Bi-temporal characterization of land surface temperature in relation to impervious
surface area, NDVI and NDBI, using a sub-pixel image analysis
Youshui Zhanga, Inakwu O.A. Odehb,
,
and Chunfeng Hana
a
College of Geography, Fujian Normal University, Fuzhou 350007, China
b
Faculty of Agriculture, Food and Natural Resources, The University of Sydney, Sydney, NSW
2006, Australia
Received 1 August 2008;
accepted 10 March 2009.
Available online 9 April 2009.
Abstract
As more than 50% of the human population are situated in cities of the world, urbanization has
become an important contributor to global warming due to remarkable urban heat island (UHI)
effect. UHI effect has been linked to the regional climate, environment, and socio-economic
development. In this study, Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper
Plus (ETM+) imagery, respectively acquired in 1989 and 2001, were utilized to assess urban
area thermal characteristics in Fuzhou, the capital city of Fujian province in south-eastern
China. As a key indicator for the assessment of urban environments, sub-pixel impervious
surface area (ISA) was mapped to quantitatively determine urban land-use extents and urban
surface thermal patterns. In order to accurately estimate urban surface types, high-resolution
imagery was utilized to generate the proportion of impervious surface areas. Urban thermal
characteristics was further analysed by investigating the relationships between the land surface
temperature (LST), percent impervious surface area, and two indices, the Normalized
Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI). The
results show that correlations between NDVI and LST are rather weak, but there is a strong
positive correlation between percent ISA, NDBI and LST. This suggests that percent ISA,
combined with LST, and NDBI, can quantitatively describe the spatial distribution and temporal
variation of urban thermal patterns and associated land-use/land-cover (LULC) conditions.
Keywords: Urban heat island; Land surface temperature; Impervious surface area; NDVI; NDBI
Article Outline
1. Introduction
2. Methods
2.1. Study area and data
2.2. Image pre-processing
2.3. Derivation of LST, NDVI and NDBI from TM and ETM+ imageries
2.3.1. LST
2.3.2. NDVI and NDBI
2.4. The derivation of urban percent ISA
3. Results and discussion
3.1. Land surface imperviousness and LST
3.2. Relationship among imperviousness, NDVI, NDBI and LST
3.3. Relationship between UHI and LULC patterns
4. Conclusions
References
1. Introduction
Changes in land cover (which is the biophysical attributes of the earth's surface) and land use
(the utilization of land for a given human purpose) have been reported to be the main driver of
environmental change, while climatic change and climate variability may influence land-use
preferences differently in different parts of the world (Brunsell, 2006). Therefore accurate and
up-to-date information on land cover and the state of the environment is critical to environmental
monitoring, management and planning (Assefa, 2004). However, the interaction between landuse/land-cover (LULC) change and the spatial–temporal climatic variability is poorly understood
which requires the development of new models linking the climate variability with the changes in
land use and land cover, especially at the urban–rural regional scale.
Recent developments in remote sensing and image analysis have identified land surface
temperature (LST) as one of the key parameters controlling the physical, chemical and
biological processes at the interface between the Earth and the atmosphere. It is an important
factor for the study of urban climate ([Voogt and Oke, 2003] and [Small, 2006]). LST has been
shown to be an effective means of partitioning latent heat fluxes and thus surface radiant
temperature response as a function of varying surface soil water content and vegetation cover
(Owen et al., 1998). These findings have encouraged investigations of the relationship between
LST and vegetation abundance (e.g., [Gallo and Owen, 1998a], [Gallo and Owen,
1998b], [Weng, 2001] and [Weng et al., 2004]). Urbanization and industrialization can lead to
modification of land surface and near-surface atmospheric conditions, which in turn could cause
change in thermal properties of urban areas causing them to be warmer than the surrounding
non-urbanized areas. This phenomenon is called urban heat island (UHI), which is mainly
caused by replacement of vegetated areas by non-evaporating and impervious materials such
as asphalt and concrete ([Dousset and Gourmelon, 2003], [Kim, 1992] and [Ruiliang et al.,
2006]). The UHI phenomenon can influence the radiative fluxes in the near-surface flow
because in urban areas, the higher level of sensible heat fluxes is caused by LULC changes
caused by the removal of the original vegetated areas that were characterized by lower heat
fluxes. Furthermore, urbanization generally leads to reduced evapotranspiration and more rapid
runoff of rainwater.
The monitoring of UHI phenomenon and the physical processes associated with it have
traditionally been conducted by ground-based observations taken from fixed thermometer
networks or by traversing a targeted area with a thermometer mounted on vehicles ([Voogt and
Oke, 2003] and [Weng et al., 2004]). However, the advent of remote sensing technology has
made it possible to study UHIs using satellite remote sensing data, especially thermal data
taken by either satellite-borne or air-borne sensors. Rao (1972) was the first to demonstrate the
possibility of identifying urban areas based on the analyses of thermal infrared data acquired by
a satellite sensor. Following this, Gallo et al. (1995) reviewed the validity and utility of UHI
derived from the satellite-acquired imagery. (Gallo and Owen, 1998a) and (Gallo and Owen,
1998b) and (Streutker, 2002) and (Streutker, 2003) derived land surface temperature and
evaluated the UHI phenomenon using National Oceanic and Atmospheric Administration
Advanced Very High-Resolution Radiometer (NOAA AVHRR) data for regional-scale urban
temperature mapping. Similar studies by Chen et al. (2006) andWeng (2001) used Landsat
Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) thermal infrared (TIR)
data to assess the local patterns of UHI. Generally, with increasing availability of thermal remote
sensing data from Landsat Thematic Mapper, Advanced Space-borne Thermal Emission and
Reflection Radiometer (ASTER), and the NOAA AVHRR, research of LST, using thermal
images, has been a topic of great interest in the remote sensing community for the past three
decades. The application of low spatial resolution remotely sensed data is requisite for
understanding the affects of UHI on climatic patterns. However, understanding the thermal
response of individual land covers within a city is equally valuable, as radiative transfer over
such intensively developed environment varies significantly over a short space due to the
diversity of urban land covers and their respective physical properties (Renee et al., 2006).
