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A new method to quantify surface urban heat island intensity

  • Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences

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Reliable quantification of urban heat island (UHI) can contribute to the effective evaluation of potential heat risk. Traditional methods for the quantification of UHI intensity (UHII) using pairs-measurements are sensitive to the choice of stations or grids. In order to get rid of the limitation of urban/rural divisions, this paper proposes a new approach to quantify surface UHII (SUHII) using the relationship between MODIS land surface temperature (LST) and impervious surface areas (ISA). Given the footprint of LST measurement, the ISA was regionalized to include the information of neighborhood pixels using a Kernel Density Estimation (KDE) method. Considering the footprint improves the LST-ISA relationship. The LST shows highly positive correlation with the KDE regionalized ISA (ISAKDE). The linear functions of LST are well fitted by the ISAKDE in both annual and daily scales for the city of Berlin. The slope of the linear function represents the increase in LST from the natural surface in rural regions to the impervious surface in urban regions, and is defined as SUHII in this study. The calculated SUHII show high values in summer and during the day than in winter and at night. The new method is also verified using finer resolution Landset data, and the results further prove its reliability.
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A new method to quantify surface urban heat island intensity
Huidong Li
, Xiaoma Li
, Xun Wang
, Sahar Sodoudi
Institute of Meteorology, Freie Universität Berlin, Berlin, Germany
Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA, USA
Forestry Experiment Center of North China, Chinese Academy of Forestry, Beijing, China
Quantifyingsurface urbanheat island in-
tensity using the relationship between
LST and Impervious Surface Areas.
The impervious surface areas was re-
gionalized within the footprint of remote
sensing observation using a Kernel Den-
sity Estimation method.
Linear functions of LST were well tted
using the regionalized impervious sur-
face areas.
Slope of the linear function of LST was
dened as the surface urban heat island
abstractarticle info
Article history:
Received 17 August 2017
Received in revised form 30 November 2017
Accepted 30 November 2017
Available online xxxx
Reliable quantication of urban heat island (UHI) can contribute to the effective evaluation of potential heat risk.
Traditional methods for the quantication of UHI intensity (UHII) using pairs-measurements are sensitive to the
choice of stations or grids. In order to get rid of the limitation of urban/rural divisions, this paper proposes a
new approach to quantify surface UHII (SUHII) using the relationship between MODIS land surface temperature
(LST) and impervious surface areas (ISA). Given the footprint of LST measurement, the ISA was regionalized to in-
clude the information of neighborhood pixels using a Kernel Density Estimation (KDE) method. Considering the
footprint improves the LST-ISA relationship. The LST shows highly positive correlation with the KDE regionalized
). The linear functions of LST are well tted by the ISA
in both annual and daily scales for the city of
Berlin. The slope of the linear function represents the increaseinLSTfromthenaturalsurfaceinruralregionstothe
impervious surface in urban regions, and is denedas SUHII in this study.The calculatedSUHII show highvalues in
summer and during the day than in winter and at night. The new method is also veried using ner resolution
Landset data, and the results further prove its reliability.
© 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://
Surface urban heat island
Land surface temperature
Impervious surface area
Kernel density estimation
Footprint of remote sensing observation
1. Introduction
Urban areas show higher temperature than the surrounding rural
areas, which is well known as Urban Heat Island (UHI) effect. Since its
rst observation by Howard in London (Mills, 2008), UHI phenomenon
has been widely reported in different sized cities (e.g. Arneld, 2003;
Zhang et al., 2010; Zhou et al., 2017). Warmer air caused by UHI increases
heat load stress of urban residents, potentially raising the threat of mor-
tality (e.g. Tan et al., 2010; Constantinescu et al., 2016). Meanwhile,
higher temperature increases energy consumption and associated
greenhouse gas emissions due to the use of air conditioning (e.g. Zhou
and Gurney, 2010; Zhou et al., 2012). Under the background of fast ur-
banization (e.g. Kuang et al., 2013, 2016b) and global change (e.g.
Science of the Total Environment 624 (2018) 262272
Corresponding author.
E-mail address: (S. Sodoudi).
0048-9697/© 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (
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journal homepage:
Grimm et al., 2008), residential living in cities is suffering from higher
risk of heat wave (e.g. Zhou et al., 2014, 2015; Yang et al., 2017).
Concerning the increasing possible hazards cased by UHI, more and
more attention has been paid to the studies of UHI (Inouye, 2015;
McDonnell and MacGregor-Fors, 2016; Lee et al., 2015).
Accurate quantication of UHI can help to efciently evaluate the po-
tential heat risk and to guide the city management and development for
government and city planners. Urban heat island intensity (UHII), the
difference in temperature between urban and surrounding rural regions,
is the classical indicator to quantitatively describe UHI effect (Rizwan et
al., 2008; Stewart, 2011). Traditionally, the detection of UHII is conducted
at two xed in-situ stations, one in urban and the other in rural regions
(e.g. Yang et al., 2013; Earl et al., 2016). Similarly, the study of the surface
UHII (SUHII) using remote sensing data is conducted over selected pixels
that are located in the urban and rural regions, separately (e.g. Stewart,
2011). The estimation of UHII (SUHII) relies on the denitions of urban
and rural stations or pixels (e.g. Roth et al., 1989; Azevedo et al., 2016;
Du et al., 2016). However, urban regions are strongly affected by
human activities with high heterogeneity over the urban surface, and
even the surrounding rural areas may have different ecosystems
(Buyantuyev and Wu, 2010; Cadenasso et al., 2007). The urban-rural di-
chotomy alone cannot sufciently guide the choice of the stations
(Stewart and Oke, 2012). Schwarz et al. (2011) compared eleven ap-
proaches for quantifying SUHII with MODIS land surface temperatures
for European cities, and found that the calculated SUHIIs using different
rural pixels showed weak correlations. The different denitions of the
urban/rural regions make the intercomparison study of UHII among dif-
ferent cities challenging. Stewart (2011) argued that previous UHI stud-
ies that used two stations measurements are often not comparable
because of the different denitions of the measurement stations and
the lack of the crucial description of these stations. On the other hand,
xed stations or pixels only represent the local micro-climate around
these stations or pixels (Oke, 2006). Limited measurements only reect
parts of the characteristics of UHI (SUHI), and cannot identify the spatial
variation and the structure of UHI within a whole city, especially for the
cities which have multiple UHI centers (e.g. Li and Yin, 2013; Dou et al.,
2015). The shape of cities could signicantly inuence the amplitude of
UHI (Zhang et al., 2012; Zhou et al., 2017). To overcome the problems
mentioned above, a promising way for the quantication of UHII
(SUHII) should try to get rid of the limitation of urban/rural divisions,
and consider the comprehensive conditions of cities by integrating the
urban surface properties.
Land use change caused by urbanization is the primary driving factor
of UHI (e.g. Cheval and Dumitrescu, 2015; Du et al., 2016; Li et al., 2017).
The lower albedo and higher sealing degree of urban areas signicantly
alter the surface energy budget and lead to higher temperature than
rural areas (Oke, 1982, 1988; Kuang et al., 2015a, 2015b). Near surface
temperatures are closely related to urban indicators, such as Impervious
Surface Area (ISA). Yuan and Marvin (2007) found that there was a
strong linear relationship between LST and ISA for all seasons in Minne-
sota. Rajasekar and Weng (2009) pointed out that the areas with high
heat signatures had a strong correlation with impervious surfaces in cen-
tral Indiana. Imhoff et al. (2010) concluded that ISA was the primary
driver for the increase in temperature, explaining 70% of the total vari-
ance in LST for 38 the most populous cities combined in the continental
United States. Zhang et al. (2010) pointed that N60% of the total LST var-
iance was explained by ISA for urban settlements within forests at mid to
high latitudes globally. Li et al. (2011) reported a strong positive relation-
ship between LST and ISA in Shanghai. Schatz and Kucharik (2014) found
that ISA within the footprint of measurement stations was the dominant
driver of air temperature and accounted for 74% and 80% of the explained
spatial variation of the air temperature at night and during the day, re-
spectively, in Madison, Wisconsin. Kuang et al. (2017) found that the
highly dense impervious surface areas signicantly increased land sur-
face temperature. Wang et al. (2017) found that ISA was responsible
for 31%38% and 49%54% of air temperature variability during the day
and at night, respectively in Beijing. Compared with UHII, SUHII is usually
more dependent on ISA. This is because that the land cover is the single-
most dominant factor of LST, while the air temperature is affected by
land cover, air advection and anthropogenic heat emission, together
(Azevedo et al., 2016). To summer up, as a good indicator of urban
land use, ISA could reect the spatial pattern of UHI (SUHI). The relation-
ship between temperature and ISA can be a potential powerful tool for
the quantication of UHII (SUHII).
