Forest fire occurrence prediction remains challenging despite advances in operational fire danger rating systems. In South Korea, the Korea Forest Fire Danger Rating Index (KFDRI) incorporates meteorological conditions, terrain (elevation, aspect), and forest type to assess regional fire danger. While KFDRI successfully assesses environmental fire danger at the pixel level, it does not explicitly account for human activity patterns that create substantial occurrence variability among locations with similar environmental conditions. This limitation is critical in human-dominated landscapes where where the main source of fire occurrence is anthropogenic. This study developed a Random Forest (RF) model to predict forest fire occurrence probability and propose management priorities during the forest fire prevention season (November–May) along the eastern coast of Korea, explicitly integrating human proximity variables (distance to agricultural areas and roads) with topographical (elevation, slope, aspect), surface fuel load, and meteorological variables (SMAP soil moisture, cumulative precipitation). Using forest fire occurrence records (1112 fire occurrence records) and background samples from 2015 to 2024, the model was trained with monthly stratified sampling and 10-fold cross-validation. The model achieved stable classification performance, with an overall F1-score of 0.515 and accuracy of 0.733. According to the SHAP (SHapley Additive exPlanations) analysis, distance to agricultural areas, elevation, slope, aspect, 5-day cumulative precipitation, and forest type were the most influential predictors. In particular, occurrence probability tended to increase in areas close to agricultural land (<180 m), at low elevations (≤200 m), on moderately steep slopes (≥8°), on south- and west-facing aspects, and under dried conditions. These results emphasize that fire occurrence risk is primarily structured by human proximity within areas of similar environmental danger. We propose an operational integration in which the RF model provides a 30 m “where-to-focus” occurrence layer that is used alongside KFDRI’s daily danger rating to prioritize prevention and patrol efforts.

Wildfire activity is intensifying under climate change, particularly in temperate East Asia where human-driven ignitions interact with extreme fire-weather conditions. This study examines wildfire risk during the March 2025 large wildfire event in Korea by applying explainable machine-learning models to assess ignition-prone environments and their spatial relationship with socio-environmental features relevant to exposure and management. CatBoost and LightGBM models were used to estimate wildfire susceptibility based on climatic, topographic, vegetation, and anthropogenic predictors, with SHAP analysis employed to interpret variable contributions. Both models showed strong predictive performance (CatBoost AUC = 0.910; LightGBM AUC = 0.907). Temperature, relative humidity, and wind speed emerged as the dominant climatic drivers, with ignition probability increasing under hot (>25 °C), dry (6 m s⁻¹) conditions. Anthropogenic factors—including proximity to graves, mountain trails, forest roads, and contiguous coniferous stands (≥30 ha)—were consistently associated with elevated ignition likelihood, reflecting the role of human accessibility within pine-dominated landscapes. The socio-environmental overlay analysis further indicated that high-susceptibility zones were spatially aligned with arboreta, private commercial forests, and campsites, highlighting areas where ignition-prone environments coincide with active human use and forest management. These results suggest that wildfire risk in Korea is shaped by the spatial concurrence of climatic extremes, fuel continuity, and socio-environmental exposure. By situating explainable susceptibility modeling within an event-conditioned risk perspective, this study provides practical insights for identifying Wildfire Priority Management Areas (WPMAs) and supporting risk-informed prevention, preparedness, and spatial decision-making under ongoing climate change.

기후적응을 위해 산림부문에서 풀어가야 할 핵심과제는 산림정책에 기후적응을 주류화하고, 적응 조치의 효과성 모니터링 및 평가를 포함하는 적응의 선순환 체계를 정립하는 것이다. 우리나라 산림의 장기적인 관리 계획의 기본 방향을 제시해주는 것은 산림청이 산림기본법 제11조 및 같은 법 시행령...

