Multi-hazard mapping of droughts and forest fires using a multi-layer hazards approach with machine learning algorithms

Abstract

The frequency of forest fires in Gangwon-do has increased in recent years due to advanced climate change and dry weather. The Gangwon-do area, the largest forest area in South Korea, has rich forest resources and ecological diversity, therefore there is an urgent need for more effective monitoring and prevention of forest fires. This study proposed a method to establish a multi-hazard probability map (MHPM) for two related hazards (forest fires and droughts) based on a multi-layer hazards approach and machine learning algorithms for monitoring forest fire susceptibility areas. First, extreme drought years were selected using the standardized precipitation index (SPI). An inventory drought map was constructed using the enhanced vegetation index (EVI) based on satellite image data. Then, 70% of the inventory maps based on forest fires and droughts were used to construct hazard susceptibility maps and 30% of these were used to validate three machine learning models: Classification and Regression Trees (CART), Random Forest (RF) and boosted Regression Trees (BRT). Eleven conditioning factors related to climate, topography, hydrology, and human activities were considered for the analysis. The results of the three models were then validated using the area under the receiver operating characteristic (ROC) curve (AUC), and the best performing model was selected (BRT; forest fire: 85%, drought: 80%). Finally, the susceptibility maps of forest fires and droughts were combined to construct the MHPM for the Gangwon Province, South Korea. The results show that the MHPM of forest fires and droughts constructed in this study is valid and reliable. This multi-hazard map can provide key information for planners and decision-makers to develop forest fire prevention and management plans and to more effectively prevent and reduce the frequency of forest fires.