A remote sensing derived dataset of 100 million individual tree crowns for the National Ecological Observatory Network
- University of Florida, United States
- 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
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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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
