Land use and land cover (LULC) transformations, due to urban growth and agro-forestry-pastoral activities, have caused changes in the surface energy balance. In this context, the variation of Land Surface Temperature (LST) in the Itapetininga municipality Itapetininga, São Paulo State, from 2003 to 2023, was analyzed in relation to the Normalized Difference Vegetation Index (NDVI) and Land Use/Land Cover, Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the MapBiomas Project were used. An increase of NDVI and a decline of LST were observed over the 21-year period analyzed. The regulatory effect of vegetation on LST was confirmed by the Mann–Kendall and Sen’s Slope trend tests, as well as by the Spearman and Mann–Kendall correlation analysis. A statistically significant trend was observed only for NDVI (τ = 0.1359; p < 0.001). The relationship between the variables showed a negative and significant correlation (ρ = –0.50; τ = –0.36). The Shapiro–Wilk normality test indicated that the data distribution does not meet parametric assumptions, justifying the use of nonparametric statistical methods. Furthermore, LULC changes between 2003 and 2023 revealed the expansion of agriculture (8.86%), reforestation (6%), and forest (0.69%), contributed, in part, to the decline in LST. Reforestation stood out as an important thermal mitigator, although its isolated influence still depends on specific analysis. It is concluded that the local thermal dynamics results from the interaction between climatic indicators and anthropogenic changes, reinforcing the importance of integrated monitoring using orbital and ground-based data to support territorial planning and climate adaptation strategies.

The study analyzes flood susceptibility in the Banat Hydrographic Area (Romania) using an integrated GIS framework based on MCDA–AHP multicriteria analysis and the multitemporal evaluation of static and dynamic factors for two scenarios (2005 and 2023). The results highlight differences between the two scenarios, mainly driven by variations in precipitation: although the moderate class remains dominant (~56% of the area), the share of high and very high susceptibility classes is lower in 2023 (~6%) compared to 2005 (~17%), accompanied by an expansion of the low susceptibility class (~26% to ~37%). Validation using flood extent data from April 2005 shows that approximately 99% of the affected area falls within the moderate, high, and very high susceptibility classes (χ2 = 9475, p < 0.001). The multitemporal analysis indicates high stability (75% of the territory), while 25.35% exhibits transitions toward lower susceptibility classes. Dynamic factors show differentiated roles: precipitation exerts a dominant regional control (95.44% of the area), while LULC changes contribute locally. The differences between scenarios should be interpreted as a model response to climatic variability rather than as structural changes in intrinsic susceptibility. The approach provides a reproducible framework for susceptibility assessment and supports spatial planning and risk management.

Abstract Thermal remote sensing is a valuable tool for assessing Surface Urban Heat Islands (SUHI). To quantify the SUHI intensity, the Urban Thermal Field Variance Index (UTFVI) is increasingly used as a proxy for urban heat distribution, e.g., by public authorities in Germany. The UTFVI is an ordinal-scaled metric that shows the spatial variability of LST in relation to the average LST of an area of interest. Numerous scientific studies utilize the UTFVI for detecting spatiotemporal increases in SUHI intensities attributed to Land Use/Land Cover (LULC) changes, such as rapid urbanization. However, UTFVI analyses often rely on only a few time steps over extended periods, ignoring the effects of weather patterns on actual UTFVI distributions. To address this research gap, this study investigates the influence of weather conditions of varying durations (up to 21 days) on seasonal UTFVI distributions in four Hessian municipalities (Germany) with less than 300,000 inhabitants and only minor LULC changes in the urban area over time. The analysis is based on more than 100 Landsat 4–9 Level‑2 datasets spanning a 40-year period. To reduce rural influences, only urban areas are considered. Results reveal high seasonal and intra-seasonal variability in UTFVI. Statistical tests (Friedman and Wilcoxon) show significant differences of UTFVI distributions even within one summer (2023). Spearman’s rank correlation coefficients indicate that spatial patterns of the UTFVI are influenced by the temperature intensity of preceding weather phases: short-term warming leads to an increase of the UTFVI categories indicating high LST levels, while they are less frequent during prolonged warmth. The presented study provides for the first time a comprehensive analysis of long-term UTFVI developments that focusses on factors altering the UTFVI which have not been investigated so far. These new insights support a better understanding and a more distinct interpretation of the UTFVI for potential users.

Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), is transforming the modeling of complex spatiotemporal urban processes such as urban growth, sprawl, shrinkage, redevelopment, and Land Use/Land Cover Change (LULCC). However, despite rapid methodological innovation, applications remain fragmented, and there is limited synthesis of how AI-based models complement, extend, or supersede conventional approaches. This study addresses this gap through a systematic review of 6356 records, from which 120 articles were selected for detailed analysis. It investigates: (i) how ML/DL techniques are embedded within spatiotemporal modeling frameworks; (ii) their use in simulating urbanization dynamics and land-use (LU) transitions; (iii) methodological and performance gains relative to traditional statistical and rule-based models; and (iv) emerging research frontiers and limitations. The review shows that LULCC dominates current applications, with Artificial Neural Networks (ANNs) as the most prevalent ML method, increasingly complemented by DL architectures. Across cases, AI is primarily used to learn non-linear transition dynamics, represent spatial and temporal dependencies, identify influential drivers, and improve classification performance and computational efficiency. Building on these insights, the paper synthesizes the roles of AI in spatiotemporal urban modeling and outlines forward-looking research directions to support more robust, transparent, and policy-relevant applications for urban sustainability.

Rapid urbanization has led to a significant increase in land use carbon emission (LCE), putting great pressure on ecological security. The coupling relationship between LCE and the ecological security index (ESI) is the key to sustainable development. Based on land use/cover change (LUCC) and Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) data, the LCE of the Jilin Border Cities (JLBCs) from 2013 to 2023 was estimated. Twenty-seven indicators were selected from both natural and socioeconomic aspects to evaluate the ESI using the Driving forces–Pressure–State–Impact–Response–Management (DPSIRM) model. The spatial interaction between LCE and ESI was analyzed using the coupling degree model and spatial autocorrelation. The results show that from 2013 to 2023, the main LCE areas in the JLBCs were concentrated in central urban districts, while the total LCE remained negative but exhibited a clear upward trend. The ESIs in Tonghua City and Baishan City have continued to improve, but those in Yanbian Autonomous Prefecture have gradually deteriorated, with ecological security warnings intensifying progressively toward the east. The spatial variation in the LCE–ESI coupling degree is significant, predominantly exhibiting low coupling with differences across scales. Within the study area, coupling degree shows a strong positive correlation, revealing distinct spatial clustering patterns dominated by low clusters and cold spots. Future efforts should focus on promoting low-carbon development models, strengthening protection and restoration, while implementing targeted measures to enhance the overall ecology of JLBCs.

The East and South Eastern Coastal Plain Zone (ESECPZ) of Odisha, situated along the eastern coast of India, present a mosaic of urban and rural regions. The present study aims to estimate the land use land cover change and extent of urbanization in ESECPZ over the span of two decades, from 2000 to 2020. Multi-year Landsat data underwent supervised maximum likelihood classification to estimate LULC change. The ESECPZ of Odisha underwent substantial transformations during the study period. Forest cover decreased from 33.95% to 18.98%, agricultural land decreased from 24.98% to 21.84%, as against the settlements which surged from 21.52% to 39.67%. The maximum, minimum and average Land Surface Temperature (LST) showed steady increase during the period of study. About 5°C increase in the minimum LST range and 3°C increase in maximum LST were recorded. The increase in LST was attributed to the expansion of settlement areas and reduction in the vegetation cover due to population growth and urbanization. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) showed gradual decreasing and increasing trends, respectively. Strong positive correlation was found between LST and NDBI while a strong negative correlation was found between LST and NDVI during all the years under study. This research provides valuable insights into the often-overlooked impacts of urbanization on (LST) and vegetation cover; the findings offer a positive outlook for the future, providing a foundation for forward-thinking initiatives aimed at addressing these environmental challenges.

