Search Results (49)

Surface urban heat island intensity (SUHII) is a critical indicator of the urban heat island (UHI) effect. However, discrepancies in estimation methods may introduce uncertainty in SUHII values. While previous studies have examined the responses of SUHII to different methods at large scales, further analysis is needed for plateau cities in southwestern China, which have complex geographical features. This study investigates the spatiotemporal patterns and influencing factors of SUHII in 200 plateau cities across southwestern China via nine estimation methods that incorporate rural ranges and elevation-based conditions. The results show that: (1) The annual average daytime and nighttime SUHII for these cities were 0.97 ± 0.78 °C (mean ± std) and 0.21 ± 0.87 °C, respectively. For 22% of the cities during the day and 26% at night, the choice of different SUHII estimation methods resulted in the transformation between a surface urban heat island (SUHI) and a surface urban cold island (SUCI) due to the exclusion of rural pixels more than ±50 m from the median urban elevation. Compared with other regions, high-altitude plateau cities exhibited a slightly lower daytime SUHII but a significantly higher nighttime SUHII because of the lower atmospheric pressure in plateau areas, which limits the conduction and retention of heat. Consequently, heat dissipates more quickly at night, increasing SUHII values. (2) The mean ΔSUHII_AD (absolute difference in SUHII values across methods) was 0.51 ± 0.01 °C during the day and 0.44 ± 0.02 °C at night. (3) In high-altitude plateau cities, for all methods, the correlation of the SUHII with influencing factors was stronger, highlighting their sensitivity to both environmental and anthropogenic influences. These results enhance our understanding of plateau UHI dynamics and highlight the importance of considering appropriate rural definitions for cities with varying geographical characteristics. Full article

The urban heat island (UHI) effect, marked by higher temperatures in urban areas compared to rural ones, is a key indicator of human-driven environmental changes. This study aims to identify the key morphological parameters that primarily contribute to the development of surface urban heat island intensity (SUHII) and investigates the relationship between SUHII and urban morphology using land surface temperature (LST) data from the Sentinel-3 satellite. The research focuses on Milan and Lecce, analyzing how urban geometry affects SUHII. Factors such as building height, aspect ratio, sky visibility, and surface cover are examined using approximately 1000 satellite images from 2022 and 2023. The study highlights seasonal and diurnal variations in SUHII, with particular emphasis on HW periods. Through multicollinearity and multiple regression analyses, the study identifies the main morphological drivers influencing SUHII in the two cities, specifically the Impervious Surface Fraction (ISF) and Mean Building Height (HM). Milan consistently exhibits higher SUHII, particularly during HWs, while Lecce experiences a negative SUHII, especially during the summer, due to lower urban density, more vegetation, and the low soil moisture around the urban area. Both cities show positive SUHII values at night, which are slightly elevated during HWs. The heat wave analysis reveals the areas most susceptible to overheating, typically characterized by high urban density, with ISF and HM values in some cases above the 90th percentile (0.8 and 13.0 m, respectively) compared to the overall distribution, particularly for Milan. The research emphasizes the importance of urban morphology in influencing SUHII, suggesting that detailed morphological analysis is crucial for developing climate adaptation and urban planning strategies to reduce urban overheating and improve urban resilience to climate change. Full article

This study evaluates the effectiveness of sustainable urban regeneration projects in mitigating Urban Heat Island (UHI) effects through a place-based approach. Geographic Information Systems (GIS) and satellite imagery were integrated with machine learning (ML) models to analyse the urban environment, human activities, and climate data in Turin, Italy. A detailed analysis of the ex-industrial Teksid area revealed a significant reduction in Surface Urban Heat Island Intensity (SUHII), with decreases of −0.94 in summer and −0.54 in winter following regeneration interventions. Using 17 variables in the Random Forest model, key determinants influencing SUHII were identified, including building density, vegetation cover, and surface albedo. This study quantitatively highlights the impact of increasing green spaces and enhancing surface materials to improve solar reflectivity, with findings showing a 19.46% increase in vegetation and a 3.09% rise in albedo after mitigation efforts. Furthermore, the results demonstrate that integrating Local Climate Zones (LCZs) into urban planning, alongside interventions targeting these key variables, can further optimise UHI mitigation and assess changes. This comprehensive approach provides policymakers with a robust tool to enhance urban resilience and guide sustainable planning strategies in response to climate change. Full article

