• Industrialization in Ennore is unauthorized, unsustainable, infringed and unjust • Industrialization and urbanization have decreased the area of wetlands by 89.34% • Subnational government had manipulated 1996-CZMP Map to favour political elites • Manipulation had served political elites’ interests, to favor that of global elites • Peri-urban environmental injustice is caused by regulatory capture by the elites Industrialization, urbanization and population growth are the major drivers behind abominable ‘Land-Use Land-Cover Change (LULCC)’, and the loss of local ecosystem services and environmental quality, at peri-urban interfaces. Such dynamics indicate the need to analyse the LULCC pattern, and explore the political drivers behind unsustainable LULCC. This paper, taking ‘Ennore Peri-Urban Region’ as the study area, has adopted a ‘Geospatial Mixed-Methods Case-Study Approach’ that synergises ‘Quantitative LULCC Analysis’ and ‘Qualitative Political Discourse Analysis’. The quantitative LULCC analysis was performed by utilizing ‘Supervised Image Classification’ and ‘Change Detection Analysis’. Quantitative results have revealed that total area of wetland, waterbody and cropland/shrubland has decreased by 89.34%, 14.43% and 10.61% respectively, in the period 1988-2023. Especially, cropland/shrubland has been severely affected in the core industrial region. Such unsustainable LULCC has occurred due to an intensive peri-urban industrialization, and a gradual peri-urbanization. The area under settlement and dense-vegetation have increased by 507.84% and 3.42%, respectively. Qualitative-political discourse analysis has revealed that such an unsustainable peri-urban LULCC has occurred due to the five-year delay in preparing the ‘Coastal Zone Management Plan (CZMP)’ and its map, and the unauthorized manipulation of 1996-CZMP Map by the subnational ‘Government of Tamil Nadu’, without the approval of the national ‘Government of India’. Such delay and manipulation had initially favoured the vested interests of political elites, and eventually that of global urban business elites, through regulatory capture by the latter. These indicate an inefficient, unfair, unequitable, unjust, incoherent and non-transparent intergovernmental environmental governance, and a weak public participation in decision-making.

Land-use change and surface warming in Uganda’s oil-rich Albertine region (1995–2025): A geodetector analysis

Extractive activities drive land transformation in many mineralised river basins. However, linking these changes to observable and attributable water-quality outcomes remains methodologically challenging. This study applies an integrated monitoring framework to examine how multi-decadal Land-use and land-cover change (LULC) translates into spatially differentiated river water quality in the Ankobra River basin, Ghana. Using harmonised Landsat and Sentinel imagery, LULC dynamics was reconstructed for 1986, 2002, 2016, and 2025. Field-based measurements of key physico-chemical water-quality parameters were collected to support the analysis. Spatial interpolation using Ordinary Kriging and redundancy analysis was then applied to assess the extent to which land-use composition explains the observed variation in water quality. The results showed a shift from forest-dominated land cover towards agriculture, settlement, and mining-related disturbance during the study period. Bareland/Mining expanded from less than 1% of the basin in 1986 to approximately 3.7% by 2025 (>100 km2), while combined forest cover declined overall throughout the study period. Water-quality patterns exhibited strong spatial gradients, with turbidity ranging from approximately 114 to more than 1000 NTU and total suspended solids (TSS) from around 100 to nearly 3000 mg L-1. Redundancy analysis indicated that land-use composition explained approximately 47.5% of the variance in water quality, with the mining-related land cover exerting the strongest influence (F=13.66, p<0.001) and showing robust positive associations with turbidity and TSS. Closed forest cover displayed a significant buffering effect, while agricultural land use did not show significant association on the spatial scale examined. These findings demonstrate how integrated Earth observation and field data can move sustainability assessment beyond descriptive convergence towards diagnostic clarity. The analytical framework offers a transparent and scalable approach for prioritising regulatory attention and monitoring in extractive landscapes where environmental pressures are spatially uneven and governance capacity is constrained.