In comparison with thermal remote sensing of natural and agricultural surfaces, thermal remote
sensing of urban areas has been slow to advance beyond qualitative description of thermal
patterns and simple correlation analysis (Ruiliang et al., 2006). Recently, Voogt and Oke
(2003) reviewed most of previous research on UHI study and listed three themes of research:
examination of the spatial structure of urban thermal patterns and their relation to urban surface
characteristics (e.g., [Balling and Brazel, 1988] and [Dousset and Gourmelon, 2003]); thermal
remote sensing for urban surface energy balances (e.g., [Assefa, 2004] and [Kim, 1992]); and
study on the relation between atmospheric heat islands and surface urban heat islands
(e.g., [Ben-Dor and Saaroni, 1997] and [Caselles et al., 1991]). The main objective of this study
is to address the first theme.
Many of the previous remote sensing studies of the urban environment used Normalized
Difference Vegetation Index (NDVI) as a major indicator of urban climate ([Lo et al.,
1997], [Gallo and Owen, 1999] and [Yuan and Bauer, 2007]). However, NDVI is subject to
seasonal variations which may influence the results of land surface UHI analysis. Moreover, the
relationship between NDVI and LST is well known to be nonlinear, due to the predominantly
bare ground surfaces which tend to exhibit larger variation in surface radiant temperature than
the densely vegetated LULC types ([Price, 1990], [Gillies and Carlson, 1995], [Owen et al.,
1998] and [Chen et al., 2006]). The variability and nonlinearity suggest that NDVI alone may not
be sufficient to quantitatively study UHI. The intensity of UHI is related to the spatial extent and
composition of vegetation and built-up areas and their temporal changes. Quantitative studies of
the relationship between LULC patterns and LST are important for land-use management and
planning. Furthermore while NDVI has been used for the estimation of vegetation productivity
and rainfall in semi-arid areas ([Chen et al., 2004]and [Wang et al., 2004]), the Normalized
Difference Built-up Index (NDBI) has been developed for the identification of urban and built-up
areas (Zha et al., 2003). It is therefore possible that the utilization of both NDVI and NDBI as
surrogates of LULC can reveal the relationships between different indices such as NDVI, NDBI,
and land surface temperature in UHI studies.
Impervious surfaces, defined as land-cover types that impede water infiltration are primarily
associated with transportation (streets, highways, parking lots and sidewalks) and building
rooftops. In remote sensing, classified impervious surface area (ISA) has been used to quantify
and map the degree of urbanization and extent of urban land use ([Yuan and Bauer,
2007], [Xian and Crane, 2006] and [Civco et al., 2002]). With increased concern regarding
global climate change, it is important to analyse the relationship between the LST and percent
ISA in an urbanized environment as an alternative approach to the study of urban expansion.
Compared to the NDVI, the percent ISA is more stable and less affected by seasonal changes
in landscape conditions, which means that percent ISA may provide an additional metric for the
analysis of LST and urban thermal patterns.
The aims of this study are to investigate the relationships of LST with NDVI, and those of NDBI
with percent ISA using Landsat TM and ETM+ data obtained for the city of Fuzhou in southeastern China; and to quantitatively compare the patterns and intensity of UHI with LULC types.
In order to generate verifiable estimates of change in urban extent, the LULC types were
quantitatively determined by implementing sub-pixel percent imperviousness estimation and
selecting certain threshold values of percent ISA. In developing this method of quantifying the
urban LULC types and associated surface thermal distribution using remote sensing data, it is
envisaged that the method could be used for other similar geographical regions in China and
indeed elsewhere.
2. Methods
2.1. Study area and data
The study area is Fuzhou City, located in the southeast coast of China (Fig. 1). With a
population of over 5.75 million, Fuzhou is the major coastal city located between Hong Kong
and Shanghai. The city is on a subtropical plain sandwiched between the Gu and Qi mountains
with potential for expansion in all directions. Like many other Chinese cities, the population of
Fuzhou is rapidly increasing leading to increased urban expansion. This urban growth is
encroaching into the adjacent agricultural and other non-urban land. With sweltering summer
and mild winter, the city has several advantages that make it suitable for our study. It is
characterized by a diversity of land-cover types transversed by the Min River. The city is also
characterized by high-, medium- and low-density urban developments in the central portion and
several rural land-cover types – predominantly agricultural fields, forests, water and bare land in
the surrounding landscapes. The built environment consists of buildings and roofs made up of
concrete, brick tiles and metal plates, and majority of the roads are covered by asphalt and
concrete. The city is therefore ideally suitable for the analysis of UHI phenomenon due to its
diversity of land-cover types and the rapid urbanization.
Full-size image (57K)
High-quality image (545K)
Fig. 1. Location map of study area showing the aerial photograph image.
To quantitatively derive LST and compute UHI intensity, Landsat 5 TM image (acquired on June
15, 1989) and Landsat 7 ETM+ image (acquired on March 4, 2001) were used. While bands 1–
5 and 7 images have nominal spatial resolution of 30 m, the thermal infrared band (band 6) has
120 m spatial resolution for TM image and 60 m for ETM+ image. In addition, an IKONOS
image (acquired on October 29, 2000) with 1 m spatial resolution and aerial photographs
(acquired on May 20, 1988) with 2 m spatial resolution, rectified to the Universal Transverse
Mercator (UTM) coordinate system, were used to calculate the percent ISA. Land-cover
classification of the remote sensing imageries was carried out using ancillary data obtained from
1:10,000 scale digital topographic maps.
2.2. Image pre-processing
To analyse the changes in temperature in relation to LULC types, the bi-temporal TM/ETM+
images were geo-referenced to a common UTM coordinate system based on the rectified highresolution IKONOS image, aerial photograph and the 1:10,000 scale topographic maps. The
RMSE of rectification is less than 0.3 pixels (≈9 m). Using the radiometric correction method
of Schroeder et al. (2006), the original digital numbers of bands 1–5 and 7 images were
converted to at-satellite radiance, at-satellite reflectance, and further converted to surface
reflectance. While bands 1 through 5 and band 7 are at a spatial resolution of 30 m, the thermal
band (band 6) comes at an original spatial resolution of 120 m for TM and 60 m for ETM+. In
order to carry out further analysis on a common spatial resolution, bands 1–5 and band 7 of
both Landsat imageries were resampled onto 120 m using the cubical convolution algorithm.