The temperature at each site is also affected by the surrounding envi-
ronment (Rannik et al., 2000). There is a footprint for the temperature
measurement (Oke, 2006). The measured temperature is related to the
overall land use information within the footprint. Schatz and Kucharik
(2014) and Wang et al. (2017) considered the footprint when examining
the relationship between in-situ air temperature and ISA. As for the re-
mote sensing, there is a mismatch between the observation results and
its ground source, especially for the mixed pixels over heterogeneous
areas, due to the variation of the view zenith angles and gridding pro-
cesses. Campagnolo and Montano (2014) found that the width of the
ground-projected instantaneous elds of view (IFOV) of MODIS products
was larger than the nominal resolutions, and increased with the view ze-
nith angles. The IFOV errors also exist in the Lansat data, especially in the
thermal band (Lee et al., 2004). The satellite observation result in each
pixel also contains information from neighboring pixels. Townshend et
al. (2000) found that parts of the signal in MODIS pixels come from the
surroundings. Peng et al. (2015) found that the size of the signal source
of MODIS pixels is larger than the nominal resolution of the pixel. As
thus, it is necessary to consider the inuence of the footprint of remote
sensing observation when study LST-ISA relationship.
This paper comes up with an approach to calculate SUHII based on
the linear relationship between LST and ISA. Given the footprint of LST
measurement, the ISA was regionalized to include the information of
neighborhood pixels within the footprint using a Kernel Density Estima-
tion (KDE) method. The linear regression function of LST was tted using
the KDE regionalized ISA (ISA
). The regression slope of the tted func-
tion was used as SUHII. The temporal variations of the calculated SUHII of
Berlin in 2010 were investigated. In addition, the new developed ISA
was compared with the raw ISA in terms of the tted functions of LST
and the calculated SUHII. The goal of this paper is to develop a promising
approach for the quantication of SUHII.
2. Study area, data, and methodology
2.1. Study area
The study area is Berlin, the capital city of Germany. Berlin (52.34°
52.68° N, 13.10°13.77° E) is located in Northeast Germany with a at to-
pography (34122 m, altitude), and covers an area of about 900 km
cording to a report in the year of 2015 of the Statistical Ofce of Berlin-
Brandenburg (, Berlin has
N3.6 million inhabitants, with one third living in the inner city in an
area of about 88 km
. It is the second most populous city in the European
Union. Fig. 1 shows the spatial pattern of CORINE land cover (Feranec et
al., 2007) version 2012 in the study area. Berlin is an urbanized region
with about 35% built-up areas. In addition, transportation and infrastruc-
ture areas cover about 20% of the city. Berlin's built-up areas create a mi-
croclimate with noticeable urban heat island effects, leading to a higher
potential heat stress risk in the central inner-city areas (Dugord et al.,
Berlin has a temperate maritime climate with a mean annual temper-
ature of 9.5 °C and annual precipitation of 591 mm (data based on the
German Meteorological Ofce Dahlem station measurements in the 30-
years period of 19812010). Affected by the prevailing westerlies and
abundant water vapor from Atlantic, Berlin has a windy and cloudy cli-
mate (Kottek et al., 2006). Fig. 2 exhibits the seasonal variations of the
cloud fraction, wind speed, and precipitataion in 2010. Most of the win-
ter days were cloudy with more than half of the sky covered by clouds,
263H. Li et al. / Science of the Total Environment 624 (2018) 262272
while a lot of the summer days had clear skies. The winter days had
stronger winds compared to the summer days. The precipitation was
not very concentrated. More than half of the days had precipitation larg-
er than 0.1 mm/d, and almost one-third of the days had precipitation
larger than 1.0 mm/d. Although the intensity of the precipitation was
not strong, the frequency was high. Most of the typical weather for UHI
with few clouds and calm wind occurred in summer.
2.2. LST data
Daily MODIS LST products (MOD11A1&MYD11A1) collection 005
with 1000 m resolution grids in 2010 were used in this study (Coll et
al., 2009). MODIS products have high temporal resolution, four observa-
tions per day. Moreover, MODISLST data have high quality. Wan (2008)
reported that the accuracy of MODIS LST product (collection 5) is better
than 1 K in most cases. Rigo et al. (2006) reported an accuracy of MODIS
LST b5%, even in urban environments. The freely available dataare easy
to access. MODIS LST data have been widely used to study SUHI in differ-
ent regions, such as Europe (e.g. Schwarz et al., 2011; Azevedo et al.,
2016), Asia (e.g. Kuang et al., 2015a, 2015b), and North America (e.g.
Zhang et al., 2014; Hu and Brunsell, 2015; Hu et al., 2016).
Affected by the variation in sensing geometry of the MODIS instru-
ments, the effective signal source is larger than the size of a MODIS
pixel. Wolfe et al. (2002) estimated that the width of the instantaneous
eld of view of the MODIS observation cells reached approximately 2.0
times and 4.8 times the nadir resolution in the track and scan directions
at the scan edge. Campagnolo and Montano (2014) found that in near
optimal locations, the effective resolution of the 250 m MODIS gridded
products varied between 344 and 835 m along rows and between 292
and 523 m along columns, respectively.
Two MODIS sensors are mounted on the Terra and Aqua satellites,
separately. Everyday, the Aqua satellite passed over Berlin at UTC time
around 11:50 and 01:20, and the Terra satellite passed over Berlin at
UTC time around 20:50 and 10:20. As for the MODIS daily LST images,
there is slight difference in the observation time among the pixels,
around 9 & 13 min for the two MOD images and 9 & 14 min during
the study period. Special care needs to be taken into this problemduring
the interpretation of the results. Clouds absorb the longwave radiation
from the earth surface, and then block the observation of LST
(Williamson et al., 2013). Here, the cloud-covered pixels are ltered
out for each image. The images with too much missing pixels, in partic-
ular within urban regions, cannot show the real feature of SUHII well.
Meanwhile, too many clouds could weaken UHI effect (Morris et al.,
2001). In this study, only the images havingN90% of valid pixels within
both the study areas and the urban areas (ISA larger than 25%) are cho-
sen for analysis. Berlin has a cloudy climate. Most days are cloudy days,
especially in winter (Fig. 2). Eventually, a total of 47, 34, 76 and 61 im-
ages at four observation times 10:20, 11:50, 20:50, and 01:20 are kept.
The mean LST for the whole year, summer (May to September) and
winter (October to April) are calculated using these selected images.
2.3. ISA data
High Resolution Layer imperviousness product version 2012 from
Copernicus Land Monitoring Service Pan-European Component (http:// was applied in this study. HRL prod-
ucts are produced from multi-source high resolution satellite imagery
through a combination of automatic processing and interactive rule-
based classication (Lefebvre et al., 2013). The imperviousness shows
the percentage of articial impervious cover (0100%), referring to the
built-up areas that are characterized by the substitution of the original
natural land cover or water surface with an articial surface (Langanke,
Fig. 1. Spatial pattern of the CORINE land cover in the study area.
50 100 150 200 250 300 350
Wind speed Cloud fraction Precipitation
Wind speed (m/s)
Julian day
Precipitation (mm)
Cloud fraction (%)
Fig. 2. Seasonal variations of the cloud fraction (blue curve), wind speed (red curve), and
precipitation (black curve) in 2010 for Berlin. (For interpretation of the references to
colour in this gurelegend, the reader is referred to the web version of this article.)
