Urban Land Surface Temperature Research Articles

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.

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

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

The Heterogeneous Effects of Microscale-Built Environments on Land Surface Temperature Based on Machine Learning and Street View Images

Global climate change has exacerbated alterations in urban thermal environments, significantly impacting the daily lives and health of city residents. Measuring and understanding urban land surface temperatures (LST) and their influencing factors is important in addressing global climate change and enhancing the well-being of residents. However, due to limitations in data precision and analytical methods, existing studies often overlook the microscale examination closely related to residents’ daily lives, and lack a deep exploration of the spatial heterogeneity of the influencing factors. This leads to these results being ineffective in guiding the planning and construction of cities. Taking Shenzhen as a case study, our study investigates the effects of various microscale build environment characteristics of LST using street view images and machine learning. A convolutional neural network model adopting the SegNet architecture is used to perform semantic segmentation on street view images, extracting features of the microscale urban-built environment. The LST is inverted through the Google Earth Engine (GEE) platform. By using Multiscale Geographically Weighted Regression (MGWR) models, our study reveals the comprehensive impact of the urban-built environment on LST and its significant spatial heterogeneity. The findings indicate that the proportions of sky, roads, and buildings are positively correlated with LST, while trees have a significant cooling effect. Although earth and water can reduce LST, their overall contribution is minimal due to limitations in their area and distribution patterns. This study not only reveals the key factors affecting urban LST at the microscale but also emphasizes the necessity of considering the spatial heterogeneity of these factors’ impacts. This suggests the need for targeted strategies for different areas to effectively improve the urban thermal environment and achieve sustainable urban development.

Spatial coupling relationship between architectural landscape characteristics and urban heat island in different urban functional zones

The process of urbanization exacerbates the urban heat island phenomenon. In order to reasonably identify urban functional zones, and reveal the influence of buildings on land surface temperature (LST). Guiyang, a multi-mountainous city in China, was taken as the research area, Open Street Map (OSM) and land use type data were used to identify urban functional zones. Subsequently, the correlation between urban building morphology metrics and LST was analyzed using Pearson correlation and boosted regression tree (BRT) model. The results showed that: (1) There were significant differences in LST among different urban functional zones. (2) The relative contribution value of building density (BD) to land surface temperature was high, and it significantly exacerbated the urban heat island effect across all urban functional zones, with the maximum impact on LST reaching 2.5 °C in Utility zone. (3) Mean building height (MBH), Southerly wind speed (S_Wind speed) and Landscape shape index of building (LSI_Building) had the effect of alleviating thermal environment, and the mitigation degree of land surface temperature was the highest in Residential zone and Ecological zone, reaching 0.6 °C, 0.8 °C and 2.0 °C, respectively. This study enhances our understanding of the relationship between urban functions and LST, providing valuable insights for mitigating the urban thermal environment and promoting sustainable development.

Towards inclusive urbanism: An examination of urban environment strategies for enhancing social equity in Chengdu's housing zones

With rapid urbanization, the urban heat island (UHI) effect has become a major problem worldwide. This study focuses on the relationship between Urban Green Space (UGS) and Land Surface Temperature (LST), providing insights into how natural environment, urban construction, and human activities influence this relationship by analyzing housing areas of different tiers. Utilizing the LightGBM machine learning approach, this study takes Chengdu City in Southwest China as a research area and finds that green space has a more significant cooling effect on LST in the middle-market housing and affordable housing areas. Furthermore, within the NDVI range of 0.37 to 0.48, the LST decreases by 0.63℃ for every 0.1 increase. Additionally, the study reveals that urban construction characteristics, such as building density and building height, exert a more significant influence on LST in middle-market housing and affordable housing areas, with a contribution rate of 23.61 % observed in middle-market housing areas and the lowest contribution rate of 17.84 % observed in high-end housing areas. Overall, our research emphasizes the importance of addressing urban thermal disparities, particularly in diverse housing areas, by prioritizing green space augmentation and optimizing built environments, thereby offering actionable insights for equitable and sustainable urban planning.