2.3. Derivation of LST, NDVI and NDBI from TM and ETM+ imageries
2.3.1. LST
LST is the radiative skin temperature of the land surface which plays an important role in the
physics of the land surface through the process of energy and water exchanges with the
atmosphere. The derivation of LST from satellite thermal data requires several procedures:
sensor radiometric calibrations, atmospheric and surface emissivity corrections, characterization
of spatial variability in land-cover, etc. As the near-surface atmospheric water vapour content
varies over time due to seasonality and inter-annual variability of the atmospheric conditions, it
is inappropriate to directly compare temperature values represented by the LST between
multiple periods. Therefore the focus here is on the UHI intensity and its spatial patterns across
the study region. UHI intensity is estimated as the difference between the peak temperatures
(LST) of the urban area and the background non-urban temperatures (Chen et al., 2006). This
UHI effect can be determined for the individual thermal images and then compared between two
or more periods. However, before we compute UHI effect, we must first derive the LST based
on different methods for TM and ETM+ images.
As described above the TM and ETM+ thermal infrared band (10.4–12.5 μm) data were used to
derive the LST. Yuan and Bauer (2007) proposed a method of deriving LST in three steps:
Firstly, the digital numbers (DNs) of band 6 are converted to radiation luminance or top-ofatmospheric (TOA) radiance (Lλ, mW/cm2 sr) using:
(1)
where b6 is the pixel digital number for band 6, Lmax = 1.896 (mW/cm2 sr), and Lmin = 0.1534
(mW/cm2 sr). In the case of Landsat 7 ETM+ image, TOA is derived by:
(2)
where QCALmin = 1, QCALmax = 255, and Lmax = 17.04 W/(m2 sr μm), and Lmin = 0.
Secondly, the TOA radiance is converted to surface-leaving radiance by removing the effects of
the atmosphere in the thermal region. An atmospheric correction tool – MODTRAN 4.0 for the
thermal band of Landsat sensors was applied. This tool uses the MODTRAN radiative transfer
code and a suite of integration algorithms to estimate three parameters – atmospheric
transmission, and upwelling and downwelling radiance – which enable the calculation of the
surface-leaving radiance – LT or the radiance of a blackbody target of kinetic temperature T, in
the form of (Eq. (3)):
(3)
where TOA is the radiance derived for the instrument, Lμ is the upwelling or atmospheric path
radiance, Ld is the downwelling or sky radiance, τ is the atmospheric transmission, and is the
emissivity of the surface specific to the target type. Radiance values are in units of W/(m2 sr μm)
and the transmission and emissivity is unitless. The emissivity could be based on the land-cover
classification (Yuan et al., 2005) or the emissivity values as derived by Snyder et al. (1998).
Lastly, the radiance (LT) is converted to surface temperature (LST) using the Landsat specific
estimate of the Planck curve (Eq. (4)) (Chander and Markham, 2003):
(4)
where LST is the temperature in Kelvin (K), K1 is the pre-launch calibration constant in
W/(m2 sr μm) and K2 is another pre-launch calibration constant in Kelvin. For Landsat 5
TM, K1 = 607.76 W/(m2 sr μm) and K2 = 1260.56 K; for Landsat 7
ETM+, K1 = 666.09 W/(m2 sr μm) and K2 = 1282.71 K.
The LST image from the thermal band of ETM+ image (band 6) with original spatial resolution of
60 m was resampled to 120 m using the nearest neighbour algorithm to match the pixel size of
the LST image from TM image.
2.3.2. NDVI and NDBI
The NDVI and NDBI indices were required to characterize the LULC types and to explore the
quantitative relationships between LULC types and UHI. NDVI (Eq. (5), which has generally
been used to express the density of vegetation (Purevdorj et al., 1998) is of the form
(5)
where ρ3 is the reflectance value of red band (band 3) and ρ4 is the reflectance value of nearinfrared band (band 4), both of the Landsat images. The NDVI values range from −1 to 1, with
positive values indicating vegetated areas and negative values signifying non-vegetated surface
features.
Another index used in this study that is sensitive to the built-up area is NDBI (Zha et al., 2003),
derived as
(6)
where b5 and b4 are the respective digital numbers of mid-infrared band (band 5) and nearinfrared band (band 4) of the Landsat images.
In this study both the NDVI and NDBI were used to differentiate the LULC types by setting the
appropriate threshold values. It is worth noting that these indices may have different range of
values for different land-cover types, depending on the study regions, image acquisition time
and different conditions of atmosphere and precipitation.
2.4. The derivation of urban percent ISA
Several studies have shown ISA to be highly correlated with urban land-cover types and their
spatial patterns ([Jennings et al., 2004] and [Xian and Crane, 2005]). However, the identification
of impervious surfaces is a challenge because of the complexity of urban and suburban
landscapes and the limitation of remotely sensed data in terms of spectral and spatial
resolutions. The heterogeneity of urban landscapes and the difficulty in selecting training
samples are major causes of LULC misclassification, as the impervious surfaces consist of a
mixture of different surfaces. Recently, research in impervious surface identification has moved
toward per-pixel image classification, decision tree modelling, and sub-pixel classification (Lu
and Weng, 2006). Sub-pixel percent ISA can be used to characterize urban LULC at resolutions
smaller than the pixel size of the target imagery, and thus enhances the quantification of
heterogeneous of urban LULC. While high-resolution imagery can be used to identify urban
land-cover heterogeneity, the medium-resolution Landsat imagery could be used to extrapolate
ISA over large regions (Xian and Crane, 2006).