In the context of sustainable urban development, elucidating urban heat island (UHI) dynamics in arid regions is crucial. By thoroughly examining the characteristics of UHI variations and potential driving factors, cities can implement effective strategies to reduce their impacts on the environment and public health. However, the driving factors of a UHI in arid regions remain unclear. This study analyzed seasonal and diurnal variations in a surface UHI (SUHI) and the potential driving factors using Pearson’s correlation analysis and an Optimal Parameters-Based Geographic Detector (OPGD) model in 22 cities in Xinjiang, northwest China. The findings reveal that the average annual surface urban heat island intensity (SUHII) values in Xinjiang’s cities were 1.37 ± 0.86 °C, with the SUHII being most pronounced in summer (2.44 °C), followed by winter (2.15 °C), spring (0.47 °C), and autumn (0.40 °C). Moreover, the annual mean SUHII was stronger at nighttime (1.90 °C) compared to during the daytime (0.84 °C), with variations observed across seasons. The seasonal disparity of SUHII in Xinjiang was more significant during the daytime (3.91 °C) compared to nighttime (0.39 °C), with daytime and nighttime SUHIIs decreasing from summer to winter. The study also highlights that the city size, elevation, vegetation cover, urban form, and socio-economic factors (GDP and population density) emerged as key drivers, with the GDP exerting the strongest influence on SUHIIs in cities across Xinjiang. To mitigate the UHI effects, measures like urban environment enhancement by improving surface conditions, blue–green space development, landscape optimization, and economic strategy adjustments are recommended. Full article

Understanding the driving mechanisms behind surface urban heat island (SUHI) effects is essential for mitigating the degradation of urban thermal environments and enhancing urban livability. However, previous studies have primarily concentrated on central urban areas, lacking a comprehensive analysis of the entire metropolitan area over distinct time periods. Additionally, most studies have relied on regression analysis models such as ordinary least squares (OLS) or logistic regression, without adequately analyzing the spatial heterogeneity of factors influencing the surface urban heat island (SUHI) effects. Therefore, this study aims to explore the spatial heterogeneity and driving mechanisms of surface urban heat island (SUHI) effects in the Guangzhou-Foshan metropolitan area across different time periods. The Local Climate Zones (LCZs) method was employed to analyze the landscape characteristics and spatial structure of the Guangzhou-Foshan metropolis for the years 2013, 2018, and 2023. Furthermore, Geographically Weighted Regression (GWR), Multi-scale Geographically Weighted Regression (MGWR), and Geographical Detector (GD) models were utilized to investigate the interactions between influencing factors (land cover factors, urban environmental factors, socio-economic factors) and Surface Urban Heat Island Intensity (SUHII), maximizing the explanation of SUHII across all time periods. Three main findings emerged: First, the Local Climate Zones (LCZs) in the Guangzhou-Foshan metropolitan area exhibited significant spatial heterogeneity, with a non-linear relationship to SUHII. Second, the SUHI effects displayed a distinct core-periphery pattern, with Large lowrise (LCZ 8) and compact lowrise (LCZ 3) areas showing the highest SUHII levels in urban core zones. Third, land cover factors emerged as the most influential factors on SUHI effects in the Guangzhou-Foshan metropolis. These results indicate that SUHI effects exhibit notable spatial heterogeneity, and varying negative influencing factors can be leveraged to mitigate SUHI effects in different metropolitan locations. Such findings offer crucial insights for future urban policy-making. Full article