Linkages between urban growth and land surface temperature variations in the Seoul metropolitan area: A spatial first-order difference approach

This study investigates the effects of urban development and expansion on Land Surface Temperature (LST) changes from 2000 to 2018, using the Seoul Metropolitan area (SMA) as a case study area. Along with descriptive analysis, this study uses a spatial first-order difference model to statistically analyze the longitudinal relationship between land use features and LST, including impervious surface (IS), enhanced vegetation index (EVI), anthropogenic factors, and urban spatial and topographical factors as well as urban development types as influential factors on LST variations. Our findings support prior research indicating the cooling effects of vegetation and warming effects of imperviousness. We also found a positive association between detached housing units, manufacturing activities and LST changes and a negative relationship between apartment units, urban parks, service activities and LST changes. More importantly, substantial LST reduction was found in the large-scale high-rise apartment complexes redeveloped from the low-rise, detached housing sites in inner-city infill areas, contrasting with significant LST rise in the suburban new town development. We suggest redevelopment of inner-city low-rise compact areas into high-rise but greener blocks as a LST mitigation strategy, equipped with cooling strategies such as green infrastructure and climate-sensitive design considering local microclimate and natural ventilation.

Spatiotemporal assessment of the nexus between urban sprawl and land surface temperature as microclimatic effect: implications for urban planning.

Rapid urbanisation has led to significant environmental and climatic changes worldwide, especially in urban heat islands where increased land surface temperature (LST) poses a major challenge to sustainable urban living. In the city of Abha in southwestern Saudi Arabia, a region experiencing rapid urban growth, the impact of such expansion on LST and the resulting microclimatic changes are still poorly understood. This study aims to explore the dynamics of urban sprawl and its direct impact on LST to provide important insights for urban planning and climate change mitigation strategies. Using the random forest (RF) algorithm optimised for land use and land cover (LULC) mapping, LULC models were derived that had an overall accuracy of 87.70%, 86.27% and 93.53% for 1990, 2000 and 2020, respectively. The mono-window algorithm facilitated the derivation of LST, while Markovian transition matrices and spatial linear regression models assessed LULC dynamics and LST trends. Notably, built-up areas grew from 69.40 km2 in 1990 to 338.74 km2 in 2020, while LST in urban areas showed a pronounced warming trend, with temperatures increasing from an average of 43.71°C in 1990 to 50.46°C in 2020. Six landscape fragmentation indices were then calculated for urban areas over three decades. The results show that the Largest Patch Index (LPI) increases from 22.78 in 1990 to 65.24 in 2020, and the number of patches (NP) escalates from 2,531 in 1990 to an impressive 10,710 in 2020. Further regression analyses highlighted the morphological changes in the cities and attributed almost 97% of the LST variability to these urban patch dynamics. In addition, water bodies showed a cooling trend with a temperature decrease from 33.76°C in 2000 to 29.69°C in 2020, suggesting an anthropogenic influence. The conclusion emphasises the urgent need for sustainable urban planning to counteract the warming trends associated with urban sprawl and promote climate resilience.

Spatio-temporal urban land surface temperature variations and heat stress vulnerability index in Thiruvananthapuram city of Kerala, India

ABSTRACT Land use/Land cover (LU/LC) modifications leads to rise in surface temperature is a global concern. This study investigates the effects of LU/LC changes in Land Surface Temperature (LST) variations and effects of Urban Heat Island (UHI) in Thiruvananthapuram city. Furthermore, the study proposed a novel heat stress vulnerability index (HSVI) using Analytical Hierarchy Process (AHP) method. Results shows that transformation of all land uses is to built-up areas, which shows an increase of +118.46% where as others such as vegetation (-24.27%), open area (-64.315) and waterbody (-43.88%) shows drastic decrease over 34 years. The relationship between Normalized Difference Vegetation Index and LST shows a significant change with R2 values between 0.223 in 1988, 0.382 in 2000 and 0.253 in 2022. Moreover, this also contributed to the increase of the mean LST of 26.96 ºC in 1988 to 28.52 ºC in 2022. HSVI of the study indicates that 30.83% of the study area is categorized as very low heat stress vulnerable area followed by 37.22% area in low heat stress vulnerability, 17.88% in moderate heat stress vulnerability, 8.87% in high heat stress vulnerability and 5.19% of the area falling in very high heat stress Vulnerability class.

