Monitoring Changes in Landsat Thermal Features in Urban and Non-Urban Interfaces from 1986 to 2023 in Two International Urban Centers: Implications for Climate and Global Issues

Urbanization leads to changes in landscape configuration and land use/land cover (LULC) patterns, and these changes are important factors affecting the surface urban heat island (SUHI) effect. However, from the perspective of spatiotemporal changes, quantitative analytical results regarding the impacts of the LULC composition, configuration, and pattern in inland plateau lakeside cities on the SUHI effect, and the responsive relationships among these factors remain unclear. By combining satellite remote sensing data with analytical methods, such as urban-rural gradients, spatial statistics, and landscape pattern indices, the impacts of LULC changes on the SUHI effect in Kunming, China, are revealed. The results show the following. (1) The explosive growth in impervious surfaces (ISs) caused by urbanization, leading to changes in the LULC composition, configuration and pattern, is the main reason for the deterioration of the SUHI effect. Over the past 30years, Kunming's ISs have increased by 304.58 km2, SUHI has expanded by 764.26 km2, and the regional average land surface temperature (LST) has increased by 1°C. (2) This study also found that a large area of bare ground is another important reason for the sharp rise in LST, explaining why bare land (BL) has the highest average LST (28.72°C). (3) The pattern of LULC can well explain the spatial distribution characteristics of SUHIs. The normalized difference built-up index (NDBI), normalized difference bareness index (NDBaI), and LST have the same change curve along the urban-rural gradient, while the normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), and LST have opposite trends. (4) ISs and water body (WB) are the main types of warming and cooling, respectively, but the warming effect of ISs is greater than the cooling effect of WB. From the average value of the correlation coefficient with LST, NDBI (0.84) > MNDWI (-0.63). (5) Kunming's remote sensing index values do not have simple linear relationships with the LST. NDBaI, NDBI, and LST show significant exponential relationships, and NDVI, MNDWI, and LST show significant quadratic polynomial relationships. (6) The dominant landscape type determines the correlation between the landscape shape index (LSI) and the LST of green spaces (GSs). (7) Adopting a simple and regular landscape layout can effectively reduce the SUHI effect. These research results could provide a scientific decision-making basis for the spatial urban planning and ecological construction of Kunming and could have practical significance for guiding the green, healthy, and sustainable development of the city.

Abstract. Exploring changes in land use and land cover (LULC) in the city area and its surrounding is important to understand the variation of surface urban heat island (SUHI) and surface urban heat island intensity (SUHII). The SUHII can be calculated based on the local climate zone by using land use and land cover compossition of the city and based on the urban rural zone . The objective of this research is to examine the spatiotemporal changes of LULC and the impact of its composition for the formation of SUHI in Addis Ababa City, Ethiopia based on the urban rural zones. The mean center of the central business district of the Addis Ababa City was considered as the central point of the study area. We represented the 30 km × 30 km geographical area as a study area with a 15km radius from the central point. As data sources, multi-temporal satellite data provided by the United States Geological Survey (USGS) were used in respect to the years of 1986, 2001, and 2016. In the methodology, we first completed the classification of LULC by using pixel-oriented method for the three years and the validation of the classification has been made. For the classification five LULC classes were identified such as forest, impervious surface, grass land, bare land and crop land. Afterward, land surface temperature (LST) has been computed for three years respectively. Finally, urban rural gradient zones (URGZs) have been generated as a set of polygons with 210m distance in each zone from the central point of the study area. In order to evaluate the SUHII along the URGZs in respect to the LULC, the following analyses were accomplished: (i) the relationship between mean LST and composition of the LULC was computed, (ii) the SUHII was calculated based on the LST variation of main LULC categories and the temperature difference between URGZs, (iii) multi-temporal and multi-directional SUHII was computed, and (iv) linear regression analyses were used to assess the correlations of the mean LST with composition of LULC. The results of the analyses show that (i) distribution pattern of SUHII has changed over the study period as results of changes in LULC, and (ii) mean LST gradually declines from city centre to outside of the city , then it can be seen increasing trends due to the effect of bare lands in rural area. This pattern can be seen over the three years as the result of multi-directional approach. The methodology presented will be able to apply other cities which are showing similar growth pattern by making necessary calibration, and our finding can be used as a proxy indicator to introduce appropriate landscape and town planning in a sustainable viewpoint in Addis Ababa City.

