Semi-automatic classification for rapid delineation of the geohazard-prone areas using Sentinel-2 satellite imagery

The study of land use land cover has become increasingly significant with the availability of remote sensing data. The main objective of this study is to delineate geohazard-prone areas using semi-automatic classification technique and Sentinel-2 satellite imagery in Bhutan. An open-source, semi-automatic classification plugin tools in QGIS software enabled efficient and rapid conduct of land cover classification. Band sets 2-8, 8A, and 11-12 are utilized and the virtual colour composites have been used for the clustering and creation of training samples or regions of interest. An iterative self-organizing data analysis technique is used for clustering and the image is classified by a minimum distance algorithm in the unsupervised classification. The Random Forest (RF) classifier is used for the supervised classification. The unsupervised classification shows an overall accuracy of 85.47% (Kappa coefficient = 0.71) and the RF classifier resulted in an accuracy of 92.62% (Kappa coefficient = 0.86). A comparison of the classification shows a higher overall accuracy of the RF classifier with an improvement of 7.15%. The study highlights 35.59% (512,100 m 2 ) of the study area under the geohazard-prone area. The study also overlaid the major landslide polygons to roughly validate the landslide hazards. Article highlights Semi-automatic classification technique was applied to delineate the geohazard-prone area in the heterogeneous region of Bhutan Himalaya. Unsupervised and supervised classification technique were used to perform land cover classification using the semi-automatic classification plugin (SCP). The Random Forest classifier predicted higher accuracy and the application is rapid and efficient compared to the unsupervised classification.