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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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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. |
format | Online Article Text |
id | pubmed-9910190 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
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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