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International Journal of Civil & Environmental Engineering IJCEE-IJENS Vol: 11 No: 05 34
112805-7676 IJCEE-IJENS © October 2011 IJENS
I J E N S
Abstract— Facing the concern of the population to its
environment and to climatic change, city planners are now
considering the urban climate in their planning choices. The urban
climate, representing different urban morphologies across the
central Bangkok metropolitan area (BMA), were used to
investigate the effects of both the composition and configuration of
variables of urban morphology indicators on the summer diurnal
range of urban climate, using correlation analyses and multiple
linear regressions. Landsat TM image data acquired in summer
were used to estimate land surface temperature (LST). It was
found that approximately 81.1% of the variation of the average
daytime near-surface air temperature (Ta) were explained by the
surface temperature (Ts) on the summer diurnal range. The urban
canopy cover features that most significantly affect the magnitude
of surface temperature is the percentage covered of buildings. We
found that the configuration of urban morphology indicators was
more important in determining the Ta than the composition of
urban canopy cover features. The results indicate that
approximately 92.6% of the variation in Ta was explained jointly
by the two composition variables of urban morphology indicators,
including open space ratio (OSR) and floor area ratio (FAR). On
the other hand, the green coverage ratio (GCR) had the high
negative correlation in mitigating of urban climate. These results
suggest that the impact of urbanization on urban climate can be
mitigated not only by balancing the relative amounts of various
urban canopy cover features, but also by optimizing their spatial
configuration. This research expands our scientific understanding
of the effects of urban canopy cover pattern on urban environment
and climatology by explicitly quantifying the effects of
configuration. In addition, it may provide important insights for
urban planners and natural resource managers on mitigating the
impact of urban development on urban climate.
Index Term— Urban climate, Urban morphology,
Near-surface air temperature, Surface temperature, Urban
canopy cover
I. INTRODUCTION
CITIES are also responsive to climate instability and inconstant,
the highest densities of population and many urban residents are
terrible and particularly weakest to climatic instability.
Furthermore, cities have afore changed their own climates. For
instance, temperatures are significantly warmer than its
Manat Srivanit is a PhD candidate of Graduate School of Science and
Engineering, Saga University, Saga, JAPAN (phone: +81(0)80-3224-2629;
e-mail: [email protected]).
Hokao Kazunori is a Professor PhD of Graduate School of Science and
Engineering, Saga University, Saga, JAPAN (e-mail: [email protected]).
surrounding rural areas; a phenomenon called an urban heat
island (UHI) effect, ventilation is weaken and poor outdoor air
quality, which further compounds sensitivity to future global
changes [1]. The outdoor thermal environment and ventilation
condition within the climate below the roof tops in the spaces
between buildings or the urban canopy layer of the city are
meaningful in the analytical processes of the urban climatic
environmental assessment. To thoroughly understand the effect,
it is significant to specify the key variables influence on an
urban climatic environment. Previous researches suggest that
the physical profile within the urban canopy layer significantly
affects the physics of urban climatic environment [2-7]. The
problem, however, is that different researchers look at the
problem from a different angle using different urban indicators,
and it is very difficult to conclude which particular factors
would be more important in determining the urban climatic
scenario within an urban context. Oke [8] defined four
significant controls on urban climate including urban structure
(dimensions of the buildings and the spaces between them,
street widths and spacing), urban cover (fractions of built-up,
paved, vegetated, bare soil and water), urban fabric
(construction and natural materials), and urban metabolism
(heat, water, and pollutants due to human activity). These four
controls, playing important roles in creating certain urban
climatic environments, all are related to urban morphology.
Correspondingly urban planning determines urban
morphology, influencing modes of living and impacting on
urban climate. Urban planning is crucial globally, for aesthetics,
efficiency, and the urban climatic environment. In addition, the
integrated effect of urban climate can influence global climate,
for example the urban heat island phenomenon has resulted in
changes in climatic mean and variability at local, regional,
national, and global scales [9]. Urban climate is a crucial factor
not only influencing regional and global climates but also urban
liveability; it can be modified and improved to fulfill resident's
needs by urban planning means [10]. Current priorities placed
on sustainable urban development have encouraged urban
planners to examine the various parameters of urban climate
modeling and incorporate them into planning and design efforts.
But while they may understand the importance of interactions
between urban morphology and urban microclimate condition,
Manat Srivanit and Hokao Kazunori
The Influence of Urban Morphology Indicators
on Summer Diurnal Range of Urban Climate in
Bangkok Metropolitan Area, Thailand
International Journal of Civil & Environmental Engineering IJCEE-IJENS Vol: 11 No: 05 35
112805-7676 IJCEE-IJENS © October 2011 IJENS
I J E N S
they lack basic knowledge of urban climatology [11]. Therefore
the incorporation of urban climate knowledge in the urban
planning process has become crucial.
Currently, the knowledge of the climate inside a city can be
developed from observations either in-situ (ground-based, fixed
or mobile) or by remotely sensed measurements. Satellite or
airborne remote sensing allows a spatially exhaustive
monitoring of the climate at the urban/rural interface [12].
Remote sensing is particularly useful for measuring and
mapping the surface temperatures that contribute to the
modulation of air temperature, and thus determine building
thermal ambiance that affects urban comfort [13]. Surface and
air temperatures may differ considerably from each other [14].
The air temperature refers to an ambient temperature, resulting
from the mixing of the heat fluxes emitted by the surface, the
human activities and the background temperature of the
surrounding landscape components. However, if current
research has demonstrated a strong need to better link surface
temperatures and quantitative descriptors (physical properties)
of the urban landscape [13], there also exists need to better link
climate measurements used to monitor urban heat island (UHI)
and urban landscapes in order to generate meaningful urban
climate information than can be used by city planners or public
authorities.
