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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2023-02-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/14557.pdf |
| _version_ | 1797413281576517632 |
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| author | Shiqi Zhang Maoyang Bai Xiao Wang Xuefeng Peng Ailin Chen Peihao Peng |
| author_facet | Shiqi Zhang Maoyang Bai Xiao Wang Xuefeng Peng Ailin Chen Peihao Peng |
| author_sort | Shiqi Zhang |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T05:16:01Z |
| format | Article |
| id | doaj.art-73a309a5274448aa811f796a5cab8d21 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T05:16:01Z |
| publishDate | 2023-02-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-73a309a5274448aa811f796a5cab8d212023-12-03T12:45:56ZengPeerJ Inc.PeerJ2167-83592023-02-0111e1455710.7717/peerj.14557Remote sensing technology for rapid extraction of burned areas and ecosystem environmental assessmentShiqi Zhang0Maoyang Bai1Xiao Wang2Xuefeng Peng3Ailin Chen4Peihao Peng5College of Earth Sciences, Chengdu University of Technology, Chengdu, ChinaCollege of Earth Sciences, Chengdu University of Technology, Chengdu, ChinaSchool of Architecture and Civil Engineering, Chengdu University, Chengdu, ChinaCollege of Tourism and Urban-Rural Planning, Chengdu University of Technology, Chengdu, ChinaSichuan Earthquake Agency, Chengdu, ChinaCollege of Earth Sciences, Chengdu University of Technology, Chengdu, ChinaForest 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.https://peerj.com/articles/14557.pdfForest fireBurned areasSentinel-2Remote sensing environment indexGEE platformOTSU threshold |
| spellingShingle | Shiqi Zhang Maoyang Bai Xiao Wang Xuefeng Peng Ailin Chen Peihao Peng Remote sensing technology for rapid extraction of burned areas and ecosystem environmental assessment PeerJ Forest fire Burned areas Sentinel-2 Remote sensing environment index GEE platform OTSU threshold |
| 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 | Forest fire Burned areas Sentinel-2 Remote sensing environment index GEE platform OTSU threshold |
| url | https://peerj.com/articles/14557.pdf |
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