Search Results (205)

The effects of extreme heat events on Surface Urban Heat Island Intensity (SUHII) were investigated in Lecce (southern Italy) during the summer months (June–August) from 2018 to 2025. The analysis began with the identification of heatwave frequency, duration, and intensity using the Warm Spell Duration Index (WSDI), based on a homogenized long-term temperature record, which indicated a progressive increase in persistent extreme events in recent years. High-resolution ECOSTRESS land surface temperature (LST) data were then processed and combined with CORINE Land Cover (CLC) information to examine the thermal response of different urban fabrics, compact residential areas, continuous/discontinuous urban fabric, and industrial–commercial zones. SUHII was derived from each ECOSTRESS acquisition and evaluated across multiple diurnal intervals to assess temporal variability under both normal and WSDI conditions. The results show a consistent diurnal asymmetry: daytime SUHII becomes more negative during WSDI periods, reflecting enhanced rural warming under dry and highly irradiated conditions, despite overall higher absolute LST during heatwaves, whereas nighttime SUHII intensifies, particularly in dense urban areas where higher thermal inertia promotes persistent heat retention. Statistical analyses confirm significant differences between normal and extreme conditions across all classes and time intervals. These findings demonstrate that extreme heat events alter the urban–rural thermal contrast by amplifying nighttime heat accumulation and reinforcing daytime negative SUHII values. The integration of WSDI-derived heatwave characterization with multi-year ECOSTRESS observations highlights the increasing thermal vulnerability of compact urban environments under intensifying summer extremes. Full article

Rapid urban growth in South Indian coastal cities such as Chennai has intensified the Urban Heat Island (UHI) effect, with paved parking lots, walkways, and open spaces acting as major heat reservoirs. This study specifically compares conventional construction materials with natural and low-thermal-inertia alternatives to evaluate their relative ability to mitigate Surface Urban Heat Island (SUHI) effects. Unlike previous studies that examine isolated materials or single seasons, this pilot provides a unified, multi-season comparison of nine urban surfaces, offering new evidence on their comparative cooling performance. To assess practical mitigation strategies, a field pilot was conducted using nine surface types commonly employed in the region—concrete, interlocking tiles, parking tiles, white cooling tiles, white-painted concrete, natural grass, synthetic turf, barren soil, and a novel 10% coconut-shell biochar concrete. The rationale of this comparison is to evaluate how conventional, reflective, vegetated, and low-thermal-inertia surfaces differ in their capacity to reduce surface heating, thereby identifying practical, material-based strategies for SUHI mitigation in tropical cities. Surface temperatures were measured at four times of day (pre-dawn, noon, sunset, night) across three months (winter, transition, summer). Results revealed sharp noon-time contrasts: synthetic turf and barren soil peaked above 45–70 °C in summer, while reflective coatings and natural grass remained 25–35 °C cooler. High thermal-mass materials such as concrete and interlocked tiles retained heat into the evening, whereas grass and reflective tiles cooled rapidly, lowering late-day and nocturnal heat loads. Biochar concrete performed thermally similarly to conventional concrete but offered co-benefits of ~10% cement reduction, carbon sequestration, and sustainable reuse of locally abundant coconut shell waste. Full article

The COVID-19 pandemic presented an unprecedented opportunity to assess the environmental effects of reduced anthropogenic activity on urban climates. This study investigates the impact of COVID-19-induced lockdowns on land surface temperature (LST) and the intensity of the surface urban heat island (SUHI) in Nowshera District, Khyber Pakhtunkhwa Province, Pakistan, which is experiencing rapid urbanization. Using Landsat 8/9 imagery, we assessed thermal changes across three periods: pre-lockdown (April 2019), during lockdown (April 2020), and post-lockdown (April 2021). Remote sensing indices, including NDVI and NDBI, were applied to evaluate the relationship between land cover and LST. Our results show a significant reduction in average LST during lockdown, from 31.38 °C in 2019 to 25.34 °C in 2020, a 6 °C decrease. Urban–rural LST differences narrowed from 9 °C to 6 °C. A one-way ANOVA confirmed significant differences in LST across the three periods (F (2, 3) = 3691.46, p < 0.001), with Tukey HSD tests indicating that the lockdown period differed significantly from both the pre- and post-lockdown periods (p < 0.001). SUHI intensity fell from 35.10 °C to 28.89 °C during lockdown, then rebounded to 35.37 °C post-lockdown. The indices analysis shows that built-up and rangeland areas consistently recorded the highest LST (e.g., 35.36 °C and 37.09 °C in 2021, respectively), while vegetation and water bodies maintained lower temperatures (34.68 °C and 32.69 °C in 2021). NDVI confirmed the cooling effect of green areas, while high NDBI values correlated with increased LST in urban areas. These findings underscore the impact of human activity on urban heat dynamics and highlight the role of sustainable urban planning and green infrastructure in enhancing climate resilience. By exploring the relationships among land cover, anthropogenic activity, and urban climate resilience, this research offers policymakers and urban planners’ valuable insights for developing adaptive, low-emission cities amid rapid urbanization and climate change. Full article

