Urban Land Surface Temperature Research Articles

Synergistic effects of carbon and heat under disturbance of human activities: Evidence from a resource-based city of China.

For resource-based cities, the rapid development of industrialization and urbanization has led to significant carbon emissions (CEs), accelerated the rise of urban land surface temperatures (LSTs) and hindered sustainable urban development. This study constructed a model to measure the carbon-heat relationship to clarify the complex relationship between LSTs and CEs in resource-based cities. The results show that:1) High-temperature areas are primarily concentrated around the urban center and large industrial zones, with average LSTs reaching a peak of 35.7°C in 2015, indicating severe temperature polarization; 2) CEs exhibited an overall upward trend with a diffusion effect, particularly pronounced in the urban center and industrial zones. Areas with extremely significant, strong significant, and generally significant growth in CEs accounted for 4.64%, 3.81%, and 81.35%, respectively, showing a concentrated increase in the urban center; 3) A positive correlation between CEs and LSTs of the city was identified, and the distribution of urban heat island and the high value area of CEs are concentrated and similar; 4) The synergistic effects between LSTs and CEs varied between urban center, suburban and peripheral areas, due to human activities. Areas with a high positive correlation between CEs and LSTs are concentrated in urban centers and peripheral areas, while for urban suburbs, the correlation is weak or even absent. To mitigate the negative effects of carbon-heat accumulation, urban centers should avoid high population concentrations, and the carbon sink potential of green spaces near industrial zones and peripheral areas should be fully utilized. These insights provide actionable strategies for sustainability of resource-based cities, particularly in the governance of urban thermal environments and the mitigation of CEs.

A cross-scale indicator framework for the study of annual stability of land surface temperature in different land uses

Urban Land Surface Temperature (LST) is crucial in surface urban heat island (SUHI) and microclimate studies. Currently, research has focused on seasonal LST differences across land uses, but annual LST fluctuations (ΔLST) within the same land use and their drivers remain underexplored. To explore the impact of land characteristics and urban elements on seasonal LST differences, we propose annual LST stability. We constructed a new indicator framework based on Land Use and Land Cover (LULC), supplemented by Land Morphology (LM) and Land Properties (LP), for cross-scale ΔLST research. We identified land use ratios and key characteristics of urban plots with high stability. The results show an interactive effect of the green land ratio to other land on ΔLST. For residential and office land, the green space ratio (GSR) is key to annual LST stability. Residential land needs a GSR of more than 24 %. The floor area ratio (FAR) for residential and office land has a significant nonlinear effect on annual LST stability, with FARs of 1.8 for residential land and 1.5 for office land being most detrimental to the LST stability. For practical implications, we conducted cluster analyses on residential, office, and green lands, providing strategies to improve stability. These conclusions help balance land economic benefits with urban climate resilience and guide urban planning and design to address the challenges of heat and cold waves.

Exploring the Configurational Relationships between Urban Heat Island Patterns and the Built Environment: A Case Study of Beijing

The spatial heterogeneity of land surface temperature (LST) within cities is profoundly influenced by the built environment. Although significant progress has been made in the study of the urban thermal environment, there is still a lack of research on how the pattern and structural layout of the built environment affects the thermal environment. In this study, we take the Fifth Ring Road of Beijing as an example, invert the urban LST on the basis of multisource spatial data, characterize the built environment, and use k-means cluster analysis to investigate the main influencing factors of the LST in different functional areas and building patterns within the city, as well as the spatial relationship between the built environment and the urban LST. The results show the following: (1) The urban heat island (UHI) effect occurs to varying degrees over a large part of the study area, and these UHI areas are mainly concentrated in the southwestern part of the city, forming a large contiguous area between the second and fifth ring roads. (2) Class 1 is dominated by transport blocks, Class 3 is dominated by commercial blocks, and Class 5 is dominated by green space blocks, with a clustering index of 0.38. (3) The high-density, high-height class (HH-Class 2) has a greater number of blocks distributed in a ring shape around the periphery of the second ring road. The high-density, low-height class (HL-Class 2) has a relatively small number of blocks but a relatively large area, and the largest blocks are located in the western part of the study area. (4) In the HH and HL building patterns, extreme heat scenarios often occur; from the perspective of functional areas, the probability of extreme heat in the transport block is much higher than that of other functional areas, and except for the HH scenario, the green space functional area plays a very important role in reducing the temperature. This study explores the characteristics of the built environment that influence the urban LST from the perspective of different urban functional zones in cities to provide decision support for quantitative territorial spatial planning, optimization, and management.

