Search Results (5,775)

With the acceleration of global urbanization and intensified agricultural activities, approximately 61% of the world’s wetlands have degraded over recent decades, significantly weakening their carbon sequestration capacity. The Shibalianwei Wetland, a crucial tributary system of Lake Chaohu in China, has suffered severe degradation due to land use and cover change, nutrient loading and hydrological disruption. In response, large-scale ecological restoration has been implemented since 2018. To quantify the restoration outcomes, this study integrated remote sensing, GIS, and machine learning techniques, employing the XGBoost model to evaluate and predict carbon sequestration in 2017 and 2024 based on 2010 carbon data. The results reveal that the average carbon density increased from 48.70 t ha⁻¹ in 2017 to 90.18 t ha⁻¹ in 2024, representing an overall increase of 85.2% in total carbon storage. This substantial enhancement is primarily attributed to land use transitions and ecosystem-scale restoration effects, including vegetation recovery and hydrological rehabilitation. Model validation indicated moderate prediction errors (RMSE = 0.47–0.74), with consistent performance across repeated iterations. Together with complementary MAE and R² metrics, the results suggest that the XGBoost model is capable of capturing relative spatial patterns and restoration-induced changes in wetland carbon sequestration, while retaining reasonable predictive stability under changing landscape conditions. Overall, the findings demonstrate that large-scale wetland restoration can rapidly and effectively enhance regional carbon sink capacity and highlight the potential of data-driven modeling frameworks to support wetland management and carbon-neutrality strategies. This provides important guidance for policymakers to promote sustainable land use and optimize ecosystem management under China’s dual-carbon development goals. Full article

Accurate monitoring of oil palm plantations is important for balancing economic development with environmental conservation in Southeast Asia. However, existing plantation maps often suffer from low spatial resolution and a lack of recent temporal coverage, impeding effective surveillance of rapid land-use changes. In this study, we propose a deep learning framework to generate 10 m resolution oil palm plantation maps for Indonesia and Malaysia from 2020 to 2024, utilizing Sentinel-2 imagery without requiring new manual annotations. To address the resolution mismatch between coarse 100 m historical labels and 10 m imagery, we employ a U-Net architecture optimized with Determinant-based Mutual Information (DMI). This approach effectively mitigates the influence of label noise. We validated our method against 2058 manually verified points, achieving overall accuracies of 70.64%, 63.53%, and 60.06% for the years 2020, 2022, and 2024, respectively. The gradual decline in accuracy with time is consistent with a growing temporal mismatch between the 2016 historical reference labels and the later prediction years. At the regional scale, the mapped oil palm area suggests a peak in 2022 followed by a lower mapped extent in 2024. Land cover transition analysis further indicates exchanges with cropland and flooded vegetation, which should be interpreted together with the reported accuracy and uncertainty. Given the moderate per-year accuracies and the temporal mismatch between the 2016 supervision and the later prediction years, these results should be interpreted as regional-scale indicators rather than pixel-level change maps. The generated maps can support regional monitoring, sustainability assessment, and prioritization of areas for further validation. Full article

Long-term forest landscape change in mountainous rural areas reflects interactions among land-use pressure, forest structure and human activities. This study had two primary objectives: (i) to quantify 53 years of change in forest extent, stand structure and landscape configuration in the Kümbet Planning Unit in the Eastern Black Sea Region of Türkiye and (ii) to evaluate associations of annual registered forest crime counts with election-year status and broader-scale population and real per capita income indicators while treating the crime records as contextual indicators of recorded pressure rather than as causal measures of landscape change. Forest stand-type maps from 1971, 2013 and 2024 were analyzed using LULC transition matrices and landscape metrics. Annual registered forest crime records for 1981–2025 were examined in relation to election-year status, the provincial population and real per capita income using correlation analysis and negative binomial regression, with linearly interpolated annual forest area included as an exposure offset. Total forest area declined from 6747.8 to 6298.1 ha, whereas agricultural land increased from 637.3 to 2177.7 ha. Degraded forest and open areas decreased, and 44.3% of the study area changed land-cover type. Landscape fragmentation increased between 1971 and 2013, followed by partial spatial consolidation between 2013 and 2024. Election-year status and real per capita income were not significantly associated with annual registered forest crime counts. Within the 1981–2025 study period, the provincial population showed a positive temporal association with registered forest crime counts. Each increase of 10,000 persons corresponded to an approximately 7.5% increase in the expected registered forest crime rate, but this relationship should not be interpreted as a direct local demographic effect. Registered forest crime density did not differ significantly among management-related periods. The temporal correspondence between higher crime density and stronger fragmentation was descriptive and did not establish causality. These findings demonstrate that forest conservation and landscape-restoration planning should integrate forest extent, stand structure, land-cover transitions, landscape configuration and appropriately scaled administrative indicators of recorded forest-related pressure. Full article

