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British colonization initiated colonial capitalist dispossession of Adivasi-Dalit-Nontribal Forest Dwellers (ADNTFD) in India. Post-independence development continued this trend, accentuated by the neoliberal turn in the early 1990s orchestrated by the then Congress government and intensified by saffron (Hindu nationalist) authoritarian neoliberalism under the recently re-elected Bharatiya Janata Party in 2014 and 2019, leading the National Democratic Alliance regime. Neocolonial continuities and current neoliberal colonial capitalist dispossession and resistance in the forest belt, or India's contemporary 'land wars', are dialectically linked to potential spaces for Participatory Action Research (PAR) with/in ADNTFD struggles. This paper explores the imbrications of the neoliberal agenda, land wars and PAR in India, and by extension, for similar contexts addressing the vicissitudes of neoliberal authoritarian capitalism in the neo-colonies.

This paper traces the kinds of learning engendered through Adivasi trans-local and local subaltern social movement (SSM) action addressing state-corporate developmental collusions, state-caste interests and the resulting dispossession of Adivasis from land, forest and their ways of life given the economic liberalization drive to exploit resources in the rural hinterlands in India since 1991. The paper draws upon insights from the author's association with the Adivasi since 1992 and funded research into "Learning in Adivasi movements." (Contains 6 notes.)

The phenomenon of urban heat island (UHI) is characterized by industrial, economic development, unplanned and unregulated land use as well as a rapid increase in urban population, resulting a warmer inner core in contrast to the surrounding natural environment, thus requiring immediate attention for a sustainable urban environment. This study examined the land use/land cover (LULC) change, pattern of spectral indices (Normalized Difference Vegetation Index, NDVI; Normalized Difference Water Index, NDWI; Normalized Difference Built-up Index, NDBI and Normalized Difference Bareness Index, NDBaI), retrieval of land surface temperature (LST) and Urban Thermal Field Variance Index (UTFVI) as well as identification of UHI from 2000 to 2022. The relationship among LST and LULC spectral indices was estimated using Pearson's correlation coefficient. The Landsat-5 (TM) and Landsat-8 (OLI/TIRS) satellite data have been used, and all tasks were completed through various geospatial tools like ArcGIS 10.8, Google Earth Engine (GEE), Erdas Imagine 2014 and R-Programming. The result of this study depicts over the period that built-up area and water bodies increased by 119.78 and 35.70%, respectively. On the contrary, fallow and barren decreased by 55.33 and 32.31% respectively over the period. The mean and maximum LST increased by 3.61 °C and 2.62 °C, and the study reveals that a high concentration of UTFVI and UHI in industrial areas, coal mining sites and their surroundings, but the core urban area has observed low LST and intensity of UHI than the peripheral areas due to maintained vegetation cover and water bodies. An inverse relationship has been found among LST, NDVI and NDWI, while adverse relationships were observed among LST, NDBI and NDBaI throughout the period. Sustainable environment planning is needful for the urban area, as well as the periphery region and plantation is one of the controlling measures of LST and UHI increment. This work provides the scientific base for the study of the thermal environment which can be one of the variables for planning of Asansol City and likewise other cities of the country as well as the world. [ABSTRACT FROM AUTHOR]

Groundwater plays an important part in protecting the ecological values of the earth's systems. Assessment of groundwater (GW) recharge due to change in land use/land cover (LULC) and climate variables is essential for integrated water management and adaptation. This study is an attempt to check the combined influence of change in LULC and climate variables on GW recharge in Kallada river basin, Kerala, India. The LULC map is predicted for the year 2030 based on LULC maps during the years 1985, 1995, 2005 and 2015 using Markov chain and multilayer perceptron model. The major LULC category in the study area is plantation with an area of 797.8 km2 (49.6% in 2015) and dense forest with an area of 366.5 km2 (22.8% in 2015). Considering the period 1985–2005, a decrease of 7.43% dense forest and an increase of 2.55% built area were observed. The predicted LULC shows that there is a reduction in plantation (3.77%) and dense forest (1.28%) and increase in built-up land (4.12%) by 2030 as compared to the year 2015. The CMIP5 General Circulation Model is used for future projections of climate variables, namely daily precipitation, maximum temperature and minimum temperature under RCP 4.5 and RCP 8.5 scenarios. Multilayer perceptron neural network model was used for statistical downscaling of GCM projections. The SWAT model was used to estimate the groundwater recharge for the year 2030 using the predicted LULC map and hydro-meteorological variables. The results illustrate a significant drop in GW recharge in Kallada river basin by the year 2030. Average GW recharge in the Vettikavala and kottarakkara, which is in the western region of the basin, is reduced to 4.6% and 9.3% under RCP 4.5 and RCP 8.5 scenarios, compared to the average GW recharge in 2005, which was 24.8% of rainfall. The decline in the groundwater recharge in the western regions may be due the increase in built-up area and decrease in plantation. The proposed model results provide a reliable insight for water resources planners in conducting future research on groundwater resources. [ABSTRACT FROM AUTHOR]

In the context of environmental and social applications, the analysis of land use and land cover (LULC) holds immense significance. The growing accessibility of remote sensing (RS) data has led to the development of LULC benchmark datasets, especially pivotal for intricate image classification tasks. This study addresses the scarcity of such benchmark datasets across diverse settings, with a particular focus on the distinctive landscape of India. The study entails the creation of patch-based datasets, consisting of 4000 labelled images spanning four distinct LULC classes derived from Sentinel-2 satellite imagery. For the subsequent classification task, three traditional machine learning (ML) models and three convolutional neural networks (CNNs) were employed. Despite facing several challenges throughout the process of dataset generation and subsequent classification, the CNN models consistently attained an overall accuracy of 90% or more. Notably, one of the ML models stood out with 96% accuracy, surpassing CNNs in this specific context. The study also conducts a comparative analysis of ML models on existing benchmark datasets, revealing higher prediction accuracy when dealing with fewer LULC classes. Thus, the selection of an appropriate model hinges on the given task, available resources, and the necessary trade-offs between performance and efficiency, particularly crucial in resource-constrained settings. The standardized benchmark dataset contributes valuable insights into the relative performance of deep CNN and ML models in LULC classification, providing a comprehensive understanding of their strengths and weaknesses. [ABSTRACT FROM AUTHOR]

Flood disasters are frequent natural disasters that occur annually during the monsoon season and significantly impact urban areas. This area is characterized by impermeable concrete surfaces, which increase runoff and are particularly susceptible to flooding. Therefore, this study aims to adopt Bi-variate statistical methods such as frequency ratio (FR) and weight of evidence (WOE) to map flood susceptibility in an urbanized watershed. The study area encompasses an urbanized watershed surrounding the Chennai Metropolitan area in southern India. The essential parameters considered for flood susceptibility zonation include geomorphology, soil, land use/land cover (LU/LC), rainfall, drainage, slope, aspect, Topographic Wetness Index (TWI), and Normalized Difference Vegetation Index (NDVI). The flood susceptibility map was derived using 70% of randomly selected flood areas from the flood inventory database, and the other 30% was used for validation using the area under curve (AUC) method. The AUC method produced a frequency ratio of 0.806 and a weight of evidence value of 0.865 contributing to the zonation of the three classes. The study further investigates the impact of urbanization on flood susceptibility and is further classified into high, moderate, and low flood risk zones. With the abrupt change in climatic scenarios, there is an increase in the risk of flash floods. The results of this study can be used by policymakers and planners in developing a preparedness system to mitigate economic, human, and property losses due to floods in any urbanized watershed. [ABSTRACT FROM AUTHOR]

