Throughout history, human-wildlife conflicts have posed persistent challenges for wildlife conservation. With global population growth and extensive development, these conflicts have intensified, leading to casualties among both humans and wildlife. This pioneering study focuses on human-wildlife conflicts in villages near the less explored Pakhro range of Jim Corbett National Park, situated in the Himalayan foothills of India. Through extensive ground surveys, the study identifies conflict hotspots and assesses their severity, revealing the significant impact on local populations. It is revealed through this study a significant amount of population permanently migrated from the villages like Godi and Amsaur in past few years. Animal attacks and wildlife sightings are common occurrence in the region. In addition to this, the study also identifies conflict with herbivores like Elephants and Deers in the region which results in crop damage. To discover the factors driving these conflicts, a thorough MaxEnt-based analysis was conducted, integrating anthropogenic, topographic, and environmental variables based on literature and expert opinions with 86 location points where wildlife conflicts were reported. The model elucidates the impact of these factors on conflict occurrence in the region. It is clearly indicated from the results that parameters like LULC, proximity from waterbodies, slope, aspect, elevation play a key role in assessment of such conflict zones. The results highlight the significance of adopting a holistic approach, whereby considering multiple predictor variables enhances our ability to comprehensively understand and forecast human-animal conflict dynamics.

Rural and indigenous communities still rely heavily on medicinal plants and ethnobotanical knowledge to support primary health care and provide cultural-based treatments. However, a significant amount of this knowledge remains scattered, underutilized, and undigitized in modern public health systems. In order to improve Digital Public Health systems, this conceptual review explores the integration of citizen science, Artificial Intelligence (AI) and Machine Learning (ML), and geospatial technology. AI and ML improve species identification, prediction, and knowledge synthesis; citizen science encourages community involvement and data collecting; and geospatial tools facilitate the mapping of medicinal plant resources and accessibility. In order to support conservation, health equity, and sustainable resource management, the article suggests an integrated system that connects citizen-generated ethnobotanical data with geographic and AI/ML analytics. This two-way system links policy-relevant digital outputs with community knowledge. The necessity for open, participative, and culturally sensitive digital platforms that support both biodiversity conservation and public health outcomes is highlighted by enduring challenges, including data interoperability, ethical governance, and inclusiveness.

Cloud-to-ground lightning is recognized as a major weather hazard in India, with mortality and losses persisting. A reproducible lightning-risk framework for India is developed and demonstrated for the peninsular state of Andhra Pradesh, using lightning-occurrence data together with topography from CartoDEM, land cover from NRSC's LULC, and socio-economic and infrastructure indicators derived from SECC-2011 and OpenStreetMap. Guided by the UNDRR hazard-exposure-vulnerability concept and FEMA's National Risk Index factorization, the study combines a Lightning Hazard Index (LHI) and a six-factor Lightning Vulnerability Index (LVI) to generate seasonal Lightning Risk Index (LRI) maps. Hazard mapping reveals a monsoon concentration along the north-coastal corridor, a post-monsoon southward shift, and minimal winter risk, while vulnerability peaks along the urban-industrial chain and within the Krishna-Godavari deltas. These season-resolved, decision-ready LRI maps are expected to be highly useful for targeted lightning protection, early-warning placement, and community preparedness. The proposed framework offers a transferable model for lightning risk mapping across India, supporting climate-aware disaster mitigation strategies.

Migratory birds face immense challenges while migrating long distances across countries in their pathways as well as in their destination site. Due to increasing human population, urbanization, anthropogenic activities and changing climate, the migration route of the birds are affected, thereby influencing the pattern of migraton. India is home to many migratory birds, various birds visit India throughout the year. India particularly has 3 major seasons namely summer, monsoon and winter. Therefore, one migratory bird for each season is considered for better analysis of changing climatic conditions. The birds are Blue-tailed Bee-eater (Merops philippinus - summer migrant), Jacobin cuckoo (Clamator jacobinus monsoon migrant) and Rosy starling (Pastor roseus - winter migrant). This study projects probable habitat distribution in current and future climatic conditions under four Shared Socioeconomic Pathways (SSPs) i.e. SSP 126, 245, 370 and 585. To predict the habitat suitability, different AI/ML algorithm was run and based on the ROC plots and AUC values, random forest (RF) algorithm was selected to perform the further analysis. First the model is run to predict the current suitable habitat areas for all three birds. Then its run for all four future scenarios using ACCESS-ESM1-5 modeled CMIP future data for the years (2041-60, 2061-80, 2081-100). In this study its found that the suitable habitat of Clamator jacobinus seems to shift towards the southern part of India in future climatic conditions, under different SSP's. Similarly for the winter migrant to India i.e Pastor roseus the suitable habitat area appears to be decreased in future indicating rise in temperature. But negligible change is observed in suitable area for the summer migrant Merops philippinus. By examining the spatial distribution of suitable habitats for key migratory species, the study seeks to provide insights into the potential risks and opportunities for conservation planning in the face of climate change.