The purpose of this study is to explore the relationship
between the composition and configuration of variables of
urban morphology indicators highly utilized in the Bangkok
metropolitan area (BMA) and urban climate indicators. The
urban morphology indicators are normally determined in the
very beginning of urban planning, and serve as a basis for the
entire planning and design process. It is thus crucial to identify
the factors amongst the many indicators available to investigate
which ones are more important. This research aims to address
this issue. The results can provide a reference for urban planners
to understand which urban morphology indicators could modify
the local temperature and thermal responsiveness, which is
considered here as the summer diurnal range of the urban
canopy layer air temperature. It could potentially contribute to
climate change adaptation, which is currently a research focus in
global climate change studies.
II. THE STUDY AREA
Bangkok is the capital of Thailand and is among the larger
cities in Asia, with an estimated population well in excess of 10
million people in its 1,576 sq.km area. Bangkok Metropolitan
Administration has divided the city into three zones, inner,
middle, and outer zone, in accordance with the population
density. Bangkok is subdivided into 50 districts, distributed by
zones are 21 (207 sq.km area), 18 (485 sq.km area) and 11 (884
sq.km area) in the inner, middle and outer zones respectively.
The summer period, or hot and humid season, is from March to
June. At this time, temperatures in Bangkok average around
34˚C, but in April has highest solar intensity and longer days
and thus can become quite hot (Fig.1), it could affect a
community's environment and quality of life. As an economic
magnet, Bangkok‟s population is continually increasing through
in-migration from the Thai countryside. This rapid rise in
population, capital investment, factories and employees in
Bangkok city have caused the community numbers to increase
leading to the development of road networks, real estate
developments, land value and advanced technologies which had
resulted in expansion of the city to the surrounding areas. This
rapid urbanization has led to several environmental problems
such as air pollution, water pollution, land subsidence as well as
the effect to excess urban heat.
Fig. 1. Monthly rainfall, evapotranspiration and daily high temperature of
Bangkok based on 30-year historical average data [15]
Furthermore, as cities continue to grow in population and
physical size, these urban–rural differences in temperature also
increased as reported by long-term temperature records.
Boonjawat et al. [16] found an increase of 1.23 ˚C in lowest air
temperature in the UHI of Bangkok for the last 50 years and the
peak temperature of metropolitan such as Bangkok can be
higher than the surroundings by 3.5 ̊ C and detected during clear
and calm night in dry season. Increased temperatures due to the
UHI effect may increase water consumption and energy use in
urban areas and lead to alterations to biotic communities [17].
Kiattiporn et al. [18] found an increase in 1˚C of the temperature
that will result in an increase of 6.79% electricity consumption
in BMA. Excess heat may also affect the comfort of urban
dwellers and lead to greater health risks [19]. In addition, higher
temperatures in urban areas increase the production of ground
level ozone which has direct consequences for human health
[20, 21]. It is, therefore, the analyzing patterns of UHI in BMA
and its relationship with urban surface characteristics are
significant to understand in order to lessen the ever worsening
urban climate problem in the region.
III. METHODOLOGICAL AND DATASET
A. Measuring climate indicators based on in situ and remotely
sensed data
The methodological approach based on samples
surrounding meteorological ground stations of the UCL, were
conducted in April 2009 in order to better understand the effects
of urban morphology indicators on the summer diurnal range of
International Journal of Civil & Environmental Engineering IJCEE-IJENS Vol: 11 No: 05 36
112805-7676 IJCEE-IJENS © October 2011 IJENS
I J E N S
urban climate. The climate indicators had been assessed with
two measurement techniques. First, in-situ measurements of
daytime near-surface air temperature were carried out
simultaneously at 13 locations (Figs.2a). The instruments were
placed in locations where air temperature could be
representative of a scale larger than the street around the
instrument [8]. Air temperatures are usually measured at about
1.5 meters above the ground, where standard weather
observations are taken. For this study, a variety of sources can
be used to take these measurements including the Thai
meteorological department (TMD) and the Pollution control
department (PCD). The climate indicators used include hourly
mean, maximum and minimum air temperatures.
The surface temperature is of prime importance to the study
of urban climatology. It modulates the air temperature of the
lowest layer of the urban atmosphere, is central to the energy
balance of the surface, helps to determine the internal climates
of buildings and affects the energy exchanges that affect the
comfort of city dwellers [13]. Remotely sensed land surface
temperature (LST) records the radiative energy emitted from the
ground surface, including building roofs, paved surfaces,
vegetation, bare ground, and water [22, 13]. Therefore, the
pattern of land cover in urban landscapes may potentially
influence LST [22, 23].
Fig. 2. (a) Location of the Bangkok metropolitan area (BMA) and the network of meteorological stations, (b) typical of meteorological stations and (c) the circle
of influence within the radius of a temperature sensor and the results of the composition and configuration of urban morphology features
This study focuses on the effects of land cover composition
on LST. Surface temperatures were derived from Landsat
thematic mapper (TM) images acquired in summer (acquisition
time on April 25, 2009 was approximately 3:25 p.m., a day with
a highly clear atmospheric condition) and the thermal infrared
band (10.4–12.5 m) data was used to derive the LST. As
surface temperatures are generally stronger and exhibit greater
spatial variations during the daytime [22, 24], a selection of a
daytime image in the summer is appropriate for this study. Yuan
and Bauer [25] proposed a method of deriving LST in three
steps: Firstly, the digital numbers (DNs) of thermal infrared
band are converted to radiation luminance or
top-of-atmospheric (TOA) radiance ( L , mW/(cm2 sr·m)
using [Eq.1] [26]:
minmin
minx
minx )()(
LQCALDNQCALQCAL
LLL
ma
ma
Sensor
A circle of influence
Surface
Temperature form
thermal remote
sensor
Building
configuration &
impervious surface
Pervious surface
Above and below
green cover
Water cover
Classifying urban canopy parameters
Meteorological stations Air quality monitoring stations
(a.)
