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Temporal characteristics of thermal satellite images for urban heat stress and heat island mapping
Abstract
The reconstruction of urban climate is still challenging to
climatologists in spite of over five decades of research including
direct data measurement and model building. Methods for measuring and
monitoring urban climate have strengths and weaknesses depending on the
application. The mapping of patterns of urban heat stress over a city is
not useful if the patterns depicted apply only to the time of data
acquisition. Since thermal satellite sensors can now provide detailed
temperature data covering whole cities and beyond, their adoption in
urban planning depends on demonstrating their relevance to commonly
prevailing conditions. This research investigates and presents a
methodology based on four summertime ASTER thermal satellite images of
Hong Kong for urban heat stress mapping at detailed level. It
demonstrates that satellite images obtained under certain climatic
conditions, and accompanied by adequate 'in situ' ground data, can
provide a basis for an operational heat stress mapping system. The
temporal limitation of thermal satellite images is examined for both day
and nighttime images by comparison of image-derived air temperatures
with ground data representing extended periods and other hot days and
nights outside the image acquisition times. The nighttime images were
found to be more representative of air temperature at other times than
the daytime images, due to a more stable boundary layer, with lower wind
speeds and temperature inversion at night. The nighttime images showed
high and significant correlations with ground level air temperatures for
an average 13-h period surrounding the image time 10.42pm, from 6 pm to
4-8 am the next day. Additionally they were highly and significantly
correlated with ground air temperature distributions on 93% of all hot
summer nights in the same years. Therefore the nighttime images can be
considered representative of a commonly occurring summer nighttime
situation in Hong Kong, and can be used to determine the locations of
areas where temperatures commonly exceed hot weather warning thresholds.
Notably, the images were better able than climate stations to represent
areas in the urbanized Kowloon Peninsula and several smaller satellite
towns which exceeded hot weather warning thresholds. Many areas exceeded
the thresholds, even when no hot weather warning was in force, due to
the unrepresentative location of climate stations. The images were also
more able than climate stations to indicate the hottest and coolest
areas over the Hong Kong territory, thereby enabling measurement of the
magnitude and extent of the urban heat island.
... Conversion of the digital number (DN) into spectral radiance (L λ ) As every item is capable of discharging thermal electromagnetic energy, accordingly the signals received by thermal sensors can, therefore, be transformed into the sensor radiance. The following equation explains how the spectral radiance (L) can be arrived at (Grover & Singh, 2015;Lee, Lee, & Wang, 2012;Nichol & To, 2012): ...
... Conversion of spectral radiance (L λ ) into at-satellite brightness temperatures (TB) Based on the nature of land cover, any rectifications for emissivity (ε) would be applied to the radiant temperatures. In practice, vegetation areas are assigned a value of 0.95 and non-vegetation areas a value of 0.92 (Grover & Singh, 2015;Lee et al., 2012;Nichol & To, 2012;Siu & Hart, 2013). The emissivitycorrected surface temperature was derived by using Grover and Singh's (2015) analysis: ...
... Subsequently, it is essential to rectify the spectral emissivity (ε) in order to obtain the temperature values with a black body. Such rectifications can be achieved based on the nature of land cover or by referring to emissivity values from the normalised difference vegetation index (NDVI) values for each pixel (Grover & Singh, 2015;Lee et al., 2012;Nichol & To, 2012;Siu & Hart, 2013). The emissivity-corrected LSTs were calculated based on Grover and Singh (2015); ...
This paper evaluates the impact of land-use and land-cover (LULC) changes on land surface temperature (LST) in the Kuala Lumpur metropolitan city using multi-spectral and multi-temporal satellite data. The spectral radiance model was used to extract the LST from Landsat 8 OLI and Landsat 5 TM. The analysis on LULC changes revealed a phenomenal increase in the urban (high built-up area) areas and a decrease in the forest land area. The distribution of average changes in LST shows that urban (high built-up area) areas recorded the highest increase in temperature followed by urban (low built-up area) areas, grass land area, forest land area and waterbodies. The LST and normalised difference vegetation index (NDVI) were computed based on changes in LULC which indicates that a strong correlation value was observed between LST and NDVI for urban (high and low built-up areas) areas, grass land area and forest land area. This study demonstrated that an increase in non-evaporating surfaces and a decrease in the vegetation area have increased the surface temperature and modified the temperature of the study area. Remote-sensing techniques were found to be efficient, especially in reducing the time for analysis of urban expansion, and are useful tools to evaluate the impact of urbanisation on LST.
... BIAS finally refers to the mean error. Table 1 shows the results for the urban stations Köln-Stammheim und München-Stadt based on the available data for the test year (2012). The reference model resulted in RMSEs of 3.28 K and 3.12 K and Rs of 0.94 and 0.95 for the station in Cologne (Köln-Stammheim) and in Munich (München-Stadt). ...
... Generally, the multi-temporal predictor sets outperformed the zero-model. Table 1 Results for the urban stations Köln-Stammheim and München-Stadt based on the available data for the test year (2012). Npred: number of multi-temporal predictors: Ntrain: size of the training set. ...
... Recent relevant studies focusing on urban air temperatures comprise Chen, Quan, Zhan and Guo (2016), who found an RMSE of 2.5 K, Keramitsoglou, Kiranoudis, Sismanidis and Zakšek (2016) with an RMSE of 2.3 K, and the Bechtel et al. (2014) and as Pichierri et al. (2012) with an RMSEs of 1.8 K each. Other approaches were less successful (Ho et al., 2014;Kloog, Nordio, Coull, & Schwartz, 2014;Kloog, Chudnovsky, Koutrakis, & Schwartz, 2012;Nichol and To, 2012). ...
Timely meteorological data of high accuracy and spatiotemporal resolution can contribute to sustainable urban planning and management in terms of human thermal comfort, heat wave warning, air pollution prediction, as well as building and transport management among others. Today, the scientific knowledge about the specific urban climate is not fully exploited by these applications. Of particular relevance is thereby the thermal impact which currently is either observed in situ or modelled. Both approaches have advantages but also shortcomings in terms of costs and data needs. In this study we investigated air temperatures estimations from satellite data as alternative source. In particular, we focused on dense time series of land surface temperature as predictors for urban and rural air temperature. We applied empirical models which used LST in predefined time intervals to estimate air temperature using a simple but robust method for empirical modelling. The root mean square errors of the model substantially decreased with the multi-temporal predictors and were below 2 K for most stations. Additionally, the representation of the diurnal cycle improved and the annual cycle was well represented. This method is seen as a great enhancement of existing satellite based air temperature monitoring methods especially for urban applications.
... Mapping T air is thus important to understand the dynamics of urban microclimates; however, T air is usually recorded by weather stations, which measure T air between 1.5 and 2 m above ground and are distributed sparsely, thus failing to provide synoptic spatial coverage [2,3]. Moreover, urban areas are more heterogeneous than rural areas; hence, the effective coverage of weather stations providing long-term observational data tends to be narrow [4,5], leaving large swathes of urban areas unobserved. This is a reason to use remote sensing data for T air prediction; remote sensing offers a possibility to track its seasonal behavior and especially its spatial distribution. ...
... Cross-validation analysis on temperature predictions across a station-centered 1000 m circular area revealed quite a high correlation (R 2 Val = 0.77, RMSE Val = 1.58) between the predicted and measured T air from the test set. 4. ...
Machine learning (ML) was used to assess and predict urban air temperature (Tair) considering the complexity of the terrain features in Yerevan (Armenia). The estimation was performed based on the Partial Least-Squares Regression (PLSR) model with a high number (30) of input variables. The relevant parameters include a newly purposed modification of spectral index IBI-SAVI, which turned out to strongly impact Tair prediction together with land surface temperature (LST). Cross-validation analysis on temperature predictions across a station-centered 1000 m circular area revealed quite a high correlation (R2Val = 0.77, RMSEVal = 1.58) between the predicted and measured Tair from the test set. It was concluded the remote sensing is an effective tool to estimate Tair distribution where a dense network of weather stations is not available. However, further developments will include incorporation of additional weather parameters from the weather stations, such as precipitation and wind speed, as well as the use of non-parametric ML techniques.
... For the delivered product, Landsat TIR data are resampled into a 30 m grid. The problem with the employment of LST in describing ambient thermal conditions in high spatial resolution is the large difference between the LST and the AT (Unger et al., 2009;Schwarz et al., 2012;Nichol and Pui, 2012;Azevedo et al., 2016). Hence, considering the LST as representing the temperature experienced by a city dweller may lead to inappropriate conclusions. ...
... To address these issues, the use of remote sensing data for UHI studies has been suggested and applied. As remote sensing thermal sensors detect temperatures of surfaces rather than that of the air and therefore, they do not provide information directly comparable to ATs detected with thermometers (Unger et al., 2009;Caihua et al., 2011;Nichol and Pui, 2012). However, due to the lack of direct AT observations, it is common to refer to remotely sensed LST when estimating the thermal conditions of a region (e.g., Streutker, 2003;Jin, 2012;Feizizadeh and Blaschke, 2013;Mirzaei et al., 2020;Monteiro et al., 2021), potentially leading to misinterpretations. ...
Sustainable city planning requires detailed information on spatial temperature variations. Remotely sensed land surface temperature (LST) is known to differ substantially from air temperature (AT) causing misinterpretations of the ambient conditions. We demonstrate a reliable and cost-efficient method for AT modelling in urban environments using open data and few temperature observations. The study area is the city of Turku SW Finland, where we have a dense in situ AT observation network of 64 Onset Hobo temperature loggers as a reference. Landsat 8 thermal data from different seasons were used to extract pixel-based LST by employing MODIS and ASTER emissivity libraries and CORINE land cover classification. The LSTs were analysed against the in situ AT first with the correlation analysis. Except for December, the Pearson's correlation coefficients were statistically significant (0.449–0.654, p ≤ 0.001). Seasonally adjusted linear regression models were applied to predict spatially continuous air temperatures (ATp) based on the extracted LST. Our results demonstrate that it is possible to predict urban ATs reliably - within ca. half-a-degree accuracy (MAE 0.36–0.62 °C). The prediction works best in spring, summer and autumn. It improves the capacity to produce reliable high spatial resolution AT information even if in situ observations are sparse.
... For the delivered product, Landsat TIR data are resampled into a 30 m grid. The problem with the employment of LST in describing ambient thermal conditions in high spatial resolution is the large difference between the LST and the AT (Unger et al., 2009;Schwarz et al., 2012;Nichol and Pui, 2012;Azevedo et al., 2016). Hence, considering the LST as representing the temperature experienced by a city dweller may lead to inappropriate conclusions. ...
... To address these issues, the use of remote sensing data for UHI studies has been suggested and applied. As remote sensing thermal sensors detect temperatures of surfaces rather than that of the air and therefore, they do not provide information directly comparable to ATs detected with thermometers (Unger et al., 2009;Caihua et al., 2011;Nichol and Pui, 2012). However, due to the lack of direct AT observations, it is common to refer to remotely sensed LST when estimating the thermal conditions of a region (e.g., Streutker, 2003;Jin, 2012;Feizizadeh and Blaschke, 2013;Mirzaei et al., 2020;Monteiro et al., 2021), potentially leading to misinterpretations. ...
3rd International Winter School at Sigmund Freud University
on Human Behavior and Global Climate Change
... Urbanicity rasters were developed because in densely constructed areas, urban heat island effects are expected to influence temperatures (Nichol and To, 2012;Shi et al., 2018), and therefore urbanicity may be an important predictor in climate interpolation. High-rise buildings can influence temperature by blocking wind, creating shade, acting as heat sinks, and producing thermal pollution. ...
... Second, while our temperature rasters should accurately represent air temperature in open areas, they do not reflect the high spatial variation in temperature found in urban microclimates. For example, although the manned Kowloon HKO weather station is inside a densely populated area, as pointed out by Nichol and To (2012), it is still in a small parklike area surrounded by trees and therefore is not representative of the most densely urbanized areas of Hong Kong. Other stations in urban areas are similarly near green spaces or otherwise open areas. ...
The recent proliferation of high-quality global gridded environmental datasets has spurred a renaissance of studies in many fields, including biogeography. However, these data, often 1 km at the finest scale available, are too coarse for applications such as precise designation of conservation priority areas and regional species distribution modeling, or purposes outside of biology such as city planning and precision agriculture. Further, these global datasets likely underestimate local climate variations because they do not incorporate locally relevant variables. Here we describe a comprehensive set of 30 m resolution rasters for Hong Kong, a small tropical territory with highly variable terrain where intense anthropogenic disturbance meets a robust protected area system. The data include topographic variables, a Normalized Difference Vegetation Index raster, and interpolated climate variables based on weather station observations. We present validation statistics that convey each climate variable's reliability and compare our results to a widely used global dataset, finding that our models consistently reflect greater climatic variation. To our knowledge, this is the first set of published environmental rasters specific to Hong Kong. We hope this diverse suite of geographic data will facilitate future environmental and ecological studies in this region of the world, where a spatial understanding of rapid urbanization, introduced species pressure, and conservation efforts is critical. The dataset (Morgan and Guénard, 2018) is accessible at 10.6084/m9.figshare.6791276.
... Influencing by the subtropical climate, there are multiple Very Hot Days (! 33 C) and Hot Night (!28 C) as observed by the weather station located at the Hong Kong Observatory (HKO) in Kowloon Peninsula [24]. During these extremely hot days and nights, urban heat islands with extremely high temperatures can occur across the high-density environment and, as a result, these can induce the significance of thermal discomfort, morbidity risks, and even mortality risks for its inhabitants [9,21,25]. However, a study focusing on intra-urban temperature difference has also observed a phenomenon of "urban cool island" across this high-density environment [26]. ...