The anisotropy of MODIS LST in urban areas: A perspective from different time scales using model simulations

Remote sensing is one of the effective means to obtain urban land surface temperature (LST), but the observed temperature varies with sensor viewing angle due to urban thermal anisotropy (UTA) and biased sensor viewing angle. The anisotropy of satellite-based LST products (e.g., MODIS LST) varies at different time scales. Previous researches focus on the anisotropy of MODIS LST at the seasonal scale or at other time scales (e.g., hourly) but only for individual cities. The characteristics of anisotropy at different time scales and for cities at different latitudes are still unknown. In this study, we focus on analyzing the characteristics of anisotropy at hourly, daily, seasonal, and annual time scales separately for 80 global cities using the simulation data. The simulation data were generated by coupling a microscale urban surface temperature model and a thermal remote sensing model. In addition, the impact factors (i.e., sensor factor, meteorological factor, and surface factor) at different time scales are discussed and validated using satellite observations. The results show that the daytime instantaneous anisotropy varies from −4.2 K to 1.1 K at the hourly scale. For each city, the smaller anisotropy is captured at the local time 12:00 more easily for both Terra and Aqua overpass during daytime. This is because the magnitude of urban thermal anisotropy is more influenced by the sensor factor (i.e., sensor zenith angle), which varies with the overpass time and the relative position of the city in the track. However, if the seasonally or yearly aggregated MODIS LST is utilized to study mean surface temperature, the intensity of angular effect is much smaller with the value ranging from 0.1 K to 3.3 K at the seasonal scale (from 0.2 K to 2.1 K at the yearly scale) because the variation of sensor zenith angle is smoothed. At seasonal and yearly scales, the strength of the angular effect is greater in the morning during daytime, in summer among all seasons, and in the cities with lower latitudes, which is due to larger solar radiation. Among all meteorological factors, wind speed also has a larger impact on the magnitude of surface thermal anisotropy besides the downward surface shortwave radiation. Our study provides a reference for the correction of the angular effect in the MODIS LST products.

Machine-Learning-Assisted Characterization of Regional Heat Islands with a Spatial Extent Larger than the Urban Size

Surface urban heat islands (SUHIs) can extend beyond the urban boundaries and greatly affect the thermal environment of continuous regions over an agglomeration. Traditional urban-rural dichotomy depending on the built-up and non-urban lands is challenged in characterizing regional SUHIs, such as how to accurately quantify the intensity, spatial pattern, and scales of SUHIs, which are vulnerable to SUHIs, and what the optimal scale for conducting measures to mitigate the SUHIs. We propose a machine-learning-assisted solution to address these problems based on the thermal similarity in the Yangtze River Delta of China. We first identified the regional-level SUHI zone of approximately 42,328 km2 and 38,884 km2 and the areas that have no SUHI effects from the annual cycle of land surface temperatures (LSTs) retrieved from Terra and Aqua satellites. Defining SUHI as an anomaly on background condition, random forest (RF) models were further adopted to fit the LSTs in the areas without the SUHI effects and estimate the LST background and SUHI intensity at each grid point in the SUHI zone. The RF models performed well in fitting rural LSTs with a simulation error of approximately 0.31 °C/0.44 °C for Terra/Aqua satellite data and showed a good generalization ability in estimating the urban LST background. The RF-estimated daytime Aqua/SUHI intensity peaked at approximately 6.20 °C in August, and the Terra/SUHI intensity had two peaks of approximately 3.18 and 3.81 °C in May and August, with summertime RF-estimated SUHIs being more reliable than other SUHI types owing to the smaller simulation error of less than 1.0 °C in July–September. This machine-learning-assisted solution identified an optimal SUHI scale of 30,636 km2 and a zone of approximately 23,631 km2 that is vulnerable to SUHIs, and it provided the SUHI intensity and statistical reliability for each grid point identified as being part of the SUHI. Urban planners and decision-makers can focus on the statistically reliable RF-estimated summertime intensities in SUHI zones that have an LST annual cycle similar to that of large cities in developing effective strategies for mitigating adverse SUHI effects. In addition, the selection of large cities might strongly affect the accuracy of identifying the SUHI zone, which is defined as the areas that have an LST annual cycle similar to large cities. Water bodies might reduce the RF performance in estimating the LST background over urban agglomerations.

