KJCKorea
Journal Central

Abstract: In the transition toward carbon neutrality, the deployment of renewable-energy-linked water electrolysis hydrogen production facilities is expected to expand. Although electrolysis produces almost no direct emissions, indirect greenhouse gas emissions depending on the electricity source, water use, and safety perception emerge as key environmental considerations. This study systematizes major considerations and application directions from an environmental impact assessment (EIA) perspective. Literature and technical materials were analyzed to identify environmental impact pathways of the electrolysis process and compare them with existing EIA categories. The review focused on greenhouse gas emission characteristics by electricity source, operational water use, site conditions, and social perception factors. Results indicate that environmental performance is largely determined by the electricity source rather than the process itself, and that large-scale facilities require verification considering regional water resource conditions and operational characteristics. The study distinguishes applicable existing EIA items and additional considerations, providing reference information for environmental review of hydrogen production facilities.

This study systematically reviews research trends in studies on wind power landscapes in Korea to identify key issues and future tasks. Literature selected from the Korea Citation Index(KCI) was analyzed for research topics, methodologies, and spatial scopes. The analysis reveals that studies tend to differentiate their analytical focus according to spatial hierarchy. Regional studies have primarily focused on exploring generalized landscape images and overall patterns of public acceptance, whereas site-based studies have emphasized experiential acceptance embedded in residents’ daily lives. Methodologically, a dichotomy is observed between qualitative and quantitative approaches, which has led to a discrepancy between determinants of acceptance and factors used for landscape evaluation. Beyond a descriptive review of the literature, this study identifies overlooked issues. Specifically, the disconnection between social acceptance and physical indicators, the lack of empirical verification in predictive assessments, and the bias toward individual project scales are identified as challenges. This study suggests establishing an integrated framework that strengthens links between spatial scales and harmonizes qualitative-quantitative and predictive-empirical approaches.

In current Environmental Impact Assessment (EIA) practices, the estimation of damaged tree volume primarily relies on quadrat-based sampling methods. However, such approaches have inherent limitations in terms of accessibility, safety, and representativeness, particularly in steep slopes and densely vegetated forest areas. This study aims to quantitatively estimate damaged tree volume using LiDAR point cloud data and the Cloth Simulation Filter (CSF) algorithm, and to propose an analytical workflow applicable to EIA practices. The study area is a forest site subject to small-scale EIA located in Yangji-ri 527, Onam-eup, Namyangju-si, Gyeonggi-do, South Korea. High-density point cloud data were acquired using the SATLAB SL9 system. The workflow consisted of point cloud preprocessing, CSF-based ground filtering, noise removal of non-ground points using Statistical Outlier Removal (SOR), height normalization, individual tree segmentation using Treeiso, diameter at breast height (DBH) estimation via circle fitting, and volume calculation using both 2.5D volume and allometric equations. The results show that the total number of points in the study area was 291,003,897. After CSF application, 170,879,807 points (58.72%) were classified as ground points and 120,124,090 points (41.28%) as non-ground points. Following SOR filtering, 105,683,270 points remained. After height normalization and removal of points below 0.3 m, 104,848,559 points were used for Treeiso segmentation. A total of 2,397 individual tree objects were extracted, which were further classified into 600 accepted trees, 1,565 multi-stem or merged candidates, and 232 removed objects. Among the accepted trees, 207 were classified as overstory trees, 182 as midstory trees, and 211 as understory vegetation. The total estimated tree volume was 174.0263 m³. These findings demonstrate that LiDARbased analysis enables precise quantification of individual tree attributes, including location, height, DBH, and volume, even in complex forest environments such as steep terrains. Compared to conventional sampling-based approaches, this method provides detailed three-dimensional structural information and volume-based estimation of damaged trees. Therefore, it is expected to contribute to the development of a more objective and reproducible framework for estimating damaged tree volume in EIA practices.