Flooding is one of the most frequent and damaging natural disasters, accounting for nearly half of global disasters and posing a major challenge in Indonesia, where floods represent approximately 77% of all nationally recorded disaster events. Rapid urbanisation, land-use change, and climate-induced extreme rainfall have intensified flood risks nationwide. However, existing vulnerability assessments remain fragmented and localised, limiting their relevance for national-scale adaptation planning. This study develops a measurable and explainable framework for assessing urban flood vulnerability across Indonesia using cloud-based geospatial data and interpretable machine learning. The approach integrates CEMS-GLOFAS (flood hazard), WorldPop (population exposure), SRTM (topography), and ESA WorldCover (land cover) datasets within Google Earth Engine (GEE). Flood vulnerability is quantified through a modified Flood Vulnerability Index (FVI) combining hazard, exposure, and physical vulnerability components. The Extreme Gradient Boosting (XGBoost) model predicts FVI values, while SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs) enhance model transparency and identify the influence of key variables such as flood depth, population density, and elevation. The model achieved high predictive accuracy (R² = 0.89; RMSE = 0.04728 FVI units, dimensionless) and revealed substantial spatial heterogeneity across 514 districts, with the highest FVI (0.75–0.85) in Banda Aceh, Mojokerto, Pasuruan, Samarinda, and Merauke. The integration of GEE and explainable AI offers a transparent, scalable framework to support data-driven flood risk mitigation and urban climate resilience in Indonesia. Full article

The Cascade Reservoir System (CRS) in the upper Yellow River delivers integrated benefits (flood control, water supply, hydro-power generation, and ecological regulation), but it also alters the natural runoff regime and exerts non-negligible impacts on the regional eco-environment. However, the long-term trajectory of reservoir-cascade effects on land use/land cover (LULC) in alpine basins has not yet been systematically quantified. Here, we focused on the Hualong-Xunhua reach and delineated two impact domains—the Reservoir Influence Zone (RIZ, enclosed by the first-order mountain ridge lines closest to the river channel representing direct hydrological impacts) and the Local Microclimate Influence Zone (LMIZ, spanning from the first-order ridges to the outer watershed boundary representing indirect climatic impacts)—to investigate the spatiotemporal dynamics of LULC associated with reservoir development. Results show that, from 1985 to 2023 in the CRS area, cropland and shrub-land decreased by 89.56 km² (−16.09%) and 9.41% (−9.41%), respectively, whereas forest and grassland increased by 79.92 km² (+14.36%) and 7.74% (+7.74%). Within the RIZ, cropland declined by 29.49 km² (−20.14%), while water bodies increased markedly by 32.19 km² (+22%); forest cover also expanded by 9.09 km² (+6.21%). In the LMIZ, forest and grassland exhibited pronounced increases of 70.83 km² (+17.27%) and 39.37 km² (+9.60%), respectively. Correlation analysis indicates that GDP and air temperature are strongly and positively correlated with forest, water bodies, and impervious surfaces (Pearson’s r > 0.9), whereas cropland shows significant negative correlations with GDP, forest, and grassland (Pearson’s r < −0.8). Overall, the distinct spatiotemporal contrasts between the RIZ and LMIZ, coupled with the temporal alignment of cropland-to-forest transitions post-2000, suggest that reservoir-cascade construction and the Grain-for-Green Project are associated with these major LULC transitions, serving as contributing factors, while temperature rise and GDP growth represented the background environmental and socioeconomic context. These findings provide data support and a conceptual basis for long-term monitoring and assessment of eco-environmental responses to reservoir cascade development, and offer scientific evidence particularly relevant to reservoir planning and management in high-altitude cold regions. Full article