Influence of urban functional zone change on land surface temperature using multi-source geospatial data: A case study in Nanjing City, China

The urban heat island effect is a prevalent urban concern, and studies on its influencing factors are essential to mitigate its negative effects and promote sustainable urban development. Although many studies have revealed the impact of land cover change on land surface temperature (LST) during urbanization, most have focused only on built-up areas without considering the relationship between urban internal functional structure and LST. To clarify the influence of changes in urban functional zones (UFZs) on LST, focusing on the downtown area of Nanjing City, China, this study proposed an effective framework using multi-source geospatial data for qualitative and quantitative analysis. Specifically, very high spatial resolution images and point of interest data were jointly used to generate UFZ maps using an advanced deep learning approach, and LST distribution maps were retrieved from Landsat 8 images using the radiative transfer equation. The UFZ and LST maps were then overlaid and analyzed to reveal the impact of UFZ change on LST. The results showed that in 2013, 2018, and 2022, the residential region constituted the largest proportion of area, comprising 23.52%, 24.52%, and 27.28%, respectively. The areas of unused region and transport region experienced the most rapid decrease and increase of 6.08% and 4.05% from 2013 to 2022, respectively. Furthermore, the area of sub-high temperature zone decreased the most, by 3.47%, whereas that of the high-temperature zone increased the most, by 6.05%. In the study area, water and green space maintained relatively low LSTs, whereas residential region and unused region exhibited higher LSTs. From 2013 to 2022, UFZ changes contributed to a 0.71°C increase in LST. Transfers from green space, water, commercial region, transport region, and industrial region mostly resulted in a higher LST, which was associated with a decrease in fractional vegetation cover (FVC). Transfers from unused region, residential region, and public service region mostly contributed to reducing LST, which was caused by the increase in FVC. The findings and the proposed framework can provide reliable references for urban managers to optimize urban spatial layout and promote sustainable urban development.

Quantitative assessment of interplay between urbanization dynamics and land surface temperature variations using generalized additive model coupled PDP for sustainable urban planning and management.

The mountainous region of Asir is experiencing rapid and unsystematic urbanization leading to an increase in land surface temperatures (LST), which poses a challenge to human well-being and ecological balance. Therefore, it is necessary to study the interaction between land use and land cover (LULC)-induced urbanization and LST using advanced geostatistical techniques. In addition, understanding the urbanization process and urban density is essential for effective urban planning and management. The aim of this study was to investigate the interaction between the urbanization process, urban density and the associated LST. Using the Random Forest Algorithm, LULC mapping was conducted for the years 1990, 2000 and 2020. Metrics such as land cover change rate (LCCR), land cover index (LCI), landscape expansion index (LEI), mean landscape expansion index (MLEI) and area-weighted landscape expansion index (AWLEI) were used to understand urbanization processes and LULC changes. Convolutional kernels were used to model urban density, and the mono-window algorithm was applied to analyse LST in the selected years. In addition, the study assessed the Surface Urban Heat Island (SUHI) contribution index to LULC and used Generalized Additive Models (GAMs) in conjunction with Partial Dependence Plots (PDPs) to understand the relationship between urbanization processes, urban density and LST. In a detailed 30-year study, the application of the RF algorithm showed significant shifts in LULC with an overall validation accuracy of over 85%. Urban areas grew dramatically from 69.40 km2 in 1990 to 338.74 km2 in 2020, while water areas decreased from 1.51 to 0.54 km2. Dense vegetation increased from 43.36 to 52.22 km2, indicating positive ecological trends. The LST analysis showed a general warming, with the mean LST increasing from 40.51°C in 1990 to 46.73°C in 2020 and the highest temperature category (50-60°C) increasing from 0.78 to 33.35 km2. The built-up area of cities tripled between 1990 and 2020, with the Landscape Expansion Index reflecting significant growth in suburban areas. The modeling of urban density shows increasing urbanization in the centre, which will expand significantly to the east by 2020. The contribution of LULC to LST and the Urban Heat Island (SUHI) effect was evident, with built-up areas showing a constant temperature increase. GAMs confirmed a statistically significant relationship between urban density and LST, with different effects for different types of urban expansion. This comprehensive study quantitatively sheds light on the complicated dynamics of urbanization, land cover change and temperature variation and provides important insights for sustainable urban development.

