Urban Thermal Patterns Research Articles

Green space-building integration for Urban Heat Island mitigation: Insights from Beijing's fifth ring road district

In this research, we delve into the complex arrangement of urban landscapes, where green spaces and buildings are not merely co-existing but are interwoven into a cohesive fabric that shapes the thermal environment. Our approach transcends the conventional methods of analysis, which typically isolate the roles of greenery or built environments. Instead, we adopt a synergistic perspective that recognizes the collective influence of these landscape constituents on the urban thermal pattern. Key insights are: (1) A linear decrease in average land surface temperature with increasing green space coverage is observed. However, substantial temperature variations (up to 8 °C) within the same coverage interval highlight the significant impact of built-up pattern on thermal conditions; (2) High Building Height and Floor Area Ratio, and low Building Coverage Ratio and Sky View Factor, are linked to cooler temperatures in areas with up to 50 % green space; (3) The study suggests that low-temperature areas can inform the adjustment of built-up patterns in high-temperature areas, offering a strategy for thermal environment optimization within specific green space coverage intervals. This research contributes insights into the integrated planning of green spaces and buildings, with implications for urban development and renewal initiatives aiming to enhance the urban thermal environment.

Ground Urban Heat Island: Strengthening the Connection Between Spaceborne Thermal Observations and Urban Heat Risk Management.

As urbanization progresses under a changing climate, urban populations face increasing threats from chronically higher heat exposures and more frequent extreme heat events. Understanding the complex urban thermal exposure patterns becomes crucial for effective heat risk management. The spatial advantage of satellite thermal observations positions surface urban heat islands (SUHI) as a primary measure for such applications at the city scale. However, satellite-inherent biases pose considerable uncertainties. To improve the representation of human-relevant heat exposure, this study proposes a simple but effective satellite-based measure- ground urban heat island (GUHI), focusing solely on radiant temperatures from urban ground elements. Leveraging ECOSTRESS land surface temperature product and radiation-based statistical downscaling, diurnally representative GUHIs were evaluated over NYC. The findings reveal that overall GUHI is consistently warmer than SUHI diurnally. However, GUHI exhibits complex spatial contrasts with SUHI, primarily influenced by vegetation coverage. Various indicators associated with urban structures and materials were examined, showing important but dissimilar roles in shaping the spatial dynamics of GUHI and SUHI. This study highlights the value of satellite thermal observations compared to air temperature while addressing uncertainties in widely adopted practices of using them. By improving the depiction of human-related urban heat patterns from Earth observations, this research offers valuable insight and more reliable measures to address the urgent requirements for urban heat risk management globally.

Dynamics of Heat Island Intensity in a Rapidly Urbanizing Area and the Cooling Effect of Ecological Land: A Case Study in Suzhou, Yangtze River Delta

Ecological land could play an important function in climate regulation to mitigate urban heat islands (UHIs) and enhance the quality of the living environment. In this work, we chose Suzhou as our case study for urban agglomeration in the Yangtze River Delta (YRD), eastern China. In this city, we analyzed the dynamics of heat island intensity from 2000 to 2021 by retrieving land surface temperature (LST). Subsequently, we examined the relationship between the urban thermal environment pattern and land use change, and finally, we explored the cooling effect provided by ecological land. The results indicated that, in 2000, the city’s UHI effect primarily centered around the central urban region as a singular patch; however, since 2014, the patch UHI effect in the central urban region has been mitigated, and the original small hotspots have converged into a large, contiguous expanse spreading outward. As the shift has occurred from low- to high-temperature zones, the proportion of conversion between ecological land has been decreasing, while the opposite trend has been seen for the proportions of ecological land transferred out and for unchanged artificial surfaces. The normalized difference built-up index was found to be the main contributor to the UHI effect, followed by the normalized difference vegetation index. These findings provide novel insights into the regulation of ecosystem services during urban expansion and offer a reference for improving the function of the cooling effect through urban renewal activities and the optimization of spatial planning.

