Search Results (891)

The rapid advancement of global urbanization has rendered Impervious Surface Area (ISA) a critical indicator for monitoring urban ecological and thermal environments. However, traditional sub-pixel ISA estimation methods, such as Spectral Mixture Analysis (SMA) and machine learning regression, are significantly constrained by spectral variability and a scarcity of high-quality training samples. To address these limitations, this study proposes a novel sub-pixel Impervious Surface Fraction (ISF) retrieval framework leveraging high-resolution airborne hyperspectral data. By simulating physically consistent multispectral reflectance and generating high-accuracy reference ISF via spatial aggregation, we construct a robust and noise-resistant training dataset. Experimental results on Landsat data demonstrate that this simulation-based approach effectively mitigates sample uncertainty, significantly enhances retrieval accuracy, and accurately preserves spatial details and boundary structures. Theoretically, the framework exhibits strong cross-sensor adaptability, as it allows for the generation of sensor-consistent training datasets for various medium-resolution satellite platforms by simply substituting the target sensor’s spectral response functions. Combined with this inherent scalability and the potential for cross-sensor model migration, this method provides a reliable and systematic paradigm for long-term, high-precision ISF mapping across multiple satellite constellations. Full article

Urban Heat Islands (UHIs) are widely analyzed using Land Surface Temperature (LST), yet most studies remain limited to single cities, rely on a single machine-learning model, analyze LST alone, and use inconsistent Surface Urban Heat Island Intensity (SUHII) definitions, which restrict cross-city comparability and broader generalization. This study introduces an explainable artificial intelligence (XAI) framework implemented in Google Earth Engine (GEE) to analyze census-tract summer surface heat (2018–2024) across eight climatically contrasting U.S. cities. The main novelty is a standardized tract-scale cross-city framework that jointly models LST and SUHII using a consistent SUHII definition, a common physical predictor set, city-held-out nested cross-validation, and SHAP-based interpretation, allowing absolute surface heat to be distinguished from relative within-city heat anomaly; this combination is rarely implemented within a single urban heat study. Multiple machine-learning models were evaluated, with ensemble trees performing best: Extreme Gradient Boosting (XGBoost) best predicted SUHII (R² = 0.879; RMSE = 0.213), while Extra Trees best predicted LST (R² = 0.908; RMSE = 0.745 °C). SHapley Additive exPlanations (SHAP) indicate that SUHII is driven primarily by impervious surface fraction and surface moisture availability, whereas LST is structured by latitude and mean summer air temperature. Overall, the framework provides interpretable multi-city attribution of urban surface heat drivers with demonstrated cross-city generalization. Full article

In the context of rapid urbanisation, the accelerating conversion of ecological land into built-up areas has intensified conflicts between urban expansion and ecological sustainability, making accurate simulation and forecasting of land-use development increasingly important for sustainable spatial planning. This challenge is particularly urgent in cities where ecological functions are closely linked to water resources and landscape structure. The present study adopts Jinan, designated the “City of a Thousand Springs”, as a paradigmatic example of a mountain–spring–urban composite ecosystem. The study systematically analyses how disparate development pathways influence regional water yield, carbon storage, and their interactions. Land-use patterns for 2030 were simulated with the PLUS model under three scenarios: natural development (NDS), ecological spring protection (ESPS), and economic development (UDS). The InVEST model was used to quantify water yield, carbon storage and water–carbon coupling coordination for 2020 and each scenario. Results show that between 2000 and 2020, construction land expanded by 954.85 km² while cropland declined by 632.46 km². Rising impervious surface coverage led to modest increases in total water yield across scenarios (0.65~1.07%), with the smallest increase under ESPS. High-yield areas remained concentrated in built-up zones. Carbon storage declined by 0.41~0.75%, most notably under UDS, and maintained a stable “high south-low north” spatial pattern. Water–carbon coupling was dominated by initial to moderate coordination, while trade-off areas were mainly distributed across plains. The results provide a spatial basis for the promotion of sustainable land use, integrated ecosystem management and urban ecological security planning, offering practical insights for advancing sustainability-oriented development in rapidly urbanising regions. Full article

