The Surface Urban Heat Island of Dublin derived from remotely sensed data: 2007-2014

A Thesis submitted to Department of Geography National University of Ireland Maynooth

in partial fulfilment of the requirements for the degree of

Bachelor of Arts (Honours)in Geography

By

Paul, M, Lynch

Peter Thorne, Jan Rigby

Maynooth, KildareApril, 2015

Copyright 2014 by Paul, M, Lynch

All Rights Reserved

44 Pages

11,704 Words with 16 Figures and 3 Tables

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The Surface Urban Heat Island of Dublin derived from remotely sensed data: 2007-2014

Paul, M, Lynch

Thesis Supervisor: Peter Thorne Thesis Advisor: Paul John Alexander

ABSTRACTCurrently there exists no investigation into the surface urban heat island (SUHI) of Dublin.

Recent data released by USGS allows the use of remotely sensed data to examine the Land

Surface Temperatures (LST) of Dublin city. These data captured by the MODIS senor on

board the Terra and Aqua satellites allow for the analysis of the SUHI of Dublin. This study

investigates the January and June, day and night-time SUHI of Dublin city, Ireland, for the

period 2007-2014. These data were processed in a GIS to construct and analyse a visual map

of the SUHI from the data obtained from MODIS. The Local Climate Zone classification

system was used to examine the relationship between LST and different urban forms. This

allowed for a clearer understanding of the link between land cover and LST values. The

results quantify the magnitude of the Dublin SUHI while also indicating the spatial variation

as a result of varying land cover. The SUHI intensity for June was found to be greatest of the

two months, particularly during the day while January had the smallest SUHI intensity which

was found to be strongest during the night. Warm thermal signals corresponded spatially with

intensely built up urban areas, while cool signals were found in areas with higher vegetative

coverage for example, the Phoenix Park was found to be as much as 8.5°C cooler than the

nearby inner city areas of Dublin. The results presented by this study establish a baseline

SUHI intensity, which will aid future research into the relationship between the SUHI and

atmospheric canopy layer UHI in Dublin.

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Acknowledgements

The author wishes to thank several people. I would like to thank Hannah for her continued patience and support through the last few months. Alan and Kate for the many cups of tea and kindness that made home a sanctuary to study in. Furthermore, I would also like to thank Mr. Paul Alexander and Professor. Peter Thorne for their constructive and helpful advice and guidance all along the process of producing this thesis.

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List of Figures

Figure 1: Comparison of the surface and urban energy balance.............................................................................5Figure 2: Overview of the Local Climate Zone Classification System....................................................................6Figure 3: True colour and thermal Satellite Image of London city..........................................................................7Figure 4: Urban Canopy Layer location...................................................................................................................9Figure 5: Urban Geometry.....................................................................................................................................10Figure 6: Urban vs Non-urban evapotranspiration.................................................................................................11Figure 7: The effect of urban geometry on wind influence....................................................................................12Figure 8: Diagram of the Urban Boundary Layer..................................................................................................13Figure 9: Turbulent air movement within the UBL ..............................................................................................14Figure 10: Green roofing on top of an urban building...........................................................................................16Figure 11: Types of Urban Heat Island Source: Modified from............................................................................18Figure 12: Study area overview.............................................................................................................................20Figure 13: Overview of the Methodology 1...........................................................................................................23Figure 14: Overview of Methodology 2.................................................................................................................23Figure 15: January and June LST 8-year mean for both DT AND NT..................................................................26Figure 16: The co-location of the warmest and coolest pixels June DT................................................................28

List of TablesTable 1 National Population Growth in Ireland for the Period 1985-2015...........................................17Table 2 Connection between Urban Energy Balance and features of urbanisation...............................19Table 3: Maximum urban LST value (Turban) compared to minimum LST value (TNon-Urban).................26

List of Abbreviations / Non-Standard Notation UHI Urban Heat IslandUCL Urban Canopy LayerUBL Urban Boundary LayerSUHI Surface Urban Heat IslandSEB Surface Energy BalanceUEB Urban Energy BalanceLST Land Surface TemperatureDT Day TimeNT Night Time

Q* Net RadiationQH Sensible Heat FluxQE Latent Heat Flux∆QS/QS Storage Heat FluxQF Anthropogenic Heat FluxK↓ Incoming Shortwave RadiationK↑ Outgoing Shortwave RadiationL↓ Incoming Longwave RadiationL↑ Outgoing Longwave RadiationK* Net Shortwave RadiationL* Net Longwave Radiation

Table of Contents

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ABSTRACT...........................................................................................................................................................iii

Acknowledgements.................................................................................................................................................v

List of Tables.........................................................................................................................................................vi

List of Abbreviations / Non-Standard Notation.................................................................................................vi

Chapter 1 – Introduction.......................................................................................................................................1

1.1 Theoretical and Contextual Background................................................................................................1

1.2 Scope of Thesis.......................................................................................................................................1

