Surface Urban Heat Island Effect Research Articles

Quantitative Analysis and Spatial Pattern Research of Built-Up Environments and Surface Urban Heat Island Effect in Beijing’s Main Urban Area

Quantitative Analysis and Spatial Pattern Research of Built-Up Environments and Surface Urban Heat Island Effect in Beijing’s Main Urban Area

Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine

Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (Tair), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, this study analyzes time-series data from Landsat and MODIS satellites, alongside Tair climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in Tair and LST during the summer months from 1984 to 2023, with a notable positive correlation between Tair and LST across both datasets. MODIS data exhibit a stronger correlation (R2 = 0.879) compared to Landsat (R2 = 0.663). The application of a supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations: a 70.3% increase in urban land and a decrement in vegetative cover comprising a 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse vegetation into urban land, underscoring a pronounced trajectory towards urbanization. Temporal and seasonal LST variations across different LULC classes were analyzed using kernel density estimation (KDE) and boxplot analysis. Urban areas and sparse vegetation had the smallest average LST fluctuations, at 2.09 °C and 2.16 °C, respectively, but recorded the most extreme LST values. Water and dense vegetation classes exhibited slightly larger fluctuations of 2.30 °C and 2.24 °C, with the bare land class showing the highest fluctuation 2.46 °C, but fewer extremes. Quantitative analysis with the application of Kolmogorov-Smirnov tests across various LULC classes substantiated the normality of LST distributions p > 0.05 for both monthly and annual datasets. Conversely, the Shapiro-Wilk test validated the normal distribution hypothesis exclusively for monthly data, indicating deviations from normality in the annual data. Thresholded LST classifies urban and bare lands as the warmest classes at 39.51 °C and 38.20 °C, respectively, and classifies water at 35.96 °C, dense vegetation at 35.52 °C, and sparse vegetation 37.71 °C as the coldest, which is a trend that is consistent annually and monthly. The analysis of SUHI effects demonstrates an increasing trend in UHI intensity, with statistical trends indicating a growth in average SUHI values over time. This comprehensive study underscores the critical role of remote sensing in understanding and addressing the impacts of climate change and urbanization on local and global climates, emphasizing the need for sustainable urban planning and green infrastructure to mitigate UHI effects.

Spatial-temporal analysis of urban climate dynamics in major Hungarian cities

Increasing heatwaves are making cities and their populations more vulnerable, parallel to urban sprawl and the aging population in Hungary. The increasing number of hot days is predicted to worsen urban climate anomalies at the local scale, which, in parallel with changing land use patterns, may contribute to a significant increase in vulnerability to heatwaves. Local stakeholders and decision-makers need to understand the critical role of spatiotemporal land use—land cover (LULC) patterns and urban climate aspects to address relevant challenges for urban development. The current literature does not contain a synthesis analysis of major Hungarian cities that includes urban climate and sustainability findings hand by hand; therefore, this study aims to analyze LULC patterns, urban hotspots and surface urban heat island effects. In addition, the Normalized Difference Vegetation Index (NDVI) was determined as an important indicator for assessing the health and density of green spaces in major Hungarian cities from 2006 to 2018 using remote sensing data. Our results show that each city experienced significant urban sprawl, while above-average NDVI areas decreased over time. The average increase in the share of built-up areas was 1.3% from 2006 to 2018, while the calculated average decline in agricultural areas was 2%, so the expansion of residential areas and artificial areas is not the only driving force of this shrinking trend in agricultural areas. Furthermore, we found that urban hotspots are generally concentrated in industrial areas and represent new spaces of heat islands on the outskirts of cities. Székesfehérvár has the most intense industrial heat islands, with the largest proportion of urban hotspots (approximately 3.5% of the total area) concentrated in industrial zones. Our study contributes to uncovering inter-urban processes of land use patterns and urban climate issues in major Hungarian cities, moreover revealing sustainability-related issues from a lock-in perspective.

Contrary to expectation: The surface urban heat island intensity is increasing in population shrinking region while decreasing in population growing region-A comparative analysis from China.