(b.)
(c.)
Classification urban morphology features
International Journal of Civil & Environmental Engineering IJCEE-IJENS Vol: 11 No: 05 37
112805-7676 IJCEE-IJENS © October 2011 IJENS
I J E N S
[Eq.1]
Where DN is the pixel digital number for thermal infrared
band, maxQCAL = 255 is Maximum quantized calibrated
pixel value corresponding to maxL , minQCAL = 0 is
Minimum quantized calibrated pixel value corresponding
to minL , maxL = 17.04 (mW/cm2sr·m) is spectral at-sensor
radiance that is scaled to maxQCAL and minL = 0
(mW/cm2sr·m) is spectral at-sensor radiance that is scaled to
minQCAL .
Secondly, the radiance was converted to surface
temperature in Celsius degree using the Landsat specific
estimate of the Planck curve [Eq.2] [26]:
11
2
L
KIn
KTk [Eq.2]
Where kT is the temperature in Kelvin ( K ), 1K is the
prelaunch calibration of constant 1 in unit of W/(m2 sr·m) and
2K is the prelaunch calibration constant 2 in Kelvin. For
Landsat TM, 1K is about 607.76 W/(m2 sr·m) and 2K is
about 1260.56 W/(m2 sr·m) with atmospheric correction [27].
The final apparent surface temperature on Celsius (˚C) can be
calculated the following equation:
15.273 kc TT [Eq.3]
Where cT is the temperature in Celsius (˚C), kT is the
temperature in Kelvin ( K ).
The mean LST was summarized for each surrounding
meteorological ground station by overlapping the urban
canopy cover boundaries and the image layer of emissivity
corrected LST. The mean of LST was used as the response
variable in later statistical analysis.
B. Classification of composition and configuration of urban
morphology based on GIS and remotely sensed techniques
All weather station sites are essentially defined by a
circle of influence (also known as source area or footprint) of
the instrument which depends on its height and the
characteristics of the process transporting the surface property
to the sensor [8]. In this study, using the circle of influence on
a temperature sensor is thought to have a radius of about 300
meters typically in stable conditions. The first objective is to
automatically compute all meteorological stations, where these
urban morphology indicators are using GIS, remotely sensed
data and techniques within the circle of influence. Images
were derived from overlaying building elevation obtained from
airborne imagery and photogrammetric techniques.
Identification of urban surfaces still remains a challenge
because each city shows composition and structure
specificities and no universal urban classification method
exists [28]. The image fusion technique of an aerial orthophoto
(acquired in 2009 at 1 m. resolution) with a Landsat TM image
(acquired on April 25 2009, 30 m. resolution), could enhance
the automated supervised object-based approach for urban
canopy classification [29]. Some confusions were removed by
overlaying building spatial extent as impervious surface on the
classification. The maximum-likelihood classification (MLC)
algorithm was applied to classify the fraction images into six
classes; building coverage, impervious surface (mainly
artificial structures such as pavements of roads, sidewalks,
driveways and parking lots), pervious surface (including bare
soil/gravel), water coverage (mainly including rivers, canals,
creeks, ponds, and lakes), above green (tree canopy) and
below green (grass and shrubs canopy) coverage for each
surrounding meteorological stations and is based on a
multi-resolution image clustering [30].
Water surface and vegetation detection was optimized
using Normalized Difference Vegetation Index (NDVI), and
was extracted from computation of calculated from the visible
and near-infrared light reflected by plants to investigate
vegetation cover from remote sensing imagery and then the
image removed vegetation and water surface was impervious
surface [31-33]. Accuracy assessment of the classification map
was based on a stratified random sampling and visual
assessment of the true color photography, with an overall
classification accuracy of 96% being achieved. This
classification is spatially limited within the circle of influence
on a temperature sensor.
This study investigates the effects of composition, whether
the configuration of urban morphology features significantly
affects urban climate indicators. The results from this study
can enhance our understanding of how urban climate varies
with changing urban morphology patterns. In addition,
important insights can be provided to urban planners and
natural resource managers on how to mitigate the impact of
urbanization on urban climate through urban design and
management. For this study, we selected the most frequently
used composition variables and the percent cover of each
urban canopy cover features, were calculated based on a high
spatial resolution land cover classification map obtained from
an object-based classification approach and the six
configuration variables were used as predictor variables in the
statistical analyses to examine the relationship between urban
climate indicators. The major configuration of urban
morphology indicators, used in this study are includ;
Floor area ratio (FAR): is the ratio of the total floor area
of the building to the area of the land on which it is located. It
is a building density parameter used in urban planning and
design disciplines. It captures the impact of vertical frictional
surfaces in urban land due to high-rise surfaces of buildings
and used in urban canopy parameterization of drag and
turbulence production. On the other hand, it is a major
parameter showing development intensity and refers to the
intensity of activities taking place within a specified land area
and obviously has implications on urban climate that reflects
International Journal of Civil & Environmental Engineering IJCEE-IJENS Vol: 11 No: 05 38
112805-7676 IJCEE-IJENS © October 2011 IJENS
I J E N S
the number of prominent obstacles that affects air flow [6, 34,
35];
Building coverage ratio (BCR): means percentage of the
total ground area of a site occupied by any building or
structure as measured from the outside of its surrounding
external walls. Building coverage includes exterior structures
such as impervious surfaces mainly artificial structures such as
pavements of roads, sidewalks, driveways and parking lots.
