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Geomorphological evolution of landslides near an active normal fault in northern Taiwan, as revealed by lidar and unmanned aircraft system data
Several remote sensing techniques, namely traditional aerial photographs, an unmanned aircraft system (UAS), and airborne lidar, were used in this study to decipher the morphological features of obscure landslides in volcanic regions and how the observed features may be used for understanding landsl...
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Published in: | Natural hazards and earth system sciences 2018-03, Vol.18 (3), p.709-727 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Several remote sensing techniques, namely traditional aerial photographs, an unmanned aircraft system (UAS), and airborne lidar, were used in this study to decipher the morphological features of obscure landslides in volcanic regions and how the observed features may be used for understanding landslide occurrence and potential hazard. A morphological reconstruction method was proposed to assess landslide morphology based on the dome-shaped topography of the volcanic edifice and the nature of its morphological evolution. Two large-scale landslides in the Tatun volcano group in northern Taiwan were targeted to more accurately characterize the landslide morphology through airborne lidar and UAS-derived digital terrain models and images. With the proposed reconstruction method, the depleted volume of the two landslides was estimated to be at least 820 ± 20 × 106 m3. Normal faulting in the region likely played a role in triggering the two landslides, because there are extensive geological and historical records of an active normal fault in this region. The subsequent geomorphological evolution of the two landslides is thus inferred to account for the observed morphological and tectonic features that are indicative of resulting in large and life-threatening landslides, as characterized using the recent remote sensing techniques. |
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ISSN: | 1684-9981 1561-8633 1684-9981 |
DOI: | 10.5194/nhess-18-709-2018 |