Floor litter can reduce the amount of water reaching the soil layer through rainfall interception. The rainfall interception capacity of floor litter varies with the physical features of the litter and rainfall characteristics. This study aimed to define the maximum and minimum interception storages ($C_{mx}, C_{mn}$) of litter layers using rainfall simulation experiments, and examine the effects of litter type and rainfall characteristics on rainfall retention and drainage processes that occur in the litter layer. Different types of needle-leaf and broadleaf litters were used: Abies holophylla, Pinus strobus, Pinus rigida, Quercus acutissima, Quercus variabilis, and Sorbus alnifolia. Our results indicate a wide variation in interception storage values of needle leaf litter, regardless of the rainfall intensity and duration. The A. holophylla needle-leaf litter showed the highest $C_{mx}$ and $C_{mn}$ values owing to its short length and low porosity. Conversely, the lowest interception storage values were determined for the P. strobus needle leaf litter. No significant differences in interception storage were established for the broadleaf litter. Moreover, except for A. holophylla litter, the broadleaf litter retained more water than the needle leaf litter. An increase in the intensity or duration of rainfall events leads to an increase in the water retention storage of litter. However, these factors do not influence the litter’s drainage capacity, which depends primarily on the force of gravity.

Forest fires have increased over the last several decades in many regions. Quantifying the general patterns of frequency, areal extent, and seasonality is crucial for understanding fire dynamics. This study aimed to investigate whether the spatial and temporal trends in forest fires have changed across South Korea. The Mann–Kendall test and Sen’s slope estimation were used to analyze the temporal trends in forest fire statistics from 1991 to 2020. The spatial dispersion of fire activity was detected using a standard deviation ellipse and hotspot analysis. An average of 451 fires have occurred annually over the last 30 years, with a yearly increase of 5.82 fires. The burned area in April and May accounted for 80.7% of the annual burned area. The length of the fire season in 2006–2020 was 25 days longer than that in 1991–2005. The risk of large fires is increasing and becoming more concentrated in the northeastern region, such as the Gwangwon and Gyeongsangbuk Provinces of South Korea. Both climate change and forest recovery have led to South Korea becoming more prone to fires. However, forest fires are not burning more intensely nor charring more areas than they did previously. This is probably due to the implementation of surveillance and initial attack systems. Targeted forest fire suppression policies can help to effectively reduce the risk of forest fires in South Korea.

A probabilistic estimation model for forest biomass using unmanned aerial vehicle (UAV) photography was developed. We utilized a machine-learning-based object detection algorithm, a mask region-based convolutional neural network (Mask R-CNN), to detect trees in aerial photographs. Subsequently, Bayesian regression was used to calibrate the model based on an allometric model using the estimated crown diameter (CD) obtained from aerial photographs and analyzed the diameter at breast height (DBH) data acquired through terrestrial laser scanning. The F1 score of the Mask R-CNN for individual tree detection was 0.927. Moreover, CD estimation using the Mask R-CNN was acceptable (rRMSE = 10.17%). Accordingly, the probabilistic DBH estimation model was successfully calibrated using Bayesian regression. A predictive distribution accurately predicted the validation data, with 98.6% and 56.7% of the data being within the 95% and 50% prediction intervals, respectively. Furthermore, the estimated uncertainty of the probabilistic model was more practical and reliable compared to traditional ordinary least squares (OLS). Our model can be applied to estimate forest biomass at the individual tree level. Particularly, the probabilistic approach of this study provides a benefit for risk assessments. Additionally, since the workflow is not interfered by the tree canopy, it can effectively estimate forest biomass in dense canopy conditions.

Fires can alter the hydraulic properties of burned soils through the consumption of organic matter on the ground surface. This study examined the effects of rainfall on the presence of soil pore clogging with varying ash layer thickness using laboratory rainfall simulator experiments. The image analysis with resin impregnation showed that rainfall impact caused plugging of soil pores at 22.2% with soil particles and 14.3% with ash particles on near surface soils (0–5 mm below). High rainfall intensities enhanced soil pore clogging by ash particles, particularly at shallow soil depths (0–10 mm). Ash deposits on the soil surface increased the water-absorbing capacity of ash-covered soils compared with that of bare soils. The rainfall simulation experiments also showed that ash cover led to a reduction in soil hydraulic conductivity, owing to the combined effects of surface crust formation and soil pore clogging. The complementary effects of soil pore clogging and water absorption by ash cover could hamper the accurate understanding of the soil hydrologic processes in burned soils.

Tree detection and fuel amount and distribution estimation are crucial for the investigation and risk assessment of wildfires. The demand for risk assessment is increasing due to the escalating severity of wildfires. A quick and cost-effective method is required to mitigate foreseeable disasters. In this study, a method for tree detection and fuel amount and distribution prediction using aerial images was proposed for a low-cost and efficient acquisition of fuel information. Three-dimensional (3D) fuel information (height) from light detection and ranging (LiDAR) was matched to two-dimensional (2D) fuel information (crown width) from aerial photographs to establish a statistical prediction model in northeastern South Korea. Quantile regression for 0.05, 0.5, and 0.95 quantiles was performed. Subsequently, an allometric tree model was used to predict the diameter at the breast height. The performance of the prediction model was validated using physically measured data by laser distance meter triangulation and direct measurement from a field survey. The predicted quantile, 0.5, was adequately matched to the measured quantile, 0.5, and most of the measured values lied within the predicted quantiles, 0.05 and 0.95. Therefore, in the developed prediction model, only 2D images were required to predict a few of the 3D fuel details. The proposed method can significantly reduce the cost and duration of data acquisition for the investigation and risk assessment of wildfires.

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.