Land Surface Temperature In Urban Areas Research Articles

Spatiotemporal Analysis of Land Surface Temperature in Response to Land Use and Land Cover Changes: A Remote Sensing Approach

Rapid urbanisation in the global south has often introduced substantial and rapid uncontrolled Land Use and Land Cover (LULC) changes, considerably affecting the Land Surface Temperature (LST) patterns. Understanding the relationship between LULC changes and LST is essential to mitigate such effects, considering the urban heat island (UHI). This study aims to elucidate the spatiotemporal variations and alterations of LST in urban areas compared to LULC changes. The study focused on a peripheral urban area of Phnom Penh (Cambodia) undergoing rapid urban development. Using Landsat images from 2000 to 2021, the analysis employed an exploratory time-series analysis of LST. The study revealed a noticeable variability in LST (20 to 69 °C), which was predominantly influenced by seasonal variability and LULC changes. The study also provided insights into how LST varies within different LULC at the exact spatial locations. These changes in LST did not manifest uniformly but displayed site-specific responses to LULC changes. This study accounts for changing land surfaces’ complex physical energy interaction over time. The methodology offers a replicable model for other similarly structured, rapidly urbanised regions utilising novel semi-automatic processing of LST from Landsat images, potentially inspiring future research in various urban planning and monitoring contexts.

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.

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.

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.

Relationships between Land Surface Temperatures and Neighboring Environment in Highly Urbanized Areas: Seasonal and Scale Effects Analyses of Beijing, China

Urban environments have a strong influence on the land surface temperature (LST) in urban areas. Understanding the relationship between LST and urban environmental factors can help develop effective strategies to reduce high LSTs in urban areas, which is critical for mitigating the urban heat island effect. Previous studies have focused on the correlation between LST and the environmental factors that drive its formation, without considering the influences of the neighboring environment and the vertical expansion of highly urbanized areas. Notably, the correlation between LST and its neighboring environment in different seasons remains unclear. In this study, we selected central Beijing in China as our study area and employed the moving window method to characterize the environmental factors of the neighboring environment of the central LST cell. We explored eight environmental factors from three layers: normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), modified normalized difference water index (MNDWI), building density (BD), building height (BH), building volume (BV), sky view factor (SVF), and road density (RD). The Pearson correlation and extreme gradient boosting (XGB) regression methods were applied to measure the correlation between LST and the different factors in moving windows of different sizes. The results indicated that the correlation between NDVI, MNDWI, and LST was considerably different in the winter and other seasons. However, NDBI was positively correlated with LST in all seasons, although the correlation was strongest/weakest in summer/winter. Among building-related factors, BD and BH were more strongly correlated with LST, and the positive/negative correlation between BD/BH and LST was stronger in summer/winter. The correlation between LST and its neighboring environment varied with increasing window size, and this variation differs significantly between winter and other seasons. In spring, summer, and autumn, the strength of the correlation between LST and its neighboring environment showed an “inverted V” pattern with increasing window size. The optimal spatial scales to explore the influence of neighboring environments on the LST of 30-m cells were 210 m and 270 m. This study revealed the seasonal correlation between LST and its neighboring environment while explaining the variation at a spatial scale. Notably, this study can provide a new perspective for understanding the driving mechanism of the urban thermal environment, while contributing to its scientific optimization and management.

ASSESSING LAND SURFACE TEMPERATURE IN URBAN AREAS USING OPEN-SOURCE GEOSPATIAL TOOLS

Abstract. Land surface temperature (LST) in urban areas can be traditionally observed by thermal remote sensors. The increasing availability of 3D city models provides an alternative approach based on geospatial modeling. Using solar radiation tools and scripting in GRASS GIS, we have developed a physically-based LST model that can be used to estimate LST in urban areas represented by vector 3D city models. It uses standard input parameters such as solar irradiance, albedo or convection heat transfer coefficient for urban surfaces. The solar irradiance is estimated using the v.sun solar radiation add-on module in GRASS GIS. The LST values are calculated using map algebra operations using a Python script. The suggested methodology was applied to the study area in the city of Košice, Slovakia. Results indicate that urban morphology has a strong impact on spatial distribution of LST during the daylight hours. The accurate parameterization of urban surfaces can increase the accuracy of the model that can be used for urban planning, optimization of energy use in buildings or mitigation of urban heat island effects.

