Summer Land Surface Temperature Research Articles

Coupling effects of building-vegetation-land on seasonal land surface temperature on street-level: A study from a campus in Beijing

The land surface temperature (LST) of urban streets is significantly influenced by the surrounding urban environment. In the cold areas of northern China, this influence exhibits a seasonal pattern. Currently, in areas with noticeable seasonal variations, the coupling effects of urban elements on the annual stability of street LST remain unclear. This study developed a new metric, Normalized Winter-Summer Land Surface Temperature Difference, to measure the year-round LST stability of campus streets. Three key urban elements affecting street-level LST in winter and summer were identified: building, vegetation, and land. The influencing characteristics for winter and summer LST were quantified and ranked respectively. The study reveals that the green space ratio dominates summer LST, while fraction vegetation coverage, leaf area index and average building height dominate winter LST. We found that morphological characteristics of buildings and vegetation cannot fully explain the impact of the urban physical environment on street-level LST. For winter street LST studies, greater emphasis should be placed on non-morphological characteristics and land. Based on the impact mechanisms and critical thresholds of key features in winter and summer, this study establishes an interpretable spatial classification method. By assigning seasonal weights to influencing features, it provides comprehensive design strategies that consider both winter and summer features to enhance annual LST stability and climate resilience in urban spaces facing extreme meteorological conditions.

The contrasting trend of global urbanization-induced impacts on day and night land surface temperature from a time-series perspective

Understanding the warming effect of the impervious surface area (ISA) quantitively is crucial to alleviate the urban heat island effect. However, the quantitative analyses of urbanization-induced land surface temperature (LST) change globally remain to be determined during the global warming hiatus. Here, we first characterized the response of LST to ISA (i.e., δLST) in 369 global cities using MODIS LST product (1 km) and the global artificial impervious area dataset (30 m). We then investigated the spatiotemporal changes of δLST and its associated driving factors, including enhanced vegetation index (1 km), albedo (ALB, 500 m), population (100 m), air temperature (AT, 0.1°), and precipitation (0.1°). The analysis was conducted using linear regression models from 2000 to 2010 at the pixel level. The results illustrate that (1) the daytime δLST in urban areas shows a negative trend with –0.2971 °C/% and –0.0870 °C/% in arid and temperate regions, and the nighttime δLSTs were 0.0938 °C/% and 0.0048 °C/%, respectively. In tropical regions, the δLST remains positive throughout the day, while it exhibits an opposite trend in snow regions. (2) The magnitude of nighttime δLST is stronger compared to daytime globally, which is more significant in arid regions with the largest ΔδLST values (i.e., diurnal variations) in urban (0.3909 °C/%) and rural (0.3262 °C/%) areas. (3) In temperate regions, the response of ALB to ISA (δALB, negative effect) and the response of AT to ISA (δAT, positive effect) are dominant factors in regulating δLST during daytime and nighttime, respectively However, in arid regions, the daytime and nighttime δLSTs are negatively correlated by urban population and vegetation, respectively. These findings provide a quantitative understanding on long-term LST variations and driving factors behind at the pixel level, suggesting that urban greening and increasing surface albedo are effective strategies to mitigate the urbanization-induced surface warming.

Response of summer Land surface temperature of small and medium-sized cities to their neighboring urban spatial morphology

Buildings, hard surfaces, vegetation, and water bodies are the four main urban surface types in the study region that are categorized in this study. It uses summer land surface temperature (LST)1 data obtained with a spatial resolution of 1 × 1 m using thermal imaging mounted on a drone, with a case study centering on a small to medium-sized Chinese town. To assess urban spatial morphology indicators within different ranges around the sample sites, buffer zones at 30 m, 50 m, 100 m, and 150 m were developed. The study determined the effective response range, identified the important urban spatial morphology indicators influencing LST, and examined the link between summer LST and various urban spatial morphologies for the four surface types. The results show that, close to the sample locations, Natural Environment Indicators (NEI) often have a cooling effect whereas Building Indicators (BI) typically have a warming effect. The influence of spatial morphologies on summer LST was primarily centered between 30 m and 50 m from the sample sites. The primary spatial morphological indicators determining LST for various underlying surface types within various spatial ranges are the building structure index (BSI) and water surface rate (WP). The response modes, response ranges, and response intensities of LST, however, varied significantly among various underlying surface types. This work provides new insights into how urban morphological characteristics affect the fine-scale spatial variation of LST within urban heat islands (UHI).

