DEM

DEM
Squeaks 41 · CC BY-SA 4.0 · source
Name	DEM
Caption	Digital elevation representation
Type	Geospatial dataset
Introduced	20th century

DEM is a raster representation of terrain elevations used across geospatial sciences, cartography, and remote sensing. It encodes topography as a regularly spaced grid of elevation values to support mapping, hydrology, geomorphology, and infrastructure planning. Developers, researchers, and agencies integrate DEM data into workflows that include surveying, satellite missions, geographic information systems, and numerical modeling.

Definition and Types

A digital elevation model exists in multiple forms including digital terrain model, digital surface model, and bathymetric grids derived from sonar surveys and satellite altimetry. Common DEM formats include GeoTIFF, ESRI Shapefile-derived rasters, and proprietary arrays produced by missions such as Shuttle Radar Topography Mission and Advanced Spaceborne Thermal Emission and Reflection Radiometer. Variants differ by whether they represent bare earth (for example, after removing vegetation and structures) or include above-ground features captured by platforms like Light Detection and Ranging sensors. Resolutions span broad classes from global ~30 arc-second products (e.g., ETOPO compilations) to submeter local surveys conducted by companies such as Leica Geosystems or agencies like the Ordnance Survey.

Data Acquisition and Sources

DEM production draws on remote sensing, field surveying, and cartographic interpolation. Radar missions including Shuttle Radar Topography Mission and TanDEM-X deliver wide-coverage interferometric datasets; optical stereo systems aboard Landsat and ASTER enable photogrammetric DEMs; and airborne sensors—principally LiDAR—provide high-density point clouds for municipal and ecological projects. Nautical DEMs originate from multibeam echosounders aboard vessels commissioned by organizations like the National Oceanic and Atmospheric Administration and the United Kingdom Hydrographic Office. National mapping authorities such as US Geological Survey, Geoscience Australia, Institut Géographique National, and Natural Resources Canada distribute standardized DEM products. Commercial providers including Planet Labs and Maxar Technologies also supply stereo-derived elevation datasets for defense, energy, and insurance sectors.

Processing and Modeling Techniques

Processing pipelines convert raw observations into gridded elevations via core algorithms and software ecosystems. Interferometric synthetic aperture radar workflows apply phase unwrapping and baseline estimation techniques used in Sentinel-1 processing; photogrammetry uses bundle adjustment and structure-from-motion common in tools like Agisoft Metashape and Pix4D. Point cloud classification routines distinguish ground returns from vegetation and buildings using methods developed in the Point Cloud Library and implemented in packages such as LAStools and PDAL. Hydrological conditioning applies pit-filling and flow-direction algorithms associated with models like D8 and D-infinity; terrain derivatives—slope, aspect, and curvature—are calculated in geographic platforms including GRASS GIS and ArcGIS. Error propagation and vertical datum transformations reference standards set by entities such as International Association of Geodesy and national vertical datum agencies.

Applications and Use Cases

DEM products underpin a diverse set of practical and scientific applications. Flood modeling and inundation mapping are implemented in frameworks like Hydrologic Engineering Center models and used by organizations such as Federal Emergency Management Agency and European Flood Awareness System. Infrastructure siting and line-of-sight analysis support projects by utilities and telecoms employing tools from Bentley Systems and Trimble. Ecological studies utilize terrain metrics in research conducted at institutions like Smithsonian Institution and universities including University of Cambridge and Stanford University. Geomorphological investigations reference classic field sites catalogued by United States Geological Survey and regional programs like European Space Agency initiatives. Archaeological prospection applies high-resolution DEMs in projects coordinated with the Council for British Archaeology and museums such as the British Museum.

Accuracy, Errors, and Validation

DEM accuracy depends on sensor characteristics, processing choices, and surface conditions. Systematic vertical biases arise from geoid-model mismatches managed via standards from International Association of Geodesy; random errors reflect point spacing, incidence angles in SAR acquisitions such as SRTM, and canopy penetration limits of LiDAR. Common error sources include voids in radar mosaics, blunders in photogrammetric tie points, and tidal aliasing in bathymetric synthesis as encountered by the National Oceanic and Atmospheric Administration. Validation approaches compare DEMs to ground control points surveyed with Global Navigation Satellite System receivers, terrestrial laser scans produced by companies like Faro Technologies, and cross-comparisons against high-accuracy products distributed by agencies like US Geological Survey. Quality metrics include root mean square error, mean bias, and spatially explicit uncertainty grids used by research programs at institutions like Massachusetts Institute of Technology.

Visualization and Analysis Tools

Visualization and analysis of DEMs use desktop and web-based platforms. Desktop GIS software such as QGIS and ArcGIS enables hillshade generation, contour extraction, and integration with vector layers from repositories like OpenStreetMap. 3D rendering and simulated flightthroughs are supported by engines from Cesium and Google Earth Engine. Specialized toolchains for large datasets rely on cloud services provided by Amazon Web Services and Google Cloud Platform together with tiling standards promoted by projects like Mapbox. Scientific workflows integrate DEMs in scripting environments such as Python (programming language) with libraries including GDAL and Rasterio, and in statistical platforms like R (programming language) using packages maintained by academic groups at universities including University of California, Berkeley.

Category:Geospatial data