This study developed and tested a neighborhood-scale framework that integrates unmanned aerial vehicle (UAV)-based multispectral mapping and georeferenced socioeconomic data to identify inequities in urban green infrastructure and translate them into an operational prioritization tool for inclusive planning. Using object-based image analysis, impervious surfaces, low vegetation, and woody vegetation (trees and shrubs) were mapped across 33 Neighborhood Units in Temuco, Chile, and landscape metrics describing dominance, edge, isolation/connectivity, and diversity were derived. Socioeconomic conditions were summarized through Principal Component Analysis, and their relationships with vegetation metrics were evaluated using Generalized Additive Models. The results revealed strongly nonlinear and metric-specific associations, with the most robust relationships observed for woody-structure metrics, particularly total woody edge and built-environment isolation, whereas landscape diversity showed weaker but still significant dependence on resource-access gradients. To support inclusive planning, a dimensionless Green Infrastructure Prioritization Index (GIPI) was computed by combining standardized green deficit and standardized social vulnerability with equal weights. GIPI values ranged from 0.318 to 0.740 (median = 0.528), identifying 11 high-priority units characterized by higher social vulnerability and less favorable woody structure, including lower largest-patch dominance and greater isolation. Sensitivity analyses varying the deficit weight from 0.30 to 0.70 showed that 10 of the 11 high-priority units remained in the same class in at least 80% of weighting scenarios, indicating a stable priority set. Further classification of high-priority units according to dominant deficit type supported a staged intervention strategy, in which woody canopy is first increased in deficit nodes and subsequently reinforced through corridor-oriented greening to improve structural connectivity. These findings demonstrate the value of coupling fine-scale vegetation mapping with socioeconomic gradients to support more equitable urban green infrastructure planning. Full article

Understanding vegetation-mediated mitigation of urban heat islands (UHI) is essential for sustainable urban adaptation strategies. Although vegetation responses to extreme heat events have been widely explored using satellite remote sensing and statistical methods, evidence remains limited regarding how these responses vary along urban–rural gradients, particularly in terms of resistance and recovery dynamics. This study focuses on the North Tianshan Slope Urban Agglomeration (TNSUA) in Xinjiang, China. Based on Enhanced Vegetation Index (EVI) data from 2000 to 2022, an urban–rural gradient was delineated using impervious surface fraction. Vegetation resistance and recovery during extreme heat events were quantified to reveal spatiotemporal response patterns. Generalized additive models (GAMs) and Random Forest (RF) models were applied to identify key driving factors and to evaluate their relative importance across multiple spatial scales. The results indicate that rural land cover along the gradient provides a strong cooling effect, particularly in areas with an urban development intensity (UDI) of 70–85%. Vegetation responses show pronounced seasonal differences, with urban vegetation generally exhibiting lower resistance and recovery than rural vegetation. At the county scale, local UHI intensity is the dominant driver of vegetation responses, whereas at the pixel scale, precipitation and vapor pressure deficit (VPD) play the most critical roles. Overall, this study improves the understanding of vegetation responses to extreme heat events in arid regions and provides scientific support for nature-based urban heat adaptation strategies. Full article

The intensification of Global Warming and Urban Heat Island phenomena necessitates advanced, computationally effective tools for evaluating outdoor thermal comfort and microclimatic dynamics by means of Mean Radiant Temperature assessment. However, existing high-resolution physical models often suffer from prohibitive computational costs. This research proposes LUCIDiT (Lean Urban Comfort Intelligent Digital Twin), a physically based modeling framework implemented for a quick mean radiant temperature assessment inside complex urban morphologies. The method integrates a simplified balance of mutual radiative heat exchanges with recursive time-series filtering to account for the thermal inertia of different urban materials, alongside greenery heat exchange due to evapotranspiration. This architecture creates an operational urban comfort digital twin that reduces computational times by orders of magnitude for large-scale mappings, without sacrificing physical accuracy. Validation against drone-acquired thermographic data and the established Urban Multi-scale Environmental Predictor model demonstrates high reliability and coherence with the real physical phenomena and context. The application to an urban pilot site in Florence reveals that strategic interventions, such as substituting impervious surfaces with irrigated greenery and arboreal canopies, can mitigate radiant loads by up to 20 °C. Findings show that the proposed urban comfort digital twin can be a robust, scalable instrument for designing evidence-based climate adaptation strategies and quick testing mitigation scenarios to enhance urban resilience. Full article