Modeling the relationship between urban tree canopy, landscape heterogeneity, and land surface temperature: A machine learning approach

Cities across the United States and around the globe are embracing urban greening as a strategy for mitigating the effects of rising temperatures on human health and quality-of-life. Better understanding how the spatial configuration of tree canopy influences land surface temperature should help to increase the positive impacts of urban greening. This study applies a machine learning approach for modeling the relationship between urban tree canopy, landscape heterogeneity, and land surface temperature (LST) using data from nine cities located in nine different climate zones of the United States. We collected summer LST data from the U.S. Geological Survey (USGS) Analysis Ready Data series and processed them to derive mean, minimum, and maximum LST in degrees Fahrenheit for each Census block group within the cities considered. We also calculated the percentage of each block group comprised by the land cover designations in the 2016 or 2019 National Land Cover Database (NLCD) maintained by the USGS, depending on the vintage of the available LST data. High resolution tree canopy data were purchased for all the study cities and the spatial configuration of tree canopy was measured at the block group level using established landscape metrics. Landscape metrics of the waterbodies were also calculated to incorporate the cooling effects of waterbodies. We used a Generalized Boosted Regression Model (GBM) algorithm to predict LST from the collected data. Our results show that tree canopy exerts a consistent and significant influence on predicted land surface temperatures across all study cities, but that the configuration of tree canopy and water patches matters more in some locations than in others. The findings underscore the importance of considering the local climate and existing landscape features when planning for urban greening.

Impacts of urban landscape pattern changes on land surface temperature in Southeast Brazil

Urban sprawl is a global concern, considering the current climate change scenario. The conversion of natural habitats or other land uses to urban areas increases the Land Surface Temperature (LST), impacting people mainly in tropical regions, such as Brazil, where the temperature is aggravated by high air humidity. In this context, we assessed the seasonal variation of LST in response to the land-use/land-cover (LULC) changes over a large city in southwest Brazil during the last 36 years (1985–2020). We used Mapbiomas LULC data and LST retrieved from the Landsat series with the Google Earth Engine platform. The methodology involved the change detection analysis from 1985 to 2020 for all LULC types, a non-parametric Dwass-Steel-Critchlow-Fligner test to estimate the LST spatio-temporal variability over different LULC types, and a non-parametric Sperman's rho test and scatter plots to relate LST changes to the LULC patterns. The results presented areas exchanging under an intense urbanization process, wherein nearly 13% of the transitions were from agricultural to urban areas. These transitions occurred jointly with an increase in temperature for all LULC types. Forest formation registered the lowest LST and a moderate to strong negative correlation with LST in both seasons. On the other hand, urban areas showed the highest temperatures and a moderate to strong positive correlation with LST in summer. From 1985 to 2020, the mean temperature in urban (built-up) areas during the summer increased by 7 °C. In 2020, the mean temperature in urban areas was 32.5 °C and 27.8 °C in forested areas during the summer. The main contribution of this study is to provide evidence-based support for establishing LULC governance at the city scale. It offers essential scientific information about the detrimental impact of urban sprawl against the climate benefits of green infrastructure on the city scale, even in tropical regions with high forest fragmentation, temperatures, and long periods with high air humidity.

Characterisation of Morphological Patterns for Land Surface Temperature Distribution in Urban Environments: An Approach to Identify Priority Areas

