Landscape transition-induced ecological risk modeling using GIS and remote sensing techniques: a case of Saint Martin Island, Bangladesh.

Uncontrolled human activity and nature are causing the deterioration of Saint Martin Island, Bangladesh's only tropical island, necessitating sustainable land use strategies and ecological practices. Therefore, the present study measures the land use/cover transition from 1974 to 2021, predicts 2032 and 2042, and constructs the spatiotemporal features of the Landscape Ecological Risk Index based on land use changes. The study utilized Maximum Likelihood Classification (MLC) on Landsat images from 1974, 1988, 2001, 2013, and Sentinel 2B in 2021, achieving ≥ 80% accuracy. The MLP-MC approach was also used to predict 2032 and 2042 LULC change patterns. The eco-risk index was developed using landscape disturbance and vulnerability indices, Bayesian Kriging interpolation, and spatial autocorrelations to indicate spatial clustering. The research found that settlements increased from 2.06 to 28.62 ha between 1974 and 2021 and would cover 41.22 ha in 2042, causing considerable losses in agricultural areas, waterbodies, sand, coral reefs, and vegetation. The area under study showed a more uniform and homogenous environment as Shannon's diversity and evenness scores decreased. The ecological risk of Saint Martin Island increased from 4.31 to 31.05 ha between 1974 and 2042 due to natural and human factors like erosion, tidal bores, population growth, coral mining, habitat destruction, and intensive agricultural practices and tourism, primarily in Nazrul Para, Galachipa, and Western Dakhin Para. The findings will benefit St. Martin Island stakeholders and policymakers by providing insights into current and potential landscape changes and land eco-management.

Impact of Land Use and Land Cover (LULC) Changes on Carbon Stocks and Economic Implications in Calabria Using Google Earth Engine (GEE).

Terrestrial ecosystems play a crucial role in global carbon cycling by sequestering carbon from the atmosphere and storing it primarily in living biomass and soil. Monitoring terrestrial carbon stocks is essential for understanding the impacts of changes in land use on carbon sequestration. This study investigates the potential of remote sensing techniques and the Google Earth Engine to map and monitor changes in the forests of Calabria (Italy) over the past two decades. Using satellite-sourced Corine land cover datasets and the InVEST model, changes in Land Use Land Cover (LULC), and carbon concentrations are analyzed, providing insights into the carbon dynamics of the region. Furthermore, cellular automata and Markov chain techniques are used to simulate the future spatial and temporal dynamics of LULC. The results reveal notable fluctuations in LULC; specifically, settlement and bare land have expanded at the expense of forested and grassland areas. These land use and land cover changes significantly declined the overall carbon stocks in Calabria between 2000 and 2024, resulting in notable economic impacts. The region experienced periods of both decline and growth in carbon concentration, with overall losses resulting in economic impacts up to EUR 357.57 million and carbon losses equivalent to 6,558,069.68 Mg of CO 2 emissions during periods of decline. Conversely, during periods of carbon gain, the economic benefit reached EUR 41.26 million, with sequestered carbon equivalent to 756,919.47 Mg of CO 2 emissions. This research aims to highlight the critical role of satellite data in enhancing our understanding and development of comprehensive strategies for managing carbon stocks in terrestrial ecosystems.

Predicting Prayagraj's Urbanization Trajectory using CA-ANN Modelling: Population Pressures and Land Use Dynamics.