The effect of urban form parameters on annual and diurnal cycles of land surface temperature with 30-meter hourly resolution

Understanding dynamic changes in urban land surface temperature (U-LST) is vital for improving urban thermal environment. Previous studies noted a close association between two/three-dimensional (2D/3D) urban form parameters (UFPs) and U-LST, but divergent conclusions emerged due to study scale and data spatiotemporal resolution. This study investigated the effect of 2D/3D UFPs on multi-temporal (hourly, diurnal, and annual) U-LSTs with 30-m spatial resolution, which is the first exploration of such dynamics at this fine scale. In addition, the influence of local climate zones (LCZs) was considered. Results showed that 2D/3D vegetation UFPs have the greatest summer cooling effect at 21:00, while 3D building UFP sky view factor (SVF) impacts minimum nighttime temperature more (5:00). The independently total explained variances (R2) of 2D UFPs (0.2–0.7) on seasonal average U-LSTs were higher than that of 3D UFPs (0.1–0.4) in most LCZs. LCZ 3 and LCZ 2 had the highest seasonal and daytime U-LST, respectively. Interestingly, SVF replaced normalized difference vegetation index as the dominant factor in LCZs 4–6 and LCZs A-C after summer. Findings of this study are useful for guiding urban planning, formulating urban heat island mitigation policies, and promoting sustainable development goals (SDGs 11, 13, 15) of cities and communities.

Assessment of Landscape Change and its Relationships with Surface Temperature and Geo-spatial Indices during 1992 - 2022 at Haldia Industrial Region, West Bengal, India

Unplanned local development and alterations in Land Use and Land Cover (LULC) are an early alert for towns and cities around the world. The population pressure has resulted in the conversion of natural land into impermeable areas, which has altered the surface energy budget and created microclimatic differences locally. The main objective of the present study is to estimate LULC changes and its relation to increases Land Surface Temperature (LST) in the Haldia Industrial Region (HIR) of West Bengal using multi-temporal Landsat 5 Thematic Mapper (TM) of 1992 and Landsat 8 Operational Land Imager (OLI) of 2022 with 30-m spatial resolution images were obtained from the USGS website. The supervised classification method was applied by using Maximum Likelihood classification (MLC) to classify the satellite images into various LULC classes such as settlement, waterbody, vegetation, agriculture land, and fallow land, respectively. The result suggested an overall increase of settlement area from 13.49 km2 in 1992 to 44.54 km2 in 2022. On the other hand, the agriculture land decreased from 214.26 km2 to 152.34 km2 due to fast urbanization, decline green lands, and expansion of barren lands. The outcome result from LST was exhibited that the LST value has increased 3.77°C during 1992 to 2022 in this study area. The Normalized Difference Vegetation Index (NDVI) was showed a strong negative relationship with LST with a determination coefficient (R2) values found as 0.491 (1992) and 0.356 (2022). The positive determination coefficient between LST with Normalized Difference Water Index (NDWI) was found as 0.0002 (1992), and 0.0417 (2022). R2 values were estimated as 0.174 (1992), and 0.0347 (2022) between LST with Normalized Difference Built-up Index (NDBI) that indicated a positive determination coefficient. The findings enhanced understanding of the relationship between urban LST and LULC in developing an inclusive climate resilience policy and making the HIR more sustainable to the effects of climate change.

