Patterns Of Urban Heat Island Research Articles

Assessment of Urban Heat Island Patterns in Bangkok Metropolitan Area Using Time-Series of LANDSAT Thermal Infrared Data

Assessment of Urban Heat Island Patterns in Bangkok Metropolitan Area Using Time-Series of LANDSAT Thermal Infrared Data

Urban heat island behaviors in dryland regions

Urban heat island (UHI) characteristics and mitigation strategies for dryland cities differ from those for wetter urban regions. Whereas the latter typically see daytime surface UHIs, the rapid heating and cooling of deserts surrounding arid cities often produces daytime ‘urban cool islands’ and nighttime UHIs. Degrees of aridness, extent of vegetation, elevation, latitude, humidity, topography, and typical building types are likely to influence dryland UHI dynamics. This study analyzes variations in thermal effects at multiple scales for 10 dryland urban regions representing varied geographies worldwide with an aim to establish a broader understanding of the spectrum of UHI patterns in dryland cities. We used GIS to assemble daytime and nighttime satellite imagery, determined land surface temperature and vegetation at a 30-meter scale, and analyzed typical neighborhood-scale examples of six land cover types in each region. The 10 regions showed large variation in thermal effects. We found a strong daytime surface UHI in only one. Nighttime heat islands were more pronounced. However, all regions showed strong small-scale variation in temperature, averaging a 12.3 °C difference between mean top-quintile and bottom-quintile surface temperatures. Samples of urban forest landscapes cooled daytime temperatures an average of 5.6 °C compared to metro averages. Irrigated lawn and multistory building land cover samples also had a substantial cooling effect. Xeriscaped landscapes amplified daytime heating. Our results indicate that UHIs for dryland cities are unlikely to be reduced by xeriscape strategies, but that shade-maximizing urban forestry and built form hold promise to reduce heat islands.

High‐Resolution, Multilayer Modeling of Singapore's Urban Climate Incorporating Local Climate Zones

AbstractWe applied Weather Research and Forecasting (WRF) model's Multilayer Urban Canopy Model (MLUCM) to simulate the urban climate of Singapore during a hot period in April 2016. The high‐resolution local climate zone (LCZ) map was used as urban land use/land cover data in order to study the intraurban variability in different LCZ classes. The LCZ map for Singapore was developed by adopting the World Urban Database and Access Portal Tools (WUDAPT) methodology based on satellite remote sensing imageries and building height data. The coupled WRF/MLUCM model was validated using meteorological data from stations across Singapore. Higher index of agreement compared to observations and lower root‐mean‐squared error of 2‐m temperature, relative humidity, and global horizontal irradiance showed satisfactory model performance. A sensitivity analysis of initial and boundary conditions helped in determining the model configuration with the least error for quantifying the urban heat island (UHI) effect. The diurnal cycle and the spatial pattern of UHI were investigated, and it was found that the mean UHI intensity peaked in the early morning at 2.2 °C, reaching 3.6 °C in the Compact High Rise (LCZ1) areas. The anthropogenic heat due to indoor air conditioning was found to play a major role in all the processes studied, while the effect of the different land use types was most pronounced during nighttime and least visible near noon. The UHI circulation developed near the central catchment is found to prevent sea breezes from further propagating inland.

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.

On the relationship between normalized difference vegetation index and land surface temperature: MODIS-based analysis in a semi-arid to arid environment

This work focused on studying the relationships between Normalized Difference Vegetation Index (NDVI) and daytime and nighttime Land Surface Temperature (LST) in winter, spring, summer and fall and investigating the effects of land cover on these variables in Jordan, which represents a typical semi-arid to arid environment. Using MODIS-based data for the year 2017, multiple procedures were applied: one-way analysis of variance followed by comparison between means, Pearson correlation coefficient, global Moran’s index, simple linear regression, second-order polynomial regression, recursive-partitioning regression and geographically weighted regression. The results showed that land cover explained fair amount of the variability in NDVI but small amount of the variability in daytime and nighttime LST. In addition, an inverted surface urban heat island pattern was observed in daytime. Finally, applying different regression procedures produced different perspectives about the complex and variable relationships between daytime and nighttime LST and NDVI in different seasons.

