Integrated assessment of groundwater quality dynamics and Land use/land cover changes in rapidly urbanizing semi-arid region.

Rapid urbanization worldwide, poses numerous environmental challenges between escalating land use land cover (LULC) changes and groundwater quality dynamics. The main objective of this study was to investigate the dynamics of groundwater quality and LULC changes in Sargodha district, Punjab, Pakistan. Groundwater hydrochemistry reveals acceptable pH levels (<8) but total dissolved solids (TDS), electrical conductivity (EC) and HCO3- showed dynamic fluctuations by exceeding WHO limits. Piper diagrams, indicated dominance by magnesium and bicarbonate types, underscoring the influence of natural processes and anthropogenic activities. Major ion relationships in 2010, 2015, and 2021 showed a high correlation (R2 > 0.85) between Na+ and Cl-, suggesting salinization. whereas, the poor correlation (<0.17) between Ca2+ and HCO3- does not support calcite dissolution as the primary process affecting groundwater composition. The examination of nitrate contamination in groundwater across the years 2010, 2015, and 2021 was found to be high in the municipal sewage zone, suggesting a prevailing issue of nitrate contamination attributed to urban activities. The Nitrate Pollution Index (NPI) reveals a concerning trend, with a higher proportion of samples classified under moderate to high pollution categories in 2015 and 2021 compared to 2010. The qualitative assessment of nitrate concentration on spatiotemporal scale showed lower values in 2010 while a consistent rise from 2015 to 2021 in north-east and western parts of district. Likewise, NPI was high in the north-eastern and south-western regions in 2010, then reduced in subsequent years, which may be attributed to effective waste management practices and alterations in agricultural practices. The health risk assessment of 2010 indicated Total Health Hazard Quotient (THQ) within the standard limit, while in 2015 and 2021, elevated health risk was observed. This study emphasizes the need to use multiple approaches to groundwater management for sustainable land use planning and regulations that prioritize groundwater quality conservation.

Assessment of universal thermal climate index (UTCI) using the WRF-UCM model over a metropolitan city in India.

Rapid urbanization increases urban air temperature, considerably affecting health, comfort, and the quality of life in urban spaces. The accurate assessment of outdoor thermal comfort is crucial for urban health. In the present study, a high-resolution mesoscale model coupled with a layer Urban Canopy Model (WRF-UCM) is implemented over the city of Hyderabad (17.3850° N, 78.4867° E) to simulate urban meteorological conditions during the summer and winter period of 2009 and 2019. The universal thermal climate index (UTCI) has been estimated using the model-derived atmospheric variables and a human biometeorology parameter to assess the linkages between the outdoor environment and thermal comfort. Results revealed that during summer, the city experiences nearly 50 h of very strong thermal stress, whereas about 120 h of slight cold stress are experienced during winter. The urban area in Hyderabad expanded from 5 to 15% during the study period, leading to a 2.5℃ (2.8 ℃) increase in land surface temperature, and a 1.2 (1.9 ℃) rise in air temperature at 2 m height and 1.5 (2.5 ℃) UTCI during summer (winter) time. The analysis reveals that the maximum UTCI values were noticed over built-up areas compared to other land classes during daytime and nighttime. The results derived from the present study have shown that the performance of WRF-UCM-derived UTCI reasonably portrayed the significant impact of urbanization on thermal comfort over the city and provided useful insights with regard to urban comfort and welfare.

Assessing and decoupling ecosystem services evolution in karst areas: A multi-model approach to support land management decision-making.