Both groundwater and surface water are essential sources of freshwater worldwide. In urban and peri-urban areas of Sub-Saharan Africa, such as Dar es Salaam city (Tanzania), the scarcity of good quality surface water has led to a groundwater overexploitation in the coastal aquifer. This is due to the growing water demand driven by demographic and economic development of the city. The rapid and uncontrolled urban expansion, together with the increase of impermeable areas, have negative impacts on the aquifer recharge, water table decrease and runoff increase, causing higher risk of flooding, erosion and water shortage. This paper presents the separate assessments of land use land cover change (LULCC) and climate variability impacts on water budget in the study area, using remote sensing datasets, which have followed the evolution of Dar Es Salaam City during the period 2002-2022. The assessments show how rapid urbanization has increased runoff and reduced aquifer recharge, leading to flooding risks and groundwater degradation. In 20 years, within the hydrogeological basin, the aquifer lost 30% of the recharge water volume, on average. Climate variability has affected the specific annual recharge and runoff volumes, especially in the last decade, due to an important rainfall increase. This paper demonstrates that combining the analysis of LULCC and climate variability in catchment areas with geomorphological ones and hydrogeological water budget method can provide a thorough understanding of uncontrolled urban spread effects on water resources balance and support evidence based decision-making spatial planning and environmental management. The study is part of the WECOAdapt project (Water management through ECOhydrology for climate change ADAPTation) that focuses on the reduction of negative impacts on floods and droughts due to unsustainable urban development, aiming to reverse/reduce the degradation of water and land resources.

Accelerated urbanization in Ulaanbaatar, Mongolia, has driven substantial changes in Land Use and Land Cover (LULC), threatening sustainable urban ecosystems. This study investigates historical LULC dynamics (2000–2021) and simulates future expansion scenarios through 2050 using a hybrid Machine Learning (ML) and Cellular Automata-Artificial Neural Network (CA-ANN) approach. Multi-temporal classification was performed using Support Vector Machine (SVM) and Random Forest (RF) algorithms. Both classifiers demonstrated high and comparable accuracy; SVM achieved an average Kappa coefficient of 0.8939 while RF achieved 0.8917, a marginal difference that should be interpreted with caution. Change detection analysis revealed a continuous expansion of built-up areas at the expense of dense forest and grassland, a trend driven largely by accessibility factors. Future projections indicate that even as the rate of urbanization may slow, encroachment on green spaces will persist without policy intervention. This research presents a replicable methodological workflow for monitoring urban sprawl and provides evidence to inform sustainable land management and reforestation strategies in rapidly developing urban regions.

ABSTRACT This study aims to investigate the effects of Land use/cover change (LUCC) and spatial processes on carbon sequestration (CS) in the Karaj landscape (KL), Iran that has experienced rapid urbanization in recent decades. This research is structured around the central hypothesis that rapid urbanization drives carbon sequestration loss primarily through the spatial processes of landscape fragmentation and the attrition of carbon-rich land covers, and that these impacts are quantifiable both biophysically and economically. The study uses a combination of Markov chains, InVEST, and decision trees to evaluate two scenarios of LUCC and CS for the years 2006, 2023, and 2040. The results show that the KL has undergone significant changes in LUCC and CS from 2006 to 2023, and these changes are projected to continue until 2040. The main changes include the expansion of settlements and the decrease of rangeland, which have led to substantial reductions in CS in the landscape. The results also show that the spatial processes that have the most negative impact on CS are “creation” in settlements and “attrition” in cropland and rangeland, which result in the loss of carbon-rich land cover types. The study also estimates the economic losses associated with the reduction of CS from 2006 to 2040, which amount to millions of dollars. The study provides useful insights for urban planners and policymakers who are concerned with the effects of LUCC and spatial processes on CS and other ecosystem services in the KL.