High-resolution land cover classification is critical for monitoring environmental change and managing natural resources. This study presents an unsupervised framework with externally guided feature prioritization that integrates Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical imagery at 10 m spatial resolution. A cloud-native export protocol in Google Earth Engine (GEE) enables the generation of consistent, cloud-free, and snow-free seasonal composites across Ontario, Canada. A comprehensive feature engineering pipeline combines spectral indices, radar backscatter metrics, terrain derivatives from digital elevation models (DEMs), and temporal statistics to create a rich multi-sensor input space. Dimensionality reduction is performed using Sparse Principal Component Analysis (SparsePCA) and mutual-information-based feature selection. Clustering is conducted using three complementary algorithms: centroid-based K-means, density-based Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), and reachability-based Ordering Points To Identify the Clustering Structure (OPTICS). Final land cover labels are assigned via a majority-voting ensemble, with prediction ties resolved deterministically using OPTICS. OPTICS is particularly effective for modeling heterogeneous landscapes due to its ability to detect clusters of varying density without requiring a global threshold. This study is designed as a pilot-site methodological demonstration using three representative 2 km × 2 km regions in Ontario, rather than a full provincial-scale land cover product. The resulting classification maps are validated against reference land cover data, demonstrating the effectiveness and potential scalability of the proposed external-label guided unsupervised mapping approach. Full article

Climate change is increasingly altering forest ecosystems worldwide, reshaping species phenology, productivity, and resilience. In this study, we evaluate the phenoclimatic responses of European beech (Fagus sylvatica L.) forests across Europe by assessing their phenological responses to climate change across climatic zones and altitudinal gradients using remote-sensing data. We used 24 years of satellite-derived land-surface phenology and climate data to quantify phenological trends at 356 beech-dominant locations from the EUFGIS database, of which 274 remained after land-cover homogeneity and data-quality filtering. To reduce land-cover mixing at the MODIS resolution, we applied a land-cover homogeneity filter based on ESA WorldCover. The analysis was structured across the seven climatic zones in Europe. Phenological responses to climate change were assessed through climate–phenology sensitivity analyses and a composite phenoclimatic departure index integrating climatic trends, phenological shifts, and interannual variability. Phenological sensitivity varied across climatic zones and phenological phases. Temperature-related sensitivity was most evident in spring in several continental zones, whereas precipitation sensitivity was more apparent for growing-season length and autumn timing in some regions. The composite phenoclimatic departure analysis showed that regional profiles were not uniform across the European beech range. Although warming was widespread, precipitation trends, phenological shifts, and interannual variability differed strongly among zones. These findings demonstrate heterogeneous and location-specific phenoclimatic responses across Europe, but the departure index should not be interpreted as a direct measure of ecological vulnerability or risk. Full article

The current trend towards decarbonization has increased the pressure towards land use change through the installation of solar parks on agricultural fields. The usefulness of RothC to model the evolution of soil carbon after the installation of the solar park has been validated in a field with historic data. The model has been applied to evaluate the impact of a large-scale modification of land use in Valencia (Spain), a mediterranean region with an ambitious plan for the installation of renewable energy. The removal of the orange trees for the installation of a solar park would generate a carbon release in CO₂ eq to 72 Mg ha⁻¹. If the soil is left vacant of vegetation, another 28 Mg ha⁻¹ would be emitted in 30 years. By contrast, if the soil is covered by scrubland, an overall CO₂ capture of −226 Mg ha⁻¹ could be achieved, including the impact of the initial plant removal. If we consider the Valencia region, the installation of 12.000 hectares of solar parks could generate up to 1.2 × 10⁶ Mg of CO₂ emissions or capture 2.7 × 10⁶ Mg of CO₂. Also, a sensitivity analysis to evaluate the effect of the main labels has been performed, revealing that the original carbon content is the most relevant label, followed by plant input and the % of soil covered by the solar panels. The limited availability in experimental data means that this study should be considered an exploratory evaluation of the impact of including plantations in solar parks. Full article