1.3 Research Questions.................................................................................................................................1

1.4 Rationale........................................................................................................................................................2

1.5 Thesis Layout.................................................................................................................................................2

Chapter 2 – Literature Review.............................................................................................................................3

2.1 The Urban Heat Island (UHI).........................................................................................................................3

2.1.1 Overview.....................................................................................................................................................3

2.1.2 Causes.........................................................................................................................................................3

2.1.3 Variation and intensity................................................................................................................................5

2.2 The Surface Urban Heat Island (SUHI).........................................................................................................7

2.3 The Urban Canopy Layer (UCL)...................................................................................................................8

2.4 The Urban Boundary Layer (UBL)..............................................................................................................12

2.5 UHI Mitigation Strategies.............................................................................................................................15

2.5.1 Planting and Vegetation............................................................................................................................15

2.5.2 Altering Pavement and Roofing albedo....................................................................................................15

2.5.3 Green Roofs..............................................................................................................................................16

2.6 Previous UHI Research for Dublin City......................................................................................................16

2.7 Chapter 2 Summary.......................................................................................................................................18

Chapter 3 - Methodology.....................................................................................................................................20

3.1 Methodology...................................................................................................................................................20

3.1.1 Study Area................................................................................................................................................20

3.1.2 MODIS Data.............................................................................................................................................21

3.1.3 Experimental Design.................................................................................................................................21

3.2 Data Processing..............................................................................................................................................22

3.3 Analysis Procedure.........................................................................................................................................24

3.4 SUHI intensity................................................................................................................................................24

Chapter 4 – Results..............................................................................................................................................25

4.1 January and June 8-year mean SUHI intensity............................................................................................25

4.2 Correlation with LCZ...................................................................................................................................27

4.3 SUHI Intensity.............................................................................................................................................28

Chapter 5 – Discussion and Conclusions...........................................................................................................30

Chapter 6- Bibliography......................................................................................................................................34

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Chapter 1 – Introduction

1.1 Theoretical and Contextual Background The construction of cities represents the un-natural altering of the land. New surfaces along with

varying building height are constructed wherein complex geometries are created that alter the surface

energy balance of that location. This altering of the natural landscape also upsets the “radiative,

aerodynamic, thermal and moisture properties of the atmosphere in and around the urban areas”

(Alexander & Mills, 2014). This often results in the change in the surfaces ability to reflect in coming

solar radiation. By converting natural land into a urban surface, the location will now absorb an

increased amount of solar radiation. This can ultimately result in the warming of an urban area

compared to areas where natural land cover and vegetation still exist. This contrast in surface

temperature between the urban area and the non-urban area is known as the Surface Urban Heat

Island (SUHI).

1.2 Scope of ThesisThis thesis examines land surface temperature (LST) in Dublin city during the period 2007-2014.

While the urban heat island (UHI) phenomenon occurs over a considerably shorter time period

(hours) the scope of this thesis was to consider a “climatology” of LST hence, the SUHI. Therefore

daily values, which are closer in temporal scale to traditional studies, are not considered here, but

rather mean LST values across an entire month. The annual cycle of SUHI intensity is also not

considered here, rather two periods, which together represent a minimum and maximum in terms of

solar radiation receipt, are considered, namely January 2007-2014 and June 2007-2014.

1.3 Research QuestionsSince the SUHI of Dublin does not feature in published literature, the overall aim of the present

volume was to establish a baseline for the SUHI in Dublin City.

The aim of this research project is:

Determine the extent of the SUHI for Dublin city for the months of January and June

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The Objectives are:

To analyze the LST data provided by the MODIS sensor. To construct visual maps of the resulting data using ArcMap GIS. To provide theoretical reasoning for the most prolific trends identified in the data and to

compare the results with previous studies findings.

The specific research questions that inform the approach of this thesis are:

1. When and where the SUHI is most intense in Dublin City?2. What influence do day and night have on the magnitude of the Surface Urban Heat Island

intensity? 3. Related to 2. above: is the SUHI in Dublin more or less intense during the daytime or night

time?4. Is the intensity (difference between minimum and maximum LST values) similar in June and

January?

1.4 RationalePrevious Irish research has focused on the Urban Canopy Layer-Urban Heat Island (UCL-UHI).Three

substantially works stand out that study the UCL-UHI. (Sweeney, 1987) studied the extent of the

winter UHI while (Graham, 1993) investigated the summer UHI. (Alexander & Mills, 2014) added to

the research by constructing a Local Climate Zone map that displayed the influence land cover has on

the UHI formation. This thesis attempts to conduct a study into the SUHI in order to fill a research

gap currently existing in the understanding of the Dublin UHI.

1.5 Thesis LayoutThis thesis is divided into 6 Chapters:

Chapter 2 provides a review previous research and concepts that inform the present study

Chapter 3 outlines the methodology employed to address the research question stated above

Chapter 4 presents the results of the analysis arising from the methodology presented in Chapter 3

Chapter 5 outlines the main implications of Chapter 4 and states the conclusions of this study

Chapter 6 contains a bibliographic list of other works cited in the body of this work.