264 H. Li et al. / Science of the Total Environment 624 (2018) 262272
2013). Copernicus provides ISA products at both 20 m and 100 m resolu-
tion. The accuracy of the data was accessed based on a stratied system-
atic sampling approach using the EUROSTAT Land Use/Cover Area from
statistical Survey sampling frame (Sannier et al., 2016). The results sug-
gested that the Copernicus ISA data reached or even exceeded the re-
quired level of accuracy with b1015% error for both omission and
commission errors. This accuracy is similar to the other studies (Kuang
et al., 2016a). To make ISA match with LST in grids, the nearest approach
was applied to re-sample the ISA data from the resolution of 100 m to
1000 m on the platform of ArcGIS.
2.4. Regionalization of ISA using a kernel density estimation method
Land surface temperature measurement over each pixel has a foot-
print due to the adjacency effect (Justice et al., 1998). The measurements
data are not only affected by the surface of corresponding pixel, but also
affected by the surrounding pixels. In order to take the inuence of the
0 255075100
LST (°C)
Rural Suburban Urban
Fig. 3. Schematic diagram of the quantication of SUHII based on the linear regression
relationship between LST and ISA
and LST
mean the LST in the urban and rural
areas, respectively.
Fig. 4. Spatial distributions of the mean LST for Berlin in the year 2010.Three columns representthe mean values during different time periods: (left) wholeyear, (middle) summer,and
(right) winter. Rows (a), (b), (c), and (d) present the results at four observation times.
265H. Li et al. / Science of the Total Environment 624 (2018) 262272
footprint on the LST measurement into account, the ISA was regionalized
using a Kernel Density Estimation (KDE) method. As one of the most
well-established spatial techniques, KDE method could calculate the
contribution of the surrounding points. The density (f
) of a specic
point (x
) was calculated as the sum of the weights of neighbor points
) within a circular neighbor areas as follows
fKDE x0
 ð1Þ
where ris the kernel radius and controls the size of the circular neighbor-
hoods around x
.Kis the kernel function and controls the weight of the
neighbor points.
In this paper, the KDE calculation was conducted on the platform of
ArcGIS. Firstly, the raster image of ISA was converted to point feature
using the function of Raster to Pointunder the toolbox Conversion
Tools. Then the function of Kernel Densityunder the toolbox of Spatial
Analyst Toolswas used to calculate the density of each pointin a neigh-
borhood around each output raster cell with a resolution of 1000 m.
During this process, a smoothly curved surface (kernel surface) is tted
over a circular neighborhood of each point based on quartic kernelfunc-
tion (Silverman, 1986). The surface value is largest at the location of the
point and decreases with increasing distance fromthe point, reaching to
zero at the border of the circular neighborhood. The density at each out-
put raster cell is calculated byadding the values ofall the kernel surfaces
where they overlay the rastercell center. Then the f
were normalized
to the range of 0 to 100% as follows
max fKDE
min fKDE
The kernel radius could affect the KDE calculation results (Anderson,
2009; Thakali et al., 2015). Based on the calculation process of the KDE
method, the kernel radius should be larger than the distance between
the centers of two neighbor pixels (value of the spatial resolution of
the pixels, 1000 m). Meanwhile, the largest radius should be b4.8
times (4800 m) the nadir resolution of MODIS LST data based on the
study of Wolfe et al. (2002). In order to nd outthe optimal kernel radi-
us, a sensitivity test was conducted using kernel radius ranging from
1500 m to 5000 m with an interval of 500 m. The LST shows best
relationship with KDE regionalized ISA using kernel radius of 3000 m.
The details of the sensitivity study are discussed in the Section 4.1.
2.5. Quantication of SUHII
dominant reason for SUHI. Usually, the temperature increases with the
increase in ISA from rural to urban regions, presenting positive linear
trend (e.g. Yuan and Marvin, 2007; Schatz and Kucharik, 2014, 2015).
A linear regression function of LST can be tted using ISA (Fig. 3). The
slope of the tted function refers to the increase of LST versus ISA in-
creasing from 0% to 100%. The regression slope can be used to dene
SUHII, if a good regression function is tted. In this study, the regional-
ized ISA
is chosen to t the linear function of LST. Urban surface has
high heterogeneity. LST may vary largely among the grids that have sim-
ilar ISA
due to the difference in material, elements conguration and
morphological characteristics (Oke, 2006; Wang et al., 2017). Here, the
Table 1
Min-Max range of the mean LST (°C) for differentperiods and times of the day in Berlin.
Observation times Annual mean Summer mean Winter mean
MOD 10:20 9.20 11.09 7.91
MYD 11:50 11.24 12.76 8.88
MOD 20:50 6.32 6.45 5.97
MYD 01:20 8.52 8.35 5.40
Fig. 5. Spatial distributions of the (a) raw ISA and (b) KDE regionalized ISA using kernel
radius of 3000 m.
Fig. 6. Longitudinal proles of (ad)the mean LST and (e)the ISA
acrossthe city within
the latitude extent between 52.34° N and 52.68° N (the administrative border of Berlin).
The mean LST of the whole year (black curve), summer (red curve), and winter (green
curve) were calculated, separately. The blue bars indicate the grids that strongly affected
by water bodies. (For interpretation of the references to colour in this gure legend, the
reader is referred to the web version of this article.)
266 H. Li et al. / Science of the Total Environment 624 (2018) 262272
zonal analysis method was applied referring to Yuan and Marvin (2007).
All the pixels within the study areas were divided into 50 parts with 2%
interval of the ISA
. Given the sub-interval variation of LST caused by
the heterogeneity of urban surface and different ecosystems of the
rural surface, the mean values of LST and ISA
were calculated within
each interval to t the function of LST. Least square method was applied
to t the linear regression function. The coefcient of determination (R
was used to evaluate the accuracy of the tted function. Water bodies
have strong thermal properties, which may skew the trend of LST with
the change in ISA (Hu and Brunsell, 2015). Given the large areas of
water bodies in the study areas (6.7% based on Corine land cover data),
the pixels with more than one-quarter of water bodies areas were ex-
cluded during the calculation process.
3. Results
3.1. Spatial patterns of the LST
Fig. 4 shows the spatial variations of the mean LST at the four obser-
vation times for the different periods in 2010 for Berlin. The LST within
the city center is obviously higher than those within the surrounding
rural regions, presenting pronounced surface urban heat island effect.
The distribution of high LST areas is corresponding with the urban land
cover distribution shown in Fig. 1. The max-min ranges of the annual
mean LST are up to 9.20, 11.24, 6.32 and 8.52 °C at the four observation
times (Table 1). The LST shows larger urban-rural difference in daytime
and summer than in nighttime and winter.
3.2. Spatial patterns of the ISA
Fig. 5 shows the spatial distributions of the raw ISA and KDE region-
alized ISA (ISA
) using the kernel radius of 3000 m for Berlin. Most of
the high ISA areas are located in the central regions of the city. In the sur-
rounding rural regions, the pixels with large and small values crossly dis-
tribute. The spatial pattern of the ISA is very similar to that of the LST in
Fig. 4. The ISA
shows a smoother and more continuous distribution,
compared with the raw ISA. The Standard Deviation (STD) of the ISA
(20.91%) is smaller than that of the raw ISA (25.74%).
3.3. Longitudinal variations of the LST versus the ISA
In order to clearly show the spatial conguration of LST-ISA
mean longitudinal variations of the LST and ISA
are presented in Fig.
6. It shows that as the areas change from rural to inner-city and the
0 20406080100
0 204060801000 20406080100
Mean LST (°C)
Annual mean Summer mean Winter mean
Mean LST (°C)Mean LST (°C)
Mean LST (°C)
(%) ISA
(%) ISA
Fig. 7. Variationsof the (left) annul mean LST, (middle)summer mean LST and (right) winter mean LST versusISA
. Red lines are thetted linear regressionfunctions. The valuesof the
SUHII (K) were calculated using the regression slope. Rows (a), (b), (c), and (d) present the results at four observation times. All the correlations are signicant here (p b0.01). (For
interpretation of thereferences to colour in this gure legend, the reader is referred to the web version of this article.)