In this study, the percent ISA was used as an indicator of urban spatial extent and level of
development. To accurately estimate urban surface types, the sub-pixel imperviousness
detection method was used to spatially quantify the urban LULC types following the steps
illustrated in Fig. 2. In the case of Landsat ETM+ obtained in 2001, this method classified urban
and non-urban areas of the 1 m resolution IKONOS image (produced by the fusion of IKONOS
1 m high-resolution panchromatic image and 4 m multi-spectral images, acquired in 2000)
based on unsupervised classification, and further classified urban areas by thresholding. The
ISA result was used to calculate the percent ISA based on the 120 m vector data and then
convert to 120 m raster percent ISA image for each pixel of LST derived from 2001 ETM+
imagery. In the case of 1989 TM imagery, the 2 m resolution aerial photograph, acquired in May
1988, was utilized to extract urban areas and then converted to 120 m raster percent ISA
image. In the case of rural areas for which there was no high-resolution IKONOS imagery or
aerial photographs, we used the relationships of urban percent ISA and NDBI (derived from
TM/ETM+ using Eq. (6)) to obtain percent ISA images through thresholding of NDBI values. The
results of these analyses are two 120 m resolution images of percent ISA representing the
general characteristics of rural–urban landscape mix for 1989 and 2001, respectively.
Full-size image (41K)
High-quality image (346K)
Fig. 2. Flow chart showing the steps for deriving percent impervious surface area (ISA) and
associated land surface temperature (LST).
3. Results and discussion
3.1. Land surface imperviousness and LST
The digital remote sensing method described here provides not only a measure of the
magnitude of surface temperatures, but also the spatial extent of the surface UHI effects. The
percent ISA, which was estimated within a continuous range of between 0% and 100%, was
mapped for the region. As illustrated by the imperviousness maps in Fig. 3, the higher percent
ISA threshold values captured almost all of the developed land including low-, medium-, and
high-density residential areas, as well as the central business districts (CBD). Pixels were
classified as urban at various levels of development when the percent ISA is equal to or greater
than 10% and those pixels of less than 10% were classified as non-urban. Thus the urban
development densities were defined by the ISA threshold values as 10–30% for low-density;
31–50% for medium-density; and >50% for high-density. Such detailed information on urban
land-cover types also reveals CBD and urban residential areas with varying densities and
patterns, rural developed centres and relatively undeveloped areas.
Full-size image (367K)
High-quality image (5360K)
Fig. 3. Spatial distribution patterns of land surface temperature (LST) for different percent
impervious surface area (ISA) from the TM image acquired on June 15, 1989 (a, c, e, g) and
ETM+ image acquired on March 4, 2001 (b, d, f, h).
The spatial patterns of LST estimated for the different categories of percent ISA are shown
in Fig. 3. While Fig. 3g and h indicates relatively high ISA percentages and associated LST
values, Fig. 3e, f and c, d are respectively indicative of medium and medium low percent ISA
and associated LST. However, the lowest of the percent ISA and associated LST appear to be
restricted to the rural areas (Fig. 3a and b). In both 1989 and 2001, the spatial extent and
distribution patterns of LST show increasing values from natural landscape to the high density
CBD where LULC types are predominantly made of concrete, stone, and metal. They also show
a general UHI effect with a variety of thermal distributions in the urban area (yellow and red
patterns in Fig. 3g and h), compared with other LULC types (Fig. 3a–e). The “hot spots”,
observed by both satellite sensors, are more pronounced in downtown CBD in 1989 (Fig. 3g).
As urbanization expanded, new “hot spots” have appeared in the western, southern and southeastern outskirt of the city by 2001 (Fig. 3h). The thermal gradient from the centre to the edge of
the city is from the hot (high percent ISA) to warm (medium percent ISA) to cool (low percent
ISA). The results show that the spatial distribution patterns of the UHIs have changed from a
scattered pattern in 1989 to a more contiguous pattern in 2001, which is indicative of urban
filling and expansion.
Table 1 shows the areal extent of the different categories of percent ISA in Fuzhou city. Overall
the areal extent of percent ISA >10% is 763.94 km2 in 1989, increasing to approximately
1213.64 km2 in 2001. This increase occurred more in the categories of 31–50% ISA and >50%
ISA, which are more than compensated by the decrease in areal extent of the <10% ISA
category. The largest increase of about 89.0% had occurred in the category of 31–50% ISA,
which means that the medium density urban development had occurred quite significantly within
12 years.
Table 1.
The spatial extent (km2) of each category of percent ISA in 1989 and 2001 and change in
spatial extent between the two periods.
Year/category of percent ISA
<10% ISA 10–30% ISA 31–50% ISA >50% ISA
2001 (km2)
530.71
352.71
498.38
362.55
1989 (km2)
980.41
297.69
263.69
202.56
Difference (km2)
−449.7
55.02
234.69
159.99
18.5
89.0
79.0
Percent change from 1989 to 2001 −45.9
The mean and standard deviation (SD) of LST for each imperviousness category are shown
in Table 2. Generally, the high-density urban area (percent ISA categories >50% ISA) have
higher mean LST for 1989, but in 2001 the mean LST is nearly the same as that of the mediumand high-density urban areas with the value slightly over 289 K. The mean LST values for
relatively developed areas (percent ISA >10%) in both years are about 2–3 K higher than those
of rural areas (percent ISA <10%). In comparative terms, the increase is probably because high-
density urban development areas were, on average, much larger in 1989 (mean LST of about
2.8 K) than in 2001 (2.46 K). This means that there is some homogenisation and expansion of
urban development over the study region in 2001 compared to 1989. This is further supported
by the comparative SD of LST for the percent ISA categories for each year (Table 2). The SDs
of LST are much larger in 1989 compared to 2001, although this difference in SD of LST could
also be attributed to seasonal fluctuations of LST, as mean LST and its SD in early spring
(March 2001) would be expected to be less than mean LST in summer (June 1989). Explained
in different context, the SDs of LST are generally larger for urban areas than the rural areas,
indicating that the urban landscapes would have experienced wider variation in LST than the
natural vegetation because of mix of LULC types and different building structures and
construction materials. This explains the thermal heterogeneity that characterizes these areas.
Table 2.
Mean LST of categories of percent ISA and associated standard deviation (SD) in 1989 and
2001.
