THE POTENTIAL IMPACT OF URBANISATION ON CLIMATE CHANGE:

BRUNEI DARUSSALAM CASE STUDY

Dr. Jose Edgardo L. Aban*, Dr. William Duane* and Mr. Gabriel Yit Vui Yong**

*Senior Lecturer, Environmental Studies and Geography & Development

Faculty of Arts and Social Sciences, Universiti Brunei Darussalam

** Lecturer, Environmental Studies and Geography & Development

Faculty of Arts and Social Sciences, Universiti Brunei Darussalam

Abstract

Urbanisation and development over the past 3-4 decades have reduced the amount of pristine

rainforest cover in Brunei Darussalam to less than 50% of the country’s land area. Low-density

urban sprawl is most pronounced in the Brunei-Muara District, where commercial shop blocks,

housing estates and infrastructure are built at rapid rates. By-products of this process include

increasing number of decaying, dilapidated urban spots and bare areas (exposed rock and soil)

created by abandoned or stalled projects, which quickly become bad lands. The drastic change in

land cover is expected to have a significant impact on the local climatic regime as the heat and

water absorption and distribution capacities of rainforest differ significantly from that of bare

ground, landscaped green spaces, built features, and even secondary forest. While carbon dioxide

emission has dominated the discourse on climate change, land cover/land use change is increasing

being recognised as a major contributor to global climate change. This is particularly pronounced

in developing regions, where the original land cover in the recent past was pristine natural

vegetation. This paper aims to assess the potential impact of urbanisation in Brunei Darussalam

on climate change by measuring the change in relative heat distribution pattern (as exemplified by

urban heat islands) and vegetation indices, using multi-year satellite imagery, in particular,

Landsat thermal and near-infrared bands. The remote sensing study is augmented by ground

measurements of ambient heat profiles in selected land cover types, particularly in different

categories of green spaces. It is hoped that the study will be beneficial to land use and urban

planning and its management, particularly in the refinement and enforcement of green space

requirements.

1. Introduction

The urban heat island (UHI) refers to the phenomenon of higher atmospheric and surface

temperatures occurring in urban areas than in the surrounding rural (highly vegetated or forested)

areas due to urbanisation (Voogt & Oke, 2003). It is exemplified by a large expanse of non-

evaporating impervious materials covering a majority of urban areas with a consequent increase

in sensible heat flux at the expense of latent heat flux (Oke, 1982; Owen et al., 1998). UHIs

develop when a large fraction of the natural land-cover in an area are replaced by built surfaces

that trap incoming solar radiation during the day and then re-radiate it at night (Oke, 1982;

Quattrochi et al., 2000).

UHI effects are exacerbated by the anthropogenic heat generated by traffic, industry and domestic

buildings, impacting the local climate through a city's compact mass of buildings that affect

exchange of energy and levels of conductivity. The higher temperatures in urban heat islands

increase air conditioning demands, raise pollution levels, and may modify precipitation patterns.

In addition, increased electricity generation by power plants leads to higher emissions of sulfur

dioxide, carbon monoxide, nitrous oxide and suspended particulates, as well as carbon dioxide, a

greenhouse gas (GHG) known to contribute to global warming and climate change. As a result,

the magnitude and pattern of UHI effects have been major concerns of many urban climatology

studies.

Heat islands can be characterized for different layers of the urban atmosphere and for various

surfaces and divided into three categories: canopy layer heat island (CLHI), boundary layer heat

island (BLHI), and surface urban heat island (SUHI). The urban canopy layer extends

upwards from the surface to approximately mean building height, whereas the urban boundary

layer is located above the canopy layer (Voogt & Oke, 2003). The CLHI and the BLHI are

atmospheric heat islands since they denote a warming of the urban atmosphere, whereas the

SUHI refers to the relative warmth of urban surfaces compared to surrounding rural areas. It is

known that atmospheric UHIs are larger at night while surface UHIs are larger during the day

(Roth et al, 1989). Surface UHI is typically characterized as land surface temperature (LST)

through the use of airborne or satellite thermal infrared remote sensing, which provides a

synoptic and uniform means of studying SUHI effects at regional scales. Satellite-measured

LST has been utilized in various heat-balance, climate modeling, and global-change studies

since it is determined by the effective radiating temperature of the Earth's surface, which

controls surface heat and water exchange with the atmosphere. Voogt and Oke (2003) suggested

three major applications of thermal remote sensing to the study of urban climates. Two of them

focus on examining relations either between spatial structure of urban thermal patterns and urban

surface characteristics or between atmospheric and surface heat islands; the third is centered

on studying urban surface energy balances by coupling urban climate models with remotely

sensed data. This study addresses the first application area.