Comparative study and effects of urban green scape on the land surface temperature of a large metropolis and green city

Previous studies have provided valuable insights into the impact of green space (GS) on land surface temperature (LST). However, there is a need for in-depth comparative research on changing landscape patterns in cities and their effects on the urban thermal environment. This study investigates the spatial arrangement of GS and the influence of impervious surfaces on LST in urban areas, examining their cooling and warming effects in the urban landscapes of Beijing and Islamabad. The study aims to assess the impact of the spatial arrangement of GS on LST using a moving window of 1 km2 to analyze the overall effect of landscape patterns on the urban environment. Using Gaofen (GF–2) and Landsat–8 satellite data, we examined the biophysical surface properties of core urban areas. The results indicate a significant difference in the mean LST of 5.44 °C and 3.31 °C between impervious surfaces and GS in Beijing and Islamabad, respectively. The barren land and GS in Islamabad experience a higher LST of 3.39 °C compared to Beijing, which accounts for 1.39 °C. In Beijing, configuration metrics show no significant effect on urban LST, while edge density (ED) exhibits a slightly negative trend. In contrast, in the city of Islamabad, the landscape shape index (LSI), patch density (PD), and number of patches (NP) metrics have a significant influence on LST. The cooling effect of GS patches (0.1–0.5 ha) is more pronounced, while that of GS patches of 15–20 ha shows no significant effect on LST. The temperature difference (TD) of 5.01 °C was observed from the edge of GS in Beijing and 3.3 °C in Islamabad. Considering Islamabad's lush green scape compared to Beijing, this study suggests that Islamabad may experience an increase in LST in the future due to urbanization. This study's findings may assist urban policy-makers in designing sustainable green city layouts that effectively address future planning considerations.

Quantifying the Impact of Urban Growth on Urban Surface Heat Islands in the Bangkok Metropolitan Region, Thailand

The urban built environment, comprising structures, roads, and various facilities, plays a key role in the formation of urban heat islands, which inflict considerable damage upon human society. This phenomenon is particularly pronounced in urban areas characterized by the rapid growth and concentration of populations, a global trend, notably exemplified in megacities such as Bangkok, Thailand. The global trend of urbanization has witnessed unprecedented growth in recent decades, with cities transforming into megametropolises that profoundly impact changes in urban temperature, specifically the urban heat island (UHI) phenomenon induced by the rapid growth of urban areas. Elevated urban concentrations lead to increased city density, contributing to higher temperatures within the urban environment compared to the surrounding areas. The evolving land-use surface has assumed heightened significance due to urban development, necessitating accelerated efforts to mitigate urban heat islands. This study aims to quantify the influence of urban growth on urban surface temperature in Bangkok and its surrounding areas. The inverse relationship between urban temperature and land surface temperature (LST), coupled with urban area density, was examined using Landsat 5 and 8 satellite imagery. The analysis revealed a positive correlation between higher temperatures and levels of urban growth. Areas characterized by high-rise structures and economic activities experienced the most pronounced impact of the heat island phenomenon. The city exhibited a notable correlation between high density and high temperatures (high–high), signifying that increased density contributes to elevated temperatures due to heat dissipation (significant correlation of R2 = 0.8582). Conversely, low-temperature, low-density cities (low–low) with a dispersed layout demonstrated effective cooling of the surrounding area, resulting in a significant correlation with lower local temperatures (R2 = 0.7404). These findings provide valuable insights to assist governments and related agencies in expediting planning and policy development aimed at reducing heat in urban areas and steering sustainable urban development.

Evaluating the thermal environment of urban land surfaces in Yakutsk, a city located in a region of continuous permafrost

Rapid urbanization has led to changes in the urban land surface thermal environment. However, there are still much unknown about the urban land surface thermal conditions in permafrost regions. Permafrost is a unique geological environment, changes in the urban land surface thermal environment may trigger geological disasters caused by permafrost degradation. This study utilized remote sensing data and geographic detectors to identify the dynamic changes in land surface temperature (LST) and land use/land cover (LU/LC) in Yakutsk, as well as the potential factors contributing to LST variations. Between 1992 and 2020, the built-up area in Yakutsk increased by 36%, and the annual average LST in Yakutsk has risen by 6.67 °C, accompanied by an expansion of high-temperature areas. Despite ongoing greening efforts, rapid urbanization poses a threat to these green spaces. Changes in the normalized difference built-up index (NDBI) and land use transfer (LDT) were identified as the primary drivers of urban LST changes. By integrating geographic detector technology and artificial neural network models, we optimized the selection of input factors in the prediction model and used it to explore the future changes in LST in Yakutsk. The average LST in Yakutsk is expected to reach 23.4 °C and 25.1 °C in 2030 and 2040, respectively, with a further increase in high-temperature areas. This study provides a reference for ecological, hydrological, and geological assessments of cities in permafrost regions.

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.

