Geomorphometry, or geomorphometrics (Ancient Greek: γῆ, romanized: gê, lit. 'earth' + Ancient Greek: μορφή, romanized: morphḗ, lit. 'form, shape' + Ancient Greek: μέτρον, romanized: métron, lit. 'measure'), is the science and practice of measuring the characteristics of terrain, the shape of the surface of the Earth, and the effects of this surface form on human and natural geography.[1] It gathers various mathematical, statistical and image processing techniques that can be used to quantify morphological, hydrological, ecological and other aspects of a land surface. Common synonyms for geomorphometry are geomorphological analysis (after geomorphology), terrain morphometry, terrain analysis, and land surface analysis. Geomorphometrics is the discipline based on the computational measures of the geometry, topography and shape of the Earth's horizons, and their temporal change.[2] This is a major component of geographic information systems (GIS) and other software tools for spatial analysis.
In simple terms, geomorphometry aims at extracting (land) surface parameters (morphometric, hydrological, climatic, etc.) and objects (watersheds, stream networks, landforms, etc.) using input digital land surface model (also known as digital elevation model, DEM) and parameterization software.[3] Extracted surface parameters and objects can then be used, for example, to improve mapping and modeling of soils, vegetation, land use, geomorphological and geological features and similar.
With the rapid increase of sources of DEMs today (and mainly due to the Shuttle Radar Topography Mission and LIDAR-based projects), extraction of land surface parameters is becoming more and more attractive to numerous fields ranging from precision agriculture, soil-landscape modeling, climatic and hydrological applications to urban planning, education, and space research. The topography of almost all Earth has been sampled or scanned today so that DEMs are available at resolutions of 100 m or better at a global scale. Today, land surface parameters are successfully used for both stochastic and process-based modeling, the only remaining issue being the level of detail and vertical accuracy of the DEM.