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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]

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]

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]

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]

Land use changes in urbanization, industrialization, and agricultural processes will continue to have negative impacts on water quality at all scales. The impact of land use change on water quality is generally studied by analysing the relationship between land use and water quality indicators. Water samples were collected from the surface of Pampa River basin, and analysed for physical, chemical and micro biological parameters. Water quality parameters such as electrical conductivity, Total dissolved solids, Biochemical oxygen demand, Total Coliform, and Fecal Coliform etc., were found to be high in the urban environment. On the other hand water pH and 'DO' were high in the forest area and nitrate values and E.Coli were maximum in the SMT land use. The water pollution index (WPI) showed that the river water was very pure in the forest area, moderately polluted in the paddy fields, polluted in the urban environment and heavily polluted in the SMT land use. The findings demonstrated that significant effect of land use change on water quality. [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]

• Greening in China and India at administrative and built-up levels. • Greening status in relatively poor regions of China and India. • Comparing greening measures and their pros and cons in China and India. • China and India's greening experiences offer insights for sustainable development. Vegetation greening not only offers climate benefits but also enhances residents' well-being in urban areas. China and India lead the world in terms of global greening. However, systematic studies summarizing the green development levels of both countries are still lacking. Here, we explore the regional significant greening levels and spatial distribution characteristics of China and India across multiple spatial scales using the significant greening level as an indicator. Additionally, we analyze the diverse relationships between significant greening in China and India and factors such as land use, economy, and population. The results indicate that areas with significant greening in China are primarily located in some mountainous regions in the central and eastern parts, while in India, areas with significant greening are mainly distributed in plain and plateau regions. Within significantly greening areas, 55.57% in China are forests, and 95.67% in India are croplands. The greening level in built-up areas in China is mostly positively correlated with economic factors, but in economically disadvantaged areas, the greening level is not necessarily the lowest. In Indian built-up areas, the greening level is negatively correlated with the economy. Regarding the relationship with urbanization, the greening level in the surrounding areas of built-up areas in both China and India generally decreases with the increasing degree of urbanization's influence, but the damage caused by urban development to the greening level in India is much higher than in China. 83% of cities in China have medium-range per capita greening levels, while 60% of cities in India have per capita greening levels in the lower range. A systematic analysis and summary of China and India's greening development can provide strategic references for other developing countries to enhance greening initiatives and improve their level of sustainable development. [ABSTRACT FROM AUTHOR]

The understanding of spatio-temporal variation in land use and land cover (LULC) patterns is crucial for managing catchment land use planning, as it directly influences of tropical reservoir water quality and the subsequent Nutrient Contamination (NC) of unmonitored water bodies. The current research attempts to accurately measure the influence of LULC and its associated determinants on the quantities of NC loads by using Chl-a as a proxy, within tropical reservoirs, i.e. Bhadra and Tungabhadra, located in same river catchment. This Chl-a spread calculated by the Maximum Chlorophyll Index (MCI) derived from Sentinel 2 satellite data products covering the period from July 2016 to June 2021 were done using Google Earth Engine (GEE) platform. The validation analysis confirms the robustness of the methodology with a strong correlation between MCI-calculated values and EOMAP (Earth Observation and Environmental Services Mapping) Chl-a (μg/L) data points for both reservoirs, Bhadra (R2 = 0.64) and Tungabhadra (R2 = 0.68). The findings reveal that, Tungabhadra reservoir consistently exhibits an excessive spatial distribution of Chl-a spread area (17 km2 to 335 km2), reflecting nutrient-rich water inflows, particularly evident during the post-monsoon period. This notable rise could be linked to harvesting the Kharif crop, resulting in elevated nutrient concentrations. In contrast Bhadra reservoir, dominated by forested areas, maintains relatively lower Chl-a spread areas (<20 km2), highlighting its pivotal role in maintaining water cleanliness and serves as a riparian boundary. In addition, the changes in LULC classes show a strong relationship with variation in Chl-a during the studied period, for the Bhadra Reservoir R2 = 0.51 (F- statistics = 3.983, p = 0.021), and the Tungabhadra Reservoir R2 = 0.802 (F- statistics = 7.489, p = 0.0143). This highlights how changes in land use significantly shape contamination dynamics, deepening our understanding of nutrient inputs and contamination drivers in tropical reservoirs. • Sentinel 2 has potential for Chl-a estimation using Maximum Chlorophyll Index. • Demonstrates suitability for different trophic states of water bodies. • Heterogenous catchments LULC significantly influences water quality. [ABSTRACT FROM AUTHOR]

