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

Karma Tempa*, Komal Raj Aryal

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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 m2) of the study area under the geohazard-prone area. The study also overlaid the major landslide polygons to roughly validate the landslide hazards.

Original languageEnglish
Article number141
Number of pages14
JournalSN Applied Sciences
Issue number5
Early online date9 Apr 2022
Publication statusPublished - May 2022

Bibliographical note

Copyright © The Author(s), 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit


  • Bhutan
  • Geohazard
  • Random Forest
  • Semi-automatic classification
  • Sentinel-2


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