Built footprints obstruct urban wind flow and increase thermal
mass of urban fabric that could heat up the neighborhood [34];
Complete aspect ratio (CAR): is defined as the summed
surface area (summing the surface area of the buildings which
including the area of rooftops) of roughness elements and
exposed ground divided by the total plan area because the
temperature of the air among the buildings is affected by the
temperatures of both horizontal and vertical surfaces. This
multiple impact and the magnitudes of the effects of individual
factors are very difficult to determine. Voogt and Oke [36]
introduced the concept of complete surface temperature which
cannot be measured directly, but it can be calculated or
estimated as a result of the radiation originating from all of the
(horizontal and vertical) surfaces. High of building envelopes
in terms of complete aspect ratio may have impacts by
reducing heat gain or discharge, but reduces urban ventilation
[37, 38];
Open space ratio (OSR): is the percentage of open space to
the area of the land. An open-to-sky space without a roof is
considered an open space. The location, size, distribution and
surface nature of open spaces could change the local
environment by altering the air flow, humidity and heat
balance with the urban canopy layer [37].
Green coverage ratio (GCR): is the percentage of the total
area of all green spaces (including above green and below
green coverage) to the area of the land. Trees and smaller
plants such as shrubs, vines, grasses, and ground cover, help
cool the urban environment. Thus, GCR is an important
parameter in describing urban surface cover, which is affects
urban climate such as radiation and surface temperature
through shading and evapotranspiration. In the summer,
generally 10 to 30 percent of the sun‟s energy reaches the area
below a tree, with the remainder being absorbed by leaves and
used for photosynthesis, and some being reflected back into
the atmosphere [39] and;
Water coverage ratio (WCR): is the percentage of water
coverage to the area of the land, which is an increase in the
amount of cooling that normally associated with the
evaporation of moisture. On the other hand, surface water
bodies affects wind flow and also heat exchanges. Moreover
water bodies on land such as lakes and rivers are regarded as a
thermal sink for urban air pollutants [40].
Proportion of urban canopy cover classification
Above green coverBelow green coverWater bodyImpervious surfacePervious surfaceBuilding coverage
#
#
#
#
#
#
#
#
#
#
#
#
#
PCD1
PCD2
PCD3
PCD4
PCD5
PCD6
PCD7
PCD8
PCD9
TMD1
TMD2
TMD3
PCD10
PCD1
PCD2
PCD3
PCD4
PCD5
PCD6
PCD7
PCD8
PCD9
TMD1
TMD2
TMD3
PCD10
5 0 5
Kilometers
5 0 5
Kilometers
FARBCRCAR
GCRWCR
The results of the measuring urban morphology indicators
OSR
(a.) (b.)
Figs. 3. Maps of (a.) the proportion of urban canopy cover classification and, (b.) the urban morphology indicators for each surrounding meteorological stations
International Journal of Civil & Environmental Engineering IJCEE-IJENS Vol: 11 No: 05 39
112805-7676 IJCEE-IJENS © October 2011 IJENS
I J E N S
1.Bansomdejchaopraya Rajabhat
University (PCD1)2.Rat Burana Post Office
(PCD2)3.Chandrakasem Rajabhat
University (PCD3)4.Huaykwang - National Housing
Authority Stadium (PCD4) 5.Nonsi Withaya School
(PCD5)
6.Singharaj Pittayakom School
(PCD6)
7.Thonburi Power Substation
(PCD7)
8.Chokechai 4 Police Box
(PCD8) 9.Dindaeng - National Housing
Authority (PCD9)10.Badindecha School
(PCD10)
11.Sirikit Center (TMD1) 12.Bangna (TMD2) 13.Don Muang Airport (TMD3)
Fig. 4. Spatial pattern of urban canopy cover classification for each surrounding meteorological stations
Fig. 5. Characteristics of building configuration for each surrounding meteorological stations
1.Bansomdejchaopraya Rajabhat
University (PCD1)2.Rat Burana Post Office
(PCD2)3.Chandrakasem Rajabhat
University (PCD3)4.Huaykwang - National Housing
Authority Stadium (PCD4) 5.Nonsi Withaya School
(PCD5)
6.Singharaj Pittayakom School
(PCD6)
7.Thonburi Power Substation
(PCD7)
8.Chokechai 4 Police Box
(PCD8) 9.Dindaeng - National Housing
Authority (PCD9)10.Badindecha School
(PCD10)
11.Sirikit Center (TMD1) 12.Bangna (TMD2) 13.Don Muang Airport (TMD3)
International Journal of Civil & Environmental Engineering IJCEE-IJENS Vol: 11 No: 05 40
112805-7676 IJCEE-IJENS © October 2011 IJENS
I J E N S
Fig. 6. Spatial pattern of surface temperature observed with LANDSAT TM on April25, 2009 (summer daytime)
Taking into the review of urban climatic studies, it is clear
that different work argues differently in different contexts and
there is no consensus on which are the most important factors.
There are also no systematic methods to determine the
relationship between urban morphological factors. This gap of
urban climatic knowledge and urban planning are where we
would like to insert an effort into. This study proposes the
following question: Is there a relationship between urban
climate concentration and urban morphology in dense
residential areas, and if there is, it is thus crucial to identify the
factors amongst the many indicators available to investigate
which ones are most important. A Pearson correlation was first
developed to examine the strength of bivariate associations
between urban climate indicators and the variables of
composition and configuration of urban morphology indicators
each surrounding meteorological stations. The stepwise
regressions method is used to explore the relationship between
urban climate indicators and the variables of composition and
configuration of urban morphology indicators.