... It is due to the limitation of data of nighttime satellite images for reconstructing historical dataset and for conducting a comprehensive spatiotemporal analysis. Previous study has attempted to use ASTER satellite image to estimate the spatial distribution of nighttime urban temperature [9]. However, lack of cloud-free images and lack of acquisition in the earlier past years have limited the ability of using ASTER images to reconstruct historical datasets. ...
The high-rise/high-density environment of a compact city can influence the microclimate resulting in lower living quality. Previous studies have analyzed the relationships between high-rise/high-density environment and microclimates, by either a temporal study or a spatial approach, while a strategy for investigating the spatiotemporal relationship has yet to be developed. This study initiated a set of innovative strategies to map the historical built environment/microclimates of a compact city, with a spatiotemporal approach to analyze the relationships between building structures and urban climates, for developing a sustainable protocol for future urban planning. Three major components were reconstructed, including 1) the annually averaged Land Surface Temperature (LST) for determining the relative temperature across a compact city; 2) 3D building datasets for representing the building morphology; and 3) sets of urban morphological data derived from building datasets for analyzing microclimate and thermal distress.
... Urban heat island (UHI) and magnitude of the difference in observed ambient air temperature between cities and their surrounding rural regions have been a concern for more than 60 years (Landsberg, 1981). Nichol and Hang (2012) reported that there is a clear cut difference of temperature between rural and urban region and this gap is usually 3-4C. One of the earliest UHI studies was conducted in 1964 (Nieuwolt, 1966) in the urban southern Singapore. ...
... One of the earliest UHI studies was conducted in 1964 (Nieuwolt, 1966) in the urban southern Singapore. Extensive urbanized surfaces modify the energy and water balance processes and influence the dynamics of air movement (Nichol and Hang, 2012). Afterward, many scientists (Giridharan et al., 2004;Neteler, 2010;Schwarz et al., 2011;Xiong et al., 2012;Zhang et al. 2013;Li et al. 2014;Kuang et al., 2015b;Alavipanah et al., 2015) have worked in this field emphasizing different cognitive issues. ...
Rapid urbanization and change of landuse/landcover results in changes of the thermal spectrum of a city even in small cities like English Bazaar Municipality (EBM) of Malda district. Monitoring the spatio-temporal surface temperature patterns is important, therefore, the present paper attempts to extract spatio-temporal surface temperature from thermal band of Landsat imageries and tries to validate it with factor based Land Surface Temperature (LST) models constructed based on six proxy temperature variables for selected time periods (1991, 2010 and 2014). Seasonal variation of temperature is also analyzed from the LST models over different time phases. Landsat TIRS based LST shows that in winter season, the minimum and maximum LST have raised up 2.32°C and 3.09°C in last 25 years. In pre monsoon season, the increase is much higher (2.80°C and 6.74°C) than in the winter period during the same time frame. In post monsoon season, exceptional situation happened due to high moisture availability caused by previous monsoon rainfall spell. Trend analysis revealed that the LST has been rising over time. Expansion and intensification of built up land as well as changing thermal properties of the urban heartland and rimland strongly control LST. Factor based surface temperature models have been prepared for the same period of times as done in case of LST modeling. In all seasons and selected time phases, correlation coefficient values between the extracted spatial LST model and factor based surface temperature model varies from 0.575 to 0.713 and these values are significant at 99% confidence level. So, thinking over ecological growth of urban is highly required for making the environment ambient for living.
... Rio das Almas, which divides the cities of Ceres and Rialma, has lower intensities due to the heat capacity of water and the lower intensity of solar radiation that reaches the inclined surfaces. It can be verified that the urban area of Ceres and Rialma had, on average, the highest intensities of SUHIs (Figure 6), which was verified in numerous studies [1,5,26,27,[29][30][31][32]. As shown in Figure 7and Table 3, in the study area, there was predominance of SUHIs at the expense of SUCIs. ...
... Rio das Almas, which divides the cities of Ceres and Rialma, has lower intensities due to the heat capacity of water and the lower intensity of solar radiation that reaches the inclined surfaces. It can be verified that the urban area of Ceres and Rialma had, on average, the highest intensities of SUHIs (Figure 6), which was verified in numerous studies [1,5,26,27,[29][30][31][32]. Table 3, in the study area, there was predominance of SUHIs at the expense of SUCIs. ...
In recent years, SUHIs (surface urban heat islands) have been greatly emphasized in urban climate studies, since it is one of the climate phenomena most influenced by human action. In this study, temporal and spatial variations of SUHIs in the cities of Ceres and Rialma (Brazil) were investigated; satellite Landsat 8 TIRS/OLI images from 2013 to 2016 were used for this purpose. The results showed that in all seasons, two relationships were observed, one positive and one negative. An N D V I (Normalized Difference Vegetation Index) of 0.2 is the divider of this relationship: up to this value, the relationship is positive, that is, the higher the N D V I value, the higher the surface temperature, while the relationship is negative at an N D V I greater than 0.2. There was high seasonal variation in the SUHIs, with the highest intensities recorded in the spring and summer (±12 °C), and the lowest in the winter. These temporal variations were attributed to the annual cycle of precipitation, which directly involves the robustness of the Cerrado vegetation. SUHIs occupied, on average, an area three times larger than the area of SUCIs (surface urban cool islands). The highest values of SUCIs were observed in water bodies and in valley bottoms. Overall, SUHIs showed high intensities; however, a more intense core area, such as in large cities, was not observed.
... At present, the methods for studying the urban heat island effect mainly encompass the following: meteorological observation [10], site observation [11], numerical simulation [12], and remote sensing technology analysis [13][14][15], etc. Due to the characteristics of remote sensing technology such as wide simultaneous detection range, short data period, high spatial resolution, and spatial continuity, it is progressively becoming the mainstream approach for quantitative calculation and analysis of urban heat island effect. Scholars globally have produced a lot of research on the quantitative assessment [16,17], distribution characteristics [18,19], and analysis of influencing factors of urban heat islands [20][21][22], leveraging remote sensing methods. ...
Urban agglomerations significantly alter the regional thermal environment. It is urgent to investigate the evolution and influence mechanisms of urban agglomeration heat island intensity from a regional perspective. This study is supported by Google Earth Engine long-term MODIS data series. On the basis of estimating surface urban heat island intensity (SUHI) in the Yangtze River Delta urban agglomeration from 2001 to 2020 based on the suburban temperature difference method, the causes of heat islands in the urban agglomeration were analyzed by using geographical detector analysis. Additionally, the heat island proportion (PHI) and SUHI indicators were used to compare and analyze the changing characteristics of the urban heat island effect of ten representative cities. The research reveals the following: (1) The average SUHI of the study area increased from 0.11 °C in 2001 to 0.29 °C in 2020, with an average annual increase rate of 0.009 °C. (2) According to the results of the geographical detector analysis, SUHI was influenced by several driving factors exhibiting obvious seasonal variations. (3) SUHI difference between cities is significant in the summer (1.52 °C), but smallest in the winter; the PHI difference between cities is larger in the autumn (46.7%), while it is smaller in the summer. The research findings aim to effectively serve the formulation of collaborative development plans for the Yangtze River Delta urban agglomeration.
... Each object is capable of discharging thermal electromagnetic energy. As indicated in Eq.(1) (Aakriti & Ram, 2015;Nichol & To, 2012). Where (Lλ) is a Top of Atmosphere (TOA) spectral radiance (Watts/m2 × srad × µm), ML is the multiplicative scaling factor for band-specific radiance, QCAL is the DN of given Pixel (Band 6,10 and 11), and AL is the band-specific additive rescaling factor. ...
Recent climate change has had a negative impact on a wide range of human and natural systems, and it is clear that humans influence the climate. Because, as anthropogenic influence increases, the heat output from the land surface increases, speeding up the rate of climate change. In this regard, the use of RS and GIS techniques has provided various opportunities for research to examine these changes. The current analysis is based on the Landsat 1989, and 2020. Over the study period of 31 years, the built-up regions increased in size from 44.23 km2 to 154.56 km2. Whereas, the area covered by scrubland, water bodies, and vegetation cover has significantly decreased. The LST study further supports the outcome, showing that the mean and standard deviation increased from 14.81°C±1.32(1989) to 18.82°C±1.57(2020). The study also made an effort to examine how LULC affected LST; while vegetation cover has consistently helped to lower mean LST, built-up areas and scrubland are the main drivers of mean LST rise. The LST and NDBI revealed a positive correlation, while the NDVI/SLOPE and LST showed a negative correlation. Subsequently, the multiple linear regression (MLR) models concluded that the BUAs has evolved into a serious threat to the increase in LST, but increase in vegetation cover and SLOPE would result in slight decrease in LST. the study recommended that the government create policies that restrict future land encroachment and conversion, notably of forested area and water bodies, and make an immediate effort to increase the quantity and quality of urban green cover in the study area. So that we may, respectively, minimize the potential hazard posed by future LST rise and LULC change.
... However, it is also important to note that Landsat has limitations in acquiring data on the thermal environment. It is usually collected around 10:00 a.m. and can only be used to represent the SUHI conditions during a certain period [44]. Subject to this limitation, this data is not ideal enough for studying the daily changes in UHI [45]. ...
In recent years, many studies on urban morphology have investigated the urban heat island. However, most of the studies have focused on the whole city rather than on heat aggregation areas. Also, their assessments rarely consider local variations of LST impacting factors, and their results lack planning applicability. In this study, the main urban area of Nanjing was used as a case study. Using open data, heat aggregation areas were identified and the impact of urban morphological parameters on LST in daytime was assessed. Based on the urban morphological parameters and their responses to LST, some morphological optimization strategies for heat aggregation areas with spatial heterogeneity were proposed. The results show that forest index (FI) (91.71%), water index (WI) (90.23%) and floor area ratio (FAR) (91.94%) suggest a negative effect on LST, while impervious surface index (ISI) (79.31%) and building density (BD) (95.55%) seem to have a positive effect on LST. And the correlation with the indicators in three dimensions and LST is unclear. Spatial heterogeneity within heat aggregation areas also exists. Based on this, this study suggests targeted local strategies for LST mitigation based on the overall optimization strategy. This study contributes to sustainable urban planning by providing useful insights into the influence of urban morphological parameters on LST.
... The research methods of urban heat island effect generally include meteorological data analysis Song et al. 2003), field measurements and analysis (Dong et al. 2011), numerical simulation (He et al. 2020Giannaros et al. 2013) and remote sensing (Yang et al. 2018;Zhang et al. 2015;Nichol and Hang 2012). Among them, remote sensing is widely used in the comparative study of surface heat island effect in different urban development periods (Yang et al. 2018), which belongs to macro scale research (Strategic Consulting Center of Chinese Academy of Engineering 2020). ...
Alleviating the urban heat island effect is of great significance to improve thermal comfort, energy saving and carbon reduction, and realize sustainable urban development. At present, several methods are developed to investigate urban heat island effect, including meteorological observation data analysis, mesoscale WRF numerical simulation and remote sensing image analysis, etc. Among them, remote sensing image is widely used in the comparative study of heat island effect in different urban development periods. The local climate zone theory (LCZs), proposed by Stewart and Oke (Bull Am Meteorol Soc 93:1879–1900, 2012) provides a new tool for the downscaling study of urban heat island effect and forms a systematic classification scheme for different urban forms and surface landscapes. The results currently using LCZs to study the heat island effect, usually illustrate the horizontal differentiation at pedestrian level. However, the high-rise compact urban canopy of megacities in China is characteristic of three-dimensional space pattern, leading to the three dimensional differentiation of urban thermal and wind environment. Together with the local climate zones, the two-layer analysis scheme of the surface building-vegetation mixing layer and high building effect layer is thus proposed in this short review to understand the three-dimensional differentiation of urban canopy. This two-layer analysis scheme will provide a new insight for the study of urban heat islands and heat mitigation, deepening the existing local climate zone theory.
... The lower LST values exhibited by forests and agricultural lands are attributed to their contributions to the photosynthetic pool of the area, thereby reducing the heat 24 . Therefore, urbanization involving buildings incorporated with vegetation (green buildings) and less concrete structures has been suggested as a way of reducing the LST of an area 26,27 . ...
The ecological changes in vegetation and land of an area can be monitored and managed through the assessment of its past and present land use and land cover (LULC). In this study, we assessed the changes in the LULC of Penang Island between 2010 and 2021. We also determined the corresponding impacts on the land surface temperature (LST) and vegetation index in the form of normalized difference vegetation index (NDVI). Landsat-5 and Landsat-8 were selected for the study. The LULC types were classified using both supervised and unsupervised multivariate maximum likelihood techniques. The LULC change analysis revealed a considerable increase in the urbanized areas (45.71%), a slight increase in the forests (1.57%) and a sizeable reduction in the agricultural/herbaceous areas (− 33.49) of the city within the stipulated period. The urbanized areas were observed to have the highest LST in 2010 and 2021 (28.75–34.0 °C) followed by the bare land (29.76–29 °C). The increase in temperature could have been driven by the reduction in the greenness of the city coupled with the openness of vegetation cover. Similarly, strong positive correlations were observed between the LST and NDVI in the urbanized areas (R ² = 0.92), and bare lands (R ² = 0.86). We, therefore, hypothesize that urbanization is the main driver of the LULC changes on Penang Island.
... Although meteorological stations can provide long-term air temperature data, they are spatially dispersed, so the air temperature data are only effective in a certain area near the station [3]. The lack of air temperature data on a spatiotemporal scale limits the ability to analyze spatiotemporal changes in air temperature in heterogeneous areas [4,5]. Therefore, accurate estimation of spatiotemporal distributions of the air temperature is important. ...