Since 2025, 45% of the world’s population of 8.2 billion people has lived in cities, and by 2050, that number is expected to increase to 66%. As the number of people living in cities increases, natural landscapes will be transformed into impervious surfaces, leading to serious challenges and resulting in a phenomenon named the urban heat island (UHI) effect. Although urban thermal variation has been studied globally, few studies have examined the impact of land use transitions on local surface temperatures. This study aims to address this gap by investigating the impact of LULC transitions on the land surface temperature (LST) and the urban thermal field variation index (UTFVI) in the five most populated cities in Saudi Arabia between 2000 and 2024: Riyadh, Jeddah, Makkah, Madinah, and Dammam. This study provides not only a comprehensive overview of the cities in Saudi Arabia but also a detailed analysis of each city using a novel approach that integrates thermal land use analysis. In this study, Landsat TM-5, OLI-TIRS-8, and OLI2-TIRS2-9 were used to process the LULC using random forest machine learning and thermal indices. Fifteen LULC maps were generated and assessed based on four classifications across the cities and time periods: urban area, barren land, vegetation, and water. The difference-in-difference (DiD) analytical approach was used to compute the thermal effect size and compare the specified changed pixels (barren-to-urban, vegetation-to-urban) with stable urban. Then, the relationship between the LST and the NDVI–NDBI were investigated. The results show that the overall accuracy of the 15 LULC classifications ranged from 89.00% to 97.00%. The urban area increased across all the cities, with the greatest changes being 448.84, 179.67, 177.96, 126.33, and 95.69 km² in Riyadh, Jeddah, Dammam, Madinah, and Makkah, respectively. Furthermore, the vegetation cover increased in most of the cities over time. The LST of the urban areas increased by 8.31 °C in Riyadh, 5.24 °C in Jeddah, and 1.41 °C in Makkah in 2024 compared to 2000, while those in Dammam and Madinah decreased by 2.67 °C and 0.60 °C, respectively. This study delivers robust insights into two decades of urban surface temperature dynamics across major Saudi Arabian cities, offering critical evidence to inform UHI mitigation strategies and support the long-term sustainability of urban environments. Full article

This study examines near-surface air-temperature variability during extreme heatwaves in Coimbra (Portugal), focusing on Urban Heat Island (UHI) dynamics through a hotspot-based assessment of intra-urban thermal hotspots (IUTHs), defined as localized zones of recurrent elevated near-surface temperatures. Using an extensive multi-site dataset collected at multiple times of the day across heterogeneous urban environments, the analysis evaluates how urbanization intensity, surface cover, green infrastructure, and site-specific context influence diurnal temperature contrasts and patterns of heat exposure. Statistical results reveal clear spatial thermal disparities, with densely built-up and highly impervious areas such as Santana and the Seminary surroundings consistently emerging as intra-urban hotspots, particularly during afternoon peak temperatures. In contrast, green spaces (Botanical Garden and Mermaid Garden) act as cooling refugia, exhibiting lower near-surface air temperatures and reduced thermal amplitude compared with surrounding urban areas. Proximity to water bodies further moderates ambient conditions, highlighting the buffering role of blue infrastructure during extreme heat periods. These findings demonstrate that analysing UHI intensity through fine-scale intra-urban hotspot patterns provides valuable insights for urban climate adaptation. The results support the strategic integration of green spaces and nature-based solutions in urban planning to mitigate heat risk, strengthen climate resilience, safeguard public well-being, and promote more adaptive and liveable cities. Full article

Rapid land-cover transformations associated with urban expansion have increasingly altered hydrological processes, modifying runoff generation and flood response at the catchment scale. This study applied the Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) to examine rainfall–runoff dynamics in the Prosna River catchment (Poland) and the Morava e Binçës River catchment (Kosovo) for 2006–2021. Land-use changes were quantified using CORINE Land Cover (CLC) data from 2006, 2012, and 2018, and their hydrological effects were evaluated through changes in the Curve Number (CN) parameter. The model was calibrated and validated for the Prosna catchment, achieving satisfactory performance (NSE = 0.72 during calibration and 0.56 during validation), confirming its reliability under varying hydrometeorological conditions. Due to the lack of continuous discharge data in Kosovo, a parameter-transfer approach was used, applying calibrated parameters from the Prosna to the Morava e Binçës. Scenario-based simulations assessed the combined effects of urban growth and meteorological variability. Under wetter conditions, increased precipitation and expanded impervious surfaces markedly amplified simulated discharge, with maximum daily differences reaching 86.9 m³ s⁻¹. These findings underscore the sensitivity of catchment response to interacting land-use and precipitation changes and highlight the need for improved hydrological monitoring in data-scarce regions. Full article

