The Influence of Urban Morphology Indicators on … · variables of urban morphology indicators on the summer diurnal ... (construction and natural materials), ... As an economic

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

mailto:[email protected]

mailto:[email protected]



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



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



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



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



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






(PCD5)


(PCD6)


(PCD7)




(PCD10)




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






(PCD5)


(PCD6)


(PCD7)




(PCD10)




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 0.05 level (one-tailed)



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



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


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):



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.



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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