REMOTE SENSING IMAGE-BASED ANALYSIS OF THE URBAN HEAT ISLAND IN DENPASAR, INDONESIA.

8th International Symposium on Lowland Technology
September 11-13, 2012 in Bali, Indonesia

REMOTE SENSING IMAGE-BASED ANALYSIS OF THE URBAN HEAT ISLAND IN
DENPASAR, INDONESIA
Abd. R. As-syakur 1, I W. Nuarsa1, I W Arthana1, M. S. Mahendra1, I W. S. Adnyana1, I N. Merit1,
R. Suyarto1, and K. A. Lila1

ABSTRACT: Denpasar city with a large population and urban size located in the Bali province, Indonesia, was chosen
to study and quantify the entire urban heat island (UHI) as a continuously varying surface and its spatial expansion.
Extensive urbanized surfaces modify the energy and water balance processes and influence the dynamics of air
movement. Landsat 5 TM and 7 ETM+ images of Denpasar on 1995 and 2003 were selected to retrieve the brightness
temperatures and urban heat island areas. The purpose of this study was to characterize the complete urban heat island
in magnitude and spatial extent without the use of in situ measurements. The result showed in the center of the city that
has density built up area created high surface radiant temperature in both observation times. Temperature changes are
quite large due to the addition of population size and land use land cover changes. Vegetation abundance from urban
forest or holy area has an important role controlling the cold surface radiant temperature in the center of city. The cross
section analyses indicate that the land surface temperature (LST) intensity decreases gradually in the central parts of the
city towards the surrounding. Indicated UHI effect may have occurred in Denpasar.
Keywords: Urban heat island (UHI); land surface temperature; landsat; normalized difference vegetation index (NDVI);
denpasar


INTRODUCTION

In fact, it has been detected in cities with populations of
less than 10,000 (Karl et al. 1988).
The UHI is the characteristic warmth of urban areas
compared to their (non-urbanized) surroundings.
Generally, it refers to the increase in air temperatures,
but it can also refer to the relative warmth of surface or
subsurface materials (Voogt 2003). Until recently studies
of urban effects on meteorology and climate have been
conducted for isolated locations and with in situ
measurements (Streutker 2002). With the advent of
thermal remote sensing technology, remote observation
of UHI’s became possible using satellite data and has
provided new avenues for the observation of UHI’s and
the study of their causation through the combination of
thermal remote sensing and urban micrometeorology
(Voogt and Oke 2003).
Remote sensing is a tool for mapping and monitoring

urban areas (Rao 1972). One important advantage of
using remotely sensed surface temperature is its wall-towall coverage of the urban area, explicitly revealing its
spatial patterns (Li et al. 2011). Thermal infrared
radiometry from aircraft or satellite platforms has often
been advocated as a means of providing more spatially
representative measurements of surface temperature over
larger areas of cities than is feasible using ground-based

Urbanization and the conversion of the Earth’s
surface to urban uses are among the most visible and
rapid anthropogenic changes (Roth 2007). Urbanization
is a dynamic process which involves expansion of urban
related area and conversion of natural physical landscape
to more economic land uses. Urbanization, not only
leads to degradation of environment quality but it also
affecting ecosystem health. Urbanization, an important
driver of climate change and pollution, alters both biotic
and abiotic ecosystem properties within, surrounding,
and even at great distances from urban areas (Grimm et
al. 2008).

A most noticeable phenomenon that has arisen as a
result of city expansion is that urban climates are warmer
and more polluted than their rural counterparts (Lo and
Quattrochi 2003). Higher urban heat mainly caused due
to the anthropogenic heat released from vehicles, power
plants, air conditioners and other heat sources, and due
to the heat stored and re-radiated by massive and
complex urban structures (Memon et al. 2008). This
phenomenon called the urban heat island (UHI) effect.
This effect is not restricted to large metropolitan areas.

