Search Results (3,331)

Rising urban temperatures have become a critical constraint to urban ecosystem resilience and livability due to rapid urbanization. This study proposes a novel intra-city zoning scheme, named component morphological blocks (CMBs), which classifies built-up areas into six types characterized by multidimensional urban canopy morphologies. The XGBoost-SHAP model, optimized via Bayesian tuning, was employed to examine the relative contributions of 16 potential driving variables to block-scale land surface temperature (LST). The results show that: (1) LST gradually increases with increasing building density in the warm seasons. The average building height (BH) exhibits a positive correlation with shaded area, thereby reducing LST on the block scale; (2) hotspots are mainly concentrated in function-oriented blocks with hotspot distribution indices of 1.85, 1.96, 1.24, and 1.14, respectively. Coldspots are largely observed in blue–green space in the warm seasons; (3) BH dominates the LST across seasons, while the building-related factors make a prominent impact on LST in warm seasons. The contribution of vegetation canopy density is followed by BH during autumn and winter (12.2%, 10.9%); (4) a distinct transition occurs between summer normalized difference built-up index (NDBI) and fractional vegetation cover around an NDBI of 0.1. In winter, the interaction between 2D and 3D vegetation factors indicates a shift in their relative contributions from negative to positive as they increase. This study demonstrates that CMBs serve as an effective choice for characterizing LST patterns at the block scale, providing insights for sustainable urban development aimed at mitigating the urban heat island effect. Full article

Uneven urbanization can lead to significant quantities of vacant and abandoned land while exacerbating urban heat island (UHI) effects and simultaneously adversely affecting socioeconomically disadvantaged communities. This study examines the correlation between land surface temperature (LST) and urban vacant and abandoned land in socially vulnerable neighborhoods in Houston, TX, USA, where extreme heat can present significant environmental and public health challenges. Six critical study locations exhibiting a social vulnerability index (SVI) over 0.7 and average land surface temperature (LST) values surpassing 82 °F (27.8 °C) are analyzed through spatial analytics and drone footage. Findings indicate that vegetated vacant spaces help mitigate urban heat by decreasing land surface temperature, but abandoned structures exacerbate temperatures due to heat retention from non-permeable surfaces. Findings suggest that elevated socioeconomic vulnerability correlates with increased land surface temperature, exacerbating heat-related hazards in at-risk communities. In this six-site sample, the abandonment rate exhibited a positive correlation with the site mean land surface temperature (exploratory linear fit: +2.42 °F [0.74, 4.11]/+1.35 °C [0.41, 2.28] per +1% increase in abandonment; to be interpreted as exploratory and potentially confounded). Results provide critical insights for climate resilience planning and urban heat reduction through high-resolution thermal and geographical analysis, highlighting the impact of vacant and abandoned land on LST. Such findings endorse certain urban cooling techniques, including land reutilization and green infrastructure, to enhance environmental equality and adaptation. Full article

Wildfires are a major natural hazard causing extensive ecological damage and endangering human survival. Previous studies on wildfires in China have mostly focused on specific regions or individual drivers, with limited systematic assessments at the long-term and national scales. The spatiotemporal patterns of wildfires and their multiple driving mechanisms under China’s diverse climatic regimes remain insufficiently understood. To bridge this gap, we combined MCD64A1 burned area data (2001–2023) with multi-source natural (meteorological, vegetation, and topographic) and anthropogenic factors, using random forest models at both the national and regional scales to examine the spatiotemporal patterns, dominant drivers, and response mechanisms of wildfires in China. The results revealed that: (1) Spatially, wildfires were concentrated in northeastern and southern China, which accounted for 86.20% of the total burned area. Temporally, northern wildfires were primarily a spring-dominated fire regime, with peak activity in March and April, whereas southern wildfires were winter-dominated, peaking in February. (2) At the national scale, elevation was the key topographic factor influencing wildfire occurrence (relative importance = 0.49), with low-elevation and gentle-slope areas being more fire-prone. At the regional scale, the driving factors exhibit spatial differentiation, forming a spatial pattern of topography-dominated and climate-dominated. (3) Partial dependence plot analysis revealed nonlinear and threshold responses. Fire probability increases rapidly when the soil moisture is below 20 mm, while extremely high land surface temperatures in arid regions suppress fire occurrence due to fuel limitations. This study enhances the understanding of spatially heterogeneous wildfire drivers in China and provides a scientific basis for region-specific wildfire prevention and management strategies. Full article

