rasterandmete

Rasterandmete, commonly known as raster data in geographic information systems, describes geographic phenomena using a grid of equally sized cells, or pixels. Each cell holds a value that represents the attribute of the landscape at that location. The grid has a defined spatial resolution, extent, and coordinate reference system. Resolution determines the level of detail: smaller cells provide finer detail but require more storage. Cells may contain continuous values (such as elevation or temperature) or discrete categories (such as land cover classes). NoData values indicate missing or unavailable information.

Raster data are typically created from remote sensing imagery, aerial photography, scanned maps, or derived products like digital elevation models. Formats frequently used to store raster data includeGeoTIFF, IMG, and various proprietary formats, as well as multi-band data in NetCDF or HDF. Metadata records describe the coordinate system, pixel size, georeferencing, temporal resolution, and data provenance, enabling interoperability and proper interpretation.

Processing and analysis of raster data include a range of operations. Raster algebra and map algebra perform per-cell computations across datasets. Resampling and reprojection adjust resolution and alignment when combining rasters with different grids. Focal, neighbourhood, and global operators enable filtering, neighborhood statistics, terrain analysis (for example, slope and aspect), and interpolation. Classification and change detection are common tasks, while indices such as NDVI are derived from multi-band imagery to monitor vegetation.

Applications of rasterandmete span land use planning, environmental monitoring, agriculture, hydrology, climate science, and disaster management. Key considerations include data size and performance, how cell size influences accuracy, and careful handling of coordinate systems and edge effects when integrating with vector data.