IV. RESULTS AND DISCUSSIONS
A. Classification of the composition and configuration of urban
morphology features
Results of urban morphology classifications are shown in
Figs.4. The differences in urban morphology among the
thirteen meteorological stations are reflected by urban and
environment planning indicators, such as FAR, BCR, OSR,
GCR and WCR (Figs.3). FAR, a major indicator of
development intensity and livability has the highest value
(1.425) for the core area and the lowest value (0.416) for the
suburb area. The two highest FAR values are all located in the
inner area of BMA, namely Dindaeng National Housing
Authority (PCD9) and Huaykwang National Housing
Authority Stadium (PCD4); low-income housing projects
under the control of the Thai government. The average GCR
value for the inner area stations about 10.84, increasing to
26.12 for the middle area stations and 38.42 for the outer area
stations of BMA. Sirikit center station (TMD1) in the inner
area has the highest values that it was containing open space of
Benjakiti Park. Thus the station adjacent to open space was
more likely to have higher GCR value (20.891). It is not
surprising the Chokechai 4 Police Box (PCD8) has the lowest
GCR value (6.104) given it has the highest building coverage.
B. Climate behaviors on the summer diurnal range based on
near-surface air temperature and surface temperature
On a sunny the summer day, the main cause of the urban
climate is modification of the land surface by urban
development which uses materials which effectively retain
heat. Thus, surface temperature is an important condition for
studies of the urban climatology. In the Figs.8 and Figs.9, the
climate behaviors based on surface temperatures during the
day in summer (April 25, 2009) are quite similar to those
observed with near-surface air temperature. It was found that
1.Bansomdejchaopraya Rajabhat
University (PCD1)2.Rat Burana Post Office
(PCD2)3.Chandrakasem Rajabhat
University (PCD3)4.Huaykwang - National Housing
Authority Stadium (PCD4) 5.Nonsi Withaya School
(PCD5)
6.Singharaj Pittayakom School
(PCD6)
7.Thonburi Power Substation
(PCD7)
8.Chokechai 4 Police Box
(PCD8) 9.Dindaeng - National Housing
Authority (PCD9)10.Badindecha School
(PCD10)
11.Sirikit Center (TMD1) 12.Bangna (TMD2) 13.Don Muang Airport (TMD3)
International Journal of Civil & Environmental Engineering IJCEE-IJENS Vol: 11 No: 05 41
112805-7676 IJCEE-IJENS © October 2011 IJENS
I J E N S
approximately 81.1% of the variation the average daytime
near-surface air temperature (Ta) was explained by the average
surface temperature (Ts) on the summer diurnal range (TABLE I
and Fig.7b) and this result could be related to the results of
some studies [41-43], there is more consistent relationship
between these two. Among the different stations, it was found
that the highest average surface temperature (Mean±S.D.) in
the Bansomdejchaopraya Rajabhat University (PCD1) was
about 42.41±1.45˚C, followed by Thonburi Power Substation
(PCD7) and Nonsi Withaya School (PCD5) were
41.99±1.32˚C and 41.66±1.21˚C, respectively, all of which are
located in the inner area of BMA. The lowest average surface
temperature site is located in Bangna (TMD2) was
39.24±1.46˚C are relatively the highest green coverage and
significant influence on lowest average air temperatures in this
area, indicating the contribution that parks and green spaces
make in reducing surface temperature in urban areas. The
standard deviation (S.D.) of surface temperature is much larger
in Sirikit Center (TMD1) was 39.48±1.51˚C, indicating that
the landscapes would have experienced wider variation in
surface temperature than the natural vegetation because of mix
of land use/land cover types and different building structures
and construction materials. The S.D. of surface temperature is
relatively small for the Huaykwang National Housing
Authority Stadium (PCD4) was 41.34±0.65˚C because of the
homogeneity of construction types contributing to low surface
temperature variation in these areas (Fig.7a).
TABLE I
RELATIONSHIPS BETWEEN THE AVERAGE DAYTIME NEAR-SURFACE AIR
TEMPERATURE (Ta) AND THE AVERAGE SURFACE TEMPERATURE (Ts) IN SUMMER
OBTAINED BY SINGLE LINEAR REGRESSION MODEL.
Variable Mean
±S.D.
Corr.
Coeff.
Regression analysis
R2(Adj.) F P-value
Surface
Temp.(Ts)
40.746
±1.104 0.901**
0.811
(0.794) 47.266 <0.001
**Correlation is significant at the 0.01 level (one-tailed)
C. Effects of the composition of urban canopy cover features on
summer daytime surface temperature
The Pearson correlation coefficients show that all of the
composition variables, except pervious surfaces and water
bodies, were significantly related to Ts that derived from
Landsat TM thermal infrared image acquired in summer
(TABLE II), with some variables having stronger relationships
with Ts than others. Composition variables such as percent
cover of buildings, impervious surfaces and below and above
green cover had relatively strong relationships with Ts, while
percent cover of pervious surface and water were only weakly
related to Ts. Fig.7c and Fig.7d shows the single linear
regression models between variables of composition of urban
canopy features and Ts. A positive coefficient for an
independent variable indicates that the variable has a positive
effective on Ts, or that Ts increases with the increase of the
value of that variable; whereas a negative coefficient indicates
Ts decreases with the increase of the value of that variable. For
example, both coefficients of percent cover of building and
impervious surface were positive, suggesting that an increase
in the percent cover of building and pavement would increase
surface temperature. In contrast, the negative coefficients of
percent cover of below green, above green and water indicated
that surface temperature would decrease with the increase of
relative abundances of vegetation and water.
