Urban Land Surface Temperature Research Articles

Land use change impact on urban land surface temperatures: A GIS-supported satellite-based case study

This paper illustrates the use of GIS techniques and satellite data in order to analyze the impact of land use change on the local urban microclimate. Specifically, a case study is presented that concerns the city of Vienna. Thereby, satellite-based images were used to classify the city of Vienna into four zones toward the computation of land surface temperatures in two reference years. The classified maps were then statistically projected into the future, resulting in predicted land surface temperatures. The findings highlight the relationship between urbanization and temperature rise in the urban context. The study used data from Landsat 8 satellite in 2013 and 2020. Land cover maps were generated with QGIS for past and current conditions and future land cover maps were projected and corresponding land surface temperatures were predicted. The analysis of satellite data highlighted land surface temperature increase in the city of Vienna. This rise in land surface temperatures correlates with urbanization-driven change in land use and land cover.

A novel approach to estimating urban land surface temperature by the combination of geographically weighted regression and deep neural network models

Growing concerns on excessive urban heat call for better approaches to modeling urban thermal environment and developing effective mitigation strategies. A hybrid model integrating the geographically weighted regression (GWR) and deep neural network (DNN) was developed to estimate land surface temperature (LST). This model was compared with three other data-driven approaches to predicting LST, including the ordinary least squares (OLS) regression, GWR, and DNN. Sixteen satellite image datasets (a total of 155,728 data points) during a four-year period in Hong Kong were used for model development, validation, and comparison. The datasets cover two distinguishable geographical regions and consist of sixteen explanatory variables from five groups, including (1) land use and land cover (LULC) composition and surface characteristics, (2) LULC configuration, (3) urban form, (4) anthropogenic activities, and (5) location and local climate. The results indicate that the hybrid model performs the best in terms of model fitness and prediction accuracy, with R2 equal to 0.85 and 0.73 and the mean squared error (MSE) equal to 0.52 and 0.70 in the two regions, respectively. Compared to the OLS, DNN, and GWR models, the overall R2 for all the datasets of the hybrid model increases by 97.3%, 16.6%, and 6.9%, respectively. The promising result of the hybrid model is due to its ability to capture both spatial heterogeneity and address possible correlations between explanatory variables. Sensitivity of LST to various explanatory variables is also discussed and strategies to mitigate excessive heat are recommended. This study is anticipated to contribute to model development in urban LST estimation and quantitative evaluation of those factors driving LST variations.

The spatial configuration of buildings: A vital consideration impossible to ignore in regulating urban land surface temperature? Evidence from 35 Chinese cities

Land urbanization not only efficiently met the requirements of social development but also brought severe heating effects, especially the changes in Land Surface Temperature (LST). The effects of building density on LST and driving relation of the configuration of buildings remain poorly understood over large areas. Using Landsat 8 satellite imagery acquired from the summer of 2019, this study quantified the heating effects (k) of building density on LST across 35 cities in China, and further analyzed the driving relation of the configuration of buildings such as the size, shape, and spatial distribution on k. Here the regression analysis results showed that the building density had a significant relationship with LST, and the k varied from 1.10 to 7.27 amount of the different cities. The size and aggregation distribution of buildings were the main positive drivers for the effects of building density on LST, and the shape of buildings was not obviously related to the k-value. The results of thermal environment simulation showed that the major reason might be that the close spatial relationship reduced the heat exchange between buildings and the atmosphere, resulting in higher LST. These conclusions will provide an important reference for urban planning and design.

