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Weinstein BG, Marconi S, Zare A, Bohlman SA, Singh A, Graves SJ, Magee L, Johnson DJ, Record S, Rubio VE, Swenson NG, Townsend P, Veblen TT, Andrus RA, White EP. Individual canopy tree species maps for the National Ecological Observatory Network. PLoS Biol 2024; 22:e3002700. [PMID: 39013163 PMCID: PMC11251727 DOI: 10.1371/journal.pbio.3002700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 06/05/2024] [Indexed: 07/18/2024] Open
Abstract
The ecology of forest ecosystems depends on the composition of trees. Capturing fine-grained information on individual trees at broad scales provides a unique perspective on forest ecosystems, forest restoration, and responses to disturbance. Individual tree data at wide extents promises to increase the scale of forest analysis, biogeographic research, and ecosystem monitoring without losing details on individual species composition and abundance. Computer vision using deep neural networks can convert raw sensor data into predictions of individual canopy tree species through labeled data collected by field researchers. Using over 40,000 individual tree stems as training data, we create landscape-level species predictions for over 100 million individual trees across 24 sites in the National Ecological Observatory Network (NEON). Using hierarchical multi-temporal models fine-tuned for each geographic area, we produce open-source data available as 1 km2 shapefiles with individual tree species prediction, as well as crown location, crown area, and height of 81 canopy tree species. Site-specific models had an average performance of 79% accuracy covering an average of 6 species per site, ranging from 3 to 15 species per site. All predictions are openly archived and have been uploaded to Google Earth Engine to benefit the ecology community and overlay with other remote sensing assets. We outline the potential utility and limitations of these data in ecology and computer vision research, as well as strategies for improving predictions using targeted data sampling.
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Affiliation(s)
- Ben G. Weinstein
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, United States of America
| | - Sergio Marconi
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, United States of America
| | - Alina Zare
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, United States of America
| | - Stephanie A. Bohlman
- School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, Florida, United States of America
| | - Aditya Singh
- Department of Agricultural & Biological Engineering, University of Florida, Gainesville, Florida, United States of America
| | - Sarah J. Graves
- Nelson Institute for Environmental Studies, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Lukas Magee
- School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, Florida, United States of America
| | - Daniel J. Johnson
- School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, Florida, United States of America
| | - Sydne Record
- Department of Wildlife, Fisheries, and Conservation Biology, University of Maine, Orono, Maine, United States of America
| | - Vanessa E. Rubio
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Nathan G. Swenson
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Philip Townsend
- Department of Forest & Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Thomas T. Veblen
- Department of Geography, University of Colorado, Boulder, Colorado, United States of America
| | - Robert A. Andrus
- Department of Geography, University of Colorado, Boulder, Colorado, United States of America
- School of Environment, Washington State University, Pullman, Washington, United States of America
| | - Ethan P. White
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, United States of America
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Meier CL, Thibault KM, Barnett DT. Spatial and temporal sampling strategy connecting
NEON
Terrestrial Observation System protocols. Ecosphere 2023. [DOI: 10.1002/ecs2.4455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023] Open
Affiliation(s)
- Courtney L. Meier
- National Ecological Observatory Network, Battelle Boulder Colorado USA
| | | | - David T. Barnett
- National Ecological Observatory Network, Battelle Boulder Colorado USA
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