The validated influence of urban biophysical structure on environmental processes within urban areas has heightened the emphasis on studies examining morphological patterns to determine precise locations and underlying causes of urban climate conditions. The present study aims to characterise morphological patterns describing the distribution of Land Surface Temperature (LST) based on a prior classification of biophysical variables, including urban density (building intensity and average height), surface characteristics, shortwave solar radiation (broadband albedo), and seasonal variations in vegetation cover (high, medium, and low levels), retrieved from multisource datasets. To describe the distribution of LST, the variables were calculated, classified, and subsequently, analysed individually and collectively concerning winter and summer LST values applied in an urban neighbourhood in Madrid, Spain. The results from the analytical approaches (observation, correlations, and multiple regressions) were compared to define the morphological patterns. The selection of areas resulting from the morphological patterns with the most unfavourable LST values showed agreement of up to 89% in summer and up to 70% for winter, demonstrating the feasibility of the methods applied to identify priority areas for intervention by season. Notably, low and high vegetation levels emerged as pivotal biophysical characteristics influencing LST distribution compared to the other characteristics, emphasising the significance of integrating detailed seasonal vegetation variations in urban analyses.

Dynamic Impact of Urban Built Environment on Land Surface Temperature Considering Spatio-Temporal Heterogeneity: A Perspective of Local Climate Zone

Thermal environment deterioration has seriously threatened urban habitat quality and urban sustainable development. The evolution of the urban built environment (UBE) is an important cause for urban thermal environment variation. However, the dynamic effect of the UBE on the land surface temperature (LST) is rarely studied by combining the local climate zone (LCZ) theory and spatio-temporal heterogeneity. Based on a case study of Beilin District in Xi’an, China, this paper identified LCZ types of Beilin District in 2010, 2015, and 2020 using the GIS method. It also analyzed the spatial–temporal characteristics of the LST in summer based on the remote sensing retrieval method and explored the effects of the built environment on the LST by Geodetector and geographically weighted regression (GWR). The results showed the following: (1) The area share of dense building zones in Beilin District was greater than that of open building zones and natural surface zones, while the share of mid- and high-rise dense building zones continued to increase and the share of low-rise dense building zones continued to decrease during the study period. (2) The LST of different LCZ types in Beilin District was obviously different, and the LST of dense building zones was generally higher than that of open building zones and natural surface zones. Meanwhile, the LST of mid- and low-rise dense building zones increased gradually, and the LST of high-rise open building zones decreased gradually, but the overall warming area was obviously more than the cooling area. (3) The effects of the UBE factors on the LST varied greatly, with their interaction having an enhancement effect. The direct and interactive influence of the two-dimensional (2D) UBE indicators on the LST were greater than those of the three-dimensional (3D) indicators, but there was a gradual decrease in the force of the 2D indicators and a simultaneous diminution, enhancement, and invariance of the force of the 3D indicators. (4) Vegetation cover (VC) and floor area ratio (FAR) acted negatively, and the building height (BH) was changing from a positive to a negative role, with the average action intensity of VC changing from −0.27 to −0.15, FAR from −0.20 to −0.16, and BH from 0.05 to −0.04. The impervious surface area (ISA), building area (BA), and space congestion (SC) acted positively, with the average action intensity of the ISA changing from 0.12 to 0.20, BA from 0.12 to 0.19, and SC was stable at 0.04. The framework enables a deeper portrayal of LST changes in different LCZs, reflecting the direct and interactive effects of different UBE indicators on LST, as well as local variations in the impact effects and provides a basis for urban managers or planners to improve urban heat resilience.

Study on the Response of the Summer Land Surface Temperature to Urban Morphology in Urumqi, China

Increases in urban temperature affect the urban ecological environment and human health and well-being. In urban morphology, building characteristics are important factors affecting the land surface temperature (LST). Contemporary research focuses mainly on the effects of land use, urban tissue configuration, and street networks on the LST, and the effects of building characteristics on the LST need to be further understood. The mean LST and the urban morphology indicators of a single grid were calculated via a remote sensing inversion and a spatial analysis, and a geographically weighted regression (GWR) model was established to explore the influence of the building coverage ratio (BCR), mean building height (BH_mean), floor area ratio (FAR), and mean sky view factor (SVF_mean) on the LST. The results show that the correlations between the urban morphology indicators and the LST at a scale of 100~500 m are of different degrees, and the correlations are more significant at a scale of 200 m. Therefore, the optimal spatial scale for studying the influence of urban morphology indicators on the LST is 200 m. The fitting effect of the GWR model is significantly better than that of the ordinary least squares (OLS) method, and the effects of each indicator on the thermal environment have spatial non-stationarity. The BCR, BH_mean, FAR, and SVF_mean differ in their ability to raise and lower the temperature in different spatial zones, and the order of influence is as follows: BCR > SVF_mean > FAR > BH_mean. This study will provide a reference for the urban planning of Urumqi.

