Urban Heat Island Intensity Research Articles

Quantifying of surface urban heat island intensity in Isfahan metropolis using MODIS\Terra\LST data

Heat island characteristics depend on the background climate of the site where the city is located. Therefore, an index was defined for the Isfahan metropolitan area to quantify the surface urban heat island intensity. This new index is based on the representative pixels of urban and non-urban areas. For this purpose, MODIS land cover type product (MCD12Q1) data were used to distinguish between urban and non-urban areas. Also, data from the MODIS/Terra land surface temperature product (MOD11A1) from 2000 to 2018 were utilized for daytime and nighttime to study the surface heat island intensity. Then, the representative pixels of urban and non-urban areas were identified using the spatial correlation method, and the heat island index was calculated for the metropolitan area of Isfahan. The study showed that the frequency distribution of the nighttime heat island index follows a normal distribution and is often 3.5 to 4º K above the temperature of the surrounding areas of the city. The 365-day floating mean of the surface urban heat island reveals that this index has increased in recent years. The research of temporal behavior showed that the intensity of the surface urban heat island reaches its maximum in January and becomes weaker in summer, while the survey of spatial behavior showed that the core of the surface urban heat island extends towards downtown areas, where the oldest part of the city is located.

Direct and indirect impacts of land use/cover change on urban heat environment: a 15-year panel data study across 365 Chinese cities during summer daytime and nighttime

ContextLand use/cover change (LUCC) can directly and indirectly affect surface urban heat island intensity (SUHII) and the effects need to be decomposed.ObjectivesTo perform long-term trend analyses of contribution indexes (CIs) of land use types to urban heat environment in cities and to deconstruct direct and indirect effects of LUCC on SUHII within geographical regions.MethodsMann–Kendall test and Sen’s slope were used to examine the trends of CIs and SUHII in 365 cities during summer of 2005–2019. Structural equation models were established to quantify direct and indirect effects of land use types’ CIs on SUHII in six geographical regions of China.ResultsFirst, SUHII in 78.08% and 73.70% of the Chinese cities increased during summer daytime and nighttime, respectively. Second, the CI of built-up land significantly increased across more than half of the cities in all the six regions. Third, not all land use types exerted both direct and indirect effects on SUHII. At daytime, the CI of cropland (direct) was the dominant factor in East China (1.386), South-central (− 0.637), and Northwest (− 0.399) regions. At nighttime, the CI of water bodies (both direct and indirect) was the dominant factor in Northwest (0.506) and Northeast (0.697) regions while CI of built-up land (both direct and indirect) determined in North China (0.476).ConclusionsSeparation of direct and indirect effects of land use types on SUHII had practical implications for cities to optimize the structures and functions of ecosystems and to take regionally based actions improving the urban heat environment.

Evaluation of surface urban energy and water balance scheme (SUEWS) using scaled 2D model experiments under various seasons and sky conditions

Urban energy balance models are essential for better understanding of urban climate processes and mitigating urban heat island intensity. In this study, the Surface Urban Energy and Water Balance Scheme (SUEWS) is evaluated using scaled outdoor experiments of idealized city models consisting of street canyons (aspect ratio H/W = 2, H = 0.5) and hollow concrete building models, under various seasonal and sky conditions in a humid subtropical climate in 2020, using three evaluation approaches. The results indicate that the model effectively simulates net all-wave radiation (Q∗), with the coefficient of determination (R2) exceeding 0.970. Storage heat flux (ΔQS) derived from the Objective Hysteresis Model (OHM) is compared to those obtained from the residual term method (RTM) and element surface temperature method (ESTM), revealing the RTM tend to overestimate ΔQS compared to the observation-based ESTM. Compared to using default OHM coefficients, applying fitted OHM coefficients obtained under seasons and sky conditions improves the simulations of ΔQS and sensible heat flux (QH), reducing statistical errors by approximately 50% for ΔQS and increasing R2 in rainy summer from 0.353 to 0.793 for QH, respectively. Additionally, the improved available energy simulation suggests the importance of accurately partitioning between QH and latent heat flux (QE) for the future development of SUEWS.

