Incorporating the effect of ALS-derived DEM uncertainty for quantifying changes due to the landslide in 2011, Mt. Umyeon, Seoul. Issue 3 (2nd April 2020)

• Record Type: Journal Article
• Authors: Kim, Mi-Kyeong; Sohn, Hong-Gyoo; Kim, Seongsam
• Is Part Of: GIScience & remote sensing. Volume 57:Issue 3(2020)
• Page Start: 287
• Page End: 301
• Publication Date: 2020-04-02
• Subjects: Landslide -- ALS -- DEM -- probabilistic analysis -- DoD -- uncertainty estimation; Geodesy -- Periodicals; Cartography -- Periodicals; Aerial photogrammetry -- Periodicals; Remote sensing -- Periodicals; 526.05
• DOI: 10.1080/15481603.2019.1687133
• Languages: English
• ISSNs: 1548-1603

Abstract

ABSTRACT: 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.