Near-surface air temperature (Ta) is an important parameter in agricultural production and climate change. Satellite remote sensing data provide an effective way to estimate regional-scale air temperature. Therefore, taking Gansu section of the upper Weihe River Basin as the study area, using the filtered reconstructed high-quality long-time series normalized difference vegetation index (NDVI), interpolated reconstructed land surface temperature (LST), surface albedo, and digital elevation model (DEM) as the input data, the back-propagation artificial neural network algorithm (BP-ANN) was combined with a multiple linear regression method to estimate regional air temperature, and the influencing factors of air temperature estimation were analyzed. This method effectively compensates for the fact that air temperature data provided by a single station cannot represent regional air temperature information. The result shows that the temperature estimation accuracy is high. In terms of interannual variation, the air temperature in the study area showed a slightly increasing trend, with an average annual increase of 0.047°C. The calculation results of the interannual variation rate of temperature showed that the area with increased air temperature accounted for 75.8% of the total area. In terms of seasonal variation, compared with that in summer and winter, the air temperature rising trend in autumn was obvious, and the air temperature in the middle of the study area decreased in spring, which is prone to frost disasters. LST and NDVI in the study area were positively correlated with air temperature, and their positive correlation distribution areas accounted for 93.62% and 94.34% of the total study area, respectively. NDVI, LST and DEM influence the temperature change in the study area. The results show that there is a significant positive correlation between NDVI and air temperature, and the change of NDVI has a positive effect on the spatiotemporal variation of air temperature. The correlation coefficient between LST and air temperature in the southeast of the study area is negative, and there is a difference. In addition, the correlation coefficient between LST and air temperature in other areas of the study area is positive. The air temperature decreased with elevation, air temperature decreases by 0.27°C every hundred meters.
... The quantity and growth of UHI are measured throughout the land surface temperature (LST) and emissivity ratio over the surface. LST is an excellent index of surface energy and signifies the quantity of growth in UHI (Nichol and Hang 2012;Miliaresis 2016;Pramanik and Punia 2019;Fitrahanjani et al. 2021). As the intensity of LULC increases, so does the LST change. ...
Lakes are known to be the bearers and carriers of any ecosystem. Lakes appropriately assist in local weather regulation in a growing urban area. But as urban areas expand, these urban lakes continue to shrink, and the impact of the urban heat island (UHI) on these lakes continues to grow. This paper highlights the impact of land use change on the urban lakes of three different climates in West Bengal, India, and their role in mitigating the seasonal UHI situation. From 2000 to 2020, the urban area and LST in the vicinity of Rabindra Sarobar in Kolkata, Saheb bandh in Purulia and Mirik Lake in Darjeeling have been steadily increasing. On the other hand, the water and vegetation index have been declining in some cases. Also, validating the LST obtained from the satellite with the surface temperature shows that the relationship between these two elements is quite good. Of these three urban lakes, Mirik Lake is the best in overall condition, but Rabindra Sarobar is more conserved than the other two lakes. Not only that, the effect of LST on the types of land use is most noticeable in settlements and the LST increases with distance from wetlands towards the surrounding urban area. Even if the weather changes, it is expected that people will save these urban lakes in the future for their own needs.
... To define urban heat islands, LST derived from remote sensing data is widely used as an indicator. Satellitebased LST is considered superior to other types of sensors (e.g., urban weather station, flux tower) for tracking spatial distributions of temperature (Nichol and To, 2012). Thermal remote sensing sources can retrieve LST across a wide range of the earth surface at various temporal and spatial scales (Chen et al., 2006). ...
Human-induced climate change is bringing warmer conditions to the Southwestern United States. More extreme urban heat island (UHI) effects are not distributed equally, and often impact socioeconomically vulnerable populations the most. This study aims to quantify how land surface temperature (LST) changes with increasing green vegetation landscapes, identify disparities in urban warming exposure, and provide a method for developing evidence-based mitigation options. ECOSTRESS LST products, detailed land use and land cover (LULC) classes, and socioeconomic variables were used to facilitate the analysis. We examined the relationship between LST and the fractions of LULC and socioeconomic factors in the city of Phoenix, Arizona. A machine learning approach (Random Forest) was used to model LST changes by taking the LULC fractions (scenario-based approaches) as the explanatory variables. We found that vegetation features—trees, grass, and shrubs—were the most important factors mitigating UHI effects during the summer daytime. Trees tended to lower surface temperature more effectively, whereas we observed elevated daytime LST most often near roads. Meanwhile, higher summer daytime temperatures were observed on land with unmanaged soil compared to the built environment. We found that affluent neighborhoods experienced lower temperatures, while low-income communities experienced higher temperatures. Scenario analyses suggest that replacing 50% of unmanaged soil with trees could reduce average summer daytime temperatures by 1.97°C if the intervention was implemented across all of Phoenix and by 1.43°C if implemented within the urban core only. We suggest that native trees requiring little to no additional water other than rainfall should be considered. We quantify mitigation options for urban warming effect under vegetation management interventions, and our results provide some vital insight into existing disparities in UHI impacts. Future UHI mitigation strategies seriously need to consider low-income communities to improve environmental justice. These can be used to guide the development of sustainable and equitable policies for vegetation management to mitigate heat exposure impacts on communities.
... The Urban Heat Island (UHI), the phenomenon of elevated urban temperatures referenced to rural temperatures, is a widespread outcome of urbanization (Oke, 1982;Oke et al., 2017). Increasing attention has been paid to the UHI in the past few decades (Chakraborty et al., 2020;Chakraborty and Lee, 2019;Clinton and Gong, 2013;Hu and Brunsell, 2015;Maimaitiyiming et al., 2014;Nichol et al., 2009;Nichol and To, 2012;Pichierri et al., 2012;Stewart and Oke, 2012;Wang et al., 2017), mostly because of its direct impacts on human thermal comfort and the urban environment (Gong et al., 2012;Grimm et al., 2008;Knapp et al., 2010). ...
Estimating future temporal patterns of Surface Urban Heat Islands (SUHIs) on multiple time scales is an ongoing research endeavor. Among these time scales, estimation of next-day SUHIs is of special significance to urban residents, yet we currently lack a simple but efficient approach for making such estimations. In the present study, we propose a statistical strategy for estimating next-day nighttime SUHIs, based on incorporating various SUHI controls into a support vector machine regression (SVR) model. The majority of both the surface controls (including factors related to land cover and solar radiation) and meteorological controls (including temperature fluctuations, relative humidity, accumulated precipitation, wind speed, aerosol optical depth, and soil moisture) that have previously been found to account for daily SUHI variations were used as estimators, and we provide estimations for both the overall SUHI intensity (SUHII) and pixel-by-pixel Gaussian-based LSTs over 59 Chinese megacities. For the overall SUHII, the mean absolute error (MAE) is 0.67 K on average, and the mean absolute percentage error (MAPE) is no more than 25% for more than 90% of the cities. For the pixel-by-pixel LSTs, the associated MAE is less than 2.0 K in most scenarios. In addition, the contribution from each selected estimator to SUHII estimation is assessed comprehensively. Among all the estimators, the contribution from relative humidity is the greatest, followed by rural surface temperature and surface air temperature. Moreover, for nearly 78% of the cities, the estimators related to day-today SUHI variations make a larger contribution than those related to intra-annual SUHI variations. We conclude that our simple yet effective statistical approach for estimating next-day SUHIs can potentially help urban residents to better adapt to urban heat stress.
... But Wenq (2001) and Zhang et al. (2007) have used TM and ETM + data on estimating the LST on the local scale. Nichol and To (2012) used ASTER data to investigate the urban stress and heat island of Hong Kong city of China. D. Wang et al. (2021) used MODIS data to model the angular effect of LST. ...
Land Cover (LC) is going through a dramatic change due to rapid urbanization, especially in urban areas. The impervious land covers (built-up areas) are replacing the natural land covers (vegetation and waterbody) rapidly, which significantly contributes to the increase of Land Surface Temperature (LST). Increased LST deteriorates the meteorological condition in urban areas and causes Surface Urban Heat Island (SUHI) effect. The vulnerability of the SUHI effect can be described quantitatively and qualitatively by Urban Thermal Field Variance Index (UTFVI) phenomenon. The study investigates the changing pattern of the SUHI and its magnitude with Landsat TM/OLI time-series satellite imageries from 1988 to 2018 and establishes the relationship between LC change and LST variations in Chattogram city. The relationships are defined by correlation analysis, cross-section profiles, and Simple Linear Regression Models (SLRM). The results suggest that vegetation cover, water body, and bare-soil have decreased by 2%, 7%, and 10%, respectively, where the built-up area has increased by 19% in the study area during the study period. The average LST of the study area has increased by approximately 10°C in the last 30 years. Weightage of the SUHI affected area has also increased over time due to its direct linkage with LST. No area was affected by the SUHI phenomenon in 1988, which was found more than 35% in 2018. Only 1.69% area was found under the strongest UTFVI phenomenon in 1988, which increased significantly by 27.53% in 2018. In statistical analysis, regression models are used to define LC and LST relationship for every LC type. The LC and LST relationship analyses demonstrate a significant positive correlation with the built-up area while it is negative with vegetation, waterbody, and bare soil. The findings of this study will help city officials and policymakers to prepare a sustainable urban land development plan to minimize the negative consequences of unplanned urbanization and heat stress-related issues.
... Only a limited number of studies evaluate the potential of LST as a proxy for thermal comfort. Existing studies look, for example, at the potential of LST for estimating Temperature Humidity Index (THI) [71] or for heat stress mapping [72]. ...
Urban Heat Islands (UHIs) and Urban Cool Islands (UCIs) can be measured by means of in situ measurements and interpolation methods, which often require densely distributed networks of sensors and can be time-consuming, expensive and in many cases infeasible. The use of satellite data to estimate Land Surface Temperature (LST) and spectral indices such as the Normalized Difference Vegetation Index (NDVI) has emerged in the last decade as a promising technique to map Surface Urban Heat Islands (SUHIs), primarily at large geographical scales. Furthermore, thermal comfort, the subjective perception and experience of humans of micro-climates, is also an important component of UHIs. It remains unanswered whether LST can be used to predict thermal comfort. The objective of this study is to evaluate the accuracy of remotely sensed data, including a derived LST, at a small geographical scale, in the case study of King Abdulaziz University (KAU) campus (Jeddah, Saudi Arabia) and four surrounding neighborhoods. We evaluate the potential use of LST estimates as proxy for air temperature (Tair) and thermal comfort. We estimate LST based on Land-sat-8 measurements, Tair and other climatological parameters by means of in situ measurements and subjective thermal comfort by means of a Physiological Equivalent Temperature (PET) model. We find a significant correlation (r = 0.45, p < 0.001) between LST and mean Tair and the compatibility of LST and Tair as equivalent measures using Bland-Altman analysis. We evaluate several models with LST, NDVI, and Normalized Difference Built-up Index (NDBI) as data inputs to proxy Tair and find that they achieve error rates across metrics that are two orders of magnitude below that of a comparison with LST and Tair alone. We also find that, using only remotely sensed data, including LST, NDVI, and NDBI, random forest classifiers can detect sites with "very hot" classification of thermal comfort nearly as effectively as estimates using in situ data, with one such model attaining an F1 score of 0.65. This study demonstrates the potential use of remotely sensed measurements to infer the Physiological Equivalent Temperature (PET) and subjective thermal comfort at small geographical scales as well as the impacts of land cover and land use characteristics on UHI and UCI. Such insights are fundamental for sustainable urban planning and would contribute enormously to urban planning that considers people's well-being and comfort.
... In changing environmental conditions, satellite images taken at a single instant may be unrepresentative. However, Nichol and To (2012) found that in Hong Kong, due to a more stable boundary layer at night, nighttime ASTER thermal images were representative of commonly occurring climatic conditions for a 13-h period surrounding the image acquisition time, and were significantly correlated with ground air temperatures over the city, for 93% of hot summer nights. ...
This chapter depicts the state of the art in remote sensing for urban pollution monitoring, including urban heat islands, urban air quality, and water quality around urban coastlines. Recent developments in spatial and temporal resolutions of modern sensors, and in retrieval methodologies and gap-filling routines, have increased the applicability of remote sensing for urban areas. However, capturing the spatial heterogeneity of urban areas is still challenging, given the spatial resolution limitations of aerosol retrieval algorithms for air-quality monitoring, and of modern thermal sensors for urban heat island analysis. For urban coastal applications, water-quality parameters can now be retrieved with adequate spatial and temporal detail even for localized phenomena such as algal blooms, pollution plumes, and point pollution sources. The chapter reviews the main sensors used, and developments in retrieval algorithms. For urban air quality the MODIS Dark Target (DT), Deep Blue (DB), and the merged DT/DB algorithms are evaluated. For urban heat island and urban climatic analysis using coarse- and medium- resolution thermal sensors, MODIS, Landsat, and ASTER are evaluated. For water-quality monitoring, medium spatial resolution sensors including Landsat, HJ1A/B, and Sentinel 2, are evaluated as potential replacements for expensive routine ship-borne monitoring.
... Ces dernières s'avèrent être d'un grand recours puisqu'il n'existe pas, jusqu'à présent, sur Lyon, et dans la plupart des grandes agglomérations, de réseaux de stations météorologiques fixes suffisamment déployés en centre-urbain. C'est une réelle opportunité, d'autant plus que la température de l'air évolue à une échelle métrique, à moins de 100 mètres (Nichol et To, 2012 ;Tsin et al., 2016). De plus, l'utilisation d'informations obtenues à partir de capteurs aéroportés ou de satellites pour observer la surface de la terre depuis le ciel ou l'espace est une méthodologie qui évalue efficacement la distribution spatiale des variables de la surface terrestre à l'échelle locale et régionale (Mira et al., 2017). ...
