Combining Theory, Deep Learning, and Lidar to Test Climate and Slope Controls on Tree Throw Production on Hillslopes

当树木倒下并连根拔起时，它们会突然将土壤和岩石从土壤覆盖层深处推到地表。这一过程被称为树抛，是山丘上沉积物运输的重要贡献者，并影响森林景观中的土壤、水、碳和生态，但量化此类事件的频率具有挑战性，因为事件不常见。然而，树扔留下了地形特征：一个坑的位置倒下的树和一个土堆的“抛出”沉积物立即下坡。树木抛掷的地形特征持续了几十年或几个世纪，因此陆地表面代表了树木抛掷事件的历史，并提供了量化过程的机会。此外，由于树木抛掷往往是由极端天气驱动的，树木抛掷的地形特征可以作为极端事件的档案。研究人员以描述因周期性产生坑和土丘而导致的地表粗糙度的理论为基础，将利用高分辨率地形数据、理论和机器学习的地形特征来绘制大面积树木投掷实例。该小组将通过印第安纳州大学的环境变化教育计划，包括为期一周的研讨会，重点是体验式学习和课程开发，吸引K-12教师。最后，通过将地球科学和深度学习相结合，研究该补助金的研究生将接受跨学科方法的培训，并能够解决科学，工业和信息学相关领域的问题。当极端大气事件对森林树冠施加的力量超过土壤和根系的强度时，就会发生树木倒伏。连根拔起在森林地面上创造了一个地形特征，这就像爬行一样，重复劳动和退化。因此，地形粗糙度的空间格局包含了树木抛洒率和驱动它们的事件的过程信息。这个项目将建立新的方法，自动映射坑丘对联地形数据和理论解释粗糙度的过程为基础的条款。研究人员将在印第安纳州、西弗吉尼亚州、宾夕法尼亚州、南卡罗来纳州和田纳西州的选定地点使用激光雷达数据来识别坑丘对联。他们还将使用配备激光雷达装置的无人驾驶飞行器收集更高分辨率的激光雷达数据集，以增强公开可用的数据。为了在大面积上绘制坑丘对联，研究人员将开发和训练深度学习算法，自动绘制坑丘对联的位置。他们预计将在印第安纳州南部绘制数百万个特征，他们已经在那里展示了高密度的树木投掷坑丘对联。该研究小组将联合收割机与现有的理论相结合，自动绘制树木投掷事件的清单，以提供控制树木投掷的速率和空间模式的新见解。该项目由地球科学理事会和高级网络基础设施办公室合作共同资助，以支持人工智能/地球科学中的ML和开放科学活动。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的评估被认为值得支持。影响审查标准。

When trees fall over and uproot, they suddenly heave soil and rock from deep in the soil mantle to the surface. This process, called tree throw, is an important contributor to sediment transport on hills and influences soils, water, carbon, and ecology in forested landscapes, yet quantifying the frequency of such events is challenging because events are infrequent. However, tree throw leaves a topographic signature: a pit in the location of the fallen tree and a mound of “thrown” sediment immediately downslope. The topographic signature of tree throw persists for many decades or centuries so that the land surface represents a history of tree throw events and offers an opportunity to quantify the process. Further, because tree throw is often driven by extreme weather, the topographic signature of tree throw may serve as an archive of extreme events. Building on theory that describes the roughness of the land surface due to the periodic creation of pits and mounds, the investigators will leverage topographic signatures from high resolution topographic data, theory, and machine learning to map tree throw instances across large areas. The team will engage K-12 teachers through the Indiana University’s Education for Environmental Change program that consists of a week-long workshop that focuses on experiential learning and curriculum development. Finally, by combining Earth science and deep learning, graduate students working on this grant will be trained in cross-disciplinary methods and will be able to address problems in science, industry, and informatics-related fields. Tree throw occurs when extreme atmospheric events exert forces on forest canopies that can exceed soil and root strengths. The uprooting creates a topographic signature in forest floors, which creep-like processes rework and degrade. Thus, the spatial patterns of topographic roughness contain process information of tree throw rates and the events that drive them. This project will establish new methods for automated mapping of pit-mound couplets in topographic data and theory to interpret roughness in process-based terms. The researchers will use lidar data at select sites in Indiana, West Virginia, Pennsylvania, South Carolina, and Tennessee to identify pit-mound couplets. They will also augment publicly available data with higher resolution lidar datasets that they will collect using an unmanned aerial vehicle equipped with a lidar unit. To map pit-mound couplets across large areas, the researchers will develop and train deep learning algorithms that automatically map the locations of pit-mound couplets. They anticipate mapping several million features across southern Indiana where they have already demonstrated a high density of tree throw pit-mound couplets. The research team will combine the automatically mapped inventory of tree throw events with existing theory to provide new insights on what controls the rates and spatial patterns of tree throw.This project is co-funded by a collaboration between the Directorate for Geosciences and Office of Advanced Cyberinfrastructure to support AI/ML and open science activities in the geosciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.