Exploring the complex relationship between population change and surface urban heat island (SUHI) effect has important practical significance for the ecological transformation development of shrinking cities in the context of the prevalence of urban shrinkage and the global climate change. This paper compares the population change and SUHI effect between population shrinking region (Northeast Region, NR) and population growing region (Yangtze River Delta, YRD) in China, and explores their differences in driving mechanisms, using GIS spatial analysis and Geodetector model. Our results indicated that there are significant differences in population changes and SUHI intensity between these two regions. About 72.22% of the cities in the NR were shrinking, while their SUHI intensities increased by an average of 1.69°C. On the contrary, the urban population in the YRD shows a linear growth trend, while their SUHI intensities decreased by 0.11°C on average. The results of bivariate Moran's I index also indicated that the spatial correlation between the urban population changes and the SUHI intensity changes are not significant in the above regions. Furthermore, there are significant differences in the primary drivers of SUHI variations between these two regions. In the NR, underlying surface changes, including the changes of green coverage and built-up areas, are the most important driving factors. However, atmospheric environment changes, such as carbon dioxide emission and sulfur dioxide emission, are the key drivers in the YRD. Northam's theory of three-stage urbanization and environmental Kuznets curve hypothesis are powerful to explain these differences.

Determination of land surface temperature and urban heat island effects with remote sensing capabilities: the case of Kayseri, Türkiye

Kayseri, a densely urbanized province in Türkiye, grapples with pressing challenges of air pollution and limited green spaces, accentuating the need for strategic urban planning. This study, utilizing Landsat 8 and Landsat 9 satellite imagery, investigates the evolution of land surface temperatures (LST) and urban heat island (UHI) effects in key districts—Kocasinan, Melikgazi, Talas, and Hacılar—between 2013 and 2022. This research has been complemented with an analysis of the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-Up Index (NDBI), exploring correlations among the LST, UHI, NDVI, and NDBI changes. The findings indicate that a significant portion (65% and 88%) of the study area remained unchanged with respect to the NDVI and NDBI differences. This research’s findings reveal that a substantial portion (65% and 88%) of the study area exhibited consistency in the NDVI and NDBI. Noteworthy increases in the NDVI were observed in 20% of the region, while only 4% exhibited higher NDBI. Strikingly, the UHI displayed strong negative correlations with the NDVI and robust positive correlations with the NDBI. The LST changes demonstrated a reduced temperature range, from 21 to 51 °C in 2013, to 18 to 40 °C in 2022. Localized environmental factors, notably at the National Garden site, showcased the most significant temperature variations. Notably, the UHI exhibited strong negative correlations with the NDVI and strong positive correlations with the NDBI. The study’s results emphasize the interplay among the NDBI, LST, and UHI and an inverse relationship with the NDVI and NDBI, LST, and UHI. These findings hold implications for urban planning and policymaking, particularly in the context of resilient and sustainable land use planning and the UHI mitigation. This research underscores the intricate interplay among the NDBI, LST, and UHI, highlighting an inverse relationship with the NDVI. These findings hold crucial implications for resilient and sustainable urban planning, particularly in mitigating the UHI effects. Despite limited vacant spaces in Kayseri, geospatial techniques for identifying potential green spaces can facilitate swift UHI mitigation measures. Acknowledging Kayseri’s complex dynamics, future research should delve into the UHI responses to urban morphology and design, extending this methodology to analyze the UHI effects in other Turkish cities. This research contributes to a broader understanding of UHI dynamics and sustainable urban planning practices, offering valuable insights for policymakers, urban planners, and researchers alike.