Reverse Thinking: The Logical System Research Method of Urban Thermal Safety Pattern Construction, Evaluation, and Optimization

The acceleration of urbanization has significantly impacted the changing regional thermal environment, leading to a series of ecological and environment-related problems. A scientific evaluation of the urban thermal security pattern (TSPurban) strongly benefits the planning and layout of sustainable development and the construction of comfortable human settlements. To analyze the characteristics of the TSPurban under cross-regional differences and provide targeted solutions to mitigate the urban heat island effect in later stages, the logical system research framework of the TSPurban based on the “construction–evaluation–optimization” model was explored using reverse thinking. This study selected the Wuhan metropolitan area in China as the research object. First, a morphological spatial pattern analysis (MSPA) model was used to extract the top 30 core heat island patches, and Conefor 2.6 software was used for connection analysis to evaluate their importance. Second, based on the characteristics of various land cover types, the friction (cost) map of surface urban heat island (SUHI) diffusion was simulated. The spatial attributes of the heat island resistance surface were examined using a standard deviation ellipse and hot spot analysis. Finally, this paper used circuit theory to find 56 low-cost heat island links (corridors) and circuit scape software to find widely distributed vital nodes. The optimization of the TSPurban network was then investigated using a reverse thinking process. Heat island patches, corridors, and vital nodes are among the crucial components of the TSPurban. By obstructing corridor links and disturbing important nodes, it is possible to appropriately and proficiently reduce the TSPurban network’s connection efficiency and stability, which will have a positive influence on regional climate mitigation and the heat island effect.

Pedestrian thermal perception: studies around two high-rise buildings in the Mediterranean climate

ABSTRACT High-rise buildings affect the microclimate around them at ground level and, as a result, also affect the pedestrian activities that take place there. The implications of this statement are especially amplified by the increasing numbers of high-rise buildings within the urban environment. This paper focuses on the urban microclimate in the vicinity of two high-rise buildings, with a focus on urban thermal comfort and wind patterns at pedestrian level relative to building height. The studies take place in the Mediterranean climate of Tel Aviv. The methodology includes collecting and combining various data, e.g. microclimatic monitoring, spot measurements, meteorological stations data, and surveys performed by administering questionnaires. Results showed that enhanced wind velocity is perceived as positive in summer, but unpleasant in winter, for the specific climatic conditions. The case study emphasizes the importance of microclimatic studies as part of high-rise building design, and the inclusion of design guidelines at the pedestrian level of high-rise buildings. In this process, the evaluation of outdoor human comfort perception through field surveys is highlighted.

An Application of the LCZ Approach in Surface Urban Heat Island Mapping in Sofia, Bulgaria

This article presents the results of the thermal survey of the capital of Bulgaria (Sofia) carried out in August 2019, with the application of an unmanned aerial system (UAS). The study is based on the concept of local climate zones (LCZs), taking into account the influence of the features of land use/land cover and urban morphology on the urban climate. The basic spatial units used in the study are presented in the form of a regular grid consisting of 3299 cells with sides of 250 × 250 m. A total of 13 types of LCZs were identified, of which LCZs 6, 5, 8, 4, D, and A form the largest share. In the thermal imaging of the surface, a stratified sampling scheme was applied, which allowed us to select 74 cells, which are interpreted as representative of all cells belonging to the corresponding LCZ in the urban space. The performed statistical analysis of the thermal data allowed us to identify both the most thermally loaded zones (LCZs 9, 4, and 5) and the cells forming Urban Cool Islands (mainly in LCZs D and C). The average surface temperature in Sofia during the study period (in the time interval between 8:00 p.m. and 10:00 p.m.) was estimated at 20.9 °C, and between the different zones it varied in the range 17.2–25.1 °C. The highest maximum values of LST (27.9–30.6 °C) were registered in LCZ 4 and LCZ 5. The relation between the spatial structure of the urban thermal patterns and urban surface characteristics was also analyzed. Regression analysis confirmed the hypothesis that as the proportion of green areas increases, surface temperatures decrease, and, vice versa, as the proportion of built-up and impermeable areas increases, surface temperatures increase. A heat load map (via applying a z-transformation to standardize the temperature values), a map of the average surface temperature, and a map of the average intensity of the heat island on the surface were generated in the GIS environment. The results of the study adequately reflect the complex spatial model of the studied phenomenon, which gives grounds to conclude that the research approach used is applicable to similar studies in other cities.