The marginal effect of landscapes on urban land surface temperature within local climate zones based on optimal landscape scale

The rising urban land surface temperature (LST) has undoubtedly caused an imminent threat to human habitat. Landscape patterns are among the most significant factors related to LST. However, the marginal effects and thresholds of landscape at optimal grid scale on LST within diverse local climate zones (LCZs) are poorly understood. This study employed the boosted regression trees (BRT) model to evaluate the relative influences and marginal effects of built-up, water, and green landscape metrics on LST at the optimal grid scale across various LCZs. The findings reveal that: (1) The spatial spread trend of LST closely matches the expansion trend of built-up landscapes. (2) Significant variations in average summer LST were observed among LCZs, with LCZ8 (large low-rise) exhibiting the highest temperatures and LCZ11 (dense trees) the lowest. (3) At the optimal scale of 1000 m, the DIVISION metric for water landscapes significantly influenced LCZ6 (open low-rise), LCZ8, and LCZ14 (low plants). (4) In LCZ6 and LCZ8, effective heat mitigation requires increasing water area to 50% and optimizing the water body separation index to 0.88. These findings suggest that heterogeneous landscape heat mitigation strategies should be considered across diverse LCZs in urban areas.

Spatial effect of urban morphology on land surface tempature from the perspective of local climate zone

Urban expansion results in the intrusion of artificial surfaces into natural environments, giving rise to phenomena such as urban heat islands and heightened temperatures within inhabited areas. This study examined the dynamic evolution of urban morphology and land surface temperature through the lens of local climate zone. Furthermore, the spatial relationship between urban morphology indices and land surface temperature was analyzed within the context of loops. The investigation yielded several noteworthy findings: (1) LCZ8, LCZ6, and LCZD were the most significant transfer-in and transfer-out classes within the study area. (2) The heat contribution of residential LCZs (LCZ 2,3,5) decreased over time, ranging from 0.415 to 0.171, while that of industrial LCZs (LCZ 8,10) increased within the range of 0.348–0.553. (3) Urban heat island mitigation can be achieved by regulating building height and surface fraction. Establishing a judicious threshold for reducing LST is imperative for sky view factor and impervious surface fraction factors. Surface albedo exhibited a substantial inhibitory effect in 2020. The observation of urban morphological changes and long-term local climate zone considerations integrate the responsibility of mitigating urban heat islands into urban planning.

Seasonal dynamics of land surface temperature and urban thermal comfort with land use land cover pattern in semi-arid Indian cities: Insights for sustainable Urban Management

The cities in semi-arid and arid regions generally exhibit distinctive land use land cover (LULC) pattern and seasonal variation in urban thermal comfort (UTC) and land surface temperature (LST). This study examines the seasonal variation in LST and UTC and quantify the importance of LULC pattern in influencing LST in eight Indian semi-arid cities using Landsat 8 datasets. Random forest regression (RFR) model has been applied to quantify the influence of LULC in determining LST. The study shows that bare soil and open spaces in outskirts of selected cities have comparatively high LST than the core of cities. The mean LST varies seasonally from 12.84 °C in Udaipur to 26.18 °C in Jaipur in winter and summer. Analysis of UTC shows that autumn and winter seasons have better UTC than spring and summer seasons. RFR analysis shows a robust correlation (R2 > 0.85) between LULC and LST across all cities. Notably, built-up areas, open land, and vegetation cover exert the greatest influence on LST. Study suggests that targeted LULC planning may effectively address UTC problem in semi-arid cities and enhance sustainability. Study may help in understanding the seasonality thermal environment and assist in mitigating UHI and maintain UTC in these cities.