Investigating urban heat island through spatial analysis of New York City streetscapes

Cities experience the urban heat island (UHI), which continue to pose challenges for humanity's increasingly urban population. Past research has revealed that land cover composition and configuration, along with other geographical phenomena (i.e., albedo), can explain much of the spatial pattern of UHI, yet advances await. In response, this research was made to: (i) assess the spatial pattern of mean ambient night temperature across 34 streetscapes in New York City (NYC); (ii) create and differentiate global and local regression models between-natural and built streetscape characteristics- and mean ambient night temperature; and (iii) use geographically weighted regression (GWR) to assess local patterns of correlated associations. Urban canopy layer (UCL) temperatures were recorded across 34 weather stations, and landscape metrics calculated from 0.914 m land cover data with 96% accuracy. Local Getis-Ord Gi* statistic exhibited significant spatial cold and hot spots of UHI in NYC. Global inferential tests revealed that sky-view factor, photosynthesis activity, elevation, and road configuration were the strongest predictors of mean ambient night temperature. Six multiple regression models were ultimately made with GWR fitting the UHI aptly (R2 = 65–74%). Important explanatory covariates were illustrated using local pseudo-t statistics and linked to mean ambient night temperature, supporting the importance of GWR for understanding local UHI interactions. Results also confirm that landscape configuration metrics are stronger predictors of UHI than composition measures. Streetscape design, particularly road patterns and process, requires more consideration when attempting to mitigate UHI during future sustainability planning, urban renewal projects, and research.

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.

The Impact Identification of Urban Heat Island in Coastal Urban Areas of Java Island

Last 20th-century urban area faced with the phenomenon of Urban Heat Island (UHI) caused over land use to meet increasing human needs. The most populous island in Indonesia is Java Island, in the year 2015 recorded population density rising 0.82% from the year 2000 to reach 20,902 people/km2. The layout of urban areas in Java is bordered by sea due to the climate is more heat, such as Jakarta, Tangerang, Serang, Semarang, Surabaya, Sidoarjo, Banyuwangi and Cilacap. The phenomenon existence of UHI in 8 cities, it's interesting to be analysed using the air temperature time series throughout the 21th-century. The impact identification of UHI on a sense of discomfort and potential drought is done by correlating values of air temperature against Discomfort Index (DI) and Accumulated Potential Water Loss (APWL). Further analysis using Weather Research and Forecasting (WRF) model for last decade is done to map the spatial and temporal patterns of UHI in Java. Based on the analysis of trend line, the 8 cities experienced a temperature increase between 0.0060°C/years to 0.0246°C/years, indicating the occurrence of UHI. The increase of temperature value cause the change of normal temperature value significantly every decade. The range values of the correlation between temperature and DI are 0.746 to 0.987 showed UHI effect is strong against impact sense of discomfort, but the effect is weak against a potential drought due to the range values of the correlation between temperature and APWL are –0.677 to 0.120. WRF output results showed during the last decade, UHI in spatial experience increased the area of 33°C to 34°C maximum temperature.

Spatial-temporal pattern of, and driving forces for, urban heat island in China

Urban heat islands (UHIs) have very large negative effects on local climate features and ecological environments. However, the larger-scale temporal and spatial patterns of UHIs are still unclear. Our research explored the large-scale spatial and temporal patterns of UHIs in 155 cities across China by collecting observational data from 310 meteorological stations over a 30-year period (1984–2013). The results suggested that the UHI intensity has linearly increased over the past 30 years and is strongest in summer across different years. The UHI intensity in inland cities is significantly higher than that in coastal cities; cities in arid areas had higher UHI intensities than those in humid areas; cities in middle or high mountain areas had higher UHI intensities compared to those located in the other landforms in China. There are no significant differences in UHI intensity between cities located in different climatic zones or between cities with different GDP levels, population sizes and industrial structures. Additionally, the relationships between the UHI intensity and several factors, such as meteorological conditions, GDP level and population size, were analyzed. Our results showed that average wind speed, average precipitation and relative humidity had a significant negative correlations with UHI intensity, whereas there was no significant correlation between anthropogenic factors and UHI intensity. Our research indicated that we should consider the local climate and landscape to eliminate UHI hazards in the future urbanization processes.