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Environmental Research Center (PPLH), Udayana University, Bali, 80232, Indonesia

997

Abd. R. As-syakur, et al.

studies (Voogt and Oke 1997). Denpasar represent an

urban city in Bali. The intense urbanization and dense
building may result in the urban heat island phenomenon.
The temperature variations may be attributable to greater
absorbency of man-made materials, and denser buildings
which restrict air-flow. It is very important to conduct an
urban heat island analysis of Denpasar and evaluate its
impact on urban environment in the city. Urban
development usually results in a dramatic alteration at
the surface, as natural vegetation removed and replaced
by reduced evaporating, non-transpiring surfaces (e.g.,
asphalt, stone, metal, concrete). Under such alteration,
the partitioning of incoming solar radiation into fluxes of
sensible and latent heat is skewed in favor of increased
sensible heat flux as evapotranspiring surfaces are
reduced (Gallo and Owen 1998).
Rao (1972) was the first to demonstrate that urban
areas identified from the analyses of thermal infrared
data acquired by the Improved TIROS Operational
Satellite (ITOS-I) Scanning Radiometer sensor with 7.4
km spatial resolution. The spatial resolution of these data

is found suitable only for small-scale urban temperature
mapping. The medium-high spatial resolution satellite
thermal infrared (TIR) data have also been utilized to
derive surface temperature (e.g. Liu and Zhang 2011;
Chen et al. 2006; Weng et al. 2004; Aniello et al. 1995).
The availability of land surface temperature (LST) from
medium-high spatial resolution, such as Landsat, with
120~60 m spatial resolution has significantly facilitated
the study of the relationship between UHI and surface
biophysical parameters (Li et al. 2011). Land surface
temperatures are strongly related to surface/building
geometry, land use, vegetation and patterns of
anthropogenic heat release. Previously, some researchers
finding the relationship between land surface
temperature and vegetation abundance (e.g. Gillies and

Carlson 1995; Gallo and Owen 1998; Weng et al. 2004).
Bottyan et al. (2005) found the relationship between UHI
and built up areas using in situ measurement. The builtup area of the city play a significant role in the
development of the UHI (Chen et al. 2006).

Previous studies have been narrating many factors
that influence of UHI distribution. However, every city
has a unique structure and varies in each region such as
in Denpasar of Indonesia. Denpasar does not have high
buildings and there is quite a lot of vegetation around
settlements area. The objective of this study was to
quantify the entire urban heat island as a continuously
varying surface. By using a surface to represent the
urban heat island, it was expected that the dependence of
measured heat island magnitude on spatial variations
could be overcome. As the satellite sensor measures
radiance, this study focussed on the surface temperature
heat island, not the air temperature heat island. Landsat 5
TM and 7 ETM+ images of Denpasar on 1995 and 2003
were selected to retrieve the brightness temperatures,
emissivity (ε), and UHI areas in order to understand
impacts of urbanization on local climate in Denpasar.

METHODS
Research Location

The research was conducted in Denpasar (BaliIndonesia), located on 08°35'31" – 08°44'49" South
latitude and 115°10'23" – 115°16'27" East longitude.
Figure 1 indicates the distribution of Denpasar city and
six graphic profiles (transects) to examine their intraurban variation. Denpasar city is the center of tourism
destination industry for Indonesia and the center of
government and business for Bali Province. In 2009,

Fig. 1 Research Location. Lines indicating the transect cross section to examine LST intra-urban variation.

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Remote Sensing Image-Based Analysis of The Urban Heat Island In Denpasar, Indonesia

T

Data and Analysis

Tra d 

Remote sensing data used in this study is daytime

Landsat 5 TM and Landsat 7 ETM+, acquisitioned on 3
February in 1995 and 21 March in 2003, respectively is
in Path 116 and Row 66. Both of data used for analysis
of urban heat island and built-up area percentage in
Denpasar. All images bands 1–5 and 7 have a spatial
resolution of 30 m, and the thermal infrared band (band
6) has a spatial resolution of 120 m for Landsat 5 TM
images and 60 m for Landsat 7 ETM+ images (Chander
et al. 2009). We used TIR (Thermal Infrared) bands 6
(10.3–12.4 m) to estimate land surface temperature.
Visible/Near-Infrared bands 3 (0.63–0.6λ m) and 4
(0.77–0.λ0 m) used to calculate emissivity (ε) with the
NDVI (Normalized Difference Vegetation Index)
approach.
Digital numbers (DN) in each band of the Landsat
TM and ETM+ images were converted to physical
measurements of the at-sensor spectral radiance (Lsat).
Conversion of DN to absolute radiance values is a
necessary procedure for comparative analysis of several
images taken by different sensors (Levin 1999). Since

each sensor has its own calibration parameters used in
recording the DN values, the same DN values in two
images taken by two different sensors may represent two
different radiance values. In order to convert the DN data
from Landsat TM and Landsat ETM+ into spectral
radiance, Equation (1) can be written as (Chander et al.
2009):

