This study examined the structural and ecological drivers of burn severity during the March 2025 wildfire in Uiseong County, Republic of Korea, with a focus on developing a predictive framework using the differenced Normalized Burn Ratio (dNBR). Seventeen candidate variables were evaluated, among which the forest type, stand age, tree height, diameter at breast height (DBH), and Normalized Difference Vegetation Index (NDVI) were consistently identified as the most influential predictors. Burn severity increased across all forest types up to the 4th–5th age classes before declining in older stands. Coniferous forests exhibited the highest severity at the 5th age class (mean dNBR = 0.3069), followed by mixed forests (0.2771) and broadleaf forests (0.2194). Structural factors reinforced this pattern, as coniferous and mixed forests recorded maximum severity within the 5–11 m height range, while broadleaf forests showed relatively stable severity across 3–21 m but declined thereafter. In the final prediction model, NDVI emerged as the dominant variable, integrating canopy density, vegetation vigor, and moisture conditions. Notably, NDVI exhibited a positive correlation with burn severity in coniferous stands during this early-spring event, diverging from the generally negative relationship reported in previous studies. This seasonal anomaly underscores the need to interpret NDVI flexibly in relation to the forest type, stand age, and phenological stage. Overall, the model results demonstrate that mid-aged stands with moderate heights and dense canopy cover are the most fire-prone, whereas older, taller stands show reduced susceptibility. By integrating NDVI with structural attributes, this modeling approach provides a scalable tool for the spatial prediction of wildfire severity and supports resilience-based forest management under climate change.

In this research, we have used spatial information analysis techniques and procedures to process storm, flood, and snow damage risks, and apply premium rates to produce a Storm and Flood Insurance Management Map. To calculate risk, we used ArcGIS’s main features to overlay, integrate, and classify data. Moreover, we designed an ArcGIS Model Builder program to process very large amounts of risk data quickly and accurately. Excel’s pivot feature was used to calculate areas and premium rates according to flood depth. In the case of Incheon metropolitan city, the average risk was 2.85 on the 4-level scale, which lies between "alert" and "danger" and corresponds to the 1st of the 4 premium rate grades. In particular, there were high risks and high premium rates in areas around ports, ocean beaches, and beaches connected to rivers. We expect that this insurance management map created using spatial information analysis techniques will provide useful data for scientific natural disaster response and prevention planning, rational insurance rate calculation and application, and promotion of policies, which identify and prevent areas at risk for frequent storm and flood damage.

Understanding long-term changes in mammal communities is essential for evaluating ecosystem dynamics under large-scale environmental transformation. This study analyzed nationwide data from the 3rd (2006–2013), 4th (2014–2018), and 5th (2019–2023) National Ecosystem Surveys to assess temporal shifts in the abundance and dominance structure of medium- and large-sized mammals across the Korean Peninsula. Using standardized survey data, we compared changes in detection frequency, dominance rank, and diversity indices among survey phases. Results revealed a significant restructuring of dominance from the 3rd to 4th survey, followed by stabilization in the 5th phase, indicating a gradual simplification of community structure. The water deer remained the most dominant species throughout all surveys, while the wild boar showed a consistent increase in relative frequency. In contrast, smaller or specialist species such as the Korean hare exhibited notable declines. These patterns correspond to ecological changes driven by forest recovery and the absence of apex predators, which have facilitated the proliferation of adaptable generalist species. The findings suggest that ongoing habitat recovery, though beneficial for biomass and productivity, may simultaneously reduce functional diversity and community balance. This study highlights the value of long-term, standardized monitoring for identifying structural transitions in wildlife communities and understanding their broader implications for biodiversity conservation on the Korean Peninsula.

This study analyzed the relationships between land-cover types and the distribution of alien and invasive plant species using data from the 5th National Ecosystem Survey of Korea (2019–2023). A total of 711,557 plant occurrence records were collected across 780 map sheets, resulting in the identification of 3842 vascular plant species, including both alien and invasive taxa. To evaluate spatial patterns and environmental drivers, multiple linear regression and spatial regression models—specifically the Spatial Lag Model (SLM) and Spatial Error Model (SEM)—were applied. The results revealed that alien and invasive species exhibited non-random, spatially clustered distributions influenced by habitat type and disturbance intensity. Alien species were more abundant in agricultural areas and wetlands, whereas forests and grasslands acted as resistant ecosystems. In contrast, invasive species were concentrated in bare lands and urbanized drylands, highlighting the importance of habitat openness and human disturbance in facilitating invasion. Spatial autocorrelation analyses (Moran’s I = 0.0777 for alien species; 0.1933 for invasive species) and the strong spatial dependence in the Spatial Error Model (λ = 0.7405 and 0.6428) confirmed that invasion patterns are shaped by spatial connectivity and environmental continuity. These findings demonstrate that invasion processes in Korea are driven by both anthropogenic disturbance and spatial dependency. Effective management therefore requires habitat-specific, spatially coordinated strategies, emphasizing early detection and rapid control in high-risk areas while reinforcing the ecological buffering capacity of forests to maintain biodiversity and ecosystem stability.

This study aimed to compare the detection rates of field surveys and camera trapping using data from forest areas within 822 survey grids of the 5th National Ecosystem Survey of South Korea (2019-2023). Detection rates for 15 mammal species were analyzed. The results showed that medium- to large-sized mammals, including Capreolus pygargus, Hydropotes inermis, Sus scrofa, Nyctereutes procyonoides, Martes flavigula, and Meles leucurus had significantly higher detection rates with camera traps. In contrast, small mammals such as Prionailurus bengalensis, Mustela sibirica, Erinaceus amurensis, Lepus coreanus, Apodemus agrarius, Eutamias sibiricus, Pteromys volans aluco, Sciurus vulgaris, and Mogera robusta exhibited low detection rates with camera traps, indicating that field surveys remain essential for monitoring these species. To improve the accuracy of mammal monitoring, a hybrid monitoring system combining field surveys and camera trapping is recommended, along with improvements in camera trap deployment. This study provides a foundation for standardizing mammal survey methodologies and supports the development of biodiversity conservation strategies in South Korea.