Integrating conservation zoning, land-use and land-cover change, and habitat integrity to support protected area management: Evidence from Golestan National Park, Iran

Use of Surface Water and Ocean Topography (SWOT) observations to support Land Use/Land Cover (LULC) change products: the case of the pacific coast of Ecuador

Intensive anthropogenic activities in high-groundwater coal basins (HGCBs) have dramatically altered land use/land cover (LULC) patterns, exerting profound impacts on regional ecosystem carbon storage. In this study, conducted in the Yanzhou Coalfield, China, we developed a refined water body classification scheme that divides water bodies into natural, artificial, seasonal, and perennial types to better capture the ecological heterogeneity of water bodies within mining-induced subsidence areas, which is often overlooked in conventional assessments. By coupling the PLUS and InVEST models, we quantified carbon storage dynamics driven by LULC changes from 2005 to 2020 and projected carbon storage levels for 2035 under four scenarios: historical trend (HT), food security (FS), ecological restoration (ER), and coordinated development (CD). The primary findings of this study are as follows: (1) Significant differences in carbon density existed among water body types, in the order natural water bodies > seasonal water bodies > perennial water bodies > artificial water bodies. Neglecting these differences can introduce systematic biases in carbon storage assessment. (2) Cropland area decreased substantially from 2005 to 2020, while built-up land and water bodies expanded continuously. (3) Intensive LULC transitions resulted in a 12.77% decline in total carbon storage (equivalent to 2.38 × 105Mg), primarily driven by the conversion of high-carbon-density cropland to built-up land and subsidence water bodies due to coal mining activities. (4) Projections for 2035 indicate that total carbon storage would decline by an additional 11.67% under the HT scenario. In contrast, the FS, ER, and CD scenarios effectively mitigated losses, with declines of 6.15%, 5.05%, and 3.13%, respectively. The CD scenario achieved the best performance through synergistic optimization of carbon sequestration, food security, and ecological restoration objectives. These findings provide critical insights for integrating carbon sequestration objectives into land management practices in coal mining areas, thereby supporting land use optimization and climate change mitigation efforts.

The economic value of ecosystem services is precious to the community and changes with land use/land cover (LULC). The main aim of this study is to examine LULC changes in the Sahiwal area between 1994 and 2024 using a Landsat time series, and to determine ecosystem service values (ESV) and the sustainability of water and vegetation resources. The innovation lies in incorporating Random Forests(RF) into Google Earth Engine (GEE) to systematically measure the dynamics of multi-decadal LULC and quantify their implications for ecosystem services and resource sustainability. Our outcomes indicated that forest and vegetation areas declined by 0.229% and 3.9%, respectively, from 1994 to 2024 in the study area. The built-up area increased by 7.83% to 16.53% from 1994 to 2024. The overall accuracy (OA) values were 96.7%, 94.2%, 91.9%, and 87.7% for 1994, 2004, 2014, and 2024, respectively. Similarly, overall kappa (OK) values were 94%, 88%, 86%, and 82% for 1994, 2004, 2014, and 2024, respectively. Results showed that vegetation ESV rose from 8669.69 to 8934.13million USD/year from 1994 to 2004, but declined to 8726.207million USD/year in 2024, while water bodies showed variability, rising from 210 to 231.5million USD/year from 2004 to 2024. The findings of this study call for improved land and water management planning, ecosystem restoration, and policy interventions to promote nature-based solutions for more sustainable water in semi-arid regions.

Remotely sensed evapotranspiration-based ensemble streamflow modeling in an ungauged watershed under climate and land use/cover change, North Korea.

Changes in land use and land cover (LULC) have a substantial impact on environmental sustainability, urban planning, and agricultural management, necessitating accurate classification using high-resolution satellite imagery. Conventional methods struggle with challenges, such as noise, edge preservation, and misclassification, due to the complexity of multispectral and hyperspectral data. To overcome these issues, this paper presents a Finite-Element Diffusion Banyan Tree Growth Kernel-Driven Attention Network (FED-BTG-KDAN) for LULC mapping. High-resolution satellite images from Sentinel-2, Google Earth Engine (GEE), and Landsat-8 are very useful for multispectral and hyperspectral image classification. Image pre-processing is done by Robust Double-Weighted Guided Image Filtering (RDWGIF) to improve the image quality and spatial resolution. Feature extraction is done by a modified ResNet-152 with Multi-Axis Vision Transformer (MAViT) to extract spatial, spectral, and contextual information. The classification process uses a Finite-Element-Integrated Neural Network (FEINN) with a Diffusion Kernel Attention Network (DKAN) to reduce misclassification and enhance spatial representation. Optimisation is done by Banyan Tree Growth Optimisation (BTGO) to ensure fast convergence and minimise classification errors. The proposed method achieves 99.9% accuracy, demonstrating superior performance in precision, robustness, and computational efficiency compared to conventional approaches. Advantages include enhanced feature extraction through multi-scale attention and optimised classification using bio-inspired learning.

