The landslide, which occurred at Umyeon mountain (Mt. Umyeon) in Seoul, Korea in 2011, was a prime example that raised awareness about the landslide in the highly urbanized area. Although many studies have been done on Umyeon landslide, there is a lack of research that detects the area where the landslide occurred and quantifies the elevation changes through remote sensing data. In this regard, this paper aims to detect and assess topographic changes quantitatively over Mt. Umyeon by using digital elevation models (DEMs) derived from airborne laser scanning (ALS) data. Since Mt. Umyeon was hilly and covered with dense trees during summer, traces of the landslide were detected by estimating the spatially distributed uncertainty of ALS-derived DEMs. The probabilistic analysis with Bayes'™ theorem considering the spatially distributed DEM of difference (DoD) uncertainty enabled to detect the landslide traces efficiently and was less affected by the influence of ALS errors. The results indicated that ALS-derived DEMs have the potential to detect landslides with their uncertainty estimation, although the ALS data were acquired in hilly and densely vegetated areas. Moreover, quantifying topographic changes due to landslides with high reliability is considered to be beneficial and practically helpful for disaster recovery.
Bibliographical noteFunding Information:
This research was supported by a grant from the Disaster and Safety Management Institute funded by Ministry of the Interior and Safety [MOIS-DP-2015-10] and the Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government [19AWMP-B121100-04]. The authors are grateful to the National Disaster Management Research Institute (NDMI) in Korea for assisting this research and providing the LiDAR data. The authors would also like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.
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All Science Journal Classification (ASJC) codes
- Earth and Planetary Sciences(all)