The Local Climate Zone (LCZ) provides a systematic approach for analyzing the differences in urban thermal environment. However, given the scarcity of accessible multi-year LCZ maps, studying the long-term thermal differences among LCZs remains challenging. This study classified LCZs using multi-year remote sensing imagery of Xiamen and analyzed the thermal differences among them. The results indicate that (1) the land surface temperatures (LSTs) of LCZ(8) Large low-rise and LCZ(10) Heavy industry are the highest, while the LSTs of LCZ(A) Dense trees and LCZ(G) Water are the lowest. Among compact LCZs(1,2,3), the LST of LCZ(1) Compact high-rise is consistently lower than that of LCZ(2) Compact mid-rise and LCZ(3) Compact low-rise. (2) The thermal contribution indices of built-up LCZs are positive, whereas those of land-cover LCZs are negative. The thermal contribution indices of LCZ(2) Compact mid-rise, LCZ(3) Compact low-rise, and LCZ(10) Heavy industry show a continuous annual increase, while the thermal contribution indices of LCZ(9) Sparsely built, LCZ(A) Dense trees, and LCZ(G) Water show a consistent annual decrease. (3) In terms of the thermal environment regulation demand index, LCZ(1) Compact high-rise, LCZ(2) Compact mid-rise and LCZ(3) Compact low-rise have the highest thermal environment regulation demand. By integrating long-term LCZ mapping with LST, TCI, and TERDI, this study provides a multidimensional and planning-oriented framework for urban thermal environment assessment and targeted mitigation. Full article

High-spatiotemporal-resolution precipitation data are essential for studies on regional hydrology, meteorology, and ecological conservation. Because the Qaidam Basin is a data-scarce region with a few ground stations and coarse-resolution remote sensing products, its utility in regional research is constrained. Therefore, high-resolution precipitation data are urgently needed. Here, longitude, latitude, the normalized difference vegetation index (NDVI), the digital elevation model (DEM), daytime and nighttime land surface temperature, slope, and aspect were selected as environmental variables. Four machine learning methods, Artificial Neural Network (ANN), Cubist, Random Forest (RF), and Support Vector Machine (SVM), were used to downscale Tropical Rainfall Measuring Mission (TRMM) precipitation data from 25 to 1 km in the Qaidam Basin and validated using ground observation stations. For annual downscaling, the accuracy ranked as Cubist > ANN > RF > SVM, and residual correction further improved performance. The Cubist model produced the best results, generating finer spatial patterns and reducing outliers in both annual and monthly products. Longitude, latitude, the DEM, and the NDVI were important contributors to the Cubist model. The resulting high-resolution dataset provides valuable support for hydrological and climate change research in the Qaidam Basin. Full article

Lakes on the Inner Mongolia–Xinjiang Plateau (IMXP) are increasingly vulnerable to eutrophication under climate change and human pressure, yet long-term monitoring remains limited by sparse field sampling. Here, we reconstruct multi-decadal trophic dynamics across the IMXP using Landsat time series and temporally transferable machine-learning models and further quantify the underlying natural and anthropogenic drivers. We compiled monthly in situ water-quality observations (chlorophyll-a, Chl-a; total phosphorus, TP; total nitrogen, TN; Secchi depth, SD; and permanganate index, COD_Mn;) and calculated the trophic level index (TLI). After rigorous quality control and monthly aggregation, we compiled a dataset of 1345 matched lake–month samples spanning 2000–2024, and divided it into a training set (n = 1076; ≤2019) and an independent test set (n = 269; 2020–2024) to evaluate temporal transferability. We utilized Google Earth Engine to generate monthly surface reflectance composites from Landsat 7 ETM+, Landsat 8 OLI, and Landsat 9 OLI-2. Four supervised regression algorithms—ridge regression (RR), support vector regression (SVR), random forest (RF), and eXtreme Gradient Boosting (XGBoost)—were trained to estimate TLI. On the independent test period, XGBoost performed best (R² = 0.780, RMSE = 3.290, MAE = 1.779), followed by RF (R² = 0.770, RMSE = 3.364), SVR (R² = 0.700, RMSE = 3.842), and RR (R² = 0.630, RMSE = 4.267); we then used XGBoost to reconstruct monthly and yearly TLI for 610 perennial grassland lakes from 2000 to 2024. From 2000 to 2024, the annual mean TLI (48–49) across the IMXP exhibited a statistically significant upward trend (slope = 0.0158 TLI yr⁻¹; 95% confidence interval (CI) = 0.0050–0.0267; p = 0.006). Meanwhile, spatial heterogeneity was distinct (TLI: 41.51–59.70). High values concentrated in endorheic and desert–oasis basins (e.g., Eastern Inner Mongolia Plateau, >51), whereas lower values characterized high-altitude regions (e.g., Yarkant River, <45). Overall, trends ranged from −0.49 to 0.51 yr⁻¹, increasing in 54% of lakes (15.6% significantly) and decreasing in 46% (15.4% significantly). Attribution analyses identified NDVI (33.92%) and temperature (21.67%) as dominant drivers (55.59% combined), followed by precipitation (13.99%) and human proxies (30.42% combined: population 10.66%, grazing 10.31%, built-up 9.45%). Across 53 sub-basins, NDVI was the primary driver in 28, followed by temperature (11), population (7), precipitation (3), grazing (3), and built-up land (1); notably, the top two drivers explained 56.6–87.1% of variations. TWFE estimates revealed bidirectional NDVI effects (significant in 31/53): positive associations in 22 basins were linked to nutrient retention, contrasting with negative effects in nine basins associated with agricultural return flows. Temperature effects were significant in 15 basins and predominantly negative (14/15), except for the Qiangtang Plateau. Overall, eutrophication risk across the IMXP lake region reflects the combined influences of climatic conditions, vegetation conditions, and human activities, with their relative contributions varying among basins. Full article