Optimizing urban spatial morphology is one of the most effective methods for improving the urban thermal environment. Some studies have used the local climate zones (LCZ) classification system to examine the relationship between urban spatial morphology and Surface Urban Heat Islands (SUHIs). However, these studies often rely on single-time-point data, failing to consider the changes in urban space and the time-series LCZ mapping relationships. This study utilized remote sensing data from Landsat 5, 7, and 8–9 to retrieve land surface temperatures in Changsha from 2005 to 2020 using the Mono-Window Algorithm. The spatial-temporal evolution of the LCZ and the Surface Urban Heat Island Intensity (SUHII) was then examined and analyzed. This study aims to (1) propose a localized, long-time LCZ mapping method, (2) investigate the spatial-temporal relationship between the LCZ and the SUHII, and (3) develop a more convenient SUHI assessment method for urban planning and design. The results showed that the spatial-temporal evolution of the LCZ reflects the sequence of urban expansion. In terms of quantity, the number of built-type LCZs maintaining their original types is low, with each undergoing at least one type change. The open LCZs increased the most, followed by the sparse and the composite LCZs. Spatially, the LCZs experience reverse transitions due to urban expansion and quality improvements in central urban areas. Seasonal changes in the LCZ types and the SUHI vary, with differences not only among the LCZ types but also in building heights within the same type. The relative importance of the LCZ parameters also differs between seasons. The SUHI model constructed using Boosted Regression Trees (BRT) demonstrated high predictive accuracy, with R² values of 0.911 for summer and 0.777 for winter. In practical case validation, the model explained 97.86% of the data for summer and 96.77% for winter. This study provides evidence-based planning recommendations to mitigate urban heat and create a comfortable built environment. Full article

Local climate zones (LCZs) and urban functional zones (UFZs) can intricately depict the multidimensional spatial elements of cities, offering a comprehensive perspective for understanding the surface urban heat island (SUHI) effect. In this study, we retrieved two types of land surface temperature (LST) data and constructed 12 SUHI scenarios over the Guangdong–Hong Kong–Macao Greater Bay Area Central region using six SUHI identification methods. It compared the SUHI sensitivity differences among different types of LCZ and UFZ to analyze the global and local sensitivity differences of influencing factors in the 12 SUHI scenarios by utilizing the spatial gradient boosting trees, geographically weighted regression, and the coefficient of variation model. Results showed the following: (1) The sensitivity of different LCZ and UFZ types to multi-scenario SUHI was significantly affected by differences in SUHI identification methods and non-urban references. (2) In the morning, the shading effect of building clusters reduced the surface urban heat island intensity (SUHII) of some built environment types (such as LCZ 1 (compact high-rise zone) to LCZ 5 (open midrise zone)). The SUHIIs of LCZ E (bare rock or paved zone) and LCZ 10 (industry zone) were 4.22 °C and 3.87 °C, respectively, and both are classified as highly sensitive to SUHI. (3) The sensitivity of SUHI influencing factors exhibited regional variability, with importance differences in the sensitivity of importance for factors such as the impervious surface ratio, elevation, average building height, vegetation coverage, and average building volume between LCZs and UFZs. Amongst the 12 SUHI scenarios, an average of 87.43% and 89.97% of areas in LCZs and UFZs, respectively, were found to have low spatial sensitivity types. Overall, this study helps urban planners and managers gain a more comprehensive understanding of the complexity of the SUHI effect in high-density cities, providing a scientific basis for future urban climate adaptability planning. Full article

With the continuous development of cities, the surface urban heat island intensity (SUHII) is increasing, leading to the deterioration of the urban thermal environment, increasing energy consumption, and endangering the health of urban residents. Understanding the spatio-temporal scale difference and gradient effect of urban spatial patterns on the impact of SUHII is crucial for improving the climate resilience of cities and promoting sustainable urban development. This paper investigated the characteristics of SUHII changes at different time periods based on local climate zones (LCZs) and downscaled land surface temperature (LST) data. Meanwhile, landscape pattern indicators and the multiscale geographically weighted regression (MGWR) model were utilized to analyze the impacts of urban spatial patterns on SUHII at multiple spatial–temporal scales. The results indicated that the SUHII of each LCZ type exhibited diverse patterns in different time periods. High SUHII occurred in summer daytime and autumn nighttime. Compact and high-rise buildings (LCZ1/2/4) showed markedly higher SUHII during the daytime or nighttime, except for heavy industry. The extent of influence and the dominant factors of LCZ spatial patterns on SUHII exhibit obvious scale differences and gradient effects. At the regional scale, highly regular and compacted built-up areas tended to increase SUHII, while single and continuously distributed built-up areas had a greater impact on increasing SUHII. At the local scale, the impact of the PLAND (1/2/4/5/10) on SUHII exhibited a trend of diminishing from urban to suburban areas. In urban areas, the PLAND of LCZ 1, LCZ 2, and LCZ4 was the major factor affecting the increase in SUHII, whereas, in suburban areas, the PLAND of LCZ 2 and LCZ 10 was the major influencing factor on SUHII. The results can provide a scientific reference for mitigating urban heat island effects and constructing an ecologically ‘designed’ city. Full article