Understanding the connections between human activities and the natural environment depends heavily on information about land use and land cover (LULC) in the form of accurate LULC maps. Environmental monitoring using deep learning (DL) is rapidly growing to preserve a sustainable environment in the long term. For establishing effective policies, regulations, and implementation, DL can be a valuable tool for assessing environmental conditions and natural resources that will positively impact the ecosystem. This paper presents the assessment of land use and land cover change detection (LULCCD) and prediction using DL techniques for the southwestern coastal region, Goa, also known as the tourist destination of India. It consists of three components: (i) change detection (CD), (ii) quantification of LULC changes, and (iii) prediction. A new CD assessment framework, Spatio-Temporal Encoder-Decoder Self Attention Network (STEDSAN), is proposed for the LULCCD process. A dual branch encoder-decoder network is constructed using strided convolution with downsampling for the encoder and transpose convolution with upsampling for the decoder to assess the bitemporal images spatially. The self-attention (SA) mechanism captures the complex global spatial-temporal (ST) interactions between individual pixels over space-time to produce more distinct features. Each branch accepts the LULC map of 2 years as one of its inputs to determine binary and multiclass changes among the bitemporal images. The STEDSAN model determines the patterns, trends, and conversion from one LULC type to another for the assessment period from 2005 to 2018. The binary change maps were also compared with the existing state of the art (SOTA) CD methods, with STEDSAN having an overall accuracy of 94.93%. The prediction was made using an recurrent neural network (RNN) known as long short term memory network (LSTM) for the year 2025. Experiments were conducted to determine area-wise changes in several LULC classes, such as built-up (BU), crops (kharif crop (KC), rabi crop (RC), zaid crop (ZC), double/triple (D/T C)), current fallow (CF), plantation (PL), forests (evergreen forest (EF), deciduous forest (DF), degraded/scurb forest (D/SF)), littoral swamp (LS), grassland (GL), wasteland (WL), waterbodies max (Wmx), and waterbodies min (Wmn). As per the analysis, over the period of 13 years, there has been a net increase in the amount of BU (1.25%), RC (1.17%), and D/TC(2.42%) and a net decrease in DF (3.29%) and WL(1.44%) being the most dominant classes being changed. These findings will offer a thorough description of identifying trends in coastal areas that may incorporate methodological hints for future studies. This study will also promote handling the spatial and temporal complexity of remotely sensed data employed in categorizing the coastal LULC of a heterogeneous landscape. [ABSTRACT FROM AUTHOR]

Soil erosion is one of the major environmental concerns, causing a reduction in soil carbon stock and soil fertility. It is generally difficult to estimate soil erosion in hilly terrains. To overcome this, different types of erodibility indices are developed and used to describe the intensity of soil erosion. The present study was conducted in Nagaland state of northeast India, where clay ratio (CR), modified clay ratio (MCR), critical level of organic matter (CLOM) and erodibility factor (K) were used to assess the severity of soil erosion under different land uses. To study these, soil samples from forest, jhum, fallow jhum and tea gardens were collected from 0 to 30 cm soil depth. The results showed that sand and clay content were significantly different under different land uses, while soil organic carbon (SOC) and stocks followed the same trend, with maximum and minimum values under tea (60.55 t C ha–1 ) and jhum lands (40.74 t C ha–1 ) respectively. Among the different erodibility indices, only CR and MCR values were found to be significantly different from each other. Higher values of CR and MCR under jhum and fallow jhum lands indicate that these land uses are more prone to erosion. The present study provides a picture of severity of soil erosion under different land uses in Nagaland and recommends that CR and MCR can be used to evaluate soil erosion in hilly terrain areas. [ABSTRACT FROM AUTHOR]

This study aimed to monitor long-term land use dynamics for understanding the natural forest integrity and intactness of the Rajiv Gandhi (Nagarhole) Tiger Reserve (RTR) pre- and post-declarations as TR. We employed multi-source data from Survey of India Toposheets (1:50 k), Landsat-7, and Sentinel-2A along with Global Ecosystem Dynamics Investigation (GEDI) vegetation canopy height (10 m) data, a high-spatial resolution CORONA (1970) images and temporal Google Earth Pro images for mapping and validation. To map vegetation type, land use and land cover (LULC) transitions, and fragmentation (1980–2022) we employed a hybrid classification approach. This study also analyzed decadal forest dynamics within TRs using India's State of Forest Reports (ISFR). Findings reveal significant forest fragmentation and habitat loss due to anthropogenic activities in the TR. Mono-plantations (teak and eucalyptus) were found inside TR, while the buffer (1 km) was occupied mainly with coffee and orange plantations which indicates the prevalence of human footprint. The overall accuracy of the current period (2022) is 92.0% with a kappa coefficient value of 0.90. These plantations were established during the British colonial period (early 1900s) for commercial purposes by clearing natural forests. The present study highlights that mono-plantations have not transitioned into natural forests even after a century. This lack of transformation could potentially compromise the integrity of the native forest. Despite its ecological significance, RTR has experienced disturbance due to human footprint. Hence, there is a need for an action plan to protect this vital landscape by replacing mono-plantations with suitable species to preserve the integrity of the forest. These issues extend beyond the protected areas, impacting surrounding regions and require regular monitoring. The proposed methods can be applied to other protected areas facing similar problems in the country and world. [ABSTRACT FROM AUTHOR]

The current study investigates the joint impact of projected land use/land cover change (LULCC) and climate change on the discharge of river Puthimari using Soil and Water Assessment Tool (SWAT). Puthimari, flowing through part of Bhutan and the northeastern region of India, earns its significance by contributing a fairly huge amount of discharge to the mainstream Brahmaputra causing frequent floods downstream, specifically in the monsoon season. The analysis was carried out from 2025 to 2099, by dividing this entire period into three sub-periods, 2025‒2049, 2050‒2074, and 2075‒2099, each of 25 years duration. To evaluate the impact of climate change, this study considered future climate data of five different CMIP5 (Coupled Model Intercomparison Project) climate models and their ensemble for RCP4.5 and RCP8.5 (Representative Concentration Pathways). The changes in LULC were incorporated by projecting the future LULC for 2035, 2065, and 2085 for each of the periods using the CA (Cellular Automata)-Markov model. SWAT performed well for both calibration and validation. The respective Nash-Sutcliffe efficiency (NSE) values for calibration and validation were found to be 0.74 and 0.77. Also, 0.75 and 0.79 coefficient of determination (R2) values were obtained for calibration and validation, respectively. The analyses reveal a 19.76% increase in rural settlement, and a 6.30%, 16.45%, and 8.76% decrease in forest, cropland, and waterbodies in the watershed by the end of this century. The average monsoon rainfall would increase by 14.16% and 38.92%, with a corresponding increase in discharge by 34.27% and 64.67%, under RCP 4.5 and RCP 8.5, respectively. This comprehensive study represents a pioneering effort to thoroughly analyze the future hydrological dynamics of the Puthimari River. This research serves as a vital resource for policymakers and government agencies, offering valuable insights to guide both structural and non-structural measures aimed at safeguarding the river from potential flood devastation. Additionally, it provides essential information to support the implementation of effective watershed management practices. [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]

The nutritive value of mulberry leaves makes it the only food of silkworms (Bombyx mori L.). It is recorded that 6.73 million hectares of area are affected by salinity and sodicity stresses covering various states of the country, which is becoming one of the major threats to popularizing sericulture in India. In the present study, chlorophyll, protein, catalase, peroxidase, and superoxide dismutase content of leaves of six mulberry germplasm viz., English Black, Kolitha-3, C776, Rotundiloba, BC259, and S1 grown under coastal saline soils of South 24 Parganas district of West Bengal, India was investigated. Results demonstrated a sharp decrease in the chlorophyll (2.35 to1.19 mg.g FW-1) and protein (30.10 to 15.20 mg.g FW-1) contents of leaves of all the mulberry germplasm with increasing soil salinity (1.60 to 22.70 dS.m-1). On the contrary, the number of stress-related antioxidant enzymes like catalase, peroxidases, and superoxide dismutase increased from 1.15 to 5.43, 1.43 to 4.76, and 8.65 to 25.15 g-1 FW.min-1, respectively. Overall, the field study indicated the superiority of Kolitha-3 and C776 grown in Canning (Canning I and II), Basanti, Namkhana, Kakdwip, and Sagar blocks of coastal regions of South 24 Parganas. The study deals with issues of the utilization of scarce land promoting income-generating avenues like sericulture in saline areas. [ABSTRACT FROM AUTHOR]