Forest cover change alters land–atmosphere exchanges of energy, water, and carbon, thereby influencing local and regional climate. This study assessed climatic patterns associated with afforestation in Khyber Pakhtunkhwa, Pakistan, from 2003 to 2023 using remote sensing data and Google Earth Engine. Land surface temperature (LST) was treated as the primary response variable, while evapotranspiration (ET) was analyzed as a secondary response variable. Air temperature; precipitation; vegetation indices, including the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI); and elevation were used as supporting variables to interpret the broader climatic and biophysical responses of afforestation. MODIS land-cover, LST, ET, and vegetation-index products, together with climate research unit (CRU) climate data and ALOS-PALSAR DEM, were used to evaluate spatiotemporal trends and variable relationships. The results showed that mean LST increased by 0.520 ± 0.070 °C across KP during 2003–2023; however, areas classified as forest gain showed a localized cooling pattern of 0.490 ± 0.050 °C during the 2013–2023 forest-cover transition assessment window. Afforested areas also exhibited increased ET, whereas forest-loss areas showed reduced ET and higher LST. Specifically, ET increased by 0.013 ± 0.002 mm/8-day in afforested areas, whereas forest-loss areas showed a decline of 0.005 ± 0.001 mm/8-day. CRU-derived regional air temperature showed an increasing tendency of 0.310 ± 0.050 °C, whereas precipitation showed only a weak and statistically non-significant regional tendency; therefore, precipitation was used only as background climatic context. The NDVI and the EVI were negatively correlated with daytime LST, and elevation showed a strong negative relationship with LST. Overall, the findings indicate that forest-cover gain was associated with localized surface cooling patterns and improved vegetation–climate regulation indicators in the study area. Full article

The Klamath-Siskiyou Ecoregion (KSE) of southwest Oregon–northern California, USA, has globally exceptional biodiversity but is experiencing mounting pressures from climate change and land uses. We conducted an ecoregional conservation assessment of the KSE and the Siskiyou Crest subregion (SCS), a proposed climate refugium within the KSE. We integrated protected area priorities based on established conservation targets with climate change planning and fire risk reduction for communities. Both areas contained very low levels (<30%) of protection (GAP status 1, 2) for nearly all land cover types (n = 17), including serpentine substrates where endemic plants are highly concentrated, older forests with potential refugia properties, and habitat for Northern Spotted Owl (Strix occidentalis caurina) and Pacific fisher (Pekania pennanti). At the ecoregional scale, high-severity fire levels were proportionately similar across GAP land-use status (“managed” vs. protected). However, high-severity fire was lowest for protected areas at the subregional scale, reflective of potential refugium properties. Most fuel treatments by federal agencies were >1 km from nearest structures, far removed from effective community fire protection in both locales. The relatively higher-elevation SCS is projected to maintain refugia properties (cooler, wetter) for longer periods than the KSE; however, that function may dissipate toward the end of the century and under a higher emissions scenario. We recommend increased protections of potential refugia combined with fire risk reduction of the built environment to more effectively maintain unique biota and prepare communities for increased likelihood of wildfire spillover events. Full article

Agricultural sustainability assessment is increasingly required to characterize how food production systems interact with land, soil, water, carbon dynamics, and broader environmental change. However, the extent to which scientific assessment methods capture these environmental-system interactions remains unclear. This study mapped methodological and thematic trends in agricultural sustainability research through text mining of 3302 bibliographic records retrieved from the Web of Science Core Collection, which was selected because of its standardized metadata structure and suitability for reproducible text-mining analysis, covering publications from 2003 to 1 March 2025. After corpus preprocessing and tokenization, term-frequency analysis, dimension-specific lexical classification, co-occurrence networks, and temporal bibliometric trends were used to identify dominant environmental themes and assessment approaches. The results revealed a clear predominance of the environmental dimension in the analyzed literature, particularly through terms associated with land, carbon, soil, and water resources, whereas social and economic dimensions displayed lower lexical representation. Food, production, and systems formed a central semantic cluster linking environmental assessment with food security. Life Cycle Assessment (LCA) was the most frequently identified methodology, reflecting the prominence of impact-oriented environmental evaluation. In contrast, integrative and farm-scale frameworks, including Driver–Pressure–State–Impact–Response (DPSIR), Sustainability Assessment of Food and Agriculture Systems (SAFA), and the Tool for Agroecology Performance Evaluation (TAPE), among others, indicated increasing attention to governance, resilience, and agroecological transitions. These findings show that text mining can support environmental research by identifying methodological biases and emerging priorities in agriculture–environment interactions. Strengthening integrated assessment approaches will be essential for managing natural resources and supporting resilient and environmentally sustainable food systems. Full article

