Search

Under the present Indian government initiative, allweather roads are being taken up for four pilgrimage locations in the Uttarakhand state of India. The Rishikesh to Gangotri road axis is a major road used by local citizens and tourists. Rainfall and numerous anthropogenic activities become the primary reasons for landslide hazards in the area. An accurate Landslide Susceptibility Map (LSM) for any area is of paramount importance for the decision makers of land-use planning. The present study gives a comparative analysis of recent advanced algorithms, i.e. CatBoost, LightGBM and deep neural network topology for generating the LSM by following pixel-based. Fourteen causative factors along with landslide inventory of 154 locations are used for the study. LSM are generated based on JENKS natural break criteria using all the algorithms and their performance comparison is evaluated. Overall accuracy for train and test data, prediction accuracy, area under receiver operating characteristics (AUROC) score for test data, and computational time for model fit on train data; are the criteria used for performance evaluation of each algorithm. In this study, it is observed that LSM can be generated at considerably fast pace if CatBoost or LightGBM is used while deep neural network-based topology gives marginally better results on all other performance measure. [ABSTRACT FROM AUTHOR]

India, as the world's second-largest rice producer, accounting for 21.7% of global rice production, plays a crucial role in ensuring global food supply stability. However, creating high-resolution rice maps for India, such as those at 10 to 30 m, poses significant challenges due to frequent cloudy weather conditions and the complexities of its agricultural systems. This study used a sample-independent mapping method for rice in India using the synthetic aperture radar (SAR)-based Rice Index (SPRI). We produced 10 m spatial resolution rice distribution maps for three years (i.e., 2018, 2020, and 2022) for 23 states in India, covering 98% of Indian rice production. The method effectively utilized the unique characteristics of rice in the vertical–horizontal (VH) backscatter coefficient time series of Sentinel-1, from ttransplantation to the maturity stage, combined with cloud-free Sentinel-2 imagery. By calculating the SPRI values for each agricultural field object using adaptive parameters, the planting locations of rice were accurately identified. On average, the user, producer, and overall accuracy over all investigated states and union territories was 84.72%, 82.31%, and 84.40%, respectively. Additionally, the regional-scale validation based on the statistical area at the district level showed that the coefficient of determination (R2) ranged from 0.53 to 0.95 for each state, indicating that the spatial distribution of the statistical planted area at the district level was reproduced well. [ABSTRACT FROM AUTHOR]

India's rivers are essential for providing fresh water for human sustenance, but their water quality has deteriorated due to pollution from industrial waste, domestic sewage, agricultural runoff, and more. Parameters such as physical, chemical, biological, and intended use criteria are used to assess water quality. Water quality monitoring is crucial for preserving freshwater resources and human health, and integrating IoT and AI has revolutionized environmental monitoring. However, India faces challenges in accessing remote locations for monitoring and a lack of manpower. To address these challenges, real-time monitoring systems and remote sensing technologies should be implemented, and wastewater treatment facilities should be upgraded. Public awareness campaigns on responsible consumption and waste disposal practices are also needed. Strengthening and enforcing regulatory measures to limit pollutant discharge and addressing deforestation and improper land use practices are essential. A comprehensive law on environmental protection is necessary for meaningful enforcement and responsibility within society. By addressing these challenges and adopting innovative solutions, India can safeguard its water resources and ensure a sustainable future. [ABSTRACT FROM AUTHOR]

The Karamnasa River, originating in Bihar's Kaimur district, serves as a vital water resource in the region. This research, conducted at coordinates 25°30'54''N latitude and 83°52'30" E longitude, aimed to identify optimal sites for water conservation structures within the Karmanasa River Basin (KRS) using Remote Sensing and GIS techniques. By analysing morphometric parameters (MP) of the basin, including slope, stream length, and drainage density, the study delineated the basin into seven sub-watersheds and five stream orders. The comprehensive analysis revealed SW2 as the most critical sub-watershed, necessitating immediate conservation efforts. Assigning ranks to parameters like bifurcation ratio and circulatory ratio, SW2 emerged as the priority sub-watershed. Fifteen potential conservation sites were identified, comprising 9 farm ponds, 3 percolation tanks, and 3 check dams. The research underscores the significance of prioritizing sub-watersheds based on morphometric characteristics, with lower parameter values indicating higher priority. The study's drainage network analysis, conducted through remote sensing and GIS, enhances understanding of the KRS hydrological features. This research highlights sustainable development through effective water resource use and targeted conservation in the Karamnasa River Basin, emphasizing community engagement and participatory methods for enduring, resilient environmental stewardship and successful land management. [ABSTRACT FROM AUTHOR]

Soil moisture plays a critical role in agricultural productivity and water resource management, especially in a diverse and populous country like India. Understanding variations in soil moisture across different regions and seasons is essential for adapting agricultural practices and water management strategies to local conditions. This study examines changes in soil moisture levels across India, comparing contemporary data from 2023 with historical averages from 2000 to 2005 using advanced remote sensing and GIS technologies. The primary objective of this study is to identify Soil Moisture Anomalies (SMA) across India, quantify their impacts on agriculture and water resources, and provide recommendations for targeted management strategies. By comparing recent soil moisture data against historical averages, the study aims to highlight trends and changes that could influence future water resource planning and agricultural practices. The research utilizes data from the Famine Early Warning Systems Network's (FEWS NET) i.e. Land Data Assimilation System (FLDAS), obtained from NASA's data archives. The study employs a systematic approach to analyze seasonal variations in soil moisture across different Indian states. Soil moisture levels were analyzed using zonal statistics in GIS to classify regions into categories based on the degree of anomaly observed. This classification helped in understanding the spatial distribution of soil moisture during the pre-monsoon, monsoon, and post-monsoon seasons. The study found significant regional and seasonal variations in soil moisture across India. During the monsoon period, areas such as Bihar and Jharkhand consistently showed significant moisture deficits, indicative of drought conditions, affecting agricultural output and necessitating urgent water conservation measures. Conversely, regions like Punjab benefited from positive soil moisture anomalies, enhancing agricultural productivity. The pre-monsoon and post-monsoon seasons also showed variations, with some areas experiencing deficits requiring careful water management while others had surpluses that increased the risk of flooding. The analysis of SMA in India underscores the need for region-specific agricultural and water management strategies that consider significant variability in soil moisture conditions. The study highlights the importance of integrating soil moisture monitoring into national policy frameworks to enhance climate resilience and sustainable agricultural practices. Future research should focus on updating soil moisture assessments with more recent data and refining predictive models to improve the accuracy and effectiveness of water management and agricultural interventions. [ABSTRACT FROM AUTHOR]

Wetlands, which serve as transitional zones between terrestrial and aquatic environments, play a crucial role in providing diverse ecosystem services for the sustenance of wildlife and mankind. Unfortunately, these delicate systems have experienced illicit human interventions in recent times, resulting in degradation and deterioration. Despite being designated as protected areas, wetlands are being exploited beyond their ecological capacity, posing a threat to their sustainability and potential existence in the near future. The present study aims to analyse the historical spatial changes in three wetlands located in Kerala, India, both before and after their declaration as Ramsar sites. Ashtamudi Lake, Sasthamkotta Lake, and Vembanad Lake were the focus areas, and the study employed multi-temporal satellite data sets from 1988 to 2023 to generate land use and land cover maps. The objective was to assess the ecological transitions, particularly the expansion of urbanization and subsequent reclamation of wetlands. By analysing the changes in aerial extent of each land cover class within the study area, the study identified spatio-temporal transitions and modifications occurring in and around the Ramsar sites. A comparison of the thematic maps from 1988 to 2023 revealed a significant increase in built-up areas and a noticeable reduction in vegetation, agriculture, and water bodies, both in terms of extent and quality. The study identified various socio-economic drivers that are responsible for these changes and emphasized the urgent need for regular monitoring and management of these wetland ecosystems. It stresses the importance of treating wetland conservation as a priority. [ABSTRACT FROM AUTHOR]

