Hyeongkeun Kweon

Rapid urban expansion into mountainous terrain, together with increasing exposure to extreme weather, has increased the need for transparent and interpretable slope-hazard assessment in forested and residential mountain environments. However, relatively few studies have integrated field-survey information with physically based simulation outputs for interpretable multi-class classification within an established slope-hazard grading framework. This pilot study developed a simulation-informed machine-learning framework for classifying composite slope-hazard grades (Grades I–III) using 71 landslide-prone and 52 debris-flow-prone sites in Sejong City, Republic of Korea. For the landslide dataset, selected field-survey indicators were integrated with SoilWorks-derived slope-stability information, whereas for the debris-flow dataset, selected field-survey indicators were integrated with FLO-2D-derived simulation information. Four machine-learning algorithms—Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)—were evaluated under severe class imbalance. RF showed the strongest overall performance in the original holdout evaluation, achieving an accuracy of 0.857 for both datasets and weighted F1-scores of 0.849 for the landslide dataset and 0.836 for the debris-flow dataset. Repeated stratified train–test analyses further showed that RF retained the highest mean overall performance, although the debris-flow dataset was more sensitive to sample allocation. Gini importance, Shapley Additive Explanations (SHAP), and targeted ablation analysis indicated that simulation-derived variables provided strong discriminatory information within the established grading framework. The proposed framework provides a structured and interpretable approach for integrating field-based assessment indicators with physically based simulation outputs and may provide supplementary support for classifying and prioritizing sites requiring further assessment. The findings also provide a basis for future investigation of reduced-input screening approaches, although the present study does not establish that reduced field surveys or simulation outputs alone are sufficient for formal grade assignment. Accordingly, the framework should be interpreted as a supplementary classification approach within the existing assessment system rather than as an independent predictor of future landslide or debris-flow occurrence.

자원과학연구(Resources Science Research)

This study examines changes in media coverage of forest roads from 2014 to 2023 using text mining techniques. As key infrastructure for sustainable forest management, forest roads also contribute to wildfire suppression and disaster prevention. By analyzing news articles, the research identifies a shift in media focus from forest management and public access to the role of forest roads in disaster management, particularly in response to recent large-scale wildfires and climate-induced extreme weather events. Using methodologies such as term frequency-inverse document frequency (TF-IDF), N-gram, and clustering, the findings provide insights into evolving societal perceptions of forest roads, offering valuable information for future policy-making and research.

The Civilian Access Control Zone (CACZ), south of the Demilitarized Zone (DMZ) separating North and South Korea, has functioned as a unique bio-reserve owing to restrictions on human use. However, it is now increasingly threatened by damaged land and slope failures. In this study, a machine-learning-based method was used to assess slope stability by introducing the random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and logistic regression (LR) approaches. These classification models were trained and evaluated on 393 slope stability cases from 2009 to 2019 to assess slope stability in the northern area of the Civilian Control Line, South Korea. For comparison, the performance of these classification models was measured by considering the accuracy, Cohen’s kappa, F1-score, recall rate, precision, and area under the ROC curve (AUC). Furthermore, 14 influencing factors (slope, vegetation, structure conditions, etc.) were considered to explore feature importance. The evaluation and comparison of the results showed that the performance of all classifier models was satisfactory for assessing the stability of the slope, the ability of LR was validated (accuracy = 0.847; AUC = 0.838), and XGBoost proved to be the most efficient method for predicting slope stability (accuracy = 0.903; AUC = 0.900). Among the 14 influencing factors, the external condition was the most important. The proposed supervised learning method offers a promising method for assessing slope status, may be beneficial for government agencies in early-stage risk mitigation, and provides a database for efficient restoration management.

Climate change affects the frequency and intensity of extreme weather, such as drought and floods. This study aimed to investigate the effects of drought stress on the growth and physiological characteristics of Z. jujuba var. inermis based on the RCP 6.0 scenario. The experiment was performed in a soil-plant daylight system (SPDS) chamber under four conditions: Control-WW, Control-DS (Drought), RCP 6.0-WW (CO 2 650 ppm, control + 3°C), and RCP 6.0-DS. Although the net photosynthetic rate (A) was higher under RCP 6.0 than the control condition, V cmax and J max , which indicate the photosynthetic capacity, were lower by 19.6% and 27.8%, respectively. Furthermore, in the RCP 6.0 group, the carbon fertilization effect tended to stimulate growth; thus, the total dry weight in RCP 6.0-WW was approximately 35% higher than that in Control-WW. When the drought treatment was included, A decreased by 57% in Control-DS compared to Control-WW, and in RCP 6.0-DS, A decreased by 73% compared to RCP 6.0-WW. Moreover, RCP-DS showed the lowest values of V cmax , J max , PI abs , DF abs , and SFI abs , meaning that the photosynthetic capacity and vitality of the photosynthetic apparatus were diminished under combined climate change and drought stress. Furthermore, stomatal limitations (Ls) regulated photosynthesis in Control-DS, whereas in RCP 6.0-DS, non-stomatal limitations, in this case V cmax , J max , PI abs , DF abs , and SFI abs , were observed. Despite a decrease in mesophyll conductance (g m), g m /g s remained relatively high. Furthermore, even with stomatal closure, g m was maintained at a relatively high level by utilizing the intercellular CO 2 concentration (C i), demonstrating the plant's response to drought stress. Although there was an increase in the overall biomass, it was noted that water consumption increased under additional drought stress, showing that growth and physiological damage were aggravated. Therefore, it is essential to conduct experiments related to the growth and physiological characteristics of commercially valuable seedlings such as Jujube to identify the adverse effects of climate change alongside extreme weather. Further, active efforts with regard to plant management should be undertaken in addition to the implemented measures to reduce greenhouse gas emissions. 12

