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Open AccessArticle

Mapping of Post-Wildfire Burned Area Using a Hybrid Algorithm and Satellite Data: The Case of the Camp Fire Wildfire in California, USA

1
Division of Science Education, College of Education # 4-301, Gangwondaehak-gil Chuncheon-si, Kangwon National University, Gangwon-do 24341, Korea
2
Geoscience Platform Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), Gajeong-dong 30, Yuseong-gu, Daejeon 305-350, Korea
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(4), 623; https://doi.org/10.3390/rs12040623
Received: 5 January 2020 / Revised: 10 February 2020 / Accepted: 11 February 2020 / Published: 13 February 2020
On November 8, 2018, a devastating wildfire, known as the Camp Fire wildfire, was reported in Butte County, California, USA. Approximately 88 fatalities ensued, and 18,804 structures were damaged by the wildfire. As a response to this destructive wildfire, this study generated a pre- and post-wildfire maps to provide basic data for evacuation and mitigation planning. This study used Landsat-8 and Sentinel-2 imagery to map the pre- and post-wildfire conditions. A support vector machine (SVM) optimized by the imperialist competitive algorithm (ICA) hybrid model was compared with the non-optimized SVM algorithm for classification of the pre- and post-wildfire map. The SVM–ICA produced a better accuracy (overall accuracies of 83.8% and 83.6% for pre- and post-wildfire using Landsat-8 respectively; 90.8% and 91.8% for pre- and post-wildfire using Sentinel-2 respectively), compared to SVM without optimization (overall accuracies of 80.0% and 78.9% for pre- and post-wildfire using Landsat-8 respectively; 83.3% and 84.8% for pre- and post-wildfire using Sentinel-2 respectively. In total, eight pre- and post-wildfire burned area maps were generated; these can be used to assess the area affected by the Camp Fire wildfire as well as for wildfire mitigation planning in the future. View Full-Text
Keywords: wildfire; hybrid model; SVM–ICA; Landsat-8; Sentinel-2; imperialist competitive algorithm wildfire; hybrid model; SVM–ICA; Landsat-8; Sentinel-2; imperialist competitive algorithm
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MDPI and ACS Style

Syifa, M.; Panahi, M.; Lee, C.-W. Mapping of Post-Wildfire Burned Area Using a Hybrid Algorithm and Satellite Data: The Case of the Camp Fire Wildfire in California, USA. Remote Sens. 2020, 12, 623. https://doi.org/10.3390/rs12040623

AMA Style

Syifa M, Panahi M, Lee C-W. Mapping of Post-Wildfire Burned Area Using a Hybrid Algorithm and Satellite Data: The Case of the Camp Fire Wildfire in California, USA. Remote Sensing. 2020; 12(4):623. https://doi.org/10.3390/rs12040623

Chicago/Turabian Style

Syifa, Mutiara; Panahi, Mahdi; Lee, Chang-Wook. 2020. "Mapping of Post-Wildfire Burned Area Using a Hybrid Algorithm and Satellite Data: The Case of the Camp Fire Wildfire in California, USA" Remote Sens. 12, no. 4: 623. https://doi.org/10.3390/rs12040623

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