REMOTE SENSING OF HEAT POLLUTION SOURCES IN THE LIPETSK URBAN AREA

Positive temperature anomalies called “urban heat islands” (UHI) are formed in cities as a result of progressing urbanization, increasing area and density of buildings, motor transport load and anthropogenic heat emissions. Such UHI reduce the comfort of the urban environment, and affect population health and quality of life. Remote sensing data from the thermal band satellite sensors are widely used to study the UHI intensity and spatial structure. We analyzed Landsat 8 and 9 satellite imageries from 2020 to 2022 representing the Lipetsk urban area in Central Russia. Taking into account the image quality requirements, 5 “winter” and 4 “summer” scenes were selected to examine thermal anomalies. As a result of their processing a map of land surface temperature (LST) excesses over the background values and a map of LST seasonal variations were compiled. The produced maps made it possible to identify 33 principal sources of anthropogenic heat in Lipetsk. 26 of them are located within industrial zones of the left bank side of Lipetsk, including. 23 sources within the territory of the NLMK Group metallurgical combine. LST of the identified heat sources were approximately 4-9°C above the background temperatures in winter and 11-14°C above the background temperatures in summer. The results of the study could become an information-methodical basis for the space monitoring of thermal pollution in Lipetsk.

Climatological analysis of urban heat island effects in Swiss cities

AbstractThis work presents a high‐resolution spatiotemporal analysis of the urban heat island (UHI) effect in Swiss cities during the last 6 years. The entire alpine country is simulated at once using the COSMO model at 1.1 km resolution, validated against a dense national measurement network. Additionally, the bulk parametrisation TERRA URB is used to model the dynamical and thermal effects of urban areas. The resulting data allows us to perform the first comprehensive analysis over the entire Switzerland, with a focus on the UHI effect with intra‐urban and across cities comparisons, and to report on the current state of urban warming. Despite the medium‐small size of the cities, the UHI is of considerable magnitude especially when considering peak values in ideal weather conditions. The present results confirm previous analyses performed for individual cities, and shed new light on the country‐wide picture by highlighting features such as the marked seasonality of the UHI intensity as well as the influence of local climate and topographical features on the urban climate. A thorough discussion is also presented highlighting the absence of simple relationships between UHI intensity and bulk city parameters and weather patterns.

Numerical investigation of thermal patterns and local wind circulations to characterize Urban Heat Island during a heatwave in Turin

Heatwaves are annually increasing in terms of intensity and frequency. In urban areas, the impact could be further exacerbated by the Urban Heat Island (UHI) effect. This work carries out an urban microclimate numerical analysis to evaluate the behavior of the UHI in the city of Turin (Italy) during a heatwave event. Turin is located in the North-West region of Italy, boarded by the Alps mountain ranges in the west and hills in the East. The study utilizes the WRF/MLUCM model and considers the heatwave period in June 2019. The high-resolution urban land use/land cover data is generated from local climate zone (LCZ) generator tool provided by the World Urban Database and Access Portal Tools (WUDAPT) portal. The simulation is validated with the data provided by ARPA (Regional Environmental Protection Agency) meteorological stations located over the region. The lower root mean squared error of air temperature and higher index of the agreement show that the simulation is in good agreement with the observational data. The simulation results are then used to analyze the rural-urban temperature distributions over the diurnal cycle and local wind circulations. According to the simulated results, the city of Turin has higher near-surface UHI intensity during the night and early morning (with maximum intensity greater than 5 °C), whereas the intensity is diminishing during the mid-day hours. The simulated wind patterns show a significant reduction in wind intensity over the urban areas. This reveals the potential of creating wind ventilation inside the city to minimize UHI effect and enhance air quality. Ultimately, this research contributes valuable insights into addressing the challenges posed by heatwaves and urban heat islands in Turin and offers a foundation for the development of sustainable urban climate strategies.

Urban Heat Island effect and Urban Thermal Field Variance Index phenomenon at the heart of ‘Chicken's Neck Corridor’ of India

Siliguri corridor, the ‘Chicken Neck Corridor of India’, is one of the largest Municipal areas in West Bengal and is of great geo-strategic importance as it lies at the crossroads between India, Nepal, Bhutan and Bangladesh connecting the north-eastern part of India with mainland India. This study uses the LANDSAT 7 ETM+ and LANDSAT 9 TIR OLI imageries from USGS Earth Explorer to determine Land Surface Temperature (LST) changes, Normalized Differential Vegetation Index (NDVI) changes; Normalized Differential Built-up Index (NDBI) changes; Land use and Land cover (LULC) changes; Urban Heat Island (UHI) effect; Urban Hot Spots (UHS) and Urban Thermal Field Variance Index (UTFVI) phenomenon. With booming trade and increasing urbanisation, Siliguri city (Municipal Corporation) has seen rise in the mean LST by 2.92 °C resulting in increased UHI intensity and UTFVI phenomena between 2000 and 2022. The average LST in UHI and UHS has risen by approx. 3 °C causing more areas to come under bad, worse and worst thermal comfort level categories of the ecological evaluation index. An attempt is made to determine the reason for such changes in this micro climate through multi-dimensional perspectives and to exhibit the land use transformations that took place between the aforesaid time periods.