Modeling the angular effect of MODIS LST in urban areas: A case study of Toulouse, France

Satellite observation of land surface temperature (LST) is an important tool for monitoring urban thermal environments but is prone to significant angular effects over non-isothermal pixels characterized by the substantial three-dimensional (3D) structure of urban areas. However, accurately characterizing the urban thermal anisotropy at the satellite pixel scale is still challenging. In this paper, comparing the simultaneous airborne observations and the temporal-aggregated signal from MODIS LST, we further investigate the seasonal and diurnal patterns of thermal anisotropy in urban areas with various simulation scenarios. The anisotropy was defined as the difference between off-nadir and nadir temperatures. First, a realistic morphological representation of urban surfaces as well as urban component temperatures measured by infrared thermometers (IRTs) were input into the discrete anisotropic radiative transfer (DART) model to simulate MODIS-scale anisotropy for Toulouse, France in summer. Extending this ‘IRT-DART’ method to other seasons was restricted because the IRTs' radiative source areas are affected by shadows when the sun elevation is low, meaning that measured sunlit temperatures are not reliably available. An energy balance model, TUF3D, and a sensor view model, SUM, for simplified urban geometry were coupled to replace ‘IRT-DART’ in winter. The cross-comparison shows that ‘TUF-SUM’ can simulate MODIS-scale anisotropy if urban structure and materials are relatively homogeneous, which provides a basis for assessing anisotropy when IRT sunlit temperatures are unavailable. The MODIS anisotropy is subject to a strong diurnal yet a discernable seasonal variation. For example, for a mid-latitude city, the anisotropy over all MODIS view angles reaches up to 6.6 K for Terra-Day (around 11:00 local time) and 4.9 K for Aqua-Day (around 13:00) in summer, and 6.1 K for Terra-Day and 4.0 K for Aqua-Day in winter. Nocturnal anisotropy is weak with values less than 0.2 K. The modeling methods can be used to quantify anisotropy of satellite LST in a broad range of urban environments and facilitate the use of multi-angular MODIS LST for a better assessment of urban thermal environments in the future.

Impact of 3-D Structures and Their Radiation on Thermal Infrared Measurements in Urban Areas

Land surface temperature (LST) is a key parameter for many fields of study. Currently, LST retrieved from satellite thermal infrared (TIR) measurements is attainable with an accuracy of about 1 K for most natural flat surfaces. However, over urban areas, TIR measurements are influenced by 3-D structures and their radiation that could degrade the performance of existing LST retrieval algorithms. Therefore, quantitative models are needed to investigate such impact. Current 3-D radiative transfer models are generally based on time-consuming numerical integrations whose solutions are not analytical, and are therefore difficult to exploit in the methods of physical retrieval of LST in urban areas. This article proposes an analytical TIR radiative transfer model over urban (ATIMOU) areas that considers the impact of 3-D structures and their radiation. The magnitude of this impact on TIR measurements is investigated in detail, using ATIMOU, under various conditions. Simulations show that failure to acknowledge this impact can potentially introduce a 1.87-K bias to the ground brightness temperature for street canyon whose ratio “wall height/road width” is 2, wall and road temperature is 300 K, wall emissivity is 0.906, and road emissivity is 0.950. This bias reaches 4.60 K if road emissivity decreases to 0.921, and road temperature decreases to 260 K. ATIMOU is also compared to the discrete anisotropic radiative transfer (DART) model. Small mean absolute error of 0.10 K was found between the models regarding the simulated ground brightness temperatures, indicating that ATIMOU is in good agreement with DART.

