Extraction of landslide-related factors near power transmission facilities using high-resolution satellite images

The majority of power transmission facilities in Korea are located in mountainous areas, and their scale is continuously expanding. However, due to the limitations of manpower-based monitoring focused on the maintenance of transmission towers, regular management has become challenging. To ensure the stable management of aging power transmission facilities, proactive monitoring systems, in addition to post-disaster damage analysis, are necessary. Although wide-area time-series monitoring using satellite imagery can assist in decision-making for disaster preparedness and recovery measures, there is currently a lack of specific monitoring strategies based on satellite imagery for surrounding environment of power transmission facilities. In particular, landslides can act in a complex manner, involving various geological and geomorphic factors, progressively impacting surrounding areas. Current studies primarily rely on direct field surveys to create landslide vulnerability maps and assess risks and damages quantitatively. However, this approach is time-consuming, difficult to sustain in the long term, and prone to subjectivity issues. Therefore, this study aimed to leverage the advantages of remote sensing in the challenging context of mountainous areas surrounding power transmission facilities. The goal was to extract landslide-related factors that accurately represent the unique characteristics of the region using readily available data, without the need for on-site data. Specifically, this study focused on extracting pertinent factors related to landslide impacts in areas exhibiting changes, using two satellite images near power transmission facilities in Gangwon Province, South Korea. PlanetScope imagery for the case of heavy rainfall in August 2022 was used and easily obtainable spatial data regarding land cover, slope, and vegetation were utilized. The study conducted two independent experiments. In the first experiment, a binary classification was performed based on spectral changes to identify highly correlated factors among the landslide-related elements. The second experiment analyzed the patterns of land cover change and their correlation with landslide-related factors. We have confirmed the necessity of monitoring slope gradient, slope direction, and forest diameter in the study area and the variables exhibiting significant correlations generally displayed evident positive relationship in most cases. This study provides evidence of the capability to consistently generate potential landslide hazards analysis results utilizing GIS data extracted from basic maps. By prioritizing elements that exhibit strong long-term correlations, the study highlights the potential to establish a time-series monitoring dataset for the surrounding environment of power transmission facilities in mountainous areas, utilizing satellite imagery.