Category of percent ISA <10% ISA 10–30% ISA 31–50% ISA >50% ISA
Mean 2001 LST (K)
287.21
287.79
289.21
289.67
SD of 2001 of LST (K)
1.30
1.40
1.33
1.45
Mean 1989LST (K)
297.78
298.40
299.20
300.58
SD of 1989 LST (K)
1.32
1.63
2.16
1.96
The SDs of LST are relatively small for the low-density residential areas because of
homogeneity of construction types contributing to low LST variation in these areas.
Theoretically, the SD for urban areas should be smaller for a given TM image than obtained for
ETM+ image because LST values were calculated from the original 120 m resolution thermal
band 6 of Landsat TM, whereas the LST values obtained from the Landsat ETM+ thermal band
were upscaled from an original 60–120 m. The LST values for ETM+ thermal band should have
relatively large variations because finer (60 m) resolution imagery was upscaled to 120 m, and
thus could have captured more detailed variability of the LST. However, probably because the
percent ISA reflects seasonal UHI fluctuations, the SDs for the urban areas are smaller for
ETM+ image (acquired in early spring) than in TM image (acquired in summer).
3.2. Relationship among imperviousness, NDVI, NDBI and LST
The land surface or near land surface temperature can be affected by the nature of land surface
cover, ranging from the bare ground to vegetation cover types of variable density. It is well
known that NDVI can be used as a surrogate for the density and vigour of vegetation. However,
in this study NDVI was used to analyse the land-cover types as classified from the Landsat
imagery and to estimate the emissivity of the surface ([Yuan et al., 2005] and [Snyder et al.,
1998]). To better understand the relationships among NDVI, percent ISA (the different ISA
categories) and LST, we analysed the mean NDVI for each percent ISA category. Sample
points from LULC types: built-up, forest, cropland and bare land (the water body was excluded
from sampling) were used to investigate the relationships of LST with NDVI and with percent
ISA (Fig. 4). Fig. 4a and b indicates the relationship between LST and NDVI is weak and
nonlinear, variable and strongly affected by season, especially as the non-vegetated locations
experience a wider variation in surface radiant temperature than densely vegetated locations.
As Fig. 4a and b shows a triangular spread of scatterplots of NDVI versus LST can be observed
in both cases. This similar to the work reported elsewhere ([Nemani et al., 1993], [Assefa,
2004] and [Yuan and Bauer, 2007]). An apparent weak nonlinearity in these relationships is less
so in spring (March 2001 than in summer (June 1989)) which suggests that NDVI alone may not
be suitable for quantitative study of UHI. However, Fig. 4c and d illustrates a better linear
relationship between LST and imperviousness or percent ISA, although less so for 2001, which
means that imperviousness, as estimated by UHI, could be used to study the spatial–temporal
variation of LST.
Full-size image (58K)
High-quality image (548K)
Fig. 4. Scatterplots of land surface temperature (LST) versus normalized difference vegetation
index (NDVI): (a) June 1989, (b) March 2001, in comparison to scatterplots of LST versus
percent impervious surface area (ISA): (c) June 1989 and (d) March 2001.
The mean NDVI for Fuzhou in 1989 and 2001 and associated SDs for different percent ISA
categories are shown in Table 3. Generally, urban areas exhibit smaller NDVI values than nonurban areas, with consistent decrease in the mean NDVI as the percent imperviousness (ISA)
increases. It is interesting to note that all of the percent ISA categories of urban areas have
negative NDVI in the summer of 1989 in comparison to positive values during spring of 2001.
This explains the fact that there was less vegetation cover interspersed within the developed
areas in 2001 in comparison to 1989. Indeed there is a consistent decline in NDVI with increase
level of urban development in both cases. The SD of NDVI for the urban areas, especially for
the highest density ISA category (>50% ISA), is higher than that for the fully vegetated rural
areas (<10% ISA) because vegetation landscapes in urban areas are interspersed with the
variegated developed urban structures.
Table 3.
Mean NDVI for the categories of percent ISA and associated standard deviation (SD) in 1989
and 2001.
Percent ISA
<10% ISA 10–30% ISA 31–50% ISA >50% ISA
Percent ISA
<10% ISA 10–30% ISA 31–50% ISA >50% ISA
NDVI (2001)
0.15
−0.01
−0.03
−0.09
SD (2001 NDVI) 0.09
0.07
0.08
0.10
NDVI (1989)
0.25
0.04
0.01
−0.11
SD (1989 NDVI) 0.05
0.06
0.14
0.20
Fig. 5 depicts the plots of the respective differences in the mean LST and NDVI for percent ISA
<10% against the respective means of NDVI and LST for the other three percent ISA
categories. In both cases (Fig. 5a and b) high average NDVI is shown to have lowered the
mean LST by nearly 4 K in 1989 and 3 K in 2001. The dense natural vegetation canopy in rural
areas reduces the surface radiant temperature leading to relatively low LST values. As ISA
increases from medium to high urban development density, mean NDVI gradually decreases
with concomitant increase in LST gradually. Such a strong negative relationships between LST
and NDVI have been reported in other studies in thermal remote sensing for urban and rural
environments ([Dousset and Gourmelon, 2003], [Gallo and Tarpley, 1996], [Lo et al.,
1997] and [Wilson et al., 2003]). The basis of these relationships is that higher levels of latent
heat fluxes are more representative of areas characterized by significant vegetation cover (e.g.,
forest and grass-land areas in this study) in comparison to areas with sparse or no vegetation
cover and low surface moisture availability (such as built-up and bare-soil types). The latter
developed surfaces are where significant sensible heat exchange occurs ([Ruiliang et al.,
2006] and [Wilson et al., 2003]). Gallo and Owen (1999) evaluated the seasonal trends in
temperature and NDVI and found that differences in NDVI and satellite-based surface
temperature accounted for 40% of the variation in urban–rural temperature differences.
Full-size image (24K)
High-quality image (190K)
Fig. 5. (a) The 1989 loss in mean normalized difference vegetation index (NDVI) and gain in
mean land surface temperature (LST) of other percent ISA categories from natural landscapes
(percent impervious surface area (ISA) ≤10%), as a function of other percent ISA categories
(with percent ISA >10%); (b) The 2001 loss in mean NDVI and gain of LST of other percent ISA
categories from natural landscapes (percent ISA ≤10%), as a function of other percent ISA
categories (with percent ISA >10%).
