Search Results (1,215)

Extreme heatwaves intensify urban heat islands and pose increasing risks to urban sustainability and human health. However, how urban morphology is associated with daytime and nighttime land surface temperature (LST) across urban functional zones (UFZs), particularly under heatwave conditions, remains insufficiently understood. To address this gap, this study integrates daytime and nighttime LST data derived from SDGSAT-1, multi-dimensional urban morphology indicators, and two interpretable ensemble models (XGBoost and GWRF) to investigate overall sample-level nonlinear model-based associations between urban morphology and LST and to explore spatial variation in local predictor importance within Beijing and Nanjing, China. Because the daytime and nighttime scenes were not always paired within the same heatwave episode, the analysis focuses on selected heatwave-condition observations. The results show marked contrasts between the selected daytime and nighttime observations in UFZ-level thermal patterns. Industrial zones generally exhibited the highest daytime LST, whereas residential zones showed the highest nighttime LST. Building density was identified as the primary model-based predictor of daytime LST in both cities, although its association with LST was nonlinear and varied across density ranges. In contrast, nighttime LST was characterized by more heterogeneous predictor associations, involving vegetation structure, sky openness, building form, anthropogenic indicators, and material-related variables, with their relative importance differing across cities and UFZ types. Local predictor-importance patterns also varied across neighborhoods, cities, and observation times, indicating that model-identified locally important predictors were not spatially uniform within each city. These findings highlight the potential of SDGSAT-1 daytime and nighttime thermal observations and interpretable machine learning for screening candidate local thermal priority areas and key morphology-related factors under heatwave conditions. Full article

Rapid urbanization is reshaping land surface conditions and local thermal environments in fast-growing coastal cities. This study examines how Land Use Land Cover (LULC) transformation influenced seasonal land surface temperature (LST) dynamics in Chattogram City Corporation (CCC), Bangladesh, over 2004–2024. Multi-temporal Landsat imagery was analyzed using a Random Forest classifier, and spectral indices (NDVI, NDBI, NDBaI, MNDWI) were derived to characterize surface biophysical conditions. Built-up land expanded by 27.71 km², largely replacing agricultural and vegetated areas. Summer mean LST rose from 36.08 °C to 36.50 °C, while winter LST rose from 25.25 °C to 26.97 °C. Only the winter warming trend is statistically significant; the summer change falls within the ±1–2 °C retrieval uncertainty of Landsat-derived LST. The summer–winter thermal gap consequently narrowed from 10.83 °C to 9.53 °C, indicating that urbanization-driven warming in this tropical coastal city is disproportionately concentrated in the cool dry season. Partial correlation and multiple regression analyses confirm that built-up intensity (NDBI) is the dominant driver of surface warming, while vegetation (NDVI) exerts a consistent cooling influence. Water bodies showed contrasting seasonal trends, with winter extent declining alongside a slight summer increase. These findings highlight the critical role of vegetation and water bodies in moderating urban heat and provide data-driven insights for climate-responsive planning in rapidly urbanizing coastal cities. Full article

Urban Heat Island (UHI) effects are increasingly acknowledged as a critical urban climate challenge with far-reaching consequences for human health and overall well-being. However, the conceptual structure, temporal evolution, and intellectual landscape of studies examining the relationships between UHI and human well-being remain fragmented, thereby constraining the development of integrated knowledge frameworks needed to guide future research, urban adaptation strategies, and evidence-based policy interventions. To this end, a total of 4857 studies were retrieved from the Scopus and Web of Science databases and screened following the PRISMA guidelines. These studies were then analyzed using Bibliometrix and VOSviewer. The results reveal a rapid and exponential growth in scientific output, particularly after 2010, with the output reaching its highest level in recent years. These outputs were shaped mostly in China and the United States with a well-established international collaboration network, while the Global South remain significantly underrepresented in scientific productions. We also found that studies are primarily structured around four dominant research clusters: urban heat island, thermal comfort, land surface temperature, and climate change. Furthermore, early studies predominantly focused on urban surface properties and built-environment characteristics, while recent research has increasingly shifted toward human health impacts, thermal stress, heat vulnerability, and well-being. Emerging research directions further highlight growing interest in nature-based solutions for mitigating UHI effects, alongside the application of advanced technologies such as machine learning and remote sensing for high-resolution urban climate assessment. Overall, our findings indicate a transition toward a more integrated urban climate–health–well-being research framework, while simultaneously revealing persistent geographical and conceptual gaps, particularly across the Global South. We therefore advocate for increased empirical research, stronger international collaboration, and context-specific urban adaptation strategies to better safeguard human well-being under intensifying urban heat conditions. Full article