Understanding seasonal contributions of urban morphology to thermal environment based on boosted regression tree approach

Urban morphology significantly affects the urban thermal environment. Seasonal impacts of two-dimensional (2D) and three-dimensional (3D) urban morphology on land surface temperature (LST) remain uncertain, and the impacts exist scale effects. Thus, taking Beijing as the study area, boosted regression tree (BRT) model was used to investigate the seasonal contributions of urban morphology to the thermal environment. Building density (BD), building height (BH), floor area ratio (FAR), sky view factor (SVF), and frontal area index (FAI), were used to comprehensively characterize urban morphology, and 13 scales ranging from 30 m to 600 m were used to investigate scale effects. The results showed that there are obvious spatial differences in LST and urban morphology indicators in the study area. 270 m was determined as the optimal scale for modeling in the study area. BH and BD are the domain indicators, which together contribute more than 75% of the variance of LST among four seasons, while the relative influences of SVF, FAR, and FAI are relatively low. Relationships between urban morphology indicators and LST are nonlinear among four seasons. The findings provide a scientific understanding for urban planners on mitigating the UHI effects through optimizing buildings.

Investigating the Trends and Drivers between Urbanization and the Land Surface Temperature: A Case Study of Zhengzhou, China

The effect of urbanization on the urban thermal environment (UTE) has attracted increasing research attention because its significant effects on local weather and climate, and serious consequences for people. However, systematic study of the relationship between urbanization and UTE has been undertaken only to a limited extent. Using quantitative thermal remote sensing and multi-buffer ring method and multiple spatial scales method, here we analyze Landsat TM/ETM+ images of Zhengzhou in Central China acquired on four different dates in 2017 to investigate the spatiotemporal variations, trends, and driving force in the land surface temperature (LST). Our results showed that LST generally increased with urbanization intensity. This trend was extremely obvious in spring and summer, weak in winter, and slightly downward in autumn. Moreover, PLAND (e.g., percentage of impervious surface in a landscape) has the most significant effect on urban LST, and generally increases as the spatial scale becomes larger. In conclusion, the study recommends that urban planning in Zhengzhou should prioritize PLAND, especially at large spatial scales. These results provide a scientific reference for urban planners who are committed to the sustainable development of Zhengzhou City.

Correlation Analysis between Urban Green Space and Land Surface Temperature from the Perspective of Spatial Heterogeneity: A Case Study within the Sixth Ring Road of Beijing

Urban greening has been widely regarded as the most effective, lasting, and economical strategy for alleviating the effects of urban heat islands (UHIs). Previous studies on the cooling effect of urban green spaces (UGSs) tend to analyze the correlation between landscape metrics and land-surface temperature (LST) based on a global parameter estimation, while ignoring urban heterogeneity and autocorrelation. This study focuses on the sixth ring road of Beijing and uses Landsat 8 imagery to retrieve the LST and extract the position of UGSs. We propose a new approach to optimize the selection of landscape metrics, to identify the least and most effective metrics to establish a geographically weighted regression (GWR) model, and to plot the distribution of local regression coefficients to investigate the spatially heterogeneous cooling effects of greenspaces. The effect of UGS landscape metrics on the LST differs according to spatial location; the method enhances our understanding of the effects of UGS spatial configuration on UHIs and better guides the planning and construction of future UGSs.

Recent trends of land surface temperature in relation to the influencing factors using Google Earth Engine platform and time series products in megacities of India

Increasing land surface temperature (LST) is one of the significant anthropogenic issues that is threatening livelihoods in urban areas. Understanding the urban LST is essential for sustainable management of the urban landscape. Various studies focused on the spatial pattern and average trend of LST in relation to influencing factors, but little is known regarding the pixel-scale spatiotemporal trends and integration of multiple influencing factors in the different seasons using time series data. Therefore, we focused on the spatiotemporal trends of LST in relation to the influencing factors using the Google Earth Engine platform and time series products. MODIS land surface temperature data was used to estimate the annual and seasonal day-night LST from 2003 to 2020. Theil-Sen slope and Mann-Kendall statistics were used to examine the magnitude and significance of the spatiotemporal trend. All-subsets regression and hierarchical partitioning (HP) were applied to explore the driving factors of LST. Average LST and range varied between the cities, but were highest in Chennai and Delhi, respectively. Except for winter nighttime in Kolkata, all cities' annual and seasonal nighttime LST are increasing (0.027–0.075 °C/year) over a large part of the study area (33.934%–100% of areas), indicating that anthropogenic force is increasing in urban areas during nighttime. Annual and seasonal daytime LST is increasing in most parts (55.545%–94.604% of areas) of Chennai and Kolkata, but daytime LST is decreasing (33.602%–98.571% of areas) in Mumbai and Delhi due to the increasing concentration of absorbing aerosol over the city. Strength of independent factors and multiple influencing factors varied in the day-night time between the cities. But nighttime LST was well explained using the all-subsets regression compared to daytime LST, except for Mumbai. Also, nighttime light density and NDBI had consistent effects on nighttime, and daytime, respectively. These findings can help to take scientific adaptation and mitigation strategies for sustainable development.

