Morphological and Environmental Drivers of Urban Heat Islands: A Geospatial Model of Nighttime Land Surface Temperature in Iberian Cities

The Urban Heat Island (UHI) effect is of critical concern for cities’ adaptation to climate change. The UHI effect shows substantial intra-urban variation at the city microscale, causing disparities in thermal comfort and energy consumption. Therefore, air temperature assessment should be prioritized for effective heat mitigation and climate adaptation. However, meteorological stations’ spatial distribution is far from meeting the scale that the UHI and its driving parameters operate. This limitation hampers demonstrating the intra-city variability of UHI and its origin of sources; for example, most studies employ Land Surface Temperature (LST), usually without demonstrating the relationship between UHI and LST. The current body of knowledge on urban climate implies a much better understanding and more detailed information on the spatial pattern of UHI and the driving factors to provide decision-makers with tools to develop effective UHI mitigation and adaptation strategies. In an attempt to address the adequacy of the use of LST and UPs in describing the intra-city variability of UHI, this study investigates the relationship between LST daytime and nighttime, and air temperature (Ta) daytime and nighttime, and driving urban parameters (UPs) of UHI together. Although it is well recognized that the intensity of the UHI is characterized by Ta, particularly at night, so-called nocturnal UHI, the use of remotely sensed LST is common, owing to the lack of spatially detailed Ta data in cities. Our findings showed that nocturnal UHI is weakly correlated with nighttime LST with a Pearson correlation (r) of 0.335 at p > 0.05 and that it is not correlated with daytime LST for the case study, highlighting the need for Ta observations for representing the intra-urban variation of nocturnal UHI. Among UPs, Sky View Factor (SVF), Building Volume Density (BVD), and Road Network Density (RND) explained 69% of the variability of Ta nighttime that characterizes nocturnal UHI. Therefore, UPs that performed well in estimating nocturnal UHI may be used in the absence of densely distributed Ta measurements. In a further investigation of the urban cooling phenomenon based on UHI diurnal changes, a particular region with high nighttime temperatures spoiled the Ta daytime and nighttime coherence. This region is characterized by high Mean Building Height (MBH), BFD, and BVD that re-emits heat, low SVF that prevents urban cooling, and high RND that releases extra heat at night. These particular UPs can be of prior interest for urban cooling. The present study, exploring the relationships of LST and Ta in a diurnal context, offers a further understanding of the preference of LST, Ta, or UPs to characterize UHI. Ta, in relation to major causative factors (UPs), provides insights into addressing the localities most vulnerable to the UHI effect and possible strategies targeting heat mitigation for sustainability and climate change resilience.

Exploring the spatial heterogeneity of urban heat island effect and its relationship to block morphology with the geographically weighted regression model

We analyzed the annual, monthly, and seasonal variations of urban heat island (UHI) intensity in Istanbul by using meteorological data measured for the period of 1960–2012 at six stations. The UHI on minimum temperature is found to be positive for all seasons, and the average UHI intensity clearly indicates seasonal changes, strongest in summer and weakest in winter. The results demonstrated increase of night time UHI intensity with 0.41–0.50 °C/decade and decrease of daytime UHI intensity with 0.13–0.18 °C/decade at the urban sites. The UHI strengthened with the expansion of the city due to increased population. The influences of meteorological variables on seasonality of the UHI intensity are examined for the days categorized depending on wind, cloud cover, and precipitation values. It is found that the UHI intensity decreases with increasing wind speed and cloud cover. The integrated response of the city atmosphere to wind speed changes differ such that daytime UHI in urban atmosphere intensifies rapidly from calm conditions to the wind speeds of 2–3 m/s, then slightly increases until 4–5-m/s wind speeds and starts to decline afterwards. On the other hand, the nighttime UHI intensities in urban sites continuously decline with the same rate until the wind speeds reach to 5–6 m/s. The difference of daytime UHI between rainy summer days and dry days is around 1 °C which is almost independent of the precipitation amount. Both nighttime and daytime UHI intensities depend on the season and site range approximately between 0.24 and 1.74 °C and − 0.62 and 2.61 °C, respectively. However, the UHI based on minimum temperature for the selected dry days with low wind and clear sky conditions increases to 1.70–3.08 °C. Land surface data from Moderate-Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra show areal extension of the UHI through the north along the Bosphorus between 2000 and 2012, especially in the night observations. The continuous increase of built-up areas, paved roads, and decrease of green areas caused the growth of UHI intensity. The estimated UHI based on land surface temperature (LST) at the most urbanized locations of Istanbul reach to 8 °C for daytime and 6 °C for nighttime.

