Surface Urban Heat Island Effect Research Articles

Analysis of Surface Urban Heat Island in the Guangzhou-Foshan Metropolitan Area Based on Local Climate Zones

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.

Daytime land surface temperature and its limits as a proxy for surface air temperature in a subtropical, seasonally wet region

Land surface temperatures (LSTs) captured via satellite remote sensing are widely used as a proxy for the surface air temperatures (SATs) experienced outdoors, a key component of human heat exposure. However, LST’s accuracy in capturing SAT can vary through space and time across climate types and geographies and has been less explored in subtropical, seasonally wet regions (where summer precipitation exceeds 570 mm). Utilizing daytime (11 AM/12 PM local time, ET/EST) Landsat 8 remote sensing data, this study derived LST and evaluated its spatiotemporal patterns, as well as its relationship with SAT retrieved from local weather stations, using the case of Miami-Dade County, Florida, USA. Over 2013–2022, a surface urban heat island effect is distinctly present (mean SUHII = 3.43°C)—most intense during spring months rather than summer months (mean spring SUHII = 4.09°C). As such, LST peaks in May/June as opposed to July/August for many other parts of the northern hemisphere. In contrast, Miami-Dade SAT is greatest in August, and the strength of its relationship with LST varies by season. LST and SAT are most correlated in winter (R = 0.91) and spring (R = 0.59) months and least correlated during the wetter fall (R = 0.40) months. The relationship between LST and SAT during the summer is statistically insignificant. In this subtropical region with a seasonally wet climate, LST effectively reflects the spatial heterogeneity of the urban thermal landscape, consistent with the literature across urban regions globally. However, because the strength of the LST-SAT relationship considerably weakens during wet season months, LST data therefore have limits as a proxy for the heat exposure people experience outdoors annually, as they may not accurately represent the magnitude of localized potential heat risks. These findings underscore important considerations in using LST data to identify urban heat exposures and inform potential adaptive responses in seasonally wet, subtropical-to-tropical regions.

Green Space Cooling Effect and Relation to Mitigate Surface Urban Heat Island Effect of Metropolitans Cities of India

Difference Vegetation Index (NDVI) to study vegetation's spatial distribution. However, MODIS thermal bands were employed to analyse Land Surface Temperature (LST) and city thermal properties. The findings show that 15.97% of Lucknow's total area is classified as a High potential SUHI zone, compared to 29.41%, classified as a Low potential SUHI zone. Jaipur has two possible SUHI zones: a high potential zone (12.69%) and a low potential zone (30.45%). In contrast, Ahmedabad exhibits an 18.37 per cent High potential SUHI Zone and a 27.62 per cent low potential SUHI Zone. Delhi exhibits a 14.98 per cent High potential SUHI Zone but is significantly higher at 39.97 per cent Low potential SUHI Zone. Analysis of LST distribution reveals correlations with vegetation cover, with areas abundant in greenery experiencing lower temperatures. This study emphasizes how crucial green infrastructure is to urban planning to improve thermal comfort in fast-urbanizing areas and reduce the negative consequences of urban heat islands.

Exploring patterns of surface urban heat island intensity: a comparative analysis of three Greek urban areas

One of the most pressing issues of our time is the destabilization of the global climate as a result of rapid urbanization. In Europe, according to United Nations reports, almost 73% of the population lives in cities and this percentage is expected to rise to 85% by 2050 (United Nations and Department of Economic and Social Affairs, ‘World Urbanization Prospects 2018 Highlights’, 2018). This observed urban development and its trends pose significant challenges to our urban environments, therefore it is critical to improve our understanding and assess its impact by measuring and analyzing its patterns. This study presents a comprehensive five-year analysis of microclimatic parameters that can contribute to this analysis, such as land surface temperature (LST) and surface urban heat island (SUHI) intensity, using remotely sensed data over three cities in Greece. Specifically, the selected cities are the city of Thessaloniki, which is the second largest city situated in the northern part of Greece, the city of Larissa on the mainland, and finally the city of Tripoli in the Peloponnese peninsula in southern Greece. Each city represents urban areas with different physical and urban characteristics. For this purpose, the vegetation index-based technique for LST extraction was applied within the Google Earth Engine (GEE) platform. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) calculations were also performed over a five-year period. The results highlighted the spatio-temporal variations and trends that occur in the studied urban areas, providing valuable insights into the annual patterns of the SUHI phenomenon in different geographical contexts within Greece. This is particularly relevant in countries like Greece, where the density of World Meteorological Organization (WMO) stations providing reliable temperature data is low, usually limited to one per city. Consequently, LST derived from satellite data can provide more accurate intra-urban results of the SUHI effect, demonstrating the value of remotely sensed data as a cost effective and supportive tool for decision-making in environmental and urban planning policies.

