Flood hazards are common in Bhutan as a result of torrential rainfall. Historical flooding events also point to flooding during the main monsoon season of the year, which has had a huge impact in many parts of the country. To account for climate change patterns in flood hazards in Bhutan, 116 historical flood events between 1968 and 2020 for 20 districts were retrieved and reviewed. The preliminary review revealed that the frequency of flood occurrence has increased by three times in recent years. In this study, seven flood vulnerability (FV) indicators were considered. Five are the attributes of historical floods, classified into a number of incidents for flood events, fatalities, affected population, and infrastructure damages including economic losses. Additionally, the highest annual rainfall and existence of a flood map were other two indicators considered. Using historical data, flood hazard and impact zonation were performed. The analytic hierarchy process (AHP) method was employed to derive a multi-criteria decision model. This resulted in priority ranking of the seven FV indicators, broadly classified into social, physical/economic, and environmental. Thereafter, an indicator-based weighted method was used to develop the district flood vulnerability index (DFVI) map of Bhutan. The DFVI map should help researchers understand the flood vulnerability scenarios in Bhutan and use these to mediate flood hazard and risk management. According to the study, FVI is very high in Chhukha, Punakha, Sarpang, and Trashigang districts, and the index ranges between 0.75 to 1.0.

Gelephu, located in the Himalayan region, has undergone significant development activities due to its suitable topography and geographic location. This has led to rapid urbanization in recent years. Assessing land use land cover (LULC) dynamics and Normalized Difference Vegetation Index (NDVI) can provide important information about urbanization trends and changes in vegetation health, respectively. The use of Geographic Information Systems (GIS) and Remote Sensing (RS) techniques based on various satellite products offers a unique opportunity to analyze these changes at a local scale. Exploring Bhutan’s mandate to maintain 60% forest cover and analyzing LULC transitions and vegetation changes using Sentinel-2 satellite imagery at 10 m resolution can provide important insights into potential future impacts. To examine these, we first performed LULC mapping for Gelephu for 2016 and 2023 using a Random Forest (RF) classifier and identified LULC changes. Second, the study assessed the dynamics of vegetation change within the study area by analysing the NDVI for the same period. Furthermore, the study also characterized the resulting LULC change for Gelephu Thromde, a sub-administrative municipal entity, as a result of the notable intensity of the infrastructure development activities. The current study used a framework to collect Sentinel-2 satellite data, which was then used for pre-and post-processing to create LULC and NDVI maps. The classification model achieved high accuracy, with an area under the curve (AUC) of up to 0.89. The corresponding LULC and NDVI statistics were analysed to determine the current status of the LULC and vegetation indices, respectively. The LULC change analysis reveals urban growth of 5.65% and 15.05% for Gelephu and Gelephu Thromde, respectively. The NDVI assessment shows significant deterioration in vegetation health with a 75.11% loss of healthy vegetation in Gelephu between 2016 and 2023. The results serve as a basis for strategy adaption required to examine the environmental protection and sustainable development management, and the policy interventions to minimize and balance the ecosystem, taking into account urban landscape.

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. 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.