To further investigate these relationships, a zonal analysis was carried out to evaluate the mean
LST at the increment of percent ISA from 0% to 100%, and at the increment of the NDVI from
−1 to 1. Fig. 6a and b shows relatively strong linear relationships (average r2 ≈ 0.68) between
the mean LST and percent imperviousness for both 1989 and 2001, suggesting that the
variations in LST can be well accounted for by percent ISA, especially during the summer period
– June 1989 (Fig. 6a). On the other hand, the associations between the mean LST and mean
NDVI are not straightforward but weak (Fig. 6c and d) for both years. In order to quantitatively
analyse the relationship between the density of built-up area and its temperature, the average
values of NDBI and corresponding temperature were used. The scatterplots of mean NDBI and
corresponding LST are shown in Fig. 7. The results indicate a statistically significance
correlation (P = 0.05; r2 = 0.87 for 1989 and 0.75 for 2001) between NDBI and LST, and the
resulting regression equation could be used to study the impact of built-up area on LST. It thus
demonstrate the usefulness of NDBI for the study of LST, and hence imperviousness of LULC
types.
Full-size image (43K)
High-quality image (358K)
Fig. 6. Linear plots of mean land surface temperature (LST) versus percent impervious surface
area (ISA): (a) 1989, (b) 2001; and plots of mean LST versus normalized difference vegetation
index (NDVI): (c) 1989 and (d) 2001.
Full-size image (19K)
High-quality image (158K)
Fig. 7. Linear plots of mean normalized difference built-up index (NDBI) versus mean land
surface temperature (LST): (a) 1989 and (b) 2001.
3.3. Relationship between UHI and LULC patterns
From the above analysis, it can be seen that NDVI, NDBI and band DNs can be used to classify
LULC types (water, bare land, built-up, forest, cropland), and that NDBI is more suitable for the
quantitative study of land-cover types and associated surface temperature than NDVI. We
further explored how different ISA categories can be used to study UHIs. To study the effect of
human activities on global warming, it is necessary to study the temporal change in
temperature, represented by UHIs, vis-à-vis LULC. Our study here indicates that the
identification of ISA categories mimic the increase in relative extent of the medium-density
residential area from 15.1% in 1989 to 28.6% in 2001. There is an areal increase in high-density
residential areas as a proportion of the total area, which varies from 11.6% in 1989 to 20.8% in
2001. Similarly there is an increase in low-density residential areas varying from 17.1% in 1989
to 20.2% in 2001. This increase over the 12-year period is more due to urban expansion in the
semi-rural areas in the vicinity of Fouzhu city. In contrast, the non-urban areas, as a proportion
of the total study area declined during the same period: from 56.2% to 30.4%. Evidently, this
change in LULC has altered the land surface temperature in the region. Due to high heat
capacity of water, temperature variation over water is less variable, and hence the temperature
difference between percent ISA and waters can be used as the measure of the UHI intensity.
We represent the mean UHI intensity for each period by calculating the difference in LST
between different ISA categories and water (Table 4).
Table 4.
The mean difference between LST values of each category of percent ISA and water.
LST of water (K)
Mean difference (K)
<10% ISA 10–30% ISA 31–50% ISA >50% ISA
2001
284.10
1.81
2.69
3.11
3.58
1989
295.30
2.48
3.10
3.40
5.28
It has been known that the UHI intensity can vary with the change of seasons, and that
agricultural activities would influence the UHI interpretation of LULC types ([Weng,
2001] and [Chen et al., 2006]). In this study we observed an increasing trend of intensity of UHI
(represented by LST) from 1989 to 2001 (Table 2 and Table 4). Using this information, we can
estimate how changes in LULC may have contributed to the change in regional temperature.
We then estimated the contribution to the net increase or decrease in temperature, which we
termed as dT, by each percent ISA category to the regional temperature dynamics (Table 5).
The estimated dT is the difference between the means of LST of each ISA category to the mean
LST of two periods (1989 and 2001). The mean LST for the two dates is 293.14 K. It is
concluded that urbanization has led to a significant temperature increase, which conforms to the
changes of the total contributions of ISA category.
Table 5.
Contribution to LST by each category of percent ISA to the local temperature.
dT
Proportion of
ISA category in
1989
1989
Contributiona
Proportion of
ISA category in
2001
2001
Contributiona
<10% ISA
−1.24 0.562
−0.725
0.304
−0.392
10–30% ISA
−0.49 0.171
0.079
0.202
0.093
31–50% ISA
0.18
0.124
0.286
0.235
0.151
>50% ISA
Total
New mean LST
dT
Proportion of
ISA category in
1989
1989
Contributiona
Proportion of
ISA category in
2001
2001
Contributiona
1.03
0.116
0.173
0.208
0.31
1
−0.349
1
0.246
292.791
293.386
Note that ‘New mean LST’ here is the sum of the contribution to the mean LST value of
293.14 K taken from LST of 1989 and 2001.
a
4. Conclusions
In this study we investigated the relationships among the LST, percent ISA, NDVI and NDBI in
Fuzhou city. The results indicate percent ISA is an accurate indicator of UHI effects with strong
linear relationships between LST and percent ISA in June 1989 and March 2001. However, the
relationship between LST and NDVI suffers perhaps due to seasonal effect. Detailed analysis of
relationships between LST and percent ISA shows that variations in surface temperature could
be better accounted for by differences in imperviousness than by the commonly used NDVI.
This implies that percent ISA can be used to analyse LST quantitatively for UHI studies
validated by NDBI.
All the analyses in this paper were based on the interpretation of remote sensing images, and
the results showed that remote sensing images are ideal for analysing UHI. In future studies,
there is the need to focus on: (i) the impact of the distribution pattern of different LULC types on
UHI; (ii) more accurate estimation of the variable conditions of LULC types; (iii) comparison of
UHIs estimated for cities of different sizes under different climatic conditions; and (iv) multitemporal studies of UHIs of a single city over four seasons’ using different satellite data.