Early summer temperature anomalies and potential impacts on achieving Sustainable Development Goals (SDGs) in National Capital Region (NCR) of India

This research aims to investigate the repercussions of an anomalous early summer Land Surface Temperature (LST) surge on food, energy, and human health within the National Capital Region (NCR) of India, with a specific focus on its potential influence on the Sustainable Development Goals (SDGs). To attain this objective, the study employed various methods to evaluate the magnitude of the deviation in LST. MODIS (Moderate Resolution Imaging Spectroradiometer) images were utilized to compute the monthly diurnal LST range, and the Standard Anomaly (StA) approach was employed to account for data dispersion. Additionally, Innovative Trend Analysis (ITA) and Detrended Fluctuation Analysis (DFA) were conducted to perform regionally specific trend analysis. Furthermore, the Ordinary Least Square (OLS) regression method was applied to investigate the relationship between StA and crop yields. The findings indicate a significant temperature increase in March, with a deviation of 3.5 °C above the average range. Additionally, the study reveals the standard anomaly (StA) of Land Surface Temperature (LST) during March fell within the range of −0.706 to 2.783 °C, while in April, it ranged from −0.781 to 3.263 °C, and in May, it ranged from −3.001 to 0.525 °C. The key significance of the study lies in the impacts of this early summer warming on the attainment of the Sustainable Development Goals. The reduction in crop yields as a result of this warming poses a substantial threat to achieving the SDG-2 target of Zero Hunger. Moreover, the adverse health effects stemming from the early summer warming impede the achievement of the SDG-3.4.1 target. Additionally, the high energy consumption induced by the warming directly affects SDG-6 on affordable and clean energy. The research underscores the critical importance of addressing the impacts of early summer warming to ensure the successful achievement of the Sustainable Development Goals in the National Capital Region of India. Policymakers and stakeholders should take into account the findings of this study to implement targeted strategies that mitigate the adverse effects of early summer warming on food, energy, and human health, and thereby contribute to the realization of the SDGs.

The Influence of Urban Form on Land Surface Temperature: A Comprehensive Investigation from 2D Urban Land Use and 3D Buildings

Urban form plays a critical role in shaping the spatial differentiation of land surface temperature (LST). However, limited research has investigated the underlying driving forces and interactions of multidimensional urban form, specifically considering two-dimensional (2D) urban land use and three-dimensional (3D) buildings, on LST. Furthermore, their multi-scale outcomes remain unclear. Taking the main urban area of Wuhan City as an example, a total of nine indicators—the proportion of administration land (PA), the proportion of commercial land (PB), the proportion of industrial land (PM), the proportion of residential land (PR), the proportion of water area (PE), the building density (BD), the building height (BH), the floor area ratio (FAR), and the sky view factor (SVF)—were selected; this paper used the geographic detector model to investigate the driving force of LST spatial differentiation in winter and summer, as well as the interaction of various influencing factors from a multi-scale perspective. The results showed that (1) the average LST in industrial land was higher than that in commercial land, both in summer and winter. The LST in administration land was higher than that in residential land, while in winter, it is the opposite. (2) The spatial differentiation of summer LST was mainly dominated by 3D buildings, while the spatial differentiation of winter LST was mainly dominated by 2D land use. (3) BD was the leading driving force of LST spatial differentiation in summer, and the interaction between BD and any other indicator showed the most significant explanatory power, which is the same for PM in winter. (4) As scale increased, the explanatory power of 2D urban land use for LST spatial differentiation gradually increased both in winter and summer, while the explanatory power of PE on LST spatial differentiation decreased. The explanatory power of BD, FAR, and SVF on LST spatial differentiation remains basically unchanged. The explanatory power of BH on summer LST spatial differentiation decreases with increasing scale, while the explanatory power of BH on winter LST spatial differentiation remains in a stable state. (5) The interaction among all urban form factors primarily increases as the scale increases, except for the interaction between BH and 2D urban land use in summer, and the interaction between PE and PR in winter. The research can provide scientific decision-making support for the collaborative optimization of multiscale urban forms to improve the urban thermal environment.