267H. Li et al. / Science of the Total Environment 624 (2018) 262272
land covers change from natural to urban, both the LST and ISA
crease. In the city center, the mean ISA
are close to 80%, and the tem-
peratures maintain high values, presenting a typical urban heat island
phenomenon. Then back from the city center towards rural regions,
the LST and ISA
decrease with the land covers changing from urban
to natural. The longitudinal variation of the LST shows good agreement
with the ISA
. Daytime and summer show more signicant urban-
rural variation in LST than nighttime and winter. The grids that are affect-
ed by water show lower LST during the day and higher LST at night than
other land cover types. These pixels are ltered out when calculating the
3.4. ISA
tted functions of LST and calculated SUHII in annual scale
Fig. 7 shows the variations of the mean LST versus ISA
for the dif-
ferent time periods and observation times. In general, the LST increases
smoothly with the increase in ISA
, showing a good agreement. ISA
accounts for most of the variation in LST. The regression functions of LST
were well tted,withhighR
for all seasons and times of the day. The
values of R
for the tted function of the annual mean LST are up to
0.96, 0.97, 0.97 and 0.98, respectively at the four observation times. The
regression functions of LST tted by ISA
can be used to quantify
SUHII. The slopes of the tted functions of LST are dened as the SUHII,
and are also shown in Fig. 7. At an annual scale, the values of the SUHII
are 5.43, 5.39, 3.58 and 3.95 K at 10:20, 11:50, 20:50 and 01:20, respec-
tively. The SUHII in summer and daytime are higher than that in winter
and nighttime. This is consistent with the max-min range of the LST
within the study areas in Table 1.
3.5. Calculated daily SUHII and its temporal variation
The linear regression functions of the daily LST were tted for each
valid image in 2010 using ISA
. Most of the LST images show good re-
lationships with ISA
. More than three-quarters of the R
of the tted
functions are larger than 0.90. Wet surface caused by the precipitation
(including rain and snow) before or during the observation time should
50 100 150 200 250 300 350
Julian day
50 100 150 200 250 300 350
Julian day
50 100 150 200 250 300 350
Julian day
(c) MOD 20:50
(a) MOD 10:20 (b) MYD 11:50
(d) MYD 01:20
50 100 150 200 250 300 350
Julian day
Fig. 8. Seasonalvariationsof the daily SUHII (red dots)and its correspondingR
(blue dots)at the four observationtimes in 2010 for Berlin.Subplots (a), (b), (c),and (d) present the results
at four observation times, respectively. (For interpretation of the references to colour in this gure legend, the reader is referred to the web version of this article.)
Table 2
Statistics of daily SUHII (K)calculated using the valid regression functions of LST, with R
within the outlier boundary. The upper boundary is 1.5 times of the Interquartile Range
(IQR) largerthan the 3rd interquartile, while thelower boundary is 1.5 timesof IOR lower
than the 1st interquartile.
Max 1st
Median 3rd
Min Mean
MOD 10:20 10.16 7.07 5.88 3.87 1.93 5.73
MYD 11:50 10.33 6.57 5.25 3.49 1.67 5.38
MOD 20:50 8.22 4.95 4.04 3.29 1.35 4.09
MYD 01:20 7.18 5.60 4.56 3.62 1.42 4.61
1500 2000 2500 3000 3500 4000 4500 5000
Year Summer Winter
Search radius (m)
Fig. 9. Variations of (a) the R
of the tted functions and (b) the calculated SUHII versus
kernel radius. The LST used here are the mean values of all valid images for different
268 H. Li et al. / Science of the Total Environment 624 (2018) 262272
be the main reason for the weak LST-ISA
relationships in some days,
and is further discussed in Section 4.4. In general, the ISA
can be used
to quantify the daily SUHII well when the disturbance of precipitation is
removed. The well tted linear regression functions of LST with R
er than the lower outlier boundary (0.82) were used to calculate daily
SUHII. Seasonal variations of the daily SUHII are shown in Fig. 8.Gener-
ally, the daily SUHIIs displayhigher values in summer andlower values
in winter. It is consistent with the results at annual scale shown in Fig. 7.
The maximum value of the daily SUHII reaches up to10.32 K happening
at 11:50 on Julian day 156, while minimum value is 1.35 K happening at
01:20 on Julian day 274. The mean values are 5.73, 5.38, 4.09 and 4.61 K,
respectively, at the four observation times (Table 2). Daytime always
shows higher SUHII than nighttime.
4. Discussion
4.1. Sensitivity of the quantication of SUHII to kernel radius
In this study, the kernel radius represents the extent of the footprint
of the LST measurement over each pixel. The kernel radius could affect
the calculation results of KDE method, and then further affect the LST-
relationship. In order to obtain the optimal kernel radius for the
SUHII calculation, a sensitivity test at annual scale was conducted. The
linear functions of the mean LST during different periods were tted
using the kernel radius ranging from 1500 to 5000 m with an interval
of 500 m. The R
of the tted functions and the calculated SUHII versus
kernel radius are present in Fig. 9. The variations of the R
with kernel radius show similar trend for the whole year, summer, and
winter. The best regression functions of LST with highest R
is achieved
at radius of 3000 m (3.0 times of the resolution). Either smaller or larger
kernel radius would weaken the LST-ISA
relationship. That is because
when the radius is too small, the KDE results could not contain enough
information inside the footprint, while when the radius is too large, the
KDE results would contain some noise information and outliers outside
the footprint (Thakali et al., 2015). The calculated SUHII is also affected
by kernel radius. Larger radius tends to produce higher SUHII when the
value was b2000 m. When the radius further increase, the sensitivity
of the calculated SUHII to kernel radius disappear.
4.2. Inuence of the regionalization of ISA on the quantication of SUHII
In order to evaluate the advantage of the newly developed ISA
calculating SUHII, a comparison study between the linear functions of
LST tted by ISA
and raw ISA was conducted. Tables 3 and 4 present
the R
of the tted functions and calculated SUHII using these two
indicators. In general, the functions of LST were better tted by ISA
with higher R
for all time periods. ISA only reects the surface property,
and does not include the inuence of neighboring pixels on the LST mea-
surements within the footprint. So it cannot perform as well as ISA
terms of the tting of LST functions. The SUHII calculated using ISA
shows larger values than those calculated using raw ISA. Compared
with the map of the ISA
, the ISA clearly detects the distributions of
the peak values among pixels (Fig. 5a). The spatial variation of the ISA
is smoother than that of the raw ISA with lower STD. The urban-rural dif-
ference in the ISA
is smaller than that of the raw ISA, leading to higher
4.3. Validation of the new method using Landsat data
MODIS data can be used to study the synoptic overview and the tem-
poralvariationofurbanareas(Pu et al., 2006). In this study, MODIS LST
achieved good relationships with ISA
, and the diurnal and seasonal
patterns of the SUHII are analyzed. However, the coarse spatial resolu-
tion MODIS data cannot distinguish the ne-scale variation of urban sur-
face, limiting the accurate detection of complex urban thermal
environment in detail. Urban surface temperature varies largely in both
the city and street scales due to the high heterogeneity of urban surface.
Strong sub-pixel variations of temperature even exist within the coarse
MODIS pixels. An more accurate study of SUHI demands higher resolu-
tion thermal remote sensing data. In order to further examine the reli-
ability of the new method, Landsat data at a 30 m resolution and
Copernicus ISA data version 2012 at a 20 m resolution were used to cal-
culate the SUHII. Two Landsat 7 ETM+ images (Fig. 10a, b) were collect-
ed from the USGS website ( The LST was
calculated using a mono-window algorithm (Zhang et al., 2006). The ISA
data was resampled to 30 m rstly, and then further regionalized using
KDE method with a kernel radius of 90 m (3 times resolution of Landset
images referring to the sensitivity analysis results above). The ISA
shows a smoother spatial pattern than the raw ISA (Fig. 10c, d). The func-
tions of LST are tted using the raw ISA and ISA
, respectively, and the
slopes of the functions are used to calculate the SUHII. Table 5 shows the
statistics of the tted functions and the calculated SUHII. The R
of two
tted functions using ISA
is larger than those using the raw ISA. Com-
pared with the raw ISA, the ISA
better reects the spatial variation of
the Landsat LST. Meanwhile, the STD of ISA
(27.67%) is smaller than
the raw ISA (31.82%), which leads to larger values of the calculated
. The difference between the ISA
and the raw ISA
in tting the functions of LST and the calculated SUHII using the Landsat
data are consistent with those using MODIS data as shown in Tables 3
and 4.
