Choi Shin-Kyu, Ramirez Ryan Angeles, Lim Hwan-Hui, Kwon Tae-Hyuk
Structural and Seismic Technology Group, Korea Electric Power Research Institute (KEPRI), Daejeon, 34056, Republic of Korea.
Department of Civil Engineering, University of Santo Tomas (UST), 1008, Manila, Philippines.
Sci Rep. 2024 May 27;14(1):12048. doi: 10.1038/s41598-024-59008-4.
Landslides pose a growing concern worldwide, emphasizing the need for accurate prediction and assessment to mitigate their impact. Recent advancements in remote sensing technology offer unprecedented datasets at various scales, yet practical applications demand further case studies to fully integrate these technologies into landslide analysis. This study presents a case study approach to fully leverage variety of multi-source remote sensing technologies for analyzing the characteristics of a landslide. The selected case is a landslide with a long runout debris flow that occurred in Gokseong County, South Korea, on August 7, 2020. The chosen multi-source technologies encompass digital photogrammetry using RGB and multi-spectral imageries, 3D point clouds acquired by light detection and ranging (LiDAR) mounted on an unmanned aerial vehicle (UAV), and satellite interferometric synthetic aperture radar (InSAR). The satellite InSAR analysis identifies the initial displacement, triggered by rainfall and later transforming into a debris flow. The utilization of digital photogrammetry, employing UAV-RGB and multi-spectral image data, precisely delineates the extent affected by the landslide. The landslide encompassed a runout distance of 678 m, featuring an initiation zone characterized by an average slope of 35°. Notably, the eroded and deposited areas measured 2.55 × 10 m and 1.72 × 10 m, respectively. The acquired UAV-LiDAR data further reveal the eroded and deposited landslide volumes approximately measuring 5.60 × 10 m and 1.58 × 10 m, respectively. This study contributes a valuable dataset on a rainfall-induced landslide with a long runout debris flow, underscoring the effectiveness of multi-source remote sensing technology in monitoring and comprehending complex landslide events.
滑坡在全球范围内引发了越来越多的关注，这凸显了进行准确预测和评估以减轻其影响的必要性。遥感技术的最新进展提供了各种尺度上前所未有的数据集，但实际应用需要更多案例研究，以便将这些技术全面整合到滑坡分析中。本研究提出了一种案例研究方法，以充分利用各种多源遥感技术来分析滑坡的特征。所选案例是2020年8月7日发生在韩国谷城郡的一起伴有长距离泥石流的滑坡。所选用的多源技术包括使用RGB和多光谱图像的数字摄影测量、安装在无人机（UAV）上的光探测与测距（LiDAR）获取的三维点云，以及卫星干涉合成孔径雷达（InSAR）。卫星InSAR分析确定了由降雨引发并随后转变为泥石流的初始位移。利用无人机RGB和多光谱图像数据的数字摄影测量精确描绘了受滑坡影响的范围。该滑坡的滑距为678米，起始区域的平均坡度为35°。值得注意的是，侵蚀区和堆积区的面积分别为2.55×10平方米和1.72×10平方米。获取的无人机LiDAR数据进一步显示，侵蚀和堆积的滑坡体积分别约为5.60×10立方米和1.58×10立方米。本研究提供了一个关于伴有长距离泥石流的降雨诱发滑坡的宝贵数据集，强调了多源遥感技术在监测和理解复杂滑坡事件方面的有效性。