Quantifying the contribution of diminishing green spaces and urban sprawl to urban heat island effect in a rapidly urbanizing metropolitan city of Pakistan

Urban heat islands (UHIs) aggravate urban heat stress and, therefore, exacerbate heat-related morbidity and mortality as global warming continues. Numerous studies used surface urban heat island intensity (SUHII) to quantify the change in the UHI effect and its drivers for heat mitigation. However, whether the variations in SUHII among cities can demonstrate the physical difference and fluctuation of the urban thermal environment is poorly understood. Here, we present a comparison study on the temporal trends of SUHII and LST in urban and nonurban areas in 13 cities of the Beijing–Tianjin–Hebei (BTH) megaregion in China and further identify different types of changes in SUHII based on the temporal trends of land surface temperature (LST) in urban and nonurban areas from 2000 to 2020. We also measured the effect of the changes in four socioecological factors (i.e., population density, vegetation greenness (EVI), GDP, and built-up area) on the trends of SUHII to understand the dynamic interaction between the UHI effect and socioecological development. We found the following. (1) Nine out of thirteen cities showed a significant increasing trend in SUHII, indicating that the SUHI effects have been intensified in most of the cities in the BTH megaregion. (2) The spatial pattern of summer mean SUHII and LST in urban areas varied greatly. Among the 13 cities, Beijing had the highest mean SUHII, but Handan had the highest urban temperature, which suggests that a city with stronger SUHII does not necessarily have a higher urban temperature or hazardous urban thermal environment. (3) Four types of changes in SUHII were identified in the 13 cities, which resulted from different temporal trends of LST in urban areas and nonurban areas. In particular, one type of increasing trend of SUHII in seven cities resulted from a greater warming trend (increasing LST) in urban than nonurban areas (SUHII↑1), and another type of increasing trend of SUHII in Beijing and Chengde was attributed to the warming trends (increasing LST) in urban areas and the cooling trends (decreasing LST) in nonurban areas (SUHII↑2). Meanwhile, the third type of increasing trend of SUHII in Zhangjiakou was due to a greater cooling (decreasing LST) trend in nonurban areas than in urban areas (SUHII↑3). In contrast, three cities with a decreasing trend of SUHII were caused by the increase in LST in urban and nonurban areas, but the warming trend in nonurban areas was greater than in urban areas (SUHII↓1). (4) Among the relationship between the trend of SUHII (TrendSUHII) and the changes in socioecological factors (Trendpopulation density, TrendGDP per captica, TrendEVI, and Trendbuild-up area), a significantly positive correlation between TrendSUHII and TrendEVI indicated that the change in SUHII was significantly related to an increased rate of EVI. This is mainly because increased vegetation in nonurban areas would result in lower temperatures in nonurban areas.

The Chishui River Basin, a vital waterway in Southwest China, has experienced rapid urbanization, leading to significant ecological and environmental changes, among which the urban heat island (UHI) effect is particularly pronounced. The UHI effect not only affects the quality of life for residents but also influences urban energy consumption and climate change, underscoring the need for in-depth study of its spatial distribution and contributing factors. The unique karst topography of the region further complicates UHI research, necessitating an investigation that can inform urban planning and sustainable development strategies. This study leveraged Landsat 8 TIRS satellite remote sensing imagery to examine the land surface temperature (LST) and UHI effect in the Chishui River Basin during the summers of 2016 and 2021. Employing the Mono-window Algorithm (MWA), the research quantitatively inverted the LST and analyzed its spatial distribution and the spatiotemporal characteristics of the surface urban heat island (SUHI) effect. The findings indicated a notable increase in average summer temperatures between the 2 years, with a 1.67°C rise from 2016 to 2021. Despite this increase, there was an observed reduction in the extent of SUHI areas, suggesting potential mitigation efforts. Additionally, the study revealed that karst regions were more susceptible to forming “abnormal” heat islands due to their distinct geomorphological features. The implications of this research are critical for urban development planning and the pursuit of sustainable urbanization in the Chishui River Basin. By understanding the thermal dynamics and their relationship with urbanization and karst landscapes, policymakers and urban planners can devise strategies to minimize the adverse effects of SUHI while promoting ecological balance and environmental health. Future research should extend the temporal analysis, employ higher resolution data, compare findings with other regions, and provide a detailed examination of mitigation efforts to enhance the robustness and applicability of the conclusions, provide stronger scientific evidence for the ecological sustainability of the Chishui River Basin.