The land use/land cover (LULC) and climate change have detrimental impacts on the water balancing component (WBC) of a river basin, especially in the humid tropical regions. The aim of this study is to assess changes in the past, present, and future WBCs by analyzing the individual and combined impacts of both climate as well as LULC changes using the soil and water assessment tool (SWAT) model for a humid tropical Pamba river basin (PRB) in Western Ghats, India. To investigate the near (2021–2040), mid (2041–2070), and far (2071–2099) future effects of changing climate and LULC on WBCs, an ensemble of five general circulation models (GCM) with representative concentration pathways (RCPs) 4.5 and 8.5 scenarios was considered. Future LULCs for 2030, 2050, 2075, and 2100 were prepared using the Land Change Modeler (LCM) considering the normal LULC growth. The results show that the effects of LULC change increase the water yield and surface runoff while decreasing evapotranspiration (ET) throughout the study period (1988 to 2100). The climate change impacts show ET, water yield, and surface runoff decreases in the future for both RCP scenarios (except water yield, which increases only in near future for both RCPs). The trend of combined impacts on ET, water yield, and surface runoff for the future time was similar to the climate change trend. The results indicate climate change is dominant on WBCs in the future in the PRB due to variation in precipitation and temperature under both RCP 4.5 and 8.5 emission scenarios. This study will be useful for the overall water resources management of the river basin considered and the methodological framework can be adapted for other similar river basins. [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]

Technology-driven population expansion is closely linked to land use change. Unregulated mining, urbanization, industrialization, and forest clearing threaten land use and cover. This study used GIS and statistical methods to examine land use and cover changes in eastern India's Asansol-Durgapur Development Authority (ADDA). The Kappa coefficient was used to validate each year's LULC map accuracy. This region is changing rapidly due to industrial and urban development, which might cause environmental issues. Thus, this area is ideal for a scientific land-use change study. The central hypothesis of this study is that the LULC of an industrial area is spatially heterogeneous and that the number of hotspots is gradually increasing in response to the dynamicity of land use change over time and space. Three years (1992, 2007, and 2022) were used to determine the estimated transition rate. Hotspots of land use change were identified using autocorrelation statistics for LULC clustering using Moron's I and Gi Z statistics. The proportion of land encompassed by natural vegetation experienced a decline from 12% in 1992 to 4% in 2022. Similarly, the extent of land occupied by agricultural activities decreased from 47 to 38% during the period spanning from 1992 to 2022. The industrial and coal mining sectors experienced a modest growth rate of 1% during the period spanning from 1992 to 2022. If the current rate of land use change persists, it will gradually and consistently alter the existing landscape. This study's findings can potentially inform strategies to mitigate the adverse impacts of industrialization and urbanization on the region's natural resources. [ABSTRACT FROM AUTHOR]

Soil erosion is a significant problem in the agriculture sector and the environment globally. Susceptible soil erosion zones must be identified and erosion rates evaluated to decrease land degradation problems and increase crop productivity by protecting soil fertility. Therefore, a research study has been carried out in the Ponnaniyar River basin, an ungauged tributary of the Cauvery basin in India, primarily used for agriculture. The main purpose of this study is to assess soil erosion (SE) and sediment yield (SY) for the future in an ungauged basin by utilizing the projected land use/land cover (LULC) map of the study area. Additionally, Landsat 8 satellite dataset was only used for the classification and prediction of LULC to eliminate the variation between the resolution, bands and its wavelength of different satellites datasets. To achieve the goals of this study, three phases were followed. First, the LULC of the study area was classified using a Random Trees Classifier (RTC), a machine learning technique, followed by the projection of land cover using a Cellular Automata-based Artificial Neural Network (CA-ANN) model. The driving factors for this model include digital elevation model (DEM), slope, distance to roads, settlements, and water bodies. The accuracy level of the projected LULC map was determined by comparing it with the classified LULC map of the study area, and the results showed an overall accuracy (OA) of 85.35 percentage and a kappa coefficient (K) of 0.74, respectively. Second, the projected LULC map was used in the land management factor (C) and conversation practice factor (P) of the Revised Universal Soil Loss Equation (RUSLE) model to assess soil erosion. The model was integrated with the sediment delivery ratio (SDR) to estimate sediment yield within the study area. The accuracy of the generated erosion map based on the classified and projected LULC for the year 2022 was determined using the receiver operating characteristic curve (ROC) curve, and it was found to be in satisfactory agreement. Finally, for effective soil and water conservation measures, the basin was divided into 13 sub-watersheds (SWs) using terrain analysis in geographical information system (GIS). The SWs were prioritized based on the mean soil loss in the 4-year interval from 2014 to 2030 and integrated using the weighted average method to determine the final prioritization. From these findings, SW 11, SW 9, SW 12, and SW 1 are extremely affected by soil erosion, and immediate implementation of water harvesting structures is required for soil conservation. Also, this research might be useful for decision-makers and policymakers in land management. [ABSTRACT FROM AUTHOR]

The current high rate of urbanization in developing countries and its consequences, like traffic congestion, slum development, scarcity of resources, and urban heat islands, raise a need for better Land Use Land Cover (LULC) classification mapping for improved planning. This study mainly deals with two objectives: 1) to explore the applicability of machine learning-based techniques, especially the Random forest (RF) algorithm and Support Vector Machine (SVM) algorithm as the potential classifiers for LULC mapping under different scenarios, and 2) to prepare a better LULC classification model for mountain terrain by using different indices with combination of spectral bands. Due to differences in topography, shadows, spectral confusion from overlapping spectral signatures of different land cover types, and a lack of access for ground verification, classification in mountainous terrain is difficult task compared to plain terrain classification. An enhanced LULC classification model has been designed using two popular machine learning (ML) classifier algorithms, SVM and RF, explicitly for mountainous terrains by taking into consideration of a study area of Gopeshwer town in the Chamoli district of Uttarakhand state, India. Online-based cloud platform Google Earth Engine (GEE) was used for overall processing. Four classification models were built using Sentinel 2B satellite imagery with 20m and 10m resolutions. Two of these models (Model 'i' based on RF algorithm and Model 'ii' based on SVM algorithm) were designed using spectral bands of visible and infrared wavelengths, and the other two (Model 'iii' based on RF algorithm and Model 'iv' based on SVM algorithm) with the addition of indices with spectral bands. The accuracy assessment was done using the confusion matrix based on the output results. Obtained result highlights that the overall accuracy for model 'i' and model 'ii' were 82% and 86% respectively, whereas these were 87.17% and 87.2% for model 'iii' and model 'iv' respectively. Finally, the study compared the performance of each model based on different accuracy metrics for better LULC mapping. It proposes an improved LULC classification model for mountainous terrains, which can contribute to better land management and planning in the study area. [ABSTRACT FROM AUTHOR]

The impact of the novel coronavirus disease (COVID-19) continues unabated. Still, it seems that apart from contact and respiratory transmission, the design and development pattern of an area does echoes to be a contributing factor in virus spreadability. The present study considers land use and transportation system parameters under TOD mode of 16 BRT station provinces in Bhopal, India, and COVID-19 cases data were collected from April 2020 to August 2020. Further, the Pearson correlation and mediational analysis were employed to determine the relationship between TODness and COVID-19 spread cases. The bootstrapping method was used to evaluate the mediation effect and describe why and under what conditions they are related. The study shows that TODness and COVID-19 spread cases are positively correlated. The results show a considerable correlation at (p < 0.05) is 0.405 of the dispersed along with TODness of an area in the analysed 16 BRT station areas. In particular, dispersed demonstrated a high-level correlation of 0.681 with TOD areas, whereas a moderate correlation of 0.322 with non-TOD areas was mediated by diversity and the number of available transit service indicators. Diversity and availability of high-quality transit services effectively spread the virus, whereas population density and public transport mediation effects are insignificant. Outcomes from this study may help government authorities and policymakers devise a strategy and adopt preventive measures in subsequent waves of the pandemic. [ABSTRACT FROM AUTHOR]

