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NEON Crowns: a remote sensing derived dataset of 100 million individual tree crowns

View ORCID ProfileBen. G. Weinstein, Sergio Marconi, Stephanie Bohlman, Alina Zare, Aditya Singh, Sarah J. Graves, Ethan White
doi: https://doi.org/10.1101/2020.09.08.287839
Ben. G. Weinstein
1Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, USA
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  • For correspondence: benweinstein2010@gmail.com
Sergio Marconi
1Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, USA
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Stephanie Bohlman
2School of Forest Resources and Conservation, University of Florida, Gainesville, Florida, USA
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Alina Zare
3Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
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Aditya Singh
4Department of Agricultural & Biological Engineering, University of Florida, Gainesville, FL 32611, USA
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Sarah J. Graves
5Nelson Institute for Environmental Studies, University of Wisconsin-Madison, Madison, Wisconsin, USA
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Ethan White
1Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, USA
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Abstract

Forests provide essential biodiversity, ecosystem and economic services. Information on individual trees is important for understanding the state of forest ecosystems but obtaining individual-level data at broad scales is challenging due to the costs and logistics of data collection. While advances in remote sensing techniques allow surveys of individual trees at unprecedented extents, there remain significant technical and computational challenges in turning sensor data into tangible information. Using deep learning methods, we produced an open-source dataset of individual-level crown estimates for 100 million trees at 37 sites across the United States surveyed by the National Ecological Observatory Network’s Airborne Observation Platform. Each canopy tree crown is represented by a rectangular bounding box and includes information on the height, crown area, and spatial location of the tree. Tree crowns identified using this technique correspond well with hand-labeled crowns, exhibiting both high levels of overlap and good correspondence in height estimates. These data have the potential to drive significant expansion of individual-level research on trees by facilitating both regional analyses at scales of ~10,000 ha and cross-region comparisons encompassing forest types from most of the United States.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://zenodo.org/record/3765872#.X1esy2dKjOQ

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted September 09, 2020.
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NEON Crowns: a remote sensing derived dataset of 100 million individual tree crowns
Ben. G. Weinstein, Sergio Marconi, Stephanie Bohlman, Alina Zare, Aditya Singh, Sarah J. Graves, Ethan White
bioRxiv 2020.09.08.287839; doi: https://doi.org/10.1101/2020.09.08.287839
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NEON Crowns: a remote sensing derived dataset of 100 million individual tree crowns
Ben. G. Weinstein, Sergio Marconi, Stephanie Bohlman, Alina Zare, Aditya Singh, Sarah J. Graves, Ethan White
bioRxiv 2020.09.08.287839; doi: https://doi.org/10.1101/2020.09.08.287839

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