Variation Of Urban Heat Island Research Articles

The effects of urban land cover dynamics on urban heat Island intensity and temporal trends

ABSTRACT Assessments of surface urban heat island (UHI) have focused on using remote sensing and land cover data to quantify UHI intensity and spatial distribution within a certain time period by including land cover information. In this study, we implemented a prototype approach to characterize the spatiotemporal variations of UHI using time series of Landsat land surface temperature products and annual land change information. We analyzed UHI distribution and change in Sioux Falls, South Dakota, in the north-central United States and found that the mean UHI intensity in the region was as large as 2.2°C during the period 1986–2017 with an increasing trend of 0.02°C per year within the area with a 5-km non-urban extent. The UHI intensity associated with high intensity urban land cover usually is stronger than with low intensity urban land cover. We evaluated the impact of different non-urban reference extents on UHI variation using different non-urban buffers. The result also suggests that the overall temporal trends of UHI intensity are almost the same when using a 5-km or 10-km non-urban buffer surrounding the urban core. The prototype approach provides a framework to consistently quantify UHI and monitor its change to a large geographic extent.

Urban Heat Island associated with Land Use/Land Cover and climate variations in Melbourne, Australia

Urbanization is known as one of the most prominent global problems that alter the atmosphere and land surface properties. The intensity of Urban Heat Island (UHI) associated with surface temperature and component attributes were assessed using the Community Land Model (CLM). The variations of UHI with Land Use/Land Cover (LULC) and climate variations were also investigated to provide a link among urbanization, surface energy balance interactions, and extreme hydroclimatic events which are drought (‘big dry’ and ‘angry summer’) and heavy rainfall (‘big wet’) in Melbourne, Australia. Generally, UHI demonstrated a uniform increasing trend with an ∼1.20 ± 0.20 °C increment, along with urbanization expansion of +14.93 % from 2001 to 2014. Furthermore, urban area showed positive contribution to UHI based on Land Contribution Index (LCI). High surface temperatures also resulted in higher sensible heat flux (Qh) and lower latent heat flux (Qle). During the multiple extreme climate events, the UHI biophysical drivers were majorly related to the convection reduction during ‘big dry’ (2001−2009), surface evaporative cooling during ‘big wet’ (2010−2011), and heat storage release during ‘angry summer’ (2012–2013). Overall, this analysis demonstrated correlation of UHI intensity and its component attributes with urban expansion, which was associated with LULC and climate variations in Melbourne.

Evaluation and application of a low-cost measurement network to study intra-urban temperature differences during summer 2018 in Bern, Switzerland

The understanding of intra-urban air temperature variations is crucial to assess strategies for cities' adaptation to impacts of present and future anthropogenic climate change. Depending on extensive measurement networks, high-resolution air temperature measurements in urban environments are challenging due to high instrumentation and maintenance costs. Here, we present a low-cost measurement device (LCD) consisting of a temperature logger and a custom-made, naturally ventilated radiation shield. Besides intercomparisons with automated weather stations (AWS) at three reference sites during record-dry summer 2018, we tested the potential of the devices using a network of 79 LCDs to assess the intra-urban variability of urban heat island (UHI) patterns in the city of Bern, Switzerland. We found positive mean measurement biases between LCDs and AWS of 0.61 to 0.93 K (RMSE: 0.78 to 1.17 K) during daytime, of which up to 82.8% of the variance could be explained statistically by solar irradiance (radiative heating) and wind speed (insufficient ventilation). During night, average measurement biases were markedly lower and eventually negative with −0.12 to 0.23 K (RMSE: 0.19 to 0.34 K). Our results highlight the importance of sensor intercomparisons being conducted at multiple locations with differing urban land-cover, structure, and metabolism given that biases varied considerably between the reference sites. Data retrieved by the city-wide measurement network showed that the LCD approach is well suited for the analysis of spatiotemporal UHI patterns during night and adds considerable value compared to the few existing AWS in detecting fine-scale air temperature variability. In conclusion, the current LCD measurement approach represents a valuable option for cost-effective analyses of urban air temperature variability across multiple scales, which may be of particular value for the development, appliance, and monitoring of adaptation strategies to climate change in cities with restricted financial resources.