Assessing the impacts of vegetation loss and land surface temperature on Surface Urban Heat Island (SUHI) in Gazipur District, Bangladesh

Rapid replacement of vegetated land with impermeable land (built-up areas) is a major factor in the increase in Land Surface Temperature (LST), while increased LST worsens the temperature in cities and creates the Surface Urban Heat Island (SUHI) effect. The study aims to measure vegetation loss and Land Surface Temperature of the Gazipur district between 2000 and 2020 and explore the relationship among Normalized Difference Vegetation Index (NDVI), LST, and Urban Thermal Field Variance Index (UTFVI). The Landsat TM/OLI images with minimum cloud coverage have been used to derive different indices. The mean NDVI values are 0.21, 0.16, and 0.22 in 2000, 2010, and 2020 respectively which indicates a general improvement in the health of the vegetation. Besides, the highest LST values throughout 20 years, represent a general increasing trend. As a consequence, different land covers have experienced fluctuations in mean temperature. The result shows that the mean temperature of bare land, buildup, vegetation, and waterbody has increased by 4.77, 2.01, 2.25, and 2.23 °C respectively from 2000 to 2020. The strongest SUHI zone’s area grew by about 28% between 2000 and 2020. Additionally, the highest index value of UTFVI was 0.39 in 2000 and grew to 0.43 in 2010. It changed to 0.49 in 2020, or ten years later. Thus, the SUHI effect’s increasing intensity is visible. Also, regression analysis has been used to explore the correlation between the derived indices. Stakeholders from different sectors like urban planners and policymakers may take insights from this study to work to promote greenery for a healthy urban environment.

Monitoring Changes with Spatiotemporal Resolution in Land Surface Temperature (LST) Impact on Land Cover Around Al-Dora Refinery in Downtown Baghdad, Iraq

This study analyses the differences in temperature of the land surface across various land cover types and uses geospatial approaches to explore the changes that are taking place in the Dora refinery and its surrounding areas. Landsat 8 OLI/TIRS made use of Landsat imagery during the months of April and August in the years 2013 and 2023. ArcGIS 10.8 software was used to process the satellite images. The brightness temperature data from a satellite sensor was used to calculate the land surface temperature and the study's land cover classifications were created by applying supervised classification techniques.The aim of the study is to monitor the changes occurring in Dora refinery and its surrounding areas for periods of time 2013-2023 as a result of the influence of urban surface temperature , the growth and expansion that occurring in human activities and represented in the filtering and production of fossil energy and crude oil as well. The results of the investigation indicate an increase in the land surface temperature (LST) during the year 2023, in contrast to the year 2013, while we notice an increase in( NDVI ) in 2013 and a decrease in 2023, and a decrease in (NDBI) in 2013 and an increase in 2023. An inverse relationship was discovered between vegetated area and ( LST) between 2013 and 2023, whereas a positive relationship between (NDBI) and (LST) has been obtained suggesting an expanding surface urban heat island effect, as indicated by the correlation coefficients of statistical significance. It significantly affects how the land surface temperature varies. The main differences in the ways that land cover and use impact (LST) were also emphasized by this study during the whole duration of the inquiry, thus, methods for monitoring, examining, and assessing urban growth trends and their effects on land surface temperature can be achieved through the use of remote sensing and geographic information system tools.

Microscale Temperature-Humidity Index (THI) Distribution Estimated at the City Scale: A Case Study in Maebashi City, Gunma Prefecture, Japan

Global warming and climate change are significantly impacting local climates, causing more intense heat during the summer season, which poses risks to individuals with pre-existing health conditions and negatively affects overall human health. While various studies have examined the Surface Urban Heat Island (SUHI) phenomenon, these studies often focus on small to large geographic regions using low-to-moderate-resolution data, highlighting general thermal trends across large administrative areas. However, there is a growing need for methods that can detect microscale thermal patterns in environments familiar to urban residents, such as streets and alleys. The temperature-humidity index (THI), which incorporates both temperature and humidity data, serves as a critical measure of human-perceived heat. However, few studies have explored microscale THI variations within urban settings and identified potential THI hotspots at a local level where SUHI effects are pronounced. This research aims to address this gap by estimating THI at a finer resolution scale using data from multiple sensor platforms. We developed a model with the random forest algorithm to assess THI trends at a resolution of 0.5 m, utilizing various variables from different sources, including Landsat 8 land surface temperature (LST), unmanned aerial system (UAS)-derived LST, Sentinel-2 NDVI and NDMI, a wind exposure index, solar radiation modeled from aircraft and UAS-derived Digital Surface Models, and vehicle density and building floor area from social big data. Two models were constructed with different variables: Modelnatural, which includes variables related to only natural factors, and Modelmix, which includes all variables, including anthropogenic factors. The two models were compared to reveal how each source contributes to the model development and SUHI effects. The results show significant improvements, as Modelnatural had a fitting R2 = 0.5846, a root mean square error (RMSE) = 0.5936 and a mean absolute error (MAE) = 0.4294. Moreover, when anthropogenic factors were introduced, Modelmix performed even better, with R2 = 0.9638, RMSE = 0.1751, and MAE = 0.1065 (n = 923). This study contributes to the future of microscale SUHI analysis and offers important insights into urban planning and smart city development.