Urban land surface temperature forecasting: a data-driven approach using regression and neural network models

The insinuations of the ailments associated with the unrestrained and disorganized proliferation of artificial impervious materials over natural surfaces are prevalent among city dwellers. These impacts can be comprehended by estimating land surface temperature (LST), as it is vital for evaluating urban climate, particularly to explain the intensity of urban heat islands and to define the health and welfare of the planet as well as the living beings. Urbanization-driven landscape changes severely disrupt comfortable living in almost every city, necessitating monitoring and modelling historical, current, and likely future LSTs. This research article proposes two forecasting techniques: Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models. These models have been widely accepted for the efficient prediction of climatic parameters, including LST, over an urban area. The landscape, elevation, and LST trend served as input to the models for an accurate prediction of LST. The analysis was performed over the Kolkata Metropolitan Area (KMA) with an additional 10 km buffer to understand urban growth and its effect on the LST of the entire region. The two developed models (MLR and ANN) effectively anticipated the LST over the KMA region. A continual increment in the surface temperatures ranging from 1 °C to 4 °C, over existing and likely-predicted urban areas was comprehended. It was anticipated that the regions near the urban areas will also experience severe discomfort and heat waves without proper mitigation measures. This scientific literature provides essential insights for decision-makers, stakeholders, and government officials to articulate new policies and modify the existing ones to create a sustainable and livable urban environment for the inhabitants.

Effects of 2D/3D urban morphology on land surface temperature: Contribution, response, and interaction

Urban morphology severely affects the intra-urban heat flux transport and thus directly regulates urban thermal environment. Despite previous studies suggested that urban morphology may significantly contribute to land surface temperature (LST), few studies simultaneously discuss the effects of urban morphology on LST in different urban units, particularly in the irregular block scale. Here, we explored the relationships between multi-dimensional urban morphology and LST using the eXtreme gradient boosting (XGBoost) model and the SHapley Additive exPlanations (SHAP) method. Especially, we revealed the interaction of the urban morphological metrics on LST at the block (i.e., irregular size delineated by roads) and the grid scale (i.e., a regular grid size with 200 × 200 m). The results show that the contributions of urban metrics on LST follow the rank of 3D building metrics >2D landscape metrics of impervious surface (IS) > 2D landscape metrics of urban green space (UGS). Average building height (AH) and the percentage of landscape of IS (PLAND_IS) are the most important metrics at the block and the grid scale, respectively. Moreover, some metrics have specific thresholds of the warming or cooling effects, e.g., the cooling effect when AH exceeds 19 m and warming effect when PLAND_IS >49%. Finally, we found that the interaction effects of 3D-3D metrics pairs are stronger than that of 2D-3D metrics pairs. These findings provide useful information for understanding the driving mechanism of the 2D and 3D urban morphology on LST and optimizing the urban morphology for mitigating the urban heat islands effect.

DEEPURBANMODELLER (DUM): A PROCESS-INFORMED NEURAL ARCHITECTURE FOR HIGH-PRECISION URBAN SURFACE TEMPERATURE PREDICTION

Abstract. High-resoulution downscaling of surface climate metrics like urban surface temperature, is a crucial and ongoing research challenge in urban climatology and environmental studies. In this study we propose a groundbreaking Physics-Inspired Neural Architecture for Modeling (PINAM) called DeepUrbanModeller(DUM), designed specifically for urban microclimate temperature estimation. DeepUrbanModeller(DUM) harnesses process-based modelling and satellite remote sensing, and draws upon high-accuracy 3D point clouds to deliver precise estimations of urban Land Surface Temperature (LST) at ultra-high resolutions. By incorporating high-accuracy land surface geometric data sourced from 3D point clouds and guided by the principles of atmospheric physics linked to surface temperature, DeepUrbanModeller(DUM) creates a data-driven framework, informed by physical laws, to accurately model high-resolution temperature distributions a task challenging for numerical simulations or conventional machine learning. The DeepUrbanModeller(DUM) design integrates two key components: Global Physical Feature Interpretation (GPFI) and Local Urban Surface Insight (LUSI). The GPFI captures broader urban physical parameters, ensuring the estimates comply with relevant physical laws. The LUSI enhances estimation performance at high-resolution levels by utilizing a newly proposed Urban Detail Orientation Index (UDOI) derived from 3D point clouds. Experimental results demonstrate the DeepUrbanModeller(DUM)’s superior capability in estimating urban LST on a detailed 30-by-30 meter grid, achieving an estimation error of less than 0.2 Kelvin compared to satellite measurements, a performance surpassing traditional methodologies.