Le changement climatique est un phénomène majeur actuel générant de multiples conséquences. En milieu urbain, il exacerbe celui de l’îlot de chaleur urbain. Ces deux manifestations climatiques engendrent des conséquences sur la santé des habitants et sur la sensation d’inconfort thermique ressenti en milieu urbain. Ainsi, il est nécessaire d’estimer au mieux la température de l’air en tout point d’un territoire, notamment face à la rationalisation actuelle du réseau de stations météorologiques fixes de Météo France. La connaissance spatialisée de la température de l’air est de plus en plus demandée pour alimenter des modèles quantitatifs liés à un large éventail de domaines, tels que l’hydrologie, l’écologie ou les études sur les changements climatiques. Cette étude se propose ainsi de modéliser la température de l’air, mesurée durant 4 campagnes mobiles réalisées durant les mois d’été, entre 2016 et 2019, dans Lyon par temps clair, à l’aide de modèle de régressions à partir de 33 variables explicatives issues de données traditionnellement utilisées, de données issues de la télédétection par une acquisition LiDAR (Light Detection And Ranging) ou satellitaire Landsat 8. Trois types de régression statistique ont été expérimentés, la régression partial least square, la régression linéaire multiple et enfin, une méthode de machine learning, la forêt aléatoire de classification et de régression. Par exemple, pour la journée du 30 août 2016, la régression linéaire multiple a expliqué 89% de la variance pour les journées d’étude, avec un RMSE moyen de seulement 0,23°C. Des variables comme la température de surface, le NDVI ou encore le MNDWI impactent fortement le modèle d’estimation.
... The energy and water balance of the city is strongly influenced by the urban land use, and it has impacts on the weather and climate of a region as well (Shastri et al. 2017). Developments in remote sensing technologies have enabled the acquisition of satellite images at large spectral ranges with suitable spatial resolution and at reasonable time intervals for monitoring the land surface features while in situ reading can be obtained from specific ground-based stations only (Javed Mallick et al. 2008;Nichol and To 2012;Myint et al. 2013). ...
The present study aims at investigating the impact of land cover features in enhancing or mitigating Land Surface Temperature (LST) in a semi-arid tropical metropolitan city of Bengaluru, India. Spatial distribution of LST and land cover types of the area were examined in the circumferential direction, and the contribution of land cover classes on LST was studied over 28 years. Urban growth and LST were modelled using Landsat and MODIS data for the years 1989, 2001, 2005 and 2017 based on the concentric ring approach. The study provides an efficient methodology for modelling and parameterisation of LST and urban growth by fitting an inverse S-curve into urban density (UD) and mean LST data. In addition, multiple linear regression models which could effectively predict the LST distribution based on land cover types were developed for both day and night time. Based on the analysis of remotely sensed data for LST, it is observed that over the years, urban core area has increased circumferentially from 5 to 10 km, and the urban growth has spread towards outskirts beyond 15 km from the city centre. As urban expansion occurs, the area under the study experiences an expansive cooling effect during day time; at night, an expansive heating effect is experienced in accordance with the growth in UD in the suburban area and outskirts. The regression models that were developed have relatively high accuracy with R² value of more than 0.94 and could explain the relationship between LST and land cover types. The study also revealed that there exists a negative correlation between urban, vegetation, water body and LST during day time while a positive correlation is observed during night. Thus, this study could assist urban planners and policymakers in understanding the scientific basis for urban heating effect and predict LST for the future development for implementing green infrastructure. The proposed methodology could be applied to other urban areas for quantifying the distribution of LST and different land cover types and their interrelationships.
... In France, there are only a few agglomerations with their own network of fixed meteorological stations, such as Rennes and Dijon [46,47]. The air temperature evolving on a metric scale, at less than 100 meters [48,49], a very dense measurements network is needed. However, this is not the case in Lyon, which is the study area. ...
Climate change is a major contemporary phenomenon with multiple consequences. In urban areas, it exacerbates the urban heat island phenomenon. It impacts the health of the inhabitants and the sensation of thermal discomfort felt in urban areas. Thus, it is necessary to estimate as well as possible the air temperature at any point of a territory, in particular in view of the ongoing rationalization of the network of fixed meteorological stations of Météo-France. Understanding the air temperature is increasingly in demand to input quantitative models related to a wide range of fields, such as hydrology, ecology, or climate change studies. This study thus proposes to model air temperature, measured during four mobile campaigns carried out during the summer months, between 2016 and 2019, in Lyon (France), in clear sky weather, using regression models based on 33 explanatory variables from traditionally used data, data from remote sensing by LiDAR (Light Detection and Ranging), or Landsat 8 satellite acquisition. Three types of statistical regression were experimented: partial least square regression, multiple linear regression, and a machine learning method, the random forest regression. For example, for the day of August 30th, 2016, multiple linear regression explained 89% of the variance for the study days, with a root mean square error (RMSE) of only 0.23 °C. Variables such as surface temperature, Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI) have a strong impact on the estimation model. This study contributes to the emergence of urban cooling systems. The solutions available vary. For example, they may include increasing the proportion of vegetation on the ground, facades, or roofs, increasing the number of basins and water bodies to promote urban cooling, choosing water-retaining materials, humidifying the pavement, increasing the number of public fountains and foggers, or creating shade with stretched canvas.
... Based on the urban and rural stations, we found that T urban-max is usually lower than T rural-max between 2001 and 2014 and during 2050 (predicted), while T urban-min is generally much higher than T rural-min . This is consistent with a previous study which showed that nighttime temperature in Hong Kong is more representative of the urban heat island than is the daytime temperature (Nichol and To 2012), possibly due to the effect of energy absorption and release across high-density and high-rise built environment across this compact city (Ho et al. 2019;Peng et al. 2017). During the forecast 2050 extreme heat event, T urban-min is on average 1.9 °C higher than T rural-min , while the difference of urban and rural temperatures may be even more significant in various locations, based on the landscape and elevation (Fig. 3). ...
In this study, we applied the Weather Research and Forecasting model to project 2050 urban and rural temperature. We applied a time-stratified analysis to compare it with mortality between 2001 and 2014 and between 2011 and 2014, to estimate the elevated risk of a 2050 heat event. We included change in daytime versus nighttime and urban versus rural temperatures as factors to project mortality, to evaluate the potential influence of climate change on mortality risk. Increases of 2.9 °C and 2.6 °C in maximum and minimum air temperature are projected in a 2050 heat event, with a day and a night that will have respective temperatures 9.8 °C and 4.9 °C higher than 2001–2014. Significantly higher mortality risk is forecasted in 2050 compared to 2001–2014 (IRR 1.721 [1.650, 1.796]) and 2011–2014 (IRR 1.622 [1.547, 1.701]) without consideration of temperature change. After consideration of changing temperature, change in maximum temperature in rural areas will induce the highest mortality risk during 2050, possibly due to rapid urbanization across the city, and with the second highest mortality risk induced by the change in minimum temperature in urbanized areas, possibly because local people in the city have been adapted to the maximum level of urban thermal stress during a summer day. Improvements to heat warning systems and sustainable planning protocols are urgently needed for climate change mitigation.
... Thus, approaches that can automatically filter the most important landscape metrics before subsequent analysis during a specific case study are desirable in further research. Second, our work was conducted for one region using only two daytime LST datasets, which may convey uncertainty for the imaging time (Nichol & To, 2012). This could lead to uncertainty in the detection of the dominant landscape metrics, because daytime LST varies greatly . ...
Although considerable effort has been made to investigate the complex deriving forces of land surface temperature (LST), relatively little attention has been paid to the issue of spatial hierarchy, which is an intrinsic property of the urban system. To solve this problem, we propose a multilevel statistical technique, incorporating a regression tree and an improved hierarchical partitioning model, to investigate the hierarchical effects of greenspace spatial patterns on LST. We tested the technique in Guangzhou with two Landsat 5 images acquired in summer. The results show that the proposed technique explicitly identifies the nested hierarchical structure of LST. Greenspace spatial composition is the dominant metric at the highest level, whereas some spatial configuration metrics affect LST variations more in the lower levels. One obvious advantage of the technique is its ability to identify the dominant landscape metrics of greenspace and to determine the hierarchical effects of LST formation. The technique serves as an important approach for environmental studies of urban heat, and it furthers our understanding of the complex and hierarchical mechanism of LST patterns and processes in urban areas. The idea of this multilevel statistical technique is also applicable to the study of other mechanisms in urban systems.
... Urban site characteristics (USCs), such as sky view factor (SVF), impervious surface fraction, building height, plan area index and frontal area index, are identified as crucial localscale proxies that should be exploited in predictive models of urban air temperatures (i.e. Nichol and To 2012;Wicki et al. 2018). Satellite remote sensing provides spatially continuous land surface characteristics such as the Normalized Difference Vegetation Index (NDVI), land cover fractions, LST and the temperature-vegetation index (TVX) (Goward et al. 1994). ...
Air temperatures in urban environments are usually obtained from sparse weather stations that provide limited information with regard to spatial patterns. Effective methods that predict air temperatures (Tair) in urban areas are based on statistical models which utilize remotely sensed and geographic data. This work aims to compute Tair predictions for diurnal and nocturnal time intervals using predictive models that do not exploit information on Land Surface Temperatures. The models are developed based on explanatory variables that describe the urban morphology, land cover and terrain, aggregated at 100 m × 100 m resolution, combined with in situ Tair measurements from urban meteorological stations. The case study is the urban and per-urban area of Heraklion, Greece, where a dense meteorological station network is available since 2016. Moran’s eigenvector filtering and an autoregressive moving average residual specification are implemented to account for spatial and temporal correlations. The statistical models display satisfactory predictive performance, with mean annual Mean Absolute Error (MAE) equal to 0.36 °C, 0.34 °C, 0.42 °C and 0.54 °C, for 11:00–12:00, 14:00–15:00, 22:00–23:00 and 02:00–03:00 (UTC + 2), respectively. The minimum (maximum) MAE for the estimated datasets is 0.22 °C (0.81 °C). The mean annual MAE for all Tair interpolations is 0.42 °C, the mean annual Root Mean Square Error (RMSE) is 0.49 °C and the mean annual bias < 0.01 °C. The time intervals of the analysed measurements coincide with the acquisition times of MODIS and Copernicus Sentinel 3 over Heraklion; hence, the derived estimates can be used in future spaceborne calculations of urban energy budget parameters.
... For example, a 60 m resolution air temperature model for greater Vancouver (Ho et al., 2014) included elevation, land surface temperature, NDWI, SVF, and solar radiation variables and explained 34% of the variation in the response (Ho et al., 2014). A similar model for Hong Kong (Nichol and To., 2012) predicted daytime air temperature at a 90 m resolution using ASTER land surface temperature and 10 m resolution emissivity data, and it explained 75% of the variation in the response (Nichol and To, 2012), which is comparable with our strongest individual route model. ...
Background
Extreme heat events have been associated with excess morbidity and mortality worldwide. Previous research mainly evaluated extreme heat exposures at the municipal and local scales, but individuals are exposed in much smaller areas. The goal of this study was to assess whether land use regression (LUR) models could be developed for air temperature using measurements collected by a pedestrian.
Methods
Microscale air temperature (<100 m) was measured during 42 sampling runs across 20 routes in greater Vancouver, Canada. Six independent variables were considered as potential predictors for LUR model construction for each run and for greater Vancouver as a whole. All models were evaluated using a spatial leave-ten-out cross-validation (LTOCV) approach.
Results
The most predictive LUR variables were Distance to Large Water Body, Distance to Major Road, Normalized Difference Water Index (NDWI), and Sky-View Factor (SVF). On average, the best individual route models explained 39% of the variation in microscale air temperatures for the 20 routes. The overall model explained only 10% of the variation in the 20 combined routes.
Conclusion
Mobile air temperatures were associated with geographic and built environment features at the microscale. The collected data were used to build moderately predictive LUR models for some locations, but could not be used to successfully model the entire study area.
... At the first stage, the digital number was transformed into spectral radiance by using Equation 1 for Landsat 5 (Markham, 1986) and Equation 2 for Landsat 8 (Lee et al., 2012;Nichol and To, 2012). ...
Land cover changes and land surface temperature have rosein the tropical regions of Myanmar especially in the surrounding areas of Magway city due to the rapid growth of urban sprawl. This study investigated the patterns of land cover and the trend of land surface temperature in Magway city area between 1989 and 2017. For this purpose, Landsat 5 TM and Landsat 8 OLI were used and land surface temperatures (LST) were calculated through thermal data with Normalized Difference Vegetation Index (NDVI). After obtaining the land cover map by using maximum likelihood algorithm for each study period, the accuracy of this map was tested using 100 ground checkpoints in an error matrix. A statistical analysis of the results showed the increase of the built-up area by11.7% and the declineof the vegetation area by 19.7% from 1989 to 2017. Moreover, land surface temperature has risen by 4 C during this 28 yearsperiod. Therefore, this study is intended to help the Magway city development council plan effective land cover management in the future.
... The Urban Heat Island (UHI), a phenomenon where the temperature in urban areas is elevated compared to surrounding rural areas, is one of the consequences of urbanisation that directly impact the urban population (Grimmond et al., 2016). Air temperature (T a ) is a key variable in a wide range of research applications, such as climate change and global warming (Intergovernmental Panel on Climate weather data; however, their ability to describe the spatial variation of T a in heterogeneous areas (such as cities) is limited due to their lack of appropriate spatial coverage (Ding et al., 2018;Fu and Weng, 2018;Nichol and Hang, 2012). Several publications have also reported problems with missing T a values caused by disrupted recordings, poor spatial coverage or a total lack of weather stations, such as in parts of West Africa (Stisen et al., 2007). ...
