Warmer and drier conditions in temperate regions are increasing the length of the wildfire season. Given the greater fire frequency and extent of burned areas under climate warming, greater focus has been placed on predicting post-fire tree mortality as a crucial component of sustainable forest management. This study evaluates the potential of logistic regression models to predict post-fire tree mortality in Korean red pine (Pinus densiflora) stands, and we propose novel means of evaluating bark injury. In the Samcheok region of Korea, we measured topography (elevation, slope, and aspect), tree characteristics (tree/crown height and diameter at breast height (DBH)), and bark injuries (bark scorch height/proportion/index) at three sites subjected to a surface fire. We determined tree status (dead or live) over three years after the initial fire. The bark scorch index (BSI) produced the best univariate model, and by combining this index with the DBH produced the highest predictive capacity in multiple logistic regression models. A three-variable model (BSI, DBH, and slope) enhanced this predictive capacity to 87%. Our logistic regression analysis accurately predicted tree mortality three years post fire. Our three-variable model provides a useful and convenient decision-making tool for land managers to optimize salvage harvesting of post-fire stands.

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.

In the Republic of Korea, forest fire occurred at a rate of more than 400 events yr⁻¹ from 2000 to 2016, and the burned areas are concentrated in the eastern coastal region. In this region, pine forest is extensively distributed, and pine forest is converted to oak forest through natural succession or stump-sprout regeneration following forest fires. However, there is a lack of research on oak competition and survival during the regeneration process in burned areas. Therefore, this research was carried out to evaluate the effects of oak survival ratio of Quercus serrata (glandbearing oak), Q. variabilis (oriental oak), and Q. mongolica (Mongolian oak) in burned areas. The investigation plots were set to survey tree height, diameter at breast height, sprout survival, and composition in Goseong-gun, Kangwon province, Republic of Korea. The mean tree heights of Q. serrata and Q. variabilis were approximately 9.8 m and 9.1 m, respectively, which were higher than the approximately 5.8 m heights of Q. mongolica stands 18 years after a forest fire, and the trend for tree diameter at breast height was the same for all three species stands. In the early stage of regeneration after the forest fire, the survival probability of the Q. serrata sprouts decreased at a rate of 1.7–2.0 times higher than that of other oak species in the Q. serrata-dominant stands, and this tendency was found to the survival probability in Q. variabilis-dominant stands as well. The median survival time of Q. variabilis sprouts in the Q. variabilis-dominant stands was approximately 10.1 years, which was similar to Q. serrata-dominant stands. However, the dominant stand of Q. mongolica was different from that of other dominant stands since the stand mostly located in the ridge and the upper part of the mountain where topography and soil conditions were considerably dissimilar from the other stands. The decision of dominant species seems to be determined by the survival and occurrence of sprouts during the early stage in post-fire regenerated oak forests. Therefore, it would be more desirable to coppice sprout for dominant species productivity and rapid dominance after a forest fire.

Introduction: Loblolly pine ( Pinus taeda L.) plantations in the southeastern United States are increasingly vulnerable to climate-driven disturbances that cause stem breakage and elevate tree mortality. Consequently, it is imperative to develop adaptive management strategies following stem breakage for forest managers and landowners to mitigate tree mortality and sustain forest productivity. However, predictive frameworks for post-breakage mortality remain underdeveloped, hampering adaptive management efforts. Methods: We conducted a split-plot experiment in four mid-rotation plantations, applying two whole-plot silvicultural treatments (no thinning vs. thinning) and five subplot treatments defined by the proportion of trees damaged (PTD: 0, 25, 50, 75, 100%). Stem breakage was simulated by rifle shooting of main stems, and individual trees were monitored for 12 years. We developed a multiple logistic regression model including thinning, plot-level damage (PTD), tree-level damage severity (PSD), diameter at breast height (DBH), total height (HT), and crown height (CRNHT), and used parametric bootstrapping (10,000 iterations) to quantify uncertainty. Results: Our findings highlight three key patterns: (1) Short-term mortality (<4 years) remained low but increased sharply over the long term following stem breakage; (2) Larger DBH and greater tree height reduced mortality risk; and (3) Plot-level damage proportion had a stronger influence on mortality than individual-tree damage severity. Discussion: These results highlight the dominant role of plot-level damage regimes and tree size in long-term survival after stem breakage. Incorporating these insights into adaptive thinning and disturbance management frameworks will help sustain productivity and resilience in loblolly pine plantations under escalating climate extremes.

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.