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

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.

Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review

“…The compactness defines the weight of the compactness criteria. The higher the value is, more compact the objects will be [33]. Several methods exist to select an optimal scale parameter, of which plateau objective function [31,32] and optimal scale parameter selector [34] have mostly been used.…”

Remote Sensing Approaches and Related Techniques to Map and Study Landslides

“…Some researchers used slightly different susceptible classes to develop landslide susceptibility maps such as unstable, quasi-stable, moderately stable, and stable (e.g., [12]). Some studies also used susceptibility indices: very high, high, moderate, low, and very low (e.g., [59][60][61]79]).…”

Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network

“…The process of landslide susceptibility prediction (LSP) modeling primarily includes a catalog of landslides, environmental factors extraction, model architecture construction, model training, landslide susceptibility mapping, and model evaluation [6,7]. The catalog of landslides (landslide area, boundary, locations) are measured using global positioning systems and put into a geographic information system (GIS) for landslide storage and management [8,9]. The environmental factors are extracted from the remote sensing (RS) images, such as Landsat8 TM image, digital elevation model (DEM), aerial imagery, and LiDAR, based on the GIS spatial analysis, including terrain analysis, hydrological analysis, and map algebra [10].…”