Acquiring Airborne Lidar data to study hydrologic, geomorphologic, and geochemical processes at three Critical Zone Observatories (CZO)

获取机载激光雷达数据以研究三个关键区域观测站 (CZO) 的水文、地貌和地球化学过程

• 批准号: 0922307
• 负责人: Qinghua Guo
• 金额: $ 93.55万
• 依托单位: University of California - Merced
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0922307&HistoricalAwards=false
• 关键词: Acquiring; Airborne; Lidar; data; study

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The Earth?s ?critical zone? is where water, atmosphere, ecosystems and soils interact on a geomorphic and geologic template, and extends from bedrock to the atmospheric boundary layer. Process understanding of erosion, weathering, soil formation, water movement and nutrient transport in the critical zone depends in large part on new observations, coupled with models that can take advantage of those new data. In particular, accurate, high‐resolution images of the land surface and vegetation canopy, and the structure in between, have the ability to transform our ability to describe and model those processes, and to predict how changes in climate and landcover will perturb water cycles and critical‐zone processes linked to water. Airborne LiDAR (Light Detection and Ranging) is proving to be a transformational technology that can determine the three‐dimensional structure of the critical zone, and thus enable this process research. LiDAR flights will measure canopy during leaf on/leaf off conditions, snow distribution and other physical features of the land surface at the three NSF‐supported Critical Zone Observatories (CZOs) and other three key sites. Physiographic data will be used to derive the LiDAR products, such as a high‐resolution digital elevation model, tree heights, tree diameter at breast height, leaf area index, crown cover, and snow depth. Ground‐truth data will be collected to validate and calibrate the LiDAR derived products. Advanced, state‐of‐the‐art processing will be carried out to assure that products are accurate. Intellectual merit. The resulting information will be used for hypothesis‐driven research across these sites. High‐resolution topographic data will characterize landscapes and enable testing hypotheses about the geomorphic processes that have generated these landscapes. The data will enable examining the role of aspect in geomorphic and hydrologic behavior, and the feedbacks between slope, soil moisture, weathering, soil formation and vegetation. Hydrologic simulations using emerging, physics‐based models will also be carried out, using the high‐spatial‐resolution topographic and canopy products from LiDAR. High‐resolution estimates of spatial patterns of snow depth will provide an unprecedented ground‐truth data set for modeling the physiographic controls on snow accumulation and melt. LiDAR scenes will contribute to estimating vegetation structure, which will then be used for parameter estimation in coupled hydro‐ecologic model analysis and in scaling of evapotranspiration and carbon flux. LiDAR‐based estimates of micro‐topography, the patterns of which give rise to ?hot? and ?cold? spots of soil biogeochemical cycling generated by preferential flow of nutrient‐rich litter leachate into mineral soils, will help guide sampling of litter and soil nutrient concentrations along gradients of water and biological availability. By quantifying functional controls on rates of erosion and weathering, the LIDAR data will contribute to improved understanding of how (and why) sediment and solutes move across (and through) the landscape. Broader impacts: There are two main, direct broader impacts of this project. First, making LiDAR data available for the three CZOs will enhance their potential and use as community platforms for research. The goal of CZOs is to build a network to advance interdisciplinary studies of Earth surface processes as well as foster collaboration among scientists and engineers from different disciplines. However, existing spatial data cannot meet the research needs of the CZO teams because of being incomplete, outdated and of insufficient spatial resolution and temporal scale. A second broader impact will be to make LiDAR products easily understood and widely used by researchers working at CZOs and similar study areas, building on the community nature of CZO data and resources, and well‐developed plans to share those resources and disseminate CZO products.

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。地球吗?年代?临界区?是水、大气、生态系统和土壤在地貌和地质模板上相互作用的地方，并从基岩延伸到大气边界层。对关键地带的侵蚀、风化、土壤形成、水分运动和养分输送过程的理解，在很大程度上取决于新的观测结果，以及可以利用这些新数据的模型。特别是精确，高&amp；#8208；陆地表面和植被冠层的分辨率图像，以及两者之间的结构，有能力改变我们描述和模拟这些过程的能力，并预测气候和土地覆盖的变化将如何扰乱水循环和临界温度。区域过程与水有关。机载激光雷达（光探测和测距）被证明是一种变革性的技术，可以确定三个目标。关键区域的尺寸结构，从而使本工艺研究成为可能。激光雷达飞行将在三个NSF&amp；#8208；支持关键区域天文台（czo）和其他三个关键站点。地理数据将用于导出LiDAR产品，例如high&amp；#8208；分辨率数字高程模型，树高，树胸径，叶面积指数，树冠覆盖和雪深。Ground& # 8208;收集真实数据以验证和校准激光雷达衍生产品。先进,state& # 8208; of& # 8208; amp的;# 8208;将进行美术加工，以确保产品的准确性。知识价值。结果信息将用于假设&amp；#8208；推动这些网站的研究。High& # 8208;高分辨率的地形数据将描绘景观特征，并使测试关于产生这些景观的地貌过程的假设成为可能。这些数据将有助于研究地形在地貌和水文行为中的作用，以及坡度、土壤水分、风化、土壤形成和植被之间的反馈。水文模拟使用新兴，物理&amp；#8208；基于模型也将进行，使用high&amp；#8208;spatial&#8208；分辨率地形和冠层产品。High& # 8208;对雪深空间格局的分辨率估计将提供一个前所未有的地面&amp；#8208；模拟积雪和融雪的地理控制的真值数据集。激光雷达场景将有助于估计植被结构，然后将其用于耦合水文[amp；#8208；生态模型分析及蒸散和碳通量的标度。LiDAR& # 8208;基于micro&amp；#8208；地形，其模式产生了？热？和冷?养分优先流动产生的土壤生物地球化学循环点&amp；#8208；丰富的凋落物渗滤液进入矿物土壤，将有助于指导沿水和生物有效性梯度的凋落物和土壤养分浓度采样。通过量化对侵蚀和风化速率的功能控制，激光雷达数据将有助于提高对沉积物和溶质如何（以及为什么）在景观中移动的理解。更广泛的影响：本项目有两个主要的、直接的更广泛的影响。首先，为三个czo提供激光雷达数据将增强它们的潜力，并将其用作社区研究平台。czo的目标是建立一个网络来推进地球表面过程的跨学科研究，并促进来自不同学科的科学家和工程师之间的合作。然而，现有的空间数据不完整、过时，空间分辨率和时间尺度不够，无法满足CZO团队的研究需求。第二个更广泛的影响将是使激光雷达产品更容易被CZO和类似研究领域的研究人员理解和广泛使用，建立在CZO数据和资源的社区性质之上。制定了共享这些资源和传播CZO产品的计划。