Incorporating Ecosystem Service Value (ESV) into land use planning provides a fresh perspective for informed land management decisions. ESV, influenced by socio-economic and natural factors, has complex driving mechanisms, particularly in China's southwestern karst regions. Studying mediating variables helps elucidate these mechanisms. Further research into ecosystem services interactions and effective land use policies in karst areas is needed. This study evaluates the ESV of Guizhou Province, located in southern China's karst region, using the benefit transfer approach. Combining the Guizhou Provincial Land Use Planning Outline (2006-2020) with the multi-objective programming (MOP) model optimized by genetic algorithm and the patch-generating land use simulation (PLUS) model, four future development scenarios were designed. The response of ESV to land use and land cover (LULC) changes at the county scale under four different development scenarios from 2000 to 2020 and in the future was analyzed. A partial least squares structural equation model (PLS-SEM) was used to decouple the driving mechanism affecting ESV. The results show that over the past two decades, with the implementation of various ecological restoration projects, the total ESV has increased. The ESV for natural development scenarios, ecological conservation scenarios, economic development scenarios, and sustainable development scenarios are CNY 238.278 billion, CNY 400.514 billion, CNY 283.201 billion, and CNY 323.615 billion, respectively. The direct impacts of karst surface characteristic factors (KSCF), meteorological factors (MF), socio-economic factors (SEF) and transportation location factors (TLF) on ESV are positive (0.098), negative (-0.098), positive (0.336), and positive (0.109) respectively. The total effect of KSCF on ESV through influencing socio-economic factors and LULC is (-0.738), with SEF playing a complete mediating role. MF indirectly affect ESV by influencing LULC, with LULC playing a complete mediating role in this process. The PLS-SEM model shows that under the dominant position of LULC, the interaction between natural environmental factors and socio-economic factors on ESV is very complex. This study offers valuable insights that can guide managers in this region, as well as in other karst regions globally, in the development of sustainable land use policies.

Impacts of future climate and land use/land cover change on urban runoff using fine-scale hydrologic modeling.

Future changes in land use/land cover (LULC) and climate (CC) affect watershed hydrology. Despite past research on estimating such changes, studies on the impacts of both these nonstationary stressors on urban watersheds have been limited. Urban watersheds have several important details such as hydraulic infrastructure that call for fine-scale models to predict the impacts of LULC and CC on watershed hydrology. In this paper, a fine-scale hydrologic model-Personal Computer Storm Water Management Model (PCSWMM)-was applied to predict the individual and joint impacts of LULC changes and CC on surface runoff attributes (peak and volume) in 3800 urban subwatersheds in Midwest Florida. The subwatersheds a range of characteristics in terms of drainage area, surface imperviousness, ground slope and LULC distribution. The PCSWMM also represented several hydraulic structures (e.g., ponds and pipes) across the subwatersheds. We analyzed changes in the runoff attributes to determine which stressor is most responsible for the changes and what subwatersheds are mostly sensitive to such changes. Six 24-h design rainfall events (5- to 200-year recurrence intervals) were studied under historical (2010) and future (year 2070) climate and LULC. We evaluated the response of the subwatersheds in terms of runoff peak and volume to the design rainfall events using the PCSWMM. The results indicated that, overall, CC has a greater impact on the runoff attributes than LULC change. We also found that LULC and climate induced changes in runoff are generally more pronounced in greater recurrence intervals and subwatersheds with smaller drainage areas and milder slopes. However, no relationship was found between the changes in runoff and original subwatershed imperviousness; this can be due to the small increase in urban land cover projected for the study area. This research helps urban planners and floodplain managers identify the required strategies to protect urban watersheds against future LULC change and CC.

A machine learning approach to investigate the impact of land use land cover (LULC) changes on groundwater quality, health risks and ecological risks through GIS and response surface methodology (RSM).

Groundwater resources are enormously affected by land use land cover (LULC) dynamics caused by increasing urbanisation, agricultural and household discharge as a result of global population growth. This study investigates the impact of decadal LULC changes in groundwater quality, human and ecological health from 2009 to 2021 in a diverse landscape, West Bengal, India. Using groundwater quality data from 479 wells in 2009 and 734 well in 2021, a recently proposed Water Pollution Index (WPI) was computed, and its geospatial distribution by a machine learning-based 'Empirical Bayesian Kriging' (EBK) tool manifested a decline in water quality since the number of excellent water category decreased from 30.5% to 28% and polluted water increased from 44% to 45%. ANOVA and Friedman tests revealed statistically significant differences (p < 0.0001) in year-wise water quality parameters as well as group comparisons for both years. Landsat 7 and 8 satellite images were used to classify the LULC types applying machine learning tools for both years, and were coupled with response surface methodology (RSM) for the first time, which revealed that the alteration of groundwater quality were attributed to LULC changes, e.g. WPI showed a positive correlation with built-up areas, village-vegetation cover, agricultural lands, and a negative correlation with surface water, barren lands, and forest cover. Expansion in built-up areas by 0.7%, and village-vegetation orchards by 2.3%, accompanied by a reduction in surface water coverage by 0.6%, and 2.4% in croplands caused a 1.5% drop in excellent water and 1% increase in polluted water category. However, ecological risks through the ecological risk index (ERI) exhibited a lower risk in 2021 attributed to reduced high-risk potential zones. This study highlights the potentiality in linking LULC and water quality changes using some advanced statistical tools like GIS and RSM for better management of water quality and landscape ecology.