The study addresses the challenging task of estimating toxic concentration in soils, focusing on Tirupur District, Tamilnadu, India, utilizing Landsat 8 imagery. Acknowledging the potential environmental repercussions, the research emphasizes the increase in pollution—air, water, and soil—due to toxic concentrations, particularly heavy metals resulting from industrial activities. Soil pollution, a consequence of rapid industrial development near water sources, is a key concern, exacerbated by improper waste disposal. Utilizing Landsat 8 OLI images and relevant soil standards, the study employs Kriging and regression analysis for mapping heavy metal concentrations in the soil, both through remote sensing and field data. The correlation between remote sensing and in-situ data enhances result reliability. The findings categorize soil samples from 17 locations in Tirupur District based on pollution levels, distinguishing regions as highly polluted, not polluted, or moderately polluted. This research contributes valuable insights into the spatial distribution of heavy metal pollution, offering a foundation for targeted environmental management strategies in the identified regions. [ABSTRACT FROM AUTHOR]

Geospatial information on river gauging stations, such as station locations, catchment boundaries, river flow paths and other hydrographic data are basic building blocks of hydrological analyses. Such information for India's river basins is available from Central Water Commission (CWC) and water resources information system (WRIS), but is limited in extent and unreliable. Using widely used global hydrography products, catchment boundaries were delineated for 100 gauging stations in the Ganga river basin (GRB), and their catchment areas were estimated. The delineated boundary for the GRB, and also station-wise catchment areas from the present study were much more consistent with global hydrography products than those available from CWC and WRIS. Lack of reliable, analysis-ready hydrography data is a hindrance to hydro-climatological research. The long-term objective of this study is to help build such a needed dataset using high-resolution remote sensing products. [ABSTRACT FROM AUTHOR]

The Himalayan region has been experiencing stark impacts of climate change, demographic and livelihood pattern changes. The analysis of land use and land cover (LULC) change provides insights into the shifts in spatial and temporal patterns of landscape. These changes are the combined effects of anthropogenic and natural/climatic factors. The present study attempts to monitor and comprehend the main drivers behind LULC changes (1999–2021) in the Himalayan region of Pithoragarh district, Uttarakhand. Pithoragarh district is a border district, remotely located in the north-east region of Uttarakhand, India. The study draws upon primary and secondary data sources. A total of 400 household surveys and five group discussions from 38 villages were conducted randomly to understand the climate perception of the local community and the drivers of change. Satellite imagery, CRU (Climatic Research Unit) climate data and climate perception data from the field have been used to comprehensively comprehend, analyze, and discuss the trends and reasons for LULC change. GIS and remote sensing techniques were used to construct LULC maps. This multifaceted approach ensures comprehensive and corroborated information. Five classes were identified and formed viz- cultivation, barren, settlement, snow, and vegetation. Results show that vegetation and built-up have increased whereas cultivation, barren land, and snow cover have decreased. The study further aims to elucidate the causes behind LULC changes in the spatially heterogeneous region, distinguishing between those attributed to human activities, climate shifts, and the interconnected impacts of both. The study provides a comprehensive picture of the study area and delivers a targeted understanding of local drivers and their potential remedies by offering a foundation for formulating sustainable adaptation policies in the region. [ABSTRACT FROM AUTHOR]

Changes in the environmental conditions of Land Use/Land Cover can also significantly influence surface water, mostly due to the dramatically increased biophysical characteristics of the land surfaces. Therefore, extracting multi-scale surface water bodies in urbanized areas is essential. Besides, the past relationships of surface water bodies mostly used low-resolution images. However, the water surface mapping and spatial analysis of multi-scale structure changes in urban areas with high-resolution satellite images have not yet been studied. The present study focused on multi-scale extraction and spatial analysis of growth pattern changes in urban water bodies using Sentinel-2 MSI imagery. Satellite data for the study were obtained from the location map of Chhattisgarh, in the central part of India. The available Sentinel-2 images covering the study area over the period 2012–2020. Initially, we applied image pre-processing steps to remove the shadow noise and distortions and convert the images into a suitable form for mapping. Then, the water surface mapping process combines multi-band water indices (Normalized Difference Vegetation Index and Normalized Difference Water Index (NDWI), Modified NDWI, Urban Difference WI, and Urban Difference Shadow Index and object-oriented methods to extract multi-scale water body information using high-resolution images. Study periods for change analysis were divided into sets. (a) Inter-annual variation is evaluated using the Water Area Frequency Index (WAFI) covering the entire study area over the time period 2012–2020. (b) The spatial variation of land use patterns (i.e., changes in the growth pattern of different scales) in the selected water areas is assessed using eight landscape metrics over three different time periods (2012, 2015, and 2020). These two variations in the WAFI layer are post-processed to determine three multi-scale water scenarios (rivers, streams, canals, and reservoirs): (1) Artificial Waterway (AW of canals, lakes, and reservoirs); (2) Natural Waterway (NW of rivers, streams); and (3) No Waterway (NWW). Two sets of qualitative evaluations are considered: inter-annual variation and spatial variation. The inter-annual variation of surface water areas is evaluated in terms of the WAFI metric percentage. Then the spatial variation of the landscape metrics (Shannon's Diversity Index, Largest Patch Index, Area and Edge metric Percentage, Landscape Division Index, Area-Weighted Mean Shape Index, Aggregation Index, Edge Density, and Patch Density) is analyzed in terms of changes in the selected water areas and changes in the growth pattern for three different time periods. Results and evaluation demonstrate that the accuracy assessment of water mapping methods achieves a high overall accuracy of 95 to 98% for three different time periods. In addition, the observed results of the spatial analysis (AW, NW, and NWW) cover the multi-scale water areas with the highest landscape pattern of fragmentation. Overall, the qualitative findings should therefore be useful for managing and protecting the urban environment. [ABSTRACT FROM AUTHOR]

Habitat suitability models are powerful tools in predicting species distributions and assessing the potential impacts of environmental changes. In this article, a habitat suitability model was developed for Paradoxornis flavirostris, a threatened (Vulnerable) bird species found in the northeastern part of India, using remote sensing data and machine learning techniques. The occurrence records for P. flavirostris were considered from primary as well as multiple secondary sources like GBIF & eBird, and bioclimatic variables such as temperature, precipitation, and humidity were collected from www.worldclim.org. Then, MaxEnt algorithm was used to model the habitat suitability of P. flavirostris based on the collected data. Additionally, the model was also run to project the future range of P. flavirostris under different climate change scenarios. The model also predicts potentially suitable habitats for P. flavirostris outside of its current range, suggesting areas where the species may expand or contract its distribution in the future. This research provides valuable insights into the habitat suitability and potential range dynamics of P. flavirostris, and can inform conservation planning and management efforts for this threatened bird species. [ABSTRACT FROM AUTHOR]

Floods have devastating consequences for human life and economies worldwide. The present study focuses on flash flooding in the Himalayan environment, specifically in Dharamshala, Himachal Pradesh, India. In July 2021, a low-pressure system developed in the area, intensifying into a deep depression and resulting in extreme rainfall on July 12, 2021. This extreme rainfall event resulted flash flooding, landslides, and extensive damage in Dharamshala. Utilizing satellite observation and geospatial technology, this study examined the heavy rainfall events and the vulnerability of the geology of Dharamshala to flash flooding. The area received a cumulative rainfall of more than 370 mm, with over 35 mm recorded at 09:00 Universal Time Coordinated (UTC). The steep slopes of the region, combined with sudden heavy downpours from multiple extreme rainfall events, contributed to flash flooding and landslides. The consequences were severe, resulting in the loss of seven lives and impacting thousands of people in the study area. The research findings emphasize the significance of satellite-borne remote sensing applications and geospatial technology for real-time short-lived localised rainfall events in the study area. The study also highlights the role of Bhagsu Nala in exacerbating flood risks. This research provides a valuable model for implementing effective disaster mitigation measures. Moreover, it contributes to global efforts to reduce the risk of natural catastrophes, particularly in Himachal Pradesh. The findings offer valuable insights into disaster management and mitigation strategies. [ABSTRACT FROM AUTHOR]