The dynamics of land use and land cover are profoundly affected by the growth, mobility, and demand of people. Thematic maps of land use and land cover (LULC) help planners account for conservation, concurrent uses, and land-use compressions by providing a reference for analysis, resource management, and prediction. The purpose of this research is to identify the transition of land-use changes in the Saroor Nagar Watershed between 2008 and 2014 using the Modules for Land Use Change Evaluation (MOLUSCE) plugin (MLP-ANN) model and to forecast and establish potential land-use changes for the years 2020 and 2026. To predict how these factors affected LULC from 2008 to 2014, MLP-ANN was trained with maps of DEM, slope, distance from the road, and distance to a waterbody. The projected and accurate LULC maps for 2020 have a Kappa value of 0.70 and a correctness percentage of 81.8%, indicating a high degree of accuracy. Changes in LULC are then predicted for the year 2026 using MLP-ANN, which shows a 17.4% increase in built-up area at the expense of vegetation and barren land. The results contribute to the development of sustainable plans for land use and resource management. [ABSTRACT FROM AUTHOR]

A proper understanding of soil parameters under different production systems of the world is necessary for efficient soil management. We, therefore, carried out the present study to assess the status of some selected soil properties (soil pH and electrical conductivity (EC)), phyto-available nutrients (available nitrogen (AN), available potassium (AK), exchangeable calcium (Ex. Ca), exchangeable magnesium (Ex. Mg), available sulfur (AS), and soil organic carbon (SOC) pools (SOC, total organic carbon (TOC), very labile C, labile C, less labile C, and non-labile C) and to establish relationships among the measured soil parameters at different depths of Vertisols of India under various land uses. A total of 150 composite soil samples (from 25 plots including nine from agricultural land, nine from horticultural land, three from forest land, and four from grassland) were collected from 6 soil depths viz, 0–10, 10–20, 20–40, 40–60, 60–80, and 80–100 cm under agriculture, horticulture, forest, and grassland land uses present in Central India and analyzed. The values of soil pH, EC, AN, AK, Ex. Ca, Ex. Mg, and AS in various soil depths under different land uses varied widely. The values of SOC (0.19 to 1.00%), TOC (0.58 to 2.42%), very labile C (0.14 to 0.83%), labile C (0.05 to 0.25%), less labile C (0.05 to 0.26%) and non-labile C (0.23 to 1.42%) in various soil depths under different land uses also varied significantly. Forest and grassland land uses had higher levels of SOC, TOC, very labile, and non-labile C content in all the soil depths in comparison to SOC, TOC, very labile, and non-labile C content in different soil depths under agriculture and horticulture land use. The levels of SOC, TOC, very labile, and non-labile C content under all the land uses decreased with increasing soil depths. SOC was positively and significantly correlated with AN, AK, AS, and estimated SOC pools in surface soil layers. Principal component analysis (PCA) of soil parameters in different soil depths resulted in 5 principal components (PCs) with > 1 eigenvalue and accounting for > 75% variability. This information could be used for managing SOC status and phyto-available nutrients in Vertisols under different land uses. [ABSTRACT FROM AUTHOR]

In modern days, a sustainable urban planning system requires a balance of vegetation, water, and settlement. The proportions of these surface features directly influence the land surface temperature (LST) in an urban area. LST primarily depends on the emittance of land use/land cover (LULC) categories. In an urban area, changes in LULC categories as well as local warming are the prime regulators of LST change. The study analyses the LULC change and its impact on LST in Imphal City, India. Landsat satellite data for the summer season and winter seasons for 1991, 2001, 2011, and 2021 have been used in this study. Results show that the mean LST of the study area increased at >1% rate/decade. The green area and water area decreased the LST values whereas the built-up area and fallow lands increased the LST values. The study indicates the consequences of proper land conversion to regulate the LST change. Moreover, the influence of population on LST is also determined. The continuous rising trend of population is a positive factor of increasing LST. The study may help the town and country planners to generate sustainable urban land. [ABSTRACT FROM AUTHOR]

Limited information is available on the greenhouse gas (GHG) emission rates in the peri-urban agricultural systems. This information is necessary for the changing paradigm of climate-smart agriculture with less carbon footprint. The decreasing trends in water availability, agricultural area and productivity, and negative changes in agricultural land use patterns make agriculture, especially fruit and vegetable cultivation in peri-urban areas of Hyderabad city, in India, highly vulnerable to climatic variations. Hence a study was contemplated to develop a methodological framework to study the changes in land, water and energy use and their impact on GHG emissions and, thereby, on agricultural production and productivity. Variations in GHG emissions and carbon credit in the identified peri-urban sites were calculated using the accounting framework with respect to changes in land use and energy usage. [ABSTRACT FROM AUTHOR]

Over the last three decades, the Government of India (GOI) has used watershed management as a solution to solve issues concerning sustainable agricultural output in rainfed areas. Additionally, since 2003, the GOI has made watershed management a national policy. A lot of thought is given to all of the significant crops that have disappeared from farming systems in the watershed development programs (WDPs) of India's current development plans, which are primarily focused on increasing and sustaining productivity levels. In the Sarguja division of Chhattisgarh, the present study attempted to document the on-site and off-site effects of these watershed development programs, as it observed an increase in the ground water level, a rise in the surface water and stream flow levels, a reduction in runoff as well as in soil erosion, increased agricultural and dairy production, improved livelihood and employment generation, and changes in the land use and farming patterns. These findings showed that the percentage of cropland increased in both Kharif and Rabi, because they started planting crops in Zaid, particularly cucumber, melon, and vegetables, that had been kept fallow. The patterns of land usage in the WDP regions have improved over time; due to farmers utilizing more wasteland for productive reasons, there has been a rise in the net sown area of these locations. Additionally, it has been claimed that many crops that were previously abandoned due to water shortages and other requirements are now being cultivated. Responses from the region's population have been in favor of the introduction of innovative techniques like Agroforestry systems. [ABSTRACT FROM AUTHOR]

The impact of five long term (10 years) organic land use systems viz. agroforestry (Poplar + turmeric (LS1)), vegetables (bottle gourd -- broccoli (LS2)), basmati -- wheat (LS3), sugarcane fodder (LS4) and maize + summer moong-wheat (LS5) was studied on soil quality. Results showed that LS1 has significantly higher soil organic carbon (SOC) content, mean weight diameter (MWD) of aggregates and soil moisture retention. Significantly higher soil pH, saturated hydraulic conductivity (Ks) and infiltration rate (IR) in LS4, bulk density (BD) in LS5 while significantly lower electrical conductivity (EC) in CS3 were observed. The LS1 has significantly better soil quality index (SQI) than all the other land use systems. The SQI was in the order of LS1 > LS2 > LS3=LS5> LS4. The order of overall contribution of MWD, particle density, soil moisture retention, Ks and IR to SQI was 45.3, 29.3, 16.6, 6.4 and 2.5% respectively. [ABSTRACT FROM AUTHOR]