Using Landsat 8 data to compare percent impervious surface area and normalized difference vegetation index as indicators of urban heat island effects in Connecticut, USA

This empirical research examines the normalized difference vegetation index (NDVI) and the percent impervious surface area (%ISA) as indicators of urban heat island (UHI) effects, using the relationships between land surface temperature (LST), %ISA, and NDVI. Landsat 8 Operational Land Imager and Thermal Infrared Sensor data were used to estimate the LST in Connecticut at different times. A map of the percent impervious surface was generated using the Impervious Surface Analysis Tool developed by the Center for Land Use Education and Research and distributed through the National Oceanic and Atmospheric Administration. Strong linear relationships between LST and %ISA exist, as stated in previous studies, whereas the relationship between LST and NDVI is evidently affected by the seasons. As the patterns of LST in urban areas are influenced by the UHI, the results demonstrate that %ISA is a more reliable indicator of UHI effects than NDVI. Thus, %ISA could be a promising alternative for use in the quantitative analysis of LST when studying UHI effects.

SPATIOTEMPORAL FUSION OF HIGH RESOLUTION LAND SURFACE TEMPERATURE USING THERMAL SHARPENED IMAGES FROM REGRESSION-BASED URBAN INDICES

Abstract. Obtaining spatially continuous, high resolution thermal images is crucial in order to effectively analyze heat-related phenomena in urban areas and the inherent high spatial and temporal variations. Spatiotemporal Fusion (STF) methods can be applied to enhance spatial and temporal resolutions simultaneously, but most STF approaches for the generation of Land Surface Temperature (LST) have not focused specifically on urban regions. This study therefore proposes a two-phase approach using Landsat 8 and MODIS images acquired on a study area in Beijing to first, investigate the sharpening of the fine resolution image input with urban-related spectral indices and second, to explore the potential of implementing the sharpened results into the Spatiotemporal Adaptive Data Fusion Algorithm for Temperature Mapping (SADFAT) to generate high spatiotemporal resolution LST images in urban areas. For this test, five urban indices were selected based on their correlation with brightness temperature. In the thermal sharpening phase, the Fractional Urban Cover (FUC) index was able to delineate spatial details in urban regions whilst maintaining its correlation with the original brightness temperature image. In the STF phase however, FUC sharpened results returned relatively high levels of correlation coefficient values up to 0.689, but suffered from the highest Root Mean Squared Error (RMSE) and Average Absolute Difference (AAD) values of 4.260 K and 2.928 K, respectively. In contrast, Normalized Difference Building Index (NDBI) sharpened results recorded the lowest RMSE and AAD values of 3.126 K and 2.325 K, but also the lowest CC values. However, STF results were effective in delineating fine spatial details, ultimately demonstrating the potential of using sharpened urban or built-up indices as a means to generate sharpened thermal images for urban areas, as well as for input images in the SADFAT algorithm. The results from this study can be used to further improve STF approaches for daily and spatially continuous mapping of LST in urban areas.

A COMPARATIVE STUDY OF LAND SURFACE TEMPERATURE WITH DIFFERENT INDICES ON HETEROGENEOUS LAND COVER USING LANDSAT 8 DATA

Abstract. The temperature rise in urban areas has become a major environmental concern. Hence, the study of Land surface temperature (LST) in urban areas is important to understand the behaviour of different land covers on temperature. Relation of LST with different indices is required to study LST in urban areas using satellite data. The present study focuses on the relation of LST with the selected indices based on different land cover using Landsat 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) data in Varanasi, India. A regression analysis was done between LST and Normalized Difference Vegetation index (NDVI), Normalized Difference Soil Index (NDSI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI). The non-linear relations of LST with NDVI and NDWI were observed, whereas NDBI and NDSI were found to show positive linear relation with LST. The correlation of LST with NDSI was found better than NDBI. Further analysis was done by choosing 25 pure pixels from each land cover of water, vegetation, bare soil and urban areas to determine the behaviour of indices on LST for each land cover. The investigation shows that NDSI and NDBI can be effectively used for study of LST in urban areas. However, NDBI can explain urban LST in the better way for the regions without water body.