Integrating urban development and ecological sustainability: a study on land surface temperature and urban heat island effect in a Karst River Basin

Integrating urban development and ecological sustainability: a study on land surface temperature and urban heat island effect in a Karst River Basin

Sensitivity of Local Climate Zones and Urban Functional Zones to Multi-Scenario Surface Urban Heat Islands

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.

A new two-step estimation approach for retrieving surface urban heat island intensity and footprint based on urban-rural temperature gradients

Past decades have seen substantial efforts devoted to observing, assessing, and documenting the urban heat island (UHI) phenomenon. However, the discrepant criteria of non-urban references and ambiguous distinctions between urban and rural landscapes pose great challenges in measuring UHI magnitudes and spatial extents. This study goes beyond the conventional urban-rural dichotomy and introduces a new two-step approach based on the continuous transition of thermal environments along urban-rural gradients. The approach is applied to quantify Surface UHI (SUHI) intensities and footprints across 283 Chinese cities from 2005 to 2018 using multiple satellite-derived data sources. The results include: 1) The two-step approach avoids the limitations in subjective rural reference selections and provides reliable quantification of SUHI characteristics in various cities over time. 2) The SUHI footprints extracted by our approach are more reasonable than those obtained by two existing methods, with footprint ratios generally ranging within 0 − 6 times the urban area. 3) The two-step approach provides more concentrated estimates of SUHI intensity. Typically, ignoring heat sources in non-built-up areas can cause an overestimation of SUHI effect and misidentification of remote rural areas with high temperatures. Overall, the two-step approach enables more accurate estimates of SUHI effect, thereby facilitating policy-making for SUHI mitigation.

The impact of urban configuration types on urban heat islands, air pollution, CO2 emissions, and mortality in Europe: a data science approach

The world is becoming increasingly urbanised. As cities around the world continue to grow, it is important for urban planners and policy makers to understand how different urban configuration patterns affect the environment and human health. However, previous studies have provided mixed findings. We aimed to identify European urban configuration types, on the basis of the local climate zones categories and street design variables from Open Street Map, and evaluate their association with motorised traffic flows, surface urban heat island (SUHI) intensities, tropospheric NO2, CO2 per person emissions, and age-standardised mortality. We considered 946 European cities from 31 countries for the analysis defined in the 2018 Urban Audit database, of which 919 European cities were analysed. Data were collected at a 250 m × 250 m grid cell resolution. We divided all cities into five concentric rings based on the Burgess concentric urban planning model and calculated the mean values of all variables for each ring. First, to identify distinct urban configuration types, we applied the Uniform Manifold Approximation and Projection for Dimension Reduction method, followed by the k-means clustering algorithm. Next, statistical differences in exposures (including SUHI) and mortality between the resulting urban configuration types were evaluated using a Kruskal-Wallis test followed by a post-hoc Dunn's test. We identified four distinct urban configuration types characterising European cities: compact high density (n=246), open low-rise medium density (n=245), open low-rise low density (n=261), and green low density (n=167). Compact high density cities were a small size, had high population densities, and a low availability of natural areas. In contrast, green low density cities were a large size, had low population densities, and a high availability of natural areas and cycleways. The open low-rise medium and low density cities were a small to medium size with medium to low population densities and low to moderate availability of green areas. Motorised traffic flows and NO2 exposure were significantly higher in compact high density and open low-rise medium density cities when compared with green low density and open low-rise low density cities. Additionally, green low density cities had a significantly lower SUHI effect compared with all other urban configuration types. Per person CO2 emissions were significantly lower in compact high density cities compared with green low density cities. Lastly, green low density cities had significantly lower mortality rates when compared with all other urban configuration types. Our findings indicate that, although the compact city model is more sustainable, European compact cities still face challenges related to poor environmental quality and health. Our results have notable implications for urban and transport planning policies in Europe and contribute to the ongoing discussion on which city models can bring the greatest benefits for the environment, climate, and health. Spanish Ministry of Science and Innovation, State Research Agency, Generalitat de Catalunya, Centro de Investigación Biomédica en red Epidemiología y Salud Pública, and Urban Burden of Disease Estimation for Policy Making as a Horizon Europe project.