Himalayan glaciers have shown a retreating trend since the Little Ice Age (LIA) in response to climate change. As glaciers are crucial for water security and ecological sustainability, it becomes necessary to map and monitor the glacial status from time to time. The present study is focused on the study of Haptal glacier located in the Bhuzas sub-basin of Kishtwar district of Jammu and Kashmir (UT), India, between the Pir-Panjal and Greater Himalayan range using satellite data. Results revealed that the glacier retreated continuously at progressive rates between 1980 and 2020, with an annual retreat rate of 28.25 ± 1.85 m a−1. During 1999–2009, the highest (67.93 ± 2.8 m a−1) retreat rate was observed. Maximum surface velocity was estimated during 1999–2000 (38.5 ± 4.7 m a−1), while the glacier experienced the minimum surface velocity during 2019–2020 (32.5 ± 3.7 m a−1). The glacier has lost its area (22.60 ± 8.18%), glacial length (10.95 ± 0.7%), and glacier ice volume (1.47 ± 0.56 km3; 29.63 ± 11.47%) during the study period. Modelled mean ice thickness using Glabtop2 for the glacier is estimated at 129.28 ± 13 m. The accumulation area values showed a decreasing trend of 10.89 ± 0.56 km2 to 8.15 ± 0.3 km2, indicating a change of 25.05 ± 5.8% between 1999 and 2020. Upward migration of snow line altitude from 5148 m (1999) to 5198 m (2020) indicates enhanced melting and glacier loss during the study period. The study further revealed that there is an overall decreasing trend in specific mass balance. Research highlights: Haptal glacier retreated at progressive rates between 1980 and 2020 The rate of the recession was higher during 1999–2009 The upward migration of SLA showed melting process during the study period Surface ice velocity of glacier is reduced during 1999–2021. Temperature shows increasing trend and precipitation shows decreasing trend. [ABSTRACT FROM AUTHOR]

Koh-i-Noor and other world-famous diamonds such as Hope, Orloff, Great Mogul, Nizam, and Pitt, well-known in the industry as 'Golkonda Diamonds' are very well recognised for their rare colours, large carat sizes, and because of the paucity of nitrogen atoms majority of them have been classified as Type IIa diamonds. These renowned Golkonda diamonds were recovered from placers mined on the banks of the Krishna River in southern India; however, their primary source rocks (either kimberlites or lamproites) remain questioned and untraced. Precise identification of the primary sources of such large-sized, dominantly Type IIa diamonds (i.e., CLIPPIRS) is crucial for understanding their deep mantle origin, nature and timing of magmatism carrying them and essential from economic and geological perspectives. We employed a multidisciplinary approach incorporating xenocrystic mineral composition and bulk-rock geochemistry, field geological and remote sensing (GIS) studies to locate the probable primary sources of these renowned diamonds, know the origin of Type IIa Golkonda diamonds in southern India, and to understand the mechanism and timing of diamond transport and dispersal as placers in the Krishna River basin. Our study rules out the possibility of various lamproite occurrences of the Eastern Dharwar Craton and Banganapalle conglomerates as being sources of Koh-i-Noor and other Golkonda diamonds. The absence of Type IIa diamonds in the highly diamondiferous Late Cretaceous kimberlites of Wajrakarur likewise excludes them as source of Golkonda diamonds. Among southern India's two significant kimberlite fields, i.e., Wajrakarur and Narayanpet, compositions of indicator minerals from the Wajrakarur Kimberlite Field (WKF) reveal their ultimate diamond preservation potential, presence of strong diamondiferous mantle roots and deeper source regions, hence recognising them to be the potential sources of Golkonda diamonds. GIS and remote sensing tools were used to calculate moisture content, vegetation indices, and to locate paleo-channel of the Penner River, which was primarily responsible for the transportation of diamonds from their Mesoproterozoic (ca. 1.1 Ga) source rocks at Wajrakarur to their final sites of recovery, i.e., Kolluru and other mines situated on the banks of the Krishna River. The occurrence of alluvial placer deposits in Krishna River drainage system is analogous to the Orange River drainage system in South Africa. Both areas have diamonds sourced from primary kimberlite pipes, transported by rivers, and deposited in specific areas. Similarities in the origin, mechanism of diamond transport, dispersal and deposition have played a crucial role in significant diamond production from alluvial deposits in Krishna and Orange Rivers. [ABSTRACT FROM AUTHOR]

Land use land cover (LULC) assessment is essential for monitoring naturally available land resources through spatio-temporal changing pattern to protect land resources sustainably. The primary objective is to identify the changing pattern in LULC over last 10 years in Imphal-Iril catchment. Multi-spectral datasets of Landsat 7 and 8 were used for five different time periods (2011, 2013, 2015, 2017 and 2019). Supervised classification method was employed to generate the classified LULC maps using the maximum likelihood classifier approach in ArcGIS 10.3. In this assessment, five major land use classes have been classified as vegetation, agriculture, settlement, barren land and water body, and the dominant land cover is vegetation. The classified images obtained from this technique were checked for their accuracy using statistical kappa co-efficient and overall accuracy. According to this study, settlement and barren areas have been significantly increased by 72.25% (39.03 km2) and 22.60% (118.29 km2) during the last 10 years, whereas areas assigned to agriculture, vegetation and water bodies have decreased by 27.26% (72.16 km2), 7.59% (73.22 km2) and 60.39% (11.94 km2), respectively. LULC map for the year 2030 was also predicted using MOLUSCE model in QGIS. The results of this assessment will be significant to the policy implicates for sustainable LULC management in the region. [ABSTRACT FROM AUTHOR]

Palaeochannels are remnants of rivers or stream channels filled with younger sediments over the period of time. In ancient times, these rivers/channels were thriving in phenomenal conditions, but due to frequent tectonic activities, they lost the direction of their original path and were gradually either lost or buried under thick beds of younger alluvium. Palaeochannels act as reservoirs for fresh groundwater since they are made up of coarser sediments and were formerly flowing rivers. Depending on the groundwater regime and local topography, these could either be saturated or dry. The palaeochannels have high groundwater potential if saturated. These are ideal sites for artificial groundwater recharge, if dry. The identification of palaeochannels becomes quite challenging if they are buried under thick deposits of finer younger sediments. In the present study, an attempt has been made to characterize the Saraswati River Palaeochannel in parts of Yamuna Nagar and Kurukshetra districts of Haryana by using surface and subsurface geophysical methods. Till date, the palaeochannels in this area were mainly discerned on the basis of remote sensing only; therefore, geophysical characterization of these palaeochannels has been attempted in this study. In surface geophysical methods, electrical resistivity surveys, especially gradient resistivity profiling (GRP) and vertical electrical sounding (VES), were conducted in the study area, while electrical and natural gamma logging was used as subsurface geophysical approaches to identify the coarser sands of buried palaeochannels. The main objective of the study was to characterize the Saraswati River palaeochannel and analyze the quality of the groundwater stored in the palaeochannel in the study area. The findings were compared with the well-log data and were found in good agreement. [ABSTRACT FROM AUTHOR]

Effective management of water resources is crucial for sustainable development in any region. When considering computer-aided analysis for resource management, geospatial technology, i.e., the use of remote sensing (RS) combined with Geographic Information Systems (GIS) proves to be highly valuable. Geospatial technology is more cost-effective and requires less labor compared to ground-based surveys, making it highly suitable for a wide range of agricultural applications. Effectively utilizing the timely, accurate, and objective data provided by RS technologies presents a crucial challenge in the field of water resource management. Satellite-based RS measurements offer consistent information on agricultural and hydrological conditions across extensive land areas. In this study, we carried out a detailed analysis focused on addressing agricultural water management issues in India through the application of RS and GIS technologies. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we systematically reviewed published research articles, providing a comprehensive and detailed analysis. This study aims to explore the use of RS and GIS technologies in crucial agricultural water management practices with the goal of enhancing their effectiveness and efficiency. This study primarily examines the current use of geospatial technology in Indian agricultural water management and sustainability. We revealed that considerable research has primarily used multispectral Landsat series data. Cutting-edge technologies like Sentinel, Unmanned Aerial Vehicles (UAVs), and hyperspectral technology have not been fully investigated for the assessment and monitoring of water resources. Integrating RS and GIS allows for consistent agricultural monitoring, offering valuable recommendations for effective management. [ABSTRACT FROM AUTHOR]

