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

改进流路建模和地形分类的地貌测量学进展

• 批准号: RGPIN-2016-03819
• 负责人: Lindsay, John
• 金额: $ 1.75万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=679531
• 关键词: Advances; geomorphometry; improved; flow; path

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. **

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。****地貌学受到新一代扫描激光测高（LiDAR）衍生的高分辨率dem的影响。与粗分辨率数据相比，高分辨率激光雷达数据可以实现许多新的应用领域，例如详细的流域测绘，但也存在一些数据处理方面的挑战。本研究的重点是高分辨率LiDAR DEM数据在地表排水特征表征和多尺度地貌分析中的应用，以进行地貌分类。研究活动将包括开发和测试从激光雷达dem中去除噪声的新技术，同时保留小规模排水特征（例如沟渠和沟渠），基于dem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渠系统的发展。此外，将探讨局部地形位置测量的方向依赖性，目的是为线性和流线型地形的自动分类创造改进的方法。这项工作将有助于开源地理信息系统（GIS） Whitebox GAT的持续开发，该系统将作为算法开发和测试以及数据可视化的实验平台。白盒GAT在150多个国家用于地貌学研究和教育，其正在进行的开发代表了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。**