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A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network
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 tech...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7895524/ https://www.ncbi.nlm.nih.gov/pubmed/33605211 http://dx.doi.org/10.7554/eLife.62922 |
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author | Weinstein, Ben G Marconi, Sergio Bohlman, Stephanie A Zare, Alina Singh, Aditya Graves, Sarah J White, Ethan P |
author_facet | Weinstein, Ben G Marconi, Sergio Bohlman, Stephanie A Zare, Alina Singh, Aditya Graves, Sarah J White, Ethan P |
author_sort | Weinstein, Ben G |
collection | PubMed |
description | 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 data set 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. |
format | Online Article Text |
id | pubmed-7895524 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-78955242021-02-22 A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network Weinstein, Ben G Marconi, Sergio Bohlman, Stephanie A Zare, Alina Singh, Aditya Graves, Sarah J White, Ethan P eLife Ecology 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 data set 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. eLife Sciences Publications, Ltd 2021-02-19 /pmc/articles/PMC7895524/ /pubmed/33605211 http://dx.doi.org/10.7554/eLife.62922 Text en © 2021, Weinstein et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Ecology Weinstein, Ben G Marconi, Sergio Bohlman, Stephanie A Zare, Alina Singh, Aditya Graves, Sarah J White, Ethan P A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network |
title | A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network |
title_full | A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network |
title_fullStr | A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network |
title_full_unstemmed | A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network |
title_short | A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network |
title_sort | remote sensing derived data set of 100 million individual tree crowns for the national ecological observatory network |
topic | Ecology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7895524/ https://www.ncbi.nlm.nih.gov/pubmed/33605211 http://dx.doi.org/10.7554/eLife.62922 |
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