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Remote sensing technology for rapid extraction of burned areas and ecosystem environmental assessment

Forest fires are one of the significant disturbances in forest ecosystems. It is essential to extract burned areas rapidly and accurately to formulate forest restoration strategies and plan restoration plans. In this work, we constructed decision trees and used a combination of differential normaliz...

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Autores principales: Zhang, Shiqi, Bai, Maoyang, Wang, Xiao, Peng, Xuefeng, Chen, Ailin, Peng, Peihao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9910190/
https://www.ncbi.nlm.nih.gov/pubmed/36778148
http://dx.doi.org/10.7717/peerj.14557
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author Zhang, Shiqi
Bai, Maoyang
Wang, Xiao
Peng, Xuefeng
Chen, Ailin
Peng, Peihao
author_facet Zhang, Shiqi
Bai, Maoyang
Wang, Xiao
Peng, Xuefeng
Chen, Ailin
Peng, Peihao
author_sort Zhang, Shiqi
collection PubMed
description Forest fires are one of the significant disturbances in forest ecosystems. It is essential to extract burned areas rapidly and accurately to formulate forest restoration strategies and plan restoration plans. In this work, we constructed decision trees and used a combination of differential normalized burn ratio (dNBR) index and OTSU threshold method to extract the heavily and mildly burned areas. The applicability of this method was evaluated with three fires in Muli County, Sichuan, China, and we concluded that the extraction accuracy of this method could reach 97.69% and 96.37% for small area forest fires, while the extraction accuracy was lower for large area fires, only 89.32%. In addition, the remote sensing environment index (RSEI) was used to evaluate the ecological environment changes. It analyzed the change of the RSEI level through the transition matrix, and all three fires showed that the changes in RSEI were stronger for heavily burned areas than for mildly burned areas, after the forest fire the ecological environment (RSEI) was reduced from good to moderate. These results realized the quantitative evaluation and dynamic evaluation of the ecological environment condition, providing an essential basis for the restoration, decision making and management of the affected forests.
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spelling pubmed-99101902023-02-10 Remote sensing technology for rapid extraction of burned areas and ecosystem environmental assessment Zhang, Shiqi Bai, Maoyang Wang, Xiao Peng, Xuefeng Chen, Ailin Peng, Peihao PeerJ Environmental Impacts Forest fires are one of the significant disturbances in forest ecosystems. It is essential to extract burned areas rapidly and accurately to formulate forest restoration strategies and plan restoration plans. In this work, we constructed decision trees and used a combination of differential normalized burn ratio (dNBR) index and OTSU threshold method to extract the heavily and mildly burned areas. The applicability of this method was evaluated with three fires in Muli County, Sichuan, China, and we concluded that the extraction accuracy of this method could reach 97.69% and 96.37% for small area forest fires, while the extraction accuracy was lower for large area fires, only 89.32%. In addition, the remote sensing environment index (RSEI) was used to evaluate the ecological environment changes. It analyzed the change of the RSEI level through the transition matrix, and all three fires showed that the changes in RSEI were stronger for heavily burned areas than for mildly burned areas, after the forest fire the ecological environment (RSEI) was reduced from good to moderate. These results realized the quantitative evaluation and dynamic evaluation of the ecological environment condition, providing an essential basis for the restoration, decision making and management of the affected forests. PeerJ Inc. 2023-02-06 /pmc/articles/PMC9910190/ /pubmed/36778148 http://dx.doi.org/10.7717/peerj.14557 Text en © 2023 Zhang 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, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Environmental Impacts
Zhang, Shiqi
Bai, Maoyang
Wang, Xiao
Peng, Xuefeng
Chen, Ailin
Peng, Peihao
Remote sensing technology for rapid extraction of burned areas and ecosystem environmental assessment
title Remote sensing technology for rapid extraction of burned areas and ecosystem environmental assessment
title_full Remote sensing technology for rapid extraction of burned areas and ecosystem environmental assessment
title_fullStr Remote sensing technology for rapid extraction of burned areas and ecosystem environmental assessment
title_full_unstemmed Remote sensing technology for rapid extraction of burned areas and ecosystem environmental assessment
title_short Remote sensing technology for rapid extraction of burned areas and ecosystem environmental assessment
title_sort remote sensing technology for rapid extraction of burned areas and ecosystem environmental assessment
topic Environmental Impacts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9910190/
https://www.ncbi.nlm.nih.gov/pubmed/36778148
http://dx.doi.org/10.7717/peerj.14557
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