High-spatiotemporal-resolution land surface temperature (LST) data are crucial for analyzing surface energy balance, modeling temperature-related processes, and monitoring thermal environments. However, despite advancements in multi-source fusion and reconstruction techniques, high-frequency LST data remain susceptible to anomalies such as abrupt changes and outliers due to retrieval uncertainties and varying observation conditions. Conventional statistical outlier detection methods risk misidentifying physically plausible rapid weather changes as data errors, introducing systematic biases. To address this, we propose a two-stage anomaly detection framework that follows a “temporal physical pre-screening first, spatial statistical verification later” logic. First, a piecewise empirical model, based on typical diurnal LST variation characteristics, is constructed to identify points violating physical patterns. Subsequently, a spatial consistency test using median absolute deviation (MAD) is introduced to distinguish real weather-driven fluctuations from genuine data anomalies from a spatial synergy perspective. This sequential design effectively reduces the risk of mis-correcting physically reasonable temperature variations. Validated using hourly seamless LST data (2016–2021) and ground observations in the Heihe River Basin, our method outperformed Seasonal-Trend decomposition using Loess (STL), double standardization methods, and robust Holt–Winters. For over 87% of the detected anomalies, the proposed method demonstrated positive improvement rates in RMSE, MAE, R, and R². The overall average improvement rates reached 23.61%, 18.79%, 16.46%, and 61.33%, respectively, indicating robust performance. The results underscore that explicitly incorporating physical constraints enhances the reliability and interpretability of quality control for high-temporal-resolution remote sensing LST data. Full article

Urban areas in arid environments are increasingly affected by the urban heat island (UHI) effect, which intensifies thermal stress, disrupts ecological balance, and poses challenges for sustainable urban development. Understanding and predicting spatiotemporal variations in land surface temperature (LST) and land use dynamics is therefore critical for effective urban planning. This study develops a predictive framework for Riyadh, Saudi Arabia, using long-term Landsat time series data (1993–2023) and deep learning models to evaluate urban thermal patterns via the Urban Thermal Field Variation Index (UTFVI). Artificial Neural Networks (ANNs) with six hidden layers for LST and seven for UTFVI forecast future trends up to 2043. The results indicate that urban areas expanded by 521.62 km², increasing from 8.73% to 19.56% between 1993 and 2023, and are projected to reach 1509.40 km² (25.28%) by 2043, while vegetation coverage declined from 0.771% to 0.674%. The highest average summer LST increased from 56.73 °C in 1993 to 59.89 °C in 2023 and is predicted to rise to 60.79 °C by 2033 and 61.52 °C by 2043. Winter temperatures exhibited a comparable upward trend, rising from 30.75 °C to 32.33 °C in 2023 and projected to reach 34.48 °C by 2043. UTFVI analysis revealed a substantial expansion of weak thermal field zones, which covered 2778 km² in 2023 and are expected to reach 3018.44 km² (57%) by winter 2043, accompanied by a marked contraction of strong thermal field areas. The ANN models achieved a high predictive performance, with RMSE values of 0.759 (summer) and 0.789 (winter) for UTFVI and correlation coefficients of 0.91 and 0.89, respectively. Projections further indicate that, by 2043, approximately 39.31% of the study area will experience summer temperatures between 48 °C and 53 °C, compared to 5.59% in 2023. These findings highlight the accelerating interaction between urban growth and thermal intensification in arid cities. The proposed modeling framework provides a robust decision-support tool for urban planners and policymakers to mitigate UHI impacts and promote climate-resilient and sustainable urban development. Full article