Harnessing Machine Learning Algorithms to Model the Association between Land Use/Land Cover Change and Heatwave Dynamics for Enhanced Environmental Management

As we navigate the fast-paced era of urban expansion, the integration of machine learning (ML) and remote sensing (RS) has become a cornerstone in environmental management. This research, focusing on Silchar City, a non-attainment city under the National Clean Air Program (NCAP), leverages these advanced technologies to understand the urban microclimate and its implications on the health, resilience, and sustainability of the built environment. The rise in land surface temperature (LST) and changes in land use and land cover (LULC) have been identified as key contributors to thermal dynamics, particularly focusing on the development of urban heat islands (UHIs). The Urban Thermal Field Variance Index (UTFVI) can assess the influence of UHIs, which is considered a parameter for ecological quality assessment. This research examines the interlinkages among urban expansion, LST, and thermal dynamics in Silchar City due to a substantial rise in air temperature, poor air quality, and particulate matter PM2.5. Using Landsat satellite imagery, LULC maps were derived for 2000, 2010, and 2020 by applying a supervised classification approach. LST was calculated by converting thermal band spectral radiance into brightness temperature. We utilized Cellular Automata (CA) and Artificial Neural Networks (ANNs) to project potential scenarios up to the year 2040. Over the two-decade period from 2000 to 2020, we observed a 21% expansion in built-up areas, primarily at the expense of vegetation and agricultural lands. This land transformation contributed to increased LST, with over 10% of the area exceeding 25 °C in 2020 compared with just 1% in 2000. The CA model predicts built-up areas will grow by an additional 26% by 2040, causing LST to rise by 4 °C. The UTFVI analysis reveals declining thermal comfort, with the worst affected zone projected to expand by 7 km2. The increase in PM2.5 and aerosol optical depth over the past two decades further indicates deteriorating air quality. This study underscores the potential of ML and RS in environmental management, providing valuable insights into urban expansion, thermal dynamics, and air quality that can guide policy formulation for sustainable urban planning.

How do driving factors affect the diurnal variation of land surface temperature across different urban functional blocks? A case study of Xi'an, China

A comprehensive and in-depth understanding of the formation mechanisms of the urban thermal environment is the basis for thermal environment regulation, however, there is insufficient knowledge regarding how driving factors influence daytime and nighttime land surface temperature (LST) within urban functional blocks (UFBs). We selected Xi'an, China as a case study, integrating remote sensing data including ECOSTRESS, Landsat-8, and Gaofen-1, along with geographic data including road network, areas of interest, points of interest, building footprint, and mobile phone signaling. It divided 10 types of UFBs, inverted daytime and nighttime LST, calculated 5 types of driving factors, and finally analyzed the contribution and marginal effects of driving factors on day-night LST using boosted regression tree. The results showed that LST and its driving factors differed significantly in different times and UFBs. Industrial blocks and urban villages had higher LST in the daytime, while residential blocks, commercial blocks, and public service blocks had higher LST in nighttime. Industrial blocks were the dominant blocks that drove the overall LST up during the day, while residential blocks were the dominant blocks at night. Location distance (-) and population density (+) affected LST in all UFBs during day and night, NDVI (-), building density (+), and floor area ratio (-) were key factors for most UFBs during daytime, and NDVI (+), surface albedo (-), and point density of interest (+) were key factors during nighttime. Most of the driving factors had significant influence thresholds, but there were small differences across UFBs. This paper aims to elucidate the mechanisms by which driving factors influence urban LST during day and night across different UFBs, thereby providing new support for more targeted thermal environment regulation and diurnal trade-offs at the block scale.