Long-Term Effects of Ecological Restoration Projects on Ecosystem Services and Their Spatial Interactions: A Case Study of Hainan Tropical Forest Park in China.

Ecological restoration projects aim to comprehensively intervene in damaged or deteriorating ecosystems, restore them, improve the provision of ecosystem services, and achieve harmonious coexistence between humans and nature. Implementing ecological restoration projects leads to continuous changes in land use/land cover. Studying the long-term changes in land use/land cover and their impacts on ecosystem services, as well as the trade-off and synergy between these services, helps evaluate the long-term effectiveness of ecological restoration projects in restoring ecosystems. Therefore, this study analyzes the land use/land cover, and ecosystem services of the Hainan Tropical Forest Park in China to address this. Since 2000, the area has undergone multiple ecological restoration projects, divided roughly into two stages: 2003-2013 and 2013-2021. The InVEST model is used to quantify three essential ecosystem services in mountainous regions (water yield, carbon storage, and soil conservation), and redundancy analysis identifies the primary driving factors influencing their changes. We conducted spatial autocorrelation analysis to examine the interplay among ecosystem services under long-term land use/land cover change. The results indicate a decrease in the total supply of water yield (-5.14%) and carbon storage (-3.21%) in the first phase. However, the second phase shows an improvement in ecosystem services, with an increase in the total supply of water yield (11.45%), carbon storage (27.58%), and soil conservation (21.95%). The redundancy analysis results reveal that land use/land cover are the primary driving factors influencing the changes in ecosystem services. Furthermore, there is a shift in the trade-off and synergy between ecosystem services at different stages, with significant differences in spatial distribution. The findings of this study provide more spatially targeted suggestions for the restoration and management of tropical montane rainforests in the future.

Evaluating long-term impacts of land use/land cover changes on pollution loads at a catchment scale.

Evaluating how pollutant loads react to changes in land use/land cover (LULC) is a challenging task due to the intricate relationships among the many elements within a watershed. However, the difficulty in connecting LULC change and nonpoint source (NPS) pollution loads to streams may be lessened by combining hydrological modeling with geospatial tools and multivariate statistics. The objective of this study was to investigate the long-term effects of LULC change on NPS pollution loads in a highly human-dominated catchment, in central Ethiopia. In the study, hydrologic modeling was used to estimate the NPS parameters from multispectral Landsat images, and multivariate statistical techniques were then used to extract major LULC types that explain the variances of NPS loads between 1981 and 2020. The results demonstrated that there were human-induced LULC changes in the area, as the built-up and agricultural landscapes are rising (186.4% and 5.8%, respectively), and shrub and forest lands are decreasing (67.1% and 41%, respectively). As a result of these changes, the concentrations of nitrate (NO3), total P, total N, organic N, and organic P loads were increased by 69.41, 19.83, 18.45, 18.88, and 24.05%, respectively. Reductions in natural vegetation, as well as agriculture intensification, are the major contributors to the NPS pollutant losses to surface water sources. The result also revealed that pollution nutrients are strongly related to deforestation and agricultural land expansion. Proper adaptation strategies should be implemented to minimize the negative impact of LULC changes in the area.

Impact of changing urban form and production-living-ecological space on changing ecosystem services of a smart city of Eastern India, Durgapur.