Mitigating heat stress in high-density historical districts remains a critical challenge in urban renewal due to complex morphological heterogeneity. Existing research often relies on isolated intervention measures, lacking systematic, multi-strategy assessments driven by high-precision spatial data. This study addresses this gap by establishing a quantitative framework that couples thermal infrared remote sensing with Computational Fluid Dynamics (CFD) to optimize microclimate responses in Beijing’s Liulichang Historic District. Remote sensing data were utilized to retrieve high-resolution Land Surface Temperature (LST), providing accurate thermal boundary conditions for micro-scale wind-thermal simulations. A baseline scenario (S0) and seven renewal strategies (S1–S7)—integrating varying configurations of greenery, water bodies, and permeable pavements—were evaluated using pedestrian-level comfort indices. Results reveal that single-factor interventions yield marginal improvements or thermodynamic trade-offs; specifically, adding greenery (S1) in narrow street canyons increased aerodynamic roughness, thereby obstructing ventilation and inducing localized warming. Conversely, composite strategies significantly enhanced microclimatic quality. The “greenery-water-permeable pavement” strategy (S4) achieved optimal synergistic effects, characterized by substantial cooling and spatial homogenization. Regression analysis identified water bodies as the dominant cooling driver, where a 10% increase in water coverage resulted in a temperature reduction of approximately 5.17 °C. Conversely, greenery alone showed no statistically significant cooling contribution (p > 0.05) without the synergistic presence of water or pavement modifications. This research suggests that urban renewal in high-temperature zones (>36 °C) should prioritize composite cooling networks. Furthermore, vegetation layouts near wind corridors must be precisely regulated to prevent ventilation degradation. These findings provide a scientific basis for the climate-adaptive sustainable regeneration of culturally significant, high-density urban blocks. Full article

Predictive Digital Soil Mapping (PDSM) in Eastern Amazonia faces challenges due to its environmental complexity, difficult access, and scarce legacy data. While legacy soil maps contain valuable tacit knowledge, updating them requires methods that can handle uncertainty. This study evaluates the integration of old soil maps with machine learning to update soil information in Tracuateua, Pará, with a specific focus on the performance of ensemble learning and the explicit incorporation of uncertainty metrics in soil mapping units under hydromorphic influence, which, in addition to being difficult to access, are influenced by complex pedogenetic processes. We combined 270 sampling points, equivalent to the total pixels that captured the variability of soil mapping units, with environmental covariates and historical data. Several algorithms were tested, including an ensemble approach, to predict mapping units and quantify uncertainty through entropy and confusion indices. The ensemble model demonstrated improved stability and reduced classification uncertainty compared to single models, particularly in challenging hydromorphic environments. Although accuracy gains were modest, the models captured soil–environment relationships, with climate as: Annual Mean Temperature 22,000 years ago (Tmean_22k), relief: Channel Network Base Level (CNBL and altitude) and organism variables: Land Surface Temperature (LST) emerging as the main predictors. Spatialized uncertainty estimates, expressed through entropy and the confusion index, provide a practical decision-support tool for guiding field surveys and identifying areas of low mapping reliability. By explicitly transferring the pedologist’s mental model—encoded as tacit knowledge in legacy soil maps—into ensemble learning, this study presents a robust and transferable framework for updating soil maps in data-scarce tropical regions, balancing predictive performance, spatial consistency, and uncertainty-aware interpretation. Full article

This study investigates the cooling effects of coastal wetland systems in Hue City, Vietnam. The analysis focuses on their riparian buffer zones, defined here as areas within 600 m of the wetland boundary. Landsat 8 imagery was used to derive land surface temperature (LST) from 1 March to 31 July 2025—a recent period marked by multiple heatwaves across the region. To assess the cooling performance of wetlands, data samples were collected within the buffer zones. A Light Gradient Boosting Machine was trained to characterize the relationship between cooling intensity and a set of influencing factors (e.g., distance to wetland boundary, land use/land cover, built-up density, and green space density). The model explains approximately 91% of the variation in cooling intensity around wetlands. Notably, a machine-learning-based simulation framework was proposed to attain insights into the cooling characteristics of the riparian zone. The result indicates a mean cooling effect of about 2 °C and an effective cooling distance of 210 m from the wetland boundary. Partial dependence analysis further reveals that increasing built-up density substantially weakens cooling performance and implies that, for the conditions observed in Hue City, maintaining built-up density near wetlands below roughly 45% is favorable for sustaining effective cooling of the blue space, as indicated by the model-based partial dependence analysis. Overall, the research findings provide a data-driven basis for informing urban planning and wetland management in Hue City to mitigate heat stress. Full article