The snowline exhibits significant seasonal shifts upward and downward, reflecting the ever-changing dynamics of the seasons and being influenced by climate variations, which can vary annually. These fluctuations profoundly impact the cryosphere, biota, and ecosystem processes in high mountain regions. Despite the critical role of snowline variations, comprehensive information on how actual climate variability affects snow cover trends in the central mountain range of the western Himalayas is scarce. In the 'Pindari' region of the Uttarakhand district, India, which is part of the Himalayas, these challenges are exacerbated by the unchecked growth of anthropogenic activities and the broader impacts of climate change. This study analyses snowline variations in the Pindari glacial region from 1972 to 2018. The findings revealed that the snowline elevation significantly shifted upward between 1972 and 2018. Notably, this research revealed a decrease in snow-covered areas of approximately 5.01 km2 over the course of 46 years. This decrease is attributed to a direct response to the increasing number of high-temperature events that occurred during this extended period. This study emphasizes the urgent need for conservation measures in the study region and similar high mountains to combat global warming and safeguard the snowline, which serves as a visible proxy indicator to safeguard high-altitude Himalayan glaciers. [ABSTRACT FROM AUTHOR]

• Proposed a Hybrid Model for Paddy Crop prediction based on phenological parameters. • CNN-LSTM attained RMSE value of 0.219 t/ha compared to other existing methods. • Random Forest method achieved highest accuracy of 96.6% in paddy classification. Rice is one of the predominant food sources to fulfill the dietary requirements of well-being in India. Therefore, accurate and timely paddy crop yield prediction is crucial to ensure the food security of the country. In this direction, the present study proposed a hybrid deep-learning method based on Conv-1D and LSTM layers using the classification-derived phenological with meteorological parameters for paddy crop yield prediction. The paddy crop classification has been conducted using high-resolution (10 m) multispectral imagery based on GPS coordinates collected during the paddy field visits to extract the phenological parameters for input to the prediction model. In this context, the efficiency of Random Forest, Naïve Bayes, SVM, and Gradient Tree boost classifiers was assessed. Furthermore, we have also analyzed the accuracy of Landsat-8, Sentinel-1 GRD, and Sentinel-2 satellite imagery in paddy crop classification based on area estimation. The Statistical Abstract of Haryana was utilized to validate the paddy crop area estimation and yield prediction. The classification outcomes showed that the Random Forest method attained the highest accuracy of 96.6 % compared to other GEE-based classifiers. The proposed Hybrid Deep learning approach achieved an RMSE value of 0.219 t/ha compared to CNN, LSTM, CNN-Bi-LSTM, and Regression techniques for crop yield prediction. The study conclusion highlighted that the sentinel-2 satellite imagery performed well and found that the proposed hybrid approach provided an alternative for paddy crop yield prediction. [ABSTRACT FROM AUTHOR]

Flooding is a regular danger in the Idukki district of Kerala causing serious damage to life and property. An attempt has been undertaken in this research to identify crucial zones in the Idukki district that are more susceptible to flooding. The AHP technique was used to give relative weights to 12 flood contributing parameters. The resultant flood susceptibility map shows that 1346.4 km2 of the total land in the Idukki district has a high to very high likelihood of flooding. Validation of the resultant map was achieved using the AUC-ROC approach, which showed good accuracy of 0.91. [ABSTRACT FROM AUTHOR]

Landslide is one of the natural calamities that severely affect society and economy, specifically in the mountainous regions. Monitoring of the possible landslide areas is essential to minimize the effect. Remote sensing and geographical information system are useful tools for landslide monitoring in order to prevent the loss associated with landslide. In recent years, the development of unmanned aerial vehicle (UAV) photogrammetry opened a new window in the field of geosciences for reconnaissance survey, mapping and hazard monitoring. In the present study, high-resolution aerial images are collected by DJI phantom 4 drone, with 24 megapixel camera, at an elevation of 70 m from the ground. From the aerial images, 3D point clouds, orthomosaic and DEM are generated, using Agisoft Photoscan software. The collected ground control points are used for georeferencing the model and for error estimation. The slope, slope direction and drainage pattern maps are prepared using ArcGIS. Landslide material spread over 0.365 m2 area with more than 1 million m3 of debris transported. However, the debris material is not completely washed out of the slope and stuck at the upper part of the village that has a chance of further slide. In such a devastating situation, the southern part of the Kangpokpi area could be affected in the future. With the help of this mapping and photogrammetric technique, the landslide can be monitored constantly so that future disasters can be prevented. [ABSTRACT FROM AUTHOR]

Groundwater, the most vital natural resource, plays an important role in meeting the sustainable part of domestic, agriculture, and industrial needs of any country. The management of groundwater resources is a major challenging issue being faced by scientists, planners, and managers of the world. In this connection, a systematic study of groundwater potential zones and proper strategy for its management is urgently required. The present research is focused to demarcate the different zones of groundwater potential and sites suitable for water conservation structures by using remote sensing (RS) and Geographical Information System (GIS) in the part of Central India. To achieve the goal of the research, the geospatial technique is applied to prepare a different thematic map of each influencing factor such as geology, geomorphology, soil, lineament density, drainage density, land use/land cover, and slope. The weightage and rank are assigned for various thematic layers as per their influences to recharge the aquifer. From overall results, the groundwater potential zone map has prepared and classified into high (8%) moderate (67%) and low potential zone (25%). A validation of the result is carried out by randomly selecting different dug wells present in the research area. The yield of dug wells supports the findings of the study. To manage the groundwater resources, various artificial recharge structures are suggested. The findings and suggestions of the study would be very helpful for improving the groundwater resources (quality as well as quantity) and development of the area. [ABSTRACT FROM AUTHOR]

The industrial and economic development has initiated the rapid growth of small and medium-sized towns in India. Rewari City, a part of the National Capital Region of India, is undergoing rapid urban expansion. This study analyzes the process of urban expansion in Rewari city, its effect on land use & land cover dynamics and landscape spatial patterns. The methodology of the study is reliant on open-source Landsat satellite data, GIS-based unsupervised classification, and spatial metrics analysis. The city expansion has been analyzed for a period of 31 years, from 1989 to 2020, and population growth has been studied since 1901. Within the study period, built-up area increased by 704%, with an annual expansion rate of 12.8 %. The other land cover classes, such as agriculture land, vegetation, barren land, and water bodies shrank in size over the years. Between 1989 and 2020, 69.4 % of the increase in built up area came at the expense of vegetation and agricultural land. It was also found that per capita land consumption rate increased significantly from 0.0024 to 0.0084, hinting towards dispersed and low-density development. Built-up land had a growth rate nearly 5 times higher than population, indicating urban sprawl. An evaluation of different landscape metrics revealed that the landscape of Rewari has lost land use diversity. The findings of this study offer information about the present state of urban growth. It also serves as a valuable resource for formulating comprehensive planning and development policies, ensuring the promotion of sustainable urban development. [ABSTRACT FROM AUTHOR]

Groundwater resources are essential for drinking water, irrigation, and the economy mainly in semiarid environments where rainfall is limited. Currently, unpredictable rainfall due to climate change and pollution on the Earth's surface directly affects groundwater resources. In this area, most people depend on groundwater resources for irrigation and drinking purposes, and every summer, most of the area depends on groundwater in a semiarid environment. Hence, we selected two popular methods, the analytical hierarchy process (AHP) and multiple influence factor (MIF) methods, which can be applied to map groundwater potential zones. Nine thematic layers, such as land use and land cover (LULC), geomorphology, soil, drainage density, slope, lineament density, elevation, groundwater level, and geology maps, were selected for this study using remote sensing and geographic information system (GIS) techniques. These layers are integrated in ArcGIS 10.5 software with the help of the AHP and MIF methods. The map of the groundwater potential zones in the study area revealed four classes, i.e., poor, moderate, good, and very good, based on the AHP and MF methods. The groundwater potential zone area is 241.50 (ha) Poor, 285.64 (ha) moderate, 408.31 (ha) good, and 92.75 (ha) very good using the AHP method. Similarly, the MIF method revealed that the groundwater potential classes were divided into four classes: 351.29 (ha) poor, 511.18 (ha), moderate, 123.95 (ha) good, and 41.78 (ha) very good. The results were compared to determine which methods are best for planning water and land resource development in specific areas that have basaltic rock and drought conditions. Both groundwater potential zone maps were validated with water yield data. The receiver operating characteristic (ROC) curve and area under the curve (AUC) model results are found to be 0.80 (good) and 0.93 (excellent) using the MIF and AHP methods, respectively; hence, the AHP method is best for delineation of groundwater potential zone maps and groundwater resource planning. The present study's framework and the results will be valuable for improving the efficiency of irrigation, conserving rainwater and maintaining the ecosystem in India. [ABSTRACT FROM AUTHOR]