As wildfires intensify globally due to climate change, accurate wildfire danger forecasting systems have become essential for effective disaster management and early warning. Fuel Moisture Content (FMC), defined as the ratio of water mass to dry fuel mass, plays a critical role in determining ignition probability and fire spread dynamics. This study conducts a comparative analysis of five major national wildfire danger rating systems: the National Fire Danger Rating System (NFDRS, USA), Canadian Forest Fire Danger Rating System (CFFDRS), European Forest Fire Information System (EFFIS), Australian Fire Danger Rating System (AFDRS), and the Korean Forest Fire Danger Rating System (KFDRS). Using a multi-criteria comparative framework, the systems were evaluated based on fuel classification structure, input variables, modeling approach, and spatiotemporal prediction resolution. The results reveal substantial disparities in spatial resolution (100 m to district-level), temporal resolution (hourly vs. daily), and fuel moisture modeling approaches (physics-based, index-based, and hybrid systems). Specifically, NFDRS and AFDRS provide high-frequency forecasting with hourly temporal resolution, operating at spatial resolutions of 1 km and 100 m, respectively, and incorporating dynamic fuel moisture modeling. In contrast, CFFDRS and KFDRS primarily rely on daily index-based predictions. Furthermore, while many global systems increasingly leverage remote sensing and machine learning for real-time FMC estimation, South Korea’s KFDRS remains predominantly empirical and weather-driven. The analysis identifies critical limitations in the KFDRS, including coarse spatial resolution (district-level), limited integration of Live Fuel Moisture Content (LFMC) modeling, and the lack of AI-augmented hybrid approaches. Accordingly, this study proposes a phased three-stage policy roadmap (2026–2035), emphasizing sensor-network expansion, AI–physics fusion modeling, and high-resolution (10 m) FMC mapping to enhance forecasting accuracy in complex terrains. These findings provide strategic insights for improving wildfire risk management and supporting the transition from reactive response to predictive wildfire forecasting under increasing climate variability.

Litter fuel moisture governs the ignition and early spread of forest fires, yet its meteorological controls in Korean conifer and broadleaf stands, and the correspondence of existing fire-weather index classes to locally observed moisture, remain poorly characterised. We directly measured litter fuel moisture in paired conifer and broadleaf stands at four regions in Korea during one spring fire season and classified moisture class (Dry/Intermediate/Moist) from 26 meteorological and soil predictors under time-blocked, nested cross-validation. Five classifiers performed moderately and indistinguishably (macro-F1 0.39–0.51; quadratic weighted kappa 0.20–0.47), all above naive baselines, and performance declined under leave-one-region-out transfer. A multiple-testing-corrected interaction test detected a forest-type-dependent response only for soil water content, and neither a forest-type indicator nor separate per-type models improved prediction over a single pooled model. The Fine Fuel Moisture Code (FFMC) was significantly correlated with measured litter moisture (r = −0.57 conifer, −0.48 broadleaf) and was more sensitive to Dry conditions than any classifier, but its ignition-probability-derived class boundary corresponded weakly to the observed classes, and a moisture-equivalent boundary produced a complementary rather than superior error pattern. Meteorological data provide site-calibrated discrimination of litter-moisture class; forest-type differences are present but narrow, and FFMC class boundaries require local calibration before operational use.

Military–civilian interface zones (MCIZs) adjacent to the Korean Demilitarized Zone (DMZ) represent complex wildfire environments shaped by restricted access, intensive military activities, and adjacent civilian land use. This study develops a spatially explicit wildfire ignition risk assessment framework for the DMZ and Civilian Control Zone (CCZ) in Paju, South Korea, employing Random Forest (RF), Generalized Additive Models (GAM), and Geographically Weighted Regression (GWR) in a complementary analytical design. A dataset of 318 wildfire ignition events (2001–2024), including 78 associated with military activities, was analyzed. The RF model achieved high predictive accuracy (AUC = 0.81), identifying proximity to military training zones, relative humidity, wind speed, and proximity to built infrastructure as dominant ignition drivers. GAM revealed narrow nonlinear thresholds—relative humidity at 13.8%–14.0% and wind speed at 13.5–14.0 m/s—corresponding to peak ignition probabilities. GWR demonstrated pronounced spatial heterogeneity, with military proximity exerting a stronger influence in the eastern and northern sectors, while the meteorological effects varied geographically. Based on these outputs, a unified analytical framework was established in which RF-derived ignition probabilities were interpreted alongside GAM- and GWR-based explanatory layers to provide spatially explicit ignition susceptibility assessments without numerical map fusion. The proposed approach provides a scientifically rigorous and operationally applicable method for quantifying ignition risk in politically sensitive, access-restricted landscapes, offering valuable insights for adaptive wildfire prevention and spatially informed governance of transboundary fire risk.