基于多源遥感数据的城市热环境响应与归因分析——以深圳市为例

城市热环境是城市局部气候与环境的综合表现，它与土地利用格局密切相关，但是相应的机制研究还不充分。以深圳市为例，首先基于2018年LANDSAT 8遥感影像，采用支持向量机方法和线性光谱混合模型提取土地利用与覆盖度信息，分析了城市土地覆盖对地表温度以及热量收支状况的影响。基于2003-2018的MODIS地表温度数据，进一步研究了深圳市城市热岛现象的时空变化，从地表能量的角度分析城市热岛变化背后的形成机制。结果表明，深圳市地表温度从西北到东南逐渐降低，城市不透水面温度显著高于植被覆盖区域，城市热岛效应明显。不透水面和城市植被共同影响深圳市的地表温度与热量收支状况，不透水地表与感热具有较好的相关性，城市植被与潜热具有较好的相关性。长时间序列分析表明深圳的城市热岛现象在夏季较高而冬季较低，月均热岛强度为2.14℃；对于年际变化，深圳在2003-2018表现出显著的下降趋势。归因分析显示感热通量的影响在深圳起主导作用，这一模式在全年和季节上都较为明显。结果表明深圳市经过高速扩张阶段，目前发展方向是提高建成区的利用效率，该现象强调了热传输在加强城市热岛效应过程中对近地面湍流的干扰作用。本研究可以为缓解热岛效应与景观格局优化研究提供借鉴。;Urban thermal environment is a comprehensive expression of local climate and environment. While it is closely related to land use patterns, its underlying mechanisms are largely unexplored. Urban heat island affects not only regional climate, vegetation growth and air quality, but also human health. Therefore, the urban thermal environment has been regarded as a critical variable. It is important to characterize the spatial and temporal patterns of urban thermal environments and quantify the response of the associated influencing factors. This study uses the city of Shenzhen as an example where rapid urbanization has occurred. Based on LANDSAT 8 remote sensing images collected in 2018, we first extract land cover types and coverage information using support vector machine and multiple linear spectral analysis models. The accuracies of the estimated land covers are acceptable, which provides confidence for further analyses. The impacts of urban land cover on land surface temperature and heat energy component are then investigated. Using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature dataset obtained from 2003 to 2018, this study further explores the spatiotemporal variations of urban heat island in Shenzhen, and analyzes its potential formation mechanisms from the perspective of surface energy balance. Our results show that the land surface temperature gradually decreases from northwest to Southeast of Shenzhen. This is largely attributed to the spatial distribution of impervious surfaces and the urban vegetation. Specifically, the land surface temperature of impervious surfaces is substantially higher than that of vegetation-covered area, implying that the urban heat island effect is obvious in Shenzhen. Impervious surface and urban vegetation jointly affect the surface temperature and heat budget through, for example, latent heat flux and sensible heat flux. Impervious surface is more correlated with sensible heat flux while urban vegetation is more correlated with latent heat flux. Obvious differences occur in sensible heat and latent heat among land cover types, which provides implications for the formation and elimination of urban heat island. Time series analysis regarding the period of 2003-2018 illustrates that the urban heat island effect in Shenzhen is stronger during summer season whereas relatively weaker during winter season. The urban heat island effect is remarkably exhibited using the time series dataset, with an average monthly intensity of 2.14 ℃. Regarding the internal variation, the downward trend in urban heat island intensity is shown from 2003 to 2018. This negative trend is significantly present during all seasons except spring, with a change rate of approximately 0.1 ℃/annual. Attribution analysis based on MODIS dataset and FLDAS flux dataset reveals that sensible heat flux plays a more important role in the urban heat island of Shenzhen in comparison to latent heat flux. This is consistent in the both annual and seasonal scales, and the relative contribution of latent heat flux and sensible heat flux is 42% and 58%, respectively. Results indicate that after a rapid urban expansion period in Shenzhen, the current development focus is to improve the utilization efficiency of built-up areas. This emphasizes the interference of heat transfer with the near-surface turbulence that strengthens the urban heat island effect. Our study provides reference values for mitigating the urban heat island effect and optimizing landscape patterns.