Land Use/Land Cover (LULC) dynamics provide a crucial role in the monitoring, planning, and management of resources. They also offer valuable information for developing strategies to balance conservation efforts, resolve conflicts between different land uses, and address pressures from growth. The present study focuses on the assessment of LULC dynamics, their forecasting, and their changes for Prayagraj city (including its surroundings) of India. Using long-term spatiotemporal Landsat datasets (1988-2018), we have explored the interlinkages between the change dynamics and human population pressure to explore the impact of agriculture and urbanization on the city landscape. Future growth prediction is carried out by incorporating Cellular Automaton (CA) and Artificial Neural Network (ANN) models. Six exploratory layers (viz., roads, educational institutes, railway transition, slope, river, and restricted area) are used in the learning process to determine LULC change (1997-2008) simulation. The validation of real and predicted LULC is carried out for 2018, where the correctness percentage and kappa value are found to be 90.29% and 0.87, respectively. Then, the ANN- Multilayer perceptron (MLP) and CA model are applied to predict LULC-2028 using the same trained transition probabilities. Results show that Built-land has grown highly by 10.03%, whereas Agriculture land and Forest land have significantly decreased by 13.43% and 3.03%, respectively, from 1988 to 2018. The predicted LULC of 2028 reveals that Built-land will keep growing by 2.83% during 2018-2028 at the cost of Agriculture land and Forest land, especially in northern, south-western and southern region, including city's inner sphere. United Nations' human population projection reveals that the city is expected to reach a population of 1.625 million by 2028. This indicates that tremendous pressure will be placed on land resources, particularly on agricultural, barren, and forested areas. To address this alarming scenario, it is imperative to delineate future development areas, ensuring better urban planning for the environmental sustainability and economic prosperity of Prayagraj city.

Assessing deep pools and water spread dynamics in semi-arid Banas River, India: a geospatial approach for conservation and sustainable management.

The deep pools are considered vital habitats for both aquatic and terrestrial biodiversity in arid and semi-arid rivers. These 'refugia' habitats sustain the aquatic biodiversity of local and regional importance when water flow ceases. Banas is an ecologically unique and non-perennial river in the Ganga Basin originating from the Aravalli Range and flowing through the semi-arid region of Rajasthan, India. This study maps and characterises the deep pools in the water stressed river using Sentinel-2 satellite data (2019-2022). Mapping and analysis were done using geospatial tools and field data. The composite map reported 2.18 km2 (0.6% of the total area) and 72.42 km2 (19.0% of the total area) of permanent water spread in the floodplain and reservoirs of Banas River, respectively with seasonal variations. A total of 558 contiguous habitats with varying sizes (50 to 314,422 m2) were delineated and most of them were located in the downstream of Bisalpur Dam especially along the river meandering. The composition of the area under different land use land cover classes in the riparian zone varied across the deep pools with medium land use intensity. The high proportion of vegetation and cropland near and far from the riparian buffer indicated existence of the natural and agrarian landscapes, respectively. The indications of various ecosystem services by the deep pools necessitate spatial quantification. Additionally, impact of the various anthropogenic threats on aquatic habitats recommends measures for habitat restoration and conservation planning of Banas River.

Spatiotemporal dynamics of land use land cover change and its drivers in the western part of Lake Abaya, Ethiopia.

Understanding the dynamics of land use/land cover (LU/LC) changes and what drives these changes is essential for creating effective strategies for sustainable land management. It also helps to monitor the impact on ecosystems and biodiversity, which is crucial for policy-making. This study focused on assessing the trends, rates, and extent of LU/LC change and its causes in the western part of Lake Abaya in Ethiopia. To achieve this, we used a supervised classification method with a maximum-likelihood algorithm to map different land use land cover types. Additionally, we gathered information through field observations, focus group discussions (FGDs), and key informant interviews (KIIs) to identify the factors driving LU/LC change and its consequences between 1990 and 2022. The study findings revealed that vegetation and wetlands significantly decreased over this period, while water bodies, agricultural land, and settlements expanded at the expense of other land uses. The average normalized difference vegetation index (NDVI) values decreased from 0.368 in 1990 to 0.135 in 2022, indicating declining vegetation health. Local communities point to several factors responsible for these changes, including the expansion of agricultural land, increased settlement, firewood collection, and charcoal production (as proximate/immediate drivers), as well as population growth, poverty, unemployment, climate change, and policy-related issues (as underlying causes). Thus, it needs the development and implementation of an integrated and sustainable land management system, and strong land use and restoration policies in order to halt or reduce the rapid expansion of agricultural land and settlement areas at the expense of vegetation and wetlands.