High summer land surface temperatures in a temperate city are mitigated by tree canopy cover

As climate warms, the impact of existing urban heat islands on the health of residents in towns and cities will worsen. A reduction in impervious in cities may help to reduce temperatures, but the relationship between tree canopy coverage and land surface temperature (LST) is not well characterised. Here, we quantified the summer LST of the temperate city of Leeds, UK using Landsat 8 TIRS remote sensing image and explored the spatial relationships between LST and impervious land cover, greenspace coverage, type of greenspace and canopy cover. We found a strong relationship between LST and canopy coverage across the built-up region of Leeds and use this relationship to project the impact of future canopy cover expansion. We found that of the nine main types of greenspaces in Leeds, private gardens occupied the greatest fraction of the total greenspace area and offered most potential for canopy cover expansion. Results suggest that a doubling of canopy coverage across the city, could reduce the mean LST by around 2.5 °C during the warmest summer months. Such a temperature reduction adds further weight to efforts by cities and countries globally to increase tree cover to both mitigate for and adapt to climate change.

Time-series analyses of land surface temperature changes with Google Earth Engine in a mountainous region

Studying changes in temperature is fundamental for understanding its interactions with the environment and biodiversity. However, studies in mountainous areas are few, due to their complex formation and the difficulty of obtaining local data. We analysed changes in temperature over time in Montesinho Natural Park (MNP) (Bragança, Portugal), an important conservation area due to its high level of biodiversity. Specifically, we aimed to analyse: i) whether temperature increased in MNP over time, ii) what environmental factors influence the Land Surface Temperature (LST), and iii) whether vegetation is related to changes in temperature. We used annual summer and winter mean data acquired from the Moderate-Resolution Imaging Spectroradiometer (MODIS) datasets/products (e.g. LST, gathered at four different times: 11am, 1pm, 10pm and 2am, Enhance vegetation index - EVI, and Evapotranspiration - ET), available on the cloud-based platform Google Earth Engine between 2003 and 2021). We analysed the dynamics of the temporal trend patterns between the LST and local thermal data (from a weather station) by correlations; the trends in LST over time with the Mann-Kendall trend test; and the stability of hot spots and cold spots of LST with Local Statistics of Spatial Association (LISA) tests. The temporal trend patterns between LST and Air Temperature (Tair) data were very similar (ρ > 0.7). The temperature in the MNP remained stable over time during summer but increased during winter nights. The biophysical indices were strongly correlated with the summer LST at 11am and 1pm. The LISA results identified hot and cold zones that remained stable over time. The remote-sensed data proved to be efficient in measuring changes in temperature over time.

Research on Spatial and Temporal Patterns of Heat Island Variability and Influencing Factors in Urban Center Areas: A Case Study of Beijing’s Central Area

Studying the urban heat island effect and actively exploring effective measures for its mitigation and alleviation can provide important parameters for urban ecological environment monitoring and propose rational strategies to address environmental degradation. This article, with the background of urban renewal projects in Beijing, focuses on the central area of Beijing as the research object. Landsat ETM+/OLI_ TIRS data from 2000 to 2020 are used as the main remote sensing imagery source, combined with functional information data and spatial attribute data of open spaces in the central area. Based on the mono-window (MW) algorithm, this study first quantitatively retrieves and categorizes the summer land surface temperature in Beijing’s central area and analyzes its spatiotemporal characteristics using the direction distribution method, revealing regular patterns in the temporal and spatial dimensions. The results show a gradual decrease in the size of the persistent high-temperature concentration area over time. Subsequently, the seasonal autoregressive integrated moving average (SARIMA) model is employed to predict the changing trends of the urban heat island and the occurrence time of the strongest and weakest heat islands. Higher land surface temperature (LST) years are projected for 2025 and 2035, with the lowest year being 2030. Lastly, the correlation coefficient and Moran’s index are used to analyze the correlation between the urban heat island and its corresponding influencing factors in different years. The results indicate that population density, nighttime light, and gross domestic product (GDP) have significant positive effects on the heat island intensity from a temporal perspective. Normalized difference vegetation index (NDVI) shows a significant negative relationship with the heat island intensity when analyzed over time. The research findings provide important reference for rational urban planning, layout, and construction, and hold significance for advancing urban renewal efforts.