Understanding the driving mechanisms behind surface urban heat island (SUHI) effects is essential for mitigating the degradation of urban thermal environments and enhancing urban livability. However, previous studies have primarily concentrated on central urban areas, lacking a comprehensive analysis of the entire metropolitan area over distinct time periods. Additionally, most studies have relied on regression analysis models such as ordinary least squares (OLS) or logistic regression, without adequately analyzing the spatial heterogeneity of factors influencing the surface urban heat island (SUHI) effects. Therefore, this study aims to explore the spatial heterogeneity and driving mechanisms of surface urban heat island (SUHI) effects in the Guangzhou-Foshan metropolitan area across different time periods. The Local Climate Zones (LCZs) method was employed to analyze the landscape characteristics and spatial structure of the Guangzhou-Foshan metropolis for the years 2013, 2018, and 2023. Furthermore, Geographically Weighted Regression (GWR), Multi-scale Geographically Weighted Regression (MGWR), and Geographical Detector (GD) models were utilized to investigate the interactions between influencing factors (land cover factors, urban environmental factors, socio-economic factors) and Surface Urban Heat Island Intensity (SUHII), maximizing the explanation of SUHII across all time periods. Three main findings emerged: First, the Local Climate Zones (LCZs) in the Guangzhou-Foshan metropolitan area exhibited significant spatial heterogeneity, with a non-linear relationship to SUHII. Second, the SUHI effects displayed a distinct core-periphery pattern, with Large lowrise (LCZ 8) and compact lowrise (LCZ 3) areas showing the highest SUHII levels in urban core zones. Third, land cover factors emerged as the most influential factors on SUHI effects in the Guangzhou-Foshan metropolis. These results indicate that SUHI effects exhibit notable spatial heterogeneity, and varying negative influencing factors can be leveraged to mitigate SUHI effects in different metropolitan locations. Such findings offer crucial insights for future urban policy-making.

Development of remote sensing techniques has made a significant contribution to assess climatology phenomena and determine which predictors have a noticeable influence on the intensity of the surface urban heat island (SUHI) effects. The aim of this study is to analyse the effectiveness of the geostatistical modelling of thermal properties of land surface in an expanding city, Poznan in west Poland. The applied models – Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) were used to explore the strength of the correlation between explanatory variables (e.g. porosity index, ISA, road density) and dependent variables defined as mean SUHI intensity (MSUHIiintensity ) and difference mean SUHI intensity between 2001 and 2011 ( ΔMSUHIiintensity) for each district within the city . In the research we employed two Landsat images (2001 and 2011) on the basis of which SUHI intensity and land-cover maps were generated. Classification results ( overall accuracy of 97,5% ) obtained through Artificial Neural Network (ANN) algorithm were used as explanatory variables to identify the impact of land-cover and its derived products on the SUHI effect within the city. On the grounds of the combination of the chosen predictors it was possible to examine whether land-cover types and their derivatives determined mean SUHI values (MSUHIiintensity) and if changes in land cover effected ΔMSUHIiintensity. On the basis of statistical indicators ( I-Moran index, AICc, VIF, R2) it turned out that the most suitable predictors were ISA, ΔISA and road density. The results for the cross-sectional GWR model (R2 = 0,730) were better than for the OLS (R2 = 0,470). In contrast to the cross-sectional analyses, the goodness of fit for the longitundinal OLS model (R2 = 0,501) was similar to the GWR results (R2 = 0,500). However, the GWR revealed that local regression residuals were differentiated – values for some regions in the city centre were overestimated and for the outskirts of Poznan R2 underestimation values were noted . This situation indicated that unlike other cities for which the longitundinal GWR modelling gave better results, for Poznan the GWR did not improve the modelling effectiveness (ZHOU, WANG, 2011; DEILAMI, KAMRUZZAMAN, 2017). This means that associations between dependent and explanatory variables are stationary and as a result ΔMSUHIiintensity is not spatially variable. This study has identified associations between SUHI effects and remotely-sensed land-cover parameters in Poznan. Results demonstrated that the GWR methods have proved effective in modelling using cross-sectional analysis (R2 = 0,730). In the case of estimating thermal conditions variability between 2001 and 2011 applying the GWR did not improve the modelling results (R2 = 0,500), what could be explained by the different spatial structure of the city and a moderate climate with both maritime and continental elements.

