| Title |
UAV-Based Analysis of Rainfall-Induced Terrain Change |
| Authors |
(Yun, Konghyun) ; (Lee, Hanna) ; (Kim, Gihong) |
| DOI |
https://doi.org/10.12652/ksce.2026.46.5.0511 |
| Keywords |
Landslide; Digital terrain model; Geomorphic change detection; Level of detection; Co-registration of topographic data; debris flow |
| Abstract |
Quantifying landslide-induced topographic change requires precise comparison of pre- and post-event terrain data. When the datasets differ in acquisition date, sensor type, spatial resolution, and positional accuracy, measurement error may be misinterpreted as real change. We reconstructed the pre-event terrain from 1:5,000 digital-map contours and spot heights using a triangulated irregular network (TIN), generated the post-event DTM from UAV LiDAR, and applied geomorphic change detection (GCD). Residual elevation differences over stable roads had a mean of -0.04 m and a standard deviation of 0.84 m; the mean vertical offset was therefore negligible relative to the dispersion. An empirical minimum level of detection (LoD) of 0.84 m was applied. After expanding the region of interest to include the source area and correcting the effect of fallen trees, erosion and deposition volumes were estimated as 105,391 m³ and 148,522 m³, respectively, yielding a net change of +43,131 m³. The workflow demonstrates how heterogeneous legacy and UAV-derived terrain data can be evaluated and used for reproducible landslide volume estimation when no high-resolution pre-event survey is available. |