Abstract. The cloud to ground (CG) lightning occurrence is an enigmatic atmospheric phenomenon. It is one of the major natural disasters in India with East coastal region being more vulnerable. Odisha state has been the most vulnerable states in India with last 5 years recording more than 1000 fatalities per year. Owing to its highly dynamical and short lived nature, it is important to have localized and focused mitigation planning. In view that most of the existing forecasting and now-casting efforts are incapable to provide sub-kilometer scales information, the high-resolution data-based risk analysis becomes important for taking appropriate measures to safeguard the most needed communities and infrastructures. Present study develops a comprehensive lightning risk assessment framework through geospatial integration of susceptibility and vulnerability factors to support disaster management planning. The methodology combines CG lightning data, topographic elevation, land cover, and socio-economic datasets to derive lightning risk maps. The prepared risk maps demonstrate 84 % predictive accuracy (AUC = 0.84) when validated against historical incident data and shows strong correlation with district-wise lightning fatality patterns. Such lightning risk maps can be utilized for targeted lightning protection infrastructure deployment, early warning systems, and community preparedness programs.

Vector-borne diseases pose a significant threat to human health, particularly in regions vulnerable to climate change. Among these diseases, malaria, caused by the parasite Plasmodium falciparum and transmitted through the Anopheles mosquito, remains a major global health concern, particularly in sub-Saharan Africa. This study explores the use of machine learning techniques to identify and predict the impact of climate change on the transmission dynamics of P. falciparum malaria in Africa.The research utilizes a combination of climate data, epidemiological records, and machine learning algorithms to analyze historical patterns and project future trends in malaria transmission. Key climate variables such as temperature, precipitation, humidity, and vegetation cover are integrated into predictive models to assess their influence on the abundance and distribution of mosquito vectors and the parasite's lifecycle. Through the application of machine learning models such as Maximum Entropy, this study aims to uncover complex relationships between climatic factors and malaria transmission dynamics. By training these models on historical data, they can accurately predict future scenarios under various climate change scenarios. The findings of this research will provide valuable insights into the potential impact of climate change on the spatial and temporal distribution of P. falciparum malaria in Africa. Such insights are crucial for designing targeted interventions and adaptation strategies to mitigate the anticipated rise in malaria cases and associated morbidity and mortality in the region. Moreover, the methodology developed in this study can serve as a framework for assessing and addressing the impact of climate change on other vector-borne diseases globally.

Hailstorms pose a significant threat to agricultural productivity by causing extensive damage to crop lands. The agricultural sector experiences substantial crop loss each year due to sudden changes in climatic conditions, occurring just when farmers are about to harvest their crops. This research focuses on assessing crop damage in select districts of Haryana state by utilizing satellite data obtained during the hailstorms that occurred in March 2023. The proposed methodology involved acquiring pre- and post-hailstorm satellite images from Sentinel-2 and deriving vegetation indices, such as the Disaster Vegetation Damage Index (DVDI) and Normalized Difference Vegetation Index (NDVI). Maximum Likelihood classification was used to classify cropped areas for both pre- and post-hailstorm periods. The analysis of the NDVI and DVDI profiles of the cropped area in the study region revealed significant variations between the pre- and post-hailstorm periods. The results indicate that approximately 16.85

Understanding and preserving the natural movements of wildlife within their habitats is crucial for their survival. Recent years have underscored the importance of comprehending and safeguarding animal migration and dispersal patterns in wilderness areas, as these behaviors are integral to maintaining ecosystem sustainability. Identifying regions with high movement permeability has become essential for effective habitat management, particularly for species like tigers, which can impact human-animal conflict significantly. Rajaji National Park, situated in Uttarakhand, India, serves as a vital tiger reserve, supporting a significant population of these majestic creatures. This research employs a circuit theory approach to construct a connectivity map of Rajaji National Park, focusing on facilitating the movement of tigers (Panthera tigris tigris) within the protected area. By utilizing circuit theory, this method illuminates areas of heightened connectivity crucial for tiger dispersal, aiding in conservation efforts within the foothills of the Himalayas. The study aims to assess dispersal dynamics within the protected area, pinpointing regions experiencing conservation challenges. The research follows a systematic methodology, beginning with habitat suitability analysis and the generation of a resistance surface. This surface indicates the suitability of various regions for tiger movement, derived from an extensive literature review. Land use and land cover data are utilized to generate the resistance surface, employing tools such as the Gnarly Landscape Utilities toolbox. This surface serves as input data for Circuitscape, alongside the designated start and end points of tiger movement. The results of the analysis identify significant areas crucial from a conservation perspective, highlighting zones requiring immediate attention from policymakers and conservationists. These findings offer valuable insights for enhancing wildlife management and conservation strategies, emphasizing the importance of prioritizing the preservation of key habitats and connectivity corridors.

