A remote sensing derived dataset of 100 million individual tree crowns for the National Ecological Observatory Network

  1. Ben G Weinstein  Is a corresponding author
  2. Sergio Marconi
  3. Stephanie A Bohlman
  4. Alina Zare
  5. Aditya Singh
  6. Sarah J Graves
  7. Ethan P White
  1. University of Florida, United States
  2. University of Wisconsin - Madison, United States

Abstract

Forests provide biodiversity, ecosystem and economic services. Information on individual trees is important for understanding 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 technical 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. These data have the potential to drive significant expansion of individual-level research on trees by facilitating both regional analyses and cross-region comparisons encompassing forest types from most of the United States.

Data availability

The dataset is available at https://zenodo.org/record/3765872#.X2J1zZNKjOQ

Article and author information

Author details

  1. Ben G Weinstein

    Wildlife Ecology and Conservation, University of Florida, Gainesville, United States
    For correspondence
    ben.weinstein@weecology.org
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2176-7935
  2. Sergio Marconi

    Wildlife Ecology and Conservation, University of Florida, Gainesville, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Stephanie A Bohlman

    Forest Resources and Conservation, University of Florida, Gainesville, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Alina Zare

    Electrical Engineering and Computer Engineering, University of Florida, Gainesville, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4847-7604
  5. Aditya Singh

    Department of Agricultural & Biological Engineering, University of Florida, Gainesville, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Sarah J Graves

    Environmental Science, University of Wisconsin - Madison, Madison, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Ethan P White

    Wildlife Ecology and Conservation, University of Florida, Gainesville, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6728-7745

Funding

Gordon and Betty Moore Foundation (GBMF4563)

  • Ethan P White

National Science Foundation (1926542)

  • Stephanie A Bohlman
  • Alina Zare
  • Aditya Singh
  • Ethan P White

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2021, Weinstein et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Ben G Weinstein
  2. Sergio Marconi
  3. Stephanie A Bohlman
  4. Alina Zare
  5. Aditya Singh
  6. Sarah J Graves
  7. Ethan P White
(2021)
A remote sensing derived dataset of 100 million individual tree crowns for the National Ecological Observatory Network
eLife 10:e62922.
https://doi.org/10.7554/eLife.62922

Share this article

https://doi.org/10.7554/eLife.62922

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