Accurate measurement and monitoring of ecosystem productivity play a pivotal role in comprehending the intricate dynamics of the Earth system, particularly in the light of anthropogenic forcing. Sun-induced chlorophyll fluorescence (SIF), serving as a proxy, has garnered attention for its potential to elucidate the dynamics of terrestrial productivity by the existence of its mechanistic link with photosynthesis. Nevertheless, physiological mechanisms that regulate the relationship between the dynamics of gross primary productivity (GPP) and SIF remain shrouded in mystery. To better accommodate the research community on GPP–SIF relation, our study attempts to offer potential results by integrating multi-source Earth observation SIF and in-situ eddy flux datasets at an ecosystem level in India. The results elucidate that the studied ecosystem consistently acted as a steady CO2 sink over the observed period, displaying a prominent double-hump seasonality and a solid correlation with SIF. Notably, this correlation was synchronous with the daily transitions of GPP. The integration of multi-source carbon fluxes and SIF datasets demonstrated a strong agreement with the in-situ data, reinforcing the reliability of our results. Our redundancy analysis by threshold binning underscores the significant positive influence of vapour pressure deficit (VPD) and photosynthetically active radiation (PAR), critical factors driving GPP and SIF, across a spectrum of temporal scales, ranging from the transient fluctuations of a day to annual panoramas. In essence, our study advocates for the synergistic utilisation of diverse datasets to unravel the underlying processes governing the relationship between GPP and SIF at multiple temporal scales. Our results and analyses are poised to propel interdisciplinary research forward by introducing novel dimensions to the GPP–SIF relationship in the Indian subcontinent. [ABSTRACT FROM AUTHOR]

Debris flows are geomorphological processes that affect the landscape evolution process of any region. In this study, an integrated methodology is proposed to identify the chance of further debris flows and quantify the similarities between debris flow locations, materials and rheology, using field and laboratory investigations and remote sensing data. The method was tested for four failure‐triggered debris flow sites in the Western Ghats of India, using dimensionless parametric similarity values ranging from 0 to 1. The maximum parametric similarity was observed as 0.84 when comparing the flow accumulation values of Sites 3 and 4, and the maximum overall site similarity was 0.68. The calibrated rheological parameters of one site were found to be satisfactory in modelling the shape of debris flow at all other sites. The findings can be used to identify similar hotspots in the region and to simulate debris flows for quantitative hazard assessment. [ABSTRACT FROM AUTHOR]

Globally, rice is one of the most important staple food crops. The most significant metric for evaluating the rice growth and productivity is the Leaf Area Index (LAI), which can be effectively monitored using remote sensing data. Hyperspectral remote sensing provides contiguous bands at narrow wavelengths for mapping LAI at various rice phenological stages, and it is functionally related to canopy spectral reflectance. Hyperspectral signatures for different phases of rice crop growth was recorded using Airborne Visible Near-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) along with corresponding ground based observations. Ground-based hyperspectral canopy spectral reflectance measurements were recorded with FieldSpec 3 Hi-Res spectroradiometer (ASD Inc., Forsyth County, GA, USA; spectral range: 350–2500 nm) and LAI data from 132 farmer's fields in Southern India. Among 29 hyperspectral vegetation indices tested, 8 were found promising for mapping rice LAI at various phenological stages. Among all the growth stages, the elongation stage was the most accurately estimated using vegetation indices that exhibited a significant correlation with the airborne hyperspectral reflectance. The validation of hyperspectral vegetation indices revealed that the best fit model for estimating rice LAI was mND705 (red-edge, blue, and NIR bands) at seedling and elongation, SAVI (red and NIR bands) at tillering and WDRVI (red and NIR bands) at booting stage. [ABSTRACT FROM AUTHOR]

The aim of the present study was to develop a multi‐criteria decision support system to find out possible aquaculture sites available in Nagapattinam district of Tamil Nadu, India. This study focuses on identifying possible aquaculture sites available in Nagapattinam district using remote sensing and GIS approaches. A geographical model has been developed to choose the probable aquaculture sites. The remote sensing and geographic information system techniques were applied to study the decisive parameters that play a vital role in the site suitability for aquaculture purposes. The chosen decisive parameters viz., normalized difference vegetation index, digital elevation model, soil type, normalized difference water index, land use land cover area and land surface temperature were subjected to the development of decision support system. Moreover, four different multi‐criteria decision support techniques viz., weighted overlay, analytical hierarchy process, fuzzy classified analytical hierarchy process and fuzzy classified weighted overlay were applied in the present study. The results obtained from the study rendered that the weighted overlay and the analytical hierarchy process outperforms the fuzzy classified models. The suitability ratio of analytical hierarchy process and weighted overlay methods were found to be 12.7 and 12.4 respectively. A total of 25 locations were selected and mapped in the Nagapattinam region and their accuracy was checked. The analytical hierarchy process results were obtained with an accuracy of 84%. Hence, the analytical hierarchy process provided better results than the weighted overlay technique. The study rendered that the area of 7.22% is highly suitable for aquaculture. These findings are essential for the development of sustainable aquaculture, assisting stakeholders in selecting suitable aquaculture sites that maximize productivity while minimizing environmental degradation. [ABSTRACT FROM AUTHOR]

The current research is focused on studying the land use changes in selected sites (total area of 3488 sq. km) of the Uttara Kannada district and the impacts of urbanization of forest degradation. Topographical maps from the Survey of India have been used to mark the study area's boundary. Forest degradation mapping has been conducted through spatiotemporal analysis of LANDSAT and Google Earth imageries for the years 1989, 2001, 2013, and 2020. The decadal change was evaluated throughout the period to delineate the sites of encroachment. The change analysis revealed that 56.6% of the forest area has remained primarily dense; 6.46% of the area has remained unchanged as agricultural area, while almost 0.95% of the total agricultural area has been converted into built-up land or has become barren. Spatiotemporal change analysis also revealed that, overall, 85.1% of the study area has remained unchanged and 4.7% of the area has changed in some form or other. Ground truthing through Google Earth imageries for various periods reveals an increase in the built-up land along the coastal stretch and the north-eastern part of the region. In some places, agricultural lands have been abandoned, which have then been converted into shrublands. The unorganized growth of built-up land and decrease in the forest and agricultural lands have necessitated extensive fieldwork for developing guidelines for the protection of forest areas and planning of built-up and agricultural lands. [ABSTRACT FROM AUTHOR]

Imaging spectroscopy offers great potential to characterize plant traits at fine resolution across broad regions and then assess controls on their variation across spatial resolutions. We applied permutational partial least‐squares regression to map seven key foliar chemical and morphological traits using NASA's Airborne Visible/Infrared Imaging Spectrometer‐Next Generation (AVIRIS‐NG) for six sites spanning a climatological gradient in the Western Ghats of India. We studied the variation of trait space at spatial resolutions from the plot level (4 m), community level (30 and 100 m) to the ecosystem level (1,000 m). We observed a consistent pattern of trait space across different resolutions, with one axis defined by foliar nitrogen and leaf mass per area (LMA) and another axis representing leaf structure and defense defined by fiber, lignin, and total phenolics. We also observed consistent directionality of environment‐trait correlations across resolutions with generally higher predictive capacity of our environment‐traits models at coarser resolutions. Among the seven traits, total phenolics, fiber, and lignin were strongly influenced by environmental factors (model R2 > 0.5 at 1,000 m). Calcium, sugars, and nitrogen were significantly affected by site conditions, incorporating site as a fixed effect largely improved model performance. LMA showed little dependence on environmental factors or site conditions, suggesting a stronger influence of species composition and site history on LMA variation. Our results show that reliable trait‐trait relationships can be identified in coarse resolution imagery, but that local scale trait‐trait relationships (resolutions finer than 30 m) are not sensitive to broad‐scale abiotic/biotic factors. Plain Language Summary: We combined field leaf measurements and airborne hyperspectral images to map seven key foliar functional traits across the Western Ghats in India. We analyzed the traits at different spatial resolutions, from plot‐level (4 m), community level (30 and 100 m) to ecosystem‐level (1,000 m). We found that the trait‐trait relationship remains the same at different resolutions, which shows that we can learn a lot about the trait relationships by using images that aren't very detailed. We also looked at how traits are linked to the environment. Traits like chemicals that help plants defend themselves, as well as physical defenses like fiber and lignin, were strongly influenced by broad climate conditions. We could predict these traits well at 1,000 and 100 m. Other traits like calcium, sugars, and nitrogen were more affected by where the plants were growing, and we could predict them well when we considered the specific location. However, leaf mass per area didn't seem to depend much on the environment or location, which means it's probably influenced more by species composition and site history. When we looked at traits on a finer scale (30 m), our predictions weren't as accurate, suggesting that other things might also be affecting traits at those levels. Key Points: We mapped seven key plant functional traits for six sites along the Western Ghats of India‐a diverse but under‐represented areaOur study showed that the trait‐trait relationship (or trait space) is well conserved during the spatial aggregation from 4 to 1,000 mMost traits could be predicted well at 1,000 m and moderately at 100 m by environmental parameters but not at local scales finer than 30 m [ABSTRACT FROM AUTHOR]