Spatiotemporal variability of urban heat island: Influence of urbanization on seasonal pattern of land surface temperature in the Metropolitan Region of Belém, Brazil

Abstract Cities experience the extensive urban heat island effect (UHI), which continue to pose challenges for humanity's increasingly urban population, where tropical cities have experienced a continued and rapid urbanization process in the past few decades. We present the evolution of surface UHI and its controlling factors in the Metropolitan Region of Belém, over the last 16 years (2003-2018), which has experienced unique consolidated economic growth and urban transformation under wet equatorial climate. We incorporate MODIS and Landsat satellite data and evaluate statistical techniques for estimates the variation in the land surface temperature (LST) during two seasons: wet season and dry season. Our result revealed that the regions of fast urbanization resulted in a decrease of normalized difference vegetation index and increase of LST. In addition, annual maps showed the spatial pattern of surface UHI intensities were produced based on daytime and nighttime temperature, and the analysis result indicated that the spatial distribution of high heat capacity was closely related with the densely built-up areas. These findings are helpful for understanding the urbanization process as well as urban ecology, which both have significant implications for urban planning and minimize the potential environmental impacts of urbanization in Metropolitan Region of Belém.

Monitoring Spatiotemporal Evolution of Urban Heat Island Effect and Its Dynamic Response to Land Use/Land Cover Transition in 1987–2016 in Wuhan, China

Monitoring the relationship between the urban heat island (UHI) effect and land use/land cover (LULC) is of great significance in land use planning to adapt to climate change. However, the dynamic response of the UHI effect to LULC change over space and time has not been deeply studied. In this study, a transfer matrix method was carried out to monitor the class-to-class transitions between different LULC types, as well as those between different NLST (normalized land surface temperature) levels over space and time. The spatiotemporal correlation and dynamic coupling between UHI variation and LULC change from 1987 to 2016 were simulated based on multi-temporal remote sensing data in Wuhan, China. The results showed that high temperature (level V) and sub-high temperature (level IV) were mainly concentrated in construction land, while the majority of low temperature (level I) was distributed in water bodies. During the study period, the most notable changes were the rapid increase in construction land, as well as the continuous shrinkage of farmland and water bodies. The inward transfer of construction land was mainly from farmland and water bodies, with the transferred area of 218.3 km2 (69.2%) and 78.9 km2 (25.0%). These transitions were mainly responsible for the thermal deterioration in the study area. The transition of farmland to construction land contributed the most (66.3% and 81.6%) to thermal deterioration in the original medium temperature area (level III). The transition of water bodies to construction land was the main driving force in rapidly upgrading NLST level I into level IV (55.8%) and level V (58.6%). These findings provided detailed information for decision support in optimizing land use structure to fight against the thermal deterioration caused by urbanization.

Assessing the urban heat island variations and its influencing mechanism in metropolitan areas of Pearl River Delta, South China

Urban heat island (UHI) is human-induced temperature reflected on the earth's surface. Although spatiotemporal variations and driving factors of UHI have been explored extensively, the underlying mechanisms of UHI remain uncertain. Using multi-source datasets of air temperature (Ta) and surface heat flux variables, we quantify the dynamics of urban-rural Ta difference (ΔT) at monthly, seasonal and annual time scales for nine cities in Pearl River Delta (PRD) during 2000–2017. The impact of sensible heat flux and latent heat flux on UHI variations are further investigated from the perspective of the surface energy balance theory. Results demonstrate that all PRD cities have apparent UHI, as evidenced by maximum ΔT in summer and minimum ΔT in winter. A notably declining trend of ΔT is observed in Shenzhen and Guangzhou, which have the largest population and build-up areas, during 2000–2017. However, trends of ΔT are not statistically significant in other cities. Attribution analysis reveals that sensible heat and latent heat significantly affect the UHI at different stages of urbanization. For the nine cities in PRD, the primary factor responsible for heat convection is the varied urban compositions formed by different developing trajectories. Specifically, sensible heat flux contributes substantially to ΔT changes in Dongguan and Shenzhen, which experienced rapid urban expansion and with complex urban structures. By contrast, latent heat flux dominates the ΔT changes in Zhongshan and Huizhou where relatively simple urban structures are preserved. Our study highlights the importance of urban patterns, sensible heat, and latent heat to understand the UHI dynamics and variations in PRD, which provides guidance for developing appropriate mitigation strategies in city planning to alleviate UHI-related burdens.