Rapid urbanisation and increasing heat extremes pose significant challenges for megacities in the Global South. This study develops a configuration-sensitive assessment of blue-green space (BGS) cooling in Delhi, a Global South megacity facing intensified heat. Using satellite imagery and statistical modelling, we quantify how land cover and patch structure regulate land surface temperature (LST). Satellite imagery was used to derive LST, and six land-cover classes were mapped using supervised classification. Spectral indices and proximity metrics were calculated, land-cover patches were delineated, and their thermal behaviour was analysed using patch-level LST statistics. Delhi exhibits a heterogeneous urban heat island (UHI) surface, with LST spanning 19.8–38.6 °C and built-up land dominating (743.50 km²), while BGS remains limited and fragmented. Warming scaled almost linearly with built-up patch size (R² = 0.98), with mean LST rising from 22.6 °C (<20,000 m²) to 27.4 °C (>500,000 m²). Cooling strengthened with BGS spatial dominance as dense vegetation declined from 23.8 to 22.1 °C (R² = 0.98), sparse vegetation from 24.3 to 22.2 °C, and water bodies from 21.4 to 18.8 °C (R² = 0.89) across increasing size classes. Correlations identified impervious surfaces as primary warming controls, while moisture and vegetation were cooling indicators. Random Forest-SHAP confirmed modified bare soil index (MBSI) and normalised difference built-up index (NDBI) as dominant predictors, with cooling from modified normalised difference water index (MNDWI) and comparatively conditional effects of normalised difference vegetation index (NDVI). Impervious and exposed surfaces govern Delhi’s thermal baseline, while BGS acts as a modifier whose benefits emerge when patches are large, connected, and integrated. These findings support shifting from area-based greening targets to morphology-based planning that protects connected blue-green corridors. Full article

Urban flooding is prevalent in low-lying, coastal regions, where subtle topographic variation, shallow groundwater, and impervious surfaces govern inundation dynamics. This study evaluates urban flood susceptibility across Miami-Dade County by integrating flood-conditioning factors, including elevation, slope, rainfall, land use/land cover, distance to roads and open water, stream power index (SPI), topographic wetness index (TWI), groundwater depth, and flow accumulation within an Analytical Hierarchy Process (AHP)-based weighted overlay framework. The AHP-derived weights demonstrated strong consistency (consistency ratio = 0.022) and were applied to reclassify each conditioning factor into five flood susceptibility classes—very low to very high. The model performance was evaluated using the Federal Emergency Management Agency (FEMA) flood zone, and the findings demonstrated that the AHP-based framework effectively differentiates flood susceptibility at a fine urban scale, achieving strong predictive performance; area under the Curve (AUC) = 0.85. The results also reveal pronounced spatial variability in flood susceptibility, with northeastern urbanized areas, particularly in Hialeah, Miami Gardens, Miami Lakes, and Downtown Miami, exhibiting higher susceptibility compared to the northwestern Everglades region. Overall, this study presents a robust urban flood susceptibility framework that supports improved flood risk assessment and decision-making in complex urban coastal environments. Full article

Under the dual pressures of climate change and intensive urban expansion, which jointly exacerbate urban heat risks, optimizing the urban thermal environment through vegetation has become a core pathway for climate adaptation. However, accurately quantifying the nonlinear cooling responses of vegetation under complex urban morphologies and diverse geomorphic conditions remains a major scientific challenge in achieving efficient heat-resilient urban planning. This study takes three representative megacities in China—Beijing, Shanghai, and Shenzhen—as case studies. By integrating multi-source datasets, an urban spatial morphology indicator system was constructed that encompasses key dimensions of the natural environment, urban morphology, and socioeconomic factors. Eleven machine learning models were applied to model and compare urban land surface temperature (LST). The results demonstrate that the CatBoost model exhibited superior performance in simulating complex urban thermal environments (R² = 0.683–0.873), effectively capturing the interactive effects among multidimensional factors. The findings reveal a dual differentiation pattern of “topographic constraint–morphological dominance” in urban thermal environments: in mountainous cities, elevation and mountain forests act as rigid cooling barriers that restrict the spread of heat islands; whereas in plain cities, thermal conditions are primarily governed by the synergistic warming effects of impervious surface expansion and intensive human–economic activities. More importantly, the study identifies a significant nonlinear threshold effect of vegetation cover (NDVI) on LST reduction—only when vegetation coverage exceeds a critical threshold can large-scale cooling benefits be activated to effectively offset the thermal accumulation associated with high GDP intensity. Based on these insights, the study proposes differentiated climate-adaptive spatial planning strategies: mountainous cities should strictly maintain ecological redlines at mountain fronts to safeguard macro-scale cooling sources, while high-density plain cities should focus on integrating green space patches to surpass the “cooling threshold” and enhance vertical greening systems. These findings provide a quantitative scientific basis for improving urban thermal resilience. Full article

