Abstract
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.
Highlights
The mountains in Nepal are one of the most hazardous environments in the world, with frequent landslides caused by tectonic activity, monsoonal rainfall and infrastructure development [1]
Three metrics were calculated: true positive (TP), false negative (FN), and false positive (FP). These metrics were not based on the number of landslides because segmentation-derived image objects rarely correspond to single landslides due to over or under-segmentation [53,94]
Alandslide inventory was created from very high-resolution (VHR) imagery in 2012 and 2018 using object-based image analysis (OBIA) within a 3 km buffer of the Karnali highway
Summary
The mountains in Nepal are one of the most hazardous environments in the world, with frequent landslides caused by tectonic activity, monsoonal rainfall and infrastructure development [1]. 72% of the country is hilly or mountainous, with 50% of the total population residing in these areas [2]. Long-term development and economic prosperity of this region is contingent on the availability and reliability of roads for access to infrastructure such as marketplaces, schools, and hospitals [3]. As a result, understanding the frequency, distribution, and susceptibility of landslides along Nepal’s main transportation corridors is vital for better characterizing the impact that landslides may impose on the population within this region. 2019, 11, 2284; doi:10.3390/rs11192284 www.mdpi.com/journal/remotesensing Remote Sens.
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