Digital Elevation Model

Digital Elevation Model
Public domain · source
Name	Digital Elevation Model
Type	Geospatial dataset
Introduced	20th century
Uses	Topography, hydrology, mapping

Digital Elevation Model

A Digital Elevation Model is a geospatial dataset representing terrain elevation over a continuous area. Developed through advances associated with United States Geological Survey, National Aeronautics and Space Administration, European Space Agency, Japan Aerospace Exploration Agency, National Geospatial-Intelligence Agency, and other institutions, DEMs underpin applications in fields linked to United Nations initiatives, World Meteorological Organization, International Charter on Space and Major Disasters, NASA Earth Observing System, and major national mapping programs such as Ordnance Survey and Geoscience Australia.

Overview

DEMs capture elevation using raster grids or triangulated meshes produced by agencies like USGS, NASA, ESA, JAXA, NOAA, and firms including Esri, Trimble, Hexagon AB, Maxar Technologies. Historical milestones involve projects by USGS National Elevation Dataset, Shuttle Radar Topography Mission, ASTER GDEM, ALOS World 3D, Copernicus Programme, Landsat, SPOT missions, and national surveys like Ordnance Survey and Geological Survey of Canada. Key dataset standards emerged from bodies such as Open Geospatial Consortium, ISO, Federal Geographic Data Committee, European Commission. Prominent scientific users include researchers affiliated with Massachusetts Institute of Technology, Stanford University, Imperial College London, ETH Zurich, University of Cambridge.

Data Sources and Acquisition Methods

Primary acquisition methods include radar missions exemplified by Shuttle Radar Topography Mission, interferometric synthetic aperture radar developed at Jet Propulsion Laboratory and used by TanDEM-X, optical stereo photogrammetry applied in Landsat and SPOT processing chains, and lidar campaigns conducted by firms like Leica Geosystems and RIEGL often coordinated with agencies such as NOAA and USGS. Airborne platforms include aircraft operated by National Oceanic and Atmospheric Administration, United States Geological Survey, and commercial operators like Airbus; satellite missions include Terra (satellite), Aqua (satellite), Copernicus Sentinel-1, Copernicus Sentinel-2, ALOS. Historical ground survey sources include the Ordnance Survey triangulation networks, U.S. Geological Survey topographic maps, and field campaigns by institutions such as United States Army Corps of Engineers.

Types and Representations

DEMs appear as raster grids, Triangulated Irregular Network (TIN), and point clouds. Raster DEMs include Digital Terrain Model and Digital Surface Model derivatives used by organizations including Esri, Google, Microsoft for platforms like Google Earth and Bing Maps. TIN products are common in civil engineering firms like AECOM and Halcrow Group. Point cloud exports derive from lidar vendors such as Velodyne, Quanergy, and laser scanning services by FARO Technologies. Representations tie to standards from Open Geospatial Consortium, ISO 19115, and visualization frameworks maintained by CesiumJS and Three.js.

Processing and Analysis Techniques

Core processing techniques incorporate interpolation algorithms (inverse distance weighting, kriging) developed in academic centers like University of Pennsylvania and Colorado State University, filtering and classification workflows implemented in software from Esri, GRASS GIS, QGIS Foundation, and LAStools by Rapidlasso GmbH. Hydrological analysis routines stem from work at University of Minnesota and USGS watershed modeling, while terrain derivatives such as slope, aspect, and curvature are applied in publications from National Center for Atmospheric Research and Lawrence Livermore National Laboratory. Machine learning approaches using frameworks from Google DeepMind, OpenAI, Facebook AI Research are increasingly applied for DEM enhancement and change detection, integrated with cloud platforms like Amazon Web Services, Google Cloud Platform, and Microsoft Azure.

Applications

DEMs enable flood modeling used by FEMA, urban planning by municipal authorities in London, New York City, and Tokyo, infrastructure design by firms like Bechtel and Jacobs Engineering, and terrain analysis for telecommunications by companies such as Ericsson and Nokia. Environmental applications include erosion assessment in studies associated with Intergovernmental Panel on Climate Change, habitat modeling in projects led by World Wildlife Fund, glacier monitoring by British Antarctic Survey, and volcanic hazard mapping by United States Geological Survey volcano program. DEMs support navigation and autonomous vehicles developed by Tesla, Inc. and Waymo, and archaeological prospection practiced by teams at University College London and Leiden University.

Accuracy, Errors, and Validation

Error sources include sensor biases studied at Jet Propulsion Laboratory, geoid and datum mismatches handled by agencies like National Geospatial-Intelligence Agency and National Geodetic Survey, and landcover-induced artifacts examined at University of California, Berkeley. Validation approaches employ checkpoints from national surveys (Ordnance Survey, Geological Survey of Canada), cross-comparisons with airborne lidar collected by NOAA or USGS, and community benchmarking initiatives involving OpenTopography and academic consortia at University of Washington and Purdue University.

Limitations and Future Developments

Limitations include vegetation and built-up area penetration challenges addressed by radar missions like TanDEM-X and lidar advances from RIEGL; temporal resolution constraints mitigated by constellations such as Planet Labs and interoperability efforts by Copernicus. Future developments point to higher-resolution global models from collaborations involving ESA, NASA, JAXA, and commercial providers like Maxar Technologies and Planet Labs, integration with digital twins promoted by Singapore Land Authority and Royal Institution of Chartered Surveyors, and real-time elevation updates facilitated by edge computing companies such as NVIDIA and Intel.

Category:Geographic information systems