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Chapter 2 – Literature Review

This chapter provides a review of relevant literature surrounding the research questions outlined in the

previous chapter. The focus of this review is centred on concepts and previous research which relates

to the urban heat island (UHI) at various scales. Specifically the following topics are addressed 1) the

urban heat island effect, its causes / controls and 2) the surface urban heat island which is the focus on

this thesis 3) the urban canopy and boundary layer heat islands 4) mitigation strategies to reduce the

impact of the UHI on populations 5) a review of previous research on the UHI in Dublin City.

2.1 The Urban Heat Island (UHI)2.1.1 OverviewHere, an overview of the Urban Heat Island (UHI) phenomenon, first alluded to by Luke Howard at

the beginning of the 19th Century (Mills, Luke Howard and The Climate of London, 2008) is

provided. The UHI is a climatic phenomenon that arises as a result of anthropogenic processes,

namely urbanisation. As the name suggests, the UHI is associated with urban areas. Within urban

areas (urban is loosely defined here as artificial materials comprising residential, commercial and

industrial constructs along with their associated activities) distinctive urban bio-physical features

interact and influence the local thermal and hydrological climate, creating anomalies in temperature,

moisture content and air quality of the near surface atmosphere. Generally, this results in a distinctive

urban climate, which is warmer, dryer and contains higher concentrations of particulate matter and

poorer air quality with respect to surrounding non-urban hinterlands (Fortuniak, 2009). Within the

urban climate, there is a noted tendency for urban areas to exhibited warmer air temperature than their

surrounding non-urban hinterlands. Thus, the UHI is generally taken to refer to elevated nocturnal

temperatures in urban areas which are several degrees warmer than surrounding rural areas

(Chapman, It's HOT in the city!, 2005). Empirical evidence shows the UHI is strongest approximately

4 hours after sunset in the middle latitudes (Kutler, Roßmann, F., & Barlag, A.B., 1996)

2.1.2 CausesThe conversion of natural or non-urban land cover into urban environments modifies the interaction

between the surface and the atmosphere, most notably the way in which the land absorbs and reflects

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solar radiation. The reflectivity of a surface is quantified by its albedo – a measure of the level of

absorption (and conversely reflection) of different surfaces. Natural surfaces such as grass, water and

forested areas reflect between 10-20% of insolation. Concrete and asphalt are found to have a mere 5-

15% (E.P.A, 2009). When the latter replaces the former, a significant change occurs in the areas

ability to reflect solar radiation. Urban areas therefore absorb higher amounts of solar radiation

compared to most non-urban environments. Additionally, the manner in which different surfaces

respond/interact with insolation is critical towards our understanding of the phenomenon. The

response of surfaces with respect to energy receipt from the sun is best understood in terms of the

surface energy balance:

Q* = QH + QE + QG (1)

Where Q* is net radiation, QH refers to sensible heating of the surface, QE refers to heat arising due to

evapotranspiration, that is, energy used to evaporate water or used in the process of photosynthesis.

QG refers to energy transferred to the ground (Oke T. R., 1982). The amount of energy available to a

surface may be further defined as:

Q* = (K↑-K↓) + (L↑-L↓) (2)

Where K↑ is reflected solar radiation, K↓ is received solar radiation, L↑ is emitted longwave radiation

L↓ is received longwave radiation.

Within urban areas, the surface energy balance is modified resulting in the urban energy balance

(UEB):

Q* + QF = QH + QE + ΔQS (3)

Where Q*, QH and QE are as equation (1) and (2) above. Additionally, QF refers to heat added by

human activities and ΔQS refers to the total storage of energy within the urban fabric i.e. within walls,

rooftops and the ground (Oke T. R., 1982). If we consider a comparison between equations (1) and

(3), that is, between non-urban and urban energy balance, there is an additional source of energy for

urban areas, moreover a larger proportion of energy can be stored. Generally, there is also a lower

degree of vegetation within urban areas, leading to a higher proportion of available energy being

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channelled into sensible heating of the surface and hence, the air. Figure 1 provides an overview of

the difference between equations (1), (2) and (3).

Figure 1: Comparison of the surface (left) and urban (right) energy balance. Note the width of the arrows is proportional to the magnitude of the energy channelled into each term. “Convection” = QH, “Evaporation” = QE, “Conduction” = QG, “Storage” = ΔQS, Reflection = K↑ Radiation = K↓. Source: (Archdeacon, 2010)

The difference in reflectivity and the energy balance creates an urban-rural contrast in air temperature

and surface radiance (Brazel, Selover, N., Vose, R., & Heisler, G., 2000). The high volume of low

reflectance albedo combined with varying building height allows for the retention of more energy

during the day time. After sunset the available energy can be understood as:

Q* ≈ ΔQS (4)

Buildings are slow to release absorbed energy in comparison to open spaces such as a green field.