Worldwide land use land cover (LULC) transformation become a serious issue in the last few decades due to its immense importance in environmental and human well-being perspectives. Expansion of urban areas at the expense of natural land covers and changing urban form is mainly responsible for changing environmental conditions. This study focused on identifying the impacts of LULC change on environmental conditions through the assessment of changing ecosystem services (ESs) of the Durgapur Municipal Corporation (DMC) from 1990 to 2020. Changing ESs are assessed based on changing urban forms and production-living-ecological space (PLES) components. Results found that the compactness of urban areas is increasing along with the outward expansion. The core urban area of DMC has risen from 8.11% to 30.11% during 1990-2020. Similarly, living space increased from 15.57% to 42.60%, production space decreased from 53.06% to 25.59%, and ecological space fluctuated from 1990 to 2020. This transformation of PLES components negatively affects DMC's environmental condition, affecting the achievement of Sustainable Development Goals (SDGs). These significant results may be utilized to understand changing environmental conditions and priority issues for DMC's future sustainable urban development.

Spatial and temporal classification and prediction of LULC in Brahmani and Baitarni basin using integrated cellular automata models.

Monitoring the dynamics of land use and land cover (LULC) is imperative in the changing climate and evolving urbanization patterns worldwide. The shifts in land use have a significant impact on the hydrological response of watersheds across the globe. Several studies have applied machine learning (ML) algorithms using historical LULC maps along with elevation data and slope for predicting future LULC projections. However, the influence of other driving factors such as socio-economic and climatological factors has not been thoroughly explored. In the present study, a sensitivity analysis approach was adopted to understand the effect of both physical (elevation, slope, aspect, etc.) and socio-economic factors such as population density, distance to built-up, and distance to road and rail, as well as climatic factors (mean precipitation) on the accuracy of LULC prediction in the Brahmani and Baitarni (BB) basin of Eastern India. Additionally, in the absence of the recent LULC maps of the basin, three ML algorithms, i.e., random forest (RF), classified and regression trees (CART), and support vector machine (SVM) were utilized for LULC classification for the years 2007, 2014, and 2021 on Google earth engine (GEE) cloud computing platform. Among the three algorithms, RF performed best for classifying built-up areas along with all the other classes as compared to CART and SVM. The prediction results revealed that the proximity to built-up and population growth dominates in modeling LULC over physical factors such as elevation and slope. The analysis of historical data revealed an increase of 351% in built-up areas over the past years (2007-2021), with a corresponding decline in forest and water areas by 12% and 36% respectively. While the future predictions highlighted an increase in built-up class ranging from 11 to 38% during the years 2028-2070, the forested areas are anticipated to decline by 4 to 16%. The overall findings of the present study suggested that the BB basin, despite being primarily agricultural with a significant forest cover, is undergoing rapid expansion of built-up areas through the encroachment of agricultural and forested lands, which could have far-reaching implications for the region's ecosystem services and sustainability.

Assessing the impact of anthropogenic activities on land use and land cover changes in the semi-arid and arid regions of Algeria.

Land use and land cover are critical factors that influence the environment and human societies. The dynamics of LULC have been constantly changing over the years, and these changes can be analyzed at different spatial and temporal scales to evaluate their impact on the natural environment. This study employs multitemporal satellite data to investigate the spatial and temporal transformations that occurred in Sidi Bel Abbes province, situated in the northwestern region of Algeria, spanning from the early 1990s to 2020. Notably, this province is marked by semi-arid and arid climates and hosts a wide range of areas susceptible to gravitational hazards, especially concerning alterations in land use and forest fires. The interactive supervised classification tool utilized multiple machine learning algorithms including Random Forest, Support Vector Machine, Classification and Regression Tree, and Naïve Bayes to produce land cover maps with six main classes: forest, shrub, agricultural, pasture, water, and built-up. The findings showed that the LULC in the research area is undergoing continuous change, particularly in the forest and agricultural lands. The forest area has decreased significantly from 10.80% in 1990 to 5.25% in 2020, mainly due to repeated fires. Agricultural land has also undergone fluctuations, with a decrease between 1990 and 2000, followed by a fast increase and near stabilization in 2020. At the same time, pasture lands and built-up areas grew steadily, increasing by 11% and 13% respectively. This research highlights the significant impact of anthropogenic activities on LULC changes in the study area and can provide valuable insights for promoting sustainable land use policies.