Differences in the evolution of urban and rural surface thermal environment and their responses to urban renewal in Shanghai, China.

The rapid and extensive urbanization has profound impacts on urban thermal environment. It is of great significance to comprehensively understand how urbanization affects the evolution of urban thermal environment for urban ecological safety, environmental quality, and residents' health. Based on daily land surface temperature (LST) products of MODIS Aqua satellite in the summer of 2002-2020, we investigated the evolution of urban-rural differences in surface summer thermal environment in Shanghai during 2002-2020 and its response to urban spatial renewal. We used normalized land surface temperature (NLST) and urban heat island ratio index (URI) as the surface thermal environment measurement indicators, by combining vegetation index and impervious surface cove-rage, and used M-K trend analysis and interpretation analysis. The results showed that the linear growth rate of LST in Shanghai was 0.09 ℃·a-1 (2002-2020), and that URI showed a trend of first increasing (2002-2010) and then decreasing (2010-2020). The mean summer LST was generally in the order of urban core>suburban>rural. 1.6% of the areas showed a significant cooling trend, of which 54.0% were distributed in the urban core. 39.5% of the regions showed a significant warming trend, of which 77.6% were distributed in the suburban. In general, there were concentrated significant cooling areas in the highly urbanized urban areas, while there was a significant warming trend in the suburban. The transformation from urban expansion to urban renewal was the main reason for the emergence of concentrated and significant cooling areas in the urban. Nearly 20% of the urban area showed a signi-ficant increase of vegetation coverage. Urban renewal projects such as gathering vegetation or dispersing impervious surfaces in highly urbanized areas are important ways to effectively improve the urban residential thermal environment.

Assessing the interrelation between NDVI and climate dependent variables by using granger causality test and vector auto-regressive neural network model

Changes in ecosystem structure and function can be revealed by examining the prevailing patterns in vegetation growth and the forces that shape those patterns. The mechanism of ecosystem behaviour may be better understood if the trend of vegetation change and its sensitivity to climatic variation are well understood. The interaction of vegetation and climatic factors (it's driving variables) is non-linear in behaviour and affected by time lag and time accumulation. Jharkhand has a typical plateau in eastern India, having a mixed climate (arid and semi-arid), taken as the study area, and the spatiotemporal distributions of the normalized difference vegetation index (NDVI) were explored with interaction driving factors. This study investigated the time-lag and time-accumulation effects of the NDVI response to climate factors Evapotranspiration (ET), Land Surface Temperature (LST), Potential Evapotranspiration (PET), Precipitation (PREC), and Soil Moisture (SM) and identified the primary controlling factors that affect the vegetation dynamics. The observations indicate that the correlation between NDVI and summer LST (- 0.838) was discovered to be greater than the correlation of NDVI with SM (r = 0.90) and PREC (r = 0.751), showing NDVI as more sensitive to LST when comparing to SM, and PREC, while PET exhibits the significant positive correlation (r = −0.751) with the NDVI in autumn during the studied duration. Higher NDVI values were seen during the monsoon (0.54 ± 0.12), which is correlated with a decrease in the monsoon LST (25.8 ± 0.20), followed by the winter (0.47 ± 0.13), summer (0.33 ± 0.18), and fall (0.37 ± 0.04). Vegetation growth is influenced by both the time-lag and time-accumulation effects of temperature and the time-accumulation impact of precipitation. Regarding the climate-vegetation response mechanism, the application of the Granger Causality (GC) Test and GC-based Vector Auto-Regressive Neural Network (VARNN) Model test reveals that the 0–2 month optimum time lag effect is prevalent in the study area. In addition, the LST and SM have a more prominent stimulating influence on plant growth in the study region than precipitation. The above findings highlight the need to effectively monitor vegetation dynamics under environmental changes by considering the temporal impacts of vegetation response to climate when the current climate models research vegetation-climate interactions.