L sa t  gain  DN  bias

K2

(2)
 K1

 1
ln
 L sa t 
where K1 and K2 are pre-launch calibration constants. K1
= 607.76 and 666.09 (mW cm-2 sr-1 m-1) for Landsat

TM and ETM+, respectively; and K2 = 1260.56 K and
1282.71 K for Landsat TM and ETM+, respectively.
The temperature values obtained above are
referenced to a black body. Therefore, corrections for
spectral emissivity (ε) became necessary according to the
nature of land cover (Van de Griend and Owe 1993).
Each of the land use/land cover categories was assigned
an emissivity values. The emissivity corrected land
surface temperatures in Kelvin (Trad, °K) were finally
computed as follows (Artis and Carnahan 1982):

Denpasar city population reached 649,762 people with
an average growth rate of 3.34% per year (BPS 2010),
this causes favourable for UHI development. Denpasar
city is border by Badung Regency where located in north
and west area of Denpasar, northern area is Gianyar
Regency, and the east area is Badung Strait. In the
southern part of Denpasar there is a small island which
named as Serangan Island. Denpasar has tropical climate
with monthly mean temperature around 24–32 °C and

monthly mean precipitation around 13–358 mm.
Settlement is dominant land use in Denpasar city in 2006,
followed by the paddy field areas (As-syakur et al. 2010).

T
1    T  (h  c/  )ln 

(3)

Whereμ = wavelength of emitted radiance (for which
the peak response and the average of the limiting
wavelengths ( = 11.43 m) will be used); ε = surface
emissivity; σ = Boltzmann constant (1.38×10-23 JK-1); h
= Planck’s constant (6.26×10-34 J s-1); and c = velocity of
light (2.998×108 m s-1).
In fact, the emissivity (ε) estimated by utilizing
NDVI (Valor and Caselles 1996; Van de Griend and
Owe 1993). Surface emissivity on pixel scale derived
using NDVI in conjunction with proportional vegetation
cover. NDVI computed from image data using the
following formula (Tucker, 1979):

NDVI =

Near Infra Red Band - Red Band
Near Infra Red Band + Red Band

(4)

When NDVI values range from 0.157 to 0.727, Van De
Griend and Owe (1993) gave an effective equation as
follows:

  1.0094  0.047 ln(NDVI)

(5)

For the area that the NDVI value is out of the range
(0.157–0.727), it is using the emissivity values
recommended by Zhang et al. (2006) as shown in Table
1.
Table 1 Estimation of emissivity using NDVI

(1)

NDVI
NDVI < -0.185
-0.185 ≤ NDVI < 0.157
0.157 ≤ NDVI ≤ 0.727
NDVI > 0.727

where gain is the slope of the radiance/DN
conversion function; and bias is the intercept of the
radiance/DN conversion function. The gain and bias
values can be found in header files of TM/ETM+ image.
Hereafter, the at-sensor spectral radiance converted to atsatellite brightness temperature in Kelvin, T (°K), by the
following equation:

Land surface emissivity (ε )
0.985
0.955
1.0094 + 0.047 * ln(NDVI)
0.990

To examine the distribution of estimated urban heat
islands, the surface radiant temperature was classified

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Abd. R. As-syakur, et al.

into three temperature types based on the standard
deviation as a criterion for classifying temperatures
(Smith 1986; García-Cueto et al. 2007; Zhand and Wang
2008). The thermal differences are considered normal if
they are ± 1 times of standard deviation, with regard to
the mean value of temperature; they are considered as
cold points, that is, cold islands, if the thermal
differences are < −1 times of standard deviation, and
warm points, that is, heat islands, if the thermal
differences are > +1 times of standard deviation. Finally,
six graphic profiles (transects) were outlined on both
image to estimated their intra-urban variation of land
surface radiant temperature (see Figure 1).

RESULTS AND DISCUSSION
Results
Daytime Landsat 5 TM and Landsat 7 ETM+,
acquisitioned on 3 February in 1995 (mid of wet season)
and 21 March in 2003 (before the end of wet season)
used here to analyse urban heat island in Depansar.
Summarized characters of land surface temperatures on
two dates are shown in Table 2. Land surface
temperatures in 1995 ranged from 23.18 °C to 31.04 °C,
with an average of 27.28 °C. In the 2003, land surface
temperatures ranged from 21.78 °C to 36.12 °C, with an
average of 29.44 °C. Standard deviation (SD) and
variance of LST in 1995 was lower than that of LST in
2003, indicating slightly higher variation of LST in 2003.
Table 2 Descriptive statistics of land surface temperature
in 1995 and 2003
Statistics
Mean (°C)
SD (°C)
Variance (°C)
Min (°C)
Max (°C)
Range (°C)