Assessment of urban heat islands for land use based on urban planning: a case study in the main urban area of Xuzhou City, China

The urban heat island (UHI) effect is an important issue in urban planning. In this study, the framework of the land surface temperature (LST) and UHIindex for urban development land (UDL) based on urban planning in China was proposed to examine the spatiotemporal evolution of the LST and the UHIindex for the whole city as well as the LST and the UHIindex of UDL at four node stages (1995, 2003, 2014 and 2019), reflecting the process of urbanization. The results demonstrate that the UHIindex showed an overall trend of first decreasing and then increasing in the process of urbanization over 2 decades. The UHI is characterized by the evolution of spatial distribution from a local point-like aggregation (1995) to a linear expansion along the urban construction land (2003), then to patchy areas tending towards overall homogeneous coverage gradually expanding (2019). The LST increased yearly, but the UHIindex first decreased and then increased, while the average UHIindex and the LST of UDL in the overall relationship maintained good consistency each year. The industrial and logistics concentration area with railway marshalling yard as the core area is the most important patch to maintain the urban thermal pattern. The results provide a quantitative basis for the thermal environmental planning of UDL and a reference for research on UHI under different land planning and management systems in other countries and regions of the world.

Coupling Analysis of the Thermal Landscape and Environmental Carrying Capacity of Urban Expansion in Beijing (China) over the Past 35 Years

In this study, we aim to carry out a coupling analysis of the thermal landscape and environmental carrying capacity of urban expansion in Beijing over the past 35 years to provide scientific grounding for city planning. The paper proposes a conceptual framework and develops an integrated quantitative approach to the coupling analysis between urban expansion, the urban ecological environment, and the urban landscape, including the Urban Eco-Environment Carrying Capacity Index (ECI), Landscape Spatial Structure Index, Landscape Thermal Index (LTI), and Transitional Landscape Index (TLI, Markov Chain Model). Urban expansion has been essentially dominated by policy adjustments and has been further influenced by socioeconomic development, which has contributed to four outbreaks of urban sprawl in Beijing. Urban expansion is an essential factor affecting ecological environment change. The Olympic Games in 2008 was the turning point for the urban landscape. The rate of urban expansion and improvement of the ecological landscape all changed significantly around the year 2008. The urban thermal distribution pattern coincided well with the featured landscape patches, representing an obvious reflection of the difference between urban green spaces and construction, while high-temperature areas were abundant in the urban center. Urban expansion has a positive effect on the ecological environment and landscape pattern when it is fully matured and well planned. It is expected that, by 2025, the ecological environment of Beijing will be significantly improved, and the proportion of high-temperature areas will decrease.

Local Weather Types by Thermal Periods: Deepening the Knowledge about Lisbon’s Urban Climate

Urbanized hot spots incorporate a great diversity of microclimates dependent, among other factors, on local meteorological conditions. Until today, detailed analysis of the combination of climatic variables at local scale are very scarce in urban areas. Thus, there is an urgent need to produce a Local Weather Type (LWT) classification that allows to exhaustively distinguish different urban thermal patterns. In this study, hourly data from air temperature, wind speed and direction, accumulated precipitation, cloud cover and specific humidity (2009–2018) were integrated in a cluster analysis (K-means) in order to produce a LWT classification for Lisbon’s urban area. This dataset was divided by daytime and nighttime and thermal periods, which were generated considering the annual cycle of air temperatures. Therefore, eight LWT sets were generated. Results show that N and NW LWT are quite frequent throughout the year, with a moderate speed (daily average of 4–6 m/s). In contrast, the frequency of rainy LWT is considerably lower, especially in summer (below 10%). Moreover, during this season the moisture content of the air masses is higher, particularly at night. This methodology will allow deepening the knowledge about the multiple Urban Heat Island (UHI) patterns in Lisbon.

Where and how to cool? An idealized urban thermal security pattern model

Urban green infrastructure (UGI) has been recognized as a promising approach to mitigating urban heat island (UHI); however, most of the previous studies are case-based and explore the effects of the existent landscape and its spatial configuration on UHI mitigation rather than modeling an optimized spatial pattern. We aimed to transcend the existing research logic (from case studies to obtain the patterns of the cooling effect of UGI, then propose implications for UHI mitigation) and established a hypothetical idealized urban thermal security pattern model (TSPurban). Based on two basic concepts deduced from the physical property of UGI—(threshold) size and cooling distance, as well as the simplifying assumptions we defined. Then, three proposed conceptual UGI types (ecological, efficient, and elementary—3E) and subtypes were used to frame the model. We deduced that the idealized TSPurban model conforms to a hierarchical hexagonal structure in theory and it can be calculated and applied, although it generally cannot be seen in the real world. The idealized TSPurban model can help us better-understanding UGI cooling effects when considering climate adaption planning and decision-making; it also serves as a novel pathway to study the cooling effects of UGI and mitigate the UHI effect.