Urbanisation generates greater population densities and an increase in anthropogenic heat generation. These factors elevate the urban–rural air temperature (Ta) difference, thus generating the Urban Heat Island (UHI) phenomenon. Ta is used in the fields of public health and epidemiology to quantify deaths attributable to heat in cities around the world: the presence of UHI can exacerbate exposure to high temperatures during summer periods, thereby increasing the risk of heat-related mortality. Measuring and monitoring the spatial patterns of Ta in urban contexts is challenging due to the lack of a good network of weather stations. This study aims to produce a parsimonious model to retrieve maximum Ta (Tmax) at high spatio-temporal resolution using Earth Observation (EO) satellite data. The novelty of this work is twofold: (i) it will produce daily estimations of Tmax for London at 1 km2 during the summertime between 2006 and 2017 using advanced statistical techniques and satellite-derived predictors, and (ii) it will investigate for the first time the predictive power of the gradient boosting algorithm to estimate Tmax for an urban area. In this work, 6 regression models were calibrated with 6 satellite products, 3 geospatial features, and 29 meteorological stations. Stepwise linear regression was applied to create 9 groups of predictors, which were trained and tested on each regression method. This study demonstrates the potential of machine learning algorithms to predict Tmax: the gradient boosting model with a group of five predictors (land surface temperature, Julian day, normalised difference vegetation index, digital elevation model, solar zenith angle) was the regression model with the best performance (R² = 0.68, MAE = 1.60 °C, and RMSE = 2.03 °C). This methodological approach is capable of being replicated in other UK cities, benefiting national heat-related mortality assessments since the data (provided by NASA and the UK Met Office) and programming languages (Python) sources are free and open. This study provides a framework to produce a high spatio-temporal resolution of Tmax, assisting public health researchers to improve the estimation of mortality attributable to high temperatures. In addition, the research contributes to practice and policy-making by enhancing the understanding of the locations where mortality rates may increase due to heat. Therefore, it enables a more informed decision-making process towards the prioritisation of actions to mitigate heat-related mortality amongst the vulnerable population.
... Moreover, products derived from remote sensing have never had such a temporal and spatial resolution and the data on the state of the Earth's surface, compiled in multiple bases from several satellites, have never been so numerous. This is a real opportunity, especially because air temperature changes at the microscale level, less than 100 m [26,27]. ...
With the phenomenon of urban heat island and thermal discomfort felt in urban areas, exacerbated by climate change, it is necessary to best estimate the air temperature in every part of an area, especially in the context of the on-going rationalization weather stations network. In addition, the comprehension of air temperature patterns is essential for multiple applications in the fields of agriculture, hydrology, land development or public health. Thus, this study proposes to estimate the air temperature from 28 explanatory variables, using multiple linear regressions. The innovation of this study is to integrate variables from remote sensing into the model in addition to the variables traditionally used like the ones from the Land Use Land Cover. The contribution of spectral indices is significant and makes it possible to improve the quality of the prediction model. However, modeling errors are still present. Their locations and magnitudes are analyzed. However, although the results provided by modelling are of good quality in most cases, particularly thanks to the introduction of explanatory variables from remote sensing, this can never replace dense networks of ground-based measurements. Nevertheless, the methodology presented, applicable to any territory and not requiring specific computer resources, can be highly useful in many fields, particularly for urban planners.
... Polar-orbiting satellites, however, only sample LSTs at a comparatively low frequency (only two to four times per day at most) primarily because of the tradeoff between the spatial and temporal resolution of satellite observations (Sobrino et al., 2012;Zhan et al., 2013). This temporal discontinuity in the LST records from polar orbiting satellites limits SUHI studies to discrete times during a diurnal cycle when the satellite transits (Clinton and Gong, 2013;Nichol and To, 2012;Peng et al., 2012;Shastri et al., 2017), while the true and continuous SUHI temporal pattern during a diurnal cycle (hereafter termed the DSUHI temporal pattern) has been less investigated. ...
Understanding the diurnal dynamics of surface urban heat islands (SUHIs) is an indispensable step towards their full interpretation at multiple time scales. However, because of the tradeoff between the spatial and temporal resolutions of satellite-derived land surface temperature (LST) data, the climatology, variety, and taxonomy of diurnal SUHI (DSUHI) patterns remain largely unknown for numerous cities with different bioclimates. By combining daily MODIS LST data with a newly developed four-parameter diurnal temperature cycle (DTC) model, we selected 354 Chinese megacities located in different bioclimatic zones to examine the characteristics of the DSUHI descriptors and systematically investigate the prevalent DSUHI temporal patterns.
The diurnally continuous SUHI variations demonstrate that both the daily maximum and minimum SUHI intensity (SUHII) can occur during most periods of the day, although these intensities are more likely to occur in the early morning and noon/afternoon. Our results also reveal that both strong SUHIs (SUHII > 3 K) and surface urban cool islands (SUCIs) (SUHII < 0 K) are more prevalent than those identified directly through the four MODIS transits. According to the SUHI dynamics, five typical DSUHI temporal patterns are identified: standard-spoon, weak-spoon, quasi-spoon, inverse-spoon, and straight-line patterns. A gradient was found with spoon-like patterns (DSUHI dynamics typically with a daytime valley and a roughly constant trend or a small peak at night) in North China and inverse-spoon (DSUHI dynamics with a typical daytime peak and a constant trend at night) or straight-line patterns (DSUHI dynamics virtually unchanged all day) in South China. The DSUHI shapes were found to be greatly controlled by the urban-rural contrast in the normalized difference vegetation index (NDVI) and urban geometry. Our results not only advance our understanding of the diurnal climatology of SUHIs but also provide a basis for urban surface heat mitigation by identifying the possible timing of the mitigation requirement.
... Under stable atmospheric conditions, the thermal source area for a screen-height sensor might extend few hundred meters away, yet it needs to be further investigated within the urban context (see figure 3). [14] Regardless of that, many researchers have avoided to explore this concept within the LCZs, adopting the approximation given by Stewart & Oke [14] or just choosing a cell-size that would fit with their local climate analysis: the width of a block, the pixel size from a given remote sensor technique, or the emission inventories resolution [45][46][47][48][49][50]. In fact, there might be many advances in that field of knowledge that are not linked with the urban climate study, maybe due to the current use of scientific databases, where narrow searching might lead to strong barriers for interdisciplinarity and knowledge transfer. ...
The correct contextualisation of urban measurements is one of the challenges that urban climate researchers have been dealing with for decades. The Local Climate Zones scheme (LCZs) emerges as a system for characterising these measurements from the thermal perspective. The rapid embracing of the LCZs by researchers from many disciplines, altogether with its adoption for other purposes such as planning, has led to an inexistent or, at its best, flexible use of the source area definition. This practice might call into question the contextualisation of many measurements, highlighting the imperative need to shed light on the source area methods within the urban context. In this study, a systematic review is conducted to compile previous experiences in which the source area was applied in the built environment. Results obtained from the systematic search are summarized and presented according to three scales: the inertial sublayer, the roughness sublayer, and the urban canopy layer. These previous experiences are studied according to their methodological contribution to the source area definition, emphasizing those studies that have considered this concept altogether with the LCZ scheme. This review aims at promoting the knowledge about footprint methodologies and its correct application within the LCZs.
... The rapid development of geospatial technologies since the 1990s has allowed researchers to examine the changes and effects of urban expansion on LST in cities around the world [7], [8], [9], [10], [11]. LST data derived from remote sensing imageries have achieved better accuracy than those collected from ground-based weather stations [12], [13]. Yuan and Bauer (2007) examined the effect of the impervious surfaces on the seasonal variation of LST for the City of Twin Cities, Minnesota in 2002 [14]. ...
... The rapid development of geospatial technologies since the 1990s has allowed researchers to examine the changes and effects of urban expansion on LST in cities around the world [7], [8], [9], [10], [11]. LST data derived from remote sensing imageries have achieved better accuracy than those collected from ground-based weather stations [12], [13]. Yuan and Bauer (2007) examined the effect of the impervious surfaces on the seasonal variation of LST for the City of Twin Cities, Minnesota in 2002 [14]. ...
The Saudi capital city of Riyadh has experienced rapid population growth and urban expansion over the past 4 decades. One major consequence of such growth is the rising of the city’s land surface temperature (LST). This study used Landsat 7 ETM+ sensor data to map the distribution of Riyadh’s LST and then examined and modelled the impacts of five contributing factors known to increase urban LST. The contributing factors are size/area and population density of each neighbourhood, along with amounts of impervious surfaces, vegetations, and soil/sand measured through remote sensing indices NDBI, NDVI, and NDBsI. The data were analyzed using Pearson’s Product Moment Correlation values, Path Analysis, and Multiple Regression analysis. The result shows that neighbourhood population densities and NDBsI index have strong positive correlations (r= 0.68 and r= 0.60) with LST. Neighbourhood area showed significant but low positive correlation (r= 0.33) and the NDBI and NDVI indices showed strong negative correlations (r= -0.55 and r= -0.64) with the LST. The multiple regression model explained about 77% of the total variation in the LST. The model can be used to predict and simulate future LST distribution for Riyadh as well as other cities in the Kingdom and the region.
... Mean LST has been used in many studies to identify the spatial pattern of SUHI effects in urban areas [17][18][19][20]. LST based on the thermal remote sensing better identifies the climatic conditions compared to air temperature from weather stations [9,21]. The Getis-Ord Gi* statistic is among the most commonly used tool for analyzing spatial changes in clustering patterns of hot spot and has been used in many fields of studies, including healthcare [22], disaster management [23], urbanization [24], transport management [25], infrastructure development [26], incident management [27], land cover influence [28], heat wave vulnerability [29], ecology [30], green volume estimation [31], and LST changes [9,28,32]. ...
The formation of surface urban heat islands (SUHIs) can cause significant adverse impacts on the quality of living in urban areas. Monitoring the spatial patterns and trajectories of UHI formations could be helpful to urban planners in crafting appropriate mitigation and adaptation measures. This study examined the spatial pattern of SUHI formation in the Colombo District (Sri Lanka), based on land surface temperature (LST), a normalized difference vegetation index (NDVI), a normalized difference built-up index (NDBI), and population density (PD) using a geospatial-based hot and cold spot analysis tool. Here, 'hot spots' refers to areas with significant spatial clustering of high variable values, while 'cold spots' refers to areas with significant spatial clustering of low variable values. The results indicated that between 1997 and 2017, 32.7% of the 557 divisions in the Colombo District persisted as hot spots. These hot spots were characterized by a significant clustering of high composite index values resulting from the four variables (LST, NDVI (inverted), NDBI, and PD). This study also identified newly emerging hot spots, which accounted for 49 divisions (8.8%). Large clusters of hot spots between both time points were found on the western side of the district, while cold spots were found on the eastern side of the district. The areas identified as hot spots are the more urbanized parts of the district. The emerging hot spots were in areas that had undergone landscape changes due to urbanization. Such areas are found between the persistent hot spots (western parts of the district) and persistent cold spots (eastern parts of the district). Generally, the spatial pattern of the emerging hot spots followed the pattern of urbanization in the district, which had been expanding from west to east. Overall, the findings of this study could be used as a reference in the context of sustainable landscape and urban planning for the Colombo District.
... Hong Kong has monsoon-influenced subtropical climate, experiencing very hot weather in summer. Air temperatures typically reach 33 °C on the hottest summer days, cooling to 26 °C in urban and 24 °C in rural areas at night (Nichol et al., 2012). On one hand, the temperature is continuously growing. ...
The number of extreme hot weather events have raised significantly in Hong Kong. Due to urban heat island effect, urban thermal environment of Hong Kong has been deteriorated. However, there is limited spatial understanding of intra-urban temperature variation under extreme hot weather conditions and climate-sensitive design for reducing heat load in severe heat events. Thus, there is a need to analyse the spatial distribution of intra-urban temperature variation of extreme hot weather and the impact of urban environment parameters on intra-urban temperature differences. In this paper, firstly, hourly air temperature records from 40 Hong Kong Observatory stations from 2011 to 2015 were collected to analysis hot night and very hot day. Secondly, for spatially mapping the very hot days and hot nights, kriging, a geostatistical interpolation algorithm, was adopted. Thirdly, urban environmental parameters (digital elevation model information, sky view factor and the NDVI) were incorporated in co-kriging interpolation for a more comprehensive understanding of the correlation of extreme hot weather and their spatial patterns. The generated maps of very hot day and hot night can provide better understanding of intra-urban temperature differences under extreme hot weather events and help to create climate-sensitive design strategies to cope with climate change locally.
... Night-time Aster images were also acquired from the National Aeronautics and Space Administration in the present paper since they can better represent the temperature pattern of the YRD region (Table 1) (Nichol and To, 2012). Average air temperature on Sept. 1 2015 from 28 open available national weather observational stations in the YRD region was also used to validate the temperature pattern of LCZ. ...
The concept of Local Climate Zone (LCZ) was developed to quantify the relationship between urban morphology and urban heat island (UHI) phenomenon. Each LCZ is supposed to represent homogeneous air temperature. However, there is inadequate data for verifying the air temperature differences between LCZ classes. Therefore, it is necessary to utilize alternative temperature data which allow more comprehensive assessment of the effect of LCZ on local climatic conditions. Land surface temperature (LST) acquired from satellite images can be used to establish the relationship between LST and LCZ by providing continuous data on surface temperature. This paper aims to investigate how LST represents the UHI intensity determined by using an improved method of the World Urban Database and Portal Tool (WUDAPT) to develop the LCZ map of the Yangtze River Delta (YRD) megaregion. The results show that LST in different YRD cities is generally consistent with the LCZ classes with higher LST observed in built-up LCZ classes. The diverse urban morphology and temporal vegetation variation are likely the reasons to inconsistencies in LCZ 9, and LCZ A to D. Findings of this paper provide a better understanding of how urban morphology affects local climate and more accurate delineation of LCZ classes.
... Compared to air temperatures collected from weather stations, thermal imagery provides full spatial coverage at various temporal scales (Myint et al., 2013). In addition, LST derived from remote sensing imagery might be better to show the hottest and coolest areas as compared to temperature collected from urban weather station, which is located in the tree park-like surroundings (Nichol and To, 2012). Surface temperature also has a direct interaction with LULC characteristics (Quattrochi and Luvall, 1999). ...