The velocity and volume of MultiSpectral (MS) remote sensing data have recently increased exponentially. In recent times, however, the absence of a spatial data cube to store analysis-ready data (ARD) products for the Indian sensors’ data delimits its ready use and depreciates its value. Establishing a framework for storing, managing, and providing online processing ARD products for different sensors is necessary. The current work proposes a framework to produce ARD products by radiometrically correcting the data using the 6 S atmospheric correction and Shepherd Diamond-based terrain correction method to provide normalised surface reflectance. The generated ARD product for LISS-III shows a good correlation with the Planet Lab’s surface reflectance ARD product and an excellent correlation with the SACRS2- a Scheme for Atmospheric Correction of ResourceSat-2 corrected product. A frequency-based geometric correction algorithm provides RMSE of less than half a pixel registration error compared to LANDSAT-8 OLI orthorectified imagery. Finally, A Spatial Data Cube (SDC) with CARD4L metadata standard stores the ARD products post ingestion. The paper explains the complete integrated software development with an end-to-end processing chain of LISS III, an Indian optical sensor data.

Effective biodiversity conservation strategies are paramount in addressing the persistent challenges of habitat fragmentation. This study investigates landscape connectivity for tigers across eight protected areas in Haryana, Uttarakhand, and Uttar Pradesh within the Terai Arc Landscape. Utilizing the least cost path methodology, the research identifies the most probable pathways connecting these protected areas. Additionally, circuit theory is employed to highlight crucial conservation areas, termed pinchpoints. The primary objective is to introduce a triangulation-based validation technique for predicted corridors, calculating the accuracy of predicted corridors between the eight protected areas. The results reveal several pinchpoints that require immediate action. The highest prediction accuracy is observed for the corridor between Rajaji National Park and Sonanadi Wildlife Sanctuary/Jim Corbett National Park, whereas the lowest accuracy is noted between Jim Corbett National Park and Kishanpur Wildlife Sanctuary. This research advances the precision and credibility of corridor modeling, offering significant contributions to wildlife conservation by elucidating landscape connectivity and presenting a novel validation technique. The findings provide practical implications for policymakers, conservation practitioners, and researchers, underscoring the need for rigor and validation in developing effective strategies to preserve and sustainably manage wildlife habitats.

Soil moisture is a critical factor that supports plant growth, improves crop yields, and reduces erosion. Therefore, obtaining accurate and timely information about soil moisture across large regions is crucial. Remote sensing techniques, such as microwave remote sensing, have emerged as powerful tools for monitoring and mapping soil moisture. Synthetic aperture radar (SAR) is beneficial for estimating soil moisture at both global and local levels. This study aimed to assess soil moisture and dielectric constant retrieval over agricultural land using machine learning (ML) algorithms and decomposition techniques. Three polarimetric decomposition models were used to extract features from simulated NASA-ISRO SAR (NISAR) L-Band radar images. Machine learning techniques such as random forest regression, decision tree regression, stochastic gradient descent (SGD), XGBoost, K-nearest neighbors (KNN) regression, neural network regression, and multilinear regression were used to retrieve soil moisture from three different crop fields: wheat, soybean, and corn. The study found that the random forest regression technique produced the most precise soil moisture estimations for soybean fields, with an R2 of 0.89 and RMSE of 0.050 without considering vegetation effects and an R2 of 0.92 and RMSE of 0.042 considering vegetation effects. The results for real dielectric constant retrieval for the soybean field were an R2 of 0.89 and RMSE of 6.79 without considering vegetation effects and an R2 of 0.89 and RMSE of 6.78 with considering vegetation effects. These findings suggest that machine learning algorithms and decomposition techniques, along with a semi-empirical technique like Water Cloud Model (WCM), can be effective tools for estimating soil moisture and dielectric constant values precisely. The methodology applied in the current research contributes essential insights that could benefit upcoming missions, such as the Radar Observing System for Europe in L-band (ROSE-L) and the collaborative NASA-ISRO SAR (NISAR) mission, for future data analysis in soil moisture applications.