This results in the surfaces of urban environments cooling at a different (slower) rate to the non-urban

settings which results in the nocturnal UHI (Oke T. , 1987)

2.1.3 Variation and intensityAs mentioned previously, materials have certain albedo characteristics which vary greatly from one

surface to another. It has been identified that temperature contrasts occur between non-urban and

urban environments. However, contrasts also occur within the urban setting. Within urban areas, a

variety of artificial materials with different radiative and thermal properties are present, for example,

asphalt, concrete, corrugated iron, glass, brick etc. Moreover, the level of vegetation varies in

different locations within cities, such as parks, street trees, community parks/sports fields etc. Based

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on equation (1) and (2), the level of vegetation can be seen to mitigate the intensity of the urban UHI

across different areas.

This results in varying levels of UHI intensity within the city itself. To explain the variation in UHI

intensity between different areas within cities, recent studies have employed a classification system

based on the level of thermal modification of that atmosphere known as Local Climate Zones (LCZ)

classification (Stewart & Oke, 2012). This classification system sections an urban surface into areas

of between one and several kilometres in size and classifies them based on the level and type of urban

materials present – see Figure 2. This allows thermal characteristics to be easily identified and which

in turn allows for easier analysis of temperature anomalies (Alexander & Mills, 2014).

Figure 2: Overview of the Local Climate Zone Classification System. The classification aides with the explanation of why intra-urban differences (in respect of the UHI) of nocturnal air temperature occur. Source: (Stewart & Oke, 2012).

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The level of overall urbanisation will also affect the strength of the UHI present cities. Urban

environments that are larger and more developed will often be denser with higher levels of

construction and manmade materials such as pavements and buildings.

The varying magnitude of the UHI is called the UHI intensity. UHI intensity varies greatly both

hourly and seasonally. As discussed, factors such as local topography, land use and land cover

characteristics all play an influential role in the development of the UHI (Fortuniak, 2009).

2.2 The Surface Urban Heat Island (SUHI)The SUHI is the first order heat island found at the surface below the Urban Canopy layer (see section

2.2.2). Traditional methods of measuring the UHI included comparing temperature data from in situ

urban and non-urban weather stations (Kukla, Gavin, & Karl, 1986) or by collecting temperature data

by transecting between an urban and non-urban area (Johnson, 1985). In the 1970’s, the first

investigations into satellite techniques for measuring the UHI were conducted (Matson, McClain,

McGinnis, & Pritchard, 1978). Improvement in remote sensing technology has been rapid. Satellite

systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) provide surface

temperature and reflectance data. Land surface temperature (LST) data is one of the many useful data

products this remote sensor compiles (see Figure 3). The large spatial coverage that a satellite can

provide is one of the main reasons satellites are employed in studies (Mendelsohn, Kurukulasuriya,

Basist, Kogan, & Williams, 2007). The MODIS LST product has been used already in many surface

UHI investigations of various degrees of city sizes around the globe (Jin & Shepherd, 2005).

Figure 3: LEFT – True colour Satellite Image of London city, vegetation appears as green areas, urban areas are shown as shades of dark grey. RIGHT - Thermal Satellite Image of London city's illustrating the SUHI. Note the higher temperatures marked in yellow and the cooler temperatures indicated using blue and green colours. The coolest areas are spatially correlated with water bodies and vegetated areas. Source: modified from (ASU, 2015).

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In a study conducted by (Peng, et al., 2011) that involved an assessment of SUHIs in 419 global big

cities, evidence was found that annual SUHI daytime intensity was greater than night-time SUHI.

This occurs as a result of the different driving mechanisms that create day and night time SUHIs. This

extensive study also identified a positive correlation between the distribution of night time SUHI and

albedo differences across urban and suburban areas. A similar study performed by Cheval &

Dumitrescu (2008) in Bucharest used MODIS data to discover the extent of the SUHI. By analysing

the average temperature per pixel of MODIS data (1 km2 resolution) the study was able to identify the

outline of the SUHI. A higher and more variable SUHI was present during the daytime while a

relationship was found to exist between land cover and SUHI intensity. Cheng, Zhao, & Li, (2006)

examined the Pearl River Delta region of Southern China – a region experiencing rapid urban growth.

The results showed a significant SUHI that had a scattered pattern corresponding to certain land

cover types. This highlights the importance of varying land surface characteristics and the influence

they have on SUHI development.

The most relevant study for this thesis is recent research conducted in Birmingham, UK by Chapman

et al. (YEAR). In their study, MODIS data was used to identify the variation in SUHI intensity.

