Evaluation of landslides process and potential in Shenmu sub-watersheds, central Taiwan

Lin, Bor-Shiun; Thomas, Kent; Chen, Chun-Kai; Ho, Hsing-Chuan

doi:10.1007/s10346-018-1109-7

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…This process is very time-consuming and can be subjective [31,32]. Recent advancements in remote-sensing technologies have significantly increased our ability to rapidly map landslides of various sizes, with less in situ surveys or human interaction [33][34][35]. Remote sensing of landslides can be categorized into two groups: pixel-based and object-based image analysis (OBIA).…”

Section: Introductionmentioning

confidence: 99%

Use of Very High-Resolution Optical Data for Landslide Mapping and Susceptibility Analysis along the Karnali Highway, Nepal

2019

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The Karnali highway is a vital transport link and the only primary roadway that connects the remote Karnali region to the lowlands in Mid-Western Nepal. Every year there are reports of landslides blocking the road, making this area largely inaccessible. However, little effort has focused on systematically identifying landslides and landslide-prone areas along this highway. In this study, landslides were mapped with an object-based approach from very high-resolution optical satellite imagery obtained by the DigitalGlobe constellation in 2012 and PlanetScope in 2018. Landslides ranging from 10 to 30,496 m2 were detected within a 3 km buffer along the highway. Most of the landslides were located at lower elevations (between 500–1500 m) and on steep south-facing slopes. Landslides tended to cluster closer to the highway, near drainage channels and away from faults. Landslides were also most prevalent within the Kuncha Formation geologic class, and the forested and agricultural land cover classes. A susceptibility map was then created using a logistic regression methodology to highlight patterns in landslide activity. The landslide susceptibility map showed a good prediction rate with an area under the curve (AUC) of 0.90. A total of 33% of the study arealies in high/very high susceptibility zones. The map highlighted the lower elevated areas between Bangesimal and Manma towns with the Kuncha Formation geologic class as being the most hazardous. The banks of the Karnali River, its tributaries and areas near the highway were also highly susceptible to landslides. The results highlight the potential of very high-resolution optical imagery for documenting detailed spatial information on landslide occurrence, which enables susceptibility assessment in remote and data scarce regions such as the Karnali highway.

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