This study analyzes the Surface Urban Heat Island (SUHI) effect and the spatial distribution of Land Surface Temperature (LST) during heatwaves in three climatically and geographically distinct Argentine cities: Posadas, Buenos Aires, and Neuquén. Leveraging high-resolution satellite imagery (MODIS, Landsat 8, Sentinel-2) and the Local Climate Zone (LCZ) classification, the research quantifies SUHI intensity and identifies fine-scale thermal patterns across urban, peri-urban, and rural areas. Results show that all three cities experience significant SUHI effects, particularly at night, with urban areas consistently exhibiting higher LSTs than their surroundings. However, daytime patterns vary considerably. In Neuquén’s semi-arid plateau, a negative SUHI was observed, with rural areas reaching higher temperatures than urban centers. Densely built environments—especially compact zones—demonstrate elevated heat retention, exacerbating heatwave impacts. Conversely, areas with substantial vegetation, such as parks and river corridors, consistently show lower surface temperatures, underscoring their role in urban cooling. By integrating multi-source remote sensing data with the LCZ framework, this study provides a robust and transferable methodology for analyzing intra-urban thermal variability. The findings offer valuable insights for climate adaptation and urban planning, particularly in rapidly expanding cities facing increasing exposure to extreme heat. Graphical Abstract This study examines the effects of extreme heat in urban environments by analyzing the Surface Urban Heat Island (SUHI) phenomenon and the spatial distribution of Land Surface Temperature (LST) across three climatically diverse Argentine cities: Posadas, Buenos Aires (CABA), and Neuquén. The research integrates high-resolution satellite imagery (MODIS, Landsat, Sentinel) and Local Climate Zone (LCZ) classification to capture fine-scale thermal dynamics during heatwaves. The two main goals were: (1) to assess SUHI intensity in different urban contexts, and (2) to explore how surface temperature varies within cities based on land cover and built environment characteristics. The results show that densely built zones, particularly compact LCZs, experience higher LSTs both during the day and at night. Vegetated and peri-urban areas consistently exhibit lower surface temperatures, confirming the cooling role of green infrastructure. In Posadas and CABA, the SUHI effect is strongest at night, while Neuquén presents a unique case of daytime negative SUHI, especially in semi-arid rural plateaus with low vegetation and soil moisture. The findings highlight how local geography, urban morphology, and vegetation interact to shape heat exposure, emphasizing the need for spatially targeted heat mitigation strategies. These insights are particularly valuable for guiding urban planning and climate adaptation efforts in rapidly expanding cities facing growing risks from extreme heat events.