Analyzing the Influencing Factors of Urban Thermal Field Intensity Using Big-Data-Based GIS

The effects of human activities and land cover changes on urban thermal field patterns are closely related to the land surface temperature (LST) and air temperature. At present, the number of studies on the quantitative relationship between these two indexes and the effect of the observational scale on their influence is insufficient. In this study, spatial analysis methods such as geographic modeling were combined with remote sensing images, meteorological data, and points of insert and used to investigate the composition and scale of the factors influencing the temperature field in Beijing. The results showed that there are differences in the positive and negative correlations between LST and air temperature and various influencing factors. At a spatial resolution of 90 m, LST had a strong linear relationship with the average air temperature. Indicators reflecting elements of human activity, such as buildings, roads, and entertainment, were easily measured by meteorological stations at a small scale, and the natural green space ratio could also be easily captured by satellite thermal sensors at small scales. These results have substantial implications for environmental impact assessments in areas experiencing an increasing urban heat island effect due to rapid urbanization.

Analysis of remotely-sensed ecological indexes' influence on urban thermal environment dynamic using an integrated ecological index: a case study of Xi’an, China

ABSTRACTThe spatio-temporal pattern of surface ecological status affects urban thermal environment distribution significantly. Urban thermal pattern, however, is a complicated physical phenomenon involving a series of terrestrial environmental parameters. Thus, it is insufficient to employ only one ecological parameter for depicting the variation of land surface temperature (LST). This paper begins with the analysis of four ecological parameters' influence on LST using regression analysis, based on 24 Landsat images which cover Xi’an of China from 1992 to 2014. These four parameters include greenness degree (i.e. the soil adjusted vegetation index, SAVI), soil moisture degree (i.e. the normalized difference moisture index, NDMI), dryness degree (i.e. the normalized difference soil index, NDSI) and resident aggregation degree (i.e. the normalized difference build-up index, NDBI). Besides, contribution intensity index was introduced to investigate the contribution effect of four ecological parameters on LST, and a new ecological index, integrated ecological index (IEI), was founded using the principal component analysis technique to integratedly represent its spatial and mathematical correlations with LST. Results indicate that four ecological parameters all possessed pronounced performance in impacting LST pattern in all dates: SAVI and NDMI were found to be correlated negatively with LST, whereas NDBI and NDSI correlated positively with LST. Additionally, SAVI had a profound impact on LST distribution compared with the other three parameters, and there was the biggest heating contribution in the lowest SAVI category. Further finding suggests that IEI as a new ecological index can be used to integratedly estimate the spatio-temporal change of LST, manifesting a negative correlation with LST. Our study thinks that the comprehensive characterization of surface ecological status is conducive to benefit us to better understand the spatio-temporal mechanism of thermal environment and ecosystem and to help urban decision-makers to execute effective conservation policies for the ecosystem.

Assessing the Capability of a Downscaled Urban Land Surface Temperature Time Series to Reproduce the Spatiotemporal Features of the Original Data

The downscaling of frequently-acquired geostationary Land Surface Temperature (LST) data can compensate the lack of high spatiotemporal LST data for urban climate studies. In order to be usable, the generated datasets must accurately reproduce the spatiotemporal features of the coarse-scale LST time series with greater spatial detail. This work concerns this issue and exploits the high temporal resolution of the data to address it. Specifically, it assesses the accuracy, correct pattern formation and the spatiotemporal inter-relationships of an urban three-month-long downscaled geostationary LST time series. The results suggest that the downscaling process operated in a consistent manner and preserved the radiometry of the original data. The exploitation of the data inter-relationships for evaluation purposes revealed that the downscaled time series reproduced the smooth diurnal cycle, but the autocorrelation of the downscaled data was higher than the original coarse-scale data. Overall, the evaluation process showed that the generation of high spatiotemporal LST data for urban areas is very challenging, and to deem it successful, it is mandatory to assess the temporal evolution of the urban thermal patterns. The results suggest that the proposed tests can facilitate the evaluation process.