Delineating groundwater potential zones using integrated remote sensing and GIS in Lahore, Pakistan.

Groundwater depletion and water scarcity are pressing issues in water-limited regions worldwide, including Pakistan, where it ranks as the third-largest user of groundwater. Lahore, Pakistan, grapples with severe groundwater depletion due to factors like population growth and increased agricultural land use. This study aims to address the lack of comprehensive groundwater availability data in Lahore's semi-arid region by employing GIS techniques and remote sensing data. Various parameters, including Land Use and Land Cover (LULC), Rainfall, Drainage Density (DD), Water Depth, Soil Type, Slope, Population Density, Road Density, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Moisture Stress Index (MSI), Water Vegetation Water Index (WVWI), and Land Surface Temperature (LST), are considered. Thematic layers of these parameters are assigned different weights based on previous literature, reclassified, and superimposed in weighted overlay tool to develop a groundwater potential zones index map for Lahore. The groundwater recharge potential zones are categorized into five classes: Extremely Bad, Bad, Mediocre, Good, and Extremely Good. The groundwater potential zone index (GWPZI) map of Lahore reveals that the majority falls within the Bad to Mediocre recharge potential zones, covering 33% and 28% of the total land area in Lahore, respectively. Additionally, 14% of the total area falls under the category of Extremely Bad recharge potential zones, while Good to Extremely Good areas cover 19% and 6%, respectively. By providing policymakers and water supply authorities with valuable insights, this study underscores the significance of GIS techniques in groundwater management. Implementing the findings can aid in addressing Lahore's groundwater challenges and formulating sustainable water management strategies for the city's future.

Impact of Land use dynamics on the water yields in the Gorgan river basin.

This research investigates the future dynamics of water yield services in the Gorgan River Basin in the North of Iran by analyzing land cover changes from 1990 to 2020, using Landsat images and predicting up to 2040 with the Land Change Modeler and InVEST model under three scenarios: continuation, conservation, and mitigation. The results indicate significant shifts in agricultural land impacted water yields, which fluctuated from 324.7 million cubic meters (MCM) in 1990 to 279.7 MCM in 2010, before rising to 320.1 MCM by 2020. The study uniquely assesses the effects of land use changes on water yields, projecting a 13.6 % increase in water yield by 2040 under the continuation scenario, a 3.9 % increase under conservation, and a 1.6 % decrease under mitigation, which limits changes on steep slopes to prevent soil erosion and floods. This underscores the interplay between land use, vegetation cover, and water yield, emphasizing strategic land management for water resource preservation and effective watershed management in the GRB.

Spatiotemporal and vertical variability of water quality in lentic small water bodies: implications of varying rainfall and land use conditions.

Lentic small water bodies (LSWBs) deteriorate owing to anthropogenic activities, such as untreated domestic and agricultural waste disposal. Moreover, different turnover mechanisms occur during different seasons, contributing to nutrient enrichment and consequent degradation of LSWBs. However, understanding their spatial, temporal, and vertical variations during different seasons is understudied. In addition, studies on the variation in water quality under varying rainfall and land-use conditions are limited. Therefore, in this study, three LSWBs located in Northern India were studied during the pre-monsoon and monsoon seasons (December 2022 to October 2023). Total nitrogen (TN), chlorophyll-a (Chl-a), total phosphorus (TP), temperature, pH, dissolved oxygen (DO), total dissolved solids (TDS), chemical oxygen demand (COD), secchi disk depth (SDD), and water level (WL) were measured monthly. Sentinel-2 and CHIRPS pentad data were used for land use, land cover classification, and rainfall analysis. The spatial analysis indicates that the seasonal shift affects the water quality distribution, especially near the inlets and at the edges. The overall concentrations of TN and TP decreased during the monsoon season; however, they increased significantly at the inlets of the LSWBs. On the other hand, the Chl-a concentration shifted towards the edges due to the inflow during the monsoon. Temporal analysis also suggests that the arrival of the monsoon lowers pH, DO, and TDS. However, the concentrations of TN and TP increased because of agricultural runoff. Chl-a and COD show distinct variations due to the individual LSWBs' local conditions. Vertical variability analysis demonstrated pH, temperature, and TN stratification during the pre-monsoon period. However, during the monsoon, stratification is less significant due to intermixing. Redundancy analysis (RDA) showed that land use and rainfall patterns affected the water quality of LSWB 1, 2, and 3 by 53.49%, 81.62%, and 92.64%, respectively. This shows that land use, land cover, and rainfall changes affect the water quality of LSWBs. This study highlights the negative impact of runoff from agricultural land use as the main factor responsible for increased nutrient levels in the LSWBs.