Landsat-based vegetation abundance and surface temperature for surface urban heat island studies: the tale of Greater Amman Municipality

ABSTRACTThis study aimed at characterizing the spatial and temporal characteristics of vegetation abundance (represented by At-Sensor Normalized Difference Vegetation Index (ASNDVI) and/or Land Surface Normalized Difference Vegetation Index (LSNDVI)) and surface temperature (represented by At-Sensor Brightness Temperature (ASBT) and/or Land Surface Temperature (LST)) and investigating the types of the relationships between these variables in different seasons in different years using Landsat data in Greater Amman Municipality in Jordan. Correlation analysis using Pearson’s correlation coefficient showed that using either ASNDVI or LSNDVI and either ASBT or LST should lead to the same results. Change analysis from 1987–2016 using map algebra showed that the majority of the municipality was subjected to both no change and small increase in vegetation abundance in summer and winter and both no change and small increase in surface temperature in summer and both no change and small decrease in surface temperature in winter. Vegetation abundance variability reflected partly the land cover characteristics of the municipality while surface temperature variability indicated the potential of the presence of an inverted surface urban heat island pattern. Correlation analysis using contingency tables showed the presence of negative relationship in summer and positive relationship in winter between vegetation abundance and surface temperature.

The influence of land-cover type on the relationship between NDVI–LST and LST-Tair

ABSTRACTAir temperature (T2m or Tair) measurements from 20 ground weather stations in Berlin were used to estimate the relationship between air temperature and the remotely sensed land surface temperature (LST) measured by Moderate Resolution Imaging Spectroradiometer over different land-cover types (LCT). Knowing this relationship enables a better understanding of the magnitude and pattern of Urban Heat Island (UHI), by considering the contribution of land cover in the formation of UHI. In order to understand the seasonal behaviour of this relationship, the influence of the normalized difference vegetation index (NDVI) as an indicator of degree of vegetation on LST over different LCT was investigated. In order to evaluate the influence of LCT, a regression analysis between LST and NDVI was made. The results demonstrate that the slope of regression depends on the LCT. It depicts a negative correlation between LST and NDVI over all LCTs. Our analysis indicates that the strength of correlations between LST and NDVI depends on the season, time of day, and land cover. This statistical analysis can also be used to assess the variation of the LST–T2m relationship during day- and night-time over different land covers. The results show that LSTDay and LSTNight are correlated significantly (p = 0.0001) with T2mDay (daytime air temperature) and T2mNight (night-time air temperature). The correlation (r) between LSTDay and TDay is higher in cold seasons than in warm seasons. Moreover, during cold seasons over every LCT, a higher correlation was observed during daytime than during night-time. In contrast, a reverse relationship was observed during warm seasons. It was found that in most cases, during daytime and in cold seasons, LST is lower than T2m. In warm seasons, however, a reverse relationship was observed over all land-cover types. In every season, LSTNight was lower than or close to T2mNight.

Spatial variations of intra-city urban heat island in megacity Delhi

This study examines the variation of intra-city Urban Heat Island (UHI) in megacity Delhi by the mobile transverse measurement technique and spatial maps of UHI along the mobile routes have been generated for the city by using the ordinary kriging interpolation tool of ArcGIS. Meteorological data was collected by mobile surveys under clear sky weather days in monsoon and winter months of 2014 using two HOBO data loggers installed on a vehicle along the routes which covered many parts of Delhi intersecting vertical and horizontal transects capturing different land use patterns of the city. UHI values obtained through interpolation were validated with UHI obtained from temperature measurements at five fixed sites spread over the Delhi region, which were found in good agreement (R2=0.91). The variability existing in the two seasons studied has been shown by low UHI values obtained in the monsoon season and high UHI values in winter season. The diurnal pattern of UHI showed higher UHI during the nighttime period, compared to morning and noon periods. The results show variable UHI in different regions of Delhi covered by mobile routes with high UHI values of >6°C observed during the winter period.