Spatiotemporal heterogeneity of the relationship between urban morphology and land surface temperature at a block scale

Understanding the relationship between urban morphology and land surface temperature (LST) is essential for mitigating urban heat island (UHI). This study investigates the spatiotemporal heterogeneity of the relationship between urban morphology and LST across 5 experiment sites located in different temperature zones of China at a block scale. Normalized difference vegetation index (NDVI), building density (BD), floor area ratio (FAR), average building height (ABH) and open space ratio (OSR) are adopted to indicate the physical urban morphology. The results show positive relationship between BD and LST, and negative relations between LST and NDVI, ABH and OSR from a global perspective. The relations of FAR to LST are mixed. However, variable relationship between urban morphological indicators (UMIs) and LST are significantly observed at a block scale. 12 scenes that generate local relations of UMIs and LST differing from that of the surroundings are identified. Typical strategies for land development, vegetation phenology, large natural elements and human activities may be the most important causative factors to the heterogenous relationship between urban morphology and LST. Findings derived in the study would promote studies on mechanisms of the effects of urban morphology on LST and contribute to practices of UHI mitigation.

Hourly impact of urban features on the spatial distribution of land surface temperature: A study across 30 cities

Global warming and the urban heat island effect exacerbate excessive heat in urban environments, adversely impacting human health. Effective urban planning can mitigate these effects by influencing land surface temperature (LST). While previous studies have examined the influence of urban features on LST across seasons and between day and night, detailed hour-by-hour comparisons remain unexplored. This study addresses this gap by analyzing the hourly impacts of various urban features on LST in 30 U.S. cities using geostationary weather satellite data. We employed cloud-based analytics and machine learning techniques to aggregate data from thousands of images, identifying the relative importance and correlation curve of each urban feature on LST at each hour. Our findings revealed two distinct correlation patterns: dynamic daytime patterns with significant hourly variability and stable nocturnal patterns with minimal hourly differences. These results demonstrate that sunlight intensity greatly affects the correlation between urban features and LST. Urban planners should therefore consider broader patterns rather than focusing on specific hours. These insights provide valuable guidance for landscape and urban planners in developing strategies for climate adaptation and heatwave mitigation, contributing to the growing body of literature on sustainable cities.

Modelling future land use land cover changes and their impacts on urban heat island intensity in Guangzhou, China

During rapid urbanization in developing countries, changes in land use and land cover (LULC) can significantly alter urban land surface temperatures (LST), exacerbating the urban heat island (UHI) effect and degrading the outdoor environment. In this study, taking Guangzhou, China, as an example, we used Landsat series satellite data from 1992 to 2022, classified the LULC of the study area by the Support Vector Machine (SVM) method, estimated the LST of the area by the mono-window algorithm, and classified the LST of the study area into five UHI intensity classes based on the normalized values of the LST, and explored the influence of the LULC on the distribution of the UHI intensity. The CA-ANN (cellular automata-artificial neural network) model in QGIS software was employed to forecast the distribution of LULC and UHI intensity in Guangzhou for 2032. The findings reveal a strong correlation between UHI intensity and LULC, with water bodies and vegetation primarily exhibiting low and sub-low temperatures, while urban areas exhibit sub-high and high temperatures. The prediction results show that, according to the current development trend, compared with 1992, the water body and vegetation cover in 2032 will decrease by 46.97% and 34.24%, the building land will increase by 263.71%, and the sub-high and high temperature areas will increase by 127.76% and 375.92%. By analysing the spatial and temporal changes in LULC and its relationship with the distribution of UHI intensity during urbanization, this study assists government administrations and urban planners in devising sensible urban development strategies and implementing effective measures to plan LULC rationally. This approach aims to mitigate the impacts of the urban heat island and foster sustainable urbanization.

Spatiotemporal dynamic relationships and simulation of urban spatial form changes and land surface temperature: a case study in Chengdu, China.