The Migration of the Warming Center and Urban Heat Island Effect in Shanghai During Urbanization

Urbanization leads to many negative climatic and environmental issues, such as urban heat island, environment pollution, and species extinctions. Based on the monthly air temperature data from 11 national meteorological stations in Shanghai, the construction land areas from 1980 to 2015 which derived from the satellite remote sensing TM image and population density of each district in Shanghai, the migration of urban warming center and its relationships with the expansion of construction land area and the increase of population density were analyzed in Shanghai. The temporal and spatial variations of urban heat island(UHI) were investigated as well. The results showed that the location of warming centers changed with the acceleration of urbanization since the 1980s. It migrated from Baoshan to Pudong and then moved towards Songjiang and Nanhui. The shifting of urban warming centers were highly related to the growth of construction land area and population density. The warming centers in all decades were located in the areas with the fastest development of construction land and population density. The air temperature in Shanghai increased significantly during 1961-2017 due to global warming and regional urbanization, and the intensity of UHI displayed three stages successively: slow growth, rapid growth, and obvious deceleration. The intensity of UHI in winter was stronger than that in summer, and it was greater in the nighttime than that during the daytime. With the accelerating process of urbanization, the UHI in Shanghai gradually expanded from the urban areas to the surrounding suburbs and even began to expand radially towards the southwest of rural areas in the 21st century. The results can provide references for urban heatwave prediction and mitigation of the UHI effect in Shanghai.

The Trend Inconsistency between Land Surface Temperature and Near Surface Air Temperature in Assessing Urban Heat Island Effects

The credible urban heat island (UHI) trend is crucial for assessing the effects of urbanization on climate. Land surface temperature (LST) and near surface air temperature (SAT) have been extensively used to obtain UHI intensities. However, the consistency of UHI trend between LST and SAT has rarely been discussed. This paper quantified the temporal stability and trend consistency between Moderate Resolution Imaging Spectroradiometer (MODIS) LST and in situ SAT. Linear regressions, temporal trends and coefficients of variations (CV) were analyzed based on the yearly mean, maximum and minimum temperatures. The findings in this study were: (1) Good statistical consistency (R2 = 0.794) and the same trends were found only in mean temperature between LST-UHI and SAT-UHI. There are 54% of cities that showed opposite temporal trends between LST-UHI and SAT-UHI for minimum temperature while the percentage was 38% for maximum temperature. (2) The high discrepancies in temporal trends were observed for all cities, which indicated the inadequacy of LST for obtaining reliable UHI trends especially when using the maximum and minimum temperatures. (3) The larger uncertainties of LST-UHI were probably due to high inter-annual fluctuations of LST. The topography was the predominant factor that affected the UHI variations for both LST and SAT. Therefore, we suggested that SAT should be combined with LST to ensure the dependable temporal series of UHI. This paper provided references for understanding the UHI effects on various surfaces.

Understanding land surface temperature on rift areas to examine the spatial variation of urban heat island: the case of Hawassa, southern Ethiopia

Examining the spatial variation of surface temperature is an important criterion to create livable urban environment. To examine the cause of heat island variation in the study area located within the rift zones, an algorithm that was prepared for Landsat 8 band 10 were used by taking the Normalized Difference Vegetation Index threshold method for the estimation of ground emissivity by integrating with the result obtained from MODIS night time data. The LU/LC (land use land cover) maps of the area were prepared with better accuracy using on screen classification technique. The derived LST showed that the surface temperature of the city ranges from 20.6 to 41.30 °C and the minimum temperature of the area was observed within the lake and the surrounding areas such as wetland. The maximum temperature was registered on the scattered hills and excavation areas and some parts of bare lands including industrial park of the city. The spatial variation of LST (Land Surface Temperature) in the city is the result of three major factors namely: (1) volcanic products of the geological setting, (2) the nature of the rock (high reflectance) and (3) the LU/LC type. Increasing of evergreen tree cover and rehabilitation of existing mining areas are among the recommended strategy to mitigate the UHI (urban heat island) effects in the city. For future studies in areas that are susceptible to natural heat sources, the satellite data should have high spatial resolution and derived from multiple sensors and satellites that can provide better tools to understand the UHI effect considering the geological setting of the area.

