The current study intended to geospatially analyze the potentiality and site suitability of geo-ecotourism in West Bengal, India. The state of West Bengal is a great platform for diverse tourism and has enormous potential to cultivate geo-ecotourism, as has come up in recent years. The current effort throws some valuable light on the possibility of turning the many geologically, geomorphologically and ecologically significant tourist spots of West Bengal into geo-ecotourism sites, aided with geospatial techniques. The study deals with the qualitative and quantitative investigation of the potentiality of the whole state by dividing it into several geo-ecotourism zones, based on its physiographic setting and Land Use Land Cover (LULC) features, using satellite image data. The application of geospatial technology combined with Remote Sensing (RS) and Geographic Information System (GIS) was employed for this geospatial analysis to portray the potential zones using cartographic and statistical techniques. Furthermore, nine criteria were selected to run the Analytic Hierarchy Process (AHP) method to determine the site suitability for geo-ecotourism. The present submission attempts to record the mapping and analysis of geo-ecotourism of West Bengal employing a secondary database, an expert’s opinions and primary observations, with the application of the AHP method and GIS. The outcomes of the study were found to be very significant, as they indicate a proviso for geo-ecotourism development in the state and will contribute to the formation of location-specific planning and the sustainable management of geo-ecotourism.

Studies on the assessment of land degradation, overland flow, soil loss, and nutrient loss have emerged as paramount importance for food security and rural livelihood in the mountains. The present study dealt with similar issues in the Eastern Himalayas, for which the primary data were collected from the field during 2017–18. Quantitative and qualitative methods were used to collect data on soil erosion and information on overland flow, soil loss, and nutrient loss was assessed through field experiments in the watershed of Sikkim, Eastern Himalayas. The first section of the methodology deals with the experimental analysis from different land use categories to quantify soil loss. In the second section, detailed qualitative analyses of farmers’ perceptions of soil erosion indicators were recorded through field surveys, i.e., key informant interviews (KEIs) and focus group discussions (FDGs). The results showed that the highest overland flow was in barren land (8.63%) followed by large cardamom-based agroforestry system (7.02%), and mixed cropping (4.84%), and the lowest overland flow was in terrace cultivation (4.69%). Soil loss was estimated to be the highest for barren land (7.73 Mg/ha/year (megagram/hectare/year)) followed by mixed cropping (4.32 Mg/ha/year), and terrace cultivation (3.75 Mg/ha/year), with the least soil loss estimated to be in cardamom-based agroforestry (3.23 Mg/ha/year). Loss of nitrogen (N) (4.49 kg/ha/year) and phosphorous (P) (2.43 kg/ha/year) were highest in barren land, while potassium (K) loss was highest (4.30 kg/ha/year) in mixed farming. The lowest N loss rate (3.34 kg/ha/year) was in terrace cultivation, the lowest P loss rate (8.19 kg/ha/year) was in mixed farming, and the lowest potassium loss rate (3.28 kg/ha/year) was in cardamom-based agroforestry. Approximately 33% of the farmers acknowledged light or no soil losses, while 17% of the farmers accepted moderate soil erosion. The results of field survey indicated that only 15–19% of the farmers reported high or extreme soil loss in the fields. Farmers in the watershed are practicing multiple measures to control land degradation; however, marginal farmers are still vulnerable and need strong support from the government to safeguard their land.