The urban heat island (UHI) has been a critical social problem as urbanization intensifies worldwide, significantly impacting human life by exacerbating heat-related health issues, increasing energy demand for cooling, and resulting in associated environmental problems. However, the fine-scale diurnal and spatial characteristics of UHI remain poorly understood due to the limited resolution of traditional satellite datasets. This study aims to quantify the diurnal and spatial dynamics of surface urban heat islands (SUHI) in Busan and Daegu—the two hottest metropolitan cities in Korea—by integrating high-resolution ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) (70 m) and Geostationary Korea Multi-Purpose Satellite-2A (GK-2A) (2 km) land surface temperature (LST) data. Using the combined datasets, season-representative diurnal LST variations were characterized, and locational heat intensification (LHI) was evaluated across land use types and densities at sub-district scales. The results show that the maximum SUHI intensity reached 10 °C in Daegu and 7 °C in Busan during summer, up to 8 °C higher than estimates from coarse-resolution data. Industrial areas recorded the highest LST (47 °C in Daegu and 43 °C in Busan) with rapid morning intensification rates of 2.0 °C/h and 1.9 °C/h, respectively. Dense urban land uses amplified LHI by nearly twofold compared to less dense urban areas. These findings emphasize the critical role of land use density and industrial heat emissions in shaping urban thermal environments, providing key insights for use in urban heat mitigation and climate-adaptive planning. Full article

Heatwaves are intensifying across Mediterranean cities, where the Urban Heat Island (UHI) effect amplifies thermal stress. This study updates the spatial characterization of the Surface Urban Heat Island (SUHI) in Tarragona using multi-sensor remote sensing data within a Local Climate Zone (LCZ) framework. Land surface temperature, albedo, and the Normalized Difference Vegetation Index (NDVI) were analyzed during heatwaves from 2015–2025 to assess spatial patterns and drivers of urban heating. Results reveal a daytime urban cool island associated with low albedo and scarce vegetation, and a nocturnal SUHI caused by heat retention in dense built-up areas. High-resolution mapping identifies industrial and commercial zones as hotspots, while vegetated and water-covered areas act as cooling sites. These findings clarify the spatial dynamics and key biophysical controls of SUHI and provide an actionable basis for prioritizing locally tailored adaptation strategies in Mediterranean coastal cities. Full article

Rapid urbanization elevates land surface temperature (LST) through complex urban spatial relationships, intensifying the urban heat island (UHI) effect. This necessitates efficient methods to analyze surface urban heat island (SUHI) factors to help develop mitigation strategies. In this study, we propose an efficient global–local regression (EGLR) framework by integrating XGBoost-SHAP with global–local regression (GLR), enabling accelerated estimation of LST. In a case study of Wuhan, the EGLR reduces the computation time of GLR by 44.21%. The main contribution of computational efficiency improvement lies in the procedure of Moran eigenvector selecting executed by XGBoost-SHAP. Results of validation experiments also show significant time decrease of the EGLR for a larger sample size; in addition, transplanting the framework of the EGLR to two machine learning models not only reduces the executing time, but also increases model fitting. Furthermore, the inherent merits of XGBoost-SHAP and GLR also enables the EGLR to simultaneously capture nonlinear causal relationships and decompose spatial effects. Results identify population density as the most sensitive LST-increasing factor. Impervious surface percentage, building height, elevation, and distance to the nearest water body are positively correlated with LST, while water area, normalized difference vegetation index, and the number of bus stops have significant negative relationships with LST. In contrast, the impact of the number of points of interest, gross domestic product, and road length on LST is not significant overall. Full article