References
Assefa, 2004 M.M. Assefa, Spatiotemporal dynamics of land surface parameters in the Red
River of the North Basin, Physics and Chemistry of the Earth 29 (2004), pp. 795–810.
Balling and Brazel, 1988 R.C. Balling Jr. and S.W. Brazel, High-resolution surface temperature
patterns in a complex urban terrain, Photogrammetric Engineering and Remote Sensing 54 (9)
(1988), pp. 1289–1293. View Record in Scopus| Cited By in Scopus (42)
Ben-Dor and Saaroni, 1997 E. Ben-Dor and H. Saaroni, Airborne video thermal radiometry as a
tool for monitoring microscale structures of the urban heat island, International Journal of
Remote Sensing 18 (1997), pp. 3039–3053. View Record in Scopus | Cited By in Scopus (24)
Brunsell, 2006 N.A. Brunsell, Characterization of land-surface precipitation feedback regimes
with remote sensing, Remote Sensing of Environment 100 (2006), pp. 200–211. Article |
PDF (849 K) | View Record in Scopus | Cited By in Scopus (7)
Caselles et al., 1991 V. Caselles, M.J. Lopez Garcia, J. Melia and A.J. Perez Cueva, Analysis of
the heat-island effect of the city of Valencia, Spain through air temperature transits and NOAA
satellite data, Theoretical and Applied Climatology 43 (1991), pp. 195–203. Full Text via
CrossRef | View Record in Scopus | Cited By in Scopus (9)
Chander and Markham, 2003 G. Chander and B. Markham, Revised Landsat-5 TM radiometric
calibration procedures and postcalibration dynamic ranges, IEEE Transactions on Geoscience
and Remote Sensing 41 (11) (2003), pp. 2674–2677. Full Text via CrossRef | View Record in
Scopus | Cited By in Scopus (161)
Chen et al., 2006 X.L. Chen, M.Z. Zhao, P.X. Li and Z.Y. Yin, Remote sensing image-based
analysis of the relationship between urban heat island and land use/cover changes, Remote
Sensing of Environment 104 (2006), pp. 133–146.Article |
PDF (1467 K) | View Record in
Scopus | Cited By in Scopus (25)
Chen et al., 2004 Z.M. Chen, I.S. Babiker, Z.X. Chen, K. Komaki, M.A.A. Mohamed and K. Kato,
Estimation of interannual variation in productivity of global vegetation using NDVI
data, International Journal of Remote Sensing 25 (16) (2004), pp. 3139–3150.
Civco et al., 2002 D.L. Civco, J.D. Hurd, E.H. Wilson, C.L. Arnold and M.P. Prisloe Jr.,
Quantifying and describing urbanizing landscapes in the northeast United
States, Photogrammetric Engineering and Remote Sensing 68 (10) (2002), pp. 1083–
1090. View Record in Scopus | Cited By in Scopus (44)
Dousset and Gourmelon, 2003 B. Dousset and F. Gourmelon, Satellite multi-sensor data
analysis of urban surface temperatures and land cover, Photogrammetry and Remote
Sensing 58 (2003), pp. 43–54 Sensing of Environment, 106, 375–386. Article |
PDF (1498
K) | View Record in Scopus | Cited By in Scopus (40)
Gallo and Owen, 1998a K.P. Gallo and T.W. Owen, Assessment of urban heat island: a
multisensor perspective for the Dallas-Ft. Worth, USA region, Geocarto International 13 (1998),
pp. 35–41. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (17)
Gallo and Owen, 1998b K.P. Gallo and T.W. Owen, Satellite-based adjustments for the urban
heat island temperature bias, Journal of Applied Meteorology 38 (1998), pp. 806–813.
Gallo and Owen, 1999 K.P. Gallo and T.W. Owen, Satellite based adjustments for the urban
heat island temperature bias, Journal of Applied Meteorology 38 (1999), pp. 806–813. View
Record in Scopus | Cited By in Scopus (45)
Gallo and Tarpley, 1996 K.P. Gallo and J.D. Tarpley, The comparison of vegetation index and
surface temperature composites of urban heat-island analysis, International Journal of Remote
Sensing 17 (1996), pp. 3071–3076. Full Text via CrossRef | View Record in Scopus | Cited By
in Scopus (26)
Gallo et al., 1995 K.P. Gallo, J.D. Tarpley, A.L. McNab and T.R. Karl, Assessment of urban heat
islands: a satellite perspective, Atmospheric Research 37 (1995), pp. 37–43. Article |
PDF
(378 K) | View Record in Scopus | Cited By in Scopus (21)
Gillies and Carlson, 1995 R.R. Gillies and T.N. Carlson, Thermal remote sensing of surface soil
water content with partial vegetation cover for incorporation into climate models, Journal of
Applied Meteorology 34 (1995), pp. 745–756.View Record in Scopus | Cited By in Scopus (141)
Jennings et al., 2004 D.B. Jennings, S.T. Jarnagin and C.W. Ebert, A modeling approach for
estimating watershed impervious surface area from national land cover data
92, Photogrammetric Engineering and Remote Sensing 70 (2004), pp. 1295–1307. View Record
in Scopus | Cited By in Scopus (9)
Kim, 1992 H.H. Kim, Urban heat island, International Journal of Remote Sensing 13 (1992), pp.
2319–2336. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (48)
Lo et al., 1997 C.P. Lo, D.A. Quattrochi and J.C. Luvall, Application of high resolution thermal
infrared remote sensing and GIS to assess the urban heat island effect, International Journal of
Remote Sensing 18 (1997), pp. 287–304. View Record in Scopus | Cited By in Scopus (97)
Lu and Weng, 2006 D. Lu and Q. Weng, Use of impervious surface in urban land-use
classification, Remote Sensing of Environment 102 (2006), pp. 146–160. Article |
PDF
(5246 K) | View Record in Scopus | Cited By in Scopus (25)
Nemani et al., 1993 R.R. Nemani, L. Pierce and S.W. Running, Developing satellite-derived
estimates of surface moisture status, Journal of Applied Meteorology 32 (3) (1993), pp. 548–
557. View Record in Scopus | Cited By in Scopus (119)
Owen et al., 1998 T.W. Owen, T.N. Carlson and R.R. Gillies, An assessment of satellite
remotely-sensed land cover parameters in quantitatively describing the climatic effect of
urbanization, International Journal of Remote Sensing 19(1998), pp. 1663–1681. View Record
in Scopus | Cited By in Scopus (81)
Price, 1990 J.C. Price, Using spatial context in satellite data to infer regional scale
evapotranspiration, IEEE Transactions on Geoscience and Remote Sensing 28 (1990), pp.
