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

数字高程模型（DEM）是地球地形的数字表示，由网格组成，其中每个网格单元包含地球表面上相应位置的高度。分析DEM数据的重要性是地貌测量学领域的焦点，它是坡度、地表方向和相对景观位置（即，一个地点的低洼或升高程度）影响地表沃茨的丰度和运动，并影响降水、温度、太阳辐射和风暴露。这些环境因素中的每一个都控制着土壤发育、植被分布、沉积物、必需营养素和污染物的通量。这就是为什么DEM数据和地貌测量技术，如基于DEM的流径建模，已成为植被制图，气候建模，洪水预报，灾害制图和其他应用领域必不可少的。地貌测量已受到来自扫描激光测高（LiDAR）的新一代高分辨率DEM的影响。与较粗分辨率的数据相比，高分辨率LiDAR数据可以实现许多新的应用领域，例如详细的水系制图，但也提出了一些数据处理挑战。本研究的重点是应用高分辨率LiDAR DEM数据表征地表水系特征和多尺度地貌分析进行地貌分类。研究活动将包括开发和测试从激光雷达数字高程模型中去除噪音的新技术，同时保留小规模的排水特征（例如沟壑和沟渠），基于数字高程模型的湿地测绘方法，以及应用地面激光扫描仪数据研究农业沟壑系统的发展。此外，还将探讨当地地形位置测量的方向依赖性，目的是改进线性和流线型地形自动分类的方法。这项工作将有助于目前正在开发的开放源码地理信息系统白盒GAT，它将作为试验算法开发和测试以及数据可视化的平台。150多个国家将白盒GAT用于地貌学研究和教育，其正在进行的开发是这项拟议研究的重要技术转让组成部分，有可能对地貌学实践产生重大影响。

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.