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.

This paper focuses on the monitoring of the urban heat island (UHI) effect with temporal and spatial variation, combining Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Thematic Mapper (TM) data. Our study area is located in the central urban area of Beijing, which mainly refers to the areas within the fifth ring road. For detecting UHI changes over the years 2002–2006, three ASTER images in the summers of 2003, 2004 and 2006 and two TM datasets in the summers of 2002 and 2005 were collected. For monitoring UHI changes with the seasons, three ASTER images and one TM image in 2004 in winter, spring, summer and autumn, respectively, were employed. To calculate the urban heat island intensity, the land surface temperatures were retrieved iteratively for ASTER data and using a generalized single-channel method for the TM image. Four separated regions located in four directions outside the fifth ring road were selected as representing rural comparative regions. Their averaged land surface temperature was regarded as the rural comparative temperature. The UHI intensity was computed by the difference between the pixel urban land surface temperature in the urban area and the comparative temperature in the rural area. Detection of the UHI effect over 2002 to 2006 indicated that most of the areas with high UHI effect were the industrial land use regions and the areas having a high density of buildings, roads, transportations and residents; and the areas without UHI effect were located around the regions with large areas of grassland, trees and water bodies. Our results also showed that the UHI effect was not proportional to urbanization over time. Statistical UHI data during 20 July to 20 September in 2003–2008 also support this point. The monitoring of the UHI effect over seasons (winter, spring, summer and autumn) showed that the urban area of Beijing city had a high UHI effect except in winter, when the urban area of Beijing was in an urban heat sink; the UHI effect increased in spring, summer and autumn.

Analysing the day/night seasonal and annual changes and trends in land surface temperature and surface urban heat island intensity (SUHII) for Indian cities

Impacts of increased urbanization on surface temperature, vegetation, and aerosols over Bengaluru, India

New methods of urban heat island (UHI) center/movement and usage of standard deviation (SD) of temperature for the analysis of UHI intensity (UHII) are presented in this study. UHI deviation is defined as difference between temperature of a measurement point and mean temperature of all measuring points. New definition of UHII is used as the difference between maximum and minimum UHI deviations. The UHI center is set as gravity center of relatively hot area and the movement of the UHI can be observed by the course of the center. These UHI metrics are suitable in analyzing the dense network for UHI measurement. As an application of these method, AMeDAS data is used to analyze the UHI effect in Tokyo metropolitan area (TMA) from the point of view of UHI movement and UHII. Clear difference of summer and winter pattern of the UHI in TMA was observed. In the summer pattern, the monthly average UHI area with the high UHI deviation was located from the coastal area to the north inland. About hourly change in a day, the UHI located along the coast at night and after the sunrise, the UHI gradually extended to inland. With this change, the UHI center moved from south to north and returned from north to south. In the winter pattern, the high UHI deviation area was located along the coast and the UHI center was located in the same area for all the day. The method of movement analysis is very effective to clarify the UHI characteristics of the area, especially coastal areas. The relation between the UHII and the SD of the temperature was analyzed. The UHII has strong linear relationship with the SD and the UHII is nearly four times of the SD (UHII ≅ 4×SD). Especially, in the observation of a dense network which has many measuring points, the SD is considered as more robust index of the UHII.