Rapid, precise and frugal surveying is a strategic factor in the present mechanized mining environment owing to rapid surface profile transformation in a short period. Determination of global coordinates with high accuracy using the satellite-based global navigation satellite system (GNSS) has been studied in the mining arena and is gradually becoming an essential component. A case study of Khanak Stone Mine, Haryana, India, is discussed here, highlighting the combination of GNSS system and terrestrial laser scanner for the preparation of a surface profile of a rugged terrain with the key objective to determine the precise in situ rock excavation in periodical phases. The actual tonnage of rock produced/transported from the mine has been calculated by determining the in situ density and bulk density in the laboratory. The study reveals that this survey of approach is apt to achieve quality plan and to obtain the goal of actual tonnage of rocks excavated. [ABSTRACT FROM AUTHOR]

India's metropolitan cities have been growing rapidly for many years. To keep geographical information accurate and current, it is essential to update GIS maps. Traditionally, experts have analysed new data sources and made necessary adjustments to the maps manually. Such manual monitoring is a laborious test both economically and in terms of workforce. Geographical data are transformed into digital maps by GIS mapping, making it simple to spot patterns, trends and linkages. Extraction of humanmade objects, such as roads, water bodies and buildings, from remotely sensed imageries holds significance in various urban applications, including urban land-use and land-cover assessment, geographical database updates and change detection. Cartosat-3 data can provide detailed information about buildings and their changes over time. Additionally, GIS maps are manually updated by rasterizing vector data. The suggested system consists of ResNet and U-Net architecture as its core. The bi-temporal images are initially coregistered to completely align 2020 and 2022 satellite images with respect to the coordinates. Buildings are then segmented using U-Net with ResNet as the backbone, and the resultant segments are converted from raster to vector format. The suggested model has been tested and trained using the Chandigarh dataset, which resulted in an accuracy of 95%. [ABSTRACT FROM AUTHOR]

Droughts frequently occurring in India have significant societal, economic, and environmental effects. The lack of direct measurements of groundwater in location and time hinders quantitative methods to analyse the intricate nature of groundwater drought. This work used the datasets derived from the Gravity and Climate Experiment (GRACE and GRACE-FO) and Global Land Data Assimilation System (GLDAS) to extensively analyse Groundwater Storage changes in the Lower Gangetic Basin (LGB) using unique hydrological parameters between the years 2003 and 2022. The analysis highlights that the GRACE-derived terrestrial water storage anomaly in the LGB decreased significantly (-12.12 mm/yr), and the amount of Groundwater Storage Anomaly (GWSA) decreased similarly (-10.80 mm/yr), while in the GRACE-FO period, a positive trend has been noticed in TWSA (33.96 mm/yr) and GWSA (64.8 mm/yr) respectively. A drought indicator called the GRACE-derived groundwater drought index (GGDI) has been computed for the entire LGB region. A traditional drought study viz. Standardised Precipitation Index (SPI) was performed over LGB to justify the results of the GGDI. The results from GGDI study effectively matched the periods of significant drought occurrences with the 12-month SPI time series. From the GGDI, this study examined groundwater drought's spatial distribution, temporal evolution, and trend (Modified Mann Kendall trend) aspects. According to research findings, the LGB experienced three major drought periods between 2009–2010, 2019 (moderate), and 2015–2016 (severe). The study offers reliable quantitative data on the evolution of GRACE-derived groundwater drought, which may add a new perspective to additional drought research in the densely populated study area, which depends majorly on agriculture, livestock and less skilled water-intensive industries such as leather and textile industries in a sub-tropical climate. This paradigm incorporates changes in groundwater resources caused by human activities and climate change, paving the way for measuring progress towards sustainable use and water security. [ABSTRACT FROM AUTHOR]

Water tanks as traditional rainwater harvesting systems for agriculture are widely distributed in South India. They have a strong impact on hydrological processes, affecting streamflow in rivers as well as evapotranspiration. This study aims at an accurate representation of water harvesting systems in a hydrologic model to improve model performance and assessment of the catchment water balance. To this end, spatio‐temporal variations of water bodies between the years 2016 and 2018 and the months of January and May 2017 were derived from Sentinel‐2 satellite data to parameterize the water tanks (reservoir) parameters in the Soil and Water Assessment Tool (SWAT+) model of the Adyar basin, Chennai, India. Approximately 16% of the basin is covered by water tanks. The initial model performance was evaluated for two model setups, with and without water tanks. The best model run was selected with a multi‐metric approach comparing observed and modelled monthly streamflow for 5000 model runs. The final model evaluation was carried out by comparing estimated water body areas by the model and remote sensing observations for January to May 2017. The results showed that representing water tanks in the hydrologic model led to an improvement in the representation of the seasonal variations of streamflow for the whole simulation period (2004–2018). The model performance was classified as good and very good for the calibration (2004–2011) and validation (2012–2018) periods as NSE varies between 0.67 and 0.85, KGE varies between 0.65 and 0.72, PBIAS varies between −24.1 and −23.6, and RSR varies between 0.57 and 0.39. The best fit was shown for the high and middle flow segments of the hydrograph where the coefficient of determination (R2) ranges from 0.81 to 0.97 and 0.75 to 0.81, respectively. The monthly variation of water body areas in 2017 estimated by the hydrologic model was consistent with changes observed in remote sensing surveys. In summary, the water tank parametrization using remote sensing techniques enhanced the hydrologic model's efficiency and applicability for future studies. [ABSTRACT FROM AUTHOR]

Calibration of a hydrological model is a challenging task, especially in basins that are data scarce. With the incorporation of regional information and integration with satellite data, the parameters of hydrological models can be estimated for a basin with scant or no discharge records. The main objective of this study is to calibrate and validate a hydrological model based on a limited amount of in‐situ measured and remote sensing satellite datasets in a data‐sparse region. Multiple techniques were applied for the model calibration: (1) stage‐discharge curves using a spatial proximity approach, (2) Simplified Surface Energy Balance actual evapotranspiration, (3) river discharge using a physical similarity regionalization approach, and (4) a new hybrid approach by integrating remote sensing datasets along with field measured river bathymetry data to estimate the river discharge. To demonstrate the methodology, we employed the widely used Soil and Water Assessment Tool (SWAT) hydrological model in Manipur River Basin, India. The sensitivity, calibration, and validation of the SWAT model were carried out by using the Sequential Uncertainty Fitting Technique. During calibration, the coefficient of determination (R2) and the Kling Gupta Efficiency (KGE) were found to be in the range of 0.46–0.81 and 0.41–0.83, whereas during validation R2 and KGE were found to be in the range of 0.40–0.79 and 0.53–0.77 for the four different techniques. Among all the four techniques applied in this study, calibration based on (i) stage‐discharge curve using spatial proximity approach and (ii) new hybrid approach by integrating remote sensing datasets and river bathymetry were found as the better approaches as indicated by the statistical indices. The performance evaluation of the model through a new hybrid approach by integrating remote sensing and in‐situ measured datasets for rivers with narrow width represents a promising technique for use in a data sparse region. [ABSTRACT FROM AUTHOR]