The increased reliance on thermal satellite images for urban climatic analysis requires robust temperature retrievals for urban surfaces. As the emissivity of any surface type determines the amount of thermal radiation received by a sensor, accurate emissivity values of reflecting surfaces are important in Land Surface Temperature (LST) computations. It is known that the commonly used Temperature Emissivity Separation (TES) algorithm is inaccurate over low-emissivity surfaces such as desert sand and metallic surfaces. However, in indigenous cities, much of the satellite ‘seen’ surface consists of metallic roofing materials like corrugated iron or aluminum. This study uses 853 ECOSTRESS images to examine the diurnal and seasonal pattern of LST for five indigenous cities in sub-Saharan Africa. Surface Urban Cool Islands (SUCIs) were observed in all five cities during both summer and winter, which were more pronounced during daytime than at night. This conflicts with air temperature data and published reports, as well as the dominant low-rise urban morphology, which would suggest the occurrence of Surface Urban Heat Islands (SUHIs). The influence of emissivity on urban LST was examined by allocating more realistic emissivity values to metallic surfaces. For a Landsat image, LST values for the urban area increased from 41 °C to 44, 46, and 49 °C when metallic surfaces were allocated emissivity values of 0.96, 0.83, 0.74, and 0.63, respectively, and SUHIs, rather than SUCIs, were observed. Similar results were obtained for an ECOSTRESS image. As increasing summer temperatures cause significant morbidity and mortality in the populations of these cities, accurate urban climatic data are essential. Full article

Extreme climate events such as heatwaves are becoming more frequent and pose serious challenges in cities. Urban areas are particularly vulnerable because built surfaces absorb and release heat, while human activities generate additional greenhouse gases. This increases health risks, making it crucial to study population exposure to heat stress. This research focuses on Naples, Italy’s most densely populated city, where intense human activity and unique geomorphological conditions influence local temperatures. The presence of a Surface Urban Heat Island (SUHI) is assessed by deriving high-resolution Land Surface Temperature (LST) in a time series ranging from 2013 to 2023, processed with the Statistical Mono Window (SMW) algorithm in the Google Earth Engine (GEE) environment. SMW needs brightness temperature (Tb) extracted from a Landsat 8 (L8) Thermal InfraRed Sensor (TIRS), emissivity from Advanced Spaceborne and Thermal Emission Radiometer Global Emissivity Database (ASTERGED), and atmospheric correction coefficients from the National Center for Environmental Prediction and Atmospheric Research (NCEP/NCAR). A total of 64 nighttime images were processed and analyzed to assess long-term trends and identify the main heat islands in Naples. The hottest image was compared with population data, including demographic categories such as children, elderly people, and pregnant women. A risk index was calculated by combining temperature values, exposure levels, and the vulnerability of each group. Results identified three major heat islands, showing that risk is strongly linked to both population density and heat island distribution. Incorporating Local Climate Zone (LCZ) classification further highlighted the urban areas most prone to extreme heat based on morphology. Full article

The climate and environment of metropolitan areas have been negatively impacted by swift urbanization and industrialization. Surface Urban Heat Islands (SUHIs) are among the most critical environmental phenomena. This research focused on the spatiotemporal analysis of land use/land cover (LULC) changes in relation to surface urban heat islands and their interconnections from 1992 to 2022. Land Surface Temperature (LST), LULC, and LULC indices, such as the Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI), were generated using Landsat data. Urban hot spots (UHSs) were identified, and the Urban Thermal Field Variance Index (UTFVI) was then used to evaluate the spatiotemporal variation in thermal comfort. The results indicated LST values between a low of 14.24 and a maximum of 46.30. Urban areas and exposed surfaces, such as open or bare soil, exhibit the highest surface radiant temperatures. Conversely, regions characterized by vegetation and water bodies have the lowest. Additionally, this study explored the correlation between LULC, LULC indices, LST, and SUHIs. LST and NDBI show a positive relationship because of urbanization and industrialization (R² = 0.57 for the year 1992, R² = 0.38 for the year 2010, and R² = 0.35 for the year 2022), while LST shows an inverse relationship with NDVI and NDMI. Urban development should account for thermal sensitivity in densely populated regions. This study introduced an innovative spatiotemporal framework for monitoring long-term changes in urban surface environments. Furthermore, this research can assist planners in creating urban green spaces in cities of developing nations to minimize the adverse impacts of urban heat islands and improve thermal comfort. Full article