Unveiling nonlinear effects of built environment attributes on urban heat resilience using interpretable machine learning

Built environment attributes (BEAs) play a significant role in influencing urban heat resilience (UHR). Previous research has examined the correlations and nonlinear relationships between BEAs and both land surface temperature (LST) and urban heat island (UHI) effects. Nevertheless, the investigation into the nonlinear effects of BEAs on UHR remains underexplored. Furthermore, the advantages of explainable machine learning in elucidating the mechanisms through which BEAs affect the urban thermal environment have been extensively validated. Consequently, taking Beijing, a highly urbanized city, as a case, we conducted an empirical study to investigate the nonlinear effects of BEAs on UHR. We first operationalized UHR as the differential in LST between extreme heat waves and normal heat days. Secondly, we constructed a set of influencing factors covering BEAs and control variables. Subsequently, by integrating the Gradient Boosting Decision Tree (GBDT) with SHapley Additive exPlanations (SHAP), the nonlinear relationships between BEAs and UHR are uncovered. The results demonstrate that: 1) Nonlinear relationships between BEAs and UHR are prevalent, as well as threshold effects. 2) Greening is the key BEA affecting UHR, accounting for 22.39% in contribution, with which increase, UHR increases at an accelerating rate. 3) From the city center outward, the growth of UHR exhibits a leapfrog effect, with the growth rate in the outer ring being 2.7 times that of the inner one. 4) Interactions between BEAs impact UHR. Our findings unveil the complex nonlinear effects of BEAs on UHR, clarifying the priority and optimal quantity thresholds of BEAs. We emphasize the importance of greening and urban scale, which could support decision-making for UHR planning and precise management.

Assessing the role of urban green infrastructure in mitigating summertime Urban Heat Island (UHI) effect in metropolitan Shanghai, China

The importance of urban green infrastructure (UGI) in ameliorating the urban thermal environment and reinforcing urban climatic adaptation has attracted growing concerns. In this study, 36 representative urban thermal hotspots (UTHSs) in Metropolitan Shanghai, which vary in urban setting features and exposure to surface urban heat island (SUHI) effect, were used to quantitatively assess the UGI's role in mitigating the SUHI effect measured with the satellite-retrieved land surface temperatures (LSTs). We observed significantly contrasting LST differences between UTHSs ranging from highly to slightly urbanized areas. Moreover, we found the contrasting performances of forward stepwise regression (FSWR), partial least squares regressions (PLSR), and random forest regressions (RFR) models for quantitatively assessing the UGI's role in mitigating the SUHI effect. The FSWR models showed high r2 (averaged 0.816) but presented the worst performance for interpreting the results; PLSR models showed the highest r2 (averaged 0.820), presenting the best performance for interpreting the results; RFR models showed the lowest r2 (averaged 0.708), presenting an acceptably moderate performance for interpreting the results. Our findings provided an applicable methodology with repeatability for assessing UGI's role in mitigating the SUHI effect elsewhere, regarding the complexity of urban setting features.

Spatiotemporal Analysis of Surface Urban Heat Island Dynamics in Central Yunnan City Cluster

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.

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

Spatio-temporal analysis of LST, NDVI and SUHI in a coastal temperate city using local climate zone

Extreme heat due to changing climate poses a new challenge for temperate climates. The challenge is further aggravated by inadequate research, policy, or preparedness to effectively respond and recover from its impacts. While urban morphology is crucial in mitigating urban heat, it has received limited attention in urban planning, highlighting the need for further exploration, particularly in temperate regions. To illustrate the challenge and its potential mitigations, we use the example of the coastal temperate city of Cardiff. To establish the interrelations between urban morphology and urban heat island patterns, we explored the spatiotemporal variations in land surface temperature (LST), normalised difference vegetation index (NDVI), and surface urban heat island (SUHI) to local climate zone (LCZ) classification for Cardiff. Results showed a significant variation in SUHI in the LCZ zones. Both LST and NDVI land were found to vary significantly across the LCZ zones demonstrating their association with the urban form and morphology of the locality. For built-up areas, a more compact built-environment with smaller vegetation cover and larger building density was 2.0 °C warmer than the open built-environment when comparing the mean summer LSTs. On average, the natural classes exhibit a LST that is 8.0 °C lower than the compact built-environment and 6.0 °C lower than the open built-environment. Consequently, the high-density, built-up LCZs have a greater SUHI effect compared to the natural classes. Therefore, temperate climate cities will benefit from incorporating an open built-environment that has sufficient greenery and open spaces. These findings help determine the optimal urban form for temperate climates and develop heat mitigation strategies while planning, designing, or improving the new and existing urban areas. In addition, the LCZ map applied in this study for Cardiff will enable international comparison and testing of proven climate change adaptation and mitigation techniques for similar urban areas.

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.