In this paper, the spatio-temporal variations of Yemen, Oman and west coast of India upwelling are studied using wind and Sea Surface Temperature (SST) based upwelling indices. The relative contributions of offshore Ekman transport and Ekman pumping are compared in these upwelling regions. It has been found that offshore Ekman transport is the more dominant mechanism in coastal areas of Oman from 17°N to 21°N with upwelling indices reaching up to 3–4 m2/s during June-July while for the Yemen coast (13–16.5°N), Ekman pumping is stronger. The west coast of India has much weaker upwelling during the southwest monsoon. Offshore Ekman transport is the main mechanism for upwelling activity during the southwest monsoon for the west coast of India. A combined upwelling index was developed, adding the contributions of Ekman transport and integrated Ekman pumping. Chlorophyll data from 1998 to 2017 showed an increase in chlorophyll concentration during southwest monsoon season for all three upwelling regions. The maximum increase in southwest monsoon chlorophyll was observed for northern Yemen (15–16.5°N) with an increase of 10–20 times. This is followed by coastal region of Oman (17–19°N) and southern part of west coast of India (8–10°N). [ABSTRACT FROM AUTHOR]

The ravine is often regarded as the worst instance of how water erosion is causing land deterioration. The farmers' livelihoods are often threatened by the ravine's expansion into the surrounding 50 m buffer zone, leaving them with no choice except to work as landless workers. Due to the high expense of ravine restoration, it is not economically possible to begin the process of reclamation in all of the ravine lands at once. As a result, it is necessary to recognize the prospective regions to bring in reclamation activities. In this work, the ravine vulnerability index for the Mahi ravine in Western India was developed using a cutting‐edge hybrid methodology. For the development of the ravine vulnerability index, it was determined that the sand, silt, clay, organic carbon, soil erodibility factor, slope, stream power index, topographic wetness index, sediment transport index, and cover factor were crucial components. Weights were applied to various parameters based on the perceived significance of each parameter in relation to another in the decision matrix of the analytical hierarchical process. The number of iterations made to reach a consistency ratio of under 10% to determine the final priority weights for each parameter. The sediment transport index had the lowest priority weight (1.4), whereas the sand had the highest priority weight (28.5). For reclamation purposes, the prospective active zones inside the designated ravine's 50‐m buffer zone, which covered an area of 63,031 acres, were determined. According to the ground truth‐validated ravine vulnerability index, possible reclamation zones were found in the 16,703 ha (26.50%) region of Mahi ravine, which was given extremely high priority. As per the Mahi ravine's land capacity classification, 20,275 ha (32.16%) of it is arable land, and 18,687 ha (29.65%) of it is non‐arable land that is ideal for soil and water conservation treatments. [ABSTRACT FROM AUTHOR]

It is crucial to accurately quantify land use and land cover (LULC) within a protected area to understand the implications of habitat changes on biodiversity. Today's remote sensing and GIS technologies greatly facilitate analysis of LULC, especially with regards to tracing changes over space and time. This study uses remote sensing and GIS to examine the impact of climate, herbivore, and anthropogenic pressures including invasive Mesquite Prosopis juliflora on native plant communities at Point Calimere Wildlife Sanctuary. Classification of satellite images revealed that dry evergreen, mudflat, and water bodies had transformed into open scrub from 1995 to 2018 and the shift in LULC is detected with optimal accuracy (85%). Changes in LULC are largely attributable to a rise in open scrub caused by the growth in P. juliflora from 3 to 6 km2 since 1995. GLM-based regression to examine the influence of climate, herbivores, and anthropogenic pressure including P. juliflora on native flora show native tree density, shrub density, shrub diversity, herb, and grass cover decreasing with P. juliflora cover or density. These findings imply that as the P. juliflora spreads the native plant diversity and density at Point Calimere Wildlife Sanctuary will continue to decline. P. juliflora is being eradicated in phases through management efforts, however, here we recommend a coordinated effort to curb further expansion in order to reverse ecological decline. [ABSTRACT FROM AUTHOR]

Oxbow lakes in the Indian subcontinent are niches rich in biodiversity, and contain insights into the palaeoclimate and past settlement patterns of the region. This study examines an erstwhile oxbow lake in Sonipat, Haryana, India, which possibly arose from the former course of River Yamuna. The study first confirms the presence of this lake using satellite imagery to identify its remnants. It also employs various pathways, including elevation data, Corona imagery and Survey of India maps, to discern the path of the lake in today's geography of Sersa. The reason for its disappearance has also been accounted for in this study. Apart from using satellite evidence and GIS, an on-site ground truth survey was also conducted, the results of which are discussed. Finally, further suggestions for research are provided to understand the palaeoclimate of the study site. It is also significant that this would be an understudied avenue of research on such a feature in Sonipat. [ABSTRACT FROM AUTHOR]

Remote sensing has already proven its capability in water quality studies. The technology enables the mapping of water quality parameters that affect its optical properties. Generally, multi-spectral remote sensing data has been utilized for such analysis. Mostly, the band rationing or simple regression approaches have been adapted to map the water quality, however, they result in qualitative estimates rather than quantitative. On the other hand, sensor technology has made steep advancements from multi-spectral to hyperspectral. The hyperspectral data provides contiguous spectral information over several hundred narrow spectral bands. This emerging technique provides a more in-depth means of investigating spatial, spectral, and temporal variations, leading to more quantifiable/accurate estimates of the process under consideration. At present, very few satellite-based hyperspectral data are available. Recently, the Indian Space Research Organisation has initiated an airborne hyperspectral remote sensing campaign to cover the important earth features in India using Airborne Visible/Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG) sensor in collaboration with the National Aeronautics and Space Administration (NASA). As the AVIRIS-NG hyperspectral data measures the spectral response in the wavelength range from 380 to 2510 nm at very narrow spectral bandwidth of 5 nm. These advanagtes of AVIRIS-NG datasets enable researchers to analyse the spectra of each and every pixel of the image spatially to match its spectral characteristics with the spectra collected in the field. In the present study, this spectral similarity approach has been expoilted within the context of turbidity, the optical property of water for Chilika Lake, Odisha, India; using the AVIRIS-NG data and field reference spectra. The lake was surveyed synchronous to AVIRIS-NG flights over it during the month of December 2015. A field-based spectral library with respect to various concentrations of turbidity in the lake has been generated using sophisticated instruments like field spectro-radiometer, handheld Global Navigation Satellite System (GNSS) receiver, and turbidimeter. The spectral angle mapper (SAM) spatial-spectral contextual image analysis has been carried out to map the turbidity of the lake. It was observed that almost at each and every pixel the image spectra were matching with the field-collected spectra. Further, the match between two spectra (field and image) measured in terms of SAM score was as high as 0.9 for most of the instances. Moreover, the estimated turbidity was in the agreement (R2 = 0.96) with the turbidity measured on the field. It was realized that the spectral similarity approach using hyperspectral remote sensing data provides more quantitative estimates as compared to multi-spectral data analysis. [ABSTRACT FROM AUTHOR]

In the present study, we have integrated the spectral anomaly map derived from airborne visible-infrared imaging spectrometer-next generation (AVIRIS-NG) data and residual ground geophysical anomaly maps (gravity and magnetic) for identifying the mafic cumulates and chromitites in the Sittampundi Layered-Complex (SLC), Tamil Nadu, India. A spectral map of chromite bearing mafic cumulates was prepared by implementing the matched filtering (MF) algorithm on the selected spectral bands of AVIRIS-NG data using the mixed image spectra of mafic cumulate and chromitite as an end member. A low pass filter was applied in the spectral map to extract the clusters of coherently distributed spectrally anomalous pixels representing the surface exposure of mafic cumulates. Concurrently, the ground gravity and magnetic data were processed for deriving the residual gravity and magnetic anomaly maps. Subsequently, an integration of spectral map, residual gravity, and magnetic anomaly maps was attempted using the support vector machine (SVM) algorithm to identify the potential sites of chromitite occurrence. Results were verified using the geological map and the field data on surface exposures of mafic cumulates. This research demonstrates that integration of remote sensing data and geophysical anomaly maps has immense potential to detect the surface distribution of chromite bearing mafic cumulates in the layered complex and the same methods can be replicated for carrying out mineral prospecting in the other parts of the world. [ABSTRACT FROM AUTHOR]