Remote Sensing-Based Prediction of Temporal Changes in Land Surface Temperature and Land Use-Land Cover (LULC) in Urban Environments

Pakistan has the highest rate of urbanization in South Asia. The climate change effects felt all over the world have become a priority for regulation agencies and governments at global and regional scales with respect assessing and mitigating the rising temperatures in urban areas. This study investigated the temporal variability in urban microclimate in terms of land surface temperature (LST) and its correlation with land use-land cover (LULC) change in Lahore city for prediction of future impact patterns of LST and LULC. The LST variability was determined using the Landsat Thermal Infrared Sensor (TIRS) and the land surface emissivity factor. The influence of LULC, using the normalized difference vegetation index (NDVI), the normalized difference building index (NDBI), and the normalized difference bareness index (NDBaI) on the variability LST was investigated applying Landsat Satellite data from 1992 to 2020. The pixel-level multivariate linear regression analysis was employed to compute urban LST and influence of LULC classes. Results revealed that an overall increase of 41.8% in built-up areas at the expense of 24%, 17.4%, and 0.4% decreases in vegetation, bare land, and water from 1992–2020, respectively. Comparison of LST obtained from the meteorological station and satellite images showed a significant coherence. An increase of 4.3 °C in temperature of built-up areas from 1992–2020 was observed. Based on LULC and LST trends, the same were predicted for 2025 and 2030, which revealed that LST may further increase up to 1.3 °C by 2030. These changes in LULC and LST in turn have detrimental effects on local as well as global climate, emphasizing the need to address the issue especially in developing countries like Pakistan.

Modelling microscale impacts assessment of urban expansion on seasonal surface urban heat island intensity using neural network algorithms

Investigation of surface urban heat island (SUHI) results from rapid urbanization and upsurge of land surface temperature (LST) has substantial socioeconomic and environmental impacts. This study investigates and simulates the impacts of rapid urbanization on LST and SUHI patterns in Sylhet City, Bangladesh, from 1995 to 2030. Landsat images and machine learning algorithms have been used to identify the urban growth, LST and UHI distribution patterns in several city directions. In addition, correlation analysis has been conducted between LST, SUHI and spectral indices (NDBI, NDBSI, NDVI, NDWI). Results suggested that urban expansion increased LST by 7 °C in summer and 6 °C in winter from 1995 to 2020. Increment has also occurred in summer high SUHI from 0.25 km2 to 2.65 km2. Pearson correlation demonstrated that built-up areas have a strong positive relationship with LST (0.96) and SUHI (0.911). Future simulation of urban expansion for 2025 and 2030 shows a 9 % increase, leading to a significant increase in moderate to high SUHI intensity. The study's findings can act as an effective guideline for sustainable infrastructural development and ensure environmental stability by increasing the thermal comfort level of the city.

Urban land surface temperature prediction using parallel STL-Bi-LSTM neural network

Accurate temperature prediction is of great significance to human life and social economy. A series of traditional methods and machine learning methods have been proposed to achieve temperature prediction, but it is still a challenging problem. We propose a temperature prediction model that combines seasonal and trend decomposition using loess (STL) and the bidirectional long short-term memory (Bi-LSTM) network to achieve high-accuracy prediction of the daily average temperature of China cities. The proposed model decomposes the temperature data using STL into trend component, seasonal component, and remainder component. Decomposition components and the original temperature data are input into the two-layer Bi-LSTM to learn the features of the temperature data, and the sum of prediction of three components and the original temperature data prediction result are added using learnable weights as the prediction result. The experimental results show that the average root mean square error and mean absolute error of the proposed model on the testing data are 0.11 and 0.09, respectively, which are lower than 0.35 and 0.27 of STL-LSTM, 2.73 and 2.07 of EMD-LSTM, 0.39 and 0.15 of STL-SVM, achieving a higher precision temperature prediction.