The escalating climate crisis presents a profound challenge to global environmental equilibrium, with rapid urbanization acting as a primary catalyst for land-use transformation. This study investigates the intricate relationship between land use and land cover changes (LULC) and the intensification of the Surface Urban Heat Island (SUHI) effect within the Shillong Planning Area (SPA). By integrating remote sensing data with advanced geospatial modeling and machine learning architectures which include Random Forest (RF), Support Vector Machine (SVM), and XGBoost, the research provides a comprehensive analysis of environmental shifts from 2000 to 2024, with predictive projections extending to 2034 and 2044. The analysis reveals a significant expansion in the built environment, with the Normalized Difference Built-up Index (NDBI) rising from 0.17 to 0.26. This urban growth has come at the expense of ecological health, as evidenced by a decline in the Normalized Difference Vegetation Index (NDVI) from a peak of 0.87 down to 0.74. A strong negative correlation between vegetative density and Land Surface Temperature (LST) underscores the critical role of green infrastructure in regional climate regulation. SUHI projections using the RF model, which achieved an Area Under the Curve (AUC) of 0.868, estimate SUHI values of 6.02 °C for 2034 and 6.66 °C for 2044. Predicted LULC scenarios for 2034 and 2044 suggest continued urban expansion, likely intensifying thermal stress. The application of predictive modeling through machine learning provides a robust framework to inform climate-resilient urban planning and sustainable land management. Full article

Urban Heat Island (UHI) effects are increasingly affecting Mediterranean coastal cities, where climate change, urbanization, and seasonal tourism intensify thermal stress and environmental vulnerability. In this context, climate-oriented planning and green infrastructure are recognized as key strategies for urban adaptation. This study investigates the spatiotemporal evolution of Land Surface Temperature (LST) and vegetation cover in the coastal municipality of Lignano Sabbiadoro (northeastern Italy) through the analysis of Landsat imagery acquired between 1984 and 2023. Summer LST and Normalized Difference Vegetation Index (NDVI) maps were derived from June–August observations and used to assess long-term thermal dynamics, vegetation patterns, and Urban Heat Island development. Meteorological data indicate a significant increase in mean annual air temperature, with a warming trend of approximately 0.57 °C per decade between 1984 and 2023. Correspondingly, Landsat-derived LST maps reveal a marked intensification of summer surface temperatures, with mean summer LST increasing from 32.16 °C in 1984–1993 to a peak of 35.91 °C in 2004–2013, followed by a slight decrease to 35.77 °C during 2014–2023. During the same period, the proportion of municipal surfaces characterized by temperatures above 35 °C increased from 10.7% to more than 60%, while cooler areas (<30 °C) declined from 17.7% to 2.3%. The comparison between LST and NDVI patterns revealed a persistent inverse relationship between vegetation cover and surface temperature, with coastal pinewoods, green spaces, and water bodies consistently exhibiting lower thermal values than densely urbanized sectors. A key methodological contribution of the study is the operational integration of satellite-derived thermal remote sensing into the Green Plan of Lignano Sabbiadoro. LST mapping was used to identify priority areas for climate adaptation measures, including ecological corridors, wooded landscape connections, urban green corridors, and depaving interventions. The results demonstrate how multi-temporal thermal analysis can support evidence-based planning by linking climate assessment with the spatial prioritization and design of green infrastructure strategies. The proposed workflow provides a transferable framework for integrating remote sensing into climate-informed planning processes in Mediterranean coastal cities and other urban contexts increasingly exposed to heat-related risks. Full article

Urban river corridors play an important role in reducing urban heat and supporting vegetation, yet their environmental influence is still poorly understood in rapidly growing African cities. This study examined the spatial and temporal relationship between vegetation cover (NDVI), land surface temperature (LST), and built-up intensity (NDBI) along the Kebena River corridor in Addis Ababa, Ethiopia, using Landsat 8 and 9 imagery from 2015, 2018, 2022, and 2026. A buffer-based approach (0–250 m, 250–500 m, and 500–1000 m) was applied to evaluate how these indicators change with distance from the river. The results reveal a clear environmental gradient. The 0–250 m zone consistently showed higher vegetation cover, lower surface temperature, and lower built-up intensity compared with the outer zones. Pearson correlation analysis indicated a significant negative relationship between NDVI and LST (r = −0.49 to −0.64) and a strong negative relationship between NDVI and NDBI (r = −0.83 to −0.90), while NDBI and LST were positively correlated (r = 0.51 to 0.67). Regression analysis further showed that a 0.1 increase in NDVI reduced LST by approximately 2.7–3.9 °C. The river corridor continued to provide measurable cooling benefits, notwithstanding a slight reduction in vegetation cover over the study period. The lower built-up intensity near the river in 2026 is consistent with the timing and recorded activities of the recent Kebena River rehabilitation programme. These results indicate that conservation and restoration of urban river corridors can improve ecological resilience, mitigate the effects of urban heat and support the sustainability of green infrastructure in rapidly urbanising cities. This study offers evidence to guide conservation planning and long-term management of urban riparian ecosystems in Addis Ababa and other cities facing similar environmental pressures. Full article

