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

该奖项是根据2009年美国复苏和再投资法案(公法第111-5条)资助的。水、大气、生态系统和土壤在地貌和地质模板上相互作用，从基岩延伸到大气边界层。对临界区侵蚀、风化、土壤形成、水分运动和养分运移的过程的理解在很大程度上取决于新的观测结果，以及能够利用这些新数据的模型。特别是，准确、高分辨率的陆地表面和植被冠层以及两者之间的结构图像，能够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与水相关的关键地带过程。机载激光雷达(光探测和测距)被证明是一种变革性的技术，可以确定临界区的三维结构，从而使这一过程研究成为可能。激光雷达飞行将在三个NSF&Amp；支持的临界区天文台(CZO)和其他三个关键地点测量树冠在落叶/落叶条件下的降雪分布和陆地表面的其他物理特征。地理学数据将被用来得出LiDAR产品，例如高分辨率数字海拔模型、树高直径、叶面积指数、树冠覆盖度和积雪深度。将收集地面真实数据，以验证和校准LiDAR衍生产品。将进行先进的、state&amp；#8208；of&amp；#8208；the&amp；#8208；art加工，以确保产品的准确性。智力上的优点。由此产生的信息将用于这些网站上的假设驱动的研究。高分辨率的地形数据将描述地貌的特征，并能够测试有关产生这些地貌的地貌过程的假设。这些数据将有助于研究坡向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。还将使用LiDAR的高分辨率空间地形和冠层产品，使用新兴的基于物理的模型进行水文模拟。对积雪深度空间模式的高分辨率估计将为模拟积雪和融化的地形控制提供前所未有的真实数据集。激光雷达场景将有助于估计植被结构，然后用于耦合水力和生态模型分析中的参数估计，以及蒸散量和碳通量的缩放。基于激光雷达对微观地形的估计，其模式引起了？热？然后呢？冷吗？富含养分的凋落物渗滤液优先流入矿物土壤而产生的土壤生物地球化学循环的斑点，将有助于指导凋落物和土壤养分浓度沿水分和生物有效性的梯度进行采样。通过量化侵蚀和风化速率的功能控制，激光雷达数据将有助于更好地了解沉积物和溶质如何(以及为什么)在地形上移动。更广泛的影响：该项目有两个主要的、直接的、更广泛的影响。首先，向三个CZO提供LiDAR数据将增强它们的潜力，并将其用作社区研究平台。CZOS的目标是建立一个网络，以促进对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，现有的空间数据由于不完整、陈旧、空间分辨率和时间尺度不足，不能满足CZO团队的研究需求。第二个更广泛的影响将是使在CZO和类似研究领域工作的研究人员更容易理解和广泛使用LiDAR产品，建立在CZO数据和资源的社区性质基础上，并制定计划共享这些资源和传播CZO产品。

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.