Al-Refaie district in Dhi Qar Governorate has witnessed a remarkable development in land use over the years, as it has been affected by the various economic and social transformations in the region. The study used Landsat 5 TM and Landsat 8 OLI images and Geographic Information Systems (GIS) techniques to analyze changes in Land Use/Land Cover (LULC) and calculate the Normalized Difference Vegetation-Index (NDVI) and the Normalized Difference Water-Index (NDWI) indices for 1990, 2000, 2013, and 2024. Based on the principles of urban planning and sustainable city expansion, this data is examined and its function in planning and designing residential areas and communities in the Al-Refaie district is examined. According to the findings, Al-Refaie's urban areas grew dramatically between 1990 and 2024, growing from roughly 21 km² to 65 km². In contrast, changes in LULC and human activity had a detrimental impact on agricultural land and water resources, causing agricultural lands to decrease from 540 km² in 1990 to 343 km² in 2024 and water to shrink from 304 km² in 1990 to 73 km² in 2024. Water and agricultural regions have decreased in favor of urban areas due to ongoing urban expansion. Land usage in Al-Refaie has been greatly impacted by demographic shifts, as is visible in both urban and residential regions. Decision-makers can effectively examine future patterns of urban expansion in keeping with the anticipated population growth in Iraq in the upcoming years by using long-term urban planning, which is based on land use and cost analysis.

The intensity and frequency of fatal landslides in Western Ghats of India are adversely influenced by human-induced land-use modifications and climate change. Understanding these factors are vital for effective disaster risk reduction strategy for coming days. This study evaluates landslide susceptibility and risk for the year 2050 by incorporating land-use/land-cover (LULC) and future rainfall patterns. The study integrates a Random Forest (RF)-based landslide susceptibility model with an ensemble of Coupled Model Intercomparison Project Phase 6 (CMIP6) rainfall projections, future LULC simulation using a Multilayer Perceptron-Cellular Automata (MLP-CA) framework. It further incorporates an exposure-based Landslide Risk Index (LRI) assessment under multiple Shared Socioeconomic Pathway (SSP) scenarios. The findings show a significant increase in rainfall intensity and urban development by 2050, resulting in an increased risk of landslides. Under the SSP5-8.5 scenario, the very-high susceptibility class is projected to increase from 8.85% in 2024 to 13.25% by 2050, which is approximately 1980km². In same period and SSP scenario, very-high risk area is expected to increase from 1.18% to 2.73%, which is about 697km². The most affected areas lie along major transport corridors and densely populated areas of Uttara Kannada, Kodagu and Chikkamagaluru districts in Karnataka state. These results show the combined impact of LULC and climate change on landslides. The study provides crucial information for the development of climate-resilient land-use planning and disaster risk reduction strategies considering probable future scenarios in the Western Ghats region of India.

ABSTRACT Rapid urbanization in cities, has led to significant land use land cover (LULC) transformations with implications for environmental sustainability. This study examines LULC changes and spectral indices dynamics between 2016 and 2024 using Landsat imagery. Nine spectral indices were computed: NDVI, NDWI, MNDWI, NDBI, BSI, SAVI, MSAVI, LSWI, and LULC classification. Results revealed substantial built‐up area expansion of 425.3 km 2 (5.0% increase), primarily converting rangeland which decreased by 232.7 km 2 (2.7% reduction). Water bodies declined by 93.9 km 2 (1.1%), while cropland remained stable due to protection policies. Vegetation indices showed spatial redistribution with localized decreases in urban expansion zones and increases in restoration areas. NDBI increased significantly in developing regions, while BSI unexpectedly decreased, suggesting vegetation recovery despite urbanization. The study provides crucial insights for sustainable urban planning and environmental management in rapidly urbanizing cities.

