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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...

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Autores principales: Weinstein, Ben G, Marconi, Sergio, Bohlman, Stephanie A, Zare, Alina, Singh, Aditya, Graves, Sarah J, White, Ethan P
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2021
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.
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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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