Rapid urbanization has resulted in increased environmental challenges, compounding worries about deteriorating air quality and rising temperatures. As cities become hubs of human activity, understanding the complex interplay of numerous environmental elements is critical for developing effective mitigation solutions. Recognizing this urgency, a framework to highlight the hotspots with critical environmental issues emerges as a comprehensive approach that incorporates key criteria such as the surface urban heat island intensity (SUHII), heat index (HI) and air quality index (AQI) to assess and address the complex web of environmental stressors that grip urban landscapes. Employing the multicriteria decision analysis approach, the proposed framework, named the environmental risk hotspot mapping framework (ERHMF), innovatively applies the analytic hierarchy process at a sub-criteria level, considering long-term heat island trends with recent fluctuations in the HI and AQI. Climate change impact has been symbolized through rising temperatures, as reflected by surface urban heat island intensity trends over two decades. The robustness and correctness of the weights have been assessed by computing the consistency ratio, which came out as 0.046, 0.065 and 0.044 for the sub-criteria of the SUHII, AQI and HI, respectively. Furthermore, the framework delves into the nexus between environmental stressors and vegetation cover, elucidating the role of green spaces in mitigating urban environmental risks. Augmented by spatial and demographic data, the ERHMF adeptly discerns high-risk areas where environmental stress converges with urban development, vulnerable population concentrations and critical vegetation status, thereby facilitating targeted risk management interventions. The framework’s effectiveness has been demonstrated in a regional case study in Italy, underscoring its ability to pinpoint risk hotspots and inform specific policy interventions. The quantitative study undertaken at the sub-administrative level revealed that approximately 6,000,000 m² of land in Bologna are classified as being under high to extremely high environmental stress, with over 4,000,000 m² lying only within the extremely high stress group (90–100). Similarly, 1,000,000 m² of land in Piacenza and Modena have high levels of environmental stress (80–90). In conclusion, the ERHMF presents a holistic methodology for delineating high-risk urban hotspots, providing essential insights for policymakers, urban planners and stakeholders, with the potential to enhance overall urban resilience and foster sustainable development efforts. Full article

The acceleration of urbanization has led to an increase in urban expansion and population density, exacerbating the urban heat island (UHI) effect. Moreover, the phenomenon has a significant impact on urban ecological environments and human health. Consequently, mitigating the UHI effect and enhancing the ecological environment is crucial. However previous research has primarily focused on individual cities or regional scales, with few studies analyzing all cities within urban agglomerations. This paper conducts a fine-grained spatiotemporal analysis of surface urban heat island (SUHI) effects in the Central Yunnan City Cluster from 2000 to 2021 using Landsat satellite data. We calculate the surface urban heat island intensity (SUHII) for 44 cities at the county or district level and discuss the quantitative estimation of overall SUHII changes and driving factors in the Central Yunnan City Cluster. Our findings are as follows: 1. Small cities also exhibit UHI effects, with a 75.4% probability of occurrence in the Central Yunnan City Cluster from 2000 to 2021, resulting in an overall decrease in SUHII of 1.21 °C. 2. The temperature increase rate in urban extension areas and suburban areas is faster than that in urban central areas, which is the main reason for the decreasing trend of SUHII. 3. Land use change inhibits the weakening of the SUHI effect, and population change contributes to the formation of this phenomenon. Additionally, the methods and results of this study can provide reasonable and effective insights for the future development and planning of the Central Yunnan City Cluster, thus promoting urban sustainable development. Full article