The 2004-tsunami affected the South Andaman coast experiencing dynamic changes in the coastal geomorphology making the region vulnerable. We focus on pre-and post-tsunami shoreline and Land Use Land Cover changes for the period 2004, 2005, and 2022 to analyse the dynamic change in hazard. We used GEBCO bathymetry data to calculate Run-up (m), arrival times (Min), and inundation (m) at 13 different locations using the 2004 Sumatra Earthquake source parameters. The Digital Shoreline Analysis System is used for the shoreline change estimates. The Landsat data is used to calculate shoreline and LULC change in five classes, namely Built-Up Areas, Forests, Inundation areas, Croplands, and water bodies during the above period. We examine the correlation between the LULC changes and the dynamic change in shoreline due to population flux, infrastructural growth, and Gross State Domestic Product growth. India industry estimates the Andaman & Nicobar Islands losses exceed INR 10 billion during 2004 that would see a five-fold increase in economic loss due to a doubling of built-up area, a three-fold increase in tourist inflow, and a population density growth. The unsustainable decline in the forest cover, mangroves, and cropland would affect sustainability during a disaster despite coastal safety measures. [ABSTRACT FROM AUTHOR]

The proposed facies classification approach uses three features of the GLCM with multitemporal X-band SAR data of TerraSAR-X combined with k-means clustering. This approach was tested on 15 transboundary Himalayan glaciers. They were classified into six facies namely debris, dry snow, wet snow, bare ice, percolation zone 1 and percolation zone 2 and the covered areas for these classes are 20.56%, 12.50%, 20.26%, 13.16%, 25.49% and 8.03% of total area respectively. After classification, the signature for classes are validated using the benchmark outline of East Rathong glacier, India. The accuracy achieved is 89.56% with negligible variance and kappa of 0.87. [ABSTRACT FROM AUTHOR]

Characterizing soil salinity at the regional scale remains challenging despite decades of effort in soil mapping. Using satellite remote sensing, an effort has been made to identify the coastal saline soils in India. The study made use of the OLI sensor of the Landsat-8 satellite. The images were downloaded from the USGS EarthExplorer website. For multi-temporal studies, absolute radiometric calibration was done to minimize the impacts of changing atmospheric conditions, solar inclination and sensor view angle. Images were categorized using unsupervised classification, while a ground survey and Google Earth data were used for ground truthing. Three indices, namely normalized difference vegetation index (NDVI), salinity index (SI), and canopy response salinity index (CRSI), were used to identify soil salinity regions. For testing the vegetation index with soil salinity, 192 georeferenced soil samples from the Indian Sundarbans were collected. A relationship was developed between NDVI, SI and square of CRSI (CRSISQR) with ECe (electrical conductivity of saturation paste extract) and EC1:2 (1: 2; soil: water). For the coastal region, soils with CRSISQR < 0.16 were considered to be influenced by salinity since the relationship between ECe and CRSISQR had a maximum R² (0.50). It has been estimated that India has 12.94 lakh ha of saline soil within arable lands in all the coastal districts, and Gujarat (5.28 lakh ha), West Bengal (5.08 lakh ha), and Andhra Pradesh (1.06 lakh ha) were identified as the top three-salinity affected coastal states in the country. [ABSTRACT FROM AUTHOR]

In today's world, by integrating remote sensing technology and modern state-of-the-art machine learning techniques, obtaining Land Use Land Cover (LULC) maps has become easier in comparison to traditional manual methods. The performance of a Machine Learning classifier is influenced by various factors. The objective of this study is to evaluate the impact of sampling design in rough complex terrain located in the Northern Himalayan region in Uttarakhand state, India, where reference data is often limited due to the geographical characteristics of the study area. Three sampling design strategies have been incorporated in this study, namely, stratified random sampling with a proportional number of samples (SRS)proportional, stratified random sampling with an equal number of samples (SRS)equivalent and stratified systematic sampling with an equal number of samples with a minimum distance of 10 m between the consecutive samples (SSS)D = 10 m for the LULC classification. In this study, Sentinel-2 data of 10 m spatial resolution for the study area of Dehradun district, Uttarakhand, India, has been selected. The following conclusions can be drawn from the results of this study (i) (SRS)proportional achieved the highest Overall Accuracy (OA) among all the three sampling techniques. The OA and kappa score (ka) using (SRS)proportional are OA = 90.25 and ka = 0.874 by Random Forest, OA = 88.84 and ka = 0.856 by Support Vector Machine and k Nearest Neighbours (kNN) obtained OA = 87.72 and ka = 0.842, respectively. (ii) It was found that in the case of (SRS)proportional, the majority classes like the deciduous forest, evergreen forest and cropland achieved higher recall and precision values in comparison to those obtained from the other two sampling strategies, i.e. (SRS)equivalent and (SSS)D = 10 m. (iii) The results showed that while switching from (SRS)proportional to (SRS)equivalent or from (SRS)proportional to (SSS)D = 10 m, there was a slight reduction in the precision and recall values for the majority classes and a slight increase for a few of the minority classes. [ABSTRACT FROM AUTHOR]

Agriculture is affected due to the impact of climate change on soil, land, water and the atmosphere. Agriculture is also a significant contributor to greenhouse gas emissions, directly or indirectly. The challenge lies in the reduction of emissions without impacting food production and yield. With the increase in air temperature and extreme events of rainfall as the probable indicators of climate change, there can be impacts on various components of the agroecosystem, which include crop yield, soil quality, pest and disease infestation, crop loss, irrigation water demand, etc. This will, in turn, impact food security. The development of novel adaptation techniques and strategies for agriculture and surveillance of resources is needed to tackle the adverse effects of climate change. This article discusses the impacts of climate change on various components of the agroecosystem and how to achieve resilience for sustainable development. Various national-level programmes for climate change resilience in agriculture have been implemented in India to incorporate climate information into agricultural development by integrating climate services into practices and policies for future food security. [ABSTRACT FROM AUTHOR]

Crop yield estimation has gained importance due to its vital significance for policymakers and decision-makers in enacting schemes, ensuring food security, and assessing crop insurance losses due to biotic and abiotic stress. This research focused on rice yield estimation at the field level in the Karimnagar district of Telangana during 2021 and 2022 by employing the leaf area index (LAI) as the primary criterion for integrating remote sensing technology and crop simulation models. Using Sentinel-2 satellite data, the rice crop was mapped with the help of ground data and machine learning algorithms, attaining an accuracy of 93.04%. Crop management data for the DSSAT tool were collected during the field visits; the model results revealed a 0.80 correlation between observed and predicted yields. Due to its strong correlation with LAI (0.82), the normalized difference vegetation index (NDVI) was selected as the critical element for integration with the model. A spatial LAI map was generated using the linear equation developed between the NDVI and LAI. The relationship between LAI and yield was used to create a spatial yield map. The study's findings show that assimilating remote sensing data with crop models enhances the precision of rice yield prediction for insurance companies and policy- and decision-makers. [ABSTRACT FROM AUTHOR]

Background: The mandate of journal metrics is to measure the performance of journals indexed in the concerned databases based on their citation impact. Such measurements are essential to understand the quality of research output and assess the quality of the journals in which such research gets reported. There are several such journal metrics in use and include Journal Impact Factors (JIF), CiteScore, Article Influence Factor, Eigenfactor (EF), Source Normalized Impact per Paper (SNIP), SCImago Journal Rank (SJR), h-index, i-10 index, Altmetric Attention Score, and PlumX metrics. Journal metrics help decide which journal to choose to publish one's manuscript in. Objective: The purpose of this paper is to highlight the Scopus journal metrics of Indian Earth and Planetary Sciences journals and to identify the top Indian Earth and Planetary Sciences journals based on their journal indices in Scopus for the period 2011 to 2020. Method: Data on the Scopus-indexed Indian Earth and Planetary Sciences journals from SCImago Journal and Country Rank databases available at https://www.scimagojr.com/journalrank.php was collected. These journals' metrics were collected from Scopus Source List available at https://www.scopus.com/sources.uri. Results: Indian Earth and Planetary Sciences Journals in Scopus are ranked based on their journal metrics in Scopus for the period 2011 to 2020. Conclusion: Based on the bibliometric parameters used, the Journal of Geological Society of India and the Journal of Earth System Sciences are the most impactful journals in Earth Science published in India. Journal of the Indian Society of Remote Sensing follows. Both journals are published on behalf of the most influential scientific societies in Earth Science in India by Springer. Several Indian Earth and Planetray Science journals have over four decades of publishing records but have impact factors <1.5. When the journal's prestige is considered, these three journals are joined by the Journal of Astrophysics & Astronomy also. [ABSTRACT FROM AUTHOR]