Urban heat islands (UHIs) pose escalating threats to public health and thermal comfort in dense urban environments. However, physics-based evaluations of material-specific cooling interventions and their integration into operational digital twin platforms remain limited. This study develops an integrated framework connecting computational fluid dynamics (CFD) modeling with digital twin visualization to evaluate UHI mitigation strategies. The objectives are to quantify the thermal mitigation effects of surface emissivity optimization on land surface temperature (LST) and pedestrian-level air temperature ($T_{air}$) to establish a data preprocessing pipeline converting CFD outputs into platform-independent visualization datasets, and to comparatively evaluate 2D GIS-based and 3D voxelization visualization approaches. Four emissivity scenarios were simulated using STAR-CCM+ for a 4 km² residential area in Gwacheon City, Republic of Korea. Comprehensive optimization (Case D) reduced the mean LST from 46.6 °C to 42.0 °C and T_air from 35.7 °C to 35.3 °C. Concrete-only optimization achieved 90.5% of the total thermal reduction while decreasing spatial variability (σ) from 7.1 to 5.8 during peak hours. The voxel-based 3D visualization provided a superior representation of vertical thermal stratification compared to 2D mapping. These findings establish a scalable foundation for climate-responsive urban management. 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

Rapid urbanisation in tropical megacities intensifies urban heat islands, especially during summer. Peri-urban wetlands help combat surface thermal stress through evapotranspiration, thermal inertia, and hydrological connectivity. However, their cooling effects are often oversimplified. This study assesses the complex cooling role of peri-urban wetlands, using a geospatial framework with Landsat imagery. We analyse land surface temperature (LST) variability and cooling patterns across the East Kolkata Wetlands (EKW). Results show a sharp thermal gradient, with waterbodies as the coolest surfaces (mean 25.4 °C) and dumping grounds as intense hotspots (mean 35.75 °C). Built-up areas adjacent to water are significantly cooler than urban cores. Cooling exhibits non-linear distance-decay and directional asymmetry, extending several kilometres but attenuated by dense western urban development. Internal thermal disruptions from dumping grounds create localised heat plumes. The findings demonstrate that wetland cooling is governed by hydrological connectivity and landscape permeability. Thus, conserving waterbody networks and mitigating thermally disruptive land uses are therefore critical. This positions peri-urban wetlands as dynamic climate-regulating infrastructure, offering a nature-based solution for urban heat adaptation that aligns with the sustainable development goals (SDGs). Full article

The Urban Heat Island (UHI) effect represents a major barrier to sustainable urban development, amplifying energy demand, public health risks, and climate vulnerability. This study provides an advanced geospatial assessment of UHI dynamics in Southampton, UK, using Landsat 8 and 9 imagery (2017–2023) to evaluate seasonal and interannual variations relevant to climate-resilient urban planning. This study integrates spatial techniques, including Land Surface Temperature estimation, NDVI-based emissivity modelling, hotspot analysis, and urban–rural gradient profiling, to identify persistent UHI hotspots concentrated in high-density commercial and industrial zones, with intensities reaching 2–3 °C above the citywide mean. It combines seasonal UHI mapping, hotspot analysis, and urban–rural gradient profiling to provide a comprehensive assessment of Southampton’s thermal landscape. The findings reveal persistent UHI hotspots in the city centre and industrial zones, with intensity peaks of 2–3 °C above the mean. Temporal analysis reveals winter-intensified UHI patterns, consistent with climate-sensitive processes observed in temperate coastal environments. Green spaces demonstrate measurable cooling benefits (up to ~1 °C), underscoring their role as sustainable nature-based mitigation strategies. By delivering a replicable, data-driven framework for continuous environmental monitoring, the research directly supports sustainable urban design, targeted greening interventions, and climate-adaptation policies. The findings provide practical tools for reducing heat stress, enhancing energy efficiency, and strengthening long-term urban resilience in medium-sized coastal cities. Full article