We are recruiting a Spatial Data Scientist to contribute to OFO research and development starting between January and September 2023.

Visit our community forum for discussion and help from OFO staff and fellow users, or contact us directly by emailing openforestobservatory@ucdavis.edu.

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.

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.

The OFO is funded primarily by the National Science Foundation (grants 2152671, 2152672, and 2152673), with additional support from The Nature Conservancy. We are housed in the Department of Plant Sciences at the University of California, Davis, the CIRES Earth Lab at the University of Colorado, Boulder, and the Bio5 Institute at the University of Arizona. We rely on ground-reference forest inventory data from the USDA Forest Service Pacific Southwest Region and the National Ecological Observatory Network. We rely on CyVerse and Jetstream2 computing infrastructure.