4.4. Inuence of precipitation on the new method application
Precipitation could alter surface moisture, and decrease the urban-
rural difference in both albedo and water availability, weakening the
relationship. The foundation of the hypothesis of this new
method shown in the Fig. 3 would be broken by precipitation. Fig. 11
shows the box plots of the R
of the tted functions of daily LST under
rainy and non-rainy conditions. When precipitation occurs, the R
shows a lot of small values (Fig. 11a). The 25th percentile and lower
boundary of outliers are only 0.77 and 0.48. When no precipitation oc-
curs, the R
shows high values, with the 25th percentile and the lower
boundary of outliers of 0.91 and 0.84, respectively. Here only the precip-
itation happened within 12 h before observation times are taken into
account. Strong precipitation has a longer term impact on the ground
moisture. Most of the outliers in Fig. 11b happen due to the strong pre-
cipitation before 12h of the observation times. Inaddition, the evapora-
tion and themeltingof snow in winter is very slow (Li et al., 2016). The
snow covering could last long time and weaken the UHI effect, which
leads to parts of the outliers in Fig. 11b.
Table 3
Comparison of the R
for the tted functions of LST using ISA
and raw ISA.
Observation time ISA
Year Summer Winter Year Summer Winter
MOD 10:20 0.96 0.96 0.95 0.92 0.91 0.91
MYD 11:50 0.97 0.96 0.97 0.90 0.90 0.89
MOD 20:50 0.97 0.96 0.96 0.83 0.84 0.79
MYD 01:20 0.98 0.98 0.96 0.84 0.86 079
Table 4
Comparison of the SUHII calculated using the slope of the LST functions tted by ISA
and raw ISA.
Observation times ISA
Year Summer Winter Year Summer Winter
MOD 10:20 5.43 6.55 3.81 3.23 3.90 2.27
MYD 11:50 5.30 6.55 3.81 3.01 3.79 2.11
MOD 20:50 3.58 3.91 3.05 1.74 2.00 1.38
MYD 01:20 3.95 4.74 2.89 1.83 2.25 1.26
269H. Li et al. / Science of the Total Environment 624 (2018) 262272
5. Conclusions and outlook
This paper proposed a new approach to quantify the SUHII by tting
the linear functions of LST using ISA. Given the footprintof LST measure-
ment, the ISA was regionalized using a Kernel Density Estimation ap-
proach. The spatial variation of the LST in Berlin region displayed
pronounced SUHI characteristic. The spatial patterns of the LST and
were similar. The linear functions of LST were well tted using
in both annual and daily scale. The slope of the linear regression
was dened as SUHII. The annual mean SUHII were 5.72, 5.38, 4.09and
4.61 K at UTC time 10:20, 11:50, 20:50and 01:20, respectively. The LST
showed higher correlation with ISA
than with raw ISA across all time
periods. The reliability of the new method was further veried using
ne resolution Landset data. Precipitation could weaken the depen-
dence of LST on surface imperviousness and then inuence the calcula-
tion of SUHII. The practical application of the new method should avoid
rainy days.
The well tted linear functions of LST using ISA
provide a promis-
ing approach to quantify the SUHII using remote sensing data. Compared
with the traditional approach of calculating the decit of measurements
at urban and rural stations or pixels, the new approach could avoid the
bias caused by the choice of the stations or pixels. This method can be
easily applied in other cities, which makes the comparisons of SUHI
among different cities possible. Netherless, it should be noted that the
hypothesis of the new method is that LST increases along with ISA. This
hypothesis is true for most cities in biomes dominated by forests and
grasslands. However, for the cities in desert environments, the LST's re-
sponse to ISA presented U-shaped horizontal gradient (e.g. Imhoff et
al., 2010; Zhang et al., 2010). So this method does not work for the cities
in the arid or desert environment. Compared with SUHII, UHII calculated
by air temperature is more concerned in term of heat stress. Usually,
SUHII is larger than UHII. This study only focuses on the quantication
of SUHII. As for the feasibility of this method for quantifying UHII, a fur-
ther study is needed in the next step. In addition, there is a slight differ-
ence in the acquired time among the pixels within the images of daily
MODIS LST products. This may affect the result to some extent and can
be investigated in future studies.
This research is supported by the China Scholarship Council, FUBright
Mobility Allowances for Research Stays promoted by the German Aca-
demic Exchange Service (DAAD)-Dahlem Research School of Freie
Universität Berlin, Mobility Allowance for Junior Research Stays from
University Alliance for Sustainability promoted by Freie Universität Ber-
lin. The authors thank the Beijing Ofce of Freie Universität Berlin. Spe-
cial thanks go to Dr. Hamid Taheri Shahraiyni for help in processing
Fig. 10.Spatial patternsof (a and b) the Landsat LSTand (c and d) ISA. The (a)and (b) show the retrieved LST attime 9:53 UTC, 28th April and 9:55 UTC,9th July 2010, respectively.The (c)
and (d) show the raw ISA and regionalized ISA
Table 5
Statistics of the tted functions of the Landsat LST and the calculated SUHII.
Image Indicator R
28th, April ISA 0.84 2.23
0.95 4.38
9th, July ISA 0.92 4.13
0.97 6.83
Fig. 11. Box plots of the R
for the tted functions under (a) rainy and (b) non-rainy
conditions within the 12 h before the observation times. Top and bottom of the blue
boxes represent the 75th and 25th percentile and red horizontal line within the box
indicates the median. Short top and bottom bars outside the boxes are the boundaries of
upper and lower outlier dened by 1.5 IQR, green crosses are the outliers. (For
interpretation of the references to colour in this gure legend, the reader is referred to
the web version of this article.)
270 H. Li et al. / Science of the Total Environment 624 (2018) 262272
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... Consequently, accurately quantifying the intensity of the SUHI effect (SUHII) and better understanding the driving factors has become imperative. These measures not only aid in assessing potential heatrelated risks but also contribute to future city management strategies, guiding governmental decision-making [8]. ...
... At present, the application of LST in urban environment studies requires more heat-related information at the urban district level with high spatial resolution [27]. However, high-resolution LSTs may also be derived from the Landsat series TIR channels (i.e., Landsat 5,8,and 9) at about 100 m but remain far from meeting the needs for improving SUHI monitoring accuracy. ...
... In this study, SUHII is the LST difference between urban (LST urban ) and suburban areas (LST suburban ) using the fused high spatiotemporal resolution summer LST dataset from 2002 to 2022 over Chengdu City. The formula is as follows [8]: ...
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Surface urban heat islands (SUHIs) are mostly an urban ecological issue. There is a growing demand for the quantification of the SUHI effect, and for its optimization to mitigate the increasing possible hazards caused by SUHI. Satellite-derived land surface temperature (LST) is an important indicator for quantifying SUHIs with frequent coverage. Current LST data with high spatiotemporal resolution is still lacking due to no single satellite sensor that can resolve the trade-off between spatial and temporal resolutions and this greatly limits its applications. To address this issue, we propose a multiscale geographically weighted regression (MGWR) coupling the comprehensive, flexible, spatiotemporal data fusion (CFSDAF) method to generate a high-spatiotemporal-resolution LST dataset. We then analyzed the SUHI intensity (SUHII) in Chengdu City, a typical cloudy and rainy city in China, from 2002 to 2022. Finally, we selected thirteen potential driving factors of SUHIs and analyzed the relation between these thirteen influential drivers and SUHIIs. Results show that: (1) an MGWR outperforms classic methods for downscaling LST, namely geographically weighted regression (GWR) and thermal image sharpening (TsHARP); (2) compared to classic spatiotemporal fusion methods, our method produces more accurate predicted LST images (R2, RMSE, AAD values were in the range of 0.8103 to 0.9476, 1.0601 to 1.4974, 0.8455 to 1.3380); (3) the average summer daytime SUHII increased form 2.08 °C (suburban area as 50% of the urban area) and 2.32 °C (suburban area as 100% of the urban area) in 2002 to 4.93 °C and 5.07 °C, respectively, in 2022 over Chengdu City; and (4) the anthropogenic activity drivers have a higher relative influence on SUHII than other drivers. Therefore, anthropogenic activity driving factors should be considered with CO2 emissions and land use changes for urban planning to mitigate the SUHI effect.
