Collaborative Research: Climatological, Vegetational, and Human-Related Controls on Channelization and Shallow Landsliding Quantified Through Objective Analysis of LiDAR Data

合作研究：通过激光雷达数据的客观分析量化渠道化和浅层滑坡的气候、植被和人类相关控制

• 批准号: 1063228
• 负责人: Paola Passalacqua
• 金额: $ 14.98万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2015-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1063228&HistoricalAwards=false
• 关键词: Collaborative; Research; Climatological; Vegetational; Human

The advent of one meter-resolution topographic data is revolutionizing the study of geomorphic processes. For the first time, the topographic patterns of surface flow, channelization, and landsliding can be resolved over large areas at resolutions commensurate with the scales of the governing processes. Such data provide an exciting opportunity to quantify these patterns and to investigate their dependence on climate, land cover, anthropogenic disturbance, and geology. At the same time, recently developed tools allow the objective extraction of geomorphic features and related attributes from the enormous amount of information contained in LiDAR digital terrain models (DTMs). The overall goal of this research project is to exploit this powerful combination of new data and new methods to deepen basic understanding of hillslope-channel process and form and to evaluate the dynamic interactions with vegetation, hydrologic response, and human-related disturbance. The investigators will employ new methods of geomorphic feature extraction and morphological analysis to a large inventory of LiDAR DTMs across Japan from the typhoon-dominated southwest to the temperate north in combination with detailed ground validation at key watersheds.This project will yield a deeper quantitative understanding of the ecogeomorphic processes that affect the evolution of humid upland landscapes, especially the process of initiating channels in such environments. Project results will provide valuable information and insights to enable planners and decision makers to address issues of critical social relevance, such as flood and landslide hazard assessment, the ecogeomorphological effects of climate variability, and land-use management in montane environments subject to extreme events.

一个仪表分辨率的地形数据的出现在于彻底改变了对地貌过程的研究。 首次可以在与管理过程的尺度相称的大面积上解决表面流，通道化和陆滑的地形模式。 这样的数据为量化这些模式提供了一个令人兴奋的机会，并研究了它们对气候，土地覆盖，人为干扰和地质的依赖。 同时，最近开发的工具允许从LiDAR数字地形模型（DTMS）中包含的大量信息中客观提取地貌特征和相关属性。 该研究项目的总体目标是利用这种新数据和新方法的强大组合，以加深对山坡通道过程和形式的基本了解，并评估与植被，水文反应和与人相关的干扰的动态相互作用。 The investigators will employ new methods of geomorphic feature extraction and morphological analysis to a large inventory of LiDAR DTMs across Japan from the typhoon-dominated southwest to the temperate north in combination with detailed ground validation at key watersheds.This project will yield a deeper quantitative understanding of the ecogeomorphic processes that affect the evolution of humid upland landscapes, especially the process of initiating channels in such环境。 项目结果将提供有价值的信息和见解，以使计划者和决策者能够解决批判性社会相关性的问题，例如洪水和滑坡危险评估，气候变异性的生态地形学效应以及在遭受极端事件的蒙塔尼环境中的土地利用管理。