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
(This article belongs to the Special Issue Urban/Coastal Vegetation Change and Their Impacts on Metropolitan Territories)
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
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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 StyleSyifa, Mutiara, Mahdi Panahi, and Chang-Wook Lee. 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 Sensing 12, no. 4: 623. https://doi.org/10.3390/rs12040623
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
