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

数字高程模型（DEM）是由网格组成的地球地形的数字表示，其中每个网格单元包含地球表面相应位置的高度。分析DEM数据（地貌学领域的焦点）的重要性在于，坡度、地表方向和相对景观位置（即场地的低洼或高洼程度）会影响地表水的丰度和运动，并影响降水、温度、太阳辐射和风暴露。这些环境因子中的每一个都控制着土壤发育、植被分布以及沉积物、必需营养素和污染物的通量。这就是为什么DEM数据和地貌学技术，如基于DEM的流道建模，已经成为植被制图、气候建模、洪水预报、灾害制图和其他应用领域必不可少的技术。

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.