The landslide is one of the natural disasters which claim human lives and incur huge economic losses, especially in the mountainous area. The main aim of this study is to develop different zones of landslide-prone area using the index of entropy (IOE) at the Ossey watershed area in Bhutan. During the landslide inventory, 164 landslides were identified of which 115 locations were used for the training dataset while the remaining 49 locations were used for the validation dataset. A total of ten causal factors were used for this study including elevation, slope, aspect, slope curvature, stream power index, normalized difference vegetation index (NDVI), distance from the road, distance from the river, lithology, and rainfall. The IOE was used to obtain the relationship between the landslide events and the causal factors. The most influential causal factors were NDVI, slope, and rainfall with the weightage of 0.377, 0.347, and 0.175 respectively as per the IOE. The final landslide susceptibility map was classified into five classes using the geometrical interval classification. The validation was done using the receiver operating characteristic (ROC) curves and the kappa index. The area under the curve (AUC) for the success rate and prediction rate was 0.7821 and 0.8377, respectively. The kappa index using the training dataset and validation dataset were 0.4111 and 0.4898, respectively. The final landslide susceptibility map is accurate enough for the future references by the decision-makers and the engineers.

Gitega District has experienced significant land use and land cover changes due to human activity. This has increased land degradation and environmental issues. However, there is no data on LULC change to guide land-use planning. This study assessed the rate and magnitude of LULC change over the last 35 years and also simulated future scenarios using Geoinformatics. In the first step, five LULC classes were extracted from satellite images from 1984, 2002, and 2019 using the supervised classification method. Overall accuracy and Kappa statistics of more than 85% and 82% respectively were achieved with 30 reference samples. Change analysis highlighted by Land Change Modeler (1984-2019) indicated a significant increase in Agriculture of 94 km2, a slight increase in Shrub Land and Built-up Area of 5.5 km2 and 2 km2, respectively; and a steep decrease in Trees Cover and Grass Land of 62.5 km2 and 39 km2, respectively. Markov Chain and CA-Markov models were further calibrated to simulate LULC changes in 2038 and 2057 using the 2019 base map. Evaluation and analysis of 2019-2057 simulation results showed a moderate agreement of 75% for Kappa and the same trends of LULC change: Trees Cover, Grass Land, and Shrub Land will decrease by 11.5 km2, 13 km2, 11.5 km2 respectively, whereas Agriculture and Built-up Area will increase by 30 km2 and 6 km2 respectively in 2057. These study outcomes can support decision-making towards restoration measures of land degradation and long-term environmental conservation in the region.

Samdrup Jongkhar-Tashigang National Highway (SJ-TG NH) in Bhutan experiences several landslides every year. However, there are no studies on the landslides which will assist in highway realignment. This study developed the landslide susceptibility mapping (LSM) using the information value (IV) and check the reliability of the IV. The workflow consists of landslide inventory, factor preparation, LSM development, and its validation. During the landslide inventory, a total of 130 landslides were identified from satellite image interpretation, google earth image, and field investigation. The landslide inventory was divided into a training dataset (70%) and a validation dataset (30%). Then, nine factors were used to construct a spatial database. The accuracy was conducted using the area under curve (AUC) and the reliability of the model was performed using the kappa index. The AUC for the success rate (0.7700) falls under a good category and the prediction rate (0.6798) falls under the moderate category. The kappa index (0.3407) for the IV falls under the fair reliability category. The LSM was classified into very safe (16.42%), safe (30.64%), moderately (27.67%), risky (16.18%), and high risky zones (9.09%) based on the natural break. The LSM will guide decision-makers in the realignment of the road.

With dwindling supply of surface water, Ground water is increasingly being used as a source of fresh water in many cities across the world. Consequently, there is an increasing need to evaluate groundwater potential of an area. Over the past few decades, Remote Sensing and GIS have been used for systematic investigations on potential recharge of aquifers. As in major cities of the world, the demand for water in Pune City is also increasing every year and demand outstrips the supply of surface water. This study delineated potential zones for artificial recharge across Pune City by using Multi-criteria analysis and the Analytical Hierarchy Process (AHP) techniques. Artificial recharge techniques especially the use of rainwater harvesting (RWH) are being deployed globally to augment supply of fresh water. Ground-water recharge is directly influenced by surface characteristics such as rainfall, geology, soil types, Land Use/Land Cover (LULC), drainage, lineaments/fractures, etc. Hence, six such parameters, namely, LULC, Slope, Soil texture, Rainfall, Drainage density, and Geology were considered to generate a groundwater recharge potential map. Based on the analysis, the study area was zoned into five classes, namely, low, moderate, good, very good and high groundwater potentials. About 45% of the city shows good to high potential for recharge. The results reveal that the high and good potential recharge zones lie to the western part of the city, whereas the central part (inner city) and the eastern part show medium to low potential for recharge. The results can help to identify areas for recharge and formulate a framework for systematic recharge of the existing aquifers in the area under study.

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