Different explanations for surface and canopy urban heat island effects in relation to background climate

The background climatic conditions and urban morphology greatly influence urban heat island effects (UHIs), but one-size-fits-all solutions are frequently employed to mitigate UHIs. Here, attribution models for surface UHIs (SUHIs) and canopy UHIs (CUHIs) were developed to describe UHI formation. The contribution of factors to SUHIs and CUHIs shows similar dependencies on background climate and urban morphology. Furthermore, the factors that mainly contributed to CUHIs were more complex, and anthropogenic heat was the more critical factor. Influence from urban morphology also highlights that there is no one-size-fit-all solution for heat mitigation at the neighborhood. In particular, maintaining a low building density should be prioritized, especially mitigating CUHIs. Moreover, it is more effective to prioritize urban irrigation maintenance over increasing green cover in arid regions but the opposite in humid regions. The work can provide scientific evidence to support developing general and regional guidelines for urban heat mitigation.

Spatial Characteristics and Temporal Trend of Urban Heat Island Effect over Major Cities in India Using Long-Term Space-Based MODIS Land Surface Temperature Observations (2000–2023)

The alteration of the Earth’s surface due to urbanization and the formation of urban heat islands is one of the most evident and widely discussed anthropogenic impacts on Earth’s microclimate. The elevated land surface temperature in the urban perimeter compared with the surrounding non-urban area is known as the surface urban heat island (SUHI) effect. India has experienced swift urban growth over the past few decades, and this trend is expected to persist in years to come. The literature published on SUHI in India focuses only on a few specific cities, and there is limited understanding of its geospatial variation across a broader region and its long-term trend. Here, we present one of the first studies exploring the long-term diurnal (daytime, and nighttime), seasonal, and annual characteristics of SUHI in the 20 largest urban centers of India and its neighboring countries. The study highlights a statistically significant (95% confidence interval) rise in nighttime surface temperatures across major cities based on a linear fit over 23 years (2000–2023) of MODIS land surface temperature satellite observations. The nighttime SUHI was found to be more conspicuous, positive, and consistent when compared with daytime satellite observations. The nighttime SUHI for April–May–June representing the pre-monsoon and onset of monsoon months for the top 10 cities, ranged from 0.92 to 2.33 °C; for December–January–February, representing the winter season, it ranged from 1.38 to 2.63 °C. In general, the total change in the nighttime SUHI based on linear fit (2000–2023) for the top ten cities showed warming over the urban region ranging from 2.04 to 3.7 °C. The highest warming trend was observed during the months of May–June–July (3.7 and 3.01 °C) in Ahmedabad and Delhi, cities that have undergone rapid urbanization in the last two to three decades. The study identified strongly positive annual SUHI intensity during nighttime, and weakly negative to moderately positive annual SUHI intensity during daytime, for major cities. Jaipur (India), Lahore (Pakistan), Dhaka (Bangladesh), and Colombo (Sri Lanka) showed a nighttime SUHI intensity of 2.17, 2.33, 0.32, and 0.21 °C, respectively, during the months of April–May–June, and a nighttime SUHI intensity of 2.63, 1.68, 0.94, 0.33 °C, respectively, for the months of December–January–February (2000–2023). It is apparent that the geographical location (inland/coastal) of the city has a high influence on the daytime and nighttime SUHI patterns. The current research is intended to help city planners and policymakers better understand SUHI intensity (day and night/seasonal basis) for developing strategies to mitigate urban heat island effects.

Influences of urban spatial factors on surface urban heat island effect and its spatial heterogeneity: A case study of Xi'an

Optimizing the urban spatial form to mitigate the urban heat island (UHI) effect is considered an efficient technical approach. This study focused on district-scale UHIs in typical high-density cities in northern China. The urban spatial characteristics were extracted and quantified from multiple data sources. Utilizing remote sensing inversion and spatial analysis methods, we calculated the surface urban heat island (SUHI) intensity within each block and the values of 15 urban spatial factors. Multi-scale geographically weighted regression (MGWR) was employed to explore the impact of various factors on the SUHI and their heterogeneity in spatial relationships. Additionally, the random forest model was utilized to assess the contributions of selected factors to the SUHI. The results indicate that the building height and the ratio of permeable surfaces are the dominant factors influencing the SUHI, with contribution rates of 12.0% and 10.3%, respectively. The topographic relief has the least influence on the SUHI, and it can only explain 2.5% of variance. The local variable water area ratio exhibits a distinct cooling effect, while the industrial area ratio demonstrates a noticeable warming effect. The impact of most factors on the SUHI intensity exhibits significant spatial heterogeneity, which proves that the MGWR can better elucidate the extent of spatial variations in the coefficients of factors compared with the global model. The findings help reveal the causes of the spatial heterogeneity of the SUHI and provide a theoretical basis for addressing UHI issues.