The main aim of this study is to comprehensively analyze the dynamics of land use and land cover (LULC) changes in the Bathinda region of Punjab, India, encompassing historical, current, and future trends. To forecast future LULC, the Cellular Automaton–Markov Chain (CA) based on artificial neural network (ANN) concepts was used using cartographic variables such as environmental, economic, and cultural. For segmenting LULC, the study used a combination of ML models, such as support vector machine (SVM) and Maximum Likelihood Classifier (MLC). The study is empirical in nature, and it employs quantitative analyses to shed light on LULC variations through time. The result indicates that the barren land is expected to shrink from 55.2 km2 in 1990 to 5.6 km2 in 2050, signifying better land management or increasing human activity. Vegetative expanses, on the other hand, are expected to rise from 81.3 km2 in 1990 to 205.6 km2 in 2050, reflecting a balance between urbanization and ecological conservation. Agricultural fields are expected to increase from 2597.4 km2 in 1990 to 2859.6 km2 in 2020 before stabilizing at 2898.4 km2 in 2050. Water landscapes are expected to shrink from 13.4 km2 in 1990 to 5.6 km2 in 2050, providing possible issues for water resources. Wetland regions are expected to decrease, thus complicating irrigation and groundwater reservoir sustainability. These findings are confirmed by strong statistical indices, with this study's high kappa coefficients of Kno (0.97), Kstandard (0.95), and Klocation (0.97) indicating a reasonable level of accuracy in CA prediction. From the result of the F1 score, a significant issue was found in MLC for segmenting vegetation, and the issue was resolved in SVM classification. The findings of this study can be used to inform land use policy and plans for sustainable development in the region and beyond. [ABSTRACT FROM AUTHOR]

Amid global concerns regarding climate change and urbanization, understanding the interplay between land use/land cover (LULC) changes, the urban heat island (UHI) effect, and land surface temperatures (LST) is paramount. This study provides an in-depth exploration of these relationships in the context of the Kamrup Metropolitan District, Northeast India, over a period of 22 years (2000–2022) and forecasts the potential implications up to 2032. Employing a high-accuracy supervised machine learning algorithm for LULC analysis, significant transformations are revealed, including the considerable growth in urban built-up areas and the corresponding decline in cultivated land. Concurrently, a progressive rise in LST is observed, underlining the escalating UHI effect. This association is further substantiated through correlation studies involving the normalized difference built-up index (NDBI) and the normalized difference vegetation index (NDVI). The study further leverages the cellular automata–artificial neural network (CA-ANN) model to project the potential scenario in 2032, indicating a predicted intensification in LST, especially in regions undergoing rapid urban expansion. The findings underscore the environmental implications of unchecked urban growth, such as rising temperatures and the intensification of UHI effects. Consequently, this research stresses the critical need for sustainable land management and urban planning strategies, as well as proactive measures to mitigate adverse environmental changes. The results serve as a vital resource for policymakers, urban planners, and environmental scientists working towards harmonizing urban growth with environmental sustainability in the face of escalating global climate change. [ABSTRACT FROM AUTHOR]

Soil microbial activity (SMA) is vital concerning carbon cycling, and its functioning is recognized as the primary factor in modifying soil carbon storage potential. The composition of the microbial community (MC) is significant in sustaining environmental services because the structure and activity of MC also influence nutrient turnover, distribution, and the breakdown rate of soil organic matter. SMA is an essential predictor of soil quality alterations, and microbiome responsiveness is imperative in addressing the escalating sustainability concerns in the Himalayan ecosystem. This study was conducted to evaluate the response of soil microbial and enzyme activities to land conversions in the Northwestern Himalayas (NWH), India. Soil samples were collected from five land use systems (LUSs), including forest, pasture, apple, saffron, and paddy-oilseed, up to a depth of 90 cm. The results revealed a significant difference (p < 0.05) in terms of dehydrogenase (9.97-11.83 TPF µg g-1 day-1), acid phosphatase (22.40-48.43 µg P-NP g-1 h-1), alkaline phosphatase (43.50-61.35 µg P-NP g-1 h-1), arylsulphatase (36.33-48.12 µg P-NP g-1 h-1), fluorescein diacetate hydrolase (12.18-21.59 µg g-1 h-1), bacterial count (67.67-123.33 CFU × 106 g-1), fungal count (19.33-67.00 CFU × 105 g-1), and actinomycetes count (12.00-42.33 CFU × 104 g-1), with the highest and lowest levels in forest soils and paddy-oilseed soils, respectively. Soil enzyme activities and microbial counts followed a pattern: forest > pasture > apple > saffron > paddy-oilseed at all three depths. Paddy-oilseed soils exhibited up to 35% lower enzyme activities than forest soils, implying that land conversion facilitates the depletion of microbiome diversity from surface soils. Additionally, reductions of 49.80% and 62.91% were observed in enzyme activity and microbial counts, respectively, with soil depth (from 0-30 to 60-90 cm). Moreover, the relationship analysis (principal component analysis and correlation) revealed a high and significant (p = 0.05) association between soil microbial and enzyme activities and physicochemical attributes. These results suggest that land conversions need to be restricted to prevent microbiome depletion, reduce the deterioration of natural resources, and ensure the sustainability of soil health. [ABSTRACT FROM AUTHOR]

Spatial distribution of rice cultivated area and its future projections are indispensable for providing food security, irrigation water management, greenhouse gas emission, ecosystem, and environmental protection. Accurate simulation of future rice cultivation is challenging in the context of climate change involving spatial modeling. This study introduced a hydrological data-based prediction approach integrating cellular automata-Markov chain model with multi-criteria decision analysis. Three decadal transitions of different land use land cover (LULC) in an agricultural intensified canal irrigated area were analyzed using Random Forest algorithm. The Landsat satellite imageries were classified with an overall accuracy of more than 90%. The results revealed that the area under water sources, plantation, and fallow land decreased by 53.1%, 33.9%, and 63.6%, respectively, while habitation and Boro rice cultivated area increased significantly by 1243.3% and 211%, respectively, during 1988–2018. The kappa coefficient (0.86), allocation disagreement (10.9%), and quantity disagreement (1.16%) revealed good agreement between the predicted and classified LULC map of 2018. The probable distribution of the LULC map in 2028 and 2038 indicated 54.5% and 59.3% of the command area under Boro rice cultivation as a dominant land cover, respectively. Further, the predicted LULC map anticipated that Boro rice cultivation was likely to expand at the expense of plantation and fallow land which may lead to massive water scarcity in the near future. The proposed approach covered up the limitations of spatial modeling on agricultural growth trends by integrating significant driving factors with quantitative and spatiotemporal aspects of LULC transition. These findings will provide a baseline map for sustainable policy reformation and contemplation on the repercussions that local LULC transition has on global environmental change. [ABSTRACT FROM AUTHOR]

The sole purpose of this review paper is to reveal the land use and land cover (LULC) changes in the study region (Northern India) from 2017 to 2021. The researchers discovered that no systematic review of the LULC dynamics of the study region had been conducted so far. This study used a novel systematic review procedure (i.e., the ICA framework) to conduct the review process. The data for this study were retrieved from the Web of Science of Clarivate Analytics, using keywords related to LULC. A thorough review of 29 full-text articles was done to examine the primary land use classes (LUC), techniques used, factors responsible, and dynamics of the major land classes. Most articles included agriculture, built-up, water bodies, and plantations as the main LULC categories. Most of them used on-screen digitisation and supervised algorithms to classify LULC classes and incorporated accuracy assessment to validate their results. It was also found that almost all the papers cited climate change, increasing population, urbanisation, and economic considerations as the main drivers responsible for the changes that occurred in different LULCs. The findings of the various studies revealed that horticulture and built-up areas have increased while agricultural, forest cover and open spaces have decreased to a greater extent. Environmental negatives of LULC dynamics of the region suggested that LULC change is not favourable everywhere. Thus, to control the haphazard LULC change, the government must come up with a substantial land conversion policy in the region with ground-level implantation that will restrict the people from blind land conversion. [ABSTRACT FROM AUTHOR]