Orthophoto change detection is used to find real land cover changes in urban monitoring, disaster assessment, and environmental management. In practice, however, multi-temporal orthophotos are rarely identical in geometry and radiometry even after standard preprocessing. Small residual misregistration, local building displacement, shadow movement, and illumination differences can produce edge-like responses that look like change but do not correspond to any land cover transition. These false alarms increase manual checking costs and reduce the reliability of change maps. This study addresses that practical problem by proposing an attention-guided conditional adversarial framework, named Attention-GAN, for false alarm-resistant orthophoto change detection. The aim is not to detect small perturbations as changes but to detect real land cover changes while suppressing responses to nuisance variations that should be treated as unchanged. The framework integrates a multi-scale spatial attention module, a channel attention module, and a PatchGAN discriminator. It also introduces perturbation-negative training pairs, where controlled geometric and radiometric perturbations are applied to unchanged image pairs and assigned all-zero change masks. Experiments on LEVIR-CD, WHU-CD, and DSIFN-CD show competitive or moderately higher accuracy than the selected representative baselines, with F1 scores of 91.2%, 92.45%, and 93.18%, respectively. In the ablation experiment, the false change rate on perturbation-negative validation pairs is reduced to 4.9%. Repeated-run statistics and ablation results indicate that the proposed training strategy mainly improves robustness by reducing false alarms under the evaluated perturbation range. The results support the use of controlled nuisance perturbations as a reproducible way to train and evaluate false alarm resistance, while broader validation under real multi-view, seasonal, and cross-sensor distortions remains necessary. Full article

This study evaluates the spatial variability of hydric stress in the State of Sinaloa, northwestern Mexico, through the integrated analysis of hydroclimatic variables, multispectral remote sensing indicators, and environmental factors. Historical hydroclimatic conditions were analyzed using meteorological records from 1961 to 2020, whereas Landsat 8 imagery acquired on 7 July 2025, was used to evaluate the spatial expression of hydric stress. Reference evapotranspiration (ETo) was estimated using the FAO-56 Penman–Monteith methodology, and hydrological deficit conditions were determined from the relationship between precipitation (P) and ETo. Spectral indicators including land surface temperature ($\overline{T}$_a), the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), and the NDWI/MNDWI relationship were used to evaluate vegetation response, surface moisture conditions, and thermal anomalies associated with hydric stress. The results revealed persistent conditions where ETo systematically exceeded P, with hydrological deficit values ranging from approximately −1600 mm·year⁻¹ to localized positive values near 50 mm·year⁻¹. The most severe deficits were concentrated within the northwestern and north-central agricultural valleys of Sinaloa. Statistical validation revealed significant negative relationships between hydrological deficit and all evaluated spectral indicators. The strongest association was observed for MNDWI (R² = 0.387), followed by NDWI/MNDWI (R² = 0.277), NDWI (R² = 0.220), and NDVI (R² = 0.134), confirming the sensitivity of vegetation and moisture-related indicators to long-term hydrological stress conditions. Spatial analyses revealed a strong correspondence among low NDVI, negative NDWI and MNDWI responses, elevated $\overline{T}$_a, and regions characterized by high atmospheric evaporative demand. Additional spatial validation integrating land-use and vegetation-cover changes (1993–2011), regional geology, topography, and the distribution of highly productive agricultural valleys demonstrated that the most severe hydrological deficits coincided with areas affected by vegetation-cover loss, agricultural expansion, and intensive land use. Although these datasets correspond to different observation periods, they collectively reflect the cumulative environmental effects associated with persistent hydrological stress across the region. The combined effects of hydrological imbalance, forest-cover reduction, and agricultural intensification have progressively reduced ecosystem resilience and increased environmental vulnerability throughout one of the most productive agricultural regions of northwestern Mexico. These findings provide a scientific basis for water-resource management, territorial planning, ecosystem restoration, and climate-adaptation strategies under increasing water-scarcity conditions. Full article