A number of thermal remote sensing studies have utilized the normalized difference vegetation

index (NDVI) as the major indicator of urban climate. For example, Gallo et al. (1993) assessed

the influence of the urban environment on observed minimum air temperatures by analyzing

urban–rural differences for NDVI and surface temperatures. Lo et al. (1997) studied changes in

the thermal responses of urban land cover types between day and night and examined the relation

between land cover radiance and vegetation amount using NDVI derived from Advanced Thermal

and Land Applications Sensor (ATLAS) data. Gallo and Owen (1999) evaluated seasonal trends

in temperature and NDVI and found that differences in NDVI and satellite-based surface

temperature accounted for 40% of the variation in urban–rural temperature differences. The

NDVI–temperature relationship had also been utilized in various studies to derive or evaluate

two variables — fractional vegetation cover and surface soil water content for climate

modeling (Carlson et al., 1995a; Gillies & Carlson, 1995; Gillies et al., 1997; Goward et al.,

2002).

Higher NDVI values typically indicate a larger fraction of vegetation in a satellite pixel. The

amount of vegetation determines LST by the latent heat flux from the surface to atmosphere via

evapotranspiration. Lower LSTs usually are found in areas with high NDVI. This negative

correlation between NDVI and LST is valuable for urban climate studies.

The current study comes in no better time, when Brunei-Muara is in a dynamic phase of

urbanisation, characterized by conversion of once pristine forest lands into residential, industrial

and shopping complexes.

2. Methods

2.1. Study area

Brunei-Muara is the northern-most district (daerah in Malay) in Brunei; it is the smallest of

Brunei's four districts in term of area but by far the most populous, with over half the country's

population. It is the most important and bustling district because the capital, Bandar Seri Begawan

which is also the state capital is the seat of the government ministries and departmental

headquarters as well as the center of business activities. Other major town in the Brunei-Muara

district include Muara town. The Brunei-Muara district contains the largest and deepest port in

Brunei, the Muara Port.

The Brunei-Muara district is located in the north-east, bordering the South China Sea to the north,

Brunei Bay to the east, Limbang, Sarawak (Malaysia) to the south and the Bruneian district of

Tutong to the west. The diversity of land cover types and uses, combined with the growing

urbanization makes it an ideal area to analyze SUHI effects.

2.2. Image pre-processing

Landsat data from four different seasons were obtained. One Landsat-5 Thematic Mapper (TM)

images, acquired on 2 October 1999, and two Landsat-7 Enhanced Thematic Mapper Plus

(ETM+) images, acquired on 19 May 2002 and 28 August 2007 were analyzed using ERDAS

Imagine and Multispec softwares.

The Landsat images were acquired already orthorectified to the UTM projection system (ellipsoid

WGS 84, zone 50). The original digital numbers (DN) of the TM and ETM+ images were

converted to exo-atmposhperic reflectance based on the methods provided by Chander and

Markham (2003) and the Landsat 7 Science Data Users Handbook (2006).

Table 1. Ground sensor-based records of temperature observations compared with Landsat ETM+ measured LST

(degrees Celsius).

LATITUDE LONGITUDE

AVERAGED

FIELD GROUND

& AIR

TEMPERATURES

(degrees Celsius)

SATELLITE-

DERIVED LST

(degrees Celsius)

DIFFERENCE

(FIELD

MINUS

SATELLITE)

4 58 08.61 N 114 53 16.56 E 29.77 26.4780 -3.2951

4 58 37.70 N 114 53 46.65 E 29.21 28.7500 -0.4615

4 57 40.05 N 114 54 39.59 E 29.58 30.0000 0.4205

4 45 46.10 N 114 48 42.9 E 30.70 25.0000 -5.6983

4 58 02.34 N 114 52 24.81 E 27.50 25.9100 -1.5898

4 57 06.05 N 114 53 49.90 E 26.09 25.6250 -0.4651

4 57 07.22 N 114 53 48.48 E 27.09 27.6060 0.5126

4 55 28.32 N

114 55 40.28 E 29.94 33.6450 3.7038

4 55 28.32 N

114 55 41.25 E 29.80 31.4810 1.6794

4 55 28.30 N

114 55 35.41 E 29.80 29.2810 -0.5183

4 55 16.61 N

114 55 43.24 E 29.24 28.7250 -0.5100

4 55 26.39 N

114 55 49.05 E 29.74 31.4810 1.7365

4 55 35.17 N

114 55 48.05 E 29.71 29.8340 0.1228

Figure 1. LST derived from LANDSAT ETM+ for three different dates.