Exploring the spatiotemporal dynamic evolution of local climate zones (LCZ) associated with changes in land surface temperature (LST) can help urban planners deeply understand urban climate. Firstly, we monitored the evolution of 3D urban spatial form in Chengdu City, Sichuan Province, China from 2010 to 2020, used the ordinary least squares model to fit the dynamic correlation (DR) between the changes in urban spatial patterns and changes in LST, and revealed the changes of urban spatial patterns closely related to the rise in LST. Secondly, the spatiotemporal patterns of LST were examined by the integration of the Space-Time Cube model and emerging hotspot analysis. Finally, a prediction model based on curve fitting and random forest was integrated to simulate the LST of study area in 2025. Results show the following: the evolution of the urban spatial form consists of three stages: initial incremental expansion, midterm incremental expansion and stock renewal, and late stock renewal and ecological transformation. The influence of the built environment on the rise of LST is greater than that of the natural environment, and the building density has a greater effect than the building height. The overall LST shows a warming trend, and the seven identified LST spatiotemporal patterns are dominated by oscillating and new hotspots patterns, accounting for 51.99 and 11.44% of the study area, respectively. The DR between urban spatial form and LST varies across different time periods and built environment types, whereas the natural environment is always positively correlated with LST. The thermal environment of the city will warm up in the future, and the area affected by the heat island will shift to the central of the city.

Influence of color steel buildings on urban land surface temperature: a case study in Urumqi, Xinjiang, China

Influence of color steel buildings on urban land surface temperature: a case study in Urumqi, Xinjiang, China

Urbanization signature on hourly rainfall extremes of Kuala Lumpur

This study uses high-resolution satellite data to investigate the impact of urbanization on the local climate of Kuala Lumpur, Malaysia. Nonparametric statistical methods, including trend test, spatial correlation, and quantile regression, were employed to quantify interrelationships between satellite nighttime light (NTL), Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST), and Global Satellite Mapping of Precipitation (GSMaP) extreme rainfall events. The results revealed that urbanization increased Kuala Lumpur's LST by 3.8 to 4.1 °C and the 99th percentile rainfall by 10 to 43 % compared to surrounding areas. The correlation analysis showed an association (r = 0.62) between the trends in LST and rainfall extremes, with a significance level of p < 0.05. The NTL trend analysis showed a rapid expansion of the city, particularly in the west, and thus, an expansion of high LST areas and extreme rainfall amounts. Quantile regression results confirmed the influence of increased LST on rainfall extremes. The trends for hourly rainfall revealed increases mainly between 2pm and 9pm in the city, which is attributed to the intensification of convective rainfall caused by increased LST. The study highlights the importance of mitigating urban LST to reduce climatic extremes and promote sustainable urban development.

Investigating the spatial interaction between urban expansion and the regional thermal environment in Guangxi Beibu Gulf urban agglomeration of China

Rapid urbanization and its associated surface modifications play a vital role in generating and intensifying the urban heat island (UHI). Numerous research has investigated the impact of landscape composition and structure on urban land surface temperature (LST), yet the spatial clustering and heterogeneity between urbanization and the regional thermal environment remained largely unexplored. Here, we analyzed land use dynamics and LST variations in the Guangxi Beibu Gulf urban agglomeration (GBG_UA) from 2000 to 2020. The spatial relationships between urban expansion and the regional thermal environment were explored by bivariate spatial autocorrelation and coupling coordination degree analysis. The results demonstrated that the land use in the GBG_UA remained relatively stable, except for the expansion of developed land in urban centers. The area of heat island zones clearly increased, leading to an enhancement of the UHI effect. Urban development and the regional thermal environment exhibited a robust positive spatial autocorrelation. The H–H (High-High) and L-L (Low-Low) types displayed significant aggregation effects. The H–H cluster areas dominated the central urban cores of prefecture-level cities, while the L-L correlation areas were primarily found in mountainous regions. The overall coupling coordination degree was relatively low, with most areas falling into the moderate dissonance level. While it showed a positive shift, with the regions of concordance levels expanding and coupling coordination degree increased. Our study reveals a close spatial correlation between urban sprawl and the UHI effect. The results can offer valuable insights for optimizing the urban green infrastructure to enhance urban heat mitigation and adaptation.

Urban Land Surface Temperature Downscaling in Chicago: Addressing Ethnic Inequality and Gentrification

Urban Land Surface Temperature Downscaling in Chicago: Addressing Ethnic Inequality and Gentrification