Building Information Modelling (BIM) For Estimation of Heat Flux from Streetscape Material

Streetscape elements are the major contributors to the urban heat island (UHI) phenomenon in the built urban environment. The hot surface air in this phenomenon is concentrated in urban regions and will gradually decrease in surrounding temperatures in suburban or rural regions. The effect of UHI can be seen through the increasing of land surface temperature and influencing the urban ecological systems, climates, and environments. A study has been conducted to identify the impact of urban heat island surrounding the PMU’s streetscape furniture. In this study, the UHI variation has been investigated using BEM and city scale model. The UHI obtained were analyzed and modeled using satellite imagery and GIS. The 3D models of the PMU’s façade were also been developed by using a laser scanner and thermal camera. Based on the analyses, the existing condition of seven selected PMUs are very high for Bangsar, High for Abu Bakar Baginda, Jalan Meru and Batu 4 Kuantan, Medium-high for Kuantan North and Taman Jaya and medium for Setia Alam. Land Surface Temperature (LST) in PMU facade is ranging between 23 ºC to55ºC. Building information modeling (BIM) analysis shows that the existing material used in most PMU facades with solar reflectance was uniformly low to medium-low. Building information modelling (BIM) result shows the building will give greater heat flux (+42% to +53% at 3 pm) because of the solar reflectance (%) from the facades of surrounding streetscape material used. By obtaining the information regarding the elements and factors that contribute to the UHI phenomenon within the study area, the reduction of heat absorption can be performed.

The Impact of the Land Cover Dynamics on Surface Urban Heat Island Variations in Semi-Arid Cities: A Case Study in Ahmedabad City, India, Using Multi-Sensor/Source Data

This study examines the behavior of land surface temperature (LST) and surface urban heat island (SUHI) from MODIS data over Ahmedabad city, Gujarat state (India), from 2003 to 2018. Summer and winter LST patterns were analyzed, both daytime and nighttime. Ahmedabad, one of the fastest growing metropolitan cities in India, is characterized by a semi-arid climate. The investigation focuses on the SUHI variations due to warming or cooling trends of both urban and rural areas, providing quantitative interpretations by means of multi-sensor/source data. Land cover maps, normalized differential vegetation index, surface albedo, evapotranspiration, urban population, and groundwater level were analyzed across the years to assess their impact on SUHI variations. Moreover, a field campaign was carried out in summer 2018 to measure LST in several rural and urban sites. During summer daytime, the rural zone exhibits a higher average LST than the urban area, resulting in a mean negative SUHI, typical of arid cities, while a slight positive SUHI (mean intensity of 0.4 °C) during winter daytime is present. An evident positive SUHI is found only during summer (1.8 °C) and winter nighttime (3.2 °C). The negative SUHI intensity is due to the low vegetation presence in the rural area, dominated by croplands turning into bare land surfaces during the pre-monsoon summer season. Higher LST values in the rural area than in the urban area are also confirmed by the field campaign, with an average difference of about 5 °C. Therefore, the impact of the rural LST in biasing the SUHI is evident, and a careful biophysical interpretation is needed. For instance, within the urban area, the yearly intensity of the summer daytime SUHI is not correlated with the evapotranspiration, while the correspondent summer daytime LST exhibits a significant negative correlation (−0.73) with evapotranspiration. Furthermore, despite the city growth across the years, the urban area does not generally reveal a temporal increase of the magnitude of the heat island but an enlargement of its spatial footprint.

Comparison of surface and canopy urban heat islands within megacities of eastern China

Urban heat island (UHI) can be characterized and quantified to understand the modification of urban surfaces on the local and regional climate. This study examines UHI variation across three megacities that are located in rapid urbanization regions in eastern China (Beijing, Shanghai and Guangzhou). These cities are located within a warm temperate climate zone, north subtropical climate zone, and lower subtropical climate zone, respectively. Satellite-based land surface temperature (LST) data and air temperature records from 2003 to 2016 were used to identify surface urban heat island (SUHI) and canopy urban heat island (CUHI), respectively. Generally, the average annual SUHI is higher than the CUHI, with the greatest UHIs appearing in Beijing (SUHI: 2.33 ± 0.18 °C, CUHI: 1.45 ± 0.54 °C). UHI changes across latitudes were negatively related to humidity variation, with higher UHI in drier climates. Seasonal UHI analysis suggests that a lower SUHI would occur in winter and a higher UHI in spring and summer, except for Guangzhou. CUHI in dry season was higher than in wet season for all three megacities, and the largest CUHI (2.10 ± 0.33 °C) appeared in winter in Beijing. Various patterns of seasonal cycles of SUHI and CUHI were related to monthly precipitation and solar insolation. Annual average daytime SUHI was higher than the nighttime SUHI, and larger daytime SUHI appeared in Guangzhou, contrasting with Shanghai and Beijing. The difference between SUHI and CUHI for all seasons was also high in Guangzhou. UHI changes were considered to be altered by warm and wet conditions in mega-cities of eastern China, and heat transportation from urban surface to urban canopy provided some possible understanding of the UHI change.

