Journal
GEOMATICS NATURAL HAZARDS & RISK
Volume 13, Issue 1, Pages 432-450Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/19475705.2022.2030808
Keywords
Natural hazard; Wildfire; Google earth engine; remote sensing; machine learning
Funding
- Research Institute of Agriculture and Life Sciences, Seoul National University
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This study constructed a forest fire susceptibility map in Gangwon-do, Korea using Google Earth Engine and machine learning algorithms. The results identified slope, human activity, and interference as the important factors affecting forest fire occurrence in the region.
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.
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