Immediate assessment of forest fire using a novel vegetation index and machine learning based on multi-platform, high temporal resolution remote sensing images
Forest fires pose a significant threat to ecosystems, biodiversity, and human settlements, necessitating accurate and timely detection of burned areas for post-fire management. This study focused on the immediate assessment of a recent major forest fire that occurred on March 15, 2024, in southweste...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843224005661 |
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| author | Hanqiu Xu Jiahui Chen Guojin He Zhongli Lin Yafen Bai Mengjie Ren Hao Zhang Huimin Yin Fenfen Liu |
| author_facet | Hanqiu Xu Jiahui Chen Guojin He Zhongli Lin Yafen Bai Mengjie Ren Hao Zhang Huimin Yin Fenfen Liu |
| author_sort | Hanqiu Xu |
| collection | DOAJ |
| description | Forest fires pose a significant threat to ecosystems, biodiversity, and human settlements, necessitating accurate and timely detection of burned areas for post-fire management. This study focused on the immediate assessment of a recent major forest fire that occurred on March 15, 2024, in southwestern China. We comprehensively utilized high temporal resolution MODIS and Black Marble nighttime light images to monitor the fire’s development and introduced a novel method for detecting burned forest areas using a new Shadow-Enhanced Vegetation Index (SEVI) coupling with a machine learning technique. The SEVI effectively enhances the vegetation index (VI) values on shaded slopes and hence reduces the VI disparity between shaded and sunlit areas, which is critical for accurately extracting fire scars in such terrain. While SEVI primarily identifies burned forest areas, the Random Forest (RF) technique detects all burned areas, including both forested and non-forested regions. Consequently, the total burned area of the Yajiang forest fire was estimated at 23,588 ha, with the burned forest area covering 19,266 ha. The combination of SEVI and RF algorithms provided a comprehensive and efficient tool for identifying burned areas. Additionally, our study employed the Remote Sensing-based Ecological Index (RSEI) to assess the ecological impact of the fire on the region, uncovering an immediate 15 % decline in regional ecological conditions following the fire. The usage of RSEI has the potential to quantitatively understand ecological responses to the fire. The findings achieved in this study underscore the significance of precise fire-burned area extraction techniques for enhancing forest fire management and ecosystem recovery strategies, while also highlighting the broader ecological implications of such events. |
| format | Article |
| id | doaj-art-fd9da93c6d784dd79df9dee9b8e4924f |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-fd9da93c6d784dd79df9dee9b8e4924f2024-11-16T05:10:15ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322024-11-01134104210Immediate assessment of forest fire using a novel vegetation index and machine learning based on multi-platform, high temporal resolution remote sensing imagesHanqiu Xu0Jiahui Chen1Guojin He2Zhongli Lin3Yafen Bai4Mengjie Ren5Hao Zhang6Huimin Yin7Fenfen Liu8College of Environment and Safety Engineering, Institute of Remote Sensing Information Engineering, Fuzhou University, Fuzhou 350116, China; Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Prevention, Fuzhou University, Fuzhou 350116, China; Corresponding author at: College of Environment and Safety Engineering, Fuzhou University, Fuzhou 350116, China.College of Environment and Safety Engineering, Institute of Remote Sensing Information Engineering, Fuzhou University, Fuzhou 350116, China; Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Prevention, Fuzhou University, Fuzhou 350116, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaCollege of Architecture and Urban Planning, Fujian University of Technology, Fuzhou 350118, ChinaCollege of Environment and Safety Engineering, Institute of Remote Sensing Information Engineering, Fuzhou University, Fuzhou 350116, China; Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Prevention, Fuzhou University, Fuzhou 350116, ChinaCollege of Environment and Safety Engineering, Institute of Remote Sensing Information Engineering, Fuzhou University, Fuzhou 350116, China; Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Prevention, Fuzhou University, Fuzhou 350116, ChinaSichuan Academy of Forestry, Chengdu, Sichuan 610081, ChinaCollege of Environment and Safety Engineering, Institute of Remote Sensing Information Engineering, Fuzhou University, Fuzhou 350116, China; Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Prevention, Fuzhou University, Fuzhou 350116, ChinaCollege of Environment and Safety Engineering, Institute of Remote Sensing Information Engineering, Fuzhou University, Fuzhou 350116, China; Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Prevention, Fuzhou University, Fuzhou 350116, ChinaForest fires pose a significant threat to ecosystems, biodiversity, and human settlements, necessitating accurate and timely detection of burned areas for post-fire management. This study focused on the immediate assessment of a recent major forest fire that occurred on March 15, 2024, in southwestern China. We comprehensively utilized high temporal resolution MODIS and Black Marble nighttime light images to monitor the fire’s development and introduced a novel method for detecting burned forest areas using a new Shadow-Enhanced Vegetation Index (SEVI) coupling with a machine learning technique. The SEVI effectively enhances the vegetation index (VI) values on shaded slopes and hence reduces the VI disparity between shaded and sunlit areas, which is critical for accurately extracting fire scars in such terrain. While SEVI primarily identifies burned forest areas, the Random Forest (RF) technique detects all burned areas, including both forested and non-forested regions. Consequently, the total burned area of the Yajiang forest fire was estimated at 23,588 ha, with the burned forest area covering 19,266 ha. The combination of SEVI and RF algorithms provided a comprehensive and efficient tool for identifying burned areas. Additionally, our study employed the Remote Sensing-based Ecological Index (RSEI) to assess the ecological impact of the fire on the region, uncovering an immediate 15 % decline in regional ecological conditions following the fire. The usage of RSEI has the potential to quantitatively understand ecological responses to the fire. The findings achieved in this study underscore the significance of precise fire-burned area extraction techniques for enhancing forest fire management and ecosystem recovery strategies, while also highlighting the broader ecological implications of such events.http://www.sciencedirect.com/science/article/pii/S1569843224005661Burned area mappingSEVIRandom ForestEcological impactYajiang forest fire |
| spellingShingle | Hanqiu Xu Jiahui Chen Guojin He Zhongli Lin Yafen Bai Mengjie Ren Hao Zhang Huimin Yin Fenfen Liu Immediate assessment of forest fire using a novel vegetation index and machine learning based on multi-platform, high temporal resolution remote sensing images International Journal of Applied Earth Observations and Geoinformation Burned area mapping SEVI Random Forest Ecological impact Yajiang forest fire |
| title | Immediate assessment of forest fire using a novel vegetation index and machine learning based on multi-platform, high temporal resolution remote sensing images |
| title_full | Immediate assessment of forest fire using a novel vegetation index and machine learning based on multi-platform, high temporal resolution remote sensing images |
| title_fullStr | Immediate assessment of forest fire using a novel vegetation index and machine learning based on multi-platform, high temporal resolution remote sensing images |
| title_full_unstemmed | Immediate assessment of forest fire using a novel vegetation index and machine learning based on multi-platform, high temporal resolution remote sensing images |
| title_short | Immediate assessment of forest fire using a novel vegetation index and machine learning based on multi-platform, high temporal resolution remote sensing images |
| title_sort | immediate assessment of forest fire using a novel vegetation index and machine learning based on multi platform high temporal resolution remote sensing images |
| topic | Burned area mapping SEVI Random Forest Ecological impact Yajiang forest fire |
| url | http://www.sciencedirect.com/science/article/pii/S1569843224005661 |
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