Rapid urbanization in semi-arid heritage cities is accelerating land use/land cover (LULC) transitions, with critical implications for local climate regulation, surface energy balance, and environmental sustainability. This study investigates Jaipur, Jodhpur, and Udaipur (Rajasthan, India) between 2018 and 2024 to assess the influence of spatio-temporal dynamics of LULC on urban surface metrics. Multi-temporal satellite datasets were used to derive the index-based built-up index (IBI), surface urban heat island intensity (SUHI), Albedo, urban thermal field variance index (UTFVI), and bare soil index (BSI). The results reveal substantial built-up expansion—most pronounced in Udaipur (+26.7%)—coupled with vegetation loss (up to −23.8% in Jaipur) and progressive albedo decline (Sen’s slope ≈ −0.002 yr⁻¹). These transformations highlight suppressed surface reflectivity and enhanced heat absorption. A key and novel finding is the emergence of a counter-intuitive surface urban cool island (SUCI) effect, whereby urban cores exhibited daytime cooling and nighttime warming relative to rural surroundings. This anomaly is attributed to the rapid heating and poor nocturnal heat retention of bare, sparsely vegetated rural soils, contrasted with the thermal inertia and shading of urban surfaces. By documenting negative SUHI patterns and explicitly linking them to LULC trajectories, this study advances the understanding of urban climate dynamics in semi-arid contexts. The findings underscore the need for climate-sensitive planning—strengthening peri-urban green belts, regulating impervious expansion, and adopting albedo-enhancing construction materials—while safeguarding cultural heritage. More broadly, the study contributes empirical evidence from climatically vulnerable yet culturally significant cities, offering insights relevant to global SUHI research and sustainable urban development. Full article

Local Climate Zones (LCZs) provide a critical framework for analyzing how urban morphology influences the surface urban heat island (SUHI) effect. However, the spatiotemporal heterogeneity of the driving mechanisms of urban morphology in SUHI within LCZs under urban renewal remains insufficiently understood. In this study, estimated building heights for 2018, 2021, and 2024 in the main urban area of Guangzhou were used to generate LCZ maps using GIS-based methods. Land surface temperatures (LSTs) were retrieved to quantity the SUHI effect. The Geographical-XGBoost (G-XGBoost) model was applied to evaluate the impacts of urban morphology on SUHI. The results indicated the following: (1) Building height estimation errors range from 5.92 to 7.03 m, and incorporating building height data into LCZ classification enabled sensitive detection of urban evolution dynamics. (2) Built LCZ types accounted for the majority of the study area. Between 2018 and 2024, LCZ 3 decreased markedly, by 9.57%, and land cover LCZ types declined annually to 21.35%. (3) Low-level SUHII was predominant, while the proportion of high and extremely high levels of SUHII initially rose before declining to 16.62%. LCZ 2 and LCZ 3 exhibited the highest SUHII. (4) Pervious surface fraction (PSF) is generally regarded as the most important explanatory factor across LCZ types; however, LCZ 4 represents an exception where its importance significantly decreases. This study reveals the nonlinear impacts of urban morphology evolution on SUHI under the effect of the interaction between LCZs and urban renewal, supporting efforts to optimize urban microclimates and promote sustainable development. Full article

The widespread availability and spatial coverage of land surface temperature (LST) estimates from space often result in LST being used as a proxy for near-surface air temperature in order to characterize the urban heat island (UHI) effect. High-spatial-resolution satellite-based LST estimates from sensors such as Landsat-8 provide the spatial and thematic details necessary to understand the potential effects of urban greening measures to mitigate the increased frequency and intensity of heatwaves that are projected to occur as a result of human-induced climate change. Here, we investigate the influence of land cover on Surface Urban Heat Island (SUHI) observations of LST using a technique to reduce the spatial spread of the per-pixel temperature observation. Additionally, using land cover-based linear mixture models, we downscale the surface temperature to a 2 m spatial resolution. We find a mean difference in LST, compared to the city average, of +8.94 °C (+/−1.87 °C at 95% CI) for built-up cover type, compared to a difference of −7.42 °C (+/−0.8 °C) for broadleaf trees. This highlights the potential benefits of creating urban green spaces for mitigating the UHI amplification of extreme heatwaves. Furthermore, we highlight the need for improved observations of night-time temperatures, e.g., from forthcoming missions such as TRISHNA, in order to fully capture the diurnal variability of land surface temperature and energy fluxes. Full article