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.

Future land use and land cover simulations with cellular automata-based artificial neural network: A case study over Delhi megacity (India).

According to United Nations projections, future global urban growth will mostly occur in Asian megacities. In this study, a Cellular Automata based Artificial Neural Network (CA-ANN) model is used to simulate the future land use and land cover (LULC) over Delhi megacity (India). Delhi, projected to become the world's most populated city by 2030, is an example of a data poor city in Asia, having millions of climate vulnerable people. The CA-ANN model of Modules for Land Change Simulation (MOLUSCE), an open-source plugin, is first tested to simulate the LULC for 2009. Based on good validation results-structural similarity (SSIM; 0.8288), overall accuracy (79.78 %), kappa index of agreement (KIA; 77.25 %), and minimum validation overall error (0.0379), the same model set-up is used to carry out LULC simulation for 2030. This model is found to be simple, efficient, and computationally less expensive tool, and can be used to model future LULCs with a minimal set of inputs, a constraint often found in data poor cities. Results show continued increase in built-up area from 38.3 % (2014) to 53.8 % (2030), at the expense of cultivable areas, forests, and wastelands. The study incorporates past and future LULC change trajectories to highlight the changing LULC dynamics of the megacity from 1977 to 2030. Rate of urban sprawl, calculated using compound annual growth rate (CAGR) is projected to be 2.51 % for 2014-2030, substantially higher than the estimates for 2006-2014 (0.62 %). Further, the past and future urban growth patterns for Delhi are found to mimic other big Asian cities. The database generated from the present study has wide applicability for scientific research community, governmental bodies, profit and non-profit organizations for topics concerning-future urban climate research, climate risk and adaption policy frameworks, climate finance budgeting, future town planning, etc.

Dynamics of Land use and Land cover in the Belitung Island, Indonesia.

Belitung Island, situated in the Southeast Asian tin belt, experiences substantial transformations in land use and land cover (LULC) driven by mining activities, impacting both local economic growth and the ecosystem. This study aims to elucidate the dynamic LULC changes on Belitung Island and evaluate deforestation trends. LULC data spanning from 1990 to 2020 were acquired from The Indonesian Ministry of Environment and Forestry (KLHK), employing supervised classification of satellite imageries. The dataset demonstrated an overall accuracy ranging from 0.79 to 0.92 and was reclassified into six types based on the Intergovernmental Panel on Climate Change or IPCC's classes, encompassing forest, cropland, grassland, other land, settlements, and wetlands. Our research unveiled a notable reduction of over 25 % in forest cover within the past 30 years. Notably, 2020 exhibited instances of reforestation, which subsequently transformed into cropland (0.57 %), grassland (0.32 %), and other lands (0.39 %). Belitung Island grapples with challenges related to sustainable development, environmental conservation, and food security. Forest Landscape Restoration (FLR) emerges as a potential strategy to address some of the socioeconomic and ecological issues.

A systematic review of cyclonic disaster: Damage-loss, consequences, adaptation strategies, and future scopes.

