Climate and Vegetation Factors Determine the Probability of Tropical Forest Fires in Hainan Island During the Dry Season

Forest fires significantly disturb the global ecosystem, profoundly impacting carbon cycling, biodiversity, and socio-economic sustainability. Understanding the seasonal patterns of forest fire occurrence is crucial for developing effective prevention and control strategies that support sustainable forest management and disaster risk reduction. However, research on the seasonal dynamics of forest fires in the tropical forests of China’s Hainan Island remains scarce, and the underlying driving mechanisms, particularly in relation to the distinct dry and rainy seasons, are still insufficiently understood. In this study, we analyzed the seasonal patterns and driving factors of forest fires on Hainan Island, a tropical region, from 2000 to 2022, and compared the applicability of emerging machine learning algorithms and traditional Logistic Regression algorithms in constructing tropical forest fire prediction models for the dry and rainy seasons. The results show that tropical forest fires in Hainan Island are primarily concentrated from January to May, peaking in April, with a lagged effect relative to the dry-season months (November to April of the following year). Average temperature and average rainfall were identified as common important predictors of fire occurrence probability in both seasons, with the Normalized Difference Vegetation Index additionally included in only the dry season. The predictive accuracy of the Random Forest model consistently surpassed that of the traditional Logistic Regression model in both seasons. Additionally, our models enable the generation of accurate forest fire probability maps, highlighting risk areas in Hainan Island during both seasons and providing new insights for wildfire prevention strategies and sustainable forest management in tropical regions.