Impact of Urbanization on Urban Heat Island Intensity in Major Districts of Bangladesh Using Remote Sensing and Geo-Spatial Tools

Comparison of land surface and air temperatures for quantifying summer and winter urban heat island in a snow climate city

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.

This study investigates the Urban Heat Island (UHI) effect in Pokhara Metropolitan City using remote sensing and geographic information systems (GIS). The primary objective is to analyze temporal and spatial variations in UHIs, with secondary objectives including evaluating spatial patterns of Land Surface Temperature (LST) and assessing changes in UHI intensity over time. Landsat 8 imagery from 2013 to 2022 was processed using Google Earth Engine (GEE) to derive LST maps. Data preprocessing involved band selection, cloud masking, and spatial filtering to enhance data quality. Urban and suburban areas were delineated using shapefiles, facilitating the calculation of Surface Urban Heat Island (SUHI) intensity across the city. Results indicate pronounced spatial disparities in LST, with urban areas consistently registering higher temperatures than suburban counterparts. The average LST in urban areas peaked at 39.13°C, compared to 34.43°C in suburban areas. Temporal analysis reveals a steady rise in both LST and SUHI intensity over the study period, with SUHI values increasing from 2.99 in 2013 to 5.85 in 2022, highlighting the escalating impact of urbanization on local climate dynamics. This research contributes crucial insights into the urban thermal environment of Pokhara Metropolitan City, essential for informing sustainable urban planning and climate resilience strategies in rapidly developing regions.

Many studies have investigated urban heat island (UHI) intensity for cities around the world, which is normally quantified as the temperature difference between urban location(s) and rural location(s). A few open questions still remain regarding the UHI, such as the spatial distribution of UHI intensity, temporal (including diurnal and seasonal) variation of UHI intensity, and the UHI formation mechanism. A dense network of atmospheric monitoring sites, known as the Oklahoma City (OKC) Micronet (OKCNET), was deployed in 2008 across the OKC metropolitan area. This study analyzes data from OKCNET in 2009 and 2010 to investigate OKC UHI at a subcity spatial scale for the first time. The UHI intensity exhibited large spatial variations over OKC. During both daytime and nighttime, the strongest UHI intensity is mostly confined around the central business district where land surface roughness is the highest in the OKC metropolitan area. These results do not support the roughness warming theory to explain the air temperature UHI in OKC. The UHI intensity of OKC increased prominently around the early evening transition (EET) and stayed at a fairly constant level throughout the night. The physical processes during the EET play a critical role in determining the nocturnal UHI intensity. The near-surface rural temperature inversion strength was a good indicator for nocturnal UHI intensity. As a consequence of the relatively weak near-surface rural inversion, the strongest nocturnal UHI in OKC was less likely to occur in summer. Other meteorological factors (e.g., wind speed and cloud) can affect the stability/depth of the nighttime boundary layer and can thus modulate nocturnal UHI intensity.

Ten cities with different urban sizes located in the Pearl River Delta, Guangdong Province, China were selected to study the relationships between surface urban heat island (S-UHI) intensity and land use/cover factors like urban size, development area, water proportion and mean NDVI, etc. All the cities are almost at the same latitude, show similar climate and have the same solar radiation, the influence of which shall be eliminated during our computation and comparative study. A variance-segmenting method was proposed to compute the S-UHI intensity for each city from the retrieved land surface temperature (LST). Factors like urban size, development area and water proportion were extracted directly from the classification images of the same ETM+ data. The urban mean NDVI value was used to quantify the urban vegetation abundance. Regression and correlation analyses were performed to study the relationships between the S-UHI intensity and these land use/cover factors, and the results show that S-UHI intensity is highly correlated to urban size (r = 0.83) and development area (r = 0.74). It was also proved that negative correlations existed between S-UHI intensity and urban mean NDVI (r = -0.62), and water proportion (r = -0.54). Linear and logarithm functions between S-UHI intensity and land use/cover factors were established respectively.