A Study on the Spatial and Temporal Variation of Summer Surface Temperature in the Bosten Lake Basin and Its Influencing Factors

The land surface temperature (LST) is an important indicator reflecting the ecological environment condition. As a sensitive area to climate change, mastering the spatial and temporal changes of summer LST in the Bosten Lake basin (BLB) helps gain insight into the evolution of the thermal environment in the Bosten Lake basin and for long-term monitoring of the basic ecological changes in the basin. Based on MOD11A1 data from 2005 to 2020, this paper investigates the diurnal LST spatiotemporal series variation and its influencing factors in the Bosten Lake basin by using surface temperature class classification, trending analysis, the Hurst index, and geographic probes. The results show that (1) the wetland grasslands in and around the Bayinbruck steppe in the northwestern part of the study area exhibit a heat island effect during the day, while the opposite is true at night. In terms of temporal changes, LST changes in the BLB fluctuate widely, having a general rising and then decreasing trend. (2) The decreasing trend of LST from 2005 to 2020 is significant during the daytime and vice versa at night, and the change at night is greater than during the day. The areas with significantly higher diurnal LST in the future have all expanded compared to the area occupied by them now, with an overall trend of a steady increase. (3) The dominant factor of LST variation has the strongest explanatory power when altitude and NDVI are combined during the daytime and the strongest explanatory power when NPP and temperature are combined at night.

Spatio-temporal Analysis of Environmental Criticality: Planned Versus Unplanned Urbanization

The economic boom in the Indian cities is causing rapid urban growth, mostly in an unplanned fashion. The growth of urban built-up is primarily taking place by replacing vegetation and other low radiative surfaces, increasing the magnitude and spatial extent of heat concentrations. The resulting phenomenal increase in temperature, known as Urban Heat Island (UHI), raises environmental criticality. Satellite remote sensing provides a breakthrough for monitoring the spatiotemporal variations of UHI by estimating Land Surface Temperature (LST) and surface biophysical parameters. The objective of this work is to compare the changing pattern of LST that resulted from urban growth and associated biophysical characteristics in a planned city (Kalyani city) and an unplanned city (Barasat city) in West Bengal, India from 2005 to 2019. Using Landsat data, the study retrieved summer LST along with the prepared vegetation index (NDVI) and built-up index (NDBI) for the years 2005 and 2019. The Environmental Criticality Index (ECI) was calculated for the periods from LST, NDVI, and NDBI datasets. The long-term (1988-2019) LST has been derived using the cloud computation technique to analyze the trend. Over the years, though the average LST of Kalyani is relatively high from Barasat, a rapid increase in LST is noticed for Barasat city. Between 2005 – 2019, the rapid unplanned growth in Barasat city has not only increased the LST but also raised the concern for environmental criticality as compared to Kalyani City. The correlation of LST with NDVI and NDBI suggests that urban heating is significantly controlled by the surface characteristics that need to modify through proper planning for urban sustainability. This study may assist planners, administrators, and researchers in decision-making.

The Impact of Urban Expansion on the Urban Thermal Environment: A Case Study in Nanchang, Jiangxi, China

Urban expansion has been changing the urban thermal environment. Understanding the spatial distribution and temporal trends in the urban thermal environment is important in guiding sustainable urbanization. In this study, we focused on the land use/land cover (LULC) changes and urban expansion in Nanchang city, Jiangxi province, China. The four elements in the remote sensing-based ecological index (RSEI) are heat, greenness, dryness, and wetness, which correspond to the land surface temperature (LST), NDVI, NDBSI, and WET, respectively. According to the synthetic images of the average indices, we conducted temporal trend analysis together with statistical significance test for these images. We conducted partial correlation analyses between LST and NDVI, NDVSI, as well as WET. In addition, we used the LULC maps to analyze the multi-year trends in urban expansion. Then, we superimposed the trends in daytime and nighttime LST in summer on urban expansion area to extract the LST trends at sample locations. The results showed that LULC in Nanchang has substantially changed during the study period. The areas with statistically significant trends in LST coincided with the urban expansion areas. Land cover change was the main reason for LST change in Nanchang. In particular, artificial surfaces showed the greatest increase in LST; for per 100 km2 expansion in artificial surfaces, the daytime and nighttime LST increased by 0.8 °C and 0.7 °C, respectively. Among all the study land cover types, water bodies showed the greatest differences in LST change between the daytime and nighttime. There were statistically significant correlations between increases in LST and increases in NDBSI as well as decreases in NDVI and WET. In view of the considerable impact of urban expansion on the urban thermal environment, we urge local authorities to emphasize on urban greening when carrying out urban planning and construction.