Mixed Convection in an Idealized Coastal Urban Environment With Momentum and Thermal Surface Heterogeneities

AbstractCoastal marine heatwaves (MHWs) modulate coastal climate through ocean‐land‐atmosphere interactions, but little is known about how coastal MHWs interact with coastal cities and modify urban thermal environment. In this study, a representative urban coastal environment under MHWs is simplified to a mixed convection problem. Fourteen large‐eddy simulations (LESs) are conducted to investigate how coastal cities interact with MHWs. We consider the simulations by simple urban roughness setup (Set A) as well as explicit urban roughness representation (Set B). Besides, different MHW intensities, synoptic wind speeds, surface fluxes of urban and sea patches are considered. Results suggest that increasing MHW intensity alters streamwise potential temperature gradient and vertical velocity direction. The magnitude of vertical velocity and urban heat island (UHI) intensity decrease with increasing synoptic wind speed. Changing urban or sea surface heat flux also leads to important differences in flow and temperature fields. Comparison between Set A and B reveals a significant increase of vertical velocity magnitude and UHI intensity. To further understand this phenomenon, a canopy layer UHI model is proposed to show the relationship between UHI intensity and urban canopy, thermal heterogeneity and mean advection. The effect of urban canopy is considered in terms of an additional vertical velocity scale that facilitates heat transport from the heated surface and therefore increases UHI intensity. The model can well explain the trend of the simulated results and implies that overlooking the effect of urban canopy underestimates canopy UHI in urban coastal environment.

UAS-Based Thermal Photogrammetry for Microscale Surface Urban Heat Island Intensity Assessment in Support of Sustainable Urban Development (A Case Study of Lyulin Housing Complex, Sofia City, Bulgaria)

The urban heat island (UHI) and its intensity is one of the phenomena that are of determining importance for the comfort of living in cities and their sustainable development in the face of deepening climate change. The study is objectively difficult due to the large dynamics like land cover and the considerable diversity of land use patterns in urban areas. Most of the frequently used research practice approaches provide information with problematic spatial and temporal resolution, making them difficult to apply for sustainable urban planning purposes. This paper proposes to calculate SUHI intensity as the difference between the temperature of a given point within a city and the average minimum temperature of the land cover class with the lowest surface temperatures within the same urban area. The study presents the results of the application of thermal photogrammetry based on the use of unmanned aerial systems (UAS), combined with geographic information systems (GIS), in the study of surface urban heat island intensity (SUHI), at the local level for the largest housing complex in Bulgaria–Lyulin district of the capital of Sofia city. The studies were carried out during a heat wave in July 2023. A difference of 16.5 °C was found between locations with SUHI occurrence and of the peripheral non-build and natural land cover types within the urbanized area. The information benefits of locally addressed data and their direct applicability are discussed to support decision-making processes in the planning and management of urban areas, including their climate adaptation and sustainable development.

Thermal stress and comfort assessment in urban areas using Copernicus Climate Change Service Era 5 reanalysis and collected microclimatic data

In this initial study of a research project, this paper seeks to understand the thermal conditions in the cities of Lisbon and Munich, specifically focusing on Urban Heat Island intensity and on thermal comfort using the Universal Thermal Climate Index modeling data at the Local Climate Zone scale. Based on these datasets, Munich has exhibited more unfavourable thermal conditions than Lisbon. In terms of UHII, both cities have shown that low, medium, and high rise compact urban areas and bare rock or paved areas have the highest values, while sparsely built areas have the lowest. These results differ from the UTCI, which indicates that in Lisbon and Munich, these sparsely built areas as well as areas with low plants and vegetation are the most uncomfortable. In Munich, the population was exposed to very strong heat stress, while Lisbon experienced strong heat stress conditions. Conversely, low, medium, and high rise compact urban areas and densely wooded areas in Munich, and scattered trees areas and large low-rise urban areas in Lisbon, have demonstrated the lowest monthly mean and average maximum values. These results will be further explored in future studies in the city of Lisbon and cross-checked with data obtained from roving missions. This will enable a more detailed temporal and local analysis.

