We aimed to tackle a common problem in post-fire tree mortality where the number of trees that survived surpasses the number of dead trees. Here, we investigated the factors that affect Korean red pine (Pinus densiflora Siebold & Zucc.) tree mortality following fires and assessed the statistical effects of class-balancing methods when fitting logistic regression models for predicting tree mortality using empirical bootstrapping (B = 100,000). We found that Slope, Aspect, Height, and Crown Ratio potentially impacted tree mortality, whereas the bark scorch index (BSI) and diameter at breast height (DBH) significantly affected tree mortality when fitting a logistic regression with the original dataset. The same variables included in the fitted logistic regression model were observed using the class-balancing regimes. Unlike the imbalanced scenario, lower variabilities of the estimated parameters in the logistic models were found in balanced data. In addition, class-balancing scenarios increased the prediction capabilities, showing reduced root mean squared error (RMSE) and improved model accuracy. However, we observed various levels of effectiveness of the class-balancing scenarios on our post-fire tree mortality data. We still suggest a thorough investigation of the minority class, but class-balancing scenarios, especially oversampling strategies, are appropriate for developing parsimonious models to predict tree mortality following fires.

Wildfires impact forest ecosystems, affecting tree survival and physiological responses. This study explored the effects of surface fires on Pinus densiflora and Quercus variabilis, assessing mortality, internal injuries, and canopy health. By 2024, P. densiflora had an 18.0% mortality rate, whereas Q. variabilis exhibited no crown dieback. Morphological traits, including tree height, the bark scorch index (BSI), and bark thickness, influenced fire resistance. Despite superior stand characteristics, P. densiflora showed higher mortality due to thin bark, whereas Q. variabilis maintained xylem integrity. While sonic tomography (SoT) showed no significant differences, electrical resistance tomography (ERT) detected physiological stress, with higher ERT_R and ERT_Y area ratios correlating with mortality risk. Notably, F-W-W classified trees showed elevated resistance a year before mortality, suggesting ERT as a predictive tool. ERT_R values exceeding 15.0% were associated with a 37.5% mortality rate, whereas ERT_B values below 55.0% corresponded to 42.9% mortality. Despite fire exposure, canopy responses, including chlorophyll fluorescence and photosynthetic efficiency, remained stable, indicating that the surviving trees maintained functional integrity. This study underscores ERT’s efficacy in diagnosing fire-induced stress and predicting mortality risk. The findings highlight species-specific diagnostic criteria and inform post-fire management, supporting forest resilience through the early detection of high-risk trees and improved restoration strategies.

This study aimed to develop and compare prognostic models (logistic regression, Cox proportional hazards, and random forest) to assess delayed mortality in pine (Pinus densiflora) following fires in Korea. Data from a 72-month monitoring of 734 trees across four fire areas were used, accounting for 19 variables: tree size, fire severity, multispectral indices, topography, and bioclimatic variables. Key predictors of mortality included diameter at breast height (DBH), bark scorch index (BSI), delta normalized burn ratio (dNBR), slope, topographic wetness index (TWI), precipitation of the warmest quarter, temperature seasonality and isothermality. The key results indicate that the random forest model was the most effective (AUC = 0.924; sensitivity = 0.892) in identifying trees at high risk of mortality. These results suggest that nonlinear approaches are effective for predicting delayed mortality in fire-damaged pines and can support rapid decision-making in post-fire forest management and restoration under increasing wildfire risk.

This study aimed to determine a method for restoring grasslands to forests in mountainous areas. The study was conducted in an area damaged by trampling due to construction activity and herding. Additionally, there were concerns of hindered tree growth due to the alpine region climate, characterized by the low temperatures, drying, and strong winds. Therefore, forest restoration treatments such as tillage, furrow, and shading were performed to improve growth conditions. From July 2021 to April 2023, soil temperature and moisture were monitored for each treatment plot, and changes in the initial growth and survival rate of Abies holophylla were investigated. Both soil temperature and soil moisture showed significantly higher values during the four seasons in the grassland-furrow treatment plot (p < 0.05). Compared to the grassland-control plot, the average soil temperature in winter was as high as 1.1℃, and the soil moisture in summer was as high as 20.3%. Additionally, the survival rate and relative growth rate were the largest in the grassland-furrow treatment plot. The survival rate was 87.5%, the relative growth rate in terms of height was 0.013, and the relative growth rate in terms of diameter at root collar was 0.023, indicating that furrow treatment had a positive effect on the initial survival and growth of seedlings. These results suggest that furrow treatment may be an appropriate restoration method when implementing forest restoration projects in areas where tree growth is challenged.

The Republic of Korea, with 64% forest coverage, is increasingly vulnerable to large-scale wildfires. This study employed electrical resistance tomography (ERT) to diagnose internal damage in Pinus densiflora trees following a surface fire in spring 2023. Of the 30 monitored trees, 5 died in 2023 and 6 more had died by 2024. Dead trees showed a 41% higher Bark Scorch Index (BSI) and a 10%–15% lower DBH and circumference than survivors. From July, ERT detected significant increases in high- (ERT_R) and medium-resistance (ERT_Y) areas, while low-resistance (ERT_B) regions declined. By September, ERT_R and ERT_Y were 2.2 and 1.9 times higher in dead trees. Maximum resistivity (Rs_max) rose 6.1-fold to 3724 Ωm. One year post-fire, healthy areas in dead trees dropped below 18%. These findings indicate that internal defects develop gradually and accelerate in summer and winter, correlating with thermal and freeze–thaw stress. Early diagnosis within two months post-fire was unreliable, while post-summer assessments better distinguished trees at mortality risk. This study demonstrates ERT’s utility as a non-destructive tool for tracking post-fire damage and guiding forest restoration under increasing wildfire threats.