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Spectral Characteristic Analysis of Burned Area Based on MODIS Data
Wildfires are one of the major serious disasters all over the world. The technology of satellite remote sensing provides useful data for post-fire assessment. This study aimed to investigate the optimal spectral indices for mapping post-fire-burned area. We acquired the post-fire data of Moderate Resolution Imaging Spectroradiometer (MODIS) sensor over several wildfires in boreal forest in western America, 2016. Then, we adopted empirical formula and multi-threshold method to extract the sample sets of five types (burned area, vegetation, cloud, bare soil, and shadow). The separability of spectral indices between burned area and other four types was analyzed by separability index M. Based on the spectral characteristic analysis of burned area, the value of separability index M of the six spectral indices (VI, CSI, MIRBI, NBR, NSEv1, and NSTv1) is larger than 1.0, which indicates that these indices perform well in discriminating burned area and unburned types, and NIR (0.841–0.876 μm), SWIR (2.105–2.155 μm) spectral domain, emissivity of thermal infrared band and land surface temperature are also proved to be sensitive to burned area. The optimal spectral indices obtained in this paper can be integrated into the burned area detection algorithm in further research.
Spectral Characteristic Analysis of Burned Area Based on MODIS Data
Wildfires are one of the major serious disasters all over the world. The technology of satellite remote sensing provides useful data for post-fire assessment. This study aimed to investigate the optimal spectral indices for mapping post-fire-burned area. We acquired the post-fire data of Moderate Resolution Imaging Spectroradiometer (MODIS) sensor over several wildfires in boreal forest in western America, 2016. Then, we adopted empirical formula and multi-threshold method to extract the sample sets of five types (burned area, vegetation, cloud, bare soil, and shadow). The separability of spectral indices between burned area and other four types was analyzed by separability index M. Based on the spectral characteristic analysis of burned area, the value of separability index M of the six spectral indices (VI, CSI, MIRBI, NBR, NSEv1, and NSTv1) is larger than 1.0, which indicates that these indices perform well in discriminating burned area and unburned types, and NIR (0.841–0.876 μm), SWIR (2.105–2.155 μm) spectral domain, emissivity of thermal infrared band and land surface temperature are also proved to be sensitive to burned area. The optimal spectral indices obtained in this paper can be integrated into the burned area detection algorithm in further research.
Spectral Characteristic Analysis of Burned Area Based on MODIS Data
Wu, Guan-Yuan (editor) / Tsai, Kuang-Chung (editor) / Chow, W. K. (editor) / Ba, Rui (author) / Song, Weiguo (author) / Lo, Siuming (author) / Xie, Zixi (author)
Asia-Oceania Symposium on Fire Science and Technology ; 2018 ; Taipei, Taiwan
The Proceedings of 11th Asia-Oceania Symposium on Fire Science and Technology ; Chapter: 29 ; 391-404
2020-01-01
14 pages
Article/Chapter (Book)
Electronic Resource
English
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