Comparative study of machine learning methods for mapping forest fire areas using Sentinel-1B and 2A imagery
The study focuses on identifying fireburning and burnt areas in a large-scale forest fire that occurred in Xintian County, China, in October 2022. To investigate the adaptability of machine learning methods in various scenarios for mapping forest fire areas, this study presents a comparative study o...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Remote Sensing |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frsen.2024.1446641/full |
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| author | Xinbao Chen Xinbao Chen Yaohui Zhang Shan Wang Zecheng Zhao Chang Liu Junjun Wen |
| author_facet | Xinbao Chen Xinbao Chen Yaohui Zhang Shan Wang Zecheng Zhao Chang Liu Junjun Wen |
| author_sort | Xinbao Chen |
| collection | DOAJ |
| description | The study focuses on identifying fireburning and burnt areas in a large-scale forest fire that occurred in Xintian County, China, in October 2022. To investigate the adaptability of machine learning methods in various scenarios for mapping forest fire areas, this study presents a comparative study on the recognition and mapping accuracy of three machine learning algorithms, namely, Support Vector Machine (SVM), Random Forest (RF), and Neural Network (NN), based on Sentinel-1B and 2A imagery. Initially, three sets of pre-fire, during-fire, and post-fire remote sensing data were preprocessed. Various feature parameters from Sentinel-1B and 2A imagery were combined to identify firerelated land cover types. The experimental results revealed that: (i) During the pre-fire period, the SVM method demonstrated superior accuracy compared to the other two methods. The combination of spectral and Normalized Difference Vegetation Index (NDVI) features achieved an optimal accuracy for identifying forest areas with an overall accuracy (OA) of 93.52%. (ii) In the during-fire period, RF method exhibited higher accuracy compared to the other two methods with peak fire identification accuracy reached by combining spectral and Normalized Burn Ratio (NBR) index features at an OA of 95.43%. (iii) In the post-fire period, SVM demonstrated superior accuracy compared to other methods. The highest accuracy of 94.97% was achieved when combining spectral and radar features from Sentinel-1B imagery, highlighting the effectiveness of using spectral and radar backward scattering coefficients as feature parameters to enhance forest fire recognition accuracy for burnt areas. These findings suggest that appropriate machine learning algorithms should be employed under different conditions to obtain more precise identification of forest fire areas. This study provides technical support and empirical evidence for extracting and mapping forest fire areas while assessing damage caused by fires. |
| format | Article |
| id | doaj-art-25e9f774c71b4298a8fe188d558e4bcd |
| institution | OA Journals |
| issn | 2673-6187 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Remote Sensing |
| spelling | doaj-art-25e9f774c71b4298a8fe188d558e4bcd2025-08-20T02:30:23ZengFrontiers Media S.A.Frontiers in Remote Sensing2673-61872024-12-01510.3389/frsen.2024.14466411446641Comparative study of machine learning methods for mapping forest fire areas using Sentinel-1B and 2A imageryXinbao Chen0Xinbao Chen1Yaohui Zhang2Shan Wang3Zecheng Zhao4Chang Liu5Junjun Wen6Sanya Institute of Hunan University of Science and Technology, Sanya, ChinaSchool of Earth Sciences and Spatial Information Engineering, Hunan University of Sciences and Technology, Xiangtan, ChinaSchool of Earth Sciences and Spatial Information Engineering, Hunan University of Sciences and Technology, Xiangtan, ChinaHunan Institute of Geological Disaster Investigation and Monitoring, Changsha, Hunan, ChinaSchool of Earth Sciences and Spatial Information Engineering, Hunan University of Sciences and Technology, Xiangtan, ChinaSchool of Earth Sciences and Spatial Information Engineering, Hunan University of Sciences and Technology, Xiangtan, ChinaHunan Institute of Geological Disaster Investigation and Monitoring, Changsha, Hunan, ChinaThe study focuses on identifying fireburning and burnt areas in a large-scale forest fire that occurred in Xintian County, China, in October 2022. To investigate the adaptability of machine learning methods in various scenarios for mapping forest fire areas, this study presents a comparative study on the recognition and mapping accuracy of three machine learning algorithms, namely, Support Vector Machine (SVM), Random Forest (RF), and Neural Network (NN), based on Sentinel-1B and 2A imagery. Initially, three sets of pre-fire, during-fire, and post-fire remote sensing data were preprocessed. Various feature parameters from Sentinel-1B and 2A imagery were combined to identify firerelated land cover types. The experimental results revealed that: (i) During the pre-fire period, the SVM method demonstrated superior accuracy compared to the other two methods. The combination of spectral and Normalized Difference Vegetation Index (NDVI) features achieved an optimal accuracy for identifying forest areas with an overall accuracy (OA) of 93.52%. (ii) In the during-fire period, RF method exhibited higher accuracy compared to the other two methods with peak fire identification accuracy reached by combining spectral and Normalized Burn Ratio (NBR) index features at an OA of 95.43%. (iii) In the post-fire period, SVM demonstrated superior accuracy compared to other methods. The highest accuracy of 94.97% was achieved when combining spectral and radar features from Sentinel-1B imagery, highlighting the effectiveness of using spectral and radar backward scattering coefficients as feature parameters to enhance forest fire recognition accuracy for burnt areas. These findings suggest that appropriate machine learning algorithms should be employed under different conditions to obtain more precise identification of forest fire areas. This study provides technical support and empirical evidence for extracting and mapping forest fire areas while assessing damage caused by fires.https://www.frontiersin.org/articles/10.3389/frsen.2024.1446641/fullcomparative studyforest firemachine learningSentinel-1B/2Aclassification |
| spellingShingle | Xinbao Chen Xinbao Chen Yaohui Zhang Shan Wang Zecheng Zhao Chang Liu Junjun Wen Comparative study of machine learning methods for mapping forest fire areas using Sentinel-1B and 2A imagery Frontiers in Remote Sensing comparative study forest fire machine learning Sentinel-1B/2A classification |
| title | Comparative study of machine learning methods for mapping forest fire areas using Sentinel-1B and 2A imagery |
| title_full | Comparative study of machine learning methods for mapping forest fire areas using Sentinel-1B and 2A imagery |
| title_fullStr | Comparative study of machine learning methods for mapping forest fire areas using Sentinel-1B and 2A imagery |
| title_full_unstemmed | Comparative study of machine learning methods for mapping forest fire areas using Sentinel-1B and 2A imagery |
| title_short | Comparative study of machine learning methods for mapping forest fire areas using Sentinel-1B and 2A imagery |
| title_sort | comparative study of machine learning methods for mapping forest fire areas using sentinel 1b and 2a imagery |
| topic | comparative study forest fire machine learning Sentinel-1B/2A classification |
| url | https://www.frontiersin.org/articles/10.3389/frsen.2024.1446641/full |
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