Integrated Influencing Mechanism of Potential Drivers on Seasonal Variability of LST in Kolkata Municipal Corporation, India

Increasing land surface temperature (LST) is one of the major anthropogenic issues and is significantly threatening the urban areas of the world. Therefore, this study was designed to examine the spatial variations and patterns of LST during the different seasons in relation to influencing factors in Kolkata Municipality Corporation (KMC), a city of India. The spatial distribution of LST was analyzed regarding the different surface types and used 25 influencing factors from 6 categories of variables to explain the variability of LST during the different seasons. All-subset regression and hierarchical partitioning analyses were used to estimate the explanatory potential and independent effects of influencing factors. The results show that high and low LST corresponded to the artificial lands and bodies of water for all seasons. In the individual category regression model, surface properties gave the highest explanatory rate for all seasons. The explanatory rates and the combination of influencing factors with their independent effects on the LST were changed for the different seasons. The explanatory rates of integration of all influencing factors were 89.4%, 81.4%, and 88.7% in the summer, transition, and winter season, respectively. With the decreasing of LST (summer to transition, then to winter) more influencing factors were required to explain the LST. In the integrated regression model, surface properties were the most important factor in summer and winter, and landscape configuration was the most important factor in the transition season. LST is not the result of single categories of influencing factors. Along with the effects of surface properties, socio-economic parameters, landscape compositions and configurations, topographic parameters and pollutant parameters mostly explained the variability of LST in the transition (11.22%) and summer season (15.22%), respectively. These findings can help to take management strategies to reduce urban LST based on local planning.

Divergent impact of urban 2D/3D morphology on thermal environment along urban gradients

Optimizing urban morphology is an effective way to alleviate the thermal environment, and it has always been the focus of urban planning. Although previous scholars have explored the impact of urban 2D/3D morphology on Land Surface Temperature (LST), they have rarely revealed differences along urban gradients, thus ignoring the nonlinear relationship between urban morphology and LST. In this study, we use multi-source data such as night-time lights, Landsat 8, land cover, and buildings, and employ dynamic threshold, single-window algorithm, and boosted regression trees methods to reveal the gradient and marginal effects of urban 2D/3D morphology on LST. Results showed that the proportion of built-up land (PCL), digital elevation model (DEM) and normalized difference built-up index (NDBI) have important contribution to LST. Moreover, relative influence of PCL, NDBI and building density (BD) on LST varies significantly along urban gradients, which are +25.87%, +14.85% and − 10.88%, respectively. Interestingly, we found that as the value of each variable increased, its relationship to LST changed. For example, when PCL < ∼49%, it is negatively correlated with LST, so lowering PCL can alleviate thermal environment. Our findings inform the sound practice of urban planning by accounting for marginal effects of urban morphology on thermal environment.

