Leveraging remote sensing data with AHP and geospatial analysis for landslide susceptibility hotspot assessment in Bandarban of Bangladesh

In the 21st century, climate change has exacerbated global instability, leading to a rise in landslide occurrences. In Bangladesh, mountainous areas such as Bandarban experience significant landslides during the monsoon season. This study seeks to evaluate landslide susceptibility in Bandarban and identify hotspots for optimal landslide hazard mitigation. This study examined landslide susceptibility using the analytical hierarchy process (AHP) and spatial weighted overlay (SWO). Ten conditioning factors were considered, with AHP based on responses from 100 key respondents. Using field surveys and high-resolution satellite images, 280 landslide occurrence samples were collected to rank the subfactors. Using AHP-derived weights of factors and subfactors, the SWO approach was used to create the landslide susceptibility map (LSM). The Getis-Ord (Gi∗) spatial statistics was then used to generate landslide susceptibility hotspots. The result showed that human influence weight 17.02%, making it the most crucial factor in landslide susceptibility. AHP-derived weights were reliable because their consistency ratio was <0.1. According to the study, 59.86% of the area is moderately susceptible, 20.06% is high, and 4.31% is very high. The validation of LSM by ROC curve found excellent performance (AUC ​= ​0.93) of the approaches. Specifically, 63.8% of very high susceptibility areas and 33.26% of high susceptibility areas were found within the hotspot zones with 99% confidence. The research showed the combined use of field samples and remote sensing-based spatial variables improved the accuracy of LSM. These findings can be useful for ensuring proper land use planning and implementation of landslide hazard mitigation measures.