02 October 1999

19 May 2002

28 August 2007

2.3. Calculation of LST and NDVI

The Landsat TM and ETM+ thermal infrared band (10.4–12.5 μm) data were utilized to derive

the LST. The Landsat Program is a series of Earth-observing satellite missions jointly managed

by National Aeronautics and Space Administration (NASA) and the U.S. Geological Survey.

Since 1972, Landsat satellites have collected information about Earth from space.

At satellite temperature can be determined for TM thermal data in a two-step process (Markham

and Barker, 1987). The spectral radiance, Lw, of each digital number (DN) value is calculated

using Equation (1).

The following equation was used to convert the digital numbers (DN) to space reaching radiance

or top-of-atmospheric (TOA) radiance measured by the instrument (Chander & Markham, 2003):

Where:

QCAL = Calibrated and scaled radiance in DN units

LMINw = Spectral radiance at QCAL = 0

LMAXw = Spectral radiance at QCAL = QCALMAX

QCALMAX = Range of rescaled radiance in DN

LW = Spectral radiance

QCAL, LMINW, LMAXW and QCALMAX are obtained directly from the header information for

each of the satellite scenes for each LANDSAT vehicle mission.

2.4. Effective at-satellite Temperature

The Effective at-satellite temperature, T, are calculated using Equation (2)

Where

T = Effective at-satellite temperature in Kelvin, oK

K2 = Calibration constant 2 in oK

K1 = Calibration constant 1 in m W cm -2

Um-1

LW = Spectral radiance in mWcm-2

ster-1 from Equation (2)

K2 and K1 are obtained directly from the header file for each Landsat vehicle mission.

Satellite-derived surface temperatures may possess errors of 2o Kelvin because of systematic

error in sensor gain. Because this study is interested only in the relative temperature differences

between trees/forests and MUHIs, this error was not corrected.

Once calibrated, surface temperatures can be determined at any Landsat pixel location.

Temperatures were grouped into appropriate ranges and color-coded to generate a thermal pattern

distribution map of Brunei-Muara (Fig. 3).

Figure 2. NDVI derived from LANDSAT ETM+ for three different dates.

02 October 1999

19 May 2002

28 August 2007

2.4 Normalized difference vegetation index (NDVI)

Normalized difference vegetation index maps were derived for all the four images as follows:

NDVI = R NIR - Rred

__________ (3)

R NIR + Rred

Where R NIR and Rred are the spectral reflectances in the TM and ETM+ red and near-infrared

bands. This NDVI equation produces values in the range of -1 to 1, where positive values

indicate vegetated areas and negative values signify non-vegetated surface features such as water,

barren soils and clouds.

3. Results and discussions

3.1 LST and NDVI patterns and statistics

The digital remote sensing method provides not only a measure if the magnitude of surface

temperatures of the entire Brunei-Muara area, but also the spatial extent of the surface heat

island effects (Fig. 1).

There are a number of noticeable hotspots (34-35 oC) in the 2007 image (Fig.3 and 4). Of

particular interest is the prominent hotspot areas and structure in the Brunei International

Airport complex. A closer look at these hotspots reveal built up structures which are,

intrinsically highly impervious (to water) such as the aircraft hangars, asphalt airport runway

and parking lots and roads. Other relatively hotter areas in the 2007 images are mostly in large

newly developed (or under development) residential complexes such as in Perumahan Rimba to

the left of the airport, newly-opened and bare residential lots in the Meragang area in the north,

as well, as in the conglomeration of concrete buildings in the government complex, Gadong

shopping complex, and the warehouse districts in the port of Muara. Conversely the

abovementioned impervious areas, correspond to areas of low NDVI values in the image.

Traditionally forested (and high in NDVI values) areas such as the Berakas Forest reserve,

Kampong Subok and and Kampong Kiangge remain to be consistently cool areas in all three

satellite images.

A detailed 3-dimensional LST image study is shown in Fig. 4, for two important economic posts

in Brunei-Muara district, which are the Brunei International Airport and the Muara Port.