Among the six types of urban canopy cover features,
stepwise regression for Ts (TABLE III) shows that percent cover
of buildings (PerBuild) was the most significant variable in
predicting Ts, and can explain 91.5% of the variance in surface
temperature difference among the different meteorological
stations which was significant at the 95% confidence level. To
predict daytime surface temperature with climate variable
(reference temperature) and the composition of urban canopy
cover features, the following formula could be used ([Eq.4],
TABLE IV):
PerBuildTs 115.0858.36 [Eq.4]
TABLE II
RELATIONSHIPS BETWEEN VARIABLES OF COMPOSITION OF URBAN CANOPY FEATURES (OR PERCENT COVER OF LAND COVER FEATURES) AND SURFACE TEMPERATURE
(Ts) OBTAINED BY SINGLE LINEAR REGRESSION MODELS.
Urban canopy features Percent cover
(Mean±S.D.)
Average Ts
(Mean±S.D.) Corr. Coeff.
Regression analysis
R-square (adjusted) F P-value
Building coverage 33.69±9.12 41.32±1.04 0.801** 0.642 (0.609) 19.690 0.001
Impervious surface 13.28±7.24 40.65±0.83 0.555* 0.308 (0.246) 4.907 0.049
Pervious surface 26.46±12.91 40.63±0.72 0.456 0.208 (0.135) 2.880 0.118
Above green cover 11.98±6.38 40.54±1.02 -0.502* 0.252 (0.184) 3.698 0.081
Below green cover 9.91±11.37 40.42±0.91 -0.665** 0.442 (0.391) 8.713 0.013
Water body 4.66±5.23 39.80±1.18 -0.425 0.181 (0.106) 2.429 0.147
**Correlation is significant at the 0.01 level (one-tailed)
* Correlation is significant at the 0.05 level (one-tailed)
International Journal of Civil & Environmental Engineering IJCEE-IJENS Vol: 11 No: 05 42
112805-7676 IJCEE-IJENS © October 2011 IJENS
I J E N S
y = 0.0637x + 39.8R² = 0.3085
y = 0.0254x + 39.955R² = 0.2075
y = 0.0907x + 38.266R² = 0.6416
39
40
41
42
43
44
0 5 10 15 20 25 30 35 40 45 50
Impervious surface
Pervious surface
Building coverage
Linear (Impervious surface)
Linear (Pervious surface)
Linear (Building coverage)
35.00
36.00
37.00
38.00
39.00
40.00
41.00
42.00
43.00
44.00
45.00
PCD1 PCD2 PCD3 PCD4 PCD5 PCD6 PCD7 PCD8 PCD9 PCD10 TMD1 TMD2 TMD3
y = 0.6984x + 2.8026R² = 0.8112
28.00
29.00
30.00
31.00
32.00
33.00
34.00
39.00 40.00 41.00 42.00 43.00
Meteorological stations
(a.) (b.)
(c.) (d.)
Surface temperature (Ts )
Air
tem
pe
ratu
re (T
a)
y = -0.0787x + 41.482R² = 0.2516
y = -0.0532x + 40.946R² = 0.442
y = -0.096x + 40.251R² = 0.1809
37
38
39
40
41
42
43
0 5 10 15 20 25 30 35 40
Above green cover
Below green cover
Water body
Linear (Above green cover)
Linear (Below green cover)
Linear (Water body)
Su
rfa
ce
tem
pe
ratu
re (T
s)
Su
rfa
ce
tem
pe
ratu
re (T
s)
Su
rfa
ce
tem
pe
ratu
re (T
s)
Percent cover Percent cover
Fig. 7. Distribution of the climate indicators difference with the meteorological stations; (a.) The daytime surface temperature in summer (LANDSAT TM on
April25, 2009), (b.) Relationship between of the average daytime near-surface air temperature (Ta) and the average surface temperature (Ts) in summer, (c.) and (d.)
The linear regression models between variables of composition of urban canopy features and Ts
TABLE III
THE RESULT OF STEPWISE MULTIPLE LINEAR REGRESSION ANALYSIS FOR THE
PERFORMANCE OF VARIABLES OF COMPOSITION OF URBAN CANOPY FEATURES
(OR PERCENT COVER OF LAND COVER FEATURES) THAT INFLUENCE ON SUMMER
DIURNAL RANGE OF SURFACE TEMPERATURE
Variable
Entered R R2 Adj.R2
Std. Error
of the
Estimate
F P-value
PerBuild 0.956 0.915 0.907 0.336 118.180 <0.001
Note: dependent indicator is the average surface temperature (Ts),Percent cover
of Building (PerBuild)
TABLE IV
SUMMARY RESULTS FOR A SINGLE LINEAR REGRESSION COEFFICIENT OF THE
BEST PREDICTION MODEL USED FOR INVESTIGATING THE INFLUENCE ON
SUMMER DIURNAL RANGE OF SURFACE TEMPERATURE
Model Unstd.Coeff. Std. Coeff. F Sig.
