Advances in geomorphometry for improved flow-path modeling and landform classification

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A digital elevation model (DEM) is a numeric representation of Earth's topography consisting of a grid in which each grid cell contains the height of the corresponding location on Earth's surface. The importance of analyzing DEM data, the focus of the field of geomorphometry, is that slope gradient, land surface orientation, and relative landscape position (i.e. how low-lying or elevated a site is) affect the abundance and movement of surface waters and influence precipitation, temperature, solar radiation, and wind exposure. Each of these environmental factors control soil development, vegetation distributions, and the flux of sediments, essential nutrients, and contaminants. This is why DEM data and geomorphometric techniques, such as DEM-based flow-path modeling, have become essential for vegetation mapping, climate modeling, flood forecasting, hazards mapping, and other application areas.****Geomorphometry has been impacted by the availability of a new generation of high-resolution DEMs derived from scanning laser altimetry (LiDAR). Compared with coarser resolution data, high-resolution LiDAR data enable many new application areas, e.g. detailed drainage mapping, but also present several data processing challenges. This proposed research focuses on applications of high-resolution LiDAR DEM data for the characterization of surface drainage features and multi-scale geomorphometric analysis for landform classification. Research activities will include development and testing of novel techniques for removing noise from LiDAR DEMs while preserving small-scale drainage features (e.g. gullies and ditches), DEM-based methods for wetland mapping, and the application of terrestrial laser scanner data for studying agricultural gully system development. Additionally, the directional dependence of measures of local topographic position will be explored, with the intent of creating improved methods for automated classification of linear and streamlined landforms. This work will contribute to the ongoing development of the open-source geographic information system (GIS) Whitebox GAT, which will serve as the platform for experimenting with algorithm development and testing and data visualization. Whitebox GAT is used for geomatics research and education in more than 150 countries and its ongoing development represents a significant technology transfer component of this proposed research with the potential to significantly impact geomorphometry practice. **