Landslide Displacement Observation in A Mountainous Area using Temporal Geo-spatial Data Acquired from Drone Mapping
Kim S.S., Jung Y.H.,Koo S., and Park J.W.

Disaster Scientific Investigation Division, National Disaster Management Research Insititute, Republic of Korea.

Abstract

The objective of this study is to propose a methodology for assessing the potential occurrence of additional landslides over time in mountainous areas, utilizing state-of-the-art drone photogrammetry techniques and time-series mapping analysis. The experimental equipment employed in this study includes the DJI Matrice 350 RTK UAV, mounting on the high-resolution optical sensor Zenmuse P1. A total of approximately 2,000 UAV-derived aerial photos of the landslide-affected areas of Mt. Toham were captured and processed into true ortho-image maps and point cloud data to generate 3D terrain models. These imagery maps were produced between September, October, December 2024, and March 2025, and the generated geospatial products served as experimental data for this study. Both oblique and nadir photos were simultaneously captured to produce true ortho-imagery maps, with approximately 2 cm GSD. The study area focuses on regions suspected of ground displacement due to landslides in the Mt. Toham. The total landslide area was estimated approximately 1.8 hectares, with a collapse volume of 21,244 m3.
In the time-series landslide observation method of this study, the first step was to select and extract fixed reference points on the ground which were clearly identifiable in the imagery, considering the challenging accessibility of the mountainous terrain. The subsequent step involved utilizing the orthophoto maps and 3D terrain cross-section data generated through drone-based mapping to monitor centimeter-level positional changes over successive observation periods. This approach enabled the analysis of the potential for further landslide occurrences based on temporal changes in the terrain morphology.

Keywords: UAVs, Drone photogrammetry, Temporal mapping analysis, Landslide, Change detection

Topic: Topic B: Applications of Remote Sensing