Seasonal analysis of land surface temperature using local climate zones in peak forest basin topography: A case study of Guilin

Understanding the characteristics of Land Surface Temperature (LST) is crucial to mitigate the Surface Urban Heat Island (SUHI) effect. Local Climate Zones (LCZs) framework can be applied in LST analysis and SUHI quantification. However, this framework primarily focuses on evaluating detailed local-scale SUHI, thereby failing to provide a comprehensive assessment of the overarching SUHI trends at the urban level. Additionally, existing LST analyses ignored geographic factors, resulting in partial analysis of LST, especially in areas with complex geography. To fill these gaps, the study develops a SUHI assessment method combining an improved surface urban heat intensity ratio index with LCZs-based SUHII, enabling a dual evaluation of both overall and detailed SUHI effect. The impact of geographic factors, both topography and natural features, on seasonal LST patterns is investigated, especially within built LCZs. Additionally, the spatio-temporal image fusion model is employed to address issues of cloud effects or image mutilations in LST results. Guilin, with unique peak forest basin topography, is selected as a case study to represent complex geographical areas. The results reveal the following: (1) An abnormal heat island phenomenon occurs during winter due to Guilin's distinctive geomorphological conditions. (2) Cooling effects influenced by mountains and water bodies on different built LCZs exhibit seasonal variation, with the strongest cooling effect observed within a 50-m distance. LCZ 2 consistently exhibits the strongest cooling effect. The study offers valuable insights for guiding decisions in sustainable urban planning and design, especially in cities influenced by complex local geography.

Identifying the Effects of Vegetation on Urban Surface Temperatures Based on Urban–Rural Local Climate Zones in a Subtropical Metropolis

Many studies have observed the crucial role of vegetated local climate zone (LCZ) types in mitigating the surface urban heat island (SUHI) effect. However, research analyzing the spatial variations in land surface temperature (LST) in a metropolis based on an urban–rural LCZ scheme and exploring the cooling effects of different vegetation types is still lacking. Here, our study focuses on the Guangzhou–Foshan metropolis and aims to elucidate the spatial variations in LST in subtropical cities and the regulating effect of vegetation on LST changes. We used a normalized difference vegetation index (NDVI) and LST data from space-borne MODIS products for the years 2000, 2009, and 2019, as well as LCZ maps, urban–rural gradient data, and land use and land cover (LULC) maps. Urban–rural, seasonal, daytime, nighttime, and diurnal comparative analyses were conducted using logarithmic regression, Pearson partial correlation, and comparison analysis. The results showed that LST values for built LCZ types were generally higher than those of land cover LCZ types, showing a positive correlation with building density and height. The LST decreased logarithmically across the urban–rural gradients, with a rapid decrease initially in the near-gradient urban area, followed by a flattening trend in farther-gradient suburban and rural areas. Regarding vegetated LCZ types, the NDVI metrics showed a significant negative correlation with the LST during the daytime but a positive correlation during the nighttime. The cooling effect of vegetated LCZ types was evident, with an average cooling amplitude of 1.92 °C over the three investigated years. In conclusion, urban LST changes are closely associated with LCZ types, urban–rural gradients, NDVI values, and vegetation types. The cooling ability of vegetation exhibited seasonal and diurnal variations, with a special emphasis on the cooling effect of dense evergreen broadleaf forests. Our findings offer valuable insights and can guide urban ecological construction and management by comprehensively assessing the impact of vegetation on urban surface temperatures.

Ecological monitoring of urban thermal field variance index and determining the surface urban heat island effects in Lahore, Pakistan.