Temporal and Spatial Urban Heat Islands in a Coastal Brazilian Area of Tropical Climate

This study explores the urban heat island (UHI) phenomenon in the Metropolitan Region of Vitória (MRV), Brazil. Two methods were employed: the traditional approach estimating the Atmospheric Urban Heat Island (UHI) using air temperature data, and the quantiles method assessing the Surface Urban Heat Island (SUHI) through medium spatial resolution images. The findings demonstrate the existence of an average heat island effect, as estimated by both UHI (+0.9 °C) and SUHI (+2.03 °C). The intensity of the atmospheric UHI is linked with higher air temperature values around midday. Additionally, the study reveals that UHI values in the MRV are more pronounced during spring and summer SUHI estimates peak during the summer months. The utilization of orbital remote sensing enables the identification of hotspots throughout the metropolitan area, with the highest temperatures observed in industrial areas, suburban neighborhoods, and densely populated regions. The advantage of the UHI method lies in its long time series, providing high temporal resolution and statistical significance over an extended period. Conversely, the SUHI method offers the advantage of considering data across the entire domain with high spatial resolution. Thus, these two methods complement each other in identifying the heat island phenomenon.

Comparing Characteristics of the Urban Thermal Environment Based on the Local Climate Zone in Three Chinese Metropolises

Urban landscape has important effects on urban climate, and the local climate zone (LCZ) framework has been widely applied in related studies. However, few studies have compared the relative contributions of LCZ on the urban thermal environment across different cities. Therefore, Beijing, Shanghai, and Shenzhen in China were selected to conduct a comparative study to explore the relationship between LCZ and land surface temperature (LST). The results showed that (1) both the composition and spatial configuration of LCZ had obvious differences among the three cities. Beijing had a higher area proportion of compact mid-rise and low-rise LCZ types. The spatial pattern of LCZ in Shenzhen was especially quite different from those of Beijing and Shanghai. (2) Shenzhen had the strongest summer surface urban heat island (UHI) intensity and the largest UHI region area. However, the proportion of urban cooling island areas was still the highest in Shenzhen. (3) Different LCZs showed significant LST differences. The largest LST difference between the LCZs reached 5.57 °C, 4.50 °C, and 12.08 °C in Beijing, Shanghai, and Shenzhen, respectively. Built-up LCZs had higher LSTs than other LCZ types. (4) The dominant driving LCZs on LST were different among these cities. The LST in Beijing was easily influenced by built-up LCZ types, while the cooling effects generated by LCZ G(water) were much stronger than built-up LCZs’ warming effects in Shanghai. These results indicated that the effect of the LCZ on LST had significant differences among LCZ types and across cities, and the dominant LCZs should be given more priority in future urban planning.

Assessment of the Spatiotemporal Variability of Urban Heat Island Intensity in Colombo Metropolitan Area


 
 
 During the last few decades, the Colombo Metropolitan Area (CMA) has experienced rapid population growth and unplanned urbanization, resulting in the replacement of natural landcover with more impervious surfaces. The primary approach of current study was to determine the gradients of air temperature and assess the spatiotemporal variation of atmospheric urban heat island (AUHI) intensity in the CMA considering a range of rural sites extending in different radial directions from the urban center. A total of sixteen rural observation sites that extend from an urban center in four directions were selected within the CMA, with four sites 3.5 km apart from each other in any direction. The air temperatures at each site were measured using HOBO UX-100-003 temperature data loggers for an 8-day period from 21st to 28th, of February 2022. The normalized difference vegetation index (NDVI) and normalized difference building index (NDBI) were used to determine the influence of land use/land cover on air temperatures. The results revealed that air temperature had a strong positive correlation with NDBI. Thus, the current study suggests that the impervious surface/s is a significant predictor of air temperature over the study area. The higher air temperatures were observed at the urban center in relation to the selected suburban and rural observation sites. Hence, the current study confirms that AUHI exists within the selected study area over the CMA. It was found that AUHI tends to be present throughout the day in the CMA. However, it becomes strong between 12.00 A.M. and 6.00 A.M and mostly weakens in the late afternoon hours. Furthermore, this study found that the timing and magnitudes of AUHI vary from direction to direction across the study area depending on the site-specific characteristics of the rural observation sites. The current study emphasizes that site-specific land use/land cover characteristics can have a significant impact on the interpretation of UHI study results even within a single metropolitan area. Therefore, taking only one rural site might not be effective in determining the UHI intensity and a range of sites need to be considered (to select from) for in-depth analysis of UHI in any given area. The findings of the current study will provide firsthand knowledge for urban planners to identify future trends in UHI in the CMA and will promote further UHI research in this area.
 Keywords: Rural observation site, Atmospheric urban heat island, Normalized Difference Vegetation Index
 
