the relationship between land surface temperature and land use/land cover in guangzhou, china
TRANSCRIPT
ORIGINAL ARTICLE
The relationship between land surface temperatureand land use/land cover in Guangzhou, China
Qinqin Sun • Zhifeng Wu • Jianjun Tan
Received: 9 June 2010 / Accepted: 7 June 2011 / Published online: 22 June 2011
� Springer-Verlag 2011
Abstract The integration of remote sensing, geographic
information system, landscape ecology and statistical
analysis methods was applied to study the urban thermal
environment in Guangzhou. Normalized Difference Veg-
etation Index (NDVI), Normalized Difference Build-up
Index (NDBI), Normalized Difference Barren Index
(NDBaI) and Modified Normalized Difference Water Index
(MNDWI) were used to analyze the relationships between
land surface temperature (LST) and land use/land cover
(LULC) qualitatively. The result revealed that, most urban
built-up lands were located in the middle part, and high
LST areas mostly and were in the middle and southern
parts. Therefore, the urbanization and thermal environment
in the middle and southern parts need to be determined.
Land surface temperature increased with the density of
urban built-up and barren land, but decreased with vege-
tation cover. The relationship between MNDWI and LST
was found to be negative, which implied that pure water
would decrease the surface temperature and the polluted
water would increase the surface temperature. A multiple
regression between LST and each indices as well as the
elevation was created to elevate the urban thermal envi-
ronment, which showed that NDVI, NDBI, NDBaI,
MNDWI were effective indicators for quantifying LULC
impacts on LST.
Keywords Land surface temperature � NDVI �Relationship � Regression
Introduction
The rapid urban expansions have caused land use/land
cover (LULC) changes, which affect the ecological envi-
ronmental process at local and regional levels, especially
the urban heat island (Gallo and Owen 1998; Streutker
2003; Weng 2003). Urban thermal environment is gov-
erned by the distribution of land surface temperature
(LST). Over the past years, many studies based on satellite
images have been focused on LULC impacts on LST
(Dousset and Gourmelon 2003; Xiao and Weng 2007).
Weng (2001) applied remote sensing (RS) and geographi-
cal information system (GIS) to examine the impact of
urban growth on surface temperatures, and found that
urban land development raised surface radiant temperature
by 13.01 K. Liu and Weng (2008) used landscape metrics
to examine the relationship between LULC pattern and
LST, and regarded landscape ecology as an effective tool
to quantify the LULC and LST patterns. The LULC areas
and landscape metrics can provide useful information for
assessing and monitoring urban thermal environments in an
area, but they cannot compute the LST in each area.
Based on satellite imagery, vegetation indices were used
to estimate the percent vegetation cover in each pixel
(Tobu and David 1997; Purevdorj et al. 1998), such as
Q. Sun (&)
State Key Laboratory of Marine Environmental Science,
Xiamen University, 361005 Xiamen, China
e-mail: [email protected]
Q. Sun � Z. Wu � J. Tan
Guangzhou Institute of Geochemistry,
Chinese Academy of Sciences, 510640 Guangzhou, China
Z. Wu
School of Geographical Sciences, Guangzhou University,
510006 Guangzhou, China
Z. Wu
Guangdong Institute of Eco-environment and Soil Sciences,
510650 Guangzhou, China
123
Environ Earth Sci (2012) 65:1687–1694
DOI 10.1007/s12665-011-1145-2
Normalized Difference Vegetation Index (NDVI). Zha
et al. (2003) developed the Normalized Difference Built-up
Index (NDBI) to identify urban built-up areas. Xu (2005)
developed Modified Normalized Difference Water Index
(MNDWI) to reflect the water purity degree, and detect
matter content changes of water due to its wide dynamic
data range. Zhao and Chen (2005) used Normalized Dif-
ference Barren Index (NDBaI) to classify the bare lands,
according to different values of NDBaI. These indices can
be used to classify different LULC types by setting the
appropriate threshold values, and provide the potential to
describe LULC condition in each place qualitatively (Chen
et al. 2006). Weng et al. (2004) found LST was positively
correlated with impervious surface fraction but negatively
correlated with green vegetation fraction. Li et al. (2009)
found there was a strong linear relationship between LST
and NDBI, whereas the relationship between LST and
NDVI was much less strong and varies by season. It is
possible that the relationships between NDVI, NDBI,
NDBaI, MNDWI and LST could be established, so that
urban thermal environment would be monitored and
assessed by LULC qualitatively.