Lahore is the second major metropolitan city in Pakistan in terms of urban population and built-up area, making the city a more ideal place to form the surface urban heat island (SUHI) effects. In the last two decades, the considerable land-use conversion from a natural surface (vegetation) and permeable (waterbody) surface into an impervious (built-up area) surfacehas lead to an increase in land surface temperature (LST) in Lahore. The human thermal comfort (HTC) of the residents is alsoimpacted by the higher LST. The present study uses multi-temporal Landsat (5&8) satellite imageries to examine the ecological and thermal conditions of Lahore between 2000 and 2020. The ecological and thermal conditions of Lahore are assessed by calculating the urban heat islands and UTFVI (urban thermal field variance index), based on LST data which quantitatively assessed the UHI effect and the quality of human life. The outcomes establish that the urban built-up area has increased by 18%, while urban vegetation, vacant land, and waterbody decreased by 13%, 4%, and 0.04%, respectively. In the last 20 years, the mean LST of the study region has risen by about 3.67 °C. The UHI intensity map shows intensification and a rise in surface temperature variation from 4.5 °C (2000) to 5.9 °C (2020). Furthermore, the finding shows that the ecological and thermal conditions are worse in construction sites, transition zones, and urban areasin comparison to nearby rural areas. The lower UTFVI was observed in dense vegetation cover areas while a hot spot of higher UTFVI was predominantly observed in the areas of transition zones and built-up area expansion. Those areas with higher hot spots are more vulnerable to the urban heat island effect. The main conclusions of this study are essential for educating city officials and urban planners in developing a sustainable urban land development plan to reduce urban heat island effects by investing in open green spaces for urban areas of cities.

Global distinct variations of surface urban heat islands in inter- and intra-cities revealed by local climate zones and seamless daily land surface temperature data

The continuous dynamic of surface urban heat island (SUHI) effect is highly needed in urban climate studies. However, temporal variations of SUHI intensity (SUHII) have been understudied in previous studies owing to the lack of spatially seamless and temporally continuous land surface temperature (LST) data, particularly in urban domains. Also, the relationship between SUHII variations and heterogeneous landscapes, reflecting various thermal and biophysical properties, has not been comprehensively explored globally. Here, we investigated the annual dynamic of nighttime SUHII and the inter- (i.e., city level) and intra-city (i.e., local climate zones level) SUHII variations of 1,028 worldwide cities, using the annual temperature cycle (ATC) model and seamless daily LST data and local climate zones (LCZ) data. The results reveal that the annual mean SUHII (SUHIIM) in urban areas is 0.69 °C for nighttime with an increasing trend towards high latitudes, while the annual SUHII amplitude (SUHIIA) is 0.32 °C and is relatively stable along latitudes. Global SUHIIM and SUHIIA indicate a robust pattern that decreases with decreasing buildings’ height (i.e., from LCZ1 to LCZ3 and from LCZ4 to LCZ6) and increasing openness of building layout (i.e., from compact to open types). In contrast, the annual pattern of LST is mainly regulated by the background climate. The phase shift (i.e., day of year when the LST reaches its maximum value) of annual SUHII shows a distinct time lag effect along the LCZ gradient in cold climate zone. These findings are valuable for scientific planning of resilient cities and can mitigate the impact of heat islands on urban residents by optimizing the composition and configuration of urban landscapes, e.g., changing the height and morphology of buildings.

An adaptive synchronous extraction (ASE) method for estimating intensity and footprint of surface urban heat islands: A case study of 254 North American cities

