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Forest fire susceptibility assessment using google earth engine in Gangwon-do, Republic of Korea

Cited 28 time in Web of Science Cited 32 time in Scopus
Authors

Piao, Yong; Lee, Dongkun; Park, Sangjin; Kim, Ho Gul; Jin, Yihua

Issue Date
2022-12
Publisher
Taylor and Francis Inc.
Citation
Geomatics, Natural Hazards and Risk, Vol.13 No.1, pp.432-450
Abstract
Forest fires are one of the most frequently occurring natural hazards, causing substantial economic loss and destruction of forest cover. As the Gangwon-do region in Korea has abundant forest resources and ecological diversity as Korea's largest forest area, spatial data on forest fire susceptibility of the region are urgently required. In this study, a forest fire susceptibility map (FFSM) of Gangwon-do was constructed using Google Earth Engine (GEE) and three machine learning algorithms: Classification and Regression Trees (CART), Random Forest (RF), and Boosted Regression Trees (BRT). The factors related to climate, topography, hydrology, and human activity were constructed. To verify the accuracy, the area under the receiver operating characteristic curve (AUC) was used. The AUC values were 0.846 (BRT), 0.835 (RF), 0.751 (CART). Factor importance analysis was performed to identify the important factors of the occurrence of forest fires in Gangwon-do. The results show that the most important factor in the Gangwon-do region is slope. A slope of approximately 17 degrees (moderately steep) has a considerable impact on the occurrence of forest fires. Human activity and interference are the other important factors that affect forest fires. The established FFSM can support future efforts on forest resource protection and environmental management planning in Gangwon-do.
ISSN
1947-5705
URI
https://hdl.handle.net/10371/179606
DOI
https://doi.org/10.1080/19475705.2022.2030808
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