For effective management and prevention, wildfire risk prediction needs to consider the substantial impacts of climate change on wildfire patterns. This study analyzed the probability of wildfire occurrence in South Korea using the Maximum Entropy (MaxEnt) model and predicted future wildfire occurrence under shared socioeconomic pathway (SSP) climate change scenarios. The model utilized historical fire occurrence data and was trained using 12 environmental variables encompassing climate, topography, vegetation, and socioeconomic factors. Future wildfire risk was predicted under the SSP2-4.5 and SSP5-8.5 scenarios for 2041–2060 and 2081–2100. Increased average temperature and solar radiation were key drivers of elevated wildfire risk, whereas increased precipitation and relative humidity reduced this risk. Under current conditions, 367,027 ha (6.52%) within the study area were classified as high-risk based on the MaxEnt model output (p > 0.6). Under both SSP scenarios, a decline in the at-risk area was observed over time. This study provides fundamental data for wildfire management and prevention strategies in South Korea and provides quantitative evidence on the potential impact of climate-related environmental changes on wildfires.

Forest fires pose significant threats to ecosystems, economies, and human lives. However, existing forest fire risk assessments are over-reliant on field data and expert-derived indices. Here, we assessed the nationwide forest fire risk in South Korea using a dataset of 2289 and 4578 fire and non-fire events between 2020 and 2023. Twelve remote sensing-based environmental variables were exclusively derived from Google Earth Engine, including climate, vegetation, topographic, and socio-environmental factors. After removing the snow equivalent variable owing to high collinearity, we trained three machine learning models: random forest, XGBoost, and artificial neural network, and evaluated their ability to predict forest fire risks. XGBoost showed the best performance (F1 = 0.511; AUC = 0.76), followed by random forest (F1 = 0.496) and artificial neural network (F1 = 0.468). DEM, NDVI, and population density consistently ranked as the most influential predictors. Spatial prediction maps from each model revealed consistent high-risk areas with some local prediction differences. These findings demonstrate the potential of integrating cloud-based remote sensing with machine learning for large-scale, high-resolution forest fire risk modeling and have implications for early warning systems and effective fire management in vulnerable regions. Future predictions can be improved by incorporating seasonal, real-time meteorological, and human activity data.

Topographic ridges are widely used in wildfire interpretation, suppression planning, and potential control-line design, but the claim that final fire boundaries preferentially follow ridge crests is rarely tested against local terrain availability. Remote-sensing-based burn mapping and DEM-derived terrain metrics now make this question testable at cohort scale, although most Korean wildfire studies have focused on ignition, occurrence probability, or fire-risk prediction rather than final-perimeter geometry. We therefore tested whether 118 final wildfire perimeters in the Republic of Korea (2018–2025) were non-randomly associated with ridge lines derived independently from a 30 m SRTM DEM. Sentinel-2 pre- and post-fire imagery and official fire metadata were used to generate burn masks and perimeters, which were sampled every 20 m and compared with ridge networks using a proximity endpoint (R30) and a joint distance-orientation endpoint (Aθ) under a local translate-and-rotate null model. Most fires were both more ridge-proximal and more strongly ridge-aligned than their local null perimeters, and the directional signal was the stronger of the two (mean enrichment 2.3, versus 1.5 for proximity alone). A valley-inclusive comparator showed no comparable pattern, indicating an association specific to ridges rather than to terrain lines in general. The directional signal was robust to ridge continuity, spatial scale, null design, and the exclusion of road-adjacent ridges. Because the analysis uses final mapped perimeters rather than time-resolved fire fronts, it documents a ridge-specific geometric association rather than proof that ridges stopped individual fires. These results provide an observational benchmark for terrain representation in fire-spread models.