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Carbon budget of different forests in China estimated by an individual-based model and remote sensing

Forests play a key role in the regional or global carbon cycle. Determining the forest carbon budget is of great significance for estimating regional carbon budgets and formulating forest management policies to cope with climate change. However, the carbon budget of Chinese different forests and the...

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Autores principales: Zhao, Junfang, Ai, Jinlong, Zhu, Yujie, Huang, Ruixi, Peng, Huiwen, Xie, Hongfei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10561855/
https://www.ncbi.nlm.nih.gov/pubmed/37812610
http://dx.doi.org/10.1371/journal.pone.0285790
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author Zhao, Junfang
Ai, Jinlong
Zhu, Yujie
Huang, Ruixi
Peng, Huiwen
Xie, Hongfei
author_facet Zhao, Junfang
Ai, Jinlong
Zhu, Yujie
Huang, Ruixi
Peng, Huiwen
Xie, Hongfei
author_sort Zhao, Junfang
collection PubMed
description Forests play a key role in the regional or global carbon cycle. Determining the forest carbon budget is of great significance for estimating regional carbon budgets and formulating forest management policies to cope with climate change. However, the carbon budget of Chinese different forests and their relative contributions are not completely clear so far. We evaluated the carbon budget of different forests from 1981 to 2020 in China through combining model with remote sensing observation. In addition, we also determined the relative contribution of carbon budget of each forest type to all forests in China. Eight forest types were studied: evergreen coniferous forest (ECF), deciduous coniferous forest (DCF), coniferous and broad-leaved mixed forest (CBF), deciduous broad-leaved forest (DBF), evergreen broad-leaved forest (EBF), evergreen deciduous broad-leaved mixed forest (EDBF), seasonal rain forest (SRF), and rain forest (RF). The results indicated that the Chinese forests were mainly carbon sink from 1981 to 2020, particularly the annual average carbon budget of forest from 2011 to 2020 was 0.191 PgC·a(-1). Spatially, the forests’ carbon budget demonstrated obvious regional differences, gradually decreasing from Southeast China to Northwest China. The relative contributions of carbon budget in different forests to all forests in China were different. During 2011–2020, the ECF forests contributed the most carbon budget (34.45%), followed by DBF forests (25.89%), EBF forests (24.82%), EDBF forests (13.10%), RF forests (2.23%), SRF forests (3.14%) and CBF forests (1.14%). However, the DCF forests were found mainly as carbon source. These results contribute to our understanding of regional carbon budget of forests.
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spelling pubmed-105618552023-10-10 Carbon budget of different forests in China estimated by an individual-based model and remote sensing Zhao, Junfang Ai, Jinlong Zhu, Yujie Huang, Ruixi Peng, Huiwen Xie, Hongfei PLoS One Research Article Forests play a key role in the regional or global carbon cycle. Determining the forest carbon budget is of great significance for estimating regional carbon budgets and formulating forest management policies to cope with climate change. However, the carbon budget of Chinese different forests and their relative contributions are not completely clear so far. We evaluated the carbon budget of different forests from 1981 to 2020 in China through combining model with remote sensing observation. In addition, we also determined the relative contribution of carbon budget of each forest type to all forests in China. Eight forest types were studied: evergreen coniferous forest (ECF), deciduous coniferous forest (DCF), coniferous and broad-leaved mixed forest (CBF), deciduous broad-leaved forest (DBF), evergreen broad-leaved forest (EBF), evergreen deciduous broad-leaved mixed forest (EDBF), seasonal rain forest (SRF), and rain forest (RF). The results indicated that the Chinese forests were mainly carbon sink from 1981 to 2020, particularly the annual average carbon budget of forest from 2011 to 2020 was 0.191 PgC·a(-1). Spatially, the forests’ carbon budget demonstrated obvious regional differences, gradually decreasing from Southeast China to Northwest China. The relative contributions of carbon budget in different forests to all forests in China were different. During 2011–2020, the ECF forests contributed the most carbon budget (34.45%), followed by DBF forests (25.89%), EBF forests (24.82%), EDBF forests (13.10%), RF forests (2.23%), SRF forests (3.14%) and CBF forests (1.14%). However, the DCF forests were found mainly as carbon source. These results contribute to our understanding of regional carbon budget of forests. Public Library of Science 2023-10-09 /pmc/articles/PMC10561855/ /pubmed/37812610 http://dx.doi.org/10.1371/journal.pone.0285790 Text en © 2023 Zhao et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zhao, Junfang
Ai, Jinlong
Zhu, Yujie
Huang, Ruixi
Peng, Huiwen
Xie, Hongfei
Carbon budget of different forests in China estimated by an individual-based model and remote sensing
title Carbon budget of different forests in China estimated by an individual-based model and remote sensing
title_full Carbon budget of different forests in China estimated by an individual-based model and remote sensing
title_fullStr Carbon budget of different forests in China estimated by an individual-based model and remote sensing
title_full_unstemmed Carbon budget of different forests in China estimated by an individual-based model and remote sensing
title_short Carbon budget of different forests in China estimated by an individual-based model and remote sensing
title_sort carbon budget of different forests in china estimated by an individual-based model and remote sensing
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10561855/
https://www.ncbi.nlm.nih.gov/pubmed/37812610
http://dx.doi.org/10.1371/journal.pone.0285790
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