Various cities in China have been identified as “stove cities” either in contemporary or historical times, exposing residents to extremely high temperatures. Existing studies on the heat island effect in stove cities are not representative nationwide. The outdated nature of these studies also significantly diminishes the relevance of their findings. Thus, reassessing the urban heat island (UHI) effect of stove cities is necessary in the context of global climate change and urbanization. This study focuses on seven symbolic and geographically distributed stove cities in China, including Nanjing, Chongqing, Wuhan, Fuzhou, Beijing, Xi’an, and Turpan. Using land surface temperature (LST) data, this study investigates the summer heat island effect from 2013 to 2023 and analyzes changes in the spatial distribution of the heat island effect. This paper utilizes impervious surface data and urban clustering algorithms to define urban and suburban areas. It then examines the evolution and spatial distribution of surface urban heat island intensity (SUHII) over time. Incorporating urbanization variables like population density and urban area, the study analyzes the main factors affecting the heat island effect from 2013 to 2018. We find that all cities continuously expand, with the annual average heat island effect intensifying over the years. With the exception of Beijing, the summer heat island or cool island effects in the remaining six cities show an overall intensification trend. From 2013 to 2018, SUHII has been primarily related to urban expansion and planning layout, with minimal impact from factors such as population density. Full article

The urban–rural temperature difference is widely used in measuring surface urban heat island intensity (SUHII), where the accurate determination of rural background is crucial. However, traditionally, the entire permeable rural surface has been selected to represent the background temperature, leaving uncertainty about the impact of non-uniform rural surfaces with multiple land covers on the accuracy of SUHII quantification. In this study, we proposed two quantifications of SUHII derived from the primary (SUHII₁) and secondary (SUHII₂) land types, respectively, which successively occupy over 40–50% of whole rural regions. The spatial integration and temporal variation of SUHII₁ and SUHII₂ were compared with the result from whole rural regions (SUHII) within 34 urban agglomerations (UAs) in China. The results showed that the SUHII₁ and SUHII₂ differed slightly with SUHII, and the correlation coefficients of SUHII and SUHII₁/SUHII₂ are generally above 0.9 in most (32) UAs. Regarding the long-term SUHII between 2003 and 2019, the three methods demonstrated similar seasonal patterns, although SUHII₁ (or SUHII₂) tended to overestimate or underestimate compared to SUHII. As for the multi-year integration at the regional scale, the day–night cycle and monthly variations of SUHII₁ and SUHII were found to be identical for each geographical division separately, indicating that the spatiotemporal pattern revealed by SUHII is minimally affected by the diversity of rural landcover types. The findings confirmed the viability of the urban–rural LST difference method for measuring long-term regional SUHII patterns under non-uniform rural land cover types. Full article

Urban heat islands (UHIs) aggravate urban heat stress and, therefore, exacerbate heat-related morbidity and mortality as global warming continues. Numerous studies used surface urban heat island intensity (SUHII) to quantify the change in the UHI effect and its drivers for heat mitigation. However, whether the variations in SUHII among cities can demonstrate the physical difference and fluctuation of the urban thermal environment is poorly understood. Here, we present a comparison study on the temporal trends of SUHII and LST in urban and nonurban areas in 13 cities of the Beijing–Tianjin–Hebei (BTH) megaregion in China and further identify different types of changes in SUHII based on the temporal trends of land surface temperature (LST) in urban and nonurban areas from 2000 to 2020. We also measured the effect of the changes in four socioecological factors (i.e., population density, vegetation greenness (EVI), GDP, and built-up area) on the trends of SUHII to understand the dynamic interaction between the UHI effect and socioecological development. We found the following. (1) Nine out of thirteen cities showed a significant increasing trend in SUHII, indicating that the SUHI effects have been intensified in most of the cities in the BTH megaregion. (2) The spatial pattern of summer mean SUHII and LST in urban areas varied greatly. Among the 13 cities, Beijing had the highest mean SUHII, but Handan had the highest urban temperature, which suggests that a city with stronger SUHII does not necessarily have a higher urban temperature or hazardous urban thermal environment. (3) Four types of changes in SUHII were identified in the 13 cities, which resulted from different temporal trends of LST in urban areas and nonurban areas. In particular, one type of increasing trend of SUHII in seven cities resulted from a greater warming trend (increasing LST) in urban than nonurban areas (SUHII↑₁), and another type of increasing trend of SUHII in Beijing and Chengde was attributed to the warming trends (increasing LST) in urban areas and the cooling trends (decreasing LST) in nonurban areas (SUHII↑₂). Meanwhile, the third type of increasing trend of SUHII in Zhangjiakou was due to a greater cooling (decreasing LST) trend in nonurban areas than in urban areas (SUHII↑₃). In contrast, three cities with a decreasing trend of SUHII were caused by the increase in LST in urban and nonurban areas, but the warming trend in nonurban areas was greater than in urban areas (SUHII↓₁). (4) Among the relationship between the trend of SUHII (Trend_SUHII) and the changes in socioecological factors (Trend_{population density}, Trend_{GDP per captica}, Trend_EVI, and Trend_{build-up area}), a significantly positive correlation between Trend_SUHII and Trend_EVI indicated that the change in SUHII was significantly related to an increased rate of EVI. This is mainly because increased vegetation in nonurban areas would result in lower temperatures in nonurban areas. Full article