This study focuses on the Qinjiang River Basin (QJRB) in Southern Guangxi, China, the core construction area of the Pinglu Canal within the Western Land-Sea New Corridor. Intensified human activities and frequent land use/land cover (LULC) changes here make it a suitable case area for investigating runoff and soil erosion processes under drastic land surface transitions. The traditional Soil and Water Assessment Tool (SWAT) model exhibits inherent simulation limitations in regions with frequent LULC changes, owing to its reliance on static LULC and oversimplified sediment transport mechanisms. To mitigate these drawbacks, a dynamic LULC-coupled SWAT model (DL-SWAT) was developed by integrating annually updated LULC data with an enhanced sediment transport module. Steep-slope croplands are identified as dominant soil erosion hotspots, with an average annual erosion intensity of 10.74 ton/ha/yr in upstream areas, 4.4 times the basin-wide average (2.45 ton/ha/yr). Forest expansion lowers surface runoff and soil detachment, while urbanization enhances surface runoff but restricts soil erosion due to impervious surfaces, revealing the joint regulatory effects of land use and terrain on regional hydrological and erosion processes. Integrating annual dynamic LULC updates at the HRU scale effectively regulates runoff-sediment coupling processes, reducing sediment overestimation by 56.9% during low-flow periods. The proposed dynamic coupling framework offers a physically consistent and accurate modeling strategy for hydrological and soil erosion simulations. • The current human activity impact on land surface dynamics is considered. • A dynamic LULC-SWAT (DLS) is proposed to enhance the simulation performance. • The DLS outperforms the SWAT, exhibiting the lowest level of uncertainty. • The Grain-for-Green policy and urbanization processes mitigate soil erosion. • Steep-slope agricultural lands are the main source of regional soil erosion.

Abstract Spatial and temporal variations in land use and land cover play a critical role in regulating land surface temperature and urban environmental conditions, with implications for ecological stability and human well-being. This study evaluates the spatiotemporal relationships among land use/land cover (LULC), LST, and vegetation dynamics in Baltimore, USA, over a 20-year period (2004–2024). Multi-temporal Landsat imagery was analyzed using GIS and remote sensing techniques to classify LULC into five categories—dense vegetation, developed land, herbaceous cover, barren land, and water bodies—and to derive LST alongside vegetation-related indices, including the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI). Results indicate notable LULC changes, with dense vegetation increasing from 681.61 km 2 in 2004 to 817.65 km 2 in 2024, while developed land decreased from 719.42 km 2 to 597.75 km 2 over the same period. Despite this apparent greening trend, LST exhibited an increase, with 2024 temperatures ranging from 61.51 °C to 91.7 °C, suggesting that global warming, urban heat retention, and reduced cooling effects may have contributed to the elevated surface temperatures. Correlation analysis revealed an inverse relationship between LST and NDVI, highlighting the cooling potential of vegetation through shading and evapotranspiration, whereas a positive relationship between LST and NDBI highlighted the warming contribution of built-up surfaces. The study underscores the importance of integrating vegetation restoration, urban design strategies, and continuous thermal monitoring into land management and planning frameworks. Such measures are essential for mitigating surface heat accumulation, reducing urban heat island effects, and enhancing environmental resilience in rapidly evolving urban landscapes.

