2024, Vol.33, No.2

Journal of Environmental Impact Assessment 2023 KCI Impact Factor : 0.51

pISSN : 1225-7184
https://journal.kci.go.kr/kseia

Biodiversity holds significant importance within the framework of environmental impact assessment, being utilized in site selection for development, understanding the surrounding environment, and assessing the impact on species due to disturbances. The field of environmental impact assessment has seen substantial research exploring new technologies and models to evaluate and predict biodiversity more accurately. While current assessments rely on data from fieldwork and literature surveys to gauge species richness indices, limitations in spatial and temporal coverage underscore the need for high-resolution biodiversity assessments through species richness mapping. In this study, leveraging data from the 4th National Ecosystem Survey and environmental variables, we developed a species distribution model using Random Forest. This model yielded mapping results of 24 mammalian species' distribution, utilizing the species richness index to generate a 100-meter resolution map of species richness. The research findings exhibited a notably high predictive accuracy, with the species distribution model demonstrating an average AUC value of 0.82. In addition, the comparison with National Ecosystem Survey data reveals that the species richness distribution in the high-resolution species richness mapping results conforms to a normal distribution. Hence, it stands as highly reliable foundational data for environmental impact assessment. Such research and analytical outcomes could serve as pivotal new reference materials for future urban development projects, offering insights for biodiversity assessment and habitat preservation endeavors.

As the seriousness of habitat destruction caused by development projects emerges, the importance of environmental impact assessment(EIA) is increasing to preserve biodiversity. In previous studies, research is being conducted to quantitatively evaluate the biodiversity impact of development factors and surrounding environmental factors on the landscape scale, but research on the factors affecting the reduction of biodiversity based on development projects is insufficient. This study examined whether independent variables(size of development project, type of the development, DEM, ecosystem and nature map, distance from the green land, distance from the protected area), which have been proven to effect biodiversity through the previous researches, have a significant effect on the change of richness index(RI) through multi-class logistic regression analysis, T-test, and analysis of the development type. As a result, only the size of development project and the first richness index in EIA showed p-value less than 0.05. And it was confirmed that the reduction in biodiversity was significantly changed in the following construction types: installation of sports facilities, energy development, and development of industrial location and industrial complex. Since the results of this study confirmed that the impact of the variables may be inconsistent depending on the analysis scale, additional study of necessary indicators at the development project is needed to analyze biodiversity changes in EIA accurately.

This study aims to estimate roadkill occurrences and investigate influential factors in Chungcheongnam-do, contributing to the establishment of roadkill prevention measures. By comprehensively considering weather, road, and environmental information, machine learning was utilized to estimate roadkill incidents and analyze the importance of each variable, deriving primary influencing factors. The Gradient Boosting Machine (GBM) exhibited the best performance, achieving an accuracy of 92.0%, a recall of 84.6%, an F1-score of 89.2%, and an AUC of 0.907. The key factors affecting roadkill included average local atmospheric pressure (hPa), average ground temperature (℃), month, average dew point temperature (℃), presence of median barriers, and average wind speed (m/s). These findings are anticipated to contribute to roadkill prevention strategies and enhance traffic safety, playing a crucial role in maintaining a balance between ecosystems and road development.