 

Exploring the role of strategic urban planning and greening in decreasing surface urban heat island intensity

ABSTRACT Surface urban heat islands (SUHIs) are a significant concern in urban areas worldwide owing to their adverse effects on energy consumption, human health, and urban climate. Strategic urban planning incorporating abundant vegetation is an approach to moderate the UHI effect, promoting cooler, greener and more sustainable cities. This study investigates SUHIs in Seoul and Ulsan, South Korea, from 2001 to 2022, using satellite measurements. Summer daytime SUHII holds significant importance due to the frequent presence of heatwaves and its combined effects with UHI phenomenon. Remarkably, Seoul has shown a significant decrease in summer daytime SUHIs (−0.03°C/yr in Terra and −0.05°C/yr in Aqua), attributed to increased vegetation (0.03/decade in Terra and 0.02/decade in Aqua) in the city’s core. In contrast, Ulsan saw rising SUHIs (+0.04°C/yr), linked to reduced summer vegetation. Daytime SUHII is highest in both Seoul and Ulsan during summer months with both Terra and Aqua datasets, and peaks in the month of June with values reaching up to 2.98°C in Seoul and 3.39°C in Ulsan. Increasing urban greenery, including tree planting, and expanded green spaces, proves effective in mitigating SUHIs, offering valuable insights for urban planners and policymakers to enhance urban sustainability, particularly in megacities like Seoul.

Assessing Long-Term Thermal Environment Change with Landsat Time-Series Data in a Rapidly Urbanizing City in China

The studies of urban heat islands or urban thermal environments have attracted extensive attention, although there is still a lack of research focused on the analysis of long-term urban thermal environment change with fine spatial resolution and actual exposure of urban residents. Taking the rapidly urbanizing city of Nanjing, China as an example, this study utilizes the Landsat-derived daytime time-series land surface temperature data to comprehensively assess the city’s long-term (30-year) urban thermal environment change. The results showed that: (1) The overall surface urban heat island intensity showed a noticeable trend of first increasing and then decreasing from 1990 to 2020. (2) It exhibited the detailed spatial distribution of urban heat/cold islands within the urban center boundary. The percentage of surface urban heat islands was 77.01% in 1990, and it increased to 85.79% in 2010 and then decreased to 80.53% in 2020. (3) More than 65% of the urban residents have lived in areas with a surface urban heat island intensity greater than 3.0 °C, which also showed a trend of first increasing and then decreasing from 1990 to 2020. The methods and findings of this study can provide a reference for other studies on urban thermal environment changes and urban sustainable development.

Quantification of Surface Urban Heat Island Intensity Using MODIS Satellite Imagery in Different Indian Cities

Quantification of Surface Urban Heat Island Intensity Using MODIS Satellite Imagery in Different Indian Cities

Diurnal variation in the urban thermal environment and its relationship to human activities in China: a Tencent location-based service geographic big data perspective.

The main factor of the formation and deterioration in China's urban thermal environment is human activity, which is difficult to describe and measure. A new perspective on the effect of human activity on the urban thermal environment can be obtained by examining the interaction between location-based service (LBS) data and the urban thermal environment in China. However, relevant research is still limited. In this study, we used Tencent LBS data, Terra/Aqua MODIS land surface temperature (LST) data, and land use data to investigate the relationship between LBS and the urban thermal environment, specifically the LST and surface urban heat island intensity (SUHII) across China and its provinces. Our results showed that (1) in summer, the heat island effect was an issue in 94% of the urban areas in China, which was worse during the day. The high- and low-value periods of LBS data on a given day coincided with the acquisition times of MODIS LST products during the day and at night, respectively. (2) During both the day and at night, there was a significant connection between LBS data and the urban thermal environment in China. The highest correlation coefficient (r) between LBS data and the LST could reach 0.55 (p < 0.01) at the provincial level, and the highest correlation coefficient (r) between LBS data and the SUHII could reach 0.78 (p < 0.01) at the provincial level. (3) The urban thermal environment diurnal difference and LBS data exhibited a significant relationship. The ΔLBS diurnal differences were significantly positively related to the SUHII diurnal differences in China. The overall study findings revealed that LBS data constitute an important parameter to represent the human activity intensity when investigating the formation of the urban thermal environment in China.