Exploring changes in land use land cover (LULC) to understand the urban heat island (UHI) effect is valuable for both communities and local governments in cities in developing countries, where urbanization and industrialization often take place rapidly but where coherent planning and control policies have not been applied. This work aims at determining and analyzing the relationship between LULC change and land surface temperature (LST) patterns in the context of urbanization. We first explore the relationship between LST and vegetation, man-made features, and cropland using normalized vegetation, and built-up indices within each LULC type. Afterwards, we assess the impacts of LULC change and urbanization in UHI using hot spot analysis (Getis-Ord Gi∗ statistics) and urban landscape analysis. Finally, we propose a model applying non-parametric regression to estimate future urban climate patterns using predicted land cover and land use change. Results from this work provide an effective methodology for UHI characterization, showing that (a) LST depends on a nonlinear way of LULC types; (b) hotspot analysis using Getis Ord Gi∗ statistics allows to analyze the LST pattern change through time; (c) UHI is influenced by both urban landscape and urban development type; (d) LST pattern forecast and UHI effect examination can be done by the proposed model using nonlinear regression and simulated LULC change scenarios. We chose an inner city area of Hanoi as a case-study, a small and flat plain area where LULC change is significant due to urbanization and industrialization. The methodology presented in this paper can be broadly applied in other cities which exhibit a similar dynamic growth. Our findings can represent an useful tool for policy makers and the community awareness by providing a scientific basis for sustainable urban planning and management.
Remote sensing satellite data have been a crucial tool in understanding urban climates. The variety of sensors with different spatiotemporal characteristics and retrieval methodologies gave rise to a multitude of approaches when analyzing the surface urban heat island effect (SUHI). Although there are considerable advantages that arise from these different characteristics (spatiotemporal resolution, time of observation, etc.), it also means that there is a need for understanding the ability of sensors in capturing spatial and temporal SUHI patterns. For this, several land surface temperature products are compared for the cities of Madrid and Paris, retrieved from five sensors: the Spinning Enhanced Visible and InfraRed Imager onboard Meteosat Second Generation, the Advanced Very-High-Resolution Radiometer onboard Metop, the Moderate-resolution Imaging Spectroradiometer onboard both Aqua and Terra, and the Thermal Infrared Sensor onboard Landsat 8 and 9. These products span a wide range of LST algorithms, including split-window, single-channel, and temperature–emissivity separation methods. Results show that the diurnal amplitude of SUHI may not be well represented when considering daytime and nighttime polar orbiting platforms. Also, significant differences arise in SUHI intensity and spatial and temporal variability due to the different methods implemented for LST retrieval.
Urban Heat Island (UHI) is the phenomenon where urbanization results in an increase in surface temperature among different locations within the city. UHI hotspots not only lead to poor air quality and make people’s health at higher risk, but they also tend to magnify the heat stress and level of thermal discomfort experienced by the people. This study aims to find the UHI spots using thermal remote sensing based on satellites, for the estimation of surface temperature, over a continuous spatial and temporal scale and to develop a mobile application indicating the spatial pattern of UHI and heat stress. Wet Bulb Globe Temperature (WBGT) data collected at various locations across Chennai city was evaluated to obtain the indices reflecting risk levels of heat stress in each area. This was subsequently analyzed in a GIS environment, along with the disaggregated Land Surface Temperature (LST) data, to arrive at valuable information that was used to delineate the hotspots of high heat stress and UHI intensity in the city. Finally, this data was exported to a mobile platform (Android) and an application indicating the spatial pattern of UHI and heat stress was developed, which shows the heat risk zones, mitigation measures, etc. This study confirmed the existence of UHI effect in Chennai city during summer. Temperature difference was found to be even as high as 6–7 °C in many parts of the city. The intensity of UHI was established to be strongly dependent on urban factors such as the density of built-up areas, vegetation cover and presence of water bodies. It was shown that such adverse heat conditions deteriorated the urban environment causing health problems. The results of this study indicate that the highest thermal stress is found in the South-Western and Northern part of the city, which is predominantly crowded, constructed (built-up), industrial and commercial areas.
EN: The Land Surface Temperature (LST) can affect the health and well-being of humans, agriculture, and food production, and can lead to an increase in energy demand. Therefore, monitoring the LST in relation to settlements and population centers is essential. The present study aims to evaluate the LST of Dasht-e Kavir in relation to its population centers. In order to extract temperature information, Landsat level-2 products were used, and both Landsat-8 and Landsat-9 products were used to improve the temporal resolution of the images for creating a map of the average LST of Dasht-e Kavir. According to the results, the surface temperature of Dasht-e Kavir in the summer of 2022 varied from a minimum of 44.6 to a maximum of 55.5 °C, with an average of 51.4 °C, which was classified into 5 temperature classes in this study. The results also showed that two temperature classes, Class 3 (49.1-51.1 °C) and Class 4 (51.2-53.3 °C), were influential in the summer of the study area. The 49.1-51.1 °C class, covering 54.8% of the population points in the region, was the largest class in terms of the number of population points, and the 51.2-53.3 °C class, covering 55% of the area of the region, was the largest area class in Dasht-e Kavir. Based on these results, it was evident that the majority of the area of this plain, around 91.4%, was in the warm temperature classes 3 and 4. Furthermore, according to the results, 97.6% of the population centers in this region were also located in these classes. The results of this study are effective for monitoring the status and locating permanent or temporary human population centers in Dasht-e Kavir based on the temperature parameter and human relative comfort, especially in the summer season. |
فارسی: دمای سطح زمین، میتواند بر سلامت و رفاه عمومی انسان، کشاورزی و تولید مواد غذایی تأثیر گذاشته و منجر به افزایش تقاضای انرژی شود. از این رو، پایش وضعیت دمای سطح زمین در ارتباط با سکونتگاهها و مراکز جمعیتی امری ضروری است. پژوهش حاضر با هدف ارزیابی وضعیت دمای سطح زمین در دشت کویر در ارتباط با مراکز جمعیتی آن انجام شده است. به منظور استخراج اطلاعات دمایی، از محصول لول-2 لندست و به منظور افزایش قدرت تفکیک زمانی تصاویر، از هردو سری لندست-8 و لندست-9 برای تهیه نقشه میانگین دمای سطح زمین دشت کویر استفاده کرده است. بر اساس نتایج به دست آمده، دمای سطح زمین دشت کویر در تابستان 1401 از حداقل 44/6 تا حداکثر 55/5 درجه سانتیگراد متغیر بوده و میانگین آن به 4/51 درجه سانتیگراد رسیده است که این دما در پژوهش حاضر به 5 کلاس دمایی طبقهبندی شد. نتایج همچنین نشان داد دو کلاس دمایی موثر در تابستان مطالعه شده دشت کویر، کلاس 3 (51/1 – 49/1 درجه سانتیگراد) و 4 (53/3 – 51/2 درجه سانتیگراد) حرارتی بود. کلاس حرارتی 51-49 درجه سانتیگراد با دربرگیری 54/8% از نقاط جمعیتی منطقه، بزرگترین کلاس از نظر تعداد نقاط جمعیتی و کلاس 53-51 درجه سانتیگراد با دربرگیری 55% از مساحت منطقه، بزرگترین کلاس مساحتی دشت کویر بود. بر همین اساس نتایج حاکی از آن بود که بخش اعظمی از مساحت این دشت، در حدود 91/4% در کلاسهای حرارتی گرم 3 و 4 قرار دارد. همچنین بر اساس نتایج، 97/6% از مراکز جمعیتی موجود در این منطقه نیز در همین کلاسها قرار گرفته بودند. نتایج پژوهش حاضر به منظور پایش وضعیت و مکانیابی نقاط جمعیتی دائمی یا موقتی انسانی در دشت کویر بر اساس پارامتر حرارت و رفاه نسبی انسانی خصوصا در فصل تابستان موثر است.
In towns and cities in developing countries, negligence in consistently regulating the growth of urban sprawl is commonplace. The purpose of the study was to analyze spatiotemporal changes in land use land cover (LULC) and their impact on land surface temperature (LST) in Balurghat, Dakshin Dinajpur district, West Bengal, India. The results revealed a decrease in the vegetation cover (64–44%) and an increase in the built-up area (14–39%) from 2012 to 2022. Over the study period, built-up regions and bare land had the highest temperatures, ranging from 20.6°C to 24.96°C, and waterbodies had the lowest temperatures, ranging from 17.85°C to 20.47°C. From 2012 to 2017, LST exhibited an increasing trend. However, after the lockdown, LST declined slightly in 2022. The mean LST variations in the study area from 2012 to 2022, presenting a pre- and post-pandemic scenario, were also highlighted in this study. Furthermore, this study emphasized the correlation analysis between LST and four spectral indices, which are the Normalized Difference Built-up Index (NDBI), the Normalized Difference Vegetation Index (NDVI), the Soil Adjusted Vegetation Index (SAVI), and the Modified Normalized Difference Water Index (MNDWI). Multiple linear regression (MLR) containing NDVI and MNDWI with LST has been consistently the best-fit model for 2012, 2017 and 2022. These models have been established using various statistical tools, primarily the Akaike information criterion (AIC) model selection and the Inflation Factor (VIF). The results provide a framework for sustainable urban design and development, which can serve as a resource for policymakers and increase public understanding.
Machine Learning (ML) was used to assess and predict urban air temperature (Tair) considering the complexity of the terrain features in Yerevan (Armenia). The estimation was performed based on the PLSR model with a high number (30) of input variables. The relevant parameters include a newly purposed modification of spectral index IBI-SAVI, which turned out to be strongly impacting on Tair prediction together with land surface temperature (LST). Cross-validation analysis on temperature predictions across a station-centered 1000m circular area revealed quite a high correlation (R2Val = 0.77, RMSEVal = 1.58) between predicted and measured Tair from the test set. It was concluded the remote sensing is an effective tool to estimate Tair distribution where a dense network of weather stations is not available. However, further developments will include incorporation of additional weather parameters from the weather stations such as precipitation and wind speed, and the use of non-parametric ML techniques.
Urban Heat Island (UHI) refers to the manifestation of relatively higher surface and air temperatures in the urban centres with respect to the immediate surroundings where land use/land cover is generally green in nature (forest or agriculture). It is a local climate system in which the temperatures are observed to follow a slope in the direction radially outwards from the city centre. In this study, UHI of the Srinagar City Region has been mapped along the time series from 1991 to 2020 and the scenario has been predicted also for 2030. Land Surface Temperature (LST), retrieved from the thermal bands of Landsat 5 images for 1991, 1999 and 2010, and from Landsat 8 images of 2020 using Mono-window (MW) Algorithm was used to understand the temperature trend and the evolution of UHI zones in the Srinagar City region. The results show that the mean surface temperature of the study area has shifted from 16.04 ℃ in 1991 to 26.21 ℃ in 2020. In the same time period, the area of UHI zone has grown at a rate of 2.85 km2 per year from 1991 to 2020. It expanded from 13.57 km2 (1.82% of total area) in 1991 to 96.18 km2 (12.90% of total area) in 2020. According to Multi-layer Perceptron Neural Network (MLPNN), which was used to predict the mean surface temperature, Srinagar City Region will experience 27.21 ℃ in 2030. This study also predicted the potential scenario of UHI zones for 2030 using Cellular Automata-Markov Chain Integrated Model (CA-Markov). It forecasts that in 2030, with an expansion rate of 12.57 km2 per year, UHI zone of Srinagar City Region will expand to 221.94 km2 covering 29.77% of the total area, if the present scenarios continue.
Urban heat is considered as a worrying issue in cities because of the unbearable feelings associated with heat especially on sunny days. Urban heat is not as a result of any one time event but a chain of processes associated with land use activities such as infrastructure development that replaces the green vegetation. This paper investigated how land surface temperature has changed in the Kumasi Metropolis in 10 years from an environment of low temperature to much warmer land surface temperature due to the loss of trees in the city. The objectives of the study were to assess the extent of Land Surface Temperature (LST) in the metropolis, determine the kind of correlation that exist between Land Surface Temperate and vegetation health and examine the extent to which vegetation has influenced the city temperature. Multiple methods were used to calculate Land Surface Temperature (LST) such as converting the digital numbers of Landsat 2009, 2015 and 2019 images to radiance, top of brightness temperature and at-sensor brightness temperature using the Plank’s inverse function. NDVI analysis was done by subtracting the near infrared bands from the red band, divided by addition of the near infrared band to the red Band. Study results showed a linear increase in LST from an average of about 18°C to 31°C. The NDVI result showed decline in vegetation cover as such the correlation analysis was a negative correlation showing places of high temperature had minimal vegetation cover while places of low temperature had more vegetation cover.
Elucidating the impact of Land Surface Temperature (LST) is an important aspect of urban studies. The impact of urbanization on LST has been widely studied to monitor the Urban Heat Island (UHI) phenomenon. However, the sensitivity of various urban factors such as urban green spaces (UGS), built-up area, and water bodies to LST is not sufficiently resolved for many urban settlements. By using remote sensing techniques, this study aimed to quantify the influence of urban factors on LST in the two traditional cities (i) Panaji and (ii) Tumkur of India, proposed to be developed as smart cities. Landsat data were used to extract thematic and statistical information about urban factors using the Enhanced Built-up and Bareness Index (EBBI), Modified Normalized Difference Water Index (MNDWI), and Soil Adjusted Vegetation Index (SAVI). The multivariate regression model revealed that the value of adjusted R2 was 0.716 with a standard error of 1.97 for Tumkur city, while it was 0.698 with a standard error of 1.407 for Panaji city. The non-parametric correlation test brought out a strong negative correlation between MNDWI and LST with a value of 0.83 for Panaji, and between SAVI and LST with a value of 0.77 for Tumkur. The maximum percentage share of cooling surfaces are water bodies in Panaji with 35% coverage and green spaces in Tumkur with 25% coverage. Apparently, the UGS and water bodies can help in bringing down the LST, as well as facilitating healthy living conditions and aesthetic appeal. Therefore, the significance of ecosystem services (green spaces and water bodies) should be given priority in the decision-making process of sustainable and vibrant city development.