Study of the geometry effect on land surface temperature retrieval in urban environment

This study presents a Single Channel Method using Urban Exitance Model (UEM-SCM) to retrieve land surface temperature (LST) from satellite data in an urbanized city, and evaluates the geometry effect on land surface temperature retrieval using single channel method and split-window algorithm. The UEM-SCM incorporates the effect of urban geometry and considers both reflection caused by the target pixel and its neighboring pixels. In order to evaluate the geometry effect, the retrieved LSTs with and without geometry effect were studied. Results show that the LSTs without geometry effect are generally higher than the LSTs with geometry effect. The temperature difference occurs because the material emissivity is always lower than the effective emissivity caused by multiple scattering and reflection in urban areas (cavity effect). The LST without geometry effect also cannot fully capture the variability and complexity of urban thermal patterns. The temperature difference between with and without the geometry effect can reach 2K in built-up areas. A comparison was also conducted between LST retrieved by split-window algorithm with and without geometry effect. Results show that the LST retrieved by split-window algorithm without geometry effect has generally higher values than the one with the geometry effect, e.g. 1.1K on average and 1.5–2K in built-up areas. The geometry effect will be removed and mis-deemed as atmospheric effect when the split-window algorithm without geometry effect is applied in urban areas. The split-window algorithm with the geometry effect can be used to distinguish between geometry and atmospheric effect in further study.

Characterizing bi-temporal patterns of land surface temperature using landscape metrics based on sub-pixel classifications from Landsat TM/ETM+

Landscape patterns in a region have different sizes, shapes and spatial arrangements, which contribute to the spatial heterogeneity of the landscape and are linked to the distinct behavior of thermal environments. There is a lack of research generating landscape metrics from discretized percent impervious surface area data (ISA), which can be used as an indicator of urban spatial structure and level of development, and quantitatively characterizing the spatial patterns of landscapes and land surface temperatures (LST). In this study, linear spectral mixture analysis (LSMA) is used to derive sub-pixel ISA. Continuous fractional cover thresholds are used to discretize percent ISA into different categories related to urban land cover patterns. Landscape metrics are calculated based on different ISA categories and used to quantify urban landscape patterns and LST configurations. The characteristics of LST and percent ISA are quantified by landscape metrics such as indices of patch density, aggregation, connectedness, shape and shape complexity. The urban thermal intensity is also analyzed based on percent ISA. The results indicate that landscape metrics are sensitive to the variation of pixel values of fractional ISA, and the integration of LST, LSMA. Landscape metrics provide a quantitative method for describing the spatial distribution and seasonal variation in urban thermal patterns in response to associated urban land cover patterns.

Spatial analysis of high-resolution urban thermal patterns in Vojvodina, Serbia

Main objective of this study was to establish a relationship between land cover and land surface temperature (LST) in urban and rural areas. The research was conducted using Landsat, WorldView-2 (WV-2) and Digital Mapping Camera. Normalised difference vegetation index and normalised difference built-up index were used for establishing the relation between built-up area, vegetation cover and LST for spatial resolution of 30 m. Impervious surface and vegetation area generated from Digital Mapping Camera from Intergraph and WV-2 were used to establish the relation between built-up area, vegetation cover and LST for spatial resolutions of 0.1, 0.5 and 30 m. Linear regression models were used to determine the relationship between LST and indicators. Main contribution of this research is to establish the use of combining remote sensing sensors with different spectral and spatial resolution for two typical settlements in Vojvodina. Correlation coefficients between LST and LST indicators ranged from 0.602 to 0.768.

An analysis of urban expansion and its associated thermal characteristics using Landsat imagery

There has been an increasing interest in mapping and monitoring urban land use/land cover using remote sensing techniques. However, there still exist quite a number of challenges in deriving urban extent and its expansion density from remote sensing data quantitatively. This study utilized Landsat TM/ETM+ remote sensing data to assess urban expansion and its thermal characteristics with a case study in the city of Changsha, China. We proposed a new approach for quantitatively determining built-up area, its expansion density and their respective relationship with land surface temperature (LST) patterns. An urban expansion metric was also developed using a moving window mechanism to identify urban built-up area and its expansion density based on selected threshold values. The study suggested that urban extent and its expansion density, as well as surface thermal characteristics and patterns could be identified through quantitatively derived remotely sensed indices and LST, which offer meaningful characteristics in quantifying urban expansion density and urban thermal pattern. Results from the case study demonstrated that: (1) the built-up area and urban expansion density have significantly increased in the city of Changsha from 1990 to 2001; and (2) the differences of urban expansion densities correspond to thermal effects, where a high percentage of imperviousness is usually associated with the area covered by high surface temperature.

