Collaborative Research: High-Resolution Aerial Forest Mapping Infrastructure and Database to Support Forest and Disturbance Ecology Research

森林清单是了解生物模式和过程的关键资源，但它们传统上需要耗时的地面调查。小型无人驾驶航空系统(SUA或“无人机”)和人工智能的最新进展正在推动森林研究的新时代，在这个时代，可以在不进行广泛的地面调查的情况下，在大范围内绘制、测量和识别个别树木的属或种。尽管基于无人机的低成本森林测绘技术现已存在，但使科学家能够制作和获取广泛的森林地图的基础设施有限。该项目建立并协助今后扩大一个由100多个森林清查地块组成的网络，每个地块约25公顷。精细、大范围的森林调查数据使人们能够对塑造森林社区和生态系统的复杂过程有新的见解。随着干旱和严重野火等压力因素推动美国西部和全球森林发生戏剧性变化--包括转向非森林植被--了解这些动态变得越来越紧迫。生态学家和森林管理者需要关于森林对这些新条件的反应的数据，以制定管理战略，但最近变化的速度和幅度对传统的实地数据收集方法提出了挑战。该项目通过森林生态无人机飞行员学徒项目和强调代表性不足的社区的外联活动，向下一代科学家介绍基于无人机的森林测绘工具。它利用NSF在公共网络基础设施方面的现有投资，并培训科学家将其用于云本地研究。它正在通过绘制森林地图来支持多方利益攸关方森林恢复伙伴关系，从而表明森林地图绘制基础设施与森林管理规划的相关性。在招募工作人员和学生参与者方面，该项目邀请支持代表性不足的学生和学者的团体参与，选择过程使用全面审查和远距离旅行标准。该项目涉及开发三个互补的网络基础设施创新，以支持和扩大森林生态和干扰生态学研究的能力：(1)可扩展、可复制、支持人工智能的软件工作流程，用于将来自低成本无人机的图像处理为森林清查数据(例如，按大小和属或物种划分的单个树木的地图)；(2)一个可供公众检索、可供公众查阅、可扩展的树木地图数据库，从100张25公顷的树木地图开始，这些地图与森林调查地块网络(包括国家自然基金会国家生态观测站网络，NEON)沿着重要的非生物和干扰历史梯度对齐；以及(3)文件和培训，包括虚拟讲习班和面对面讲习班，使研究人员能够编制和贡献自己的数据和分析工具。该软件工作流程将摄影测量用于三维林分结构建模和多视角计算机视觉(通过人工神经网络)用于分类分类和拒绝假阳性树检测，将科学家和资源管理人员可以实现的森林调查范围扩大了100倍。该项目利用NSF在研究网络基础设施方面的最大投资之一CyVerse进行数据处理和数据托管。由此产生的公共森林调查数据库支持云本地研究，以改进目前受有限数据限制的森林模式和过程的模型。开放源码软件开发和项目成果可在Open ForestObservator.org上获得。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Forest inventories are critical resources for understanding biological patterns and processes, but they have traditionally required time-consuming ground-based surveys. Recent advances in small uncrewed aerial systems (sUAS, or “drones”) and artificial intelligence are enabling a new era of forest research in which individual trees can be mapped, measured, and identified to genus or species across broad areas without extensive ground surveys. Although the technology for low-cost drone-based forest mapping now exists, infrastructure to enable scientists to produce and access extensive forest maps is limiting. This project establishes and facilitates future expansion of a network of over 100 forest inventory plots of approximately 25 ha each. Fine-scale, broad-extent forest inventory data allows for new insight into the complex processes shaping forest communities and ecosystems. Understanding these dynamics is increasingly urgent as stressors such as droughts and high-severity wildfires drive dramatic shifts in forests–including conversion to non-forest vegetation–in the western U.S. and globally. Ecologists and forest managers require data on forest response to these novel conditions to develop management strategies, but the rate and magnitude of recent changes challenge traditional field-based data collection approaches. This project introduces drone-based forest mapping tools to the next generation of scientists via a Forest Ecology Drone Pilot Apprenticeship and via outreach events emphasizing underrepresented communities. It leverages existing investments in public cyberinfrastructure by NSF and trains scientists in its use for cloud-native research. It is demonstrating the relevance of the forest mapping infrastructure to forest management planning by mapping forests to support a multi-stakeholder forest restoration partnership. In recruiting staff and student participants, the project engages groups supporting underrepresented students and scholars, and the selection processes use holistic review and distance-traveled criteria.This project involves development of three complementary cyberinfrastructure innovations to support and extend the capacity of forest ecology and disturbance ecology research: (1) a scalable, reproducible, AI-enabled software workflow for processing imagery from low-cost drones into forest inventory data (e.g., maps of individual trees by size and genus or species); (2) a searchable, publicly accessible, extensible database of tree maps, initiated with 100, 25-ha maps aligned with forest inventory plot networks (including the NSF National Ecological Observatory Network, NEON) along important abiotic and disturbance history gradients; and (3) documentation and training, including virtual and in-person workshops, to enable researchers to produce and contribute their own data and analytical tools. The software workflow, which incorporates photogrammetry for 3D stand structure modeling and multi-view computer vision (via artificial neural networks) for taxonomic classification and rejection of false-positive tree detections, expands the forest survey extents achievable by scientists and resource managers by 100-fold. The project leverages CyVerse, one of NSF’s largest investments in research cyberinfrastructure, for data processing and data hosting. The resulting public forest inventory database supports cloud native research to improve models of forest pattern and process currently constrained by limited data. Open-source software development and project results are available at openforestobservatory.org.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.