In this study, RS, GIS, landscape ecology and statistical
analysis methods were combined to study the relationship
between LULC and LST. The deep understanding of their
interaction effects was hoped to be useful for urban plan-
ners. The indices of NDVI, MNDWI, NDBI, NDBaI as
well as DEM were used to calculate the distribution of
LST, which could provide a new method for monitoring
and assessing urban thermal environment.
Study area and data preparation
Guangzhou is in southern China, with a typical subtropical
monsoon climate. The city has experienced a rapid urban
expansion due to accelerated economic growth since the
1980s. The urban heat-island effect occurs as a result of
speedy urbanization. Massive agricultural land converted
to urban or other land uses had raised surface radiant
temperature by 13.01 K in the urbanized area (Weng
2001). The urban land use area has increased from 29.92 to
48.33% in Guangzhou during 1990–2005 (Gong et al.
2009). It is necessary to investigate the relationship
between LST and LULC quantitatively, aiming to assist
urban planners with valuable findings.
A Landsat TM image on 23 November 2005, was used
in this study. The radiometric and geometrical distortions
were corrected. Local time of satellite overpasses was
GMT 02:40:20. The near-surface temperature and moisture
synchronously recorded in the Wushan weather station
(113�140E, 23�50N) are 291.15 K and 1.988 g/cm2,
respectively.
Materials and methods
Land use/land cover classification
The geographic correction had been conducted for the
image before used. According to the maximum-likelihood
algorithm, a supervised signature extraction was employed
to classify the TM image into six classes: water, forest,
urban built-up land, cropland, grassland and barren land.
Twenty known LULC sites were recruited for the assess-
ment of the classification accuracy, and the Kappa values
were all greater than 0.6. According to the literature (Ortiz
et al. 1997), such a result is good and effective for LULC
classification.
Retrieval of LST and classification
Conversion of digital number into spectral radiance
Equation 1 was used to convert the digital number (DN) of
Landsat TM thermal infrared spectrum band into spectral
radiance (Chander and Markham 2005):
L ¼ gain� DNþ bias ð1Þ
where L is at-sensor radiance,
gain ¼ ðLMAX� LMINÞ=Qmax ð2Þbias ¼ LMIN ð3Þ
LMAX and LMIN are provided in the Meta file.
Qmax is the maximum quantized calibrated pixel value
corresponding to LMAX.
For the used image, the gain and bias were 0.0551 and
1.2378, respectively.
Conversion of spectral radiance into at-sensor brightness
temperature
Convert spectral radiance into at-sensor brightness tem-
perature with Eq. 4 (Chander and Markham 2005)
T ¼ K2=Ln K1=Lþ 1ð Þ ð4Þ
where T is at-sensor brightness temperature in K, K1 and K2
are the constant values with 607.76 and 1260.56,
respectively.
Calculate NDVI precisely
NDVI was defined as Eq. 5 (Rouse et al. 1974),
NDVI ¼ ðq4 � q3Þ=ðq4 þ q3Þ ð5Þ
where q4 is the reflectance measured in the near infrared
wave bands and q3 is the reflectance corresponding to the
red wave bands.
1688 Environ Earth Sci (2012) 65:1687–1694
123
Calculation of emissivity
Mapping emissivity from satellite data is hard but impor-
tant for surface characterization and for atmospheric cor-
rection (Valor and Caselles 1996). When NDVI values
range from 0.157 to 0.727, Van de Griend and Owe (1993)
gave an effective equation (Eq. 6) to obtain thermal
infrared emissivity:
e ¼ 1:0094þ 0:047� Ln NDVIð Þ ð6Þ
The areas with NDVI values below 0.157 were extracted
as water with ewater = 0.995 and urban built-up land with
eurban = 0.970. For the areas with NDVI values above
0.727, it was classified as forest with eforest = 0.986 (Qin
et al. 2004).
Calculation of LST
As LST over a large area can be easily acquired by using
various thermal RS technologies (Sobrino et al. 1996;
Gillespie et al. 1998; Qin et al. 2001; Jimenez-Munoz and
Sobrino 2003), LST was used in this study rather than
brightness temperature. To acquire LST, the mono-window
algorithm was adopted in Erdas (Sun et al. 2010). Through
comparing retrieved and simulated LST, Sobrino et al. (2004)
pointed out that the results from the mono-window algorithm
were[0.4�C with radio sounding data in most situations.