940–948. Full Text via CrossRef | View Record in Scopus |Cited By in Scopus (124)
Purevdorj et al., 1998 T.S. Purevdorj, R. Tateishi, T. Ishiyama and Y. Honda, Relationships
between percent vegetation cover and vegetation indices, International Journal of Remote
Sensing 19 (18) (1998), pp. 3519–3535. View Record in Scopus | Cited By in Scopus (81)
Rao, 1972 P.K. Rao, Remote sensing of urban “heat islands” from an environmental
satellite, Bulletin of the American Meteorological Society 53 (1972), pp. 647–648.
Renee et al., 2006 G. Renee, A. Dale and C.L. Quattrochi Jeffrey, A multi-scale approach to
urban thermal analysis, Remote Sensing of Environment 104 (2006), pp. 123–132.
Ruiliang et al., 2006 P. Ruiliang, G. Peng, M. Ryo and S. Todashi, Assessment of multiresolution and multi-sensor data for urban surface temperature retrieval, Remote Sensing of
Environment 104 (2006), pp. 211–225.
Small, 2006 C. Small, Comparative analysis of urban reflectance and surface
temperature, Remote Sensing of Environment 104 (2006), pp. 168–189. Article |
PDF
(4323 K) | View Record in Scopus | Cited By in Scopus (5)
Snyder et al., 1998 W.C. Snyder, Z. Wan, Y. Zhang and Y.Z. Feng, Classification based
emissivity for land surface temperature measurement from space, International Journal of
Remote Sensing 19 (1998), pp. 2753–2774. View Record in Scopus | Cited By in Scopus (95)
Streutker, 2002 D.R. Streutker, A remote sensing study of the urban heat island of Houston
Texas, International Journal of Remote Sensing 23 (2002), pp. 2595–2608. Full Text via
CrossRef | View Record in Scopus | Cited By in Scopus (43)
Streutker, 2003 D.R. Streutker, Satellite-measured growth of the urban heat island of Houston,
Texas, Remote Sensing of Environment 85 (2003), pp. 282–289. Article |
PDF (233
K) | View Record in Scopus | Cited By in Scopus (63)
Schroeder et al., 2006 T.A. Schroeder, W.B. Cohen, C.H. Song, M.J. Canty and Z.Q. Yang,
Radiometric correction of multi-temporal Landsat data for characterization of early successional
forest patterns in western Oregon, Remote Sensing of Environment 103 (2006), pp. 16–
26. Article |
PDF (947 K) | View Record in Scopus | Cited By in Scopus (25)
Voogt and Oke, 2003 J.A. Voogt and T.R. Oke, Thermal remote sensing of urban
climates, Remote Sensing of Environment 86 (2003), pp. 370–384. Article |
PDF (817
K) | View Record in Scopus | Cited By in Scopus (118)
Wang et al., 2004 J. Wang, P.M. Rich, K.P. Price and W.D. Kettle, Relations between NDVI and
tree productivity in the central Great Plains, International Journal of Remote Sensing 25 (16)
(2004), pp. 3127–3138. View Record in Scopus |Cited By in Scopus (21)
Weng, 2001 Q. Weng, A remote sensing-GIS evaluation of urban expansion and its impact on
surface temperature in Zhujiang Delta, China, International Journal of Remote Sensing 22 (10)
(2001), pp. 1999–2014. Full Text via CrossRef |View Record in Scopus | Cited By in Scopus
(94)
Weng et al., 2004 Q. Weng, D. Lu and J. Schubring, Estimation of land surface temperature–
vegetation abundance relationship for urban heat island studies, Remote Sensing of
Environment 89 (2004), pp. 467–483. Article |
PDF (958 K) | View Record in Scopus | Cited
By in Scopus (106)
Wilson et al., 2003 J.S. Wilson, M. Clay, E. Martin, D. Struckey and K. Vedder-Risch, Evaluating
environmental influences of zoning in urban ecosystems with remote sensing, Remote Sensing
of Environment 86 (2003), pp. 303–321.Article |
PDF (2115 K) | View Record in
Scopus | Cited By in Scopus (28)
Xian and Crane, 2006G. Xian and M. Crane, An analysis of urban thermal characteristics and
associated land cover in Tampa Bay and Las Vegas using Landsat satellite data, Remote
Sensing of Environment 104 (2006), pp. 147–156.Article |
PDF (1173 K) | View Record in
Scopus | Cited By in Scopus (13)
Xian and Crane, 2005 G. Xian and M. Crane, Assessments of urban growth in the Tampa Bay
watershed using remote sensing data, Remote Sensing of Environment 97 (2) (2005), pp. 203–
215. Article |
PDF (1154 K) | View Record in Scopus | Cited By in Scopus (26)
Yuan and Bauer, 2007 F. Yuan and M.E. Bauer, Comparison of impervious surface area and
normalized difference vegetation index as indicators of surface urban heat island effects in
Landsat imagery, Remote Sensing of Environment106 (2007), pp. 375–386. Article |
PDF
(1300 K) | View Record in Scopus | Cited By in Scopus (24)
Yuan et al., 2005 F. Yuan, K.E. Sawaya, B.C. Loeffelholz and M.E. Bauer, Land cover mapping
and change analysis in the Twin Cities Metropolitan Area with Landsat remote sensing, Remote
Sensing of Environment 98 (2–3) (2005), pp. 317–328. Article |
PDF (2314 K) | View
Record in Scopus | Cited By in Scopus (47)
Zha et al., 2003 Y. Zha, J. Gao and S. Ni, Use of normalized difference built-up index in
automatically mapping urban areas from TM imagery, International Journal of Remote
Sensing 24 (3) (2003), pp. 583–659.