B Std. Error Beta
(Constant) 36.858 0.370 99.699 <0.001
PerBuild 0.115 0.011 0.956 0.871 <0.001
Note: dependent indicator is the average surface temperature (Ts),Percent cover
of Building (PerBuild)
D. Effects of the configuration of urban morphology features on
summer daytime near-surface air temperature
The correlation and regression analysis method is used to
explore the relationship between the configuration of urban
morphology features (FAR, BCR, CAR, OSR, GCR, WCR) and
average daytime near-surface air temperature (Ta) during the
day in summer. Correlation analysis was carried out on the
thirteen meteorological stations. Pearson correlations between
urban climate indicators and urban morphology indicators
statistically significant (at 0.01 and at 0.05 level one-tailed)
correlations can be found in TABLE V. The Pearson correlation
coefficients show that all of the composition indicators, except
WCR, were significantly related to Ta, with some indicators
having stronger relationships with Ta than others. Composition
indicators such as FAR, BCR, CAR, OSR and GCR had
relatively strong relationships with Ta, while WCR were only
weakly related to Ta. A positive coefficient for an independent
variable indicates that the variable has a positive effective on
Ta, or that Ta increases with the increase of the value of that
variable; whereas a negative coefficient indicates Ta decreases
with the increase of the value of that variable. For example,
three coefficients of FAR, BCR and CAR were positive,
suggesting that an increase in these variables would increase
International Journal of Civil & Environmental Engineering IJCEE-IJENS Vol: 11 No: 05 43
112805-7676 IJCEE-IJENS © October 2011 IJENS
I J E N S
Ta. In contrast, the negative coefficients of OSR, GCR and
WCR indicated that Ta would decrease with the increase of
relative abundances of vegetation and water. A simple
prediction model of urban climate on differences urban
morphology indicators was established using linear regression
analysis and scatter plot. According to the results of
correlation analysis, BCR had the highest positive correlation
with average near-surface air temperature by correlation
coefficient (R2) 0.878 and followed by CAR and FAR had a
high positive correlation with Ta by 0.781 and 0.472,
respectively. On the other hand, OSR had the lowest negative
correlation with Ta by 0.878 followed by GCR which was
0.649 (Figs.8).
y = -0.0872x + 37.028R² = 0.878
28.0
29.0
30.0
31.0
32.0
33.0
34.0
50.00 60.00 70.00 80.00 90.00
y = 2.0873x + 29.414R² = 0.4716
28.0
29.0
30.0
31.0
32.0
33.0
34.0
0.00 0.50 1.00 1.50
y = 0.0877x + 28.307R² = 0.878
28.0
29.0
30.0
31.0
32.0
33.0
34.0
0.00 10.00 20.00 30.00 40.00 50.00
y = 1.6842x + 29.094
R² = 0.7807
28.0
29.0
30.0
31.0
32.0
33.0
34.0
0.00 0.50 1.00 1.50 2.00 2.50
y = -0.0565x + 32.499R² = 0.6491
28.0
29.0
30.0
31.0
32.0
33.0
34.0
0.00 10.00 20.00 30.00 40.00
y = -0.0297x + 31.4R² = 0.0331
28.0
29.0
30.0
31.0
32.0
33.0
34.0
0.00 5.00 10.00 15.00 20.00
(a.) (b.) (c.)
(d.) (e.) (f.)
FAR BCR CAR
GCR WCR
Air
tem
pe
ratu
re (T
a)
Air
tem
pe
ratu
re (T
a)
Air
tem
pe
ratu
re (T
a)
Air
tem
pe
ratu
re (T
a)
Air
tem
pe
ratu
re (T
a)
Air
tem
pe
ratu
re (T
a)
OSR
Fig. 8. Relationships between the average daytime near-surface air temperature (Ta) and urban morphology indicators on the summer diurnal range
TABLE V
RELATIONSHIPS BETWEEN THE AVERAGE DAYTIME NEAR-SURFACE AIR
TEMPERATURE (Ta) AND URBAN MORPHOLOGY INDICATORS AGGREGATED FOR
EACH SURROUNDING METEOROLOGICAL STATIONS OBTAINED BY SINGLE LINEAR
REGRESSION MODELS.
Indicators Mean±S.D. Corr.
Coeff.
Regression analysis
R-square
(adjusted
)
F P-value
FAR 0.885±0.281 0.687** 0.471
(0.423) 9.810 0.010
BCR 33.692±9.146 0.937** 0.878
(0.867) 79.163 <0.001
CAR 1.287±0.449 0.884** 0.781
(0.761) 39.172 <0.001
OSR 66.123±9.197 -0.937** 0.878
(0.867) 79.169 <0.001
GCR 21.907±12.20
3 -0.806**
0.649
(0.617) 20.347 0.001
WCR 4.657±5.234 -0.182 0.033
(-0.055) 0.376 0.552
**Correlation is significant at the 0.01 level (one-tailed)
Since stepwise selection of the variables allows dropping
or adding variables at the various steps in either direction, it
could not happen that any significant variables are dropped or
non-significant variables are added in a model. Therefore, a
stepwise selection method was chosen, which reiterates the
analysis by each parameter in turn and independently considers
the inclusion or exclusion of the parameters with every step
(criteria:probability-of-F-to-enter<=0.05,probability-of-F-to-re
move>=0.1). The income factor with the largest probability of F
is removed.
TABLE VI shows the stepwise multiple linear regression
results in which Ta was the dependent variable and FAR, BCR,
CAR OSR, GCR were used as independent variables, except
WCR which identified as not significant and therefore removed
from the analysis. It was found that Model 1, which is the
simplest equation included only OSR variable (R2=0.878).
Then the impact from FAR is added in Model 2, which
explanation capacity is improved. By comparison with the
other models preformed, Model 2 should be regarded as the
best one, approximately 92.6% (R2=0.926) of the variation in
Ta was explained jointly by the two configuration of urban
morphology variables. Thus, the Ta for the average summer
daytime near-surface air temperature can be predicted from the
configuration of urban morphology features of the thirteen
urban meteorological stations in BMA ([Eq.5], TABLE VII):
International Journal of Civil & Environmental Engineering IJCEE-IJENS Vol: 11 No: 05 44
112805-7676 IJCEE-IJENS © October 2011 IJENS
I J E N S
FAROSRTa 791.0074.0473.35 [Eq.5]
TABLE VI
THE RESULT OF STEPWISE MULTIPLE LINEAR REGRESSION ANALYSIS FOR
PERFORMANCE URBAN MORPHOLOGY INDICATORS THAT INFLUENCE THE
SUMMER DIURNAL RANGE OF THE AVERAGE DAYTIME NEAR-SURFACE AIR
TEMPERATURE BY DIFFERENT MODELS
Model Variable
Entered R2
Adj.