Due to the relatively long lifespan and resilience of adults to environmental stressors, freshwater turtles are characterized as bioaccumulators of chronic contaminant exposure in urban ecosystems. Urbanization increases pollutants, resulting in subsequent runoff into streams. We evaluated the relationship between percent impervious surface and contaminant concentrations in turtles from 20 wetlands in Wake County, North Carolina, USA, one of the fastest-growing counties in the United States. We evaluated the concentrations of eight environmental contaminants known to cause human and environmental health issues listed under the U.S. Environmental Protection Agency’s Resource Conservation and Recovery Act: arsenic (As), barium (Ba), cadmium (Cd), chromium (Cr), lead (Pb), mercury (Hg), selenium (Se), and silver (Ag), as well as vanadium (V) and copper (Cu) due to their presence in urban environments and bioaccumulation, in the blood and claws from Chelydra serpentina and Trachemys scripta. All contaminants, except for Cd and Ag, were detected in both species and both tissue types. Carnivorous Chelydra serpentina exhibited higher concentrations of Se and Hg than omnivorous Trachemys scripta. Partial redundancy analysis indicated that species accounted for more variance in the data than % impervious surface at a 2200-m scale. Robust mixed-effects models showed that % impervious surface was not correlated with contaminant concentrations in either species. Although we documented no relationship between urbanization and contaminant concentrations, we recommend additional research to investigate the effects of urbanization over time in this rapidly developing region. Full article

Old urban areas are often prone to waterlogging and sewage contamination owing to their haphazard spatial arrangements, extensive impervious surfaces, and insufficient drainage infrastructure, thereby posing significant risks to both public safety and aquatic ecosystems. Sponge City retrofitting offers a viable solution. Currently, the study area is facing issues of waterlogging and pollution caused by rainfall. Conventional modeling approaches for optimizing the spatial allocation of Low-Impact Development (LID) practices typically quantify only the overall retrofit proportion. However, these methods fail to specify the optimal placement of individual facilities to balance hydrological benefits against construction costs. To bridge this gap between theoretical optimization and practical implementation, this study proposes an iterative approximation framework. First, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was coupled with the Storm Water Management Model (SWMM) to generate a Pareto front, from which optimal solutions were selected using the Analytic Hierarchy Process (AHP). The configuration was further refined through multiple iterations of “exhaustive search combined with Euclidean distance” analysis to determine the optimal types and locations of LID facilities. The results show that: In Scenario 3, the Euclidean distance after LID retrofitting achieved a narrowing gap from 5 to 3 to 1. This indicates that the proposed progressive approximation solving process can be directly applied to specific retrofit targets, providing concrete construction guidance for LID retrofitting in older communities’ areas. Conclusions showed that (1) the specific locations for implementing LID facilities within sub-catchments become progressively clearer, ultimately defining precise retrofitting sites. (2) The proposed progressive approximation approach effectively and systematically reduces this disparity. (3) Retrofitted LID measures effectively managed stormwater and controlled pollution. Full article

Urban expansion has intensified concerns regarding land use efficiency and sustainable urban development worldwide. Despite growing global application of Sustainable Development Goal (SDG) Indicator 11.3.1, comprehensive assessments within the United States (U.S.) remain limited. This study evaluates urban land use efficiency in the contiguous U.S. between 2000 and 2020 by examining the relationship between land consumption and population growth using the ‘Land Consumption Rate to Population Growth Rate’ (LCRPGR) framework. Urban land expansion and population change were derived from integrating impervious surface data with gridded population estimates, respectively, and the indicator was calculated for 2229 urban areas to evaluate temporal and regional patterns. Results show that urban land area increased by 23% over the study period, while population grew by 31%, indicating an overall shift toward denser urban development. The median LCRPGR declined from 0.84 during 2000–2010 to 0.63 during 2010–2020, reflecting improvements in land use efficiency, although notable regional disparities remain. Cluster analysis reveals distinct spatial growth patterns, with older metropolitan areas and western cities generally exhibiting more compact development. Findings demonstrate the applicability of SDG Indicator 11.3.1 for evaluating urban land use efficiency in a U.S. context and highlight the importance of coordinated spatial planning and policy measures to support sustainable urbanization. Full article