LAND USE ANALYSIS ON LAND SURFACE TEMPERATURE IN URBAN AREAS USING A GEOGRAPHICALLY WEIGHTED REGRESSION AND LANDSAT 8 IMAGERY, A CASE STUDY: TEHRAN, IRAN

Abstract. Due to urbanization and changes in the urban thermal environment and because the land surface temperature (LST) in urban areas are a few degrees higher than in surrounding non-urbanized areas, identifying spatial factors affecting on LST in urban areas is very important. In this regard, due to the unique properties of spatial data, in this study, a geographically weighted regression (GWR) was used to identify effective spatial factors. The GWR is a suitable method for spatial regression issues, because it is compatible with two unique properties of spatial data, i.e. the spatial autocorrelation and spatial non-stationarity. In this study, the Landsat 8 satellite data on 18 August 2014 and Tehran land use data in 2006 was used for determining the land surface temperature and its effective factors. As a result, R2 value of 0.765983 was obtained by taking the Gaussian kernel. The results showed that the industrial,military, transportation, and roads areas have the highest surface temperature.

Spatial and temporal distribution of urban heat islands

The formation of an urban heat island (UHI) is one of the most common impacts of the urbanization process. To mitigate the effects of UHI, the planning of urban forests (e.g., creation of parks, forests and afforestation streets) has been the major tool applied in this context. Thus, the aim of this study is to evaluate the spatial and temporal distribution of heat islands in Vila Velha, ES, Brazil using the mono-window algorithm. The study followed these methodological steps: 1) mapping of urban green areas through a photointerpretation screen; 2) application of the mono-window algorithm to obtain the spatial and temporal patterns of land surface temperature (LST); 3) correlation between LST and the normalized difference vegetation index (NDVI) and normalized difference build-up index (NDBI); 4) application of ecological evaluation index. The results showed that the mean values of LST in urban areas were at least 2.34 to 7.19°C higher than undeveloped areas. Moreover, the positive correlation between LST and NDBI showed an amplifying effect of the developed areas for UHI, while areas with a predominance of vegetation attenuated the effect of UHI. Urban centers, clustered in some parts of the city, received the worst ecological assessment index. Finally, the adoption of measures to guide the urban forest planning within urban centers is necessary to mitigate the effect of heat islands and provide thermal comfort in urban areas.

A Comprehensive Statistical Study on Daytime Surface Urban Heat Island during Summer in Urban Areas, Case Study: Cairo and Its New Towns

Surface urban heat island (SUHI) is defined as the elevated land surface temperature (LST) in urban area in comparison with non-urban areas, and it can influence the energy consumption, comfort and health of urban residents. In this study, the existence of daytime SUHI, in Cairo and its new towns during the summer, is investigated using three different approaches; (1) utilization of pre-urbanization observations as LST references; (2) utilization of rural observations as LST references (urban–rural difference); and (3) utilization of the SIUHI (Surface Intra Urban Heat Island) approach. A time series of Landsat TM & ETM+ data (46 images) from 1984 to 2015 was employed in this study for daytime LST calculation during summer. Different statistical hypothesis tests were utilized for the evaluation of LST and SUHI in the case studies. The results demonstrated that there is no significant LST difference between the urban areas studied, and their corresponding built-up areas. In addition, daytime LST in new towns during the summer is 2 K warmer than in Cairo. Utilization of a pre-urbanization observations approach, alongside an evaluation of the long-term trend, demonstrated that there is no daytime SUHI during the summer in the study areas, and construction activities in the study areas do not result in cooling or warming effects. Utilization of the rural observations approach showed that LST is lower in Cairo than its surrounding areas. This demonstrates why the selection of suitable rural references in SUHI studies is an important and complicated task, and how this approach may lead to misinterpretation in desert city areas with significant landscape and surface difference with their most surrounding areas (e.g., Cairo). Results showed that, although SIUHI technique can be representative for the changes of variance of LST in urban areas, it is not able to identify the changes of mean LST in urban areas.