Drought damage and waterlogging damage are the most frequent abiotic injuries throughout the lifespan of trees. Nonetheless, because the signs of visible damage are similar between these types of injury, physiological indicators must be analyzed to make a scientific differential diagnosis. This study was aimed at investigating growth and physiological indicators of drought (DS) and waterlogging stress (WL) in Prunus yedoensis. Overall growth indices such as tree height, diameter at root collar (DRC), and total dry weight could be ranked in the decreasing order “control > DS > WL.” In particular, there was a prominent decrease in root growth in WL, and DRC in DS was lower by 9.2% than DRC before the experiment. As for water saturation deficit (WSD) increased to 37.4% by 28 days after treatment initiation (DAT) in DS. WL showed a sharp decline of photosynthetic indicators such as the net photosynthesis rate (A), stomatal conductance (g_s), and maximum carboxylation rate (V_cmax) starting from 7 DAT. By contrast, DS manifested a significant decrease starting from 14 DAT. The DS had higher levels of leaf and root inorganic components than WL did, suggesting an improvement in the osmoregulatory ability. The overall nutritional status of WL was low due to deterioration of the absorption function of the roots. In both DS and WL, energy transfer rates of photochemical reactions, e.g. absorbed photon leads to reduction further than Q_A^- (Φ_PO), absorbed photon leads to electron transport further than Q_A^- (Φ_EO), and absorbed photon leads to reduction of Q_A^- (Ψ_O), decreased, as did vitality indices, e.g. the performance index on absorption basis (PI_abs), driving force on absorption basis (DF_abs), and the structure function index on absorption basis (SFI_abs). However, WL featured a decrease in Electron transport flux from Q_A to Q_B (ETo/RC) and Electron transport flux until PSI acceptors (REo/RC), whereas DS experienced a slight increase or almost no change. Consequently, PI_abs, DF_abs, SFI_abs, and the drought factor index are highly sensitive environmental-stress indicators, whereas WSD, DRC growth, chlorophyll a content, ETo/RC, and REo/RC appear to have high potential as diagnostic indicators of drought or waterlogging damage.

Forest roads are an essential facility for sustainable forest management and protection. With advances in survey technology, such as Light Detection and Ranging, forest road maps with greater accuracy and resolution can be produced. This study produced a 3D map for establishment of a forest road inventory using a Mobile Laser Scanning (MLS) device mounted on a vehicle in four study forest roads in Korea, in order to review its precision, accuracy and efficiency based on comparisons with mapping using Total Station (TS) and Global Navigation Satellite System (GNSS). We counted the points that consist of the cloud data of the maps to determine the degree of precision density, and then compared this with 50 points at 20-m intervals on the centerlines bisecting the widths of the study forest roads. Then, we evaluated the relative positional accuracy of the MLS data based on three criteria: the total length of each forest road; the Root Mean Square Error (RMSE) obtained from coordinate values of the MLS and TS surveys compared to the GNSS survey; and the ratios of the centerlines extracted by the MLS and TS surveys overlaid to the buffer zone by the GNSS survey. Finally, we estimated the time and cost per unit length for producing the map to examine the efficiency of MLS mapping compared to the other two surveys. The results showed that the point cloud data acquired by the MLS survey on the study forest roads had very high precision and so is sufficient to produce a 3D forest road map with high-precision density and a low RMSE value. Although the equipment rental cost is somewhat high, the fact that information targeting on all spatial elements of forest roads can be obtained with a low cost of labor is a benefit when evaluating the efficiency of MLS survey and mapping. Our findings are expected to provide a quantitative assessment of both maintaining sustainable effectiveness and preventing potential environmental damage of forest roads.

Distance is one of the important factors in determining transportation cost and travel time, and it can be easily estimated by measuring the circuity of road networks. This study calculated the circuity factors to estimate the network distance for 27 forest roads (about 105 km) in South Korea. For this purpose, ridge, mid-slope, and valley roads were classified according to the construction location of the mountain slope, and the weighted and unweighted circuity factor (each 500-m section) were calculated. The average value of weighted circuity was 1.55: mid-slope roads (2.09), ridge roads (1.36), and valley roads (1.09). The average unweighted circuity factors were 1.61 for mid-slope roads, 1.21 for ridge roads, and 1.07 for valley roads. This study found that the circuity of the forest road network was most affected by the mountain terrain. In addition, the circuity factor increased with increasing network distance in the mid-slope roads but was not affected by the network distance in ridges and valleys. To improve the efficiency of transportation in the forest road network, it is important to locate the ladings and properly connect with the public road network.