An early indication of changes in ecosystems can be noticed by the presence and behaviour of a few species that are highly sensitive to the change in the environment. Bird species are excellent indicators of changing ecosystems because they react fast to changes in their surroundings. Therefore, knowledge of all bird species and identifying their types are required to monitor the changing habitats and ecosystems. The performances of various pre-trained convolution neural networks (CNN) namely, MobileNet, MobileNetV2, NASNetMobile, and EfficientNets (version B0, B1, B2, B3, B4), in identifying Indian bird species using visual scene classification are evaluated in this study. As part of the Indian Bioresource Information Network (IBIN), this evaluation will aid in the development of Web/Mobile-Based Bird Species Identification Applications. The results indicate that EfficientNet B4 outperformed other architectures, however, the computational complexity is higher among all the selected CNN models. The EfficientNetB0 has demonstrated comparable performance with computational complexity comparable to MobileNet and NASNet and can be used in mobile platforms.

Animal behaviour such as dispersal and migration ensure their survival in the landscape. It has been established in the past few decades that wildlife conservation and study of their movement in the wilderness is vital for sustainable ecosystem. Thus, identification of regions having high movement permeability for planning and maintenance of functional wildlife corridors has turn out to be a fundamental requirement for habitat management. This study emphases on movement of big cats-Bengal Tiger (Panthera tigris tigris) and Leopard (Panthera pardus fusca) in the protected area of Rajaji National Park situated in Uttarakhand State of India. The National park is a designated tiger reserve with large amount of tigers and leopards at its disposal. Here, Circuitscape was used to generate connectivity map of the study area. The results were validated using occurrence points downloaded from GBIF. The habitat suitability and resistance of the landscape was estimated based on literature review and expert opinion survey. Since, both the species have comparable ecological niche, similar habitat parameters were used for generation of resistance map of the species. Occurrence points for the species were downloaded from GBIF. 60% of the points were used as nodes or focal points where species presence is recorded whereas 40% of the points were used in validation of the connectivity paths. Results depicts the current density map of the study area highlighting areas with high connectivity for the species.

Pied cuckoo Clamator jacobinus (Boddart, 1783) is a migratory, brood parasitic bird found in the Indian subcontinent and Africa. The arrival of Pied cuckoo in India is linked to the onset of monsoon in India. It makes a sudden appearance in northern India in May or early June, indicating the imminent arrival of the monsoon with its unmistakably loud metallic calls. Little information is available on how environmental factors might be affecting its migration. We used maximum entropy modelling to model the monthly and seasonal distribution pattern and identify major bioclimatic factors influencing the Pied cuckoo’s distribution in India. The predicted output shows the species distribution peaking in the months of June–September and no presence in winter in northern India. Water vapour pressure was the significant contributing variable (83.8

Hindrance in the ecological connectivity and gene transfer in the ecosystem affects numerous animals from large mammals to small invertebrates. For foraging, mating, and dispersal, many animal species need wide-ranging habitats such as ungulates—deer, elk, as well as bears, wolves, mountain lions, elephants and tigers. Their access to suitable habitats might be restricted by fragmented landscapes, which can also block essential movement corridors. Moreover, increased human inhabitants and population shift towards the edge of forests provides animals with very less or no scope of living in the wilderness thereby isolating the population. As a result, ecological connectivity analysis and landscape planning are integral part of one another. This paper gives a scoping review of the modelling techniques used to address the ecological connectivity in a landscape. The literature on existing modelling technique, highlighting its uses, advantages, limitations, and developments, is analysed and summarised in the paper. An exhaustive discussion on modelling techniques such as graph theoretic approaches (least cost path analysis, network analysis, etc.), circuit theoretic approaches, agent-based models and machine learning-based approach is compiled for improved decision-making. This review paper aims to support evidence-based decision-making by synthesising the current state of knowledge, identifying research gaps, and providing insights into future directions for advancing connectivity modelling.

Subalpine-alpine vegetation of Himalayan global biodiversity hotspot forms the highest and unique ecosystem of the world. These ecosystems inhabit diverse cold adapted plants, which are currently threatened by global warming. Deciphering vegetation forms and their ecological niches is pre-requisite for evolving conservation strategies. Emerging remote sensing datasets, processing techniques and platforms offer potential to map fine-scale vegetation patterns at ecoregion level. We conceptualised a four-fold classification scheme considering climate, vegetation physiognomy, floristics, and gregarious formations for the subalpine-alpine vegetation of the Western Himalaya spanning over 45,202 km 2 . Sentinel-2 satellite images were classified using a combination of rule-based and machine learning approach i.e. Random Forest in Google Earth Engine to generate vegetation map at regional scale. Reflectance bands alone provided an overall classification accuracy of 76.46% (kappa 0.78), while, the addition of vegetation indices improved the accuracy to 84.43%. (kappa 0.79). When topographical variables were also considered, the accuracy increased to 91.71% (kappa 0.81). The vegetation map at 10 m resolution represents in total 23 vegetation classes covering subalpine zone (9 coniferous forests, 4 broad-leaved forests, 2 scrubs, bamboo brake and grassland) and alpine zone (2 scrubs and 4 herbaceous). Study enhances knowledge on the coverage, distribution, abundance, diversity of subalpine and alpine vegetation and ecological amplitudes with respect to temperature, precipitation, elevation and aspect. The study outcomes are useful for developing landscape as well as species specific conservation planning and bioresource utilization.