Assessment of the impact of the different settlement patterns on the summer land surface temperature: Elazığ.

Currently, cities are at the center of the debate on global warming since land use and land cover change (LULC) in cities are considered to be major contributors to global climate change. In this study, Çarşı and Doğukent Neighborhood areas in the city center of Elazığ province, Turkey, were examined in terms of land use and land cover (LULC). Both areas were chosen because they have different patterns and features, such as different residential densities, street aspect ratios and orientations, impervious surfaces, vegetation, and elevations. The aim is to assess the effect of the different patterns of these settlements on the land surface temperature (LST) using Landsat 8 satellite images in the summertime, July 19, 2021. The results showed that the maximum, minimum, and average LST of the Doğukent Neighborhood, which is characterized by uniform streets with dense vegetation and streets oriented to the NE-SW or NW-SE, were recorded as 44.4, 38.4, and 41.0°C, respectively, while 45.4, 40.4, and 43.8°C were recorded in the Çarşı Neighborhood characterized by excessive residential areas and deep streets with lack of vegetation oriented to the E-W direction. However, the average difference is around 2.8°C, implying that residential areas with mid-building heights and vegetated streets oriented to NE-SW or NW-SE are thermally better than those with high aspect ratio streets and lacking vegetation and oriented to E-W. It was found that small variations in land elevation of these areas do not significantly affect the LST. The results of this study will set an example not only for the city of Elazığ, but also for the determination of urban transformation areas, new housing areas, and climate change in most cities of Turkey and other countries, and will provide support for sustainable and more livable urbanization in most cities. Transferring the data obtained by local governments to the physical plan decisions could also contribute to preventing climate change.

Modelling the impacts of land use/land cover changing pattern on urban thermal characteristics in Kuwait

• Changes of LULC, summer LST, UHI and UTFVI shift in Kuwait were analyzed. • Reduction of vegetation cover (47%) significantly increases the UHI effect. • LULC vs UTFVI relationship better explain the impacts of different land cover on thermal environment. • Summer UHI and UTFVI prediction exhibit a gradual decrease in overall thermal characteristics. • Predicted UTFVI demonstrated the highest UTFVI concentration in the built-up area. Rapid urbanization owing to population growth and economic development has made thermal environment-related studies increasingly prominent. This study aims to monitor and predict the changes in land use/land cover (LULC) and their impacts on land surface temperature (LST), urban heat island (UHI), and urban thermal field variance Index (UTFVI) in Kuwait from 1991 to 2021 for the very first time. Support Vector Machine (SVM) and Artificial Neural Network (ANN)machine learning algorithms were used to analyze and predict, respectively, using Landsat 4-5 and 8 images from 1991 to 2021 at 10 years interval. Results illustrated transformation of 27.24% bare land and 5.43% vegetation into the built-up area increased the mean LST by 5°C, resulting in an upsurge of UHI values by 0.861 and the strongest UFTVI effects by 108% from 1991 to2021. Predicted LULC, LST, UHI and UTFVI distribution modelled using the ANN-based Cellular Automata technique for 2031 shows the declination of vegetation cover (44%) and water bodies (57%) during 2021–2031, resulting upsurge in built-up areas (38%) and higher LST (10°C). Moreover, by 2031, 41% of the total study area will be covered by the strongest UTFVI affected zones. All the prediction was performed with a higher accuracy, and validated by RMSE and R 2 values with predicted and real datasets. Assessing the predicted LULC, LST, UHI, and UTFVI distribution maps will help prospective policymakers and city planners to take the necessary actions by reducing heat stress impacts and make cities sustainable.