Cooling Effects of Urban Vegetation: The Role of Golf Courses

Increased heat in urban environments, from the combined effects of climate change and land use/land cover change, is one of the most severe problems confronting cities and urban residents worldwide, and requires urgent resolution. While large urban green spaces such as parks and nature reserves are widely recognized for their benefits in mitigating urban heat islands (UHIs), the benefit of urban golf courses is less established. This is the first study to combine remote sensing of golf courses with Morphological Spatial Pattern Analysis (MSPA) of vegetation cover. Using ArborCamTM multispectral, high-resolution airborne imagery (0.3 × 0.3 m), this study develops an approach that assesses the role of golf courses in reducing urban land surface temperature (LST) relative to other urban land-uses in Perth, Australia, and identifies factors that influence cooling. The study revealed that urban golf courses had the second lowest LST (around 31 °C) after conservation land (30 °C), compared to industrial, residential, and main road land uses, which ranged from 35 to 37 °C. They thus have a strong capacity for summer urban heat mitigation. Within the golf courses, distance to water bodies and vegetation structure are important factors contributing to cooling effects. Green spaces comprising tall trees (&gt;10 m) and large vegetation patches have strong effects in reducing LST. This suggests that increasing the proportion of large trees, and increasing vegetation connectivity within golf courses and with other local green spaces, can decrease urban LST, thus providing benefits for urban residents. Moreover, as golf courses are useful for biodiversity conservation, planning for new golf course development should embrace the retention of native vegetation and linkages to conservation corridors.

Understanding the Linkage between Urban Growth and Land Surface Temperature—A Case Study of Bangalore City, India

Planning for a sustainable future involves understanding the past and present problems associated with urban centers. Rapid urbanization has caused significant adverse impacts on the environment and natural resources. In cities, one such impact is the unsettling urban growth, resulting in the urban heat island (UHI) effect, which causes considerable positive feedback in the climate system. It can be assessed by investigating the relationships between urban Land Use/Land Cover (LULC) changes and changes in land surface temperature. This study links the urban transformations in Bangalore, India, between 2001 and 2021, with the city’s changing average land surface temperatures. LULC classification was performed on Landsat satellite images for the years 2001, 2011, and 2021, using the support vector machine (SVM) classification algorithm. LULC change analysis revealed an increase in the built-up area coinciding with a decreasing trend of water bodies, vegetation, and the area under the others (wasteland/open land/barren land) category. The results show that built-up increased from 462.49 km2 to 867.73 km2, vegetation decreased from 799.4 km2 to 485.72 km2, and waterbody declined from 34.28 km2 to 24.69 km2 in 20 years. The impact of urbanization was evident in Bangalore’s land temperature changes between 2001 and 2021, showing the average temperature increased by 0.34 °C per year between the highest UHI events, contrary to 0.14 °C per year in non-urbanized areas. It is hoped that the results of this study can help the urban planners of Bangalore city identify critical areas where improvement in urban dwelling could be planned sustainably according to the global smart cities concept, an offshoot concept of the Sustainable Development Goal (SDG)-11.

Assessment of the TsHARP method for spatial downscaling of land surface temperature over urban regions

Nowadays, acquiring high spatial and temporal land surface temperature (LST) is one of the significant factors in urban climate studies. This study aims to assess the sharpening thermal imagery (TsHARP) method, which is developed for determining the LST in urban regions. Whereas the original TsHARP method correlates the Normalized Difference Vegetation Index (NDVI) with the LST, the current study seeks to evaluate the performance of the TsHARP method by correlating between the LST and NDVI over urban areas. Additionally, it is intended to investigate the relationship between the impervious surfaces (IS) and LST. At this point, the Advanced Spaceborne Thermal Emission and Reflection Radiometer ASTER image of the year 2003 was used to retrieve the IS as well as the LST of the Kuala Lumpur city, Malaysia. To retrieve the main LST, IS and NDVI, ASTER Level1B data were used. On the other hand, ASTER land surface temperature products (AST08) was utilized to evaluate the downscaled LST (DLST) image. With selection of the ~1/4 of the pixels with lowest coefficient of variation (CV), the LST has more correlation with the IS in comparison with the NDVI, while the correlation between the IS and LST was increased in this study. The IS index illustrates the most optimal correlation for the urban LST (R2 = 0.59). Furthermore, the TsHARPIS-based method depicted a good correlation between the DLST and the ASTER LST product (RMSE = 1.5 °C, R2 = 0.83) compared to the NDVI (RMSE = 4 °C, R2 = 0.73). Consequently, it is found that the application of TsHARPIS-based method to ASTER remote sensing data is a robust/low-cost approach for spatial downscaling of the LST over urban regions.