At the transition between the Amazon and the semi-arid Northeast of Brazil, a large watershed drain into a macrotidal semi-enclosed coastal system (SECS) where freshwater inflows strongly regulate estuarine dynamics. The São Marcos Estuarine Complex (SMEC) receives runoff from three major basins (Mearim, Pindaré, and Grajaú), which differ in geomorphology and land-use evolution despite being exposed to similar regional hydroclimatic forcing. This study examines how seasonal and interannual hydroclimatic variability (1984–2022) propagates from basin-scale processes to SECS sensitivity. Daily precipitation data of rain gauge stations were homogenized and spatially interpolated, watershed and basins boundaries were derived from the TOPODATA digital elevation model, and land-use data from MapBiomas project. Hydroclimatic variability was assessed using Standardized Precipitation Index (SPI), Seasonal-Trend decomposition using Loess, flow duration curves, and trend analyses (Mann–Kendall and Sen’s slope). Cluster analysis was applied to identify spatially coherent hydroclimatic patterns among basins. Results indicate strong seasonal control of discharge but contrasting long-term trends among basins, reflecting differences in geomorphology and land-use land cover changes. Trends in extreme precipitation and runoff suggest shifts in the balance between high-flow pulses and prolonged low-flow conditions, with implications for freshwater delivery to the SMEC. Such alterations may influence estuarine dynamics in respect to water residence-time and potential pollutant exposure. By explicitly linking basin-scale hydroclimatic diagnostics to estuarine vulnerability in a macrotidal semi-enclosed system, this study advances an operational source-to-sea perspective and provides a framework that can be applied to similar systems worldwide, helping mitigate climate change impacts and protect coastal socio-economic activities. • El Niño Southern Oscillation events drives up to 30% river discharge shifts, with Pindaré River Basin showing highest variability • Since 2012, rising seasonality intensifies extremes, impacting runoff • Low flushing heightens pollutant retention, eutrophication, and seawater intrusion

Landscape ecological risk assessment and Multi-Scenario simulation of proluvial plains in Northwest China

India, the world's most populous country, has reported over 1 million dengue cases and ~3,000 deaths between 2007 and 2022. With the annual state-wise data, we examined the spatiotemporal distribution of dengue in 28 states and eight union territories for 16 years across India. Using state-wise data on climatic variables, socio-economic inequities and land-use land-cover changes, potential determinants for the state-wise transmission were identified through a supervised regression model. The identified determinants were then mapped to various novel developmental scenarios, which were designed based on the existing shared socio-economic pathways. To estimate the dengue burden for each scenario, ensemble models of XGBoost and Gradient Boosting regression algorithms were developed. We note that 73% of the cases occurred between 2016 and 2022, highlighting a significant increase in dengue outbreaks across the country. All Himalayan states, which witness colder temperatures, have witnessed a growth in cases: Himachal Pradesh reported 168 times more cases between 2016 and 2022 than those observed between 2007 and 2015. The models suggest that dengue incidences may potentially change under future socioeconomic burden, although projections are associated with substantial uncertainty and should be interpreted as potential trajectories rather than definitive forecasts. We estimate that development focused on sustainability (874.2 per 10 million; 95% CI: 535.4, 1212.9) and fossil fuels (888.02 per 10 million; 95% CI: 521.2, 1254.9) will relatively cause a lesser burden across the country by the 2030s. Southern states are projected to have higher dengue outbreaks, while Jharkhand, a historically malaria-endemic state, is estimated to report twice as many cases in 2050 as what was reported in 2022. Given the uncertainty associated with long-term projections, public health strategies may benefit from adaptive approaches which are backed by climate- and socioeconomic-data integrated early warning systems that can respond to evolving climatic and socioeconomic conditions influencing dengue transmission. Our study provides insights into how the spread of dengue will change with varying models of socio-economic development, which highlights the spatial heterogeneity in potential future dengue risk, suggesting that resource allocation and surveillance efforts may benefit from region-specific prioritisation instead of a uniform policy.

Land use land cover (LULC) changes have been occurring at a fast pace in places like Mymensingh Sadar in Bangladesh, where these changes are mainly due to urbanization and agricultural expansion. These changes are a major reason for the depletion of groundwater in such densely populated deltaic regions. The remote sensing (Landsat 8-9 OLI/TIRS) and GIS tools were used in this study to look into the LULC changes of the area (2017-2023) by performing semi-supervised hybrid classification, and to determine the groundwater trends by applying inverse distance weighting interpolation. The methodology was confirmed with NDVI-derived vegetation health indices and accuracy assessments (overall accuracy: 82.35%) that were quite robust. The analysis showed that urban areas grew by 96.6%, which was related to population increase (72%) and water demand increase (66%). At the same time, the expanse of farmland was reduced by 16.65%, which in turn resulted in water use for irrigation being reduced by 24.16%. Groundwater depletion was aggravated such that the water table went down from 5.5 to 12.19 m in 2017 and 10.8 to 15.13 m in 2023, which is a 40-60% decline in aquifer levels. Though weak, there was a negative correlation between NDVI and groundwater depth (R² = 0.28 in 2023), indicating that vegetation was stressed and there was less recharge. This need is urgent because it requires an integrated water management plan to regulate urbanization and agricultural production concerning ecosystem integrity. Adopt the recommendations of enhancing water-saving practices, controlling groundwater extraction, and green infrastructures. Such research can provide the necessary basis on which policymakers can operate to mitigate the depletion of the aquifer in the rapidly developing hydrogeological environment.