The Urban Heat Island (UHI) effect is analyzed using LANDSAT8 satellite data acquired on two episodes of heatwaves over Hong Kong and processed using the split-window algorithm to retrieve the Land Surface Temperature (LST) over this area. The in situ ambient air temperatures measured by a number of local weather stations of the Hong Kong Observatory were used to validate the acquired LST. Regional temperature changes for the Hong Kong area for the 21st century generated using the climate scenario generator tool MAGICC/SCENGEN and constrained to SRES A2AIM project a rise in temperatures of between +0.9 and +5.4 °C. The results show the existence of severe UHI effects between urban and sub-urban localities during two severe heatwave events. Geospatial analysis of this local UHI problem quantifies how urban parks can minimize the UHI effect and a number of adaptation measures related to urban spatial planning are being recommended in view of a changing climate.
To better understand the relationship between energy consumption, and prevailing climatic condition, the present study uses Hong Kong’s observed air temperature records, end-use electricity consumption, and population datasets to: (a) investigate the spatial pattern of cooling energy requirement i.e. cooling degree days on a typical normal and extremely hot summer day using co-kriging geospatial mapping technique; (b) analyze the annual trend of cooling degree days in the city; and (c) quantify the impact of extreme heat events on the summer cooling energy requirements. Results revealed reasonable predictability of city-wide cooling degree days with the co-kriging method which uses two covariates i.e. “elevation of the weather station” and “building volume density within the 1000 m radius neighboring area”. Homogeneity and heterogeneity in cooling degree days’ distribution were found during the summer daytime and nighttime, respectively indicating the method’s ability to delineate the urban heat island effect with increased magnitude during extreme heat events. Quantitatively, the extreme heat events increased cooling degree days by 80–140% depending on the event type, a range consistent in recent years (2011–2015). Lastly, we provided the implications of our findings to building and urban design; and future energy planning.
The concept of Local Climate Zone (LCZ) has been developed to quantify the correlation between urban morphology and urban heat island (UHI). Each LCZ is supposed to have homogenous air temperature. However, traditional air temperature observation methods have limited spatial coverage and poor spatial resolution. Land surface temperature (LST) acquired from satellite images can be used to study the temperature characteristics of LCZ classes by providing continuous data on surface temperature. This study aims to study the relationship between LST and LCZ classes with Shanghai selected as a case study because of its high urbanization rate and serious UHI effect. This study has three major steps: Firstly, Shanghai local climate zone map was generated using the World Urban Database and Portal Tool (WUDAPT) method. Secondly, a remote sensing approach was taken to acquire Shanghai's LST from night-time Aster thermal data in different seasons. Thirdly, the LST was associated with the Shanghai LCZ map and the correlation between LCZ and LST in Shanghai was discussed. The results show that there are large variations in LST across LCZ classes in different months in Shanghai. These results will be able to offer integrated information under urban climate principles for urban planners and urban climate researchers.
This study examines and validates a technique for spatial enhancement of thermal satellite images for urban heat island analysis, using a nighttime ASTER satellite image. The technique, termed Emissivity Modulation, enhances the spatial resolution while simultaneously correcting
the image derived temperatures for emissivity differences of earth surface materials. A classified image derived from a higher resolution visible wavelength sensor is combined with a lower resolution thermal image in the emissivity correction equation in a procedure derived from the Stephan
Bolzmann law. This has the effect of simultaneously correcting the image-derived “Brightness Temperature” (Tb) to the true Kinetic Temperature (Ts), while enhancing the spatial resolution of the thermal data. Although the method has been used for studies of the urban
heat island, it has not been validated by comparison with “in situ” derived surface or air temperatures, and researchers may be discouraged from its use due to the fact that it creates sharp boundaries in the image. The emissivity modulated image with 10 m pixel size was found
to be highly correlated with 18 in situ surface and air temperature measurements and a low Mean Absolute Difference of 1 K was observed between image and in situ surface temperatures. Lower accuracies were obtained for the Ts and Tb images at 90 m resolution. The study demonstrates
that the emissivity modulation method can increase accuracy in the computation of kinetic temperature, improve the relationship between image values and air temperature, and enable the observation of microscale temperature patterns.
An urban canyon energy budget simulation model is described. The model is simple and computationally inexpensive enough to permit characterization of the spatial and temporal variability of canyon energy exchanges by running multiple cases. It is two-dimensional and dry and avoids the necessity for computational fluid dynamical calculations by using parameterizations for the down-canyon wind profile and cross-canyon vortex flow, and by employing an exchange coefficient formulation for sensible heat loss from the walls. These are combined with radiation calculations using the procedure of Arnfield (1976, 1982) and substrate heat fluxes computed using the Fourier heat conduction equation and wall and floor thermal property information. The model is validated against the measured canyon energy budget data of Nunez and Oke (1977) and reproduces these data quite well, with Willmott indices of agreement in the range 0.91 to 1.00 and root-mean-square errors of 14 to 46 W m-2 for individual facets and for the total canyon system. The poorest correspondence was for the substrate heat flux, the maxima for which were overestimated, suggesting use of excessively large facet thermal properties. These inputs were adjusted based on the measurement-simulation discrepancies, leading to further improvement in model performance. The adjusted thermal properties are consistent both with published data and with the available information on the morphological characteristics of the canyon. [Key words: urban canyon energy budget, urban climate, numerical simulation model.]
Standard automated weather data were collected over a 70-day period during summer 1989 at three sites in the Phoenix metropolitan area. Mean hourly observations of air temperature at 1.5 m and 3.0 m, infrared surface temperature, vapor pressure, incoming solar radiation, and vector wind velocity are used for a stepwise multiple regression analysis. Hourly regression equations, totalling 144 in number, are derived for each site with air temperature at 1.5 m and 3.0 m as dependent variables. Not surprisingly, surface temperature is the primary independent variable significantly correlated with both levels of air temperature. Atmospheric stability significantly decreases the surface-to-air temperature coupling process at each site. Wind velocity, which displays strong diurnal variability, decouples the surface and air temperature. The surface/air temperature relationships at the two more complex sites studied are significantly influenced by advection from adjacent land uses. Thermal admittance and evapotranspiration are determined to affect strongly the surface-air temperature relationships at a periodically irrigated grass site, and play a less significant role at the more complex sites studied. Helicopter remotely sensed temperatures provide further insights into the representativeness of the ground station data. [Key words: surface-air temperature, urban climate, scale, Phoenix.]
This paper presents the formulation and results of a simple numerical model designed to simulate the climate of an urban canyon. The model is two-dimensional in nature and is based on the observation that ambient airflow which tranverses the long-axis of a symmetrical canyon drives a circulating vortex within the canyon air space which results in the exchange of heat, mass and momentum at the canyon top. A simple model is described which consists of two components: a semi-empirical model to relate within-canyon airflow to ambient wind velocity, and an energy budget model for canyon surfaces. The model is capable of simulating many aspects of the canyon climate, including the canyon surface and top energy budget and surface and air temperatures. Sensitivity tests with the model indicate that the canyon top energy budget is remarkably stable for many changes in canyon parameters. Canyon geometry (expressed as a height/width ratio) caused the greatest changes with increased narrowness being associated with less heat exchange across the canyon top. The model's predictions appear intuitively reasonable and compare well with existing measurement data. The results suggest that the coupling between the urban boundary and canopy layers in terms of heat exchange may be a function of canyon geometry.
The paper demonstrates the relationship existing between the size of a village, town or city (as measured by its population), and the magnitude of the urban heat island it produces. This is accomplished by analyzing data gathered by automobile traverses in 10 settlements on the St. Lawrence Lowland, whose populations range from 1000 to 2 million inhabitants. The locations of these settlements effectively eliminate all non-urban climatic influences. The results are compared with previously published data.The analysis shows the heat island intensity under cloudless skies to be related to the inverse of the regional windspeed, and the logarithm of the population. A simple model is derived which incorporates these controls. In agreement with an extension of Summers' model the heat island appears to be approximately proportional to the fourth root of the population. With calm and clear conditions the relation is shown to hold remarkably well for North American settlements, and in a slightly modified form, for European towns and cities.
1] Urbanisation is estimated to result in 6 billion urban dwellers by 2050. Cities will be exposed to climate change from greenhouse gas induced radiative forcing, and localised effects from urbanisation such as the urban heat island. An urban land‐surface model has been included in the HadAM3 Global Climate Model. It shows that regions of high population growth coincide with regions of high urban heat island potential, most notably in the Middle East, the Indian sub‐continent, and East Africa. Climate change has the capacity to modify the climatic potential for urban heat islands, with increases of 30% in some locations, but a global average reduction of 6%. Warming and extreme heat events due to urbanisation and increased energy consumption are simulated to be as large as the impact of doubled CO 2 in some regions, and climate change increases the disparity in extreme hot nights between rural and urban areas.
Thermal remote sensing has been used over urban areas to assess the urban heat island, to perform land cover classifications and as input for models of urban surface atmosphere exchange. Here, we review the use of thermal remote sensing in the study of urban climates, focusing primarily on the urban heat island effect and progress made towards answering the methodological questions posed by Roth et al. [International Journal of Remote Sensing 10 (1989) 1699]. The review demonstrates that while some progress has been made, the thermal remote sensing of urban areas has been slow to advance beyond qualitative description of thermal patterns and simple correlations. Part of the difficulty lies in the tendency to use qualitatively based land use data to describe the urban surface rather than the use of more fundamental surface descriptors. Advances in the application of thermal remote sensing to natural and agricultural surfaces suggest insight into possible methods to advance techniques and capabilities over urban areas. Improvements in the spatial and spectral resolution of current and next-generation satellite-based sensors, in more detailed surface representations of urban surfaces and in the availability of low cost, high resolution portable thermal scanners are expected to allow progress in the application of urban thermal remote sensing to the study of the climate of urban areas.
A large number of urban surface energy balance models now exist with different assumptions about the important features of the surface and exchange processes that need to be incorporated. To date, no comparison of these models has been conducted; in contrast, models for natural surfaces have been compared extensively as part of the Project for Intercomparison of Land-surface Parameterization Schemes. Here, the methods and first results from an extensive international comparison of 33 models are presented. The aim of the comparison overall is to understand the complexity required to model energy and water exchanges in urban areas. The degree of complexity included in the models is outlined and impacts on model performance are discussed. During the comparison there have been significant developments in the models with resulting improvements in performance (root-mean-square error falling by up to two-thirds). Evaluation is based on a dataset containing net all-wave radiation, sensible heat, and latent heat flux observations for an industrial area in Vancouver, British Columbia, Canada. The aim of the comparison is twofold: to identify those modeling approaches that minimize the errors in the simulated fluxes of the urban energy balance and to determine the degree of model complexity required for accurate simulations. There is evidence that some classes of models perform better for individual fluxes but no model performs best or worst for all fluxes. In general, the simpler models perform as well as the more complex models based on all statistical measures. Generally the schemes have best overall capability to model net all-wave radiation and least capability to model latent heat flux.
A large number of urban surface energy balance models now exist with different assumptions about the important features of the surface and exchange processes that need to be incorporated. To date, no comparison of these models has been conducted; in contrast, models for natural surfaces have been compared extensively as part of the Project for Intercomparison of Land Surface Parameterisation Schemes. Here, the methods and first results from an extensive international comparison of 33 models are presented. The aim of the comparison overall is to understand the complexity required to model energy and water exchanges in urban areas. The degree of complexity included in the models is outlined and impacts on model performance are discussed. During the comparison there have been significant developments in the models with resulting improvements in performance (root mean square error falling by up to two-thirds). Evaluation is based on a dataset containing net all-wave radiation, sensible heat and latent heat flux observations for an industrial area in Vancouver. The aim of the comparison is two-fold: to identify those modelling approaches that minimise the errors in the simulated fluxes of the urban energy balance and to determine the degree of model complexity required for accurate simulations. There is evidence that some classes of models perform better for individual fluxes but no model performs best or worst for all fluxes. In general, the simpler models perform as well as the more complex models based on all statistical measures. Generally the schemes have best overall capability to model net all wave radiation and least capability to model latent heat flux
In 2003, a Heat Health Watch Warning System was developed in France to anticipate heat waves that may result in a large excess of mortality. The system was developed on the basis of a retrospective analysis of mortality and meteorological data in fourteen pilot cities. Several meteorological indicators were tested in relation to levels of excess mortality. Computations of sensibility and specificity were used to choose the meteorological indicators and the cut-offs. An indicator that mixes minimum and maximum temperatures was chosen. The cut-offs were set in order to anticipate events resulting in an excess mortality above 100% in the smallest cities and above 50% in Paris, Lyon, Marseille and Lille. The system was extended nationwide using the 98th percentile of the distribution of minimum and maximum temperatures. A national action plan was set up, using this watch warning system. It was activated on 1st June 2004 on a national scale. The system implies a close cooperation between the French Weather Bureau (Météo France), the National Institute of Health Surveillance (InVS) and the Ministry of Health. The system is supported by a panel of preventive actions, to prevent the sanitary impact of heat waves.
The August 2003 heat wave in France resulted in many thousands of excess deaths particularly of elderly people. Individual and environmental risk factors for death among the community-dwelling elderly were identified. We conducted a case-control survey and defined cases as people aged 65 years and older who lived at home and died from August 8 through August 13 from causes other than accident, suicide, or surgical complications. Controls were matched with cases for age, sex, and residential area. Interviewers used questionnaires to collect data. Satellite pictures provided profiles of the heat island characteristics around the homes. Lack of mobility was a major risk factor along with some pre-existing medical conditions. Housing characteristics associated with death were lack of thermal insulation and sleeping on the top floor, right under the roof. The temperature around the building was a major risk factor. Behaviour such as dressing lightly and use of cooling techniques and devices were protective factors. These findings suggest people with pre-existing medical conditions were likely to be vulnerable during heat waves and need information on how to adjust daily routines to heat waves. In the long term, building insulation and urban planning must be adapted to provide protection from possible heat waves.