ABSTRACT This study examines the changes in land-use land-cover (LULC) across India over seven years, with a particular focus on the impacts of the COVID-19 lockdown in 2020. Using high-resolution Sentinel-2 10-m data, a short-term analysis of LULC dynamics from 2017 to 2023 was conducted. The unprecedented lockdown measures in 2020 provided a unique opportunity to observe the environmental effects of reduced human activity. Key findings include significant reductions in urban expansion and industrial activity during the lockdown, leading to temporary increases in water areas, green cover, flooded vegetation, and improved air quality in many regions. Agricultural areas such as crops and rangeland experienced varying impacts, with some shifts in crop patterns due to labour shortages and supply chain disruptions. The study highlights the resilience of natural systems in the face of reduced anthropogenic pressure and underscores the importance of continuous monitoring for sustainable land management. Our analysis leverages advanced remote sensing techniques to provide detailed spatial and temporal insights into LULC changes, contributing valuable data for policymakers and environmental planners aiming to balance development and conservation in post-pandemic recovery efforts.

Spatio-temporal dynamics and drivers of carbon storage in arid ecosystems: Integrated analysis using InVEST and PLUS models with machine learning.

Impacts of LULC changes on hydrometeorological variables in forested, deforested, and transitional regions of Southeastern Australia

Accurate detection of spatiotemporal changes in land use and land cover (LULC) is crucial for urban planning and smart city development, but it faces challenges due to urban environments and satellite imagery complexities. To address these, a novel “Spectral Fusion Autoencoder with Capsule and Convolutional Long Short-Term Memory Network” is proposed for enhanced land cover classification. Additionally, sub-pixel change detection faces challenges due to spectral mixing, where small urban features share similar spectral properties with the surrounding environments, complicating accurate differentiation. To address this, a “Spectral Unmixing Residual Network with Variational Autoencoder” is proposed, separating mixed signals, capturing spatial features, and modeling nonlinear relationships for enhanced sub-pixel detection. Furthermore, geometric distortions in multi-temporal or multi-source imagery, especially in steep terrains, disrupt spatial alignment. Thus, a novel “Dynamic Attention-Driven Capsule Policy Optimization Network” addresses these issues by refining geometric corrections and enhancing spatial consistency through reinforcement learning. Moreover, gradual spectral transitions and intra-class variability complicate inter-class land cover changes. So, a novel, “Warped Spectral Fusion Network with ConvLongShort Net” integrates spectral, spatial, and temporal data to correct illumination and viewing angle variations, capturing subtle transitions effectively. The experimental results demonstrate the proposed model’s efficiency in accurately detecting the spatiotemporal changes with an accuracy of 0.99, loss of 0.003, fractal error of 0.32, and misclassification rate of 0.06.

Assessment and Prediction of LULC and LST Changes Using Remote Sensing and CA-ANN Algorithm: a Study from Porto Alegre, Brazil

Land use land cover (LULC) change is a major factor driving changes in environmental sustainability, including alterations in the hydrological cycle, declining biodiversity, and impacts on agricultural systems. Various studies have been conducted to assess LULC change in Kentucky watersheds. However, there is limited research that comprehensively evaluates the change at a Basin level. Our study examined the spatiotemporal pattern of LULC from 2002 to 2022, and we explored one of the major drivers influencing LULC in the Kentucky River Basin. This study utilized Google Earth Engine and a random forest classifier to map the LULC using Landsat 5 and Landsat 8 imagery. The accuracy of the maps classified was verified using high-resolution NAIP imagery. The overall accuracy was greater than 90%, and the Kappa was above 80%. The result shows substantial land transition in the basin, specifically, a decline in agricultural land (55.74%) and an increase in developed land (114.16%) relative to their 2002 baseline. The forest cover has a net gain of (1.81%), and barren land has a net loss of (-24.15%), indicating a major ecological restoration from 2012 to 2022, attributed to the Surface Mining Control and Reclamation Act (SMCRA). The water body has a net loss (53.05%), raising concern about the hydrological process of the basin. The regression analysis shows that population change was significant in some periods but had a weak influence on agricultural land loss. These findings indicate that a land-use change is underway in the Basin, necessitating an urgent approach that integrates sustainable land-use policy and drought-responsive management with the Basin's ecological processes.