Spatiotemporal Analysis of Urban Heat Islands and Vegetation Cover Using Emerging Hotspot Analysis in a Humid Subtropical Climate

Research on the temporal and spatial changes of the urban heat island effect can help us better understand how urbanization, climate change, and the environment are interconnected. This study uses a spatiotemporal analysis method that couples the Emerging Hot Spot Analysis (EHSA) technique with the Mann–Kendall technique. The method is applied to determine the intensity of the heat island effect in humid subtropical climates over time and space. The data used in this research include thermal bands, red band (RED) and near-infrared band (NIR), and Landsat 7 and 8 satellites, which were selected from 2000 to 2022 for the city of Sari, an Iranian city on the Caspian Sea. Pre-processed spectral bands from the ‘Google Earth Engine’ database were used to estimate the land surface temperature. The land surface temperature difference between the urban environment and the outer buffer (1500 m) was modeled and simulated. The results of this paper show the accuracy and novelty of using Emerging Hotspot Analysis to evaluate the effect of vegetation cover on the urban heat island intensity. Based on the Normalized Difference Vegetation Index (NDVI), the city’s land surface temperature increased by approximately 0.30 °C between 2011 and 2022 compared to 2001 to 2010. However, the intensity of the urban heat island decreased during the study period, with r = −0.42, so an average −0.031 °C/decade decrease has been experienced. The methodology can be transferred to other cities to evaluate the role of urban green spaces in reducing heat stress and to estimate the heat budget based on historical observations.

Identifying Urban Hotspots and Cold Spots in Delhi Using the Biophysical Landscape Framework

Urban heat islands (UHIs), which are formed by biophysical landscape transformations, have significant adverse effects on environmental quality as well as human health, resources, and facilities. Variations in UHI intensity give rise to urban hotspots (UHSs) and cold spots in different parts of the city. This study identifies such hotspots and cold spots in Delhi by classifying the city into zones of different UHI intensities using biophysical landscapes. The data on the selected biophysical landscapes were obtained from satellite images and secondary sources. The impact of different biophysical landscapes on UHI intensity was calculated using the weighted overlay method performed using ArcGIS software. The city of Delhi was thus divided into four zones, based on UHI intensity. It was found that UHSs cover about 45% of the total area and are mostly located in eastern and central Delhi. While built-up areas form the major source landscape, vegetation cover is the major sink landscape as per land surface temperature (LST) and UHI intensity. The findings of this study will help urban planners and policymakers identify UHSs and adopt suitable policies and measures to mitigate UHIs based on the different intensity zones.

The Impacts of Urban Morphology on Urban Heat Islands in Housing Areas: The Case of Erzurum, Turkey

The growing importance of climate change underlines the need to comprehend Urban Heat Islands (UHI), particularly those influenced by urban morphology. As progress has been made in understanding the macroscale relationship between urban morphology and UHIs, the microscale effects are often overlooked. This study, conducted in the city of Erzurum in Turkey, delves into the complex relationship between urban morphology and UHI intensity in different housing areas with distinct microclimates, focusing particularly on street networks, building systems, and land use. Pearson correlation analysis was performed to investigate the relationships between morphological indicators and UHIs in different housing areas. Key findings include that (1) noticeable UHI effects were observed, especially in dense areas with high-rise buildings. (2) UHIs reveal a strong correlation with both 2D and 3D urban morphological indicators. A moderate-to-high Sky View Factor (SVF) tends to reduce UHIs, while an extremely high SVF aggravates UHIs. (3) Enhancing street network integration emerges as a more effective strategy for mitigating UHI effects in mid-rise buildings compared to other morphological factors. The Normalised Difference Built-Up Index (NDBI) and Normalised Difference Vegetation Index (NDVI) may not reliably indicate UHIs in housing areas with a predominantly rural character. Consequently, this article recommends that urban morphology optimisation for UHI mitigation should prioritise spatial and indicator specificity in urban design and spatial planning for cities. Future research endeavours should investigate the influence of morphological indicators on UHI dynamics in different seasons, including various remote sensing indicators related to morphological structure, to enrich our understanding of daily UHI fluctuations within urban morphology research.