Modelling the fine-scale spatiotemporal pattern of urban heat island effect using land use regression approach in a megacity

Urban heat island (UHI) effect significantly raises the health burden and building energy consumption in the high-density urban environment of Hong Kong. A better understanding of the spatiotemporal pattern of UHI is essential to health risk assessments and energy consumption management but challenging in a high-density environment due to the sparsely distributed meteorological stations and the highly diverse urban features. In this study, we modelled the spatiotemporal pattern of UHI effect using the land use regression (LUR) approach in geographic information system with meteorological records of the recent 4years (2013–2016), sounding data and geographic predictors in Hong Kong. A total of 224 predictor variables were calculated and involved in model development. As a result, a total of 10 models were developed (daytime and nighttime, four seasons and annual average). As expected, meteorological records (CLD, Spd, MSLP) and sounding indices (KINX, CAPV and SHOW) are temporally correlated with UHI at high significance levels. On the top of the resultant LUR models, the influential spatial predictors of UHI with regression coefficients and their critical buffer width were also identified for the high-density urban scenario of Hong Kong. The study results indicate that the spatial pattern of UHI is largely determined by the LU/LC (RES1500, FVC500) and urban geomorphometry (h¯, BVD, λ¯F, Ψsky and z0) in a high-density built environment, especially during nighttime. The resultant models could be adopted to enrich the current urban design guideline and help with the UHI mitigation.

Spatial and Temporal Variability Patterns of the Urban Heat Island in São Paulo

The spatial and temporal variability patterns of the urban heat island (UHI) in the metropolitan area of Sao Paulo (MASP) were investigated using hourly temperature observations for a 10-year period from January 2002 to December 2011. The empirical orthogonal function (EOF) and cluster analysis (CA) techniques for multivariate analysis were used to determine the dominant modes of UHI variability and to identify the homogeneity between the temperature observations in the MASP. The EOF method was used to obtain the spatial patterns (T-mode EOF) and to define temporal variability (S-mode EOF). In the T-mode, three main modes of variability were recognized. The first EOF explained 66.7% of the total variance in the air temperature, the second explained 24.0%, and the third explained 7.8%. The first and third EOFs were associated with wind movement in the MASP. The second EOF was considered the most important mode and was found to be related to the level of urbanization in the MASP, the release of heat stored in the urban canopy and the release of heat by anthropogenic sources, thus representing the UHI pattern in the MASP. In the S-mode, two modes of variability were found. The first EOF explained 49.4% of the total variance in the data, and the second explained 30.9%. In the S-mode, the first EOF represented the spatial pattern of the UHI and was similar to the second EOF in the T-mode. CA resulted in the identification of six homogeneous groups corresponding to the EOF patterns observed. The standard UHI according to the scale and annual seasons for the period from 2002 to 2010 presented maximum values between 14:00 and 16:00 local time (LT) and minimum values between 07:00 and 09:00 LT. Seasonal analysis revealed that spring had the highest maximum and minimum UHI values relative to the other seasons.

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.

Mapping the Influence of Land Use/Land Cover Changes on the Urban Heat Island Effect—A Case Study of Changchun, China

The spatio-temporal patterns of land use/land cover changes (LUCC) can significantly affect the distribution and intensity of the urban heat island (UHI) effect. However, few studies have mapped a clear picture of the influence of LUCC on UHI. In this study, both qualitative and quantitative models are employed to explore the effect of LUCC on UHI. UHI and LUCC maps were retrieved from Landsat data acquired from 1984, 1992, 2000, 2007, and 2014 to show their spatiotemporal patterns. The results showed that: (1) both the patterns of LUCC and UHI have had dramatic changes in the past 30 years. The urban area of Changchun increased more than four times, from 143.15 km2 in 1984 to 577.45 km2 in 2014, and the proportion of UHI regions has increased from 15.27% in 1984 to 29.62% in 2014; (2) the spatiotemporal changes in thermal environment were consistent with the process of urbanization. The average LST of the study area has been continuously increasing as many other land use types have been transformed to urban regions. The mean temperatures were higher in urban regions than rural areas over all of the periods, but the UHI intensity varied based on different measurements; and (3) the thermal environment inside the city varied widely even within a small area. The LST possesses a very strong positive relationship with impervious surface area (ISA), and the relationship has become stronger in recent years. The UHI we employ, specifically in this study, is SUHI (surface urban heat island).