City size is an important determinant of the urban heat island (UHI) intensity. While most studies report a logarithmic dependence of UHI intensity on city size, other functions like power-law and logistic functions have also been reported. In addition, the urban form plays an important role and, intuitively, increased UHI intensity is expected for compact cities. However, how to incorporate urban size and form for modeling UHI is less clear. Based on the perception that every urban site interacts with every other one, whereas the intensity of interaction decreases with the distance, we propose an every-pair-interaction model to characterize the UHI intensity. The model combines urban size and fractal dimension non-linearly and regression on the summertime surface UHI intensity of 5,000 European cities shows that it outperforms the simple linear model. Subject to the interplay between the range of the every-pair interaction and the urban fractal shape, it also represents a generalization as it includes power-law, logarithmic, and saturating size dependence of UHI — all three possibilities have been reported empirically in the literature. Our model indicates that the surface UHI intensity saturates with urban size. Whether the UHI saturates with the expansion of the urban area or follows a continuously increasing trend is relevant for sustainable urban development. Our theoretical framework opens up new research perspectives around UHI intensity.

Monitoring and characterizing multi-decadal variations of urban thermal condition using time-series thermal remote sensing and dynamic land cover data

The definition and quantification of urban heat island (UHI), the air temperature differences between urban and rural areas, remains problematic. This is due, in large part, to the difficulty of operationalizing the terms “urban” and “rural”, especially with regard to classifying the weather stations that provide data. This thesis studies the urban heat island (UHI) intensity in Hong Kong and there are three foci in the research. The first focus of this study is the determination of the urban and rural weather stations in Hong Kong. The Local Climate Zones (LCZ) system has been employed to classify 17 weather stations and field observation was the main technique to collect the necessary metadata. Six field trips were arranged in the summer of 2009 and 2010. Hong Kong Observatory Headquarters (HKO) is considered as the only representative urban station, whilst Tsak Yue Wu station (TYW) is deemed as the representative rural station because of its Forest Zone (NCZ1) classification. Ta Kwu Ling station (TKL) is another reference rural site. The second focus is the quantification of the UHI intensities at six pairs of stations in Hong Kong and their diurnal and seasonal variations. The 19-year annual UHI intensities in Hong Kong suggest that the representative rural sites (TYW and TKL) also record representative UHI intensities for the region. The differences of the cooling rate at urban and rural stations drive the diurnal cycle of urban heat island. The seasonal variations of UHI intensities are also driven by the cooling rate differences of urban and rural stations in different seasons. Since the mean maximum urban cooling rate does not vary considerably throughout the seasons (0.4 – 0.5 °C/hr), it is the alteration of the rural cooling rate (1.0 – 1.6 °C/hr at TYW; 0.9 – 1.2 °C/hr at TKL) which determines the seasonal variations of UHI intensities. The mean daily maximum UHI intensities in Hong Kong are greatest in winter. The final focus is the meteorological impacts on the UHI intensity in Hong Kong. Five meteorological elements, including air temperature, wind speed, vapour pressure, cloud cover, and cooling rate, have been separately investigated to establish their impacts on the UHI intensity. Under fine weather conditions, the first four elements are negatively related to the UHI intensity. Sixteen regression models were built after the use of stepwise procedures which optimize the combination of independent variables. Rural air temperature is considered the most important meteorological factor on the UHI intensity. The models also suggest that there are other factors affecting the UHI intensities in spring and summer.