Whereas previous studies have focused on ideal conditions for UHI formation, Chapman et al.

explored the SUHI under a range of climatic conditions. The study identifies a means to counter the

lack of UHI components in current climate models. MODIS data has been used to provide easily

accessed information regarding average surface temperatures that can then be applied to climate

modelling scenarios. This addresses a significant gap in research. The scale of Birmingham city is

also representative of many mid latitude cities worldwide whereas previous studies focused mainly on

larger sized cities (Chapman, Tomlinson, Thornes, & Baker, 2012). Therefore, a similar investigation

can be easily conducted into SUHI extent for an urban area using the methods outlined in the

Chapman et al study.

2.3 The Urban Canopy Layer (UCL)The UCL-UHI, found above the SUHI, is defined as a simultaneous screen-level air temperature

difference between urban and non-urban sites (Stewart & Oke, 2012). The UCL begins at ground

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level and expands to the roof level of the urban structure (see Figure 4). Calm and clear nights are the

ideal conditions for UCL-UHI development. At night, long wave radiation is emitted from the urban

surface. This radiation loss is compensated by the withdrawal of heat stored in urban substrate during

the day. Oke (1982) has shown that the air temperature in a densely built UCL will cool almost

linearly after sunset while the rate of cooling in non-urban locations will cool rapidly. Empirical

evidence has shown the UCL-UHI is at its strongest around 4 hours after sunset (Oke T. , 1987).

Figure 4: Urban Canopy Layer location (TheBritishGeographer.com, 2015)

UCL-UHI presence is measured by comparing urban and non-urban temperature contrasts. To gather

these temperature data sets, observations are made using in-situ temperature recording stations or by

taking temperature measurements using auto-mobile traverse (Sundborg, 1950). Modern UCL-UHI

techniques seek to explore the elements found within an urban area that, when combined together,

create the UCL heat island. By isolating each individual element and thoroughly examining its

characteristics, a better understanding of how the UCL-UHI forms and subsequently how it may be

mitigated can be discovered. As with the SUHI, formation of the UCL-UHI can be best understood by

examining the urban energy balance (eqn. 1-3).

Studies such as those conducted by (Oke & Nunez, 1977) and (Kondo, Ueno, Kaga, & Yamaguchi,

2001) consider the effect canyon geometry and air pollution have on the UCL temperatures. Both

studies found that the uneven height and distribution of urban buildings create a larger surface area

that absorbs and stores a higher amount of K* (see Figure 5). Both studies also concluded that air

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pollution in the UCL can create greater absorption and re-emission of longwave radiation that results

in increased L↓.

Figure 5: Urban Geometry - notice the heat trap effect. (R., 2009)

(Kondo, Ueno, Kaga, & Yamaguchi, 2001) found that canyon geometry (i.e. the space created by

closely constructed building walls and the intervening pavement) can result in a reduced sky view

factor (SVF). The smaller the SVF for a surface the lower the rate of heat emittance for urban

environments. This will reduce the rate of nocturnal cooling in an urban area, resulting in decreased

L↑.

QF (anthropogenic heat - eqn. 3) is also an important part of the energy balance equation. Various

studies have identified that there is a significant addition of heat into the urban sphere due to human

activities. The construction of buildings and the excess amounts of burning fossil fuels found in urban

areas result in a direct addition of heat into the UCL heat island (Grimmond & Oke, Comparison of

heat fluxes from summertime observations in the suburbs of four North American cities., 1995)

(Ichinose, Shimodozono, & Hanaki , 1999) and (Offerle, Grimmond, , & Fortuniak, 2005).

(Grimmond & Oke, 2002) and (Grimmond & Oke, 1999) have conducted studies into the absorption

abilities of certain urban materials. Their findings show that urban materials when compared to many

non-urban materials such as vegetation exhibit a consistently higher heat capacity, thus store more

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heat during the day. These studies identify that urban materials have the capacity to store larger

quantities of heat.

Urban construction also alters the hydrological dynamics of an urban region. According to

(Grimmond & Oke, 1999) and (Sailor, 2009) urban construction techniques result in the efficient and

rapid removal of precipitation from an urban surface. This results in urban surfaces drying sooner

allowing energy that would have otherwise been used up in evaporation to be absorbed immediately

by the surface (see Figure 6).

Figure 6: Urban vs Non-urban evapotranspiration. (E.P.A, 2009)

The effect of urban geometry on wind speed has also been investigated by (Oke & Nunez, 1977). It

was found that the ability of the wind to remove the warmer air trapped in the urban environment was

diminished by the layout of the urban area. The jagged layout of the city was found to prevent a direct

passage of air through the urban area resulting in warm air being trapped within the restricting

confinements of the urban canyon (see Figure 7).

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Figure 7: The effect of urban geometry on wind influence. (TheBritishGeographer.com, 2015)

As is shown, considerable effort has been put into understanding the individual elements of the

modification of the near surface atmosphere beneath roof-level. The UCL is generally accepted as

important since it is within this the layer people live their daily lives. Therefore, degradation of

atmospheric conditions within the UCL either via enhanced air pollution or temperatures will be felt

immediately by the inhabitants. Excess temperatures caused by the UHI has been shown to increase

the intensity of heat wave events, resulting in an increased rate of heat related mortalities, this was

seen during the summers of 1975-2004 in Shanghai (Tan, Zheng, Tang, Guo, & Li, 2010).