... Green roofs are an effective approach to enhancing the percentage of green space and reducing UHI for cities without enough open space (Aflaki et al., 2017;Suter et al., 2017). Therefore, urban greening is a very important UHI mitigation approach as it cools the air by providing shade, increases latent heat flux through transpiration, and reduces the sensible heat flux as seen in many studies (Cui et al., 2021;Li et al., 2018;Rigolon et al., 2018;Deilami et al., 2018;Honjo et al., 2017;Kleerekoper et al., 2012). Moreover, cool roofs obtained by coating the building and roof surfaces with materials having high albedo values are also beneficial for reducing the urban heat island effect (Synnefa et al., 2008;Tsoka, 2017;Lynn and Lynn, 2020). ...
... The second method is to reduce the heat island intensity by cooling the surrounding of the built-up areas and providing milder surface boundary conditions by changing the partition of the surface energy balance components in which latent heat fluxes are increased and sensible heat fluxes are reduced (Sodoudi et al., 2014). The cooling effect of green space on urban heat islands was investigated in many studies (Cui et al., 2021;Li et al., 2018;Rigolon et al., 2018;Deilami et al., 2018). Greening the walls and roofs of buildings in urban areas or adding new parks can increase the amount of green space. ...
... However, it is susceptible to missing values in remote sensing images during processing; therefore, it is not widely used. Li et al. [50] indicated that the ISA can reflect the spatial pattern of the SUHI, and the relationship between the LST and ISA can be a powerful tool for quantifying the SUHII. Based on this, Li et al. proposed a method to study the slope of the linear regression function of the LST and ISA as the SUHI, which avoids bias because of the selection of urban and nonurban pixels and provides the possibility of a comparison between different SUHIIs. ...
... The results showed that changing the nonurban references changed the SUHII and the nature of the observed surface thermal island (heat or cold) in 74% and 8% of the cities, respectively. Although some statistical models exist to reduce errors caused by urban-rural divides, many uncertainties remain [50]. As a result, it is critical to carefully select the SUHI research methods for each city. ...
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Using the China National Knowledge Infrastructure (CNKI) and Web of Science (WoS) databases, 487 articles that used remote sensing methods to study the intensity of surface urban heat islands (SUHIs) over the past 20 years were obtained using keyword searches. A multidimensional analysis was conducted on these articles from the perspectives of the research methods used, spatiotemporal distribution characteristics of the research area, research development trends, and main challenges. The research found that (1) the growth trend of the various SUHI research methods over the years was similar to the overall trend in the number of publications, which has rapidly increased since 2009. (2) Among the SUHI research methods, temperature dichotomy is the most widely used worldwide; however, defining urban and rural areas is a main challenge. The Gaussian surface and local climate zoning methods have gradually emerged in recent years; however, owing to the limitations of the different urban development levels and scales, these methods require further improvement. (3) There are certain differences in the application of SUHI research methods between China and other countries.
... On the other hand, the different definitions of urban/rural regions make the inter-comparison study of SUHII among different cities particularly challenging. To overcome these limitations, the approach of Li et al. 35 is employed, allowing the calculation of SUHII (and its temporal dynamics) by exploiting the linear relationship between LST and the Percentage Impervious Surface (PIS) derived from CLMS. Given the LST footprint (provided by AP01), the PIS is regionalised to include information of neighbour pixels within the footprint by means of Kernel Density Estimation. ...
... • Improved the LST downscaling approach for local-scale observations developed by Mitraka et al. 30 and adapted it to use urban surface information derived from CLMS and atmospheric information derived from CAMS; • Adapted the ARM 36 to retrieve heat emissions, incorporating downscaled LST and surface information from CLMS for the aerodynamic parameterisation and air temperature from C3S; • Retrieved CO 2 emissions within city boundaries at neighbourhood scale, combining local scale CO 2 flux measurements with surface parameterisation based on CLMS products and scaled up according to the associated land cover/use type (CLMS) and environmental controls derived from CAMS and C3S; • Retrieved heat storage in buildings at local scale, using urban surface parameterisation based on CLMS products and the net parameterisation based on CAMS products; • Improved the approach of Li et al. 35 for assessing SUHII by exploiting imperviousness and LST estimations generated using inputs from CLMS and CAMS products; • Improved urban thermal comfort assessment 50 , based on CLMS data on urban land cover, soil sealing, vegetation and building data, as well as on C3S ERA5 re-analysis data; • Evaluated the impact of NBS in urban environment using urban surface and vegetation information derived from CLMS and Sentinel 2 and combined temperature variation parameters derived from CAMS and C3S with CURE LST and thermal comfort products as well as third-party data determining the green roof potential; • Applied building data from CLMS to the ATMO-Street modelling chain 26 to enable the EU-wide inclusion of street canyons when modelling NO 2 concentrations; • Updated the cross-cutting potential of mapping urban flood risk by combining EMS with CLMS data to assess the vulnerability of exposed assets and integrate risk financing schemes to buffer flood economic impacts; • Improved the cross-cutting potential for urban subsidence, movements and deformation risk service by coupling hazard monitoring with asset information (i.e., combined use of EMS, CLMS and potentially C3S services) to assess threats and vulnerabilities of city infrastructure. ...
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The urban community faces a significant obstacle in effectively utilising Earth Observation (EO) intelligence, particularly the Copernicus EO program of the European Union, to address the multifaceted aspects of urban sustainability and bolster urban resilience in the face of climate change challenges. In this context, here we present the efforts of the CURE project, which received funding under the European Union’s Horizon 2020 Research and Innovation Framework Programme, to leverage the Copernicus Core Services (CCS) in supporting urban resilience. CURE provides spatially disaggregated environmental intelligence at a local scale, demonstrating that CCS can facilitate urban planning and management strategies to improve the resilience of cities. With a strong emphasis on stakeholder engagement, CURE has identified eleven cross-cutting applications between CCS that correspond to the major dimensions of urban sustainability and align with user needs. These applications have been integrated into a cloud-based platform known as DIAS (Data and Information Access Services), which is capable of delivering reliable, usable and relevant intelligence to support the development of downstream services towards enhancing resilience planning of cities throughout Europe.
... Our findings of higher LST in areas of the city with higher building cover and more additional impervious cover are unsurprising, given that impervious landscape features are known to contribute to intra-urban heat (Jenerette et al. 2016;Ziter et al. 2019). Our results suggest that in order to keep LST values below ~35 °C (a target drawn from the global LST literature Li et al. 2018;Parastatidis et al. 2017)), total impervious cover (including buildings) should remain below ~60%. While the relationship between total impervious cover and increased LST has been recorded by similar studies (Tran et al. 2017), the relationship between building cover and LST separate from other impervious surfaces has rarely been considered. ...
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Cities globally are expanding at an unprecedented rate, requiring an understanding of how to grow cities in a way that minimizes environmental impact while providing ecological benefits to people. Compact cities are often advocated for due to reduced impacts on biodiversity. However, increased development within an existing urban footprint may lead to loss of ecosystem services (ES) if accompanied by a loss of greenspace. We use spatial data and remote sensing approaches to explore relationships between urban form and indicators of health-related ES (temperature regulation, air pollution regulation, greenspace accessibility) at 250 study sites across a range of percent building cover in Montreal, Canada. We ask: 1) How does building cover and associated landscape structure affect multiple biophysical indicators linked to health-based ES? 2) Is population density (as a proxy for flow of ES to recipients) related to ES provision at the scale of investigation once building cover is accounted for? Relationships between building cover and indicators of ES provision varied across the studied indicators. Loss of greenspace accompanying increased building cover did not affect air quality, for example, which depended strongly on pollutant sources. However, increased building cover – and accompanying vegetation loss – was a strong driver of higher daytime temperatures. For ES provided by greenspace access, there was a trade-off between the ability to provide public vs. private greenspace; suggesting public greenspace should be prioritized to maximize ES provision. Overall, our findings support that urban densification must be pursued with consideration for the overall landscape structure, and prioritize maintenance of vegetation in particular.
