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Detection of earthquake-induced landslides during the 2018 Kumamoto earthquake using multitemporal airborne lidar data
https://nied-repo.bosai.go.jp/records/5777
https://nied-repo.bosai.go.jp/records/57776e714be5-a40e-4642-baf0-c52e73c9b37f
Item type | researchmap(1) | |||||||||||
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公開日 | 2023-03-30 | |||||||||||
タイトル | ||||||||||||
言語 | en | |||||||||||
タイトル | Detection of earthquake-induced landslides during the 2018 Kumamoto earthquake using multitemporal airborne lidar data | |||||||||||
著者 |
Wen Liu
× Wen Liu
× Fumio Yamazaki
× Yoshihisa Maruyama
|
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抄録 | ||||||||||||
内容記述タイプ | Other | |||||||||||
内容記述 | c 2019 by the authors. 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. | |||||||||||
言語 | en | |||||||||||
書誌情報 |
en : Remote Sensing 巻 11, 号 19, 発行日 2019-10-01 |
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ISSN | ||||||||||||
収録物識別子タイプ | EISSN | |||||||||||
収録物識別子 | 2072-4292 | |||||||||||
DOI | ||||||||||||
関連識別子 | 10.3390/rs11192292 |