A comprehensive research on open surface drinking water resources in Istanbul using remote sensing technologies.

Istanbul is a megacity with a population of 15.5 million and is one of the fastest-growing cities in Europe. Due to the rapidly increasing population and urbanization, Istanbul's daily water needs are constantly increasing. In this study, eight drinking water basins that supply water to Istanbul were comprehensively examined using remote sensing observations and techniques. Water surface area changes were determined monthly, and their relationships with meteorological parameters and climate change were investigated. Monthly water surface areas of natural lakes and dams were determined with the Normalized Difference Water Index (NDWI) applied to Sentinel-2 satellite images. Sentinel-1 Synthetic Aperture Radar (SAR) images were used in months when optical images were unavailable. The study was carried out using 3705 optical and 1167 SAR images on the Google Earth Engine (GEE) platform. Additionally, to determine which areas of water resources are shrinking, water frequency maps of the major drinking water resources were produced. Land use/land cover (LULC) changes that occurred over time were determined, and the effects of the increase in urbanization, especially on drinking water surface areas, were investigated. ESRI LULC data was used to determine LULC changes in watersheds, and the increase in urbanization areas from 2017 to 2022 ranged from 1 to 91.43%. While the basin with the least change was in Istranca, the highest increase in the artificial surface was determined to be in the Büyükçekmece basin with 1833.03 ha (2.89%). While there was a 1-12.35% decrease in the surface areas of seven water resources from 2016 to 2022, an increase of 2.65-93% was observed in three water resources (Büyükçekmece, Sazlidere, and Elmali), each in different categories depending on their size. In the overall analysis, total WSA decreased by 62.33 ha from 2016 to 2022, a percentage change of 0.70%. Besides the areal change analysis, the algae contents of the drinking water resources over the years were examined for the major water basins using the Normalized Difference Chlorophyll Index (NDCI) and revealed their relationship with meteorological factors and urbanization.

Transformation of coastal wetlands in the Sundarban Delta (1999-2020).

Spanning across Bangladesh and India, the Sundarban Delta consists of over a thousand islands, the majority of which are protected. These islands are important for the rich biodiversity and unique species found here. However, these islands are also at the forefront of climate change due to the impact of rising sea levels and extreme weather events. Therefore, we analyzed the long-term transformations in the land use land cover (LULC) between 1999 and 2020. We used a variety of geostatistical methods, including optimized hot spots cold spots and join count statistics, to examine the spatial patterns of changes in LULC across the study area. The results of our analysis revealed substantial changes in the spatial patterns of mangroves and pond aquaculture. The changes revealed a distinct north-south demarcation in spatial patterns, in the form of clustering of mangroves in the uninhabited islands located in the south and pond aquaculture clustered in the northern inhabited islands. The loss of area under mangroves was concentrated in the southern edges of the islands, which were most exposed to erosion in the open ocean. Nevertheless, we observed an increase in the area under mangroves in some of the northern riverine islands (17 km2). In the case of pond aquaculture, it was mostly concentrated in inhabited islands in the north. Most of the expansions were concentrated in the Indian part of the delta (631 km2). It is noteworthy that because of effective conservation measures, there was very limited overlap between mangroves and pond aquaculture, denoting the conversion of agricultural land to pond aquaculture instead of mangroves. Thus, the results of our study revealed the importance of local level conservation policies and anthropogenic activities, such as deforestation and local level disturbance like over-extraction of water and pollution, on the changing patterns of LULC across this unique, fragile ecosystem. Future studies may incorporate a finer resolution time series of LULC changes over time and space to enable more detailed analysis.

Generation and classification of patch-based land use and land cover dataset in diverse Indian landscapes: a comparative study of machine learning and deep learning models.

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.