The single-window algorithm (Qin et al. 2001) is as
follows,
Ts ¼�
a6ð1� C6 � D6Þ þ ½b6ð1� C6 � D6Þ þ C6 þ D6�� Tsensor � D6T
�=C6 ð7Þ
where Ts is the LST in band 6, a and b are constants with
67.355351 and 0.458606, respectively,
C6 ¼ e6s6 ð8ÞD6 ¼ 1� s6ð Þ 1þ 1� e6ð Þð Þs6 ð9Þ
Tsensor is at-sensor brightness temperature in K, Ta and s6
are 285.005 K, 0.802, respectively, by recorded near-
surface temperature and moisture content in the Wushan
weather station.
LST was acquired and classified into five classes (Fig. 1)
for the following analysis. Jenks’ natural breaks method was
employed for classification, which optimizes the breaks
between classes for a given number of classes and determines
the best arrangement of values into classes. Table 1 provides
the range and description of each LST classification.
Landscape indices calculation
To determine the relationship between LULC and LST, the
distributions of LULC and LST types were delineated with
some landscape indices, including the centroids of types
and landscape patch metrics. The centroids were calculated
by the geometric centers of polygons in ArcGIS 9.3
(Bourke 1988). Landscape patch metrics including total
areas (TA), number of patches (NA) and mean area of
patches (MAP) were processed in Fragstats 3.3, where
MAP indicated the fragmentation of each LULC class.
Zonal statistical analysis
To summarize the values of NDVI and LST within each
LULC class, zonal statistic methods were used. The per-
centage of LULC associated with each LST zone was also
calculated in ArcGIS, which describes the LULC compo-
sition in different LST classes. For example, in high LST
areas, urban built-up land occupied 571 km2 and total area
of urban built-up land was 1,230 km2, which generated that
the percentage of urban built-up land was 46.4%.
Correlation analysis between LULC and LST
Besides NDVI, NDBI (Eq. 10), MNDWI (Eq. 11) and
NDBaI (Eq. 12) were calculated from the satellite image.
Two thousand independent points were selected from the
Fig. 1 Classifications of LST in Guangzhou (2005)
Environ Earth Sci (2012) 65:1687–1694 1689
123
LST image, and their corresponding LULC indices values
were extracted in ArcGIS. These data series were pro-
cessed to determine the relationship between LST and
LULC indices in SPSS. The terrain elevation was also
extracted from DEM, and added to the correlation analysis.
The LULC indices expressions are as follows,
NDBI ¼ ðd5 � d4Þ=ðd5 þ d4Þ ð10ÞMNDWI ¼ ðd2 � d5Þ=ðd2 þ d5Þ ð11ÞNDBaI ¼ ðd5 � d6Þ=ðd5 þ d6Þ ð12Þ
where, d2, d4, d5, d6 are the DN values of band 2, band 4,
band 5 and band 6 of Landsat TM, respectively.
Results
Distribution analysis of LULC and LST
The centroids of urban built-up land, cropland, grassland
and barren land were found to concentrate in the middle of
the city, while the centriods of forest and water were found
in the north and south of the city, respectively (Fig. 2). It
revealed that the forest mainly existed in the north, and
water mainly existed in the south. The centroids of LST
types were LLA, LA, NA, HA, HHA sequentially from
north to south, which indicated the temperatures were high
in the south and low in the north. Despite a common angle
of sunlight incidence, the centroids of HA and HHA all
leaned to the north. This may have resulted from the
moderating functions of the Pearl River and South Sea.
Zonal statistics of LULC and LST
Forest had the lowest mean LST and highest mean NDVI,
while urban built-up land had the highest LST but not
lowest mean NDVI (Figs. 3, 4). It illustrates the difference
in NDVI influences on LST in different LULC. The lowest
mean NDVI was found in water, but it had not the highest
mean LST. It was because the water temperature changed
slowly due to high thermal inertia and convection. Grass-
land and cropland had moderate surface temperature
because of the sparse vegetation and bare soil.