Rapid urbanization has intensified two critical urban challenges: the urban heat island effect and stormwater runoff. This study evaluates the pilot-level field performance of a low-tortuosity permeable pavement (LTPP) system in potentially contributing to improved thermal regulation and hydraulic functionality. The system comprises a reduced-tortuosity upper block incorporated with bottom ash (BA) as a recycled fine aggregate and an underlying storage unit connected through an interlocking configuration, enabling direct infiltration while reducing clogging susceptibility and improving resistance to settlement and displacement. Field tests included thermal imaging, water-spraying infiltration-storage and vehicle-loading observations, and theoretical storage analysis. Initially, conventional permeable pavement (PP) dry surface temperature was measured at 44.2 °C, whereas the LTPP system already exhibited a lower temperature of 42.4 °C. During the evaporative stage after wetting, the LTPP system showed a lower temperature recovery rate, with a 2.91% increase between 90 and 120 min compared with 3.60% for conventional permeable pavement, indicating improved surface-temperature regulation. The storage calculations approximated that the LTPP system could theoretically buffer the simulated 15.63 mm/h rainfall by 6.65 to 7.32 h. It was also determined using historical rainfall data that the LTPP system, especially when provided with an outlet or drainage system, could effectively accommodate short- to medium-duration rainfall. Water-spraying tests confirmed rapid infiltration and subsurface storage, while vehicle-loading observations showed no noticeable displacement or settlement. These findings highlight the potential of a multifunctional permeable pavement design strategy that combines low-tortuosity flow paths, functional recycled aggregate selection, and subsurface storage for surface-temperature regulation and stormwater management. Full article

Urban noise complaints reflect not only perceived acoustic disturbance, but also complaint behaviour shaped by thermal conditions and the built environment. Using Sanya, China, as a case study, this study integrated Landsat-derived land surface temperature data with the spatial distribution of noise complaints to examine how thermal environment and urban contextual factors jointly influence complaint patterns. An interpretable modeling framework combining eXtreme Gradient Boosting (XGBoost) and Multiscale Geographically Weighted Regression (MGWR) was employed to assess the associations of urban heat island intensity (UHI), road density, point of interest (POI) count, and population density on complaint occurrence and intensity, while also generating spatial predictions of complaint distribution. The results revealed a weak but statistically significant spatial association between the thermal environment and noise complaints, with urban heat island intensity showing nonlinear and spatially heterogeneous associations with complaint counts. POI count emerged as the strongest global predictor, while road density, population density, and thermal environment exhibited substantial spatial heterogeneity in their associations with complaint patterns. The integrated model outperformed both individual models alone, achieving the highest prediction accuracy. Overall, the findings suggest that urban noise complaint patterns reflect not only perceived acoustic disturbance, but also context-dependent social perception and reporting behaviour, providing empirical support for more place-sensitive and people-centered urban noise governance. Full article

The intensification of heat waves and the increase in average temperatures, particularly evident in the Mediterranean context, underline the urgency of strengthening sustainable spatial planning, introducing concrete tools to understand and manage how urban form, land-use and local microclimate interact with each other. In this direction, the paper proposes an integrated methodological path to map territorial exposure to surface heat stress in densely urbanised contexts, applying it to the case study of the Caserta conurbation. The approach uses several levels of analysis: (i) the estimation of the Normalised Difference Vegetation Index (NDVI) and the calculation of Land Surface Temperature (LST) from the Landsat series (1987–2022), using radiance/reflectance measurements and deriving LST from the Top-of-Atmosphere Brightness Temperature (BT) and from the Land Surface Emissivity (LSE); (ii) the consistency check of satellite-derived surface temperature using meteorological station data from the archive of the Multi-Risk Functional Center of the Civil Protection of the Campania Region, fed by the measurements of the sensors installed in the area by the Campania Regional Environmental Protection Agency (ARPAC); (iii) the evaluation of the intensity of the Surface Urban Heat Island (SUHI) as the difference between LST values extracted from paired urban and non-urbanised reference areas; (iv) the verification of the relationship between NDVI and LST by linear regression, with NDVI as the explanatory variable and LST as the dependent variable, showing a statistically consistent inverse relationship. The results support a multi-scalar reading of the intervention priorities, highlighting that the potential relevance of nature-based solutions (NBS) and cooling strategies varies according to local urban morphology, geographical configuration, and degree of soil sealing, requiring context-specific planning evaluations. Full article