Noise pollution is an unintentional consequence of mining activities, needing rigorous assessment, monitoring, and mitigation techniques to reduce its impact on local residents and ecosystems. The study specifically examines the noise pollution from rare earth mining activities in the Neendakara-Kayamkulam (NK) coastal belt, Kollam, Kerala, India, a region rich in ilmenite, rutile, sillimanite, zircon, and monazite. Despite the known environmental and health impacts of noise pollution, there is limited specific data on its magnitude and sources in this region, as well as a lack of effective mitigation strategies tailored to rare earth mining operations. Studies have indicated that mining operations, such as the movement of heavy mineral sands, considerably elevate noise levels, which have an effect on the environment's quality and public health. This study seeks to fill the gap by geospatial mapping and assessing the noise levels and recommend measures to effectively mitigate noise pollution. Systematic noise measurements were conducted at 48 suitable locations within the NK coastal belt, including residential, commercial, industrial, coastal, and silence zones. The noise levels vary from 49.1 dB(A) near a religious place to 82.4 dB(A) near the local industry. The study employs geospatial noise mapping and land cover superimposition to implement class-specific mitigation measures for noise pollution in a coastal vicinity mixed land use area, including natural and vegetative barriers, operational scheduling, zoning, and land use planning., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Landsat land use/land cover (LULC) data analysis to establish freshwater lakes' temporal and spatial distribution can provide a solid foundation for future ecological and environmental policy development to manage ecosystems better. Analysis of changes in LULC is a method that can be used to learn more about direct and indirect human interactions with the environment for sustainability. Neural network technology significantly facilitates mapping between asymmetric and high-dimensional data. This paper presents a methodological advancement that integrates the CA-ANN (cellular automata-artificial neural network) technique with the dynamic characteristics of the water body to forecast forthcoming water levels and their spatial distribution in "Wular Lake." We used remote sensing data from 2001 to 2021 with a 10-year interval to predict spatio-temporal change and LULC simulation. The validation of the calibration of predicted and accurate LULC maps for 2021 yielded a maximum kappa value of 0.86. Over the past three decades, the study region has seen an increase in a net change % in the impervious surface of 22.41% and in agricultural land by 52.02%, while water decreased by 14.12%, trees/forests decreased by 40.77%, shrubs decreased by 11.53%, and aquatic vegetation decreased by 4.14%. Multiple environmental challenges have arisen in the environmentally sustainable Wular Lake in the Kashmir Valley due to the vast land transformation, primarily due to human activities, and have been predominantly negative. The research acknowledges the importance of (LULC) analysis, recognizing it as a fundamental cornerstone for developing future ecological and environmental policy frameworks., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Tropical coastal wetlands have been well recognized for their role in mitigating climate change apart from possessing high biodiversity values as well as providing important habitats for fisheries and other ecosystem services. Although these wetlands play key roles in sequestering carbon (C) in the lithosphere and hydrosphere, there is a notable dearth of comprehensive yet spatially explicit accounts of their total blue carbon (TBC) stocks. The anthropogenic factors including development stressors have fundamentally altered the global C dynamics. In this respect, holistic accounting of the total C stocks of the coastal wetlands becomes the foremost task in emphasizing their recognition as integral components of climate change mitigation strategies. This study modeled TBC stocks of an intertidal mudflat of eastern India across different land use/ land covers (LULCs) by quantifying the above ground biomass (AGB), below ground biomass (BGB), and soil organic C (SOC). The distribution of SOC was modeled through spatial interpolation of point-specific C densities (R2 = 0.77; RMSE = 22.91 Mg ha−1). AGB was estimated by establishing allometric relationships between geospatial data, both of active (PALSAR-2) and passive (Pleiades-1B) nature and in-situ dendrometric variables using the artificial neural network (R2 = 0.85; RMSE = 48.91 Mg ha−1). BGB was estimated from the modeled AGB. Spatial integration of these datasets estimated the TBC stock of mudflat as 27,469.30 Mg. Overall, open mangrove represented highest mean TBC density (163.29 Mg ha−1) while herbaceous vegetation recorded largest TBC stock (9818.94 Mg). Mean TBC densities across LULCs were significantly different (p < 0.05) from each other except for the herbaceous vegetation and built-up categories, indicating the inertial effects of paleo-land covers. Accordingly, this study could be inferred as a pioneering one in developing a comprehensive geospatial model of TBC accounting in India towards strengthening the regional and national C inventories. [Display omitted] • Total blue C stocks of an intertidal mudflat including AGB, BGB and SOC estimated • Applying destructive/non-destructive sampling, elemental analysis and RS datasets • Coupling of fine resolution multispectral and SAR datasets through ANN • Effects of land use/land cover patterns on blue C dynamics assessed [ABSTRACT FROM AUTHOR]

Rainwater Management (RWM) interventions were implemented to address the scarcity of water and to restrict the degradation of land resources in the Parasai-Sindh watershed covering an areal extent of 1246 ha located in Jhansi district of Bundelkhand region, Central India. The region is constrained by several factors, the most significant of which is its high vulnerability to natural disasters and poor infrastructure development, resulting in low agricultural production and precarious livelihoods. This study's goal was to evaluate the impact of RWM intervention on groundwater dynamics and the changes in LULC that it has brought about. A total of 380 dug wells were monitored to investigate the ground water dynamics. Integration of Remote Sensing (RS) and Geographic Information Systems (GIS) were used to prepare LULC maps using Landsat images. The changes in LULC were studied before and after the implementation of RWM interventions. The study revealed enhanced groundwater availability for agricultural and domestic applications. Due to increased water availability, a significant portion of fallow land was converted into cropland. Built-up land coverings have also increased in the watershed. A decrease in the areal extent of barren rocky and scrubland was witnessed over the study period. The area under plantation was also increased.The study underlines the relevance of RWM interventions in restoring watershed land cover, assisting in poverty alleviation, and providing long-term livelihood. [ABSTRACT FROM AUTHOR]