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Individual structure mapping over six million trees for New York City USA

Individual tree structure mapping in cities is important for urban environmental studies. Despite mapping products for tree canopy cover and biomass are reported at multiple spatial scales using various approaches, spatially explicit mapping of individual trees and their three-dimensional structure...

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Autores principales: Ma, Qin, Lin, Jian, Ju, Yang, Li, Wenkai, Liang, Lu, Guo, Qinghua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9941473/
https://www.ncbi.nlm.nih.gov/pubmed/36807363
http://dx.doi.org/10.1038/s41597-023-02000-w
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author Ma, Qin
Lin, Jian
Ju, Yang
Li, Wenkai
Liang, Lu
Guo, Qinghua
author_facet Ma, Qin
Lin, Jian
Ju, Yang
Li, Wenkai
Liang, Lu
Guo, Qinghua
author_sort Ma, Qin
collection PubMed
description Individual tree structure mapping in cities is important for urban environmental studies. Despite mapping products for tree canopy cover and biomass are reported at multiple spatial scales using various approaches, spatially explicit mapping of individual trees and their three-dimensional structure is sparse. Here we produced an individual tree dataset including tree locations, height, crown area, crown volume, and biomass over the entire New York City, USA for 6,005,690 trees. Individual trees were detected and mapped from remotely sensed datasets along with their height and crown size information. Tree biomass in 296 field plots was measured and modelled using i-Tree Eco. Wall-to-wall tree biomass was mapped using relationships between field measurements and remotely sensed datasets and downscaled to individual trees. Validation using field-plot measurements indicated that our mapping products overestimated tree number, mean tree height and maximum tree height by 11.1%, 8.6%, and 5.3%, respectively. These overestimations were mainly due to the spatial and temporal mis-match between field measurements and remote sensing observations and uncertainties in tree segmentation algorithms. This dataset enables the evaluation of urban forest ecosystem services including regulating urban heat and promoting urban health, which can provide valuable insights for urban forest management and policy making.
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spelling pubmed-99414732023-02-22 Individual structure mapping over six million trees for New York City USA Ma, Qin Lin, Jian Ju, Yang Li, Wenkai Liang, Lu Guo, Qinghua Sci Data Data Descriptor Individual tree structure mapping in cities is important for urban environmental studies. Despite mapping products for tree canopy cover and biomass are reported at multiple spatial scales using various approaches, spatially explicit mapping of individual trees and their three-dimensional structure is sparse. Here we produced an individual tree dataset including tree locations, height, crown area, crown volume, and biomass over the entire New York City, USA for 6,005,690 trees. Individual trees were detected and mapped from remotely sensed datasets along with their height and crown size information. Tree biomass in 296 field plots was measured and modelled using i-Tree Eco. Wall-to-wall tree biomass was mapped using relationships between field measurements and remotely sensed datasets and downscaled to individual trees. Validation using field-plot measurements indicated that our mapping products overestimated tree number, mean tree height and maximum tree height by 11.1%, 8.6%, and 5.3%, respectively. These overestimations were mainly due to the spatial and temporal mis-match between field measurements and remote sensing observations and uncertainties in tree segmentation algorithms. This dataset enables the evaluation of urban forest ecosystem services including regulating urban heat and promoting urban health, which can provide valuable insights for urban forest management and policy making. Nature Publishing Group UK 2023-02-20 /pmc/articles/PMC9941473/ /pubmed/36807363 http://dx.doi.org/10.1038/s41597-023-02000-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Ma, Qin
Lin, Jian
Ju, Yang
Li, Wenkai
Liang, Lu
Guo, Qinghua
Individual structure mapping over six million trees for New York City USA
title Individual structure mapping over six million trees for New York City USA
title_full Individual structure mapping over six million trees for New York City USA
title_fullStr Individual structure mapping over six million trees for New York City USA
title_full_unstemmed Individual structure mapping over six million trees for New York City USA
title_short Individual structure mapping over six million trees for New York City USA
title_sort individual structure mapping over six million trees for new york city usa
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9941473/
https://www.ncbi.nlm.nih.gov/pubmed/36807363
http://dx.doi.org/10.1038/s41597-023-02000-w
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