Investigating urban heat island intensity in Istanbul

We analyzed the annual, monthly, and seasonal variations of urban heat island (UHI) intensity in Istanbul by using meteorological data measured for the period of 1960–2012 at six stations. The UHI on minimum temperature is found to be positive for all seasons, and the average UHI intensity clearly indicates seasonal changes, strongest in summer and weakest in winter. The results demonstrated increase of night time UHI intensity with 0.41–0.50 °C/decade and decrease of daytime UHI intensity with 0.13–0.18 °C/decade at the urban sites. The UHI strengthened with the expansion of the city due to increased population. The influences of meteorological variables on seasonality of the UHI intensity are examined for the days categorized depending on wind, cloud cover, and precipitation values. It is found that the UHI intensity decreases with increasing wind speed and cloud cover. The integrated response of the city atmosphere to wind speed changes differ such that daytime UHI in urban atmosphere intensifies rapidly from calm conditions to the wind speeds of 2–3 m/s, then slightly increases until 4–5-m/s wind speeds and starts to decline afterwards. On the other hand, the nighttime UHI intensities in urban sites continuously decline with the same rate until the wind speeds reach to 5–6 m/s. The difference of daytime UHI between rainy summer days and dry days is around 1 °C which is almost independent of the precipitation amount. Both nighttime and daytime UHI intensities depend on the season and site range approximately between 0.24 and 1.74 °C and − 0.62 and 2.61 °C, respectively. However, the UHI based on minimum temperature for the selected dry days with low wind and clear sky conditions increases to 1.70–3.08 °C. Land surface data from Moderate-Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra show areal extension of the UHI through the north along the Bosphorus between 2000 and 2012, especially in the night observations. The continuous increase of built-up areas, paved roads, and decrease of green areas caused the growth of UHI intensity. The estimated UHI based on land surface temperature (LST) at the most urbanized locations of Istanbul reach to 8 °C for daytime and 6 °C for nighttime.

Characterizing the Hourly Variation of Urban Heat Islands in a Snowy Climate City during Summer.

Temporal variation of urban heat island (UHI) intensity is one of the most important themes in UHI studies. However, fine-scale temporal variability of UHI with explicit spatial information is sparse in the literature. Based on the hourly air temperature from 195 meteorological stations during August 2015 in Changchun, China, hourly spatiotemporal patterns of UHI were mapped to explore the temporal variability and the effects of land use on the thermal environment using time series analysis, air temperature profiling, and spatial analysis. The results showed that: (1) high air temperature does not indicate strong UHI intensity. The nighttime UHI intensity (1.51 °C) was much stronger than that in the daytime (0.49 °C). (2) The urban area was the hottest during most of the day except the period from late morning to around 13:00 when there was about a 40% possibility for an “inverse UHI intensity” to appear. Paddy land was the coolest in the daytime, while woodland had the lowest temperature during the nighttime. (3) The rural area had higher warming and cooling rates than the urban area after sunrise and sunset. It appeared that 23 °C was the threshold at which the thermal characteristics of different land use types changed significantly.

Spatio -temporal variations in urban heat island and its interaction with heat wave

Most of the urban localities are facing the effects of Urban Heat Island (UHI) and extreme heat wave (HW) events. It is expected that these HW events are likely to be intensified by the effect of UHI in the future. As these events project to increase in both severity and frequency therefore, it is crucial to assess the intensity of UHI and examine the relationship between HW and UHI. In this study, observations for different coastal areas are used to quantify the impacts of UHI during the HW events. The spatial and temporal variability patterns of UHI in the metropolitan city of Karachi were also investigated using hourly temperature observations for a period of 10 years in two phases (a) from 1998 to 2002 (b) from 2012 to 2016. During the first phase (1998–2001), the maximum Urban Heat Island Intensity (UHII) for night time in summer was 1.9 °C, while during the second phase (2012–2016), it increased by 0.6 °C. Despite the fact that both phases have shown similar pattern for seasonal UHII, urban-rural temperature difference was found to be significant in summer especially in the night time. Temporal distribution of UHII for winter shows that average intensity of UHI during daytime varies between 0.1 °C and 3.2 °C, considering the overall time duration. The results indicate that UHII increased significantly during the HW period which caused more than 800 deaths in Karachi between 17th June and 24th June 2015.