3. 2 LST relationships to NDVI

Sample image scatter plots between LST and NDVI, and devoid of water pixels and clouds were

chosen in the three Landsat images, representing the years 1999, 2002 and 2007. In order to

facilitate the comparison and in order to avoid data outliers that come from image pixels which

were intrinsically devoid of vegetation (and thus low in NDVI values).

In the urbanized areas of the images, LST measurements typically represent the radiometric

temperatures of vegetated and non-vegetated surfaces, mainly impervious surfaces. Given the

pixel size 60 meters in Landsat +ETM and thermal imagery, the resultant temperatures depend

on the relative proportions of these surfaces. The variations in the pixel temperatures may be

Figure 3. Sample color-coded LST Map (2007) of Brunei-Muara

mostly related to vegetation surface amounts and characteristics, since non-vegetated (low

NDVI) surfaces vary more in temperature than sunlit vegetated surfaces.

This explains the linear pattern between LST and NDVI in the scatter plots shown in Fig. 5 . In

all three satellite acquisition dates, the inverse linear relationship between NDVI and LST is

very apparent and highly significant and correlated (r2 ~ 0.7++).

Air and ground temperature data from sensors deployed in different parts of Banda Seri Begawan

were compared to satellite LST estimates (Table 1). For this study, 13 climatologic

observations were made for the period between 7-30 September 2011. The satellite-based

LST are generally higher than the air temperatures, which is reasonable since daytime surface

temperatures of dry ground are generally warmer than that of air temperature. It is also known

that precise equivalence between LST and the near ground air temperature is difficult to obtain

and to estimate the surface energy fluxes over complex terrain is beyond satellite thermal sensing

data’s capability as the surface energy budget is too complicated (Carlson et al., 1995b). To

correct the differences between satellite measured LST and the near-ground air temperature, not

only concurrent in-situ ground data such as surface and soil temperatures, but also accurate

models that relate the surface and air temperatures are required. Hence, further research along this

line is being recommended to be pursued in the future.

4. Adaptation measures

The study, based on the findings, is proposing some design strategies for the mitigation of

effects of urban heat i sland phenomenon in Brunei-Muara . Among which is the use of light-

colored materials for roofing of downtown locations as well as improving the reflectivity of

pavement within the urban centers will hope to reduce UHI effects.

Figure 4. Hotspot areas (in red) identified in this 28 August 2007 Landsat 3D-rendered thermal image (left), and

compared with true color real image (right) from Google Earth, showing conglomeration of impervious structures (i.e.

runway, hangar) in the Brunei International airport (top) and Muara port (warehouse district, bottom).

Figure. 5. Relationship of Landsat-estimated LST (in degrees Celsius) vs. NDVI, in three different dates.

5. Conclusions

The methodology in this paper demonstrates the usefulness of Landsat data for mapping urban

heat islands (UHIs) in Brunei-Muara district. For this study, UHIs are radiative in nature, with

the highest LSTs on impervious structures mostly in commercial and conglomeration of such

structures, followed by residential areas and cooled outwards toward tree canopy and forest

zones. These imply that urban development contributes to the overall increase in LST by

replacing the natural environment, particularly forests with non-evaporating, non-transpiring

surfaces.

The study also investigated the relationships between the LST, and NDVI in the Brunei-Muara

district. Results indicate that NDVI is an accurate indicator of SUHI effects with strong linear

relationships between LST and NDVI for all the three images (years). The linear relationship

model between the LST and vegetated (NDVI) areas accounts for the variation in land surface

temperature dynamics. This implies that NDVI can be used as a complementary metric for

analyzing LST quantitatively for surface urban heat island studies.

This study further demonstrates that temperature and land use information can be directly

derived from remotely sensed data, which provides a powerful way to monitor urban

environment and human activities. This can effectively replace the traditional analogue

system of urban studies particularly as the study relate to the physical aspects of a city

environment. This information enhances understanding of urban environment and can be further

used to improve environmental quality and urban planning.

Acknowledgments

The authors wish to thank the technical assistance of the following individuals: Nursadrina Binti

Haji Mahadi, Amnijah Binti Haji Salleh, Safwanah Binti Haji Abu Hassan, Sia Ai Ling, Nor

Abidah Binti Duraman, Siti Nur Afifah Baidzura Binti Haji Mat Yassin, A'in Rasyidatul Akmal

Binti Haji Kassim.

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