An evaluative technique for drought impact on variation in agricultural LULC using remote sensing and machine learning.

Drought events threaten freshwater reservoirs and agricultural productivity, particularly in semi-arid regions characterized by erratic rainfall. This study evaluates a novel technique for assessing the impact of drought on LULC variations in the context of climate change from 2018 to 2022. Various data sources were harnessed, encompassing Sentinel-2 satellite imagery for LULC classification, climate data from the CHIRPS and AgERA5 databases, geomorphological data from JAXA's ALOS satellite, and a drought indicator (Vegetation Health Index (VHI)) derived from MODIS data. Two classifier models, namely gradient tree boost (GTB) and random forest (RF), were trained and assessed for LULC classification, with performance evaluated by overall accuracy (OA) and kappa coefficient (K). Notably, the GTB model exhibited superior performance, with OA > 90% and a K > 0.9. Over the period from 2018 to 2022, Fez experienced LULC changes of 19.92% expansion in built-up areas, a 34.86% increase in bare land, a 17.86% reduction in water bodies, and a 37.30% decrease in agricultural land. Positive correlations of 0.81 and 0.89 were observed between changes in agricultural LULC, rainfall, and VHI. Furthermore, mild drought conditions were identified in the years 2020 and 2022. This study emphasizes the importance of AI and remote sensing techniques in assessing drought and environmental changes, with potential applications for improving existing drought monitoring systems.

Assessment of land use and land cover change detection and prediction using deep learning techniques for the southwestern coastal region, Goa, India.

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.

Assessing the land use dynamics and thermal environment using geospatial techniques in the industrial city of Chotanagpur Plateau Region, India.

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.

Investigating the impact of urban growth on land use using spatial autocorrelation methods in Konya/Türkiye.

Land use land cover (LULC) change, global environmental change, and sustainable change are frequently discussed topics in research at the moment. It is important to determine the historical LULC change process for effective environmental planning and the most appropriate use of land resources. This study analysed the spatial autocorrelation of the land use structure in Konya between 1990 and 2018. For this, Global and Local Moran's I indices based on land use data from 122 neighbourhoods and hot spot analysis (Getis-Ord Gi*) methods were applied to measure the spatial correlation of changes and to determine statistically significant hot and cold spatial clusters. According to the research results, the growth of urban areas has largely destroyed the most productive agricultural lands in the region. This change showed high spatial clustering both on an area and a proportional basis in the northern and southern parts of the city. On the other hand, the growth in the industrial area suppressed the pasture areas the most in the north-eastern region of the city, and this region showed high spatial clustering on both spatial and proportional scales.

Explaining the role of land use changes on land surface temperature in an arid and semi-arid metropolitan area with multi-scale spatial regression analysis.

Urban Heat Islands (UHIs), Land Surface Temperature (LST), and Land Use Land Cover (LULC) changes are critical environmental concerns that require continuous monitoring and assessment, especially in cities within arid and semi-arid (ASA) climates. Despite the abundance of research in tropical, Mediterranean, and cold climates, there is a significant knowledge gap for cities in the Middle East with ASA climates. This study aimed to examine the effects of LULC change, population, and wind speed on LST in the Mashhad Metropolis, a city with an ASA climate, over a 30-year period. The research underscores the importance of environmental monitoring and assessment in understanding and mitigating the impacts of urbanization and climate change. Our research combines spatial regression models, multi-scale and fine-scale analyses, seasonal and city outskirts considerations, and long-term change assessments. We used Landsat satellite imagery, a crucial tool for environmental monitoring, to identify LULC changes and their impact on LST at three scales. The relationships were analyzed using Ordinary Least Squares (OLS) and Spatial Error Model (SEM) regressions, demonstrating the value of these techniques in environmental assessment. Our findings highlight the role of environmental factors in shaping LST. A decrease in vegetation and instability of water bodies significantly increased LST over the study period. Bare lands and rocky terrains had the most substantial effect on LST. At the same time, built-up areas resulted in Urban Cooling Islands (UCIs) due to their lower temperatures compared to surrounding bare lands. The Normalized Difference Vegetation Index (NDVI) and Dry Bare-Soil Index (DBSI) were the most effective indices impacting LST in ASA regions, and the 30×30 m2 micro-scale provides more precise results in regression models, underscoring their importance in environmental monitoring. Our study provided a comprehensive understanding of the relationship between LULC changes and LST in an ASA environment, contributing significantly to the literature on environmental change in arid regions and the methodologies for monitoring such changes. Future research should aim to validate and expand additional LST-affecting factors and test our approach and findings in other ASA regions, considering the unique characteristics of these areas and the importance of tailored environmental monitoring and assessment approaches.