Reliable information plays a pivotal role in sustainable urban planning. With advancements in computer technology, geoinformatics tools enable accurate identification of land use and land cover (LULC) in both spatial and temporal dimensions. Given the need for precise information to enhance decision-making, it is imperative to assess the performance and reliability of classification algorithms in detecting LULC changes. While research on the application of machine learning algorithms in LULC evaluation is widespread in many countries, it remains limited in Zambia and Sri Lanka. Hence, we aimed to assess the reliability and performance of support vector machine (SVM), random forest (RF), and artificial neural network (ANN) algorithms for detecting changes in land use and land cover taking Lusaka and Colombo City as the study area from 1995 to 2023 using Landsat Thematic Mapper (TM), and Operational Land Imager (OLI). The results reveal that the RF and ANN models exhibited superior performance, both achieving Mean Overall Accuracy (MOA) of 96% for Colombo and 96% and 94% for Lusaka, respectively. Meanwhile, the SVM model yielded Overall Accuracy (OA) ranging between 77% and 94% for the years 1995 and 2023. Further, RF algorithm notably produced slightly higher OA and kappa coefficients, ranging between 0.92 and 0.97, when compared to both the ANN and SVM models, across both study areas. A predominant land use change was observed as the expansion of vegetation by 11,990 ha (60.4%), primarily through the conversion of 1,926 ha of bare lands into vegetation in Lusaka during 1995–2005. However, a noteworthy shift was observed as built-up areas experienced significant growth from 2005 to 2023, with a total increase of 25,110 ha (71%). However, despite the conversion of vegetation to built-up areas during the entire period from 1995 to 2023, there was still a net gain of over 11,000 ha (53.4%) in vegetation cover. In case of Colombo, built-up areas expanded by 1,779 ha (81.5%), while vegetation land decreased by 1,519 ha (62.3%) during concerned period. LULC simulation also indicated a 160-ha expansion of built-up areas during the 2023–2035 period in Lusaka. Likewise, Colombo saw a rise in built-up areas by 337 ha within the same period. Overall, the RF algorithm outperformed the ANN and SVM algorithms. Additionally, the prediction and simulation results indicate an upward trend in built-up areas in both scenarios. The resultant land cover maps provide a crucial baseline that will be invaluable for urban planning and policy development agencies in both countries.

Uncrewed aerial systems (UASs) are becoming very popular in the domain of water resource mapping and management (WRMM). Being a cheaper and quicker option capable of providing high temporal and spatial resolution data, UAS has become a much sought-after platform for remote sensing. Still, their application in the field is in its early stage. This paper encompasses basic concepts of UAS, different payloads and sensor technologies available, various methodologies for its application in WRMM, different software available, and challenges associated with them, thus presenting a comprehensive review of multiple applications of UAS in different sub-domains of water resources. From cryosphere, rivers and lakes, and coastal areas to sub-surface water, as well as from water quality to wastewater management, the authors have discussed various applications of uncrewed aerial vehicles. At the end of the paper, the authors have identified the issues posing problems in the wider implementation of UAS in WRMM. Also, the future scope of the UAS in WRMM has been discussed.

The concept of ecotourism has experienced a significant surge in popularity over the past two decades, primarily driven by the multitude of adverse impacts associated with mass tourism. The objective of the study was to develop a comprehensive ecotourism suitability index to guide policymakers in implementing tourism development policies. Given the considerable appeal of the study area to both local and international tourists, it is essential to conduct a systematic evaluation to pinpoint suitable areas for ecotourism development. This necessity arises from the study area's placement within a fragile ecosystem and its proximity to a UNESCO World Heritage site. We employed a Geographic Information Systems (GIS) integrated environment coupled with a fuzzy Multi-Criteria Decision Analysis (MCDA) methodology. The GIS-MCDA integrated framework leverages the Analytic Hierarchy Process (AHP) and a weighted linear combination that seeks to amalgamate many features and criteria to assess ecotourism potential by integrating 20 criteria into six separate categories: landscape, topography, accessibility, climate, forest and wildlife, and negative factors. Weights were allocated to each criterion and factor based on the expert's opinions of their impact on the development of ecotourism. The final ecotourism suitability index comprised five unique classes: very high, high, moderate, less, and not suitable. Results reveal that out of the total areas, 45.4 % (259 km²) are within the high and very high suitable classes. The sensitivity analysis suggested that ecotourism potentials are more favorable to forest and accessibility variables. The generated index can be utilized as a road map since validation verified a 64 % accuracy. Given the dearth of earlier research, this study provides vital support for the development of sustainable ecotourism projects in the study area.