The urban heat island (UHI) effect has attracted great attention due to its potential impacts on rapidly growing urban areas. Using remotely sensed estimates of land surface temperature (LST), a large number of studies have focused on the surface UHI (SUHI) effect, which can be characterized by its two fundamental properties: intensity and footprint. The SUHI intensity reflects the LST difference between the urban area and the background reference area (BRA), and the SUHI footprint indicates the spatial extent influenced by the heat island. Currently, numerous methods have been developed to estimate the SUHI intensity and footprint, but are still greatly challenged by three main issues. Namely, the discrepancy in BRA selection criterion brings great uncertainty to the estimated SUHI intensity, the estimation of SUHI footprint is largely constrained by the predefined models, and the quantification of SUHI effect is potentially influenced by several confounding factors. Here, we proposed an adaptive synchronous extraction (ASE) method, which is capable of adaptively selecting the most optimal BRA while removing the influence of confounding factors, and achieving synchronous estimation of SUHI intensity and footprint. We applied the ASE method to 254 North American cities and conducted an in-depth comparative analysis to discuss its applicability and benefits. The main results include: (1) The ASE method avoids the limitations of existing methods in BRA selection and model presetting, and shows resilience to parameter variations. This makes the ASE method highly applicable to quantify the SUHI intensity and footprint in cities with various thermal characteristics. (2) The ASE method can better highlight the spatial, seasonal and day-night contrasts in the estimated SUHI intensity. This superiority is particularly evident when comparing it to methods based on the equal-area buffer or the simplified urban-extent algorithm. (3) Confounding factors pose non-negligible impacts on the quantification of the SUHI effect. Typically, ignoring the influence of topographic relief or missing LST data can lead to an overall overestimation of the SUHI intensity, while not removing surrounding urban areas will cause some underestimation of the SUHI intensity. Overall, the proposed ASE method provides a new generalizable tool for quantifying the SUHI effect, which has great potentials for future studies and urban climate assessments.

Surface urban heat island effect and its spatiotemporal dynamics in metropolitan area: a case study in the Zhengzhou metropolitan area, China

The deterioration of the urban surface thermal environment has seriously affected regional environments and human health, becoming a critical ecological problem faced by cities worldwide. This study focused on surface urban heat island effect in metropolitan area and selected the emerging metropolitan area of Zhengzhou, China, as a case study. Based on the MODIS land surface temperature data obtained from the Google Earth Engine the surface urban heat island intensity (SUHII) was calculated and its temporal and spatial dynamics were analyzed from 2003 to 2022. The main findings indicated that Zhengzhou, the core city of the metropolitan area, had the strongest urban heat island effect with day surface urban heat island intensity of 1.10°C and night SUHII of 1.39°C). Generally, the average annual SUHII was higher during the day than at night, and the maximum value was detected in summer (2.43°C). SUHII showed an increasing trend at night, especially in summer during the study period. It decreased obviously in urban centers during the day, while it increased obviously in the outer urban areas at night. The results of this study contributed to the understanding of the spatiotemporal dynamics of the urban heat island effect in the Zhengzhou metropolitan area.

The surface urban heat island effect decreases bird diversity in Chinese cities

The audiovisual experience of observing birds in cities provides numerous benefits to residents, but their diversity is endangered by urbanization. Although the magnitude of the surface urban heat island effect (hereafter SUHI) has grown in recent years, its impact on bird diversity has not been adequately investigated. Here, we calculate the SUHI in 336 Chinese cities and we document the implications of the SUHI for avian species richness and functional diversity during the 2001, 2011, and 2019 breeding and non-breeding seasons. We predict that the SUHI will result in greater species richness and functional diversity in urban areas during the non-breeding season, especially for cities located within colder regions of China where the SUHI is more likely to relax thermoregulatory costs and reduce the propensity of some species to migrate. We predict that the SUHI will result in decreased species richness and functional diversity during the breeding season due to increased physiological stress, especially for cities located within warmer regions of China. Our findings showed that the SUHI was associated with lower species richness and lower functional diversity of birds in urban areas compared to suburban areas during both the breeding and non-breeding seasons. These results suggest that the SUHI induced birds to avoid urban areas or to move to cooler suburban areas during both the breeding and non-breeding seasons. This effect persisted irrespective of a city's size or geographical location. Our findings suggest that the SUHI is degrading bird diversity in Chinese cities.