Soil erosion assessment in the Amazon basin in the last 60 years of deforestation.

Anthropic activities in the Amazon basin have been compromising the environmental sustainability of this complex biome. The main economic activities depend on the deforestation of the rainforest for pasture cattle ranching and agriculture. This study analyzes soil erosion to understand how deforestation has impacted the Amazon basin in this context, using three land-use temporal maps (1960, 1990, 2019) through the revised universal soil loss equation (RUSLE). Our results point to a significant influence of deforestation due to the expansion of agricultural and livestock activities on soil erosion rates in the Amazon Basin. The average soil erosion rate has increased by more than 600% between 1960 and 2019, ranging from 0.015 Mg ha-1 year-1 to 0.117 Mg ha-1 year-1. During this period, deforestation of the Amazon rainforest was approximately 7% (411,857 km2), clearly the leading cause of this increase in soil erosion, especially between 1990 and 2019. The south and southeast regions are the most impacted by increasing soil erosion, in which deforestation was accelerated for expanding agriculture and livestock activities, mainly in the sub-basins of the Madeira, Solimões, Xingu, and Tapajós that present soil erosion increases of 390%, 350%, 280%, and 240%, respectively. The sub-basins with the highest sediment delivery rate (SDR) are under the influence of the Andes, highlighting Solimões (27%), Madeira (13%), and Negro (6%) due to the increase in the soil erosion rate increase in these sub-basins.

Integration of hydrogeological data, GIS and AHP techniques applied to delineate groundwater potential zones in sandstone, limestone and shales rocks of the Damoh district, (MP) central India.

The Damoh district, which is located in the central India and characterized by limestone, shales, and sandstone compact rock. The district has been facing groundwater development challenges and problems for several decades. To facilitate groundwater management, it is crucial to monitoring and planning based on geology, slope, relief, land use, geomorphology, and the types of the basaltic aquifer in the drought-groundwater deficit area. Moreover, the majority of farmers in the area are heavily dependent on groundwater for their crops. Therefore, delineation of groundwater potential zones (GPZ) is essential, which is defined based on various thematic layers, including geology, geomorphology, slope, aspect, drainage density, lineament density, topographic wetness index (TWI), topographic ruggedness index (TRI), and land use/land cover (LULC). The processing and analysis of this information were carried out using Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) methods. The validity of the results was trained and tested using Receiver Operating Characteristic (ROC) curves, which showed training and testing accuracies of 0.713 and 0.701, respectively. The GPZ map was classified into five classes such as very high, high, moderate, low, and very low. The study revealed that approximately 45% of the area falls under the moderate GPZ, while only 30% of the region is classified as having a high GPZ. The area receives high rainfall but has very high surface runoff due to no proper developed soil and lack of water conservation structures. Every summer season show a declined groundwater level. In this context, results of study area are useful to maintain the groundwater under climate change and summer season. The GPZ map plays an important role in implementing artificial recharge structures (ARS), such as percolation ponds, tube wells, bore wells, cement nala bunds (CNBs), continuous contour trenching (CCTs), and others for development of ground level. This study is significant for developing sustainable groundwater management policies in semi-arid regions, that are experiencing climate change. Proper groundwater potential mapping and watershed development policies can help mitigate the effects of drought, climate change, and water scarcity, while preserving the ecosystem in the Limestone, Shales, and Sandstone compact rock region. The results of this study are essential for farmers, regional planners, policy-makers, climate change experts, and local governments, enabling them to understand the groundwater development possibilities in the study area.