Contemporary research on ecological building materials focuses on the use of industrial waste as an alternative to traditional raw materials. Utilizing byproducts such as fly ash, slag, and plastic waste not only solves the problem of landfill but often improves the technical parameters of the finished products. Contemporary research on sustainable building materials focuses on the use of industrial waste as an alternative to traditional raw materials. This review article, which opens this Special Issue, provides a comprehensive conceptual framework for sustainable materials and introduces clear criteria for selecting the main global waste streams. Besides systematizing the existing literature, the work critically addresses the most pressing challenges in the sector, such as the upcoming shortage of traditional fly ash resulting from the European energy transition and the volumetric instability of steel slag. Looking ahead, as an opening article, it outlines key avenues for further analysis—including advanced chemical surface modifications and supply chain regionalization—necessary to reduce embodied carbon footprints and combat urban heat islands. Ultimately, the work highlights the systemic possibilities of multi-stream waste upcycling, defining ecological and methodological benchmarks that justify and guide further academic research and industrial implementation in sustainable construction. Full article

To evaluate the application potential of convolutional neural network (CNN)-based Local Climate Zone (LCZ) mapping in urban thermal environment studies, this study employed a lightweight convolutional neural network model (Light-model) to classify LCZs within the area enclosed by the Fourth Ring Road of Xuzhou City. ECOSTRESS data obtained from summer (June to September) at different times were integrated to analyze the temporal and spatial changes of surface temperature (LST) and surface urban heat island intensity (SUHII). The classification results demonstrate that the Light-model achieved an overall accuracy of 84.46%, which is markedly higher than that of the random forest model (72.07%). It also outperformed random forest in built-up area identification (built-up overall accuracy: 69.41% vs. 46.91%) and non-built-up area identification (natural overall accuracy: 91.85% vs. 84.43%), as well as in Kappa coefficient and mean F1-score. Time-series analysis based on ECOSTRESS observations revealed a typical diurnal LST pattern characterized by the lowest temperatures before dawn, a peak in the afternoon, and a decline at night. High-density built-up zones (LCZ1–LCZ3) and large impervious areas (LCZ8) exhibited the highest daytime temperatures and the slowest nocturnal cooling, whereas bare soil areas (LCZF) showed the largest diurnal temperature range and the greatest fluctuations. Vegetation-covered and bare land zones (LCZA and LCZD) generally maintained lower temperatures, while water bodies (LCZG) functioned as persistent cooling sources throughout the day due to their high specific heat capacity. Overall, the findings suggest that CNN-based LCZ classification, when integrated with high-temporal-resolution LST observations, provides a reliable technical framework for urban thermal environment monitoring and regulation at the regional scale. Full article

Urban heat intensification is an increasing concern in rapidly urbanizing arid cities, where extreme climatic conditions intersect with expansive urban growth. This study examines the spatiotemporal dynamics of urban thermal patterns in the Doha metropolitan region, Qatar, by integrating multi-season remote sensing with urban morphological analysis. Seasonal composites of land surface temperature (LST), Urban Heat Island (UHI) intensity, and Normalized Difference Vegetation Index (NDVI) were derived from Landsat 8–9 Collection 2 Level-2 imagery across eight seasons from Spring 2024 to Winter 2026. Urban form was characterized using Local Climate Zones (LCZs) and quantified through class-level landscape metrics, i.e., Largest Patch Index (LPI), Number of Patches (NP), and CLUMPY. The results showed a pronounced seasonal variability, with LST ranging from approximately 12.5 °C in winter to 61.3 °C in summer, and intra-urban UHI exceeding 10 °C during peak conditions. The bare soil/sand, with relative coverage of 52.84% and LPI of 25.45%, and the large low-rise, with relative coverage of 38.60% and LPI of 14.70%, typologies dominate the landscape, forming highly aggregated spatial structures, while vegetation cover remained minimal. Weak negative relationships between NDVI and thermal indicators revealed that vegetation alone had limited explanatory power. In contrast, LCZ-based analysis revealed a better thermal differentiation across urban typologies, with compact forms associated with higher thermal intensities. These findings highlight the dominant role of urban morphology and spatial configuration in shaping thermal patterns and support the need for morphology-sensitive planning strategies in arid urban environments. Full article