This study presents the development and implementation of a web-based geospatial platform for the quantitative assessment of land use and land cover change (LULCC) based on multispectral satellite images. The system operationalizes the Rao spectral diversity metric (Rao’s Q) to detect and quantify LULCC resulting from different environmental agents. The platform supports single-band (classic mode) or multi-band (multidimensional mode) processing. Its main functionalities include the interactive de-limitation of areas of interest (AOI) and calendar-based temporal selection, allowing analyses to be performed at discrete time points or at defined intervals. Among the tools available in the application are the automated calculation of Rao’s Q surfaces and maps of change between pairs of dates. Additionally, the platform allows the selection of several spectral indices, with the aim of supporting ecosystem monitoring and the characterization of the Earth’s surface. In the use case demonstration (Reykjanes Peninsula volcanic eruption of February 2024), the Rao’s Q method applied to Sentinel-2 SWIR imagery demonstrated strong performance in lava flow detection, with the multidimensional approach (bands 11 + 12) achieving the most balanced results (OA = 83.0%, PA = 84.0%, UA = 82.4%), while band 11 alone yielded the highest precision (UA = 97.4%). By integrating spatiotemporal analysis, spectral diversity metrics, and spectral indices into an accessible and extensible framework, the platform constitutes a robust tool for monitoring LULCC and assessing environmental impacts.

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 &lt; 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 &lt; 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.

Introduction Land use and land cover (LULC) changes, and consumptive water uses are widely recognized as key drivers of alterations in basin hydrology, potentially reducing ecosystem services and threatening water security. However, the magnitude of the impacts varies strongly among basins, and many times are hidden by climate variability. In this context, this study aims to analyze the impacts that LULC changes combined with water consumption have on water availability of the Paraopeba River Basin, a strategic water supply basin of Brazil, through a scenario-based hydrological modeling framework that enables the explicit attribution of hydrological changes to anthropogenic drivers. Methods Differences in water yield were calculated through hydrological modeling considering two different scenarios: The current scenario (CS), based on LULC changes over the period 1985–2018 and averaged water consumption over the period 1985–2018; and the hypothetical scenario S1, which assumes no changes in land uses and human consumption since 1985. Results and discussion Results indicate that the S1 scenario presents higher minimum streamflows, with increases of up to 33% compared to the CS scenario. The difference in the flow-duration-curves signatures indicates that the streamflow regime has been modified because of the increase in urban and silviculture areas and human water consumption. In general, larger native vegetation areas are associated with higher evapotranspiration and canopy interception losses. Given the intense and increasing water use in the basin, current trends are likely to intensify water conflicts, threaten water security for a large population, and generate downstream impacts, including on basins that supply water to Brazil’s semiarid regions.

Carbon storage (CS) and its cycling processes in terrestrial ecosystems constitute a fundamental component of the global carbon cycle. Ecologically fragile regions, characterized by low ecosystem stability and high sensitivity to external disturbances, play a critical role in understanding regional and global carbon dynamics. Land Use/Land Cover Change (LUCC), as a major manifestation of human activities, is widely recognized as a key driver influencing CS and its spatiotemporal variations. However, for a typical ecologically fragile area such as Zhangye City in Gansu Province, there remains a lack of multi-scenario quantitative assessments regarding the impacts of LUCC on CS. In this study, the InVEST model and the PLUS model were integrated to analyze the evolution of land use patterns and CS in Zhangye City from 1990 to 2020. Furthermore, land use configurations and corresponding CS distributions under four ecological protection scenarios for 2030 were simulated. The results indicate that: (1) from 1990 to 2020, grassland and unused land decreased by 0.69% and 1.35%, respectively, while cultivated land expanded by 1.82% (approximately 698.86 km2), reflecting a notable shift in land use structure; (2) LUCC contributed to a net increase of 0.49×107 t in total CS, exhibiting a spatial pattern of gradual increase from north to south; (3) by 2030, compared with the natural development scenario, CS under low-, medium-, and high-level ecological protection scenarios increased to 38.05×107 t, 38.06×107 t (38.05546×107 t), and 38.06×107 t (38.05544×107 t), respectively, with the medium-level ecological protection scenario yielding the most optimal land use configuration. By coupling the InVEST and PLUS models, this study effectively characterizes the impacts of land use change on CS, and provides methodological support for land use optimization and CS assessment in ecologically fragile regions. Based on these findings, it is recommended to strengthen the protection of ecological land, appropriately regulate the expansion of built-up areas, and promote a coordinated balance between economic development and ecological conservation, thereby contributing to the achievement of carbon peaking and carbon neutrality goals.