By classifying LST into five levels, the percentage of
each LULC type was extracted (Fig. 5). Water was found
to mainly appear in normal LST areas, showing a normal
distribution with high values in low LST areas, which may
be attributed to high thermal inertia of water. The peak of
forest appeared in low and very low LST areas, because
forest could moderate temperature and was mostly located
in the mountains. The maximum percentage of urban built-
up land was found in high and very high LST areas.
Impervious surfaces, such as roads and concrete buildings,
etc., were responsible for this. The greatest percentage of
cropland was located in high LST areas, indicating that
cropland had high temperature. This seemed because crops
had been reaped in the end of November, and the fields
Table 1 The ranges and descriptions of LST classifications
Type Range (K) Description
LLA 282.023–288.833 Very low LST areas
LA 288.833–291.575 Low LST areas
NA 291.575–294.051 Normal LST areas
HA 294.051–296.439 High LST areas
HHA 296.439–304.663 Very high LST areas
Fig. 2 The centroids of LULC
(a) and LST (b) in Guangzhou
(2005)
1690 Environ Earth Sci (2012) 65:1687–1694
123
were not vegetated completely. Grassland mainly assem-
bled in normal LST areas and high LST areas, while barren
land was in high LST areas, probably due to low thermal
inertia of bare soil.
Landscape ecology analysis
Forest and cropland occupied most areas with relative large
MAP (Table 2). It revealed that forest and agriculture were
dominant despite rapid urban development in Guangzhou,
which played important roles in moderating LST. Urban
built-up area took up 17% in Guangzhou, but it possessed
small MAP. Barren land had the smallest MAP, and
grassland had the smaller MAP. HA and HHA occupied
more than 37% of the city in 2005. HHA had the smallest
MAP (Table 3), which was mainly composed of road or
urban buildings. The high proportions and small MAP of
urban built-up land and HHA implied that they were easily
disturbed by the human activities and needed to be
controlled.
The relationship of LST and LULC
Table 4 shows a summary of the regressions between LST
and NDVI for each LULC, R2 is the determination coef-
ficient of each regression. It was found that coefficients
between NDVI and LST were great in forest, grassland and
cropland. However, the coefficients of LST and NDVI
were small in water, barren land and urban built-up land.
Using MNDWI, NDBaI and NDBI to indicate water, bar-
ren land and urban built-up land, the correlations between
LST and each index of LULC were computed. Because the
temperature can be reduced by the high elevation, so the
DEM was added to the computation. The results showed
that the correlations between NDBI, NDBaI and LST were
significantly positive, but the correlations between
MNDWI, DEM and LST were significantly negative
(Table 5). A multiple regression between LST and the
indices was created, which is hoped to be useful for
monitoring and elevating the thermal environment based
on LULC and terrain (Eq. 13).
LST ¼ 292:974� 17:416�MNDWI� 4:660� NDBI
� 15:289� NDVI� 5:011� NDBaI� 0:01
� DEM R2 ¼ 0:71 ð13Þ
where, the unit of LST is K, and the unit of DEM is KM.
Fig. 3 Zonal LST and standard deviation with LULC
Fig. 4 Zonal NDVI and standard deviation with LULC
Fig. 5 Zonal percentage of each LULC associated with LST classes
Environ Earth Sci (2012) 65:1687–1694 1691
123
Discussion
The centroids represent the positions of LULC and LST
classes. The distribution centroids of forest and water were
in the north and the south of Guangzhou, which revealed
the characteristics of Guangzhou LULC distribution. The
centroids distribution of LST types showed the LST highly
increased from north to south, but the temperature decrease
was slowed for the water in the south. Most urban built-up
lands were located in the middle part, and high LST areas
mostly were located in the middle and southern parts.
Therefore, the urbanization and thermal environment in
middle and southern parts need to be noted.
