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

合作研究：支持森林和干扰生态学研究的高分辨率航空森林测绘基础设施和数据库

• 批准号: 2152672
• 负责人: GINIKANDA ILANGAKOON
• 金额: $ 15.48万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152672&HistoricalAwards=false
• 关键词: Collaborative; Research; Resolution; Aerial; Forest

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.

森林清查是了解生物格局和过程的关键资源，但传统上需要进行耗时的地面调查。小型无人驾驶航空系统（sUAS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，在这个新时代中，可以在没有广泛地面调查的情况下，在广阔的区域内对单个树木进行测绘，测量和识别属或种。虽然现在已经有了低成本的无人机森林测绘技术，但使科学家能够制作和获取广泛森林地图的基础设施仍然有限。该项目建立了一个由100多块森林清查地块组成的网络，每块约25公顷，并为今后扩大该网络提供便利。精细、广泛的森林清查数据使人们能够对塑造森林群落和生态系统的复杂过程有新的认识。了解这些动态是越来越紧迫的压力，如干旱和高严重性野火驱动戏剧性的变化，在美国西部和全球的森林，包括转换为非森林植被。生态学家和森林管理人员需要关于森林对这些新条件的反应的数据来制定管理战略，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学无人机试点学徒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原生研究。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，展示森林测绘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时，该项目与支持代表性不足的学生和学者的团体合作，选择过程使用整体审查和旅行距离标准。该项目涉及开发三个互补的网络基础设施创新，以支持和扩展森林生态学和干扰生态学研究的能力：（1）可扩展、可复制、支持AI的软件工作流程，用于将低成本无人机的图像处理为森林资源清查数据（例如，按大小和属或种分列的个别树木的地图）;（2）一个可搜索的、公众可访问的、可扩展的树木地图数据库，首先是100、25公顷的地图，与森林清查样地网络相一致（包括NSF国家生态观测网，氖）沿着重要的非生物和干扰历史梯度;以及（3）文件编制和培训，包括虚拟和现场研讨会，使研究人员能够制作和贡献自己的数据和分析工具。该软件工作流程结合了用于3D林分结构建模的摄影测量和用于分类分类和拒绝假阳性树木检测的多视图计算机视觉（通过人工神经网络），将科学家和资源管理人员可实现的森林调查范围扩大了100倍。该项目利用CyVerse（NSF在研究网络基础设施方面最大的投资之一）进行数据处理和数据托管。由此产生的公共森林资源清查数据库支持云原生研究，以改进目前受有限数据限制的森林模式和过程模型。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。