Tropical cyclones have direct and indirect repercussions in many coastal areas worldwide. In coastal regions, several studies have identified the driving factors of cyclonic hazards and their associated impacts. However, previous studies have focused little on cyclone-induced damage and loss, consequences, and adaptation strategies. As a result, it is critical to explore the global focus areas of cyclone-related studies. This review systematically examined cyclone-induced damage and loss, its consequences, adaptation strategies in coastal regions, and associated research gaps. Results revealed eight main types of cyclone-induced damages and losses. About 46 % of studies focused on vegetation damages, followed by water and sanitation (11 %), crop damages (8 %), income or business losses (8 %), health and injuries (8 %), land use and land cover changes (8 %), infrastructural damages (5 %), and mixed damages and losses (5 %). These damages and losses led to further consequences, including disruption of biocenoses, fish death because defoliated leaves carried carbon into the water, changes in forest structure and composition, loss of timber plantation confidence, hampering the steady supply of safe drinking water, raising drinking water costs, unsanitary circumstances, an increase in infectious diseases, a decrease in protein consumption, and business and supply chain interruptions. Approximately 35 % of the studies addressed one or more of the thirteen adaptation strategies identified in this review. Most of these studies documented the use of natural regeneration and tree planting as responses to vegetation damage and water purification and the distribution of emergency-safe water in response to water and sanitation damage. The findings have led to a proposal for an adaptation framework for cyclone-induced damage and loss. This review recommended investigating cyclone-induced land use and land cover change, damage to vegetation functional traits and patterns, health and injuries, service networks, and infrastructural damages.

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.

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.

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.

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.

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.

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.

Evaluating the impact of urbanization on the urban heat islands through integrated radius and non-linear regression approach.

Urban heat islands (UHIs) are a significant environmental problem, exacerbating the urban climate and affecting human health in the Asir region of Saudi Arabia. The need to understand the spatio-temporal dynamics of UHI in the context of urban expansion is crucial for sustainable urban planning. The aim of this study was to quantify the changes in land use and land cover (LULC) and urbanization, assess the expansion process of UHI, and analyze its connectivity in order to develop strategies to mitigate UHI in an urban context over a 30-year period from 1990 to 2020. Using remote sensing data, LULC changes were analyzed with a random forest model. LULC change rate (LCCR), land cover intensity (LCI), and landscape expansion index (LEI) were calculated to quantify urbanization. The land surface temperature for the study period was calculated using the mono-window algorithm. The UHI effect was analyzed using an integrated radius and non-linear regression approach, fitting SUHI data to polynomial curves and identifying turning points based on the regression derivative for UHI intensity belts to quantify the expansion and intensification of UHI. Landscape metrics such as the aggregation index (AI), landscape shape index (LSI), and four other matrices were calculated to assess UHI morphology and connectivity of the UHI. In addition, the LEI was adopted to measure the extent of UHI growth patterns. From 1990 to 2020, the study area experienced significant urbanization, with the built-up area increasing from 69.40 to 338.74 km2, an increase of 1.923 to 9.385% of the total area. This expansion included growth in peripheral areas of 129.33 km2, peripheral expansion of 85.40 km2, and infilling of 3.80 km2. At the same time, the UHI effect intensified with an increase in mean LST from 40.55 to 46.73 °C. The spatial extent of the UHI increased, as shown by the increase in areas with an LST above 50 °C from 36.58 km2 in 1990 to 133.52 km2 in 2020. The connectivity of the UHI also increased, as shown by the increase in the AI from 38.91 to 41.30 and the LSI from 56.72 to 93.64, reflecting a more irregular and fragmented urban landscape. In parallel to these urban changes, the area classified as UHI increased significantly, with the peripheral areas expanding from 23.99 km2 in the period 1990-2000 to 80.86 km2 in the period 2000-2020. Peripheral areas also grew significantly from 36.42 to 96.27 km2, contributing to an overall more pronounced and interconnected UHI effect by 2020. This study provides a comprehensive analysis of urban expansion and its thermal impacts. It highlights the need for integrated urban planning that includes strategies to mitigate the UHI effect, such as improving green infrastructure, optimizing land use, and improving urban design to counteract the negative effects of urbanization.

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.