Comparative Analysis of the Surface Urban Heat Island (SUHI) Effect Based on the Local Climate Zone (LCZ) Classification Scheme for Two Japanese Cities, Hiroshima, and Sapporo

The Local Climate Zone (LCZ) classification system is used in this study to analyze the impacts of urban morphology on a surface urban heat island (SUHI). Our study involved a comparative analysis of SUHI effects in two Japanese cities, Sapporo and Hiroshima, between 2000 to 2022. We used geographical-information-system (GIS) mapping techniques to measure temporal LST changes using Landsat 7 and 8 images during the summer’s hottest month (August) and classified the study area into LCZ classes using The World Urban Database and Access Portal Tools (WUDAPT) method with Google Earth Pro. The urban thermal field variance index (UTFVI) is used to examine each LCZ’s thermal comfort level, and the SUHI heat spots (HS) in each LCZ classes are identified. The research findings indicate that the mean LST in Sapporo only experienced a 0.5 °C increase over the time, while the mean LST increased by 1.8 °C in Hiroshima City between 2000 and 2022. In 2000, open low-rise (LCZ 6) areas in Sapporo were the hottest, but by 2022, heavy industry (LCZ 10) became the hottest. In Hiroshima, compact mid-rise (LCZ 2) areas were the hottest in 2000, but by 2022, heavy-industry areas took the lead. The study found that LCZ 10, LCZ 8, LCZ E, and LCZ 3 areas in both Dfa and Cfa climate classifications had unfavorable UTFVI conditions. This was attributed to factors such as a high concentration of heat-absorbing materials, impervious surfaces, and limited green spaces. The majority of the SUHI HS and areas with the highest surface temperatures were situated near industrial zones and large low-rise urban forms in both cities. The study offers valuable insights into the potential long-term effects of various urban forms on the SUHI phenomenon.

Evaluation and Analysis of the Effectiveness of the Main Mitigation Measures against Surface Urban Heat Islands in Different Local Climate Zones through Remote Sensing

The significant transformation of land use as a consequence of current population growth, together with global warming (atmospheric emissions and extreme weather events), is generating increases in ambient temperatures. This circumstance is affecting people’s quality of life, especially those considered more vulnerable or with fewer economic resources. Currently, 30% of the world’s population suffers climatic conditions of extreme heat, and forecasts indicate that in the next 20 years, this number will reach 74%. The present study analyzes the effectiveness of the main mitigation strategies for the surface urban heat island (SUHI) effect between the years 2002 and 2022 in the different local climate zones of the city of Granada (Spain). Using Landsat 5 and 8 images, the evolution experienced by the land surface temperature and the surface urban heat island was determined and connected to the following variables: normalized difference vegetation index, vegetal proportion, normalized difference building index, and albedo. Our results indicate that compact and industrial areas have higher temperatures and lower vegetation and albedo in contrast to open areas, which have lower temperatures and higher vegetation and albedo. The mitigation measures analyzed presented similar efficiencies, but a greater minimization of the SUHI was reported when vegetation was increased in open areas as opposed to in closed areas, where the increase in albedo was more effective. Our study will allow the implementation of more efficient measures based on the types of LCZs in cities.

A Technique for Generating Preliminary Satellite Data to Evaluate SUHI Using Cloud Computing: A Case Study in Moscow, Russia

The expansion of construction zones, transportation, and utilities for industry and high-tech areas due to human activities has caused the deterioration of the natural ecological environment. As cities face problems related to the surface urban heat island (SUHI) effect and environmental pollution, there is an urgent need to develop new methods for the ecological–microclimatic assessment and structural–functional planning of urban areas. The main goal of this study was to demonstrate the evolution of the surface urban heat island (SUHI) effect in Moscow over a long period and to determine the interaction between SUHIs and urban pollution islands (UPIs) using a geospatial analysis platform while optimizing vegetation classification with machine learning. Additionally, we are creating a digital database for modeling the sustainability of cities on the GEE platform using cloud computing. This study used cloud computing and remote sensing image analysis platforms for a 17-year temporal-series ecological–microclimatic assessment, which provided a sequence of values describing the ongoing process of changes in the ecological conditions of Moscow over time. Combining machine learning with the random forest algorithm (RF) improved vegetation classification accuracy while reducing computation time. The study findings demonstrated how the SUHI affected Moscow’s territory and showed the urban areas significantly impacted by this phenomenon. The locations of surface urban heat islands in Moscow and areas affected by SUHI and UPI were identified using numerical modeling of the urban thermal field variance index (UTFVI). From the findings, we identified the need to develop a new method for obtaining geospatial data for assessing the interaction between UPIs and SUHIs using cloud computing and mathematical data models.