Heterogeneous effects of the availability and spatial configuration of urban green spaces on their cooling effects in China

The impacts of the availability and spatial configuration of urban green spaces (UGS) on their cooling effects can vary with background climate conditions. However, large-scale studies that assess the potential heterogeneous relationships of UGS availability and spatial configuration with urban thermal environment are still lacking. In this study, we investigated the impacts of UGS availability and spatial configuration on urban land surface temperature (LST) taking 306 cities in China as a case study covering a multi-biome-scale. We first calculated the availability of surrounding UGS for urban built-up pixels in each city using a distance-weighted approach, and its spatial configuration was quantified through the Gini coefficient. Then, we employed various regression models to explore how the impacts of UGS availability and the Gini coefficient on LST varies across different LST quantiles and between day- and nighttime. The results revealed that UGS availability was negatively associated with both daytime and nighttime LST, while the Gini coefficient showed a positive impact solely on daytime LST, indicating that an adequate and equally distributed UGS contributes to lower environmental temperatures during the daytime. Furthermore, the impact of UGS availability on LST decreased during both day- and nighttime with increased background LST quantiles. Whereas the impact of the Gini coefficient increased only with daytime LST quantile levels, with its effect remaining almost insignificant during the night. Our findings provide new insights into the impacts of UGS on urban thermal environment, offering significant implications for urban green infrastructure planning aiming at lowering the urban heat island.

Correlation between LST, NDVI and NDBI with reference to Urban Sprawling – A Case Study of Shimla city

The present study aims to explain the impacts of urban expansion on land surface temperature and vegetation of Shimla city using integrated techniques of remote sensing (RS) and geographic information system (GIS). Remote sensing technique often analyses the thermal characteristics of any area. This study monitors the interrelationship of land surface temperature (LST) with two spectral indices (normalized difference vegetation index and normalized difference built-up index) in Shimla City of India using Landsat 8 satellite for summer season for the period 2014 to 2023. There is a synergetic relationship between urban expansion and LST. The result showed a linear correlation between increasing the built-up area and the higher Land Surface Temperature values while lower Land Surface Temperature was associated with vegetation cover which means that the there was a positive relationship between the normalized difference built-up index values and the Land Surface Temperature while the relation between the normalized difference vegetation index and Land Surface Temperature was inverse. These results provide an insight into the systematic planning of the urban environment in the study.

Assessment and simulation of thermal environments in Taiyuan urban built-up area, China

The urban heat island (UHI) effect has intensified with increases in impervious surface areas and population densities due to urbanization, which affects the quality of urban life and ecological services. Here, the Moran’s I and hot spot analysis (Getis-Ord Gi*) are used to explore spatial autocorrelation of land surface temperature (LST) in Taiyuan built-up area. Then, the built-up area is divided to 41 sub-areas to accurately explore the urban LST differences caused by different LULC types. Moreover, geographically weighted regression (GWR) is used to analysis the spatial heterogeneity of LST. Finally, we simulate the LST changes using the CA-Markov model in the study area in the year 2030. Our results showed that 1) average LST was 22.76°C in Taiyuan built-up area in 2018. The Highest-temperature areas were distributed in heavy-industry intensive areas in the north, north central, and southeast, whereas the Lowest-temperature areas mainly corresponded to rivers, lakes, urban forests, and green spaces. 2) The Moran’s I gradually decreased from 0.8635 to 0.2097 with an increase in the spatial distance threshold. The optimal recognition effect was obtained at a 400 × 400-m scale. The Getis-Ord Gi* analysis indicated that the cold area was 1248.32 km2 (12.24% of the study area) and the hot area was 43.84 km2 (11.11% of the study area) in 2018. 3) The GWR analysis showed significant spatial non-stationarity in the influence of LULC types on LST. The GWR model was calculated with reference to the observation values of the adjacent areas, so as to better reveal the spatial relationship between artificial surface, woodland, water, grassland, and bare land and LST. 4) The UHI distribution was more concentrated in 2030 than in 2021. The statistics of the proportion and transfer matrix of LST indicated that the proportion of the Highest and Lowest-temperature areas in 2030 decreased and the UHI effect will further intensify. This study could be used to guide sustainable development in cities and provides theoretical support for adjusting the urban spatial structure.