R2
Std. Error
of the
Estimate
F P-value
1 OSR 0.878 0.867 0.312 79.163 <0.001
2 OSR,FAR 0.926 0.912 0.255 62.810 <0.001
Note: dependent indicator is the average maximum daytime near-surface air
temperature (Ta) in summer
TABLE VII
SUMMARY RESULTS FOR MULTIPLE LINEAR REGRESSION COEFFICIENTS OF THE
BEST PREDICTION MODEL FOR INVESTIGATING THE INFLUENCE ON THE SUMMER
DIURNAL RANGE OF DAYTIME NEAR-SURFACE AIR TEMPERATURE
Model 2 UnStd. Coeff. Std. Coeff. F Sig.
B Std. Error Beta
(Constant) 35.473 0.809
43.874 <0.001
OSR -0.074 0.009 -0.798 -7.854 <0.001
FAR 0.791 0.309 0.260 2.558 0.028
V. CONCLUSIONS
The results of this research indicated that both the
composition and configuration of urban morphology features
significantly affects the magnitude of daytime near-surface air
temperature and surface temperature in summer. By explicitly
describing the quantitative relationships of two urban climate
indicators with the composition and configuration of urban
morphology features; this research expands our scientific
understanding of the effects of urban morphology features on
urban climate indicators in urban landscapes. These results have
important theoretical and management implications. Urban
planners and natural resource managers attempting to mitigate
the impact of urban development on urban climatology can gain
insights into the importance of balancing the relative amount of
various types of urban morphology features and optimizing their
spatial distributions.
The climate behaviors based on surface temperatures
during the day in summer (April 25, 2009) are quite similar to
those observed with near-surface air temperature. Our results
are consistent with some studies (e.g. Ben-Dor & Saaroni [41];
Nichol [42]; Nichol et al. [43]). It was found that approximately
81.1% of the variation of the average daytime near-surface air
temperature was explained by the average surface temperature
on the summer diurnal range. The effects of urban canopy cover
composition on surface temperature have been extensively
documented (e.g., Buyantuyev & Wu, [44]; Liang & Weng [45];
Weng [46]; Xiao et al., [47]; Weiqi Z. et al. [48]). Our results
are consistent with those from previous research that land cover
composition, or the percent cover of different types of urban
canopy cover features, greatly affect the magnitude of surface
temperature. Increasing vegetation cover could significantly
decrease surface temperature, and thus help to mitigate excess
heat in urban areas; whereas the increase of buildings and paved
surfaces would significantly increase surface temperature,
exacerbating the urban climatology phenomena on the summer
diurnal range.
The configuration of urban morphology features also
significantly affects the average summer daytime near-surface
air temperature. A multiple linear regression model in this study
was built to determine specific contribution of FAR, BCR, CAR,
OSR, GCR, WCR and were used as independent variables to
motivate the average near-surface air temperature which was a
dependent variable rising on the daytime summer. These simple
relationships between climate and urban planning indicators
could help decision makers and planners to take climate
adaptation into account, to ensure climate neutral development
from the beginning of a planning process. It was found that
approximately 92.6% of the variation in the average
near-surface air temperature was explained jointly by the two
composition variables including OSR and FAR. OSR has been
identified as the most significant urban morphology indicator to
affect urban thermal environment. OSR itself can explain 87.8%
for the average daytime summer air temperature and followed
by BCR, CAR and FAR which had high positive correlations
with the average daytime summer air temperature. On the other
hand, GCR had a high negative correlation with the average
daytime summer air temperature by 64.9%, except WCR were
only weakly related.
In fact, our results showed that the composition of urban
canopy cover features is a less important factor in determining
the average daytime summer near-surface air temperature than
the configuration of those features. The average daytime
summer near-surface air temperature can be significantly
increased or decreased by different spatial configurations of
those features. This is because the spatial configuration
influences obstruct urban wind flow and increase thermal mass
of urban fabric that could heat up the local climate zone and,
thus, affects urban climatology on the summer diurnal range.
Therefore, it is our recommendation that urban planners should
try to control for the effects of their composition. Vegetation
management, particularly increasing tree canopy, has been
considered an effective means to mitigate excess urban heat and
to alleviate the thermal discomfort in the summer months for
both highly urbanized areas and areas where urbanization is still
in process.
This study has its limitations. The research was conducted
for one region, using only one daytime thermal image to obtain
surface temperature in the summer. The relationship between
climate indicators and the variables of composition and
configuration of urban morphology features also varies by
seasons. Therefore, further studies that use multiple daytime
and nighttime thermal images for different seasons are
desirable. In addition, comparison studies across metropolitan
areas under different climatic conditions are recommended.
International Journal of Civil & Environmental Engineering IJCEE-IJENS Vol: 11 No: 05 45
112805-7676 IJCEE-IJENS © October 2011 IJENS
I J E N S
ACKNOWLEDGMENT
The authors would like to express their sincere thanks to the
anonymous reviewers for their constructive suggestions,
comments, and helps. This research is supported by the
Graduate School of Science and Engineering, Saga University,
Japan and Geo-Informatics and Space Technology
Development Agency (Public Organization): GISTDA,
Thailand.
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