Urbanization poses significant challenges to climate resilience, particularly in rapidly expanding cities like Kolkata in India. The extensive land use and land cover (LULC) changes resulting from unplanned urban growth have intensified urban climatic issues, notably the Surface Urban Heat Island (SUHI) and Urban Aerosol Pollution Island (UAPI) effects. This study investigates the impact of Kolkata's urbanization over the past 20 years (2000–2020), focusing on the interplay between LULC changes and the exacerbation of SUHI and UAPI phenomena. The findings reveal that the transformation of green spaces into built-up and impervious areas has significantly contributed to rising Land Surface Temperatures (LST) and deteriorating air quality. In contrast, regions with higher vegetation cover consistently recorded lower LST, often remaining below 30 °C, even in densely urbanized zones. Keeping temperatures below 30 °C reduces heat stress and mitigates emissions and are essential for achieving global health priorities and the Paris Agreement goal of limiting temperature rise to 1.5°C above pre-industrial levels. This highlights the critical role of urban greening in mitigating these adverse effects. A tailored vegetation strategy is proposed, categorizing urban areas based on road types—national highways, state highways, and residential roads. Using the i-Tree application, the study identifies suitable tree species for urban greening initiatives, considering Kolkata's unique climatic conditions, including temperature, growing season length and height constraints to achieve desired pollutant removal and eight other environmental factors. By aligning greening efforts with these classifications, the study demonstrates how nature-based solutions can effectively reduce SUHI and UAPI impacts while enhancing urban sustainability. This research underscores the importance of strategic vegetation planning to counteract the negative impacts of urbanization in tropical cities like Kolkata. By addressing LULC changes with targeted urban greening measures, cities can enhance their resilience to extreme climatic events and improve overall environmental quality.Keywords: LULC, SUHI, UAPI, Urban Greening, Nature-Based Solutions

Recognizing surface urban heat ‘island’ effect and its urbanization association in terms of intensity, footprint, and capacity: A case study with multi-dimensional analysis in Northern China

Quantitative analysis and prediction of urban heat island intensity on urban-rural gradient: A case study of Shanghai

The mountainous region of Asir is experiencing rapid and unsystematic urbanization leading to an increase in land surface temperatures (LST), which poses a challenge to human well-being and ecological balance. Therefore, it is necessary to study the interaction between land use and land cover (LULC)-induced urbanization and LST using advanced geostatistical techniques. In addition, understanding the urbanization process and urban density is essential for effective urban planning and management. The aim of this study was to investigate the interaction between the urbanization process, urban density and the associated LST. Using the Random Forest Algorithm, LULC mapping was conducted for the years 1990, 2000 and 2020. Metrics such as land cover change rate (LCCR), land cover index (LCI), landscape expansion index (LEI), mean landscape expansion index (MLEI) and area-weighted landscape expansion index (AWLEI) were used to understand urbanization processes and LULC changes. Convolutional kernels were used to model urban density, and the mono-window algorithm was applied to analyse LST in the selected years. In addition, the study assessed the Surface Urban Heat Island (SUHI) contribution index to LULC and used Generalized Additive Models (GAMs) in conjunction with Partial Dependence Plots (PDPs) to understand the relationship between urbanization processes, urban density and LST. In a detailed 30-year study, the application of the RF algorithm showed significant shifts in LULC with an overall validation accuracy of over 85%. Urban areas grew dramatically from 69.40 km2 in 1990 to 338.74 km2 in 2020, while water areas decreased from 1.51 to 0.54 km2. Dense vegetation increased from 43.36 to 52.22 km2, indicating positive ecological trends. The LST analysis showed a general warming, with the mean LST increasing from 40.51°C in 1990 to 46.73°C in 2020 and the highest temperature category (50-60°C) increasing from 0.78 to 33.35 km2. The built-up area of cities tripled between 1990 and 2020, with the Landscape Expansion Index reflecting significant growth in suburban areas. The modeling of urban density shows increasing urbanization in the centre, which will expand significantly to the east by 2020. The contribution of LULC to LST and the Urban Heat Island (SUHI) effect was evident, with built-up areas showing a constant temperature increase. GAMs confirmed a statistically significant relationship between urban density and LST, with different effects for different types of urban expansion. This comprehensive study quantitatively sheds light on the complicated dynamics of urbanization, land cover change and temperature variation and provides important insights for sustainable urban development.

