Integrating Geographic Information System and Logistic Regression for Forest Fire Susceptibility Mapping in Chom Thong District, Chiang Mai Province, Thailand

Forest fires are a major environmental concern in Northern Thailand, contributing to ecosystem degradation, biodiversity loss, and seasonal air pollution. This study identified the environmental factors influencing forest fire occurrence and developed a forest fire susceptibility map using an integrated Geographic Information System (GIS) and Logistic Regression (LR) framework in Chom Thong District, Chiang Mai Province, Thailand. Fire occurrence data were derived from Visible Infrared Imaging Radiometer Suite (VIIRS) active fire hotspots detected by the Suomi National Polar-orbiting Partnership satellite (Suomi-NPP satellite) during 2023–2025. A total of 1674 hotspots were identified (616 in 2023, 889 in 2024, and 169 in 2025). Ten environmental variables, including elevation, slope, aspect, Topographic Wetness Index (TWI), stream density, rainfall, Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Land Surface Temperature (LST), and land-use, were analyzed. The LR model was trained using 2293 training samples (70%) and validated using 983 samples (30%). The results revealed that slope, rainfall, stream density, and LST were significant predictors of forest fire occurrence, with deciduous and evergreen forests exhibiting the highest susceptibility among land-use classes. The resulting forest fire susceptibility map classified 235.12 km2 (21.16%) and 204.16 km2 (18.38%) of the district as very high and high susceptibility, respectively, primarily in mountainous forest areas. The model achieved an overall accuracy of 77.5% and an Area Under the Curve (AUC) value of 0.852, indicating good predictive performance. Furthermore, the proposed Geographic Information System-Logistic Regression (GIS-LR) framework provides an interpretable and transferable approach for forest fire susceptibility assessment and generates spatial information that can support forest fire prevention, resource allocation, and environmental management in Northern Thailand and other fire-prone regions.