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111-5）资助。s？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，从基岩延伸到大气边界层。对临界区侵蚀、风化、土壤形成、水分移动和养分迁移过程的了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，准确，高‐陆地表面和植被冠层的高分辨率图像，以及它们之间的结构，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&与水有关的关键#8208;区域过程。机载激光雷达（光探测和测距）被证明是一种变革性的技术，可以确定关键区域的三维结构，从而使这一过程的研究。激光雷达飞行将在三个NSF #8208支持的临界区观测站（CZO）和其他三个关键站点测量树叶开/关条件下的树冠、积雪分布和陆地表面的其他物理特征。地形数据将被用来获得激光雷达产品，如高分辨率数字高程模型，树高，树径在胸部高度，叶面积指数，树冠覆盖，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#8208;最先进的#8208;处理，以确保产品准确。智力上的优点。所得信息将用于这些研究中心的假设#8208;驱动研究。高‐高分辨率地形数据将描述地貌特征，并能够检验关于产生这些地貌的地貌过程的假设。这些数据将有助于研究地貌和水文行为方面的作用，以及坡度，土壤水分，风化，土壤形成和植被之间的反馈。还将使用激光雷达的高空间分辨率地形和冠层产品，利用新兴的物理模型进行水文模拟。高#8208;分辨率估计的雪深的空间格局将提供一个前所未有的地面#8208;真实数据集建模的自然控制积雪和融化。激光雷达场景将有助于估计植被结构，然后将用于耦合水文#8208;生态模型分析和蒸散和碳通量的缩放中的参数估计。基于激光雷达的微观地形估计，其模式会引起？热吗然后呢？冷吗养分优先流动产生的土壤地球化学循环点‐丰富的凋落物渗滤液进入矿物土壤，将有助于指导采样的凋落物和土壤养分浓度沿着梯度的水和生物的可用性。通过对侵蚀和风化速率的功能控制进行量化，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）穿过（和穿过）景观。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，为三个CZO提供LiDAR数据将增强它们作为社区研究平台的潜力和用途。CZO的目标是建立一个网络，以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，现有的空间数据不能满足CZO团队的研究需求，因为不完整，过时，空间分辨率和时间尺度不足。第二个更广泛的影响将是使LiDAR产品易于理解，并被在CZO和类似研究领域工作的研究人员广泛使用，建立在CZO数据和资源的社区性质基础上，并制定了共享这些资源和传播CZO产品的计划。

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.