Landscape metrics can quantify the LULC and LST
patterns. Combined with statistical analysis methods,
detailed information about the distribution of LULC and
LST was provided. It showed urban built-up land had small
sizes but large total areas, and led to high LST. Forest had
the lowest LST but highest NDVI. One reason for the low
LST could be its dense vegetation, and another reason
might be its high elevation. Water temperature changed
slowly due to high thermal inertia and convection. Sparse
vegetation and bare soil brought the high LST to grassland
and cropland. To mitigate urban heat-island effect, urban
built-up land areas should be controlled and forest areas
should be increased. The measures to enhance the
Table 2 Class metrics of
LULCType Total area (km2) Number of patches Mean area of patches (m2)
Water 626.27 5,514 11.36
Forest 2977.48 6,066 49.08
Urban built-up land 1229.79 14,532 8.46
Cropland 2245.66 7,893 28.45
Grassland 64.47 752 8.57
Barren land 33.93 1,240 2.74
Table 3 Class metrics of LSTType Total area (km2) Number of patches Mean area of patches (m2)
LLA 819.12 8,020 10.21
LA 1581.31 26,785 5.90
NA 2103.90 48,629 4.33
HA 1893.56 41,429 4.57
HHA 842.98 52,438 1.61
Table 4 Linear regression
equations between LST and
LULC
LULC Equations R2
Forest LST = -13.259 9 NDVI ? 299.357 0.43
Grassland LST = -14.262 9 NDVI ? 300.216 0.42
Cropland LST = -17.206 9 NDVI ? 301.523 0.40
Water LST = -4.0821 9 NDVI ? 291.668 0.16
Barren land LST = -7.222 9 NDVI ? 296.774 0.04
Urban built-up land LST = 5.8922 9 NDVI ? 295.344 0.01
Table 5 Correlations between LST and each indices of LULC, as well as the elevation
LST MNDWI NDBI NDVI NDBaI Elevation
LST 1 -0.176** 0.643** -0.476** 0.421** -0.698**
MNDWI -0.176** 1 -0.542** -0.574** -0.886** -0.105**
NDBI 0.643** -0.542** 1 -0.358** 0.778** -0.335**
NDVI -0.476** -0.574** -0.358** 1 0.204** 0.493**
NDBaI 0.421** -0.886** 0.778** 0.204** 1 -0.152**
Elevation -0.698** -0.105** -0.335** 0.493** -0.152** 1
** Correlation is significant at the 0.01 level (2-tailed)
1692 Environ Earth Sci (2012) 65:1687–1694
123
grassland cover and reduce the bare soil areas would also
be recommended.
NDVI has been widely applied as an indicator of veg-
etation abundance to estimate LST in studies of urban heat
islands (Carson et al. 1994; Gillies and Carlson 1995;
Weng et al. 2004). In this study, NDVI and LST were
found to be negatively correlated in vegetated lands. It is
coincident with the previous study that the vegetation can
decrease the LST (Goetz 1997; Xiao and Weng 2007; Yuan
and Bauer 2007). However, for other LULC classes, NDVI
is not an effective index to indicate LST. Employing
NDBI, NDBaI and MNDWI to analyze, positive relation-
ships were found between NDBI, NDBaI and LST. It
meant the LST would increase with the density of urban
built-up land and barren land. However, the relationship
between MNDWI and LST was found to be negative,
which implied the pure water would decrease the surface
temperature and the polluted water would increase the
surface temperature.
Conclusions
Landscape ecology is an effective tool to quantify the
LULC and LST patterns. The measurements can provide
precision characterization and quantification for the spatial
characteristics of LULC and LST. The distribution cen-
troids of forest and water were in the north and the south
of Guangzhou, respectively, while those of cropland,
grassland, barren land and urban built-up land were all
found in the middle part. Forest and cropland still pre-
dominated in Guangzhou. The areas with high LST mainly
existed in the center of the city, which was mainly com-
posed of built-up land. The high proportions and small
MAP of urban built-up land and HHA implied that they
were easily disturbed by human activities and their areas
needed to be controlled.
NDVI is an effective index of LST for vegetation-cov-
ered LULC. However, for other LULC, it is not effective
enough to indicate LST. Using MNDWI for water, NDBI
for built-up land, NDBaI for barren land, it was found that
the correlations between NDBI, NDBaI and LST were
positive, and the correlations between MNDWI, DEM and
LST were negative. The multiple regression equation was
created with LST and NDVI, NDBI, NDBaI, MNDWI as
well as the elevation, which can be used to monitor and
elevate the urban thermal environment based on LULC and
terrain altitude.
Both the qualitative and quantitative analysis results
show that the land use will influence LST. Therefore, with
appropriate land use planning, urban heat island could be
mitigated.
Acknowledgments The authors would like to express their sincere
thanks to the reviewers for their constructive suggestions, comments
and helps. This research is supported by the research fund of LREIS,
CAS (Grant No. 2010KF0006SA).
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