The Urban Heat Island Effect in the City of Valencia: A Case Study for Hot Summer Days

Extreme heat poses significant risks to the world’s growing urban population, and the heat stress to human health is likely to escalate with the anthropogenically increased temperatures projected by climate models. Thus, the additional heat from the urban heat island (UHI) effect needs to be quantified, including the spatial pattern. This study focuses on the city of Valencia (Spain), investigating the intensity and spatial pattern of UHI during three consecutive hot summer days accompanying a heat record. For the analysis, long-term in situ measurements and remote sensing data were combined. The UHI effect was evaluated using two approaches: (a) based on air temperature (AT) time-series from two meteorological stations and (b) using land surface temperature (LST) images from MODIS products by NASA with 1 km resolution. The strongest nighttime UHI estimated from AT was 2.3 °C, while the most intense surface UHI calculated as the difference between the LST of urban and rural regions (defined by NDVI) was 2.6 °C—both measured during the night after the record hot day. To assess the human thermal comfort in the city the Discomfort Index was applied. With the increasing number of tropical nights, the mitigation of nighttime UHI is a pressing issue that should be taken into consideration in climate-resilient urban planning.

Reply to Lofgren, B.M.: Comment on Hicham Bahi, et al. Effects of Urbanization and Seasonal Cycle on the Surface Urban Heat Island Patterns in the Coastal Growing Cities: ACase Study of Casablanca, Morocco. Remote Sens. 2016, 8, 829

First, we would like to thank Lofgren [1] for his comments [2]. The aim of our research is toevaluate the effect and the impact of both urbanization and the season cycle on the variation ofthe Surface Urban Heat Island (SUHI) in the Casablanca region, Morocco. The SUHI experiencesa significant emergence in the urban zone of our study area during winter while it experiencesa reciprocal effect during summer. However, some wintry days can differ from this trend andshow a significant cooling effect in the urban core. In our analysis, we notice that there are someclimate conditions that are more conducive to this reciprocal effect of the SUHI, such as cold/low airtemperature and precipitation. For example, in Figure 17, we show the strength of the link betweenthe spatial distribution of precipitation and the Surface Urban Cool Island (SUCI) pattern and howthe urban areas coinciding with the highest precipitation contribute significantly to the enhancementof the SUCI. On the other hand, we mentioned that winter cool/low air temperature values allowthe control of the SUCI. Once the air temperature exceeds the threshold of approximately the meanwinter temperature, we observe a significant emergence of the SUHI while the SUCI occurs in theother case. Figure 5d accentuates this fact and illustrates a weak SUHI due to the important decrease inair temperature compared to the threshold mentioned above. Some research shows that the presenceof low outdoor temperature keeps moisture in the soil as long as possible [3–5] and then contributes incontrolling the cooling effect. Hence, it was concluded that air temperature controls soil moisture andthe response of soil moisture to air temperature is more sensitive than precipitation [3]. Soil moisture isone of the prime environmental variables related to land surface climatology and is directly connectedwith the process of evapotranspiration and I agree with you that it is controlled by the Surface EnergyBalance. However, and as you evoked in your comment, air temperature can be considered as a factoror part of this complex process. In our paper, we did not deeply study this complex process becauseit is not the goal of our work but we have been limited to show the impact of low temperature andsoil moisture on decreasing the SUHI effect. Our statement, accompanied by the citation of Lofgren’spaper [1], has been mentioned in this perspective. Also, our expected results do not depend on thetopic of potential evapotranspiration and its physical drivers in detail.

Comment on Hicham Bahi, et al. Effects of Urbanization and Seasonal Cycle on the Surface Urban Heat Island Patterns in the Coastal Growing Cities: A Case Study of Casablanca, Morocco. Remote Sens. 2016, 8, 829

A statement in this recently published paper makes a point that is largely at odds with the main point of the paper that is cited. Stating that higher air temperatures lead to greater evapotranspiration is an oversimplification; the true story is more complex. Although this is by no means central to the conclusions of the paper being commented on, we have demonstrated the danger in taking too literally the idea that air temperature determines potential evapotranspiration.