Evaluating contributions of urbanization and global climate change to urban land surface temperature change: a case study in Lagos, Nigeria

This study develops a general method to evaluate the contributions of localized urbanization and global climate change to long-term urban land surface temperature (ULST) change. The method is based on the understanding that long-term annual ULST is controlled by three factors: (1) localized urbanization, (2) global climate change, and (3) interannual climate variation. Then the method removes the interannual climate fluctuations on long-term observed LST time series via linear regression and separates the contributions of urbanization and climate change to the impacts on long-term ULST via urban–rural comparison. The method is applied to Lagos, a fast-growing metropolis in the tropical West Africa, as an example for reference. Combined time-series daily daytime and nighttime MODIS Land Surface Temperature (LST) data over the years of 2003–2021 are used as the representation of land surface temperature. To avoid the potentioal interannual data biase due to uneven availability of data in the rainy seasons over years, only MODIS LST data from dry seasons are used in the study. The results are summarized as follows for Lagos: (1) long-term annual ULST is confirmed to be controlled by the three factors; (2) the proposed method can separate the contribution of the three factors to the ULST; (2) both localized urbanization and global warming are verified to contribute to the ULST increase with positive trends; (3) daytime ULST increased the most in the afternoon time at a mean rate of 1.429 °C per decade, with 0.985 °C (10 year)−1 contributed by urbanization and 0.444 °C (10 year)−1 contributed by climate warming; (4) nighttime ULST in Lagos increased the most after midnight at a rate of 0.563 °C (10 year)−1, with 0.56 °C (10 year)−1 contributed by urbanization and 0.003 °C (10 year)−1 contributed by climate warming; and (5) urbanization is generally responsible for around 60.97% of the urban warming in Lagos. Therefore, the increasing urbaniztion-induced urban heat island effect is the major cause for more heat-related health risks and climate extremes that many urban residents are suffering. The results of this study are of useful reference for both urbanization and climate change related issues in the geo-science field.

A Procedural Modeling Approach for Ecosystem Services and Geodesign Visualization in Old Town Pocatello, Idaho

City population has been growing rapidly worldwide due to urban expansion, which can bring negative impacts on local ecosystem services (ES). Efficient tools for urban design and visualization are essential for city planners and stakeholders to better understand the valuation impact of plans for future sustainable development. Current urban design methods are mainly based on a 2D perspective and lack vertical visualization. Although conventional 3D modeling was introduced to address these limitations, it still has some challenges, such as requiring powerful computing resources and specialized training. Procedural 3D modeling is a grammar-based set of rules that can effectively generate 3D models and enhance spatial visualization when compared with conventional 2D or 3D methods. This paper describes a framework for developing a geodesign tool in Old Town Pocatello, Idaho, USA using procedural modeling to improve planning and visualization for urban design, including (1) Geospatial data preparation in ArcGIS, (2) 3D cityscape model generation in CityEngine, and (3) interactive visualization applications for multiple platforms developed with the Unity game engine. Pocatello is a mid-sized city in southeast Idaho that faces several challenges towards integrating ecosystem services in urban design. As a case study in ecosystem service modelling, we proposed a green scenario for Old Town to demonstrate a tool where permeable surfaces were increased from 37% to 45% to help mitigate urban land surface temperature and improve stormwater management. This geodesign tool offers city planners and stakeholders an opportunity to visualize and analyze block-level scenarios in real time. The interactive applications can encourage public participation in the design process. More ES measurements can be implemented into this tool in the future. The techniques of 3D procedural modeling and ES modeling in this study are also applicable to other small to mid-sized cities worldwide.