Disproportionate exposure to surface-urban heat islands across vulnerable populations in Lima city, Peru

Climate change constitutes an unprecedented challenge for public health and one of its main direct effects are extreme temperatures. It varies between intra-urban areas and this difference is called surface urban heat island (SUHI) effect. We aimed to assess SUHI distribution among socioeconomic levels in Lima, Peru by conducting a cross-sectional study at the block-level. The mean land surface temperature (LST) from 2017 to 2021 were estimated using the TIRS sensor (Landsat-8 satellite [0.5 km scale]) and extracted to block level. SUHI was calculated based on the difference on mean LST values (2017–2021) per block and the lowest LST registered in a block. Socioeconomic data were obtained from the 2017 Peruvian census. A principal component analysis was performed to construct a socioeconomic index and a mixture analysis based on quantile g-computation was conducted to estimate the joint and specific effects of socioeconomic variables on SUHI. A total of 69 618 blocks were included in the analysis. In the Metropolitan Lima area, the mean SUHI estimation per block was 6.44 (SD = 1.44) Celsius degrees. We found that blocks with high socioeconomic status (SES) showed a decreased exposure to SUHI, compared to those blocks where the low SES were predominant (p-value < 0.001) and that there is a significant SUHI exposure variation (p-value < 0.001) between predominant ethnicities per block (Non-White, Afro-American, and White ethnicities). The mixture analysis showed that the overall mixture effect estimates on SUHI was −1.01 (effect on SUHI of increasing simultaneously every socioeconomic variable by one quantile). Our study highlighted that populations with low SES are more likely to be exposed to higher levels of SUHI compared to those who have a higher SES and illustrates the importance to consider SES inequalities when designing urban adaptation strategies aiming at reducing exposure to SUHI.

Remote Sensing Analysis of the Surface Urban Heat Island Effect in Vitoria-Gasteiz, 1985 to 2021

Vitoria-Gasteiz has taken several urban greening actions such as the introduction of a ring of parks that connect the city’s surroundings, a sustainable mobility plan, and urban green structure strategies. Previous studies establish a connection to the importance of greening to mitigate the surface urban heat island (SUHI) and evaluate the effectiveness of these measures on urban climate. In this study, land surface temperature (LST), a remote sensing (RS) parameter, recorded by Landsat satellites (5, 7, and 8) was used to evaluate the effect of SUHI in Vitoria-Gasteiz between 1985–2021. The aim was to evaluate whether the urban greening actions influenced the local thermal conditions and, consequently, helped minimize the SUHI. Thirty sampling locations were identified, corresponding to different local climate zones (LCZ), at which LST data were extracted. A total of 218 images were processed and separated into summer and winter. Four of the 30 locations had, since 2003, on-site meteorological stations with regular air temperature (Tair) measurements which were used to validate the LST data. The results showed that Spearman’s correlation between Tair and LST was higher than 0.88 in all locations. An amount of 21 points maintained the same LCZ classification throughout the analysed period and nine underwent a LCZ transformation. The highest average temperature was identified in the city centre (urbanized area), and the lowest average was in a forest on the outskirts of the city. SUHI was more intense during the summer. A significant increase in SUHI intensity was identified in areas transformed from natural to urban LCZs. However, SUHI during satellite data acquisition periods has shown a minimal change in areas where sustainable practices have been implemented. RS was valuable for analysing the thermal behaviour of the LCZs, despite the limitation inherent in the satellite’s time of passage, in which the SUHI effect is not as evident.