While the UCL has been well documented there are still some gaps in the research. In 2011, a study

conducted in the Netherlands by (Steeneveld, Koopmans, Heusinkveld, & van Hove, 2011)

highlighted the lack of academic research invested into quantifying the effects of the UHI on human

comfort in mid oceanic climate zones (Cfb). This, it was pointed out by the study, may be due to the

assumption that for cities with a mild oceanic climate, UHI heat stress may not have a great impact on

human comfort levels. However with rising global temperatures now may be the time to begin filling

this research gap for many of the undocumented cities that can be found in moderate climates.

2.4 The Urban Boundary Layer (UBL)The UBL is the uppermost layer of the urban climate and as with the surface and canopy layer

exhibits its own UHI. It extends from the rooftop level upward into the overlying atmosphere.

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Boundary layer depth is generally defined as the depth of atmosphere that exhibits clear modification

by the underlying surface. This region typically extends no more than 1.5km. The UBL heat island is

most intense during calm and clear nocturnal conditions (see Figure 8).

Figure 8: Diagram of the Urban Boundary Layer (TheBritishGeographer.com, 2015)

Of all the UHIs (i.e. the SUHI, UCL-UHI and UBL-UHI) the UBL-UHI is understood the least. One

of the first experiments into the UBL was the Urban Air Pollution Dynamic Research Network in

New York (U.S) in the 1960’s. Methods of temperature data gathering included the use of helicopters,

pilot balloons ad newly created numerical models. All these methods are still used in today’s UBL

studies. Air pollution studies were also conducted for the UBL of Mexico city by (J.C. Doran, 1998).

High levels of air pollution that remained trapped over the city due to the local topographic influence

on air movements spurred further research into the effects of urban areas on the UBL. The problem of

air pollution was another instigator of further research as the health impacts of poor air quality

became ever more important as urban populations grew.

To understand the UBL studies were conducted into the various controls on the UBL form. (Oke &

East, 1971) performed an investigation into the rate of urban cooling and found that heat-island

intensity within the UBL is at its maximum around midnight. The vertical movement of warm air is

highlighted as a key component of UBL-UHI creation (see Figure 8). (Sorbjan & Uliasz, 1982) used

two types of models to describe the thermal and dynamic structure of the urban boundary layer. The

study tests the influence density and urban building height have on the shape and form of the UBL. It

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was found that change in the processes such as those mentioned in 2.2.2 have altered the appearance

and extent of the UBL in urban areas compared to the non-urban equivalent.

Figure 9: Turbulent air movement within the UBL (Riverside, 2012)

(ZHANG Ning, 2011) noted the effect rapid urbanization had on the UBL. Their study considers the

unchecked expansion of Suzhou city in China. It notes the relationship between city expansion and

vertical growth of the UBL which between 1986 and 2006 was found to increase by 50m in height. In

2006, (M. W. Rotach, 2005) performed the BUBBLE experiment. This intensive study combined

previously established knowledge in order to perform a comprehensive study of the UBL in the city of

Bazel, Switzerland. Purpose built towers were constructed on varying land types within the city that

recorded various atmospheric details such as temperature and wind turbulence found within the UBL.

The towers performed many data gathering processes simultaneously that were only ever conducted

individually before. This meant that data gathered by the towers was done so under the same synoptic

conditions. This proved highly valuable for following studies as the BUBBLE data represented an

accurate link between different forms of data.

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By better understanding the chemical and physical processes of the UBL, methods may be developed

to reduce the air pollution levels that are so often found in the UBL due to the effects of the UHI. Data

collection towers, while they are a highly reliable and accurate tool for measuring the UBL, can be

very tall (often over 300m in height) and so they are often unsuitable for urban deployment. This may

result in data gaps for certain parts of the UBL of an area due to the limitations of tower deployment.

Therefore, new data gathering techniques that are as accurate and reliable as the towers will need to

be developed to fill these data gaps.

2.5 UHI Mitigation StrategiesUHI development has occurred as a result of the factors outlined in section 2.1.2. To lessen the extent

of an UHI, various mitigation methods have been derived. The following sections outline research

centred on mitigation of UHI intensity.

2.5.1 Planting and Vegetation(Tong H, 2005), investigated the effect of increasing the use of vegetation in land cover within a city,

on UHI production. By planting trees, vegetation and green roofs the quantity of solar energy

absorbed can be reduced (Eqn. 2). This will help limit the amount of heat being absorbed and stored,

the release of which leads to the UCL-UHI. Vegetation also helps to reduce the air pollutants which

also act negatively on the urban population and can intensify the UHI. This study indicated that by

planting vegetation, the temperature reduction of an urban area could be as much as 1.6°C. By

planting vegetation, an urban area may acquire a greater albedo, especially if the land surface is

converted from urban to non-urban.