Peri-urban area around a city is a dynamic zone that undergoes considerable changes over time in terms of its functional land use. Analogous to other Indian cities, these changes are also evident in the peri-urban villages of Burdwan city, which has seen intense land use changes between 1987 and 2017. These changes are gauged by using LANDSAT-5 Thematic Mapper (TM) for 1987; LANDSAT-7 Enhanced Thematic Mapper (ETM +) for 2002 and LANDSAT 8 (OLI) for 2017. This study makes a unique contribution by combining the use of remote sensing and intensity analysis to evaluate changes in land use and land cover. The results showed that both the 1987–2002 and 2002–2017 time periods experience rapid change and the land use transformation has been accelerated over the whole period. The profound changes in the vegetation and water bodies are amplified by rampant reclamation for built up and commercial purposes. Results show that the proportion of vegetation and waterbodies decreased by 72.3% and 56.3%, respectively, whereas built-up area has increased by 182.8%. The research also revealed a built up pattern mostly towards the north-west, north, North-East and south-east direction. The current study attempts to analyse the variables causing land transformation and the many actors and their roles in driving these alterations. Finally, the extensive explanation and spatio-temporal differentiation maps and tables created with geospatial data will undoubtedly aid in understanding the peri-urban growth dynamics process and changing form of land-use land cover and aid the decision-making process of local planners, stakeholders, and academicians. [ABSTRACT FROM AUTHOR]

Land Use/Land Cover (LULC) changes have a significant impact on Land Surface Temperature (LST). The LST is an important parameter in various environmental and climatological studies, as it plays a crucial role in understanding the Earth's surface–atmosphere interactions. The LULC changes can modify the surface energy balance and alter the radiation budget, leading to changes in LST. Urbanization, deforestation, and agricultural land use changes are some of the primary drivers of LULC change that have a significant impact on LST. Deforestation and agricultural land use changes result in a reduction in evapotranspiration, leading to an increase in LST. The main objective of the study is to analyze the spatio-temporal change in Land Use/Land Cover (LULC) and its effect on Land Surface Temperature (LST), as well as to establish a correlation of LST with the Normalized Difference Vegetation Index (NDVI) and Normalize Difference Snow Index (NDSI). Understanding the impact of LULC on LST is essential for developing effective land use policies that can mitigate the adverse effects of LULC change on the environment and human health. [ABSTRACT FROM AUTHOR]

Soil carbon sequestration is vital to reduce the atmospheric carbon concentration, which is expected to increase within future climatic scenarios. The present study aims to investigate the effects of land use changes to different plantation systems on SOC stocks, and simulate these changes using the RothC model in Mizoram, India. With this aim, four land uses, viz., secondary forest, oil palm, orange, and arecanut plantations, established on degraded shifting cultivation lands, and a control natural forest were selected for this study. The soils were sampled 0–30 cm in the secondary forest, plantations, and the natural forest, at an interval of five years. Measured SOC stocks were the highest in the secondary forest (67.0 Mg C ha−1) and the lowest under the oil palm plantation (37.4 Mg C ha−1), 10 years after land use conversion. The climate change projections for 2021–2035 and 2036–2050 indicated that temperature and rainfall changes, projected to increase by 0.8 and 2.0 °C, and 5.9 and 5.4%, respectively, will affect SOC stocks in the future differently, depending on the land use and carbon input from vegetation. Baseline climate simulations under land use change showed the highest increase in the SOC stock under the secondary forest (116%), and the lowest in the oil palm plantation (27%). Overall, the model predicted that SOC stocks would increase, but the rate of change (0.23–1.86 Mg C ha−1 yr−1) varied with different land uses, plant species, and land management practices. The model results indicated that restoring secondary forest following the abandonment of a shifting cultivation and orange plantations are the best options to improve SOC stocks within future climate change scenarios. Conversely, arecanut and oil palm need to be reduced because the SOC storage is lower. [ABSTRACT FROM AUTHOR]

We use Soil and Water Assessment Tool (SWAT) to simulate the combined effects of land use/land cover (LU/LC) and climate change on the hydrological response of the Upper Betwa River Catchment (UBRC), a semi-arid region in Central India. We execute this model for two different time periods, 1982–2000 and 2001–2018, using the LU/LC data of 1990 and 2018, respectively. We classified the Landsat satellite images of 1990 and 2018 to obtain the dominant LU/LC classes (water body, built-up, forest, agriculture, and open land) in the catchment. The water body, built-up areas, and cropland have increased by 63%, 65%, and 3%, respectively, whereas forest cover and open land decreased by 16% and 23% in the UBRC from 1990 to 2018. The observed climate data in UBRC shows an increase in the average temperature and decrease in the total rainfall during the period between 1980 to 2018. Once the model is set up, we perform the calibration and validation by using the SWAT Calibration Uncertainty Program (SWAT-CUP). We considered two time periods (1991–1994 and 2001–2007) for the calibration and (1995–1998 and 2008–2014) for the validation. For both these time periods, the calibration and validation result of our model is satisfactory. The output of our calibrated model shows a relative decrease in rainfall (12%), surface runoff (21%), and percolation (9%) in the catchment during the period between 2001–2018 as compared to 1982–2000. Finally, we simulate the surface runoff and percolation in the UBRC using the future climate change scenario. We used the bias-corrected multi-model ensemble of CMIP6 GCMs for four different climate scenarios (2023–2100) by assuming no change in the existing LU/LC. We do this for two different time slices: one from 2023–2060 and the other from 2061–2100. For all the climate scenarios, rainfall and surface runoff in the catchment are expected to decrease by 15–40% and 50–79% as compared to the baseline period of 1982–2018. Percolation in the catchment will have a mixed response. It is expected to decrease by 18% in the middle part of the catchment and increase about 25% in the remaining parts of the catchment. [ABSTRACT FROM AUTHOR]

The present study, covering a period of 52 years (1966–2017), explores changes in agricultural land use and its consequences on crop productivity, diversity, and food availability in Haryana, an agriculturally developed state of India. The time series data on different parameters (area, production, yield, etc.) were collected from the secondary sources and analyzed with the help of compound annual growth rate, trend tests (simple linear regression and Mann–Kendall), and change point detection tests such as Pettitt, standard normal homogeneity, Buishand range, and Neumann ratio. Apart from above, the relative share of area and yield to total change in output was determined using decomposition analysis. The results revealed that agricultural land use became intensive and underwent significant alteration with multifold shifting in area from coarse cereals (maize, jowar, and bajra) to fine food grains (wheat and rice). The yield of all crops, especially wheat and rice witnessed a significant increase which subsequently led to an upsurge in their production. However, the production of maize, jowar, and pulses recorded negative growth despite of an increase in their yield. The results also revealed manifold increase in use of modern key inputs during the first two periods (1966–1985), but afterwards input use rate slowed down. Additionally, the decomposition analysis revealed that yield effect remained positive in changing the production of all crops, but area contributed positively only in wheat, rice, cotton, and oilseeds. The major findings of this study imply that the production of crops can be enhanced only through improvement in yield because there is no further scope left for horizontal expansion in cultivable area of the state. [ABSTRACT FROM AUTHOR]

Current study was conducted to evaluate the effect of important land uses and soil depth on soil organic carbon pools viz. total organic carbon, Walkley and black carbon, labile organic carbon, particulate organic carbon, microbial biomass carbon and carbon management index (CMI) in the north Western Himalayas, India. Soil samples from five different land uses viz. forest, pasture, apple, saffron and paddy-oilseed were collected up to a depth of 1 m (0-30,30-60,60-90 cm). The results revealed that regardless of soil depth, all the carbon pools differed significantly (p < 0.05) among studied land use systems with maximum values observed under forest soils and lowest under paddy-oilseed soils. Further, upon evaluating the impact of soil depth, a significant (p < 0.05) decline and variation in all the carbon pools was observed with maximum values recorded in surface (0-30 cm) soils and least in sub-surface (60-90 cm) layers. CMI was higher in forest soils and lowest in paddy-oilseed. From regression analysis, a positive significant association (high R-squared values) between CMI and soil organic carbon pools was also observed at all three depths. Therefore, land use changes and soil depth had a significant impact on soil organic carbon pools and eventually on CMI, which is used as deterioration indicator or soil carbon rehabilitation that influences the universal goal of sustainability in the long run. [ABSTRACT FROM AUTHOR]