LST 1995
27.28
1.41
1.99
23.18
31.04
7.86

LST 2003
29.44
2.09
4.37
21.78
36.12
14.34

Figure 2 and 3 shows geographical distributions of
LST in Denpasar in 1995 and 2003, respectively. Areas
shaded in red correspond to the sectors particularly
affected by heat islands, the green tinted reflect a
significant presence of vegetation and water. Very clear
seen the LST in 1995 quite large increase compared to
2003. In 1995, the areas with higher surface radiant
temperature were mainly located in the central urban
area with a typical strip-shaped associated with the
traffic road systems. Within the urban central area,

Fig. 2 Geographical distribution of LST in Denpasar in
1995
numerous sub-centers of UHI with higher surface radiant
temperature were mainly located in the old and recently
developed downtowns. Highest LST is marked in areas
known as density built up. Vegetations were shown to
significantly modify surface temperature. Paddy field
areas surrounding the center of urban area lead to lowest
surface radiant temperatures. Meanwhile, a holy area
that is characterized by extensive vegetation cover
contained in center of urban area created cool LST.
In 2003, the extent of LST increased significantly.
With the growing central urban area, the extent of LST
dramatically expanded from the inner cycle highway to
the outer one, linking the suburban areas and the
substantially growing satellite towns, which were
characterized with small and obvious sub-centers with
higher surface radiant temperatures. Serangan Island
after reclamation in 1997, an area characterized with
wide bare land area was also a key contributor to
increased LST. In addition, seasonal bare land around
urban area as the effect the time of paddy field that
fallow period after harvest had higher surface radiant
temperatures. A holy area showed no change in LST
during that time.

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Remote Sensing Image-Based Analysis of The Urban Heat Island In Denpasar, Indonesia

1995
34
Temperature (o C)

Temperature ( oC)

34
32
30
28
26
24

2003

32
30
28
26

A
1

A’ 24 B
36

71

106

141

176

211

246

B’

1 36 71 106 141 176 211 246 281 316 351 386 421 456 491

281

Pixel Number

Pixel Number

34
Temperature (o C)

Temperature (o C)

34
32
30
28
26
24

32
30
28
26

C
1

C’ 24 D
36 71 106 141 176 211 246 281 316 351 386 421 456

1

D’
36

71

106

Pixel Number

211

246

281

316

34
Temperature (o C)

Temperature (o C)

176

Pixel Number

34
32
30
28
26
24

141

32
30
28
26

E
1

E’ 24 F
36

71

106

141

176

Pixel Number

211

246

281

1

F’
36

71

106

141

176

211

246

Pixel Number

Fig. 4 Daytime LST profile (transects) in 1995 and
2003 at six different places in Denpasar area extracted
from line cross from figure 1.

Fig. 3 Geographical distribution of LST in Denpasar in
2003
To examine the urban heat island area, a
classifications of UHI based on the scheme of standard
deviation threshold was done. Percentage areas of UHI
classification are shown in Table 3. This table show from
1995 to 2003 the areas of the cold island decrease from
18.54% to 16.99%. The normal areas also decrease from
64.38% to 47.20%. While the heat island areas increase
from 17.09% to 35.31% of the whole area.
Figure 4 shows daytime LST graphics profile
(transects) in 1995 and 2003 at six different places in
Denpasar area extracted from line cross from figure 1. In
the LST transects profile presented it is possible to
observe numerous “ridges”, “valleys”, “plateaus”,
“peaks” and “basins”, indicating the heterogeneous
nature of land surface temperature over the space.
Transect line generally shows LST in center of urban
Table 3. Percentages of UHI classifications area based on
the standard deviation in 1995 and 2003
UHI Classification
Cold islands area (%)
Normal area (%)
Heat islands area (%)