New methods of urban heat island (UHI) center/movement and usage of standard deviation (SD) of temperature for the analysis of UHI intensity (UHII) are presented in this study. UHI deviation is defined as difference between temperature of a measurement point and mean temperature of all measuring points. New definition of UHII is used as the difference between maximum and minimum UHI deviations. The UHI center is set as gravity center of relatively hot area and the movement of the UHI can be observed by the course of the center. These UHI metrics are suitable in analyzing the dense network for UHI measurement. As an application of these method, AMeDAS data is used to analyze the UHI effect in Tokyo metropolitan area (TMA) from the point of view of UHI movement and UHII. Clear difference of summer and winter pattern of the UHI in TMA was observed. In the summer pattern, the monthly average UHI area with the high UHI deviation was located from the coastal area to the north inland. About hourly change in a day, the UHI located along the coast at night and after the sunrise, the UHI gradually extended to inland. With this change, the UHI center moved from south to north and returned from north to south. In the winter pattern, the high UHI deviation area was located along the coast and the UHI center was located in the same area for all the day. The method of movement analysis is very effective to clarify the UHI characteristics of the area, especially coastal areas. The relation between the UHII and the SD of the temperature was analyzed. The UHII has strong linear relationship with the SD and the UHII is nearly four times of the SD (UHII ≅ 4×SD). Especially, in the observation of a dense network which has many measuring points, the SD is considered as more robust index of the UHII.

The land surface model SURFEX 7.3 was used to study climate effect of urban expansion located in oasis in arid area of Northwest China by surface and 2 m urban heat island (UHI) intensity and available energy ratio (B). We performed a true regional development scenario and three assumed scenario simulations in 1978, 1993, 2004, and 2014, respectively. The results show that 2 m UHI always displays positive twin peaks during whole day, while surface UHI only displays a positive single peak with several hours during daytime at four seasons in the four years. Moreover, 2 m UHI intensity during night is higher than that during daytime, indicating that UHI intensity is contributed more by “trap effect” from urban complex geometry or anthropogenic heat and that surface UHI according to land surface temperature cannot reflect UHI comprehensively. The oasis-urban development resulted in local warming and increasing of B, and compared with the original undeveloped environment, local climate in the study area was in a relatively balanced state in 1978 and 1993 due to the “heating effect” of urban area and the “cooling effect” of oasis, but the offsetting effect from oasis would become weaker after1993.

The spatial and temporal features of urban heat island (UHI) intensity in complex urban terrain are barely investigated. This study examines the UHI intensity variations in mountainous Chongqing using a dense surface monitoring network. The results show that the UHI intensity is closely related to underlying surfaces, and the strongest UHI intensity is confined around the central urban areas. The UHI intensity is most prominent at night and in warm season, and the magnitude could reach ~4.5 °C on summer night. Our quantitative analysis shows a profound contribution of urbanization level to UHI intensity both at night and in summer, with regression coefficient b = 4.31 and 6.65, respectively. At night, the urban extra heat such as reflections of longwave radiation by buildings and release of daytime-stored heat from artificial materials, is added into the boundary layer, which compensates part of urban heat loss and thus leads to stronger UHI intensity. In summer, the urban areas are frequently controlled by oppressively hot weather. Due to increased usage of air conditioning, more anthropogenic heat is released. As a result, the urban temperatures are higher at night. The near-surface wind speed can serve as an indicator predicting UHI intensity variations only in the diurnal cycle. The rural cooling rate during early evening transition, however, is an appropriate factor to estimate the magnitude of UHI intensity both at night and in summer.

City size is a primary determinant of the urban heat island (UHI) intensity, with its effects further nuanced by the urban form. But how to factor in the urban form into the UHI assessment remains unresolved. We propose an every‐pair‐interaction model that meaningfully incorporates urban size and fractal dimension to characterize the UHI intensity. Regression on the summertime surface UHI intensity of 5,000 European cities shows that the model outperforms the simple linear combination of logarithmic size and fractal dimension. Subject to the interplay between the range of the every‐pair interaction and the urban fractal shape, the model also represents a generalization as it includes power‐law, logarithmic, and saturating size dependence of UHI—all three possibilities have been reported empirically in the literature. Our theoretical framework indicates that the surface UHI intensity saturates with urban size, opening up new research perspectives around UHI intensity.

Comparison of the urban heat island intensity quantified by using air temperature and Landsat land surface temperature in Hangzhou, China

Improvement of spatial-temporal urban heat island study based on local climate zone framework: A case study of Hangzhou, China

Impacts of urban configuration on urban heat island: An empirical study in China mega-cities