... These studies were conducted using satellite-retrieved LST concentration. LST-based UHI has also recently been applied in multi-disciplinary research areas: i.e. urban expansion and UHI intensity (Li et al., 2018;Hu & Brunsell, 2015), phenology of urban ecosystems, spatial and temporal pattern distribution of UHI (Santos et al., 2017;Yao et al., 2017), urban atmospheric profile (Hu & Brunsell, 2015), urban weather and climatic zone (Chen et al., 2014;Bechtel et al., 2015;Huang et al., 2017), vegetative cover (Maimaitiyiming et al., 2014;Lu et al., 2017) and urban land use/urban metabolism (Fu & Weng, 2017;Tran et al., 2017). Ord and Getis (1995) proposed a model of local spatial autocorrelation statistics. ...
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Urban Heat Island (UHI) refers to a phenomenon whereby urban areas experience higher temperatures compared to the surrounding areas. Remote sensing-based Land Surface Temperature (LST) measurements can be utilized to measure UHI. This study emphasized on geostatistical remote sensing-based hot spot analysis (G Ã i) of UHI in Dhaka, Bangladesh as a way of examining the influences of Land Use Land Cover (LULC) on UHI from 1991 to 2015. Landsat 5 and 7 satellite based remote sensing indices were used to explore LULC, UHI and environmental footprints during the study period. The Urban Compactness Ratio (C oR) was used to calculate the urban form and augmented characteristics. The Surface Urban Heat Island (SUHI) intensity (ΔT) was also used to explore the effects of UHI on the surrounding marginal area. Based on our investigations into LULC, we discovered that around 71.34 per cent of water bodies and 71.82 percent of vegetation cover decreased from 1991 to 2015 in Dhaka city. Contrastingly, according to C oR readings, 174.13 km 2 of urban areas expanded by 249.77 per cent. Our hot spot analysis also revealed that there was a 93.73 per cent increase in hot concentration zones. Furthermore, the average temperature of the study area had increased by 3.26 C. We hope that the methods and results of this study can contribute to further research on urban climate.
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The estimation of surface urban heat island intensity (SUHII) is crucial for studying the urban thermal environment, which is influenced not only by the commonly known definition of rural reference but also by the delineation of urban extent. Existing studies relies on various urban extent products defined in different ways, and the influence of urban extent discrepancy (UED) on SUHII estimates still remains unclear. In this study, we collected five open-source global urban extent products (GUEPs) for the year 2015 and corresponding daily land surface temperature (LST) observations (MYD11A1). Based on these products, we quantified the UED-induced uncertainty in SUHII estimates by comparing absolute difference (ΔSUHIIAD) and relative difference (ΔSUHIIRD) in SUHII among GUEPs across 892 global cities. Besides, we introduced an ISF-constrained (ISF–C) method to reduce SUHII differences among GUEPs by constraining the impervious surface fraction (ISF) within urban and rural extents. The results show that urban extents delineated by different GUEPs are not consistent, leading to their difference in ISF as well as LST, which in turn causes uncertainties in the estimated SUHII. On average for global cities, the annual daytime and nighttime ΔSUHIIAD are 0.46 ± 0.02 °C (mean ± 95% confidence interval) and 0.24 ± 0.01 °C, with corresponding ΔSUHIIRD of 42.0 ± 2.7% and 35.2 ± 2.3%, respectively. The UED-induced uncertainty in SUHII estimates varies among climate zones, and the annual daytime ΔSUHIIRD averaged for cities located in the arid zone reaches up to 60.8 ± 6.6%, which is nearly twice as high as that in other climate zones. More importantly, both ΔSUHIIAD and ΔSUHIIRD show lower values when using the ISF-C method, implying the effectiveness of this method. This study highlights the non-negligible impact of UED on the estimation of SUHII, which requires more attention due to the inconsistency of urban extents among current products.
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Die Auswirkungen des Klimawandels beeinträchtigen die Lebensqualität in den Städten und stellen eine Bedrohung für die Stadtbewohner:innen dar. Räumlich geplante und verwaltete Anpassungsmaßnahmen wie multifunktionale Blaugrüne Infrastrukturen sind in der Lage, steigenden Temperaturen und häufigeren und extremeren Hitzewellen und Niederschlagsereignissen entgegenzuwirken. Damit jedoch insbesondere die grüne Infrastruktur die Verdunstungskühlung zur Minderung der Temperaturen aufrechterhalten kann muss sie ausreichend mit Wasser versorgt werden. Dies gestaltet sich, in Anbetracht länger anhaltender Trockenperioden, immer schwieriger, weshalb auf lange Sicht neue innovative Lösungsansätze ausgearbeitet werden müssen. Auf Basis eines Modellierungsansatzes zur Analyse kleinräumiger Land-Atmosphären-Interaktionen und Messungen vor Ort, zeigen wir die Auswirkungen unterschiedlicher Oberflächengestaltungsmöglichkeiten auf die lokale Wasser- und Energiebilanz an der Oberfläche. Die Erfahrungen aus zwei konkreten Platzumgestaltungen in Innsbruck (Österreich) aus den Projekten cool-INN (abgeschlossen) und COOLYMP (laufend) zeigen, dass integrale Planung Blaugrüner Infrastruktur aus grauen Plätzen in Städten, selbst wenn sie mit einer Tiefgarage unterbaut sind, eine generationenübergreifende Wohlfühloase machen kann. Damit jedoch ein Übergang von klimafitten zur klimaresistenten Platzumgestaltung, und in weiterer Folge zur klimaresistenten Stadtplanung, gelingen kann, ist ein strategisches und nachhaltiges Wassermanagement erforderlich, das für eine ausreichende Wasserverfügbarkeit zur Unterstützung der ökologischen Systeme und Aufrechterhaltung des Kühleffekts, sorgt.
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Urban-rural difference of land cover is the key determinant of urban heat island (UHI). In order to evaluate the impact of land cover data on the simulation of UHI, a comparative study between up-to-date CORINE land cover (CLC) and Urban Atlas (UA) with fine resolution (100 and 10 m) and old US Geological Survey (USGS) data with coarse resolution (30 s) was conducted using the Weather Research and Forecasting model (WRF) coupled with bulk approach of Noah-LSM for Berlin. The comparison between old data and new data partly reveals the effect of urbanization on UHI and the historical evolution of UHI, while the comparison between different resolution data reveals the impact of resolution of land cover on the simulation of UHI. Given the high heterogeneity of urban surface and the fine-resolution land cover data, the mosaic approach was implemented in this study to calculate the sub-grid variability in land cover compositions. Results showed that the simulations using UA and CLC data perform better than that using USGS data for both air and land surface temperatures. USGS-based simulation underestimates the temperature, especially in rural areas. The longitudinal variations of both temperature and land surface temperature show good agreement with urban fraction for all the three simulations. To better study the comprehensive characteristic of UHI over Berlin, the UHI curves (UHIC) are developed for all the three simulations based on the relationship between temperature and urban fraction. CLC- and UA-based simulations show smoother UHICs than USGS-based simulation. The simulation with old USGS data obviously underestimates the extent of UHI, while the up-to-date CLC and UA data better reflect the real urbanization and simulate the spatial distribution of UHI more accurately. However, the intensity of UHI simulated by CLC and UA data is not higher than that simulated by USGS data. The simulated air temperature is not dominated by the land cover as much as the land surface temperature, as air temperature is also affected by air advection.
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Urban climate is determined by a variety of factors, whose knowledge can help to attenuate heat stress in the context of ongoing urbanization and climate change. We study the influence of city size and urban form on the Urban Heat Island (UHI) phenomenon in Europe and find a complex interplay between UHI intensity and city size, fractality, and anisometry. Due to correlations among these urban factors, interactions in the multi-linear regression need to be taken into account. We find that among the largest 5,000 cities, the UHI intensity increases with the logarithm of the city size and with the fractal dimension, but decreases with the logarithm of the anisometry. Typically, the size has the strongest influence, followed by the compactness, and the smallest is the influence of the degree to which the cities stretch. Accordingly, from the point of view of UHI alleviation, small, disperse, and stretched cities are preferable. However, such recommendations need to be balanced against e.g. positive agglomeration effects of large cities. Therefore, trade-offs must be made regarding local and global aims.
