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.

一米分辨率地形数据的出现正在使地貌过程的研究发生革命性的变化。 这是第一次，地表水流、渠化和滑坡的地形模式可以在大面积上以与治理过程的尺度相称的分辨率得到解决。 这些数据提供了一个令人兴奋的机会来量化这些模式，并调查其对气候，土地覆盖，人为干扰和地质的依赖。 与此同时，最近开发的工具允许客观提取地貌特征和相关属性的激光雷达数字地形模型（DTM）中包含的大量信息。 本研究项目的总体目标是利用新数据和新方法的强大组合，加深对山坡通道过程和形式的基本理解，并评估与植被，水文响应和人类相关干扰的动态相互作用。研究人员将采用地貌特征提取和形态分析的新方法，对日本从台风主导的西南部到温带北部的大量LiDAR DTM进行调查，并结合关键流域的详细地面验证。该项目将对影响潮湿高地景观演变的生态地貌过程产生更深入的定量了解，尤其是在这种环境中启动信道的过程。 项目成果将提供宝贵的信息和见解，使规划者和决策者能够解决具有重大社会意义的问题，如洪水和滑坡灾害评估、气候变化的生态地貌影响以及易受极端事件影响的山地环境中的土地使用管理。