frequent methods are distance and cost-distance analyses, viewshed computations, and terrain analyses (slope, aspect, curvature, hillshade). Hydrological analyses like flow direction and accumulation, as well as multi-criteria decision analyses that aggregate several rasters, are also typical. Data sources include satellite imagery, digital elevation models, climate rasters, and derived indices such as NDVI.

Workflow typically involves defining the objective, gathering rasters, ensuring a common extent, resolution, and coordinate reference system, performing the analysis, and validating results against reference data. Results are then visualized, summarized, and exported for reporting or further modeling. Raster analyses are implemented in various GIS platforms and libraries, including QGIS, ArcGIS, GRASS GIS, and GDAL, and are foundational to fields such as environmental science, hydrology, land-use planning, agriculture, and climate research.