Heat stress brought on by the intensification of urban heat island (UHI) has caused many negative effects on human beings, which were found to be more severe in highly urbanized old towns. With the inconsistent findings on how urban spatial morphological characteristics influence land surface temperature (LST) and gaps between design practices being found, we chose Beijing Old Town (BOT) as the study area and took the basic planning implementation module “block” as a study to reveal the spatial heterogeneity of LST and its relationship to multiple urban morphological characteristics with higher spatial resolution calculated via WorldView3. Our results have shown that (1) UHI effect was significant and spatially heterogeneous in BOT, and significant hot areas with high LST value and small LST differences were found, as cold areas were the exact opposite. (2) The proportion of vegetated area, water, impervious surface, and urban spatial structure indicators i.e., building coverage ratio, mean height, highest building index, height fluctuation degree, space crowd degree and sky view factor were identified as significantly affecting the LST of blocks in BOT. (3) The effects of GBI components and configuration on LST varied within different block types; generally, blocks with GBI with larger patches that were more complex in shape, more aggregated, and less fragmented were associated with lower LST. Finally, in the context of integrating our study results with relevant planning and design guidelines, a strategy sample of adaptive GBI planning and vegetation design for blocks with different morphological features was provided for urban planners and managers to make a decision on UHI mitigation in the renewal process of BOT.

Urban morphology quantitatively expresses a city’s spatial structure, internal relationships, and physical form. It has advantages for predicting urban growth and analyzing the current state of cities in the literature. A comprehensive study on the complex relationships between urban morphology and urban heat island intensity (UHII) is of great importance for mitigating the urban heat island (UHI) effect for megacities. This study models urban morphological indicators in fine resolution based on three aspects: building morphology, ecological infrastructure, and human activities. The model accurately captures UHII by employing the definition of UHI effects. The relationship between urban morphology and UHII was further examined using extreme gradient boosting (XGBoost) and Shapley additive explanations (SHAP). By taking central Beijing, China as study area, major findings include the following: (1) Significant daytime UHI effects were observed within the research area, particularly during the summer months, when it appears to be most severe. More than 90% of the region experiences varying degrees of the UHI effects. (2) UHI is significantly correlated with both 2D and 3D urban morphological indicators. Low sky view factor (SVF) and high SVF tend to mitigate UHI, whereas moderate SVF tends to aggravate UHI. (3) In densely populated areas, tall trees may be more effective than other forms of vegetation at mitigating UHI. Based on the aforementioned findings, this article suggests that urban morphology optimization should focus on seasonality, spatial specificity, and indicator specificity for megacities in urban design and spatial planning aimed at mitigating UHI.

Effects of urban form on the urban heat island effect based on spatial regression model

Urbanization is closely associated with land use land cover (LULC) changes that correspond to land surface temperature (LST) variation and urban heat island (UHI) intensity. Major districts of Bangladesh have a large population base and commonly lack the resources to manage fast urbanization effects, so any rise in urban temperature influences the population both directly and indirectly. However, little is known about the impact of rapid urbanization on UHI intensity variations during the winter dry period in the major districts of Bangladesh. To this end, we aim to quantify spatiotemporal associations of UHI intensity during the winter period between 2000 and 2019 using remote-sensing and geo-spatial tools. Landsat-8 and Landsat-5 imageries of these major districts during the dry winter period from 2000 to 2020 were used for this purpose, with overall precision varying from 81% to 93%. The results of LULC classification and LST estimation showed the existence of multiple UHIs in all major districts, which showed upward trends, except for the Rajshahi and Rangpur districts. A substantial increase in urban expansion was observed in Barisal > 32%, Mymensingh > 18%, Dhaka > 17%, Chattogram > 14%, and Rangpur > 13%, while a significant decrease in built-up areas was noticed in Sylhet < −1.45% and Rajshahi < −3.72%. We found that large districts have greater UHIs than small districts. High UHI intensities were observed in Mymensingh > 10 °C, Chattogram > 9 °C, and Barisal > 8 °C compared to other districts due to dense population and unplanned urbanization. We identified higher LST (hotspots) zones in all districts to be increased with the urban expansion and bare land. The suburbanized strategy should prioritize the restraint of the high intensity of UHIs. A heterogeneous increase in UHI intensity over all seven districts was found, which might have potential implications for regional climate change. Our study findings will enable policymakers to reduce UHI and the climate change effect in the concerned districts.