Daily numbers of deaths at a regional level were collected in 16 European countries. Summer mortality was analyzed for the reference period 1998-2002 and for 2003. More than 70,000 additional deaths occurred in Europe during the summer 2003. Major distortions occurred in the age distribution of the deaths, but no harvesting effect was observed in the months following August 2003. Global warming constitutes a new health threat in an aged Europe that may be difficult to detect at the country level, depending on its size. Centralizing the count of daily deaths on an operational geographical scale constitutes a priority for Public Health in Europe.
Atmospheric sounding products from NOAA's polar-orbiting satellites were used to derive and test predictive equations of rural shelter-level maximum and minimum temperatures. Sounding data from both winter and summer months were combined with surface data from over 5300 cooperative weather stations in the continental United States to develop multiple linear regression equations. Separate equations were developed for both maximum and minimum temperature, using the three types of sounding retrievals (clear, partly cloudy, and cloudy). Clear retrieval models outperformed others, and maximum temperatures were more accurately predicted than minimums. Average standard deviations of observed rural shelter temperatures within sounding search areas were of similar magnitude to root-mean-square errors from satellite estimates for most clear and partly cloudy cases, but were significantly less for cloudy retrieval cases. Model validation for surrogate polar and tropical climatic regions showed success in application of the four clear retrieval models (maximum and minimum temperature, for both winter and summer). This indicates the potential adaptability of these models to estimates of rural shelter temperature in areas outside of the United States.
In recognition of the limited application of the satellite-based
`nadir' viewpoint for monitoring urban microclimate, a study was carried
out to investigate the three-dimensional temperature relationships in
high rise housing estates in Singapore. The data derived from this study
are combined with LANDSAT TM thermal data to construct an automated
3-dimensional model of the high rise urban environment which can be
varied according to Sun angle and azimuth at the time of imaging and the
viewing angle required by the user
The relations between surface temperature and air temperature on clear winter nights were investigated with regard to spatial scale and the vegetation effect at a local meteorological scale. The study was based on nighttime images obtained from the Landsat Thematic Mapper and high-density meteorological data obtained from the Automated Meteorological Data Acquisition System (AMeDAS). The correlation coefficients between the air temperatures and the surface temperatures at the AMeDAS stations were relatively high despite the simple comparison. Surface temperature alone explained 80% of the observed variation in air temperature. The spatial scales of the effect of surface temperature on air temperature and the effect of vegetation density on air temperature were related to the mean lapse rate of the atmospheric boundary layer. Air temperature was more sensitive to vegetation density when the mean lapse rate of the atmospheric boundary layer was smaller. Accuracy in the estimation of air temperature from satellite-derived surface temperature data was improved by multiple regression using the spatially averaged surface temperature and normalized difference vegetation index.
An observation program using ground and airborne thermal infrared radiometers is used to estimate the surface temperature of urban areas, taking into account the total active surface area. The authors call this the complete urban surface temperature. This temperature is not restricted by the viewing biases inherent in remote sensors used to estimate surface temperature over rough surfaces such as cities. Two methods to estimate the complete surface temperature are presented. Results for three different land-use areas in the city of Vancouver, British Columbia, Canada, show significant differences exist between the complete, nadir, and off-nadir airborne estimates of urban surface temperature during daytime. For the sites and times studied, the complete surface temperature is shown to agree with airborne off-nadir estimates of the apparent surface temperature of the most shaded walls. Some implications of using the complete surface temperature to estimate screen level air temperature and to calculate surface sensible heat flux are given.
The aim of this study is to characterize the urban heat island (UHI) intensity in Hong Kong. The first objective is to explore the UHI intensity in Hong Kong by using the mobile transverse and remote sensing techniques. The second objective is to produce a satellite-derived air temperature image by integrating satellite remote sensing with a mobile survey, the methodology involved in making simultaneous ground measurements when the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite made an overpass. The average UHI intensity of Hong Kong was about 28-3.58C, although a very high value of 128C UHI was observed on a calm winter night by ASTER. The satellite-derived surface temperature was compared with the in situ measurements. The bias was found to be only about 1.18C. A good correlation was also found between the in situ surface and air temperature pair of readings at nighttime on 31 January 2007. The linear regression lines of temperatures in urban and suburban areas were then used to convert the satellite-derived surface temperatures into air temperatures. The satellite-derived air tempera- tures showed a good correlation with temperatures observed by 12 fixed stations. It is possible to derive the nighttime air temperature from the satellite surface temperature on calm and clear winter nights.
The use of imaging spectrometry for urban applications has made considerable progress over the past few years, in tandem with advances in high-spatial resolution urban remote sensing using sensors such as IKONOS. Imaging spectrometers acquire a large number of spectral bands with narrow bandwidths, and numerous studies have taken advantage of the vast amount of spectral detail for precise identification of chemical and physical material properties (Goetz et al., 1985). Traditionally, the majority of imaging spectrometry, also referred to as hyperspectral remote sensing, has focused on natural targets such as vegetation (e.g., Roberts et al., 1993) and minerals (e.g., Clark, 1999).
Throughout the tropics, population movements, urban growth, and industrialization are causing conditions that result in elevated temperatures within urban areas when compared with that in surrounding rural areas, a phenomenon known as the urban heat island (UHI). One such example is the city of San Juan, Puerto Rico. Our objective in this study was to quantify the UHI created by the San Juan Metropolitan Area over space and time using temperature data collected by mobile- and fixed-station measurements. We also used the fixed-station measurements to examine the relationship between average temperature at a given location and the density of remotely sensed vegetation located upwind. We then regressed temperatures against regional upwind land cover to predict future temperature with projected urbanization. Our data from the fixed stations show that the average nighttime UHI calculated between the urban reference and rural stations (ΔTCBD–rural) was 2.15 °C during the usually wet season and 1.78 °C during the usually dry season. The maximum UHI value for San Juan was calculated as 4.7 °C between the urban and forest sites and 3.9 °C between the urban and an open, rural site. Comparisons of diurnal temperature trends at urban, grassland, and forested sites indicate that canopy cover reduced daytime warming. Temperature was predicted best (r2 = 0.94) by vegetation in upwind easterly directions, especially that within 180 m of the sensor. Results from the mobile measurements show that the UHI has reached the base of the Luquillo Mountains. Predictions of future development and temperatures suggest that if the present pattern of development continues, over 140 km2 of land that showed no signs of UHI in 2000 will have an average annual UHI between + 0.4 and + 1.55 °C by 2050. Furthermore, more than 130 km2 of land area with a current UHI between + 0.4 and + 1.4 °C in 2000 will have an average UHI greater than + 1.55 °C by 2050. Copyright © 2010 Royal Meteorological Society
Recently, countermeasures against the urban heat island effect have become increasingly important in Tokyo. Such countermeasures include reduction of anthropogenic heat release and enhancement of urban ventilation. Evaluations of urban ventilation require construction of a high-resolution computational fluid dynamics (CFD) model, which takes into account complex urban morphology. The morphological complexity arises from multi-scale geometry consisting of buildings, forests, and rivers, which is superimposed on varying topography. Given this background, airflow and temperature fields over the 23 wards of Tokyo were simulated with a CFD technique using a total of approximately 5 billion computational grid cells with a horizontal grid spacing of 5 m. The root mean square (RMS) error of the air temperature between the simulation and observation results at 127 points was 1.1 °C. Using the developed model, an urban redevelopment plan for two districts in metropolitan Tokyo was examined from the viewpoint of air temperature mitigation. Numerical results showed that a reduction by 1 ha in the area covered by buildings increases the area with temperatures below 30 °C by 12 ha. Copyright © 2010 Royal Meteorological Society
In summer (May–September), deaths associated with heat stroke are found to occur when the daily maximum net effective temperature (NET) exceeds 26. Using Poisson regression, it is estimated that the mean mortality associated with heat stroke would double per unit rise in NET beyond 26. In contrast to those in most temperate regions, the daily mortality for circulatory and respiratory related causes of death is not statistically correlated to the daily maximum NET or temperature in summer. In winter (November–March), there are statistically significant negative-lagged correlations between the daily minimum NET and the daily mortality attributed to circulatory and respiratory diseases. The increase in mortality per unit decrease in NET is found to be the highest for hypothermia, and slightly higher for circulatory causes than respiratory causes. Deaths associated with hypothermia start to occur when the daily minimum NET is less than 14 and the mean mortality is estimated to increase by about 1.3-fold per unit fall in NET below 14. The elderly age group (age ≧ 65 years) is found to be more vulnerable to NET changes when compared to other age groups. Copyright © 2008 Royal Meteorological Society
To bridge the gaps between traditional mesoscale modelling and microscale modelling, the National Center for Atmospheric Research, in collaboration with other agencies and research groups, has developed an integrated urban modelling system coupled to the weather research and forecasting (WRF) model as a community tool to address urban environmental issues. The core of this WRF/urban modelling system consists of the following: (1) three methods with different degrees of freedom to parameterize urban surface processes, ranging from a simple bulk parameterization to a sophisticated multi-layer urban canopy model with an indoor–outdoor exchange sub-model that directly interacts with the atmospheric boundary layer, (2) coupling to fine-scale computational fluid dynamic Reynolds-averaged Navier–Stokes and Large-Eddy simulation models for transport and dispersion (T&D) applications, (3) procedures to incorporate high-resolution urban land use, building morphology, and anthropogenic heating data using the National Urban Database and Access Portal Tool (NUDAPT), and (4) an urbanized high-resolution land data assimilation system. This paper provides an overview of this modelling system; addresses the daunting challenges of initializing the coupled WRF/urban model and of specifying the potentially vast number of parameters required to execute the WRF/urban model; explores the model sensitivity to these urban parameters; and evaluates the ability of WRF/urban to capture urban heat islands, complex boundary-layer structures aloft, and urban plume T&D for several major metropolitan regions. Recent applications of this modelling system illustrate its promising utility, as a regional climate-modelling tool, to investigate impacts of future urbanization on regional meteorological conditions and on air quality under future climate change scenarios. Copyright © 2010 Royal Meteorological Society
Summer warming trends in Western Europe are increasing the incidence, intensity and duration of heat waves. They are especially deadly in large cities owing to population density, physical surface properties, anthropogenic heat and pollutants. In August 2003, for 9 consecutive days, the Paris metropolitan area experienced an extreme heat wave that caused 4867 estimated heat-related deaths. A set of 61 NOAA-AVHRR (advanced very high-resolution radiometer) images and one SPOT-high resolution visible (HRV) image were used to analyse the spatial variations of land surface temperature (LST) over the diurnal cycle during the heat wave. The LST patterns were markedly different between daytime and night-time. A heat island was centred downtown at night, whereas multiple temperature anomalies were scattered in the industrial suburbs during the day. The heat wave corresponded to elevated nocturnal LST compared to normal summers. The highest mortality ratios matched the spatial distribution of the highest night-time LSTs, but were not related to the highest daytime LSTs. LSTs were sampled from images at the addresses of 482 elderly people (half were deceased persons and half were control ones) to produce daily and cumulative minimal, maximal and mean thermal indicators, over various periods of time. These indicators were integrated into a conditional logistic regression model to test their use as heat exposure indicators, based on risk factors. Over the period 1–13 August, thermal indicators taking into account minimum nocturnal temperatures averaged over 7 days or over the whole period were significantly linked to mortality. These results show the extent of the spatial variability in urban climate variables and the impact of night-time temperatures on excess mortality. These results should be used to inform policy and contingency planning in relation to heat waves, and highlight the role that satellite remote sensing can play in documenting and preventing heat-related mortality. Copyright © 2010 Royal Meteorological Society
A simple energy balance model which simulates the thermal regime of urban and rural surfaces under calm, cloudless conditions at night is used to assess the relative importance of the commonly stated causes of urban heat islands. Results show that the effects of street canyon geometry on radiation and of thermal properties on heat storage release, are the primary and almost equal causes on most occasions. In very cold conditions, space heating of buildings can become a dominant cause but this depends on wall insulation. The effects of the urban greenhouse and surface emissivity are relatively minor. The model confirms the importance of local control especially the relation between street geometry and the heat island and highlights the importance of rural thermal properties and their ability to produce seasonal variation in the heat island. A possible explanation for the small heat
To obtain a local-scale urban energy balance by either measurement or modeling it is necessary to determine storage heat flux (ΔQs). This flux cannot be measured directly due to the complexity of the urban surface. The Grimmond et al. Objective Hysteresis Model (OHM) [C.S.B. Grimmond, H.A. Cleugh, T.R. Oke, An objective urban heat storage model and its comparison with other schemes, Atmos. Environ., 25B (1991) 311–326] of local-scale ΔQs combines empirical equations for individual surface types in the proportion that they are present within the urban area. One surface type for which there is very limited field data is the urban canyon, which consists of the walls of adjacent buildings, the horizontal street-level area separating them (roadways, gardens, parking lots, etc.) and the enclosed air volume. Here the storage heat flux of an urban canyon and the resulting OHM parameters are investigated with a numerical model of a dry urban canyon energy budget. Substrate heat fluxes are derived from simulated surface and substrate temperatures; the latter evolve through time according to the finite difference form of the Fourier heat conduction equation. When compared against measured fluxes, the model performed satisfactorily. Numerical experiments show significant effects on the OHM parameters due to changes in the ratio of building height to separation distance and building wall thermal properties. Effects of intermediate significance were attributable to canyon orientation, wind speed and the timing of the between-building air temperature regime. Air temperature and the timing of the wind speed curve showed only minor significance.