Detection of Earthquake-Induced Landslides during the 2018 Kumamoto Earthquake Using Multitemporal Airborne Lidar Data

Abstract

A series of earthquakes hit Kumamoto Prefecture, Japan, continuously over a period of two days in April 2016. The earthquakes caused many landslides and numerous surface ruptures. In this study, two sets of the pre- and post-event airborne Lidar data were applied to detect landslides along the Futagawa fault. First, the horizontal displacements caused by the crustal displacements were removed by a subpixel registration. Then, the vertical displacements were calculated by averaging the vertical differences in 100-m grids. The erosions and depositions in the corrected vertical differences were extracted using the thresholding method. Slope information was applied to remove the vertical differences caused by collapsed buildings. Then, the linked depositions were identified from the erosions according to the aspect information. Finally, the erosion and its linked deposition were identified as a landslide. The results were verified using truth data from field surveys and image interpretation. Both the pair of digital surface models acquired over a short period and the pair of digital terrain models acquired over a 10-year period showed good potential for detecting 70% of landslides.

SAR and LIDAR Datasets for Building Damage Evaluation Based on Support Vector Machine and Random Forest Algorithms—A Case Study of Kumamoto Earthquake, Japan

“…Numerous studies have been conducted to assess the damage caused by this event, some of which were mentioned above. In this study, the combination of SAR and LIDAR data has been used for this purpose [5,8,12,18]. The location of pre-and post-event LIDAR data and the study area used in this research are illustrated in Figures 1 and 2.…”

“…LIDAR sensors collect data in the form of three-dimension cloud points. Landslide detection and damage assessment of buildings are the main capabilities of this method [7][8][9][10][11]. LIDAR data is mainly used to generate a high-resolution digital elevation model (DEM).…”

“…For example, Liu et al used multitemporal airborne LIDAR data to investigate landslides caused by the Kumamoto earthquake. Their study demonstrated the appropriate ability of this method to detect landslides [8]. Yamazaki et al conducted research using pre-and post-event LIDAR images.…”

A Semiautomatic Pixel-Object Method for Detecting Landslides Using Multitemporal ALOS-2 Intensity Images

“…Traditional image thresholds are set based on a linear relation (µ ± k · σ) between the mean (µ) and standard deviation (σ) and are globally computed [50,51]. In the past, such an approach yielded good performance using moderate-and high-resolution optical imagery because of the almost uniform surface radiance in cloud-free imagery [52]. However, due to the effects of shadows present in SAR intensity imagery, a global threshold approach is not suitable for detecting segments of increasing and decreasing SAR backscattering intensities.…”

Investigation of the Effect of the Dataset Size and Type in the Earthquake-Triggered Landslides Mapping: A Case Study for the 2018 Hokkaido Iburu Landslides

“…Active and passive remote sensing systems offer great advantages in rapid landslide mapping (Aimaiti et al, 2019). With remote sensing systems, Optical images (Zhao et al, 2017;Shao et al, 2019), synthetic aperture radar (SAR) (Aimaiti et al, 2019;Adriano et al, 2020), LIDAR systems (Liu et al, 2019), Unmanned Aerial Vehicle (UAV) systems (Comert et al, 2019) or synthesis of optical and SAR images (Shirvani et al, 2019) can be used in the landslide mapping. The most common method preferred in mapping landslides is visual image interpretation (Guzzetti et al, 2012;Rosi et al, 2018).…”