Forest fire is a major disturbance in mountainous ecosystems across the world. It impacts the composition, functioning, and structure of the forest ecosystem. Demographic pressure and the related climatic factors are thought to exacerbate the danger of forest fires, although there is a lack of statistical data to support this, particularly in South Asian countries with limited historical records. The study looks at how forest fires interact with their causative variables, such as population, topography, vegetation, and climate regimes, in the forests of Northern India. In order to do so, the paper presents a 15-year trend analysis of forest burn scars. This investigation confirmed the utility of principal component analysis using multisource spatial data and correlation matrices. The findings reveal that the occurrence and intensity of forest fires in mountainous locations are influenced by anthropogenic activities, forests near roads and settlements are extremely vulnerable. Based on the analysis, we emphasize the need for restructuring the existing policies like National Action Plan for forest fires and forest fire prevention and management scheme into more practical science-based policies. These findings may be utilized in planning effective fire control processes in time, and the technique provided in this study may be used to discover potential fire-risk zones in other areas.

Rapid industrialization, widespread urbanization and deforestation have resulted in the deterioration of air quality in many countries. Air pollution, also called the ‘silent killer’, is one of the major health risks being faced nowadays. Considering the rapidly alarming health risk due to environmental situation of deteriorating air quality in India, the Internet GIS-Based Air Quality Monitoring & Forecast System (https://airquality.iirs.gov.in) has been designed and developed using Free and Open Source Software for Geospatial Applications (FOSS4G). The system aims towards synergistic use of numerical prediction models for forecasting of dust, particulate matter (PM2.5 and PM10), gaseous pollutants (CO, O3, SO2 and NO2) and air quality index. Ground-based inputs and remote sensing-based datasets of aerosol optical depth and particulate matter (PM2.5 and PM10) are also utilized to monitor air quality of Indian region. It enables real-time dissemination of model-generated forecast fields and satellite-based inputs utilizing the dissemination potential of Internet towards monitoring and analysis of air quality over Indian region by the stakeholders, researchers and general public. It is developed solely using open-source tools, following Open Geospatial Consortium standard and Web technologies. It follows three-tier architecture and includes modules for visualizing and analysing air quality data accessible over easy to use and intuitive thin web client. Major contribution is to develop lightweight 3D animation module for meteorological data visualization. Here, compression algorithm is implemented which results in significant (5x) reduction in file size for visualizing the meteorological parameters over the web.

Biodiversity information and precise knowledge are critical for its conservation and management for sustainable development. With the advancement of information technology, various efforts have been made by the scientific and internet community to digitize the biodiversity information and put it on an open-source platform equipped with structured information, compiled knowledge, mapping, and spatial analysis tools. These portals assist researchers and conservation managers in a variety of ways, including providing open access bio-resource-related data and analysis tools, which has accelerated biodiversity and conservation research and management practices in recent years. The dissemination of knowledge about various aspect of these portals will expedite the current scenario. This paper is intended to highlight the various aspects of these portals. In this paper, globally recognized portals with rich biodiversity information, global coverage, and powerful spatial analysis tools are reviewed to characterize their vocabulary differences, strengths, and limitations. In addition to the global database, some country-specific initiatives that use biodiversity data in a geospatial context are included. It is observed that many portals have been solely developed for the purpose of creating an open access digital database of bio-resources, with the geographical aspect being ignored. Some of the global and regional initiatives such as Atlas of Living Australia (ALA), Global Biodiversity Information Facility (GBIF), etc. have attempted to fill this void by developing a variety of tools and integrating biodiversity data with geographic locations and other location-based parameters. Despite significant efforts, some issues remain unaddressed, viz., digitization of georeferenced information from natural history from across the world, the inclusion of more advanced web-GIS based tools for spatial analysis, development of agreed data standard, and so on, which could be the primary set of actions for future portals. The effort to address these issues is underway, and we anticipate that these will be resolved in the near future.