The urban heat island (UHI) effect is driven by land cover changes, reduced vegetation, anthropogenic heat emissions, dense urban morphology, and the thermal properties of construction materials, all of which alter energy balances and elevate temperatures in urban areas. UHI adversely affects thermal comfort, public health, and sustainability. The unprecedented global reduction in anthropogenic activity during the COVID-19 lockdown in 2020 provided a unique opportunity to examine how decreased anthropogenic emissions influence UHI dynamics. While previous studies suggest that lockdown conditions led to declines in atmospheric UHI (AUHI) and surface UHI (SUHI), the extent of these reductions remains uncertain due to confounding meteorological variables and urban-rural dynamics.This study investigates how the lockdown period (March–April 2020) affected AUHI and SUHI in Prague by controlling for weather variability and urban-rural contrasts. To ensure robust comparisons, we selected meteorologically similar days across the Lockdown period and a Reference period (March–April 2017–2019). SUHI intensity was assessed using MODIS satellite-derived land surface temperature, while AUHI variations were analyzed using near-surface air temperature records from Prague’s meteorological stations. Our results reveal that urban SUHI intensity declined by 15% (0.1 °C), and AUHI in the city center dropped by 0.7 °C compared to the Reference period. Satellite-based observations further indicate a 12% reduction in aerosol optical depth and a 29% decline in nitrogen dioxide levels, supporting the hypothesis that diminished anthropogenic emissions contributed to weakened UHI effects. The highest decrease in mean SUHI was observed on Prague’s outskirts, where rural land cover dominates, highlighting the importance of accounting for urban-rural dynamics when linking SUHI changes to AHF. Our findings advance the understanding of UHI dynamics by demonstrating the effects of reduced anthropogenic activities during the lockdown, providing policymakers with a comprehensive perspective on urban-rural microclimate interactions and their role in shaping the SUHI phenomenon.

<p>The increasing accessibility to high resolution land surface temperature (LST) data unbalances recently the investigation of the urban heat island (UHI) towards approaches based on these remote sensing tools. However, for a holistic assessment of UHI, a need of comparison of the resulted surface urban heat island (SUHI) with the air urban heat island(AUHI) remains of great interest. In our study we respond to this demand by taking to account all the MODIS LST images and their corresponding synchronous air temperature observations from 9 in-situ monitoring points evenly distributed over the city of Iași for 2013-2020. This way, using a total of 2901 satellite images, the main diurnal and seasonal characteristics of clear-sky SUHI have been outlined for Iași city.</p><p>The results obtained describe accurately the intensity of the SUHI, but also its relation with the urban land use categories. During summer season in daytime the spatial extent of SUHI reaches its maximum, SUHI being bounded by the 35°C isotherm in direct relation with the highest imperviousness ratio. In the winter season instead, SUHI is almost absent during the day especially due to the high frequency of temperature inversions in this area. Also, the geometry of SUHI tends to be compact and regular during the nighttime and more irregular during the daytime, as a result of the higher and more complex energy input.</p><p>The comparison with the in-situ observations indicates that the differences between SUHI and AUHI are highest during the daytime in spring and summer, when LST is 5 to 7°C higher than the air temperature in classical sheltered conditions, while during winter no major difference can be observed. For the nighttime the LST is 1 to 3°C lower than air conditions regardles of the seasons. The analysis is detailed with the influence of land use categories and imperviousness ratio on SUHI, but also on the difference between SUHI and AUHI. As well, using a k-means atmospheric circulation classification we identified the weather patterns that are capable to increase both the SUHI intensity, and the difference between SUHI and AUHI.</p>

In this paper, we present surface urban heat island (SUHI) analysis of Shanghai (China) based on the change in land use and land cover using satellite Landsat images from 2002 to 2013. With the rapid development of urbanization, urban ecological and environmental issues have aroused widespread concern. The urban heat island (UHI) effect is a crucial problem, as its generation and evolution are closely related to social and economic activities. Land-use and land-cover change (LUCC) is the key in analyzing the UHI effect. Shanghai, one of China’s major economic, financial and commercial centers, has experienced high development density for several decades. A tremendous amount of farmland and vegetation coverage has been replaced by an urban impervious surface, leading to an intensive SUHI effect, especially in the city’s center. Luckily, the SUHI trend has slowed due to reasonable urban planning and relevant green policies since the 2010 Expo. Data analyses demonstrate that an impervious surface (IS) has a positive correlation with land surface temperature (LST) but a negative correlation with vegetation and water. Among the three factors, impervious surface is the most relevant. Therefore, the policy implications of land use and control of impervious surfaces should pay attention to the relief of the current SUHI effect in Shanghai.