The Retrieved Urban LST in Beijing Based on TM, HJ-1B and MODIS

This paper comparatively analyzed the retrieved land surface temperature (LST) with Landsat Thematic Mapper (TM) sensor and HuanJing (HJ)-1B satellite sensor images using a case study in Beijing, China. The Jmnez-Muoz & Sobrino’s (JMS (2) the retrieved LST of TM, HJ-1B and MODIS was slightly different in the same area, while the distribution and the variation trend of the retrieving LST were consistent; (3) the urban heat island effect of Beijing was particularly obvious, and the vegetation showed a cooling effect. Furthermore, the surface multiplicity type is the main factor influencing the distribution of LST in urban areas. The empirical formulas on the basis of the JM&S single-channel algorithm may need to refit in retrieving LST of HJ-1B.

Examining the impact of urban biophysical composition and neighboring environment on surface urban heat island effect

Due to atmospheric and surface modifications associated with urbanization, surface urban heat island (SUHI) effects have been considered essential in examining urban ecological environments. With remote sensing technologies, numerous land cover type related variables, including spectral indices and land cover fractions, have been applied to estimate land surface temperature (LST), thereby further examining SUHI. This study begins with the reexamination of the commonly used indicators of LST using Landsat Enhanced Thematic Mapper Plus (ETM+) and Landsat Thematic Mapper (TM) images which cover four counties of Wisconsin, United States. Origin of the large variation of LST found in urban areas is then investigated by discriminating soil and impervious surfaces. Except land cover types, neighboring environment is another key factor which may affect LST in urban areas. Thus, a neighboring effect considered method is proposed at the end of the study to better understand the relationship between impervious surfaces fraction (%ISA) and LST by taking the influence of neighboring environment into account. Results indicate that spectral indices have better performance in predicting LST than land cover fractions do within the study area. However, the result remains arguable due to the complexity and uncertainty of spectral mixture analysis. Impervious surfaces are found responsible for the large variation of LST in urban areas, which indicates that impervious surfaces should not be simply considered as a single land cover type has stable negative correlation with LST. Moreover, a better relationship is found between %ISA and LST when neighboring effect is considered, when compared to the traditional method which ignores the neighboring effect.

Evaluation of the intensity of the daytime surface urban heat island: how can remote sensing help?

This study aimed at using remote sensing methodology to assess the daytime surface urban heat island (SUHI) in Lagos metropolis. Several studies have examined the SUHI, using point data from meteorological stations. However, it has been shown in the literature recently that it is practically impossible to accurately value the intensity of SUHI from ground meteorological measurement due to heterogeneity and complexity of surface temperature over land. Therefore a time series of Landsat data, from 1984 to 2012, were used in the present study to assess spatial and temporal variability in the contribution of source and sink landscape to daytime SUHI in Lagos. This study uses remote sensing methods because Lagos has a strong heterogeneity of land surface characteristics; with several drainage, vegetation, built-up and soil between the coast and mainland, thus the land surface temperature (LST) changes rapidly in space and time. The results from this study show differences in the contribution of source and sink landscape to SUHI. The main findings from the results show that source landscape contributes positively to the intensity of SUHI in Lagos metropolis. The results show a general increase in mean LST during the periods of study from 1984 to 2012. The north-west (NW) zone of Lagos has highest LST compared to other zones. In 1984, the mean LST of NW zone was 300.53 K, but increased to 301.85 K in 2000 and 302.85 K in 2012. Although contributions from the landscapes differ by zones and time, much more intensified LST was noted in the NW zone of the city. The study find out that change in landcover has been the most important driver of intensified SUHI in Lagos metropolis. NW zone recorded the highest increased in built-up area throughout the years: 320.32 km2 in 1984, 535.28 km2 in 2000 and 630.70 km2 in 2012. This study demonstrates therefore that it is possible to assess spatial distribution and long-term temporal evolution of the LST in urban area, using remote sensing data. Also the results shows that remote sensing methods offer possibility for measuring LST over complete spatially averaged rather than point values. The results from this study further our understanding that not only the SUHI is frequent to cities in developed countries, but the effects are also obvious in several urban settlements in tropical countries.