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.

中文翻译：

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将 ALS 衍生的 DEM 不确定性的影响纳入量化因 2011 年滑坡引起的变化，Mt. 首尔 乌面

摘要 2011 年发生在韩国首尔 Umyeon 山（Mt. Umyeon）的山体滑坡是提高人们对高度城市化地区山体滑坡认识的一个典型例子。尽管已经对乌面滑坡进行了许多研究，但缺乏通过遥感数据检测滑坡发生区域并量化高程变化的研究。在这方面，本文旨在定量检测和评估山上的地形变化。Umyeon 使用源自机载激光扫描 (ALS) 数据的数字高程模型 (DEM)。由于山。Umyeon 是丘陵，夏季被茂密的树木覆盖，通过估计 ALS 衍生的 DEM 的空间分布不确定性来检测滑坡的痕迹。贝叶斯概率分析 ™ 定理考虑了差异 (DoD) 不确定性的空间分布 DEM，能够有效地检测滑坡轨迹，并且受 ALS 误差的影响较小。结果表明，虽然 ALS 数据是在丘陵和植被茂密的地区获得的，但 ALS 衍生的 DEM 有可能通过其不确定性估计来检测滑坡。此外，以高可靠性量化由滑坡引起的地形变化被认为是有益的，并且对灾难恢复有实际帮助。尽管 ALS 数据是在丘陵和植被茂密的地区获得的。此外，以高可靠性量化由滑坡引起的地形变化被认为是有益的，并且对灾难恢复有实际帮助。尽管 ALS 数据是在丘陵和植被茂密的地区获得的。此外，以高可靠性量化由滑坡引起的地形变化被认为是有益的，并且对灾难恢复有实际帮助。