Urban heat island effects increasingly challenge pedestrian mobility by intensifying thermal stress and reducing the attractiveness of walking during hot periods. However, most pedestrian routing systems still prioritize distance or travel time, while environmental conditions such as heat exposure and shade are rarely incorporated into operational route generation. Existing comfort-aware approaches often rely on static maps, simulated microclimatic indicators, or descriptive greenery measures, limiting their direct integration into user-configurable pedestrian navigation. This study develops a thermal comfort-aware pedestrian routing framework that integrates heterogenic data sources including observed land surface temperature, pedestrian-perspective tree-canopy coverage, and network distance into a unified multi-criteria pathfinding model. The workflow proceeds in four steps: first, airborne thermal imagery is processed to derive a high-resolution land surface temperature layer; second, Google Street View images are sampled at street-segment locations and segmented using SegFormer to extract visible tree-canopy coverage; third, both environmental indicators are aggregated to a cleaned pedestrian network; and fourth, normalized distance, temperature, and canopy attributes are combined through a user-adjustable edge-cost formulation and solved using Dijkstra’s algorithm. The framework is implemented as an operational web-based routing tool for the historic center of Clermont-Ferrand, France. The routable graph includes 551 nodes and 796 edges, with 600 segments carrying GSV-derived canopy information and 623 segments carrying airborne-derived LST values. Across the network, we observed LST ranges from 19.5 °C to 39.1 °C, while canopy coverage ranged from 0 to 70.6%. For a representative origin–destination pair, the coolest route reduces average LST by nearly 5 °C and almost triples canopy coverage compared with the shortest path, although at the cost of a 72% longer distance. These results demonstrate that the framework can generate interpretable comfort–efficiency trade-offs and support user-comfort pathfinding as an operational approach for heat-resilient pedestrian navigation. Full article

Urban heat islands (UHIs) significantly influence microclimatic conditions, energy consumption, and public health. This research leverages ensemble models and correlation analysis based on Landsat 5-8 satellite data to forecast LST and explore its environmental relationships. This study employed the XGBoost machine learning algorithm to model seasonal LST dynamics in three rapidly urbanizing Arkansas cities, including Fort Smith, Little Rock, and Northwest Arkansas, using Landsat imagery from 2001 to 2021. The results show significant increases in urban heat, particularly in the summer, with Fort Smith seeing an increase in the area classified in higher-temperature bins (35–45 °C) from approximately 33% in 2001 to more than 83% by 2021. Model validation showed high predictive performance (R² = 0.74–0.78, RMSE ≤1.46 °C), indicating reliable project-based estimation of spatial LST variability for 2026 and 2031. The results revealed a substantial intensification of built-up area expansion, to 9.8% by 2026 and 20.7% by 2031, accompanied by cropland reductions of 13.2% and 25.5%, respectively. This rapid urban growth is projected to elevate summer LSTs above 45 °C across more than 700 km² combined, and winter LSTs to ≥25 °C across nearly 125 km² in the region by 2031. The integration of Landsat time series data and machine learning provide valuable insights for urban planners and policymakers, underscoring the critical importance of targeted climate-resilient strategies and sustainable urban development practices. Full article

Urban Heat Islands (UHIs) intensify extreme heat, raise energy demand, and risk citizen thermal comfort in densely built cities. However, spatially detailed, scenario-differentiated estimates of future UHI intensity are still limited for complex coastal mountainous cities. This study set out to forecast UHI intensity variations in Busan, South Korea, under SSP2-4.5 and SSP5-8.5 scenarios. Daily temperatures from 19 automatic weather stations (2010–2014) were spatially interpolated using Empirical Bayesian Kriging Regression (EBKR), which included elevation and coastline distance variables. Among the 16 CMIP6 Global Climate Models (GCMs) tested, CNRM-CM6-1 (r = 0.902, RMSE = 4.937 °C) was chosen and bias-corrected using Empirical Quantile Mapping (EQM). The results reveal that mean maximum UHI intensity rises gradually, with ΔUHI (compared with the 2010–2014 baseline of 0.90 °C) reaching +7.03 °C (SSP2-4.5) and +9.60 °C (SSP5-8.5) in the far future, roughly 1.43 times more under the high-emission scenario. Summer through autumn has a UHI intensity increase, whereas long-term warming concentrates in Busan’s urban core. These findings inform targeted urban heat adaptation strategies, prioritizing green infrastructure, cool urban surfaces, and energy-resilient city planning to protect human well-being. Full article