Against the backdrop of global climate change and carbon neutrality strategies, land use carbon emissions have become a prominent topic amid regional efforts toward low-carbon transformation. However, existing studies on land-use carbon emissions have predominantly focused on humid and economically developed regions, while the unique carbon metabolism pathways of arid oasis–desert ecosystems, which are characterized by extremely low environmental carrying capacity and high sensitivity to land-use disturbance, remain largely unexplored. This study takes the oasis urban cluster in the Tarim Basin in southern Xinjiang Uygur Autonomous Region as the research object. This region belongs to a typical oasis–desert composite ecosystem, with a simple structure and low environmental carrying capacity (reflected by sparse vegetation cover <20%, annual precipitation <100 mm, extremely limited water resources, and high sensitivity to land disturbance). Its carbon metabolism pathway (i.e., the dynamic balance between carbon sources and sinks induced by land-use change) is fundamentally different from that in humid areas, and thus merits dedicated investigation. This study selects the period from 2000 to 2020 as the research period, which completely covers the acceleration period of urbanization and agricultural expansion in the Tarim Basin oasis urban cluster since the advancement of China’s Western Development Initiative. The data have a temporal resolution of 5 years (samples in 2000, 2005, 2010, 2015, 2020) and a spatial resolution of 30 m for land use and prefecture level for socio-economic indicators. Based on this, to fill the above-mentioned research gap, a research framework integrating the carbon emission coefficient accounting method, landscape pattern index, spatial autocorrelation analysis and geographic detector is adopted. Specifically, this study aims to systematically quantify the spatio-temporal evolution of land use carbon emissions and identify the most robust driving factors in the Tarim Basin oasis urban cluster by integrating multiple models, an approach that has not been previously applied to arid oasis regions. The research results show: (1) Based on the carbon emission coefficient method, total carbon emissions increased from 1.4455 million tons to 22.364 million tons, following a ‘slow-then-fast’ trajectory. In terms of temporal evolution, the study period can be further divided into three sub-stages: 2000–2005 (slow diffusion, with emission center skewed toward the northern energy-intensive zone), 2005–2015 (rapid restructuring, characterized by a ‘unipolar surge’ in Aksu and spread to the central oasis belt), and 2015–2020 (high-intensity stabilization, forming a cross-regional emission belt). Meanwhile, the land use structure has undergone a significant transformation. Construction land and cultivated land have continued to expand, while ecological land has significantly shrunk, resulting in a complex transformation pattern of oasis–desert ecotone. (2) The overall landscape became increasingly fragmented and diversified, the integrity of ecological space was damaged, and the regional carbon sink function was weakened. (3) The spatial autocorrelation analysis indicates that the spatial distribution of carbon emissions shows a heterogeneous pattern, forming a high-emission concentration area centered around Aksu-Bayingol. However, the global Moran’s I index is negative (such as −0.171 in 2020, p > 0.05), suggesting that carbon emissions have not formed a significant spatial clustering. (4) Carbon emissions are dominated by human and economic factors, and the interaction of factors is significant. The geographic detector identifies population density (average q value 0.904) and the proportion of construction land (average q value 0.858) as the key determinants of spatial variation in carbon emissions, reflecting the sensitive response of the human-nature system of arid zones to the urbanization process. These findings not only clarify the spatio-temporal features and driving forces of land use carbon emissions in the Tarim Basin oasis urban cluster, but also provide a replicable analytical framework for carbon-emission research in other arid and semi-arid regions worldwide. Based on these findings, we discuss the unique driving mechanisms of carbon emissions in arid regions, conclude that construction land expansion and population density are the dominant factors, and recommend a three-tier zoning governance system (carbon source control zone, carbon sink enhancement zone, coordinated development zone) for low-carbon spatial planning in arid areas. Full article

Vegetation-impervious surface-soil (V-I-S) fractions offer a continuous sub-pixel representation of urban surface heterogeneity. In this study, the influence of urban surface characteristics represented through V-I-S fractions on hydrological processes is analyzed at decadal intervals, i.e., 2000, 2010, 2020, and the projected year 2030 for the Mula–Mutha river catchment, Maharashtra, India. Pune city, as a major urban centre in this region, is experiencing significant changes in land surface characteristics over time, which have direct implications for its hydrology. The analysis uses the Soil and Water Assessment Tool (SWAT) to model these changes and their effects on water resources. Results show that the urban area has increased from 14% (2000) to 25% (2020), with projections indicating a further rise to 34% (2030). Such transitions yielded an increase in surface runoff from 47% (2000) to 53% (2020) and projected to reach 54% (2030). Groundwater recharge has declined from 10% to 6% and is expected to fall to 4% by 2030. Model validation using discharge data at Mirawadi outlet yielded a coefficient of determination of 0.72 using land use/land cover (LULC) data and 0.79 for simulations based on runoff Curve Number (CN) derived from V-I-S fractions, indicating the improved model performance. This study presents a novel framework, which incorporates remote sensing-derived V-I-S fractions to assess the spatiotemporal impact of urban expansion on water balance components. Full article