This study examines diurnal surface temperature dynamics across major Moroccan cities during the growing season and explores the interaction between urban and vegetated surfaces. We also introduce the Urban Thermal Impact Ratio (UTIR), a novel metric designed to quantify urban thermal comfort as a function of the surface urban heat island (SUHI) intensity. The analysis is based on outputs from a land surface model (LSM) for the year 2010, integrating high-resolution Landsat and MODIS data to characterize land cover and biophysical parameters across twelve land cover types. Our findings reveal moderate urban–vegetation temperature differences in coastal cities like Tangier (1.8 °C) and Rabat (1.0 °C), where winter vegetation remains active. In inland areas, urban morphology plays a more dominant role: Fes, with a 20% impervious surface area (ISA), exhibits a smaller SUHI than Meknes (5% ISA), due to higher urban heating in the latter. The Atlantic desert city of Dakhla shows a distinct pattern, with a nighttime SUHI of 2.1 °C and a daytime urban cooling of −0.7 °C, driven by irrigated parks and lawns enhancing evapotranspiration and shading. At the regional scale, summer UTIR values remain below one in Tangier-Tetouan-Al Hoceima, Rabat-Sale-Kenitra, and Casablanca-Settat, suggesting that urban conditions generally stay within thermal comfort thresholds. In contrast, higher UTIR values in Marrakech-Safi, Beni Mellal-Khénifra, and Guelmim-Oued Noun indicate elevated heat discomfort. At the city scale, the UTIR in Tangier, Rabat, and Casablanca demonstrates a clear diurnal pattern: it emerges around 11:00 a.m., peaks at 1:00 p.m., and fades by 3:00 p.m. This study highlights the critical role of vegetation in regulating urban surface temperatures and modulating urban–rural thermal contrasts. The UTIR provides a practical, scalable indicator of urban heat stress, particularly valuable in data-scarce settings. These findings carry significant implications for climate-resilient urban planning, optimized energy use, and the design of public health early warning systems in the context of climate change. Full article

Surface urban heat island (SUHI) effects are intensifying in arid desert cities due to rapid urban expansion, limited vegetation, and increasing impervious and barren land surfaces. This leads to serious ecological and socio-environmental challenges in cities. This study investigates the relationship between landscape composition and land surface temperature (LST) in Phoenix and Tucson, two rapidly growing cities located in the Sonoran Desert of the southwestern United States. Landsat-9 OLI-2/TIRS-2 satellite imagery was used to derive the LST value and calculate spectral indices. A multi-resolution grid-based approach was applied to assess spatial correlations between land cover and mean LST across varying spatial scales. The strongest positive correlations were observed with barren land, followed by impervious surfaces, while green space showed a negative correlation. Furthermore, the Urban Thermal Field Variation Index (UTFVI) and the Ecological Evaluation Index (EEI) assessments indicated that over one-third of both cities are exposed to strong SUHI effects and poor ecological quality. The findings highlight the critical need for ecologically sensitive urban planning, emphasizing the importance of the morphological structure of cities, the necessity of planning holistic blue–green infrastructure systems, and the importance of reducing impervious surfaces to decrease LST, mitigate SUHI and SUHI impacts, and increase urban resilience in desert environments. These results provide evidence-based guidance for landscape planning and climate adaptation in hyper-arid urban environments. Full article

Urbanization has emerged as a significant driver of environmental change, particularly impacting local climates through the creation of urban heat islands (SUHIs). SUHIs, characterized by higher temperatures in urban or metropolitan areas than in their rural surroundings, have become a critical focus of urban climate studies. This study aims to examine the spatial and temporal dynamics of both thermal and vegetative indices (BT, LST, NDVI, NDBI, BUI, ECI, SUHI, UTFVI) across different land cover types in Samsun, Türkiye, in order to assess their contribution to the urban heat island effect. Specifically, brightness temperature (BT), land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), built-up index (BUI), environmental condition index (ECI), urban heat island (SUHI) intensity, and urban thermal field variance index (UTFVI) were calculated and assessed. The analysis utilized cloud-free Landsat 8 imagery sourced from the US Geological Survey via the Google Earth Engine platform, employing a one-year median for each pixel using a cloud masking algorithm. Land use and land cover (LULC) classification was conducted using the random forest (RF) algorithm with satellite composite imagery, achieving an overall accuracy of 85% for 2014 and 86% for 2023. This study provides a detailed analysis of the effects of various land use and cover types on temperature, vegetation, and structural characteristics, revealing the role of changes in different land types on the urban heat island effect. In the LULC classification, water bodies consistently maintained low LST values below 23 °C for both years, while built-up land exhibited the greatest temperature increase, from approximately 25 °C in 2014 to more than 31 °C in 2023. The analysis also revealed that LST varies with the size and type of vegetation, with a mean LST differential between all green spaces and urban areas averaging 7–8 °C, and differences reaching 12 °C in industrial zones. Full article