1995
18.54
64.38
17.09

2003
16.99
47.20
35.81

area higher than in the edges of sub-urab area. The
“valleys” and “basins” of transect line indicated high
density of vegetation from urban forest (holy area) or
growing time period of paddy field. Different time of
paddy crop period lead to different LST on paddy field
area. Build up area created “ridges” and “plateaus” of
transect line donated high surface radiant temperature,
especially in center of town. The difference “ridges” and
“plateaus” between 1λλ5 and 2003 due the possibility of
build up density changes or land use land cover changes.
Discussion
An investigation of urban heat island in Denpasar of
Indonesia using daytime Landsat 5 TM and Landsat 7
ETM+, acquisitioned on 3 February in 1995 and 21
March in 2003 is presented here. Distributions of land
surface temperature, areas of urban heat island, and
transect profile of land surface temperature performed
here on both satellite images.
The results clearly show the increasing of LST from
1995 to 2003. Areas of UHI also increase during the
time. In 1995, warm LST occurred in the center and
Southern part of the city, and created heat island areas.
The old town of Denpasar located in the center of city
and has density built up, therefore creates heat island
areas. In the southern part of Denpasat on 1995 there
have ex coastal ponds causing heat island region, while
in 2003 changed to the Mangrove area resulted

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Abd. R. As-syakur, et al.

heterogeneous heat-cold island area. The warm LST in
2003 expanded to a wider area compared to 1995. LST
intensity increases gradually in the northern parts of the
city towards the centre because the built-up density
grows gradually indicating significant thermal
modification caused by urbanization. In general,
expanded of warm LST from 1995 to 2003 causes
increasing (decreasing) the heat (cold) island areas.
Difference in UHI patterns is associated with many
factors, including changes in land use land cover, urban
surface geometry, seasonal variation, climatic and
meteorological condition, etc. (Chen et al. 2006; Voogt
and Oke 1998; Chow and Roth 2006; Xu and Chen,
2004). Possibility of land use land cover change is the
main factors causing an increase of UHI areas in
Denpasar. High urbanization process causes high
population size, impact to land changes from non-urban
uses to urban uses. Urban uses are one of land elements
with highest thermal absorptivity and low albedo.
Population size would have an effect on the number of
buildings, vehicles and factories. As the sources and
sinks for most of the material and energy movements
and interactions between the geosphere and biosphere,
changes in land use land covers will not only cause
changes in physical quantities such as vegetative
abundance and biomass, but also in thermal properties.
Denpasar is a unique city because one of its regulations
that prohibit making a building exceeds 15 meters height
(4 floors). This condition caused in the development of
urban areas toward the side expanse of non-urban areas
(paddy field, perennial plant areas, forest, etc.).
Another important factor in the managing of the heat
island is the presence of vegetation in urban areas.
Vegetation abundance from urban forest or holy area in
the center of city has an important role controlling the
cold surface radiant temperature. Several spot of cold
island areas clearly seen between heat island areas. Holy
area has a temperature of 4-5 °C lower than the
surrounding built-up area. In addition, paddy field and
mangrove forest in surrounding of city causes low
surface radiant temperature and created cold island areas.
Vegetation and moisture will have a significant effect on
the microclimate. Generally, vegetation helps cool urban
climates through shading and evapotranspiration process.
Vegetation cover controlling heat island areas measures
through partitioning solar radiation into fluxes of
sensible and latent heat (Wang et al. 2004). Less energy
will be partitioned into the sensible heat fluxes and more
into latent energy fluxes, consequently lowering the air
temperature. That is to say, vegetation is one of the main
contributors to the variations of spatial pattern spectral
radiance and texture in LST, thus to UHI.

CONCLUSIONS
In this study, thermal infrared wavelength
acquisitioned from Landsat 5 TM on 3 February in 1995
and Landsat 7 ETM+ on 21 March in 2003 have been
used to extract surface radiant temperature and areas of
urban heat island in Denpasar-Indonesia.
The UHI phenomenon in Denpasar area has been
found in 1995 and 2003. Areas of heat island dominant
occurred in central part of urban area and significantly
increased and expanded from 1995 to 2003. In general,
LST intensity decreases gradually from the central parts
of the city towards the surrounding.
Surface radiant temperature and surface urban heat
island patterns in Denpasar are characterized by urban
surface bio-physical parameters. Increasely of LST and
areas of urban heat island from 1995 to 2003 are may
causes by land use land cover change and the existing of
vegetation abundance. However, is uncertain because in
this study not doing land use land covers changes
analysis. It is necessary to doing further study to find out
the relationship between land use land cover change and
the presence of vegetation with urban heat island
distribution in this city. That use several remote sensing
indices are possible to found these relationships. The
reason of land use land cover change may not only cause
increase of land surface temperature over the entire
observation period. Further studies also should be
including such as influences of socio-economic
conditions,
seasonal
variation,
climatic
and
meteorological condition because it possibility of having
an impact on surface temperature and areas of heat
island.

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