Assessing the impacts of vegetation cover loss on surface temperature, urban heat island and carbon emission in Penang city, Malaysia

Rapid urbanization, human demands with dynamic hydrology, and land resources impact the carbon cycle and ecosystem resilience at the city level. The expansion of urban areas is continuously replacing the vegetative cover (VC), which accelerates carbon emissions (CE), raises land surface temperature (LST), and contributes to global warming. This study aimed to assess the CE dynamics in Penang City, Malaysia, resulting from land use and land cover (LULC) changes during 1996–2021 and to identify the impacts of CE dynamics on LST change and urban heat island (UHI) effects. In this regard, remote sensing and geospatial methods were applied to monitor the LULC changes and used the biomass carbon stock assessment techniques to quantify carbon storage. The findings of the study illustrate a 17% decrease in forest cover and an excessive increase in urban areas (22%), average LST (8 °C) and UHI (38%) in the last 25 years. The carbon stock (CS) concentration reveals a 24% decrease in high CS areas with more than 1% yearly decline from 1996 to 2021. In addition, due to the massive VC loss, carbon attracted the sun rays, which led to lower CS in built-up and no VC areas. The strong positive correlations between CE and LST and CE and UHI (R2 > 0.7961) at a significant level (p-value < 0.001) suggest that CE is highly responsible for the LST increase and UHI effects. This study proposes effective strategies, including afforestation, reforestation and urban greening practices through rooftop gardening, which can significantly improve the CS capacity and reduce climate change and global warming impacts in the fastest-growing cities.

How do disparate urbanization and climate change imprint on urban thermal variations? A comparison between two dynamic cities in Southeast Asia

This study assessed the combined influences of climate change and urbanization on land surface temperature (LST) and surface urban heat island (SUHI) in the Bangkok metropolitan region (BKK) and Ho Chi Minh City metropolitan area (HCM) during 1990–2020. We found climate change evidence from trends and variations of temperature extremes in both cities throughout the past three decades, especially for the significant upward trends of nighttime temperature in HCM (0.23 – 0.3 °C/decade). These cities have been undergoing rapid urbanization, in which HCM has a higher urbanization rate with an annual growth rate of 8.03%/year against BKK (4.26%/year). Landscape metrics have rapidly shifted towards urban aggregation and green space fragmentation along with urbanization. A peer comparison shows that BKK holds a greater urban size and LST intensity, while HCM leads in urban growth and SUHI magnitude. These changes jointly contribute to climbing SUHI intensity, especially the dramatic influence of surface changes. Yet, temperature extremes also play an indispensable role in regulating SUHI magnitude, in which the impact in HCM is considerable compared to the effect in BKK. The research findings provide crucial information for both cities – where incorporation of climate change resilience strategies and SUHI mitigation into the master plans for livable cities are recommended.

Can improving the spatial equity of urban green space mitigate the effect of urban heat islands? An empirical study

The role of urban green space (UGS) in mitigating the urban heat island (UHI) effect has been demonstrated in a growing body of literature. However, the potential influence of the spatial equity of UGS distribution on the UHI effect has largely been overlooked. The present study aims to identify this potential influence using the spatial equity of UGS and the land surface temperature (LST) as measures of UGS spatial distribution and UHIs, respectively. A comprehensive spatial distribution map of UGS was generated by combining the UGS coverage fraction data within urban impervious pixels and the green cover data outside urban impervious pixels. Then, the spatial equity of UGS distribution across all urban impervious pixels was determined using the Gini coefficient. In addition, an LST map was derived using the thermal infrared spectral bands of Landsat 8 OLI/TIRS products. A case study of Dongguan, a highly urbanized city in China, showed that (1) the distribution of both UGS and LSTs were spatially aggregated in all the towns of the city, (2) the LST of urban impervious pixels was negatively correlated with the area of surrounding UGS, and (3) the Gini coefficient of UGS was positively correlated with the proportion of hot and cool areas, but negatively correlated with the proportion of medium-hot and medium-cool areas. These findings indicate that increasing the amount of UGS is beneficial to the reduction of urban average LSTs, while promoting the spatial equity of UGS distribution is conducive to reducing the spatial aggregation of LSTs within urban areas, thereby improving the overall urban thermal environment. Therefore, as a nature-based solution, promoting the spatial equity of UGS distribution could enhance the overall cooling effect of UGS more effectively at the city scale, and thus further underpin the sustainable development of the urban environment.