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Open AccessArticle

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

1
Goddard Earth Sciences Technology and Research, Universities Space Research Association, Columbia, MD 21046, USA
2
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2284; https://doi.org/10.3390/rs11192284
Received: 30 August 2019 / Revised: 19 September 2019 / Accepted: 25 September 2019 / Published: 30 September 2019
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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. View Full-Text
Keywords: Karnali highway; landslides; very high-resolution optical imagery; object-based image analysis; DigitalGlobe; PlanetScope; landslide susceptibility; logistic regression; eCognition Karnali highway; landslides; very high-resolution optical imagery; object-based image analysis; DigitalGlobe; PlanetScope; landslide susceptibility; logistic regression; eCognition
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Amatya, P.; Kirschbaum, D.; Stanley, T. Use of Very High-Resolution Optical Data for Landslide Mapping and Susceptibility Analysis along the Karnali Highway, Nepal. Remote Sens. 2019, 11, 2284.

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