Background: The land use and land cover change (LULCC) reduces carbon storage by degrading vegetation and soils, worsening climate change. In Awka, rapid urbanization is turning carbon sinks into sources, highlighting the need for predictive modelling. Aims: The purpose of the study was to use mathematical models to estimate the future effect of the LULCC on carbon sequestration in the Awka Capital Territory (ACT), Nigeria. Study Design: It would be a quantitative research design that combines remote sensing and Laboratory soil analysis. Place and Duration of Study: Six communities in the Awka Capital Territory, Anambra State, Nigeria, in a 30-year study (1993-2023). Methodology: Walkley-Black took place whereby soil organic carbon (SOC) was established in depths of 0-60 cm in the four-land use cover: forest, shrubs, farmlands, and built-up land. To develop the relationship between area changes and carbon stock, Multiple Linear Regression (MLR) were carried out along with Pearson correlation analysis to predictive model. Results: Statistical confirmation rejected the null hypothesis, H 0 2, which provided significant correlations LULCC and carbon stock (0.62 to 0.99). The carbon stock became a near-perfect negative forecast of urbanization (-0.93 to -0.99). Predictive models of the individual communities indicate that the growth of urbanization causes a linear decline in the carbon sinks in the region. Conclusion: The acquired regression models are also a critical instrument that will enable the local policymakers to forecast the environmental effects of urban planning choices and emphasize the necessity of adopting adaptive land management to alleviate climate change in the ACT.

Abstract: The various human activities and process of rapid urbanisation has altered the landscape of an area or a region. It means that human beings have become the dominant agent for changing the landscape. Increased land cover/land use changes (LCLUC) can impact agricultural production efficiency including environmental impacts on urban, sub-urban, rural communities and natural areas. Human settlement / built-up increases the amount of impervious surfaces as a result of the building of roads and houses. This effect becomes an issue in the foothill, woodland. A detailed analysis of forest land covering and built-up area was conducted. This analysis provides insights into landscape-scale changes that have occurred as a result of human settlement. The significance of these changes for fire hazard, forest hydrology, and wildlife habitat are discussed. Forest is one of the key precious resources that support human well-being by providing ecosystem services. Unfortunately, the forest cover has decreased over time due to natural and anthropogenic factors. Forest cover has been declined in the study area for a variety of reasons, including fire wood collection, charcoal and timber extraction, semi-forest and settlements. Loss of forest cover can have significant implications for environmental sustainability, as forests have played an important role in ecosystem services, such as climate regulation, clean air, flood control, carbon sequestration, soil protection against soil erosion, and increased environmental resilience to the impacts of climate change. The present study aims to assess the spatiotemporal forest cover changes and built-up coverage and its implication on environmental sustainability. Low rates of economic growth indicate low adaptive capacities and therefore, high vulnerability to climate change and human induced pressures on ecosystems (Shukla et al., 2008; Lobell et al., 2008). LCLUC in the region is disrupting and perturbing biodiversity, regional climate, biogeochemical cycles, water resources and other ecosystem services (Turner and Annamalai, 2012; Madson, 2013). Understanding LCLUC requires addressing spatial scale issues, technological innovations, policy and institutional changes (IGBP, 2001).