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The urban agglomeration of Yangtze River Delta (YRD) is emblematic of China's rapid urbanization during the past decades. Based on homogenized daily maximum and minimum temperature data, the contributions of urbanization to trends of summer extreme temperature indices (ETIs) in YRD are evaluated. Dynamically classifying the observational stations into urban and rural, this study presents unexplored changes in temperature extremes during the past four decades in YRD and quantifies the amplification of the positive trends in ETIs by the urban heat island effect. Overall, urbanization contributes to more than one third of the increase of intensity of extreme heat events in the region, which is comparable to the contribution of greenhouse gases. Compared to rural stations, more notable shifts to the right in the probability distribution of temperature and ETIs are found in urban stations. The rapid urbanization in YRD has resulted in large increases in the risk of heat extremes.
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This study compared the diurnal and seasonal cycles of atmospheric and surface urban heat islands (UHIs) based on hourly air temperatures (Ta) collected at 65 out of 262 stations in Beijing and land surface temperature (Ts) derived from Moderate Resolution Imaging Spectroradiometer in the years 2013–2014. We found that the nighttime atmospheric and surface UHIs referenced to rural cropland stations exhibited significant seasonal cycles, with the highest in winter. However, the seasonal variations in the nighttime UHIs referenced to mountainous forest stations were negligible, because mountainous forests have a higher nighttime Ts in winter and a lower nighttime Ta in summer than rural croplands. Daytime surface UHIs showed strong seasonal cycles, with the highest in summer. The daytime atmospheric UHIs exhibited a similar but less seasonal cycle under clear-sky conditions, which was not apparent under cloudy-sky conditions. Atmospheric UHIs in urban parks were higher in daytime. Nighttime atmospheric UHIs are influenced by energy stored in urban materials during daytime and released during nighttime. The stronger anthropogenic heat release in winter causes atmospheric UHIs to increase with time during winter nights, but decrease with time during summer nights. The percentage of impervious surfaces is responsible for 49%–54% of the nighttime atmospheric UHI variability and 31%–38% of the daytime surface UHI variability. However, the nighttime surface UHI was nearly uncorrelated with the percentage of impervious surfaces around the urban stations.
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Regular diurnal and weekly cycles (WCs) in temperature provide valuable insights into the consequences of anthropogenic activity on the urban environment. Different locations experience a range of identified WCs and have very different structures. Two important sources of urban heat are those associated with the effect of large urban structures on the radiation budget and energy storage and those from the heat generated as a consequence of anthropogenic activity. The former forcing will remain relatively constant, but a WC will appear in the latter. WCs for specific times of day and the urban heat island (UHI) have not been analysed heretofore. We use three-hourly surface (2 m) temperature data to analyse the WCs of seven major Australian cities at different times of day and to determine to what extent one of our major city's (Melbourne) UHI exhibits a WC. We show that the WC of temperature in major cities differs according to the time of day and that the UHI intensity of Melbourne is affected on a WC. This provides crucial information that can contribute toward the push for healthier urban environments in the face of a more extreme climate.
Urban land-use/cover changes and their effects on the eco-environment have long been an active research topic in the urbanization field. However, the characteristics of urban inner spatial heterogeneity and its quantitative relationship with thermal environment are still poorly understood, resulting in ineffective application in urban ecological planning and management. Through the integration of “spatial structure theory” in urban geography and “surface energy balance” in urban climatology, we proposed a new concept of urban surface structure and thermal environment regulation to reveal the mechanism between urban spatial structure and surface thermal environment. We developed the EcoCity model for regulating urban land cover structure and thermal environment, and established the eco-regulation thresholds of urban surface thermal environments. Based on the comprehensive analysis of experimental observation, remotely sensed and meteorological data, we examined the spatial patterns of urban habitation, industrial, infrastructure service, and ecological spaces. We examined the impacts of internal land-cover components (e.g., urban impervious surfaces, greenness, and water) on surface radiation and heat flux. This research indicated that difference of thermal environments among urban functional areas is closely related to the proportions of the land-cover components. The highly dense impervious surface areas in commercial and residential zones significantly increased land surface temperature through increasing sensible heat flux, while greenness and water decrease land surface temperature through increasing latent heat flux. We also found that different functional zones due to various proportions of green spaces have various heat dissipation roles and ecological thresholds. Urban greening projects in highly dense impervious surfaces areas such as commercial, transportation, and residential zones are especially effective in promoting latent heat dissipation efficiency of vegetation, leading to strongly cooling effect of unit vegetation coverage. This research indicates that the EcoCity model provides the fundamentals to understand the coupled mechanism between urban land use structure and surface flux and the analysis of their spatiotemporal characteristics. This model provides a general computational model system for defining urban heat island mitigation, the greening ratio indexes, and their regulating thresholds for different functional zones.
Urban heat islands (UHIs) reflect the localized impact of human activities on thermal fields. In this study, we assessed the surface UHI and its relationship with types of land, meteorological conditions, anthropogenic heat sources and urban areas in the Yangtze River Delta Urban Agglomeration (YRDUA) with the aid of remote sensing data, statistical data and meteorological data. The results showed that the UHI intensity in YRDUA was the strongest (0.84°C) in summer, followed by 0.81°C in autumn, 0.78°C in spring and 0.53°C in winter. The daytime UHI intensity is 0.98°C, which is higher than the nighttime UHI intensity of 0.50°C. Then, the relationship between the UHI intensity and several factors such as meteorological conditions, anthropogenic heat sources and the urban area were analysed. The results indicated that there was an insignificant correlation between population density and the UHI intensity. Energy consumption, average temperature and urban area had a significant positive correlation with UHI intensity. However, the average wind speed and average precipitation were significantly negatively correlated with UHI intensity. This study provides insight into the regional climate characteristics and a scientific basis for city layout.
To develop an empirical model for ground snow sublimation beneath canopy, a weighing measurement experiment was conducted using snow samples with different density in the broadleaved Koreanpine mixed forest in Changbai Mountains, Northeastern China. Eddy covariance measurement for water vapor flux was used to evaluate the model performance. Eddy covariance data showed that the daytime sublimation was much larger than the nighttime sublimation, and 94.3% of daily sublimation occurred within the 8 h from 8:00 to 16:00. Daytime sublimation showed a linear relationship with snow density, and the regression coefficients between them varied with meteorological variables. The regression slope was closely correlated to solar radiation (R² = 0.92) and water vapor pressure (R² = 0.79), whereas the regression intercept was closely correlated to air temperature (R² = 0.92). Based on the regression relationships among sublimation, snow density, and meteorological variables, a nonlinear empirical sublimation model with a combination of snow density, solar radiation, water vapor pressure, and air temperature was developed. Sublimation estimation of the empirical model matched the eddy covariance data giving R² = 0.83 and root mean square error (RMSE) = 0.05 mm·d⁻¹. The surface reflectivity decreased with the increase in snow density. Dense snowpack would absorb more energy and promote snow sublimation by increasing the vertical water vapor pressure deficit between snow surface and atmosphere.
Extreme hot events and heat waves occur frequently in Bucharest during the warm season, triggering significant heat stress and thermal risks, especially in buildings with inappropriate ventilation, while climate change scenarios agree upon the warming trend along the next decades. This study investigates the impact of the Urban Heat Island (UHI) on the thermal regime of buildings, in order to develop a warning system capable to issue early warnings when the thermal risk reaches high levels in Bucharest. The warnings should be accurate regarding the intensity of the risk, the temporal fit and location, and complex information is compiled (e.g. air and land surface temperature, land cover, buildings and flat characteristics). Ground-based meteorological data and satellite products were used for computing the ambient temperature over several test areas for the summer months, and the indoor climate was dynamically modelled with an hourly resolution. The thermal risk was determined using standardized comfort indices, e.g. Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD), and the specific thermal and functional characteristics of the buildings.
The discipline of urban ecology arose in the 1990s, primarily motivated by a widespread interest in documenting the distribution and abundance of animals and plants in cities. Today, urban ecologists have greatly expanded their scope of study to include ecological and socioeconomic processes, urban management, planning, and design, with the goal of addressing issues of sustainability, environmental quality, and human well-being within cities and towns. As the global pace of urbanization continues to intensify, urban ecology provides the ecological and social data, as well as the principles, concepts and tools, to create livable cities.