Keeping in mind the detrimental effects of sprawl on the urban landscape, an accurate and thorough analysis of the spatiotemporal growth pattern is necessary for well-organized and sustainable urban progression. This study mainly focuses on urban sprawl and land use/land cover changes (LULCC) in Hisar City of Haryana, India. The extent of urban sprawl and changes in different land utilization over 30 years have been assessed using Shannon's entropy technique. Additionally, the land consumption rate and land absorption coefficient are introduced to support the quantitative evaluation of changes. Finally, the accuracy assessment (producer's, user's, and overall) with the Kappa coefficient was applied for spatial accuracy valuation to confirm the land use/land cover (LULC) reliability based on Landsat multi-temporal satellite data from 1991 to 2021. The study deals with water bodies, open/bare land, vegetation, agricultural lands and built-up area as LULC categories for changes evaluations. As per findings, the built-up area has increased more than twice (2,826.72 hectares) by the total area throughout the studied period, whereas farming land has lost almost half part (3,497.94 hectares). The urban sprawl and LULC change mechanisms have directly influenced other natural resources, including farming land. The increasing entropy values also confirm the sprawl or fragmented development in the city, mainly from the centre to the periphery. This unrestricted expansion has changed the geography of the concerned region and surpassed the land use plans. The suggested policy options may assist decision-makers in resolving the urban impediments, minimizing the additional loss of peripheral landscape, and achieving longterm urban growth planning in the study area. [ABSTRACT FROM AUTHOR]

Land use land cover (LULC) mapping and temporal observations are indispensable drivers for sustainable development. This research showed the growth trends and land use transition for the Prayagraj district in the last three decades. Supervised classification of Landsat images was performed on 5-year temporal intervals using a maximum likelihood classifier. All satellite images were organized into six major LULC feature classes viz agriculture/open land, barren land, built-up, forest, sand, and water. The overall accuracy of LULC classification was achieved by more than 89% in all seven temporal points. Furthermore, the accuracy of the classified maps was estimated through area-based error matrix. The Land Change Modeler tool of TerrSet 2020 software was used to analyze the transition of classes and to incorporate the multi-layer perceptron–Markov chain (MLP-MC) technique. The transition potentials were included in MLP-MC with the help of sensitive explanatory variables and significant transitions of classes. Furthermore, these transition potentials and the Markov chain transition matrix were used to predict the future LULC dynamics and vulnerability. The change analysis revealed that a significant portion of the agriculture/open land gradually decreased and got converted to built-up land. The results depicted that agriculture/open land was reduced by 8.03% in the last three decades while the built-up region was grown by 199.61%. Forest area was continuously decreasing while the sand area increased due to river meandering. Overall, more than 75% of accuracy was achieved in MLP. The prediction model was first validated with observed data, and then the LULC scenario of 2035 and 2050 was simulated. LULC of 2050 showed that the built-up area would likely reach 13.90% of district area whereas the forest area would remain only 0.79%. The prediction model has given the output in the form of future LULC map along with projected potential transition maps. This would be useful for sustainable urban planning to deal with the alarming rate of built-up growth and agriculture/open land shrinkage. [ABSTRACT FROM AUTHOR]

Understanding the likely impacts of climate change (CC) and Land Use Land Cover (LULC) on water resources (WR) is critical for a water basin's mitigation. The present study intends to quantify the impact of (CC) and (LULC) on the streamflow (SF) of the Parvara Mula Basin (PMB) using SWAT. The SWAT model was calibrated and validated using the SWAT Calibration Uncertainty Program (SWAT-CUP) for the two time periods (2003–2007 and 2013–2016) and (2008–2010 and 2017–2018), respectively. To evaluate the model's performance, statistical matrices such as R2, NSE, PBIAS, and RSR were computed for both the calibrated and validated periods. For both these periods, the calibrated and validated results of the model were found to be very good. In this study, three bias-corrected CMIP6 GCMs (ACCESS-CM2, BCC-CSM2-MR, and CanESM5) under three scenarios (ssp245, ssp370, and ssp585) have been adopted by assuming no change in the existing LULC (2018). The results obtained from the SWAT simulation at the end of the century show that there will be an increase in streamflow (SF) by 44.75% to 53.72%, 45.80% to 77.31%, and 48.51% to 83.12% according to ACCESS-CM2, BCC-CSM2-MR, and CanESM5, respectively. A mean ensemble model was created to determine the net change in streamflow under different scenarios for different future time projections. The results obtained from the mean ensembled model also reveal an increase in the SF for the near future (2020–2040), mid future (2041–2070), and far future (2071–2100) to be 64.19%, 47.33%, and 70.59%, respectively. Finally, based on the obtained results, it was concluded that the CanESM5 model produces better results than the ACCESS-CM2 and BCC-CSM2-MR models. As a result, the streamflow evaluated with this model can be used for the PMB's future water management strategies. Thus, this study's findings may be helpful in developing water management strategies and preventing the pessimistic effect of CC in the PMB. [ABSTRACT FROM AUTHOR]

Diurnal temperature range (DTR) which reflects the difference between the daily maximum (Tmax) and minimum temperature (Tmin) is an important indication of changing climate and a critical thermal metric to assess the impact on agriculture, biodiversity, water resources, and human health. The major aim of this study is to assess the probable future spatio-temporal changes in the Tmax, Tmin, and DTR and their long-term warming trend from 2006 to 2099 under two representative concentration pathways (hereafter RCP4.5 and RCP8.5) over diverse agroclimatic regions of India. The observed data from India Meteorological Department (IMD) was used to evaluate the performance of climate models (1970–2005). The result shows a very slight underestimation in DTR by models compared to the observed. In future projections, we found a reduction in DTR (0.001 to 0.020 °C/year) partly linked to the substantial increase in Tmin (0.020 to 0.071 °C/year) than Tmax (0.031 to 0.060 °C/year) that was stronger in far twenty-first-century future under RCP8.5. The decline in DTR was profound and consistent over northern India (up to 3 °C) surrounding the Indo-Gangetic Plain, western dry region, and part of central India with the highest decline observed in winter and pre-monsoon season. However, a decline in DTR was also anticipated over the plateau, coastal, and eastern Himalayas region. Change in land use land cover (LULC) also complimented the decline in DTR. The main findings of the study advocate implementation of a robust framework for climate change adaptation strategies to mitigate adverse consequences to the natural ecosystem and human health over specific regions arising due to declining DTR. [ABSTRACT FROM AUTHOR]

The alteration of land use and land cover caused by human activities on a global scale has had a notable impact on ecosystem services at regional and global levels, which are crucial for the survival and welfare of human beings. Merbil, a small freshwater wetland located in the Brahmaputra basin in Assam, India, is not exempt from this phenomenon. In the present study, we have estimated and shown a spatio-temporal variation of ecosystem service values in response to land use and land cover alteration for the years 1990, 2000, 2010, and 2021, and predicted the same for 2030 and 2040. Supervised classification and the CA-Markov model were used in this study for land-use and land-cover classification and future projection, respectively. The result showed a significant increase in built-up areas, agricultural land, and aquatic plants and a decrease in open water and vegetation during 1990–2040. The study area experienced a substantial rise in ecosystem service values during the observed period (1990–2021) due to the rapid expansion of built-up areas and agricultural and aquatic land. Although the rise of built-up and agricultural land is economically profitable and has increased the study site's overall ecosystem service values, decreasing the area under open water and vegetation cover may have led to an ecological imbalance in the study site. Hence, we suggest that protecting the natural ecosystem should be a priority in future land-use planning. The study will aid in developing natural resource sustainability management plans and provide useful guidelines for preserving the local ecological balance in small wetlands over the short to medium term. [ABSTRACT FROM AUTHOR]