The Effect of Urban Expansion on Urban Surface Temperature in Shenyang, China: an Analysis with Landsat Imagery

Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) images were used to assess the urban expansion dynamics and the corresponding thermal characteristics in Shenyang City, China. Unsupervised classification (ISODATA) and a hierarchy decision tree were applied to eight scenes of the Landsat images to derive the land use/land cover (LULC) around the Shenyang metropolitan region from 1986 to 2007. Landsat TM/ETM+ thermal infrared (TIR) images (band 6) were used to investigate the urban surface thermal patterns by retrieving land surface temperature (LST) using a mono-window algorithm. Results reveal that the built-up area has doubled from 1986 (20.2 %) to 2007 (42.3 %), most of which is converted from croplands around the urban fringe area. The built-up area has close association with the population increase (R 2 = 0.89), the gross domestic production (R 2 = 0.94), and fixed asset investments (R 2 = 0.95). These illustrate the contributions of socioeconomic factors to the rapid urban expansion in Shenyang. Three urban heat island (UHI) indices [i.e., heat effect contribution index (H i ), weighted heat unit index (D 1), and regional weighted heat unit index (D 2)] were used to characterize the urban thermal patterns for removing the phenological effects and to confirm the linkage between UHI and urban expansion. Results show that urban areas have an obvious daytime heating effect (heat source) that is strongly correlated with urban expansion, wherein a higher percentage of an impervious surface is usually associated with a higher surface temperature. Further analyses indicate that urban expansion is fairly correlated to H i ' (R 2 = 0.63) but strongly to D 2 (R 2 = 0.91). Additional research is needed to further quantify the inner urban area to gain a better understanding of UHI resulting from various heat fluxes and urban components.

Multiscale analysis of urban thermal characteristics: case study of Shijiazhuang, China

The urban heat island (UHI) effect caused by urbanization generally leads to adverse economic and environmental impacts. Thus, a detailed assessment of the thermal properties over individual land cover types at different spatial resolutions is required to better understand the establishment of UHI. Two scale levels are utilized to examine surface thermal characteristics in the case of Shijiazhuang, China. At the regional level, Landsat TM data are used to extract subpixel impervious surface area (ISA) and to inverse land surface temperature (LST) by means of multiple endmember spectral mixture analysis. Urban thermal characteristics are analyzed by relating the LST to normalized difference vegetation index (NDVI) and ISA. On the other hand, thermal airborne spectrographic imager data, with a high spatial resolution, are employed to describe the spatial distribution of urban thermal patterns at the local level. Results indicate that there is an approximate linear relationship among LST, NDVI, and ISA. In addition, the thermal characteristics over each land cover type are consistent at both levels, suggesting UHI is evident at Shijiazhuang and impervious surface is contributing most to this phenomenon. This confirms that different spatial scales are requested in UHI studies.

Landscape Classifications for Landscape Metrics-based Assessment of Urban Heat Island: A Comparative Study

In recent years, some studies have been carried out on the landscape analysis of urban thermal patterns. With the prevalence of thermal landscape, a key problem has come forth, which is how to classify thermal landscape into thermal patches. Current researches used different methods of thermal landscape classification such as standard deviation method (SD) and R method. To find out the differences, a comparative study was carried out in Xiamen using a 20-year winter time-serial Landsat images. After the retrieval of land surface temperature (LST), the thermal landscape was classified using the two methods separately. Then landscape metrics, 6 at class level and 14 at landscape level, were calculated and analyzed using Fragstats 3.3. We found that: (1) at the class level, all the metrics with SD method were evened and did not show an obvious trend along with the process of urbanization, while the R method could. (2) While at the landscape level, 6 of the 14 metrics remains the similar trends, 5 were different at local turn points of the curve, 3 of them differed completely in the shape of curves. (3) When examined with visual interpretation, SD method tended to exaggerate urban heat island effects than the R method.