Urban form plays a critical role in enhancing urban climate resilience amidst the challenges of escalating global climate change and recurrent high-temperature heatwaves. Therefore, it is crucial to study the correlation between urban spatial form factors and land surface temperature (LST). This study utilized Landsat 8 remote sensing data to estimate LST. Random forest nonlinear analysis was employed to investigate the interaction between the urban heat island (UHI) and six urban morphological factors: building density (BD), floor area ratio (FAR), building height (BH), fractional vegetation coverage (FVC), sky view factor (SVF), and impervious surface fraction (ISF), within the framework of local climate zones (LCZs). Key findings revealed that Xi’an exhibited a significant urban heat island effect, with over 10% of the study area experiencing temperatures exceeding 40 °C. Notably, the average LST of building-class LCZs (1-6) was 3.5 °C higher than that of land cover-class LCZs (A-C). Specifically, compact LCZs (1-3) had an average LST 3.02 °C higher than open LCZs (4-6). FVC contributed the most to the variation in LST, while FAR contributed the least. ISF and BD were found to have a positive impact on LST, while FVC and BH had a negative influence. Moreover, SVF was observed to positively influence LST in the compact classes (LCZ2-3) and open low-rise class (LCZ6). In the open mid-rise class (LCZ5), SVF and LST showed a U-shaped relationship. There is an inverted U-shaped relationship between FAR and LST, with the inflection point occurring at 1.5. The results of nonlinear analysis were beneficial in illustrating the complex relationships between LST and its driving factors. The study’s results highlight the effectiveness of utilizing LCZ as a detailed approach to explore the relationship between urban morphology and urban heat islands. Recommendations for enhancing urban climate resilience include strategies such as increasing vegetation coverage, regulating building heights, organizing buildings in compact LCZs in an “L” or “I” shape, and adopting an “O” or “C” configuration for buildings in open LCZs to aid planners in developing sustainable urban environments.

The survey of Landsat satellite image is effective in the continuous monitoring of a vast area during long periods of time. It is increasingly being used to derive and analyze spatial distribution data of both the normalized difference built-up index (NDBI) and land surface temperature (LST) that are major indicators for an analysis of urban environment. Especially, LST is one of the key parameters in physics and meteorology of land surface processes on regional and global scales. Satellite remote sensing has been expected to be effective for obtaining thermal information of the earth's surface with a high resolution. Meanwhile as more than 50% of the populations are situated in cities, urbanization has become an important contributor to global warming due to remarkable urban heat island (UHI) effect. UHI effect is meteorological phenomenon that the air temperature increases in urban area than the suburbs because grows with the progress of urbanization. UHI effect has been affected to the regional climate and environment. This study aims to examine relationships of LST with NDBI, and with surface moisture using Landsat TM and ETM+ imagery obtained for the city of Chungbuk in Korea; and to quantitatively compare the patterns and intensity of UHI with land-use/land-cover (LULC) types. Landsat TM (thematic mapper) and ETM+ (enhanced thematic mapper plus) imagery, respectively acquired in 1991, 1994, 2000 and 2006, were utilized to assess urban area thermal characteristics in Cheongju, the city of Chungbuk province in Korea. In order to accurately estimate urban surface moisture, tasseled cap model (TCM) was utilized to generate the proportion of surface moisture. The results indicate urbanization is an accurate indicator of UHI effects with strong linear relationships between LST and NDBI. This implies that surface moisture can be used to analyze temperature quantitatively for UHI studies validated by NDBI. And this suggests that surface moisture, combined with LST, and NDBI, can quantitatively describe the spatial distribution and temporal variation of urban thermal patterns and associated LULC types.

Comparison of land surface and air temperatures for quantifying summer and winter urban heat island in a snow climate city