Ravine and gully formations are both spectacular and also the worst forms of water-induced soil erosion and have in situ and ex situ impact on geomorphology, hydrology, productivity and environmental security, and they are the root causes of degradation of marginal and adjacent land along with reduced production potential. A long-term (2011–2019) study was conducted on marginal land of the Chambal ravine to assess the impact of six land uses, i.e., Agriculture (T1—Rainfed Soybean), Agri-horticulture (T2—Soybean + Manilkara achras), Horti-Pastoral (T3—Emblica officinalis + Cenchurus ciliaris), Pasture (T4—C. ciliaris), Silviculture (T5—Acacia nilotica) and Silvi-pasture (T6—A. nilotica + C. Ciliaris) on soil properties, runoff interception, sediment trapping and soil loss reduction. The lowest average annual soil loss (4.83 ton ha−1 year−1) and runoff (109.52 mm) were recorded under T4, while the highest sediment loss (8.09 ton ha−1 year−1) and runoff (136.07 mm), respectively, were under T5. The runoff coefficient of land uses was in the order of T3 (20.30%) < T4 (20.56%) < T1 (21.95%) < T2 (22.26%) < T6 (22.83%) < T5 (25.54%). The C. ciliaris improved bulk density and recorded lowest in horti-pasture (1.63 ± 0.04 g cm−3) followed by pasture (1.66 ± 0.03 g cm−3) land use system. The active SOC content in pasture, horti-pasture and silvi-pasture was 0.95, 0.87 and 0.64 times higher, respectively, than agriculture land use. Under pure C. ciliaris cover, resistance to penetration varied from 0.68 to 1.97 MPa, while in silviculture land use, it ranges from 1.19 to 2.90 Mpa. Grass cover had substantial impact on soil loss and runoff reduction, SOC content, soil aggregation and resistance to penetration. In degraded ecosystems, Cenchrus ciliaris can be used alone and in combination with plants for protection of natural resources from water-induced soil erosion, runoff conservation, soil quality improvement and maximization of precipitation water use. [ABSTRACT FROM AUTHOR]

Rivers are crucial components of human civilization, as they provide water for domestic, agricultural, and industrial use. Additionally, they transport domestic and industrial waste to the sea. The Ganga River is a major river in India, originating from Gangotri in the north, flowing through five provinces, and discharging into the Bay of Bengal. This study examined the impact of land use and land cover changes (LULC) on water quality along the River Hooghly in India. The research involved collecting water samples from different locations and analyzing them in the laboratory to estimate various parameters. The findings indicate that the expansion of built-up and agricultural lands is causing a reduction in tree cover and water bodies, leading to deteriorating water quality. The study highlights the need for sustainable land use practices and improved water management to preserve the river's ecosystem and maintain water quality. Specifically, the study identified localities in the vicinity of Dakshineshwar, Shibpur, and Garden Reach as particularly vulnerable to water quality deterioration due to LULC changes and population growth. The study's results provide valuable insights for policymakers and stakeholders in implementing strategies to address the challenges posed by land use changes and population growth. [ABSTRACT FROM AUTHOR]

One of the most valuable approaches in spatial analysis for a better understanding of the hydrological response of a region or a watershed is certainly the analysis of the well-known land use land cover (LULC) dynamicity. The present case study delves deeper into the analysis of LULC dynamicity by using digital Landsat TM and Landsat OLI data to classify the Kolkata Metropolitan Development Authority (KMDA) into seven classes with over 90% classification accuracy for decadal level assessments of 30 years (for the years 1989, 1999, 2009, and 2019). The change index, the Dematel method for analyzing the cause-effect relationship among the LULC classes, the Jaccard Similarity Index for measuring the nature of similarity among the LULC classes, and the Adherence Index for measuring the consistency of the LULC classes after the transition was used in this study to analyze the LULC transformation. In more detail, the present study considers how urban land use is altering at the expense of other land uses. Besides the shifting pattern of mean centers of the LULC classes through time, also gives a very significant insight into the LULC dynamics over 30 years of span. The current study of LULC dynamicity and transformation patterns over the 30 years of the KMDA area is expected to assist land and urban planners, engineers, and administrators in sustainable decisions and policies to ensure inclusive urbanization that accommodates population growth while minimizing the impact on potential natural resources within the whole study area. [ABSTRACT FROM AUTHOR]

Urban expansion and land surface temperature (LST) fluctuations have been negatively influenced by rapid urbanisation, which degrades the urban thermal environment. This study has endeavoured to incorporate future urban growth modelling and assess the impact of urbanization on LST over Indore City, India, using temporal Landsat satellite data for the years 2010, 2015 and 2020. The present study has utilized the Cellular Automata (CA) model to project future urban growth for the year 2025. The result shows that the built-up land has increased by 28.7% during 2010–2015 and 24.9% during 2015–2020, while a future projection of 4.7% is evident in 2025. The mean LST over the city signifies a decreasing trend during the winter and summer seasons from 2010 to 2020. Moreover, an occurrence of higher LST in the surrounding rural area is observed, witnessing cool heat island phenomenon. The urban thermal field variation index (UTFVI) map shows non-UTFVI in the urban area, which shows an increasing trend and strongest UTFVI in rural regions with decreasing trend in summer season during 2010–2020. Whereas in the winter season, the observed result suggests the opposite behaviour of UTFVI. [ABSTRACT FROM AUTHOR]

Human–wildlife conflicts (HWCs) are escalating globally because of human population growth and increased per capita demand for and consumption of natural resources. These conflicts may be exacerbated where anthropogenic land uses are encroaching on designated conservation or protected areas. Large mammals, such as the Asian elephant (Elephas maximus), are increasingly victimized in HWCs as movement across large-scale areas places them at odds with land areas used by humans. The number of human–elephant conflicts (HECs) reported in India involving the Indian elephant (E. m. indicus) have also increased. Managers require better information on the pattern and distribution of HECs to mitigate them and conserve elephants. To address this, we studied 1306 HECs reported from 2015 to 2019 in Patharia Hills Reserve Forest, southern Assam, India. We visited these 1306 sites based on our inventory of complaints filed and recorded the village where the incident occurred, the types and extent of damage, the approximate cost of damage and information about elephants. Conflicts peaked at crop maturity. Ninety-five percent of damage reported involved rice crops (644 ha) belonging to 1245 farmers, and 59 incidents involved household and property damaged. The estimated cost of HEC during the study period was 85,688 USD. The average affected cropland size of each farmer was 0.52 ha. Of the identified 23 HEC-affected villages, four villages were mildly affected (10 or < 10 incidents), 12 villages were moderately affected (11–50 incidents), three villages were highly affected (51–100 incidents), and a remaining four villages were severely affected (> 100 incidents). In villages reporting more HECs, long-term management practices, such as community training for on-sight deterrent of elephants, can be adopted to reduce conflicts. In these areas, villagers also reported the damage compensation process implemented by the government was slow. Expediting the compensation process coupled with increased community training and cultivation of deterrent crops may help reduce villagers' animosity toward elephant conservation. [ABSTRACT FROM AUTHOR]

Evaluation of a site suitability for sustainable urban settlement growth is crucial. A system for evaluating an interaction between the factors must be established to assure that critical site selection interrelationships are not neglected. To solve this problem, the proposed methodology was evaluated to identify a suitable site for sustainable residential development in Nashik, India. The Geographic information system (GIS)-based multi-influence factor (MIF) approach is used in this study to find ideal locations for future urban settlement. The assessment was based on 11 factors including vegetation, elevation, land use, industry, drainage network, slope, water bodies, road, health services, railway station and population density. The sustainable site suitability was delineated by aggregating all considered factors and their associated MIF weights using the interrelationship between the factors. The results showed that 27.26% of research area was not ideal for settlement development, 16.82% was low suitable, 30.65% was moderately suitable, 16.48% was highly suitable and 8.77% was very highly suitable. Most of the suitable sites were located near the existing habitant area, and major roads. The suitability sites were validated using receiver-operating characteristic (ROC), and area under curve (AUC) value of 0.895 indicated that the model was effective. Sensitivity analysis revealed that the distance from road, drainage network, and health services are the dominant for selecting the optimal location of settlement development in research area. The findings of the delineated ideal areas for the intensive urban settlement development will be helpful to planners and policy makers for sustainable urban growth in the near future. [ABSTRACT FROM AUTHOR]