Surface urban heat islands (SUHIs) are mostly an urban ecological issue. There is a growing demand for the quantification of the SUHI effect, and for its optimization to mitigate the increasing possible hazards caused by SUHI. Satellite-derived land surface temperature (LST) is an important indicator for quantifying SUHIs with frequent coverage. Current LST data with high spatiotemporal resolution is still lacking due to no single satellite sensor that can resolve the trade-off between spatial and temporal resolutions and this greatly limits its applications. To address this issue, we propose a multiscale geographically weighted regression (MGWR) coupling the comprehensive, flexible, spatiotemporal data fusion (CFSDAF) method to generate a high-spatiotemporal-resolution LST dataset. We then analyzed the SUHI intensity (SUHII) in Chengdu City, a typical cloudy and rainy city in China, from 2002 to 2022. Finally, we selected thirteen potential driving factors of SUHIs and analyzed the relation between these thirteen influential drivers and SUHIIs. Results show that: (1) an MGWR outperforms classic methods for downscaling LST, namely geographically weighted regression (GWR) and thermal image sharpening (TsHARP); (2) compared to classic spatiotemporal fusion methods, our method produces more accurate predicted LST images (R², RMSE, AAD values were in the range of 0.8103 to 0.9476, 1.0601 to 1.4974, 0.8455 to 1.3380); (3) the average summer daytime SUHII increased form 2.08 °C (suburban area as 50% of the urban area) and 2.32 °C (suburban area as 100% of the urban area) in 2002 to 4.93 °C and 5.07 °C, respectively, in 2022 over Chengdu City; and (4) the anthropogenic activity drivers have a higher relative influence on SUHII than other drivers. Therefore, anthropogenic activity driving factors should be considered with CO₂ emissions and land use changes for urban planning to mitigate the SUHI effect. Full article

Urban heat islands have garnered significant attention due to their potential impact on human life. Previous studies on urban heat islands have focused on characterizing temporal and spatial variations over longer periods of time. In this study, we investigated the urban heat island (UHI) in Birmingham from September 2013 to August 2014 using higher temporal resolution SEVIRI satellite surface temperature data along with data from the Birmingham Urban Climate Laboratory (BUCL) meteorological station and the UK Meteorological Office meteorological station. Our aim was to characterize the diurnal variations in the surface urban heat island intensity (SUHII) and canopy urban heat island intensity (CUHII) and to explore their relationship under the influence of three factors (day/nighttime, season, and wind speed) using regression analysis. Our findings reveal that SUHII and CUHII exhibit relatively stable patterns at night but vary significantly during the day with opposite diurnal trends. In addition, SUHII and CUHII were more variable in spring and summer but less variable in winter. During the nighttime, SUHII represents CUHII with high confidence, especially during spring and summer, but less so during the cold season. In addition, SUHII represents CUHII with greater confidence under low-wind conditions. This study deepens our understanding of the diurnal dynamics of urban heat islands and the influence of atmospheric conditions on the relationship between surface and canopy heat islands in urban areas. The results of this study can be used for heat island studies in cities that lack high-precision observation networks and to guide sustainable urban development. Full article