Seasonal monitoring of urban heat island using multi-temporal Landsat and MODIS images in Tehran

ABSTRACTThis study aimed to investigate the relationship between seasonal changes in land surface temperature (LST) and various land use/land cover (LULC) classes in Tehran in 2010. For this purpose, urban heat island (UHI) cores were detected. The relationship between spatiotemporal variations of UHIs and the LULC classes was studied. The results showed that UHIs have different causes due to the type of LULC classes in the region. The average temperature of the five LULC classes in different seasons revealed full compliance of the spreading pattern of UHIs in the city with the type of LULC. Daytime and nighttime MODIS LST maps were prepared to verify the results. The daytime LST map also confirmed the compliance of the spatial pattern of UHIs with the types of LULC. The consequences from the nighttime LST map revealed the movement and conduction of main cores of heat islands towards the central and densely populated areas of the city, which could be expected to be due to the high absorption rate of heat during the day by unnatural materials and energy emissions at night.

Studying Surface and Canopy Layer Urban Heat Island at Micro-Scale Using Multi-Sensor Data in Geographic Information Systems

Variations of Urban Heat Island (UHI) effect within urban areas cannot be studied in detail using traditional combination of satellite images with thermal infrared (IR) bands and local weather station data due to their limited spatio-temporal scale. In this article, a system has been built to supplement the current infrastructure and enhance the high spatio-temporal scale. The article progresses from initially traversing through the city of Greater Noida to continuous manual data collection on an academic campus and later by automating it with integrated sensors on a microcontroller while achieving the objective of the collection of continuous high spatio-temporal scale data. Geographic information systems (GISs) were used to integrate and visualize these data with land surface temperature (LST) and air temperature data. The system provided the diurnal cycle of urban materials and insights into nighttime UHI at micro-scale. Overall the low-cost sensing technology presented has the potential to monitor citywide UHI.

Temporal and spatial variability of urban heat island by geographical location: A case study of Ulsan, Korea

This paper investigated the temporal and spatial variability of the urban heat island (UHI) effect by geographical location in the study area of Ulsan, Korea. It focuses on the relationship between geometric features and geographical locations. For this research, air temperature measurements were collected from 44 monitoring stations in 2016. We classified these measurements into five geographical conditions and conducted trend and a random effect GLS regression analyses. The results of the trend analysis showed that the nocturnal air temperature of the urban center roads was approximately 2.5°C and 3.5°C higher during the summer and winter periods, respectively, than the temperature of the rural roads. However, when we controlled for the geometric features of the roads in the regression model, the UHI intensities of the urban center roads in the winter and summer nights decreased to 1.4°C and 1.1°C, respectively. This decrease was due to the mediation effect of the geometric features of the roads. In addition, nocturnal air temperature increased when the building surface fraction and road width increased, while the sky view factor (SVF) was positively related with daytime air temperature. Compared with other geographical contexts, the thermal conditions of the roads near the coastal area was mild in the winter and cool in the summer when the geometric features and the meteorological conditions of the roads were controlled in the regression models.

Impacts of land use and socioeconomic patterns on urban heat Island

ABSTRACTIntensive land surface change and human activities induced by rapid urbanization are the major causes of the urban heat island (UHI) phenomenon. In this article, we examined the spatial variability of UHI and its relationships with land use and socioeconomic patterns in the Baltimore–DC metropolitan area. Census data, road network as well the digital elevation model (DEM) and average water surface percentage were selected to analyse the correlation between spatial patterns of UHI and socioeconomic factors. The impervious surface (coefficient of determination R2 = 0.89) and normalized difference vegetation index (R2 = 0.81) were the two most important landscape factors, and population density (R2 = 0.57) was the most influential socioeconomic variable in contributing to the UHI intensity. Generally, the socioeconomic variables had smaller influence on the UHI intensity than the landscape variables. Based on the patch analysis, most of the socioeconomic variables influenced the UHI intensity indirectly through changing the physical environment (e.g. impervious surface or forest cover). The selected landscape and socioeconomic variables, except impervious surface percentage, demonstrated third-order polynomial correlation with the UHI intensity. The higher correlations were found within certain ranges such as forest percentage from 0% to 30% and population density from 0 to 5000 km–2. This research provides a case study to understand the urban land surface, vegetation, and microclimate for urban management and planning.