2.5.2 Altering Pavement and Roofing albedoExtensive studies have been carried out into the benefits of increasing the albedo of urban materials.

(Rosenfeld A H, 1998) found that by using lighter coloured materials in the construction of pavements

and roofs, the amount of solar radiation absorbed will be substantially less than if darker materials

were used. This study identified a 3°C reduction in UHI intensity when the urban albedo was altered.

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2.5.3 Green Roofs(Liu, 2003) studied the effects of rooftop conversion on UHI production. Green roofing involves

covering urban roofs with grass or other vegetation forms. This is a relatively cheap and easy process

that can increase rooftop albedo, aid air pollution mitigation and can even help reduce energy usage

for that building as the grass will help absorb heat and reduce the need to use cooling mechanisms.

Therefore by converting urban rooftops to green rooftops, the quantity of solar energy entering the

urban climate system can be reduced.

Figure 10: Green roofing on top of an urban building (McDonagh, 2014)

The focus of mitigation efforts is to reduce the intensity of the UHI. The methods mentioned above

have considered ways to tackle certain elements that contribute to the UHI and thus reduce their

contribution. By incorporating these mitigation strategies into urban planning we may be able to

reduce the UHI and the resulting health risks associated with it.

2.6 Previous UHI Research for Dublin CityUrbanisation is a worldwide phenomenon. As cities expand and their population grows, an increasing

number of people will be influenced by the negative effects of the UHI, at the same time the UHI will

intensify under urbanisation (section 2.1.3 above). UHI studies are being conducted in an increasing

number of global cities. In Ireland, Dublin city has been the focus of most UHI research. As Ireland

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capital, Dublin houses the majority of the urban population in Ireland and is the largest city in Ireland

– see Table 1.

Table 1 National Population Growth in Ireland for the Period 1985-2015. Population growth is expressed as a % increase/decrease. Source: (UN, 2012)

Year(s) Population Growth2010-2015 1.13

2005-2010 1.442000-2005 1.781995-2000 1.041990-1995 0.451985-1990 -0.02

In 1987, (Sweeney, 1987) studied the UCL-UHI for Dublin city during the winter months of

November and February. This study used the traditional methods of mobile traverse and from a non-

urban location to an urban location (Dublin city). Temperature data was recorded throughout the

course of the traverse. Sweeney noted that under the conditions of clear skies and light winds,

substantially higher temperatures were found in Dublin city. However, he advised consideration to the

synoptic effects of the cities close proximity to both the Dublin/ Wicklow mountains and the coast

which in itself may have a mitigating effect on the Dublin UHI, Sweeney suggested.

Six years later, (Graham, 1993) conducted a study of the Dublin UHI during the Summer months.

Graham also employed auto-mobile and bicycle traverse in order to collect temperature data of the

UCL-UHI for both day and night time. Results suggested that the UCL-UHI was minimal during the

day but more intense at night.

More recently, a study conducted by (Alexander & Mills, 2014) sought to fill the gap in land cover

classification – a relatively new approach that seeks to set a standard for land cover classification.

This study mapped the surface of Dublin city allocating it a certain LCZ (see 2.1.3 / Figure 2) based

on the surface elements. The study then went on to discover what near- surface temperatures were

recorded over the different LCZ. The study found a relationship between LCZ and UHI intensity

indicating the influence of non-urban land cover within the overall urban complex.

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However, to date, UHI investigations into Dublin city have focused only on the UCL-UHI. All these

studies have considered the air temperatures found within the UCL only. This study however, will

focus on gathering temperature data for the SUHI in order to fill the current knowledge gap in the

Dublin UHI literature.

2.7 Chapter 2 SummaryThis chapter has highlighted the main research that has bearing on the present thesis. The concept of

different UHI’s within the urban climate is summarised in Figure 11. Table 2 presents a summary of

the energetic basis of the UHI and how specific components of urbanisation are connected to its

modulation.

Figure 11: Types of Urban Heat Island Source: Modified from (Voogt J. , 2001) p. 5

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Table 2 Connection between Urban Energy Balance (UEB - eqn. 2&3) and features of urbanisation which lead to the UHI Source: (Mills, 2005)

Energy Balance Term Urban Feature Urban Effect

Increased K* Canyon GeometryIncreased surface area and trapping of radiation

Increased L↓ Air Pollution Greater absorption and re-emission

Decreased L* Canyon GeometryReduced Sky View Factor (less nocturnal loss)

QF Buildings and Traffic Direct addition of heatIncreased QS Construction Materials Increased thermal admittanceDecreased QE Construction Materials Increased water-proofingDecreased QH+QE Canyon Geometry Reduces wind speed

The following concepts have been highlighted in this chapter:

(1) The Urban Heat Island is found in virtually every settlement where natural vegetation has

been replaced with artificial materials

(2) The cause of the UHI is best understood in terms of the modification of the surface energy

budget driven by urbanisation (Table 2)

(3) The urban energy budget can be characterised as having additional sources of energy

(anthropogenic heat) and portioning more energy into sensible heating and heat storage

(4) There are three known types of UHIs (Figure 11) the surface urban heat island, the canopy

layer heat island and the boundary layer heat island

(5) The SUHI which is the focus of this thesis has two primary processes relating to day and

night

(6) During the day, surface materials (albedo and emissivity) control the level of solar radiation

absorbed, thus the temperature of the surface

(7) During the night, the rate at which the energy is released by that surface will determine its

skin-surface temperature

(8) UHI intensity can be mitigated by employing various strategies including modifying the level

of vegetation within the urban area or modifying the thermal characteristics of surfaces within

the urban area

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(9) Previous research to date on the UHI in Dublin has focussed on the UCL heat island, no

attempt has been made on investigating the SUHI

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Chapter 3 - Methodology

3.1 Methodology3.1.1 Study AreaDublin (53.5°N, 6.5°W) is the capital of the Republic of Ireland and is located at the western fringe of

Europe. The city is situated on the East coast and is flanked by the Irish Sea to the West and the

Dublin/ Wicklow mountains to the South. The river Liffey flows through the relatively flat basin

occupied by the city. Dublin city experiences a maritime-temperate climate (Koppen Cfb). Even with

relatively high latitude, Dublin city enjoys a mild climate and experiences little temperature variation

throughout the year. Day length is significantly longer during the summer (16h in June) while Winter

sees far shorter days (8h in December). The extent of the urban area that this study is investigating is

~700km2. The population of the study area is approximately 1.2 million (CSO, 2012).

The generally wet and windy climate experienced in Dublin results in the average UHI being small in

magnitude. The conditions that allow for the strong UHI formation – strong and persistent anticylonic

conditions – occur rarely. However, the few studies that have been conducted into the Dublin UHI

have been done during such ideal conditions. Figure 12 illustrates the extent of Dublin city considered

for this thesis. The study area encompasses 891 Km2 (33 x 27 Km) from North of Dublin Airport

South past Tallaght. With the exception of the high elevation to the South, the majority of the study

area occupies low lying land.

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Figure 12: Study area overview

3.1.2 MODIS DataThis study uses the MODIS product MYD11A1 – MODIS/Aqua Land Surface Temperature (LST)

and Emissivity Daily L3 Global 1Km Grid SIN. The MODIS LST product uses split window

algorithms and techniques (Wan Z, 1996) that correct for atmospheric effects such as rate of

absorption and emission along with surface emissivity. The MODIS LST product does this by

utilising multiple bands (36 in total) available on the sensor. By doing so, many of the problems

associated with remote sensing measurements of LST data can be addressed. The MODIS sensor

aboard the Aqua satellite was used as it captures night images for Dublin city (at approximately

1330hr and 0130hrs local time) due to the near polar sun synchronous. Night images allow for a more

precise LST calculation as there is no incoming solar radiation to alter the surface radiation balance.

(Rigo G, 2006) has shown night time MODIS LST data to be more accurate. The timing of the image

collection compliments the time indicated by (Oke T. , 1987) when UHI magnitude is at its maximum

3-5hrs after sunset. The advantages of the easy access to MODIS LST data is highlighted by

(Chapman, Tomlinson, Thornes, & Baker, 2012) (see section 2.2.1).

3.1.3 Experimental DesignThe months of January and June were examined for the Dublin city study area for an eight year period

from 2007-2014. It was anticipated that the synoptic contrast between the months of January and the

June would provide an interesting study for UHI magnitude comparison. The January data are

representative of the typical Irish winter weather, with the exception of 2010 which was exceptionally

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cold and a-typical since snow cover was persistent for much of the month. June represents the peak of

solar radiation for Ireland (i.e. longest daylight and highest sun-angle) thus was select to represent the

“maximum” SUHI intensity.

MODIS data for Dublin city is only available from 2004. However, it was found that the synoptic

conditions for much of January 2004-2006 were unsuitable for MODIS data collection due to high

cloud cover content and wet surfaces that contaminated any LST data available. Although for the

same period June LST data was available, a successful UHI study for these years was impossible

without the January LST data to act as a comparison. Therefore, the January 2007 – 2014 data was

used as accurate LST scenes were available for this period. Likewise, June LST data was widely

available. This allowed for a comparison study to be conducted for the period 2007-2014 for both

months.

Data for each day of each of the selected months were collected for each of the eight years being

studied. The information collected in the form of LST ‘scenes’ were then processed using the model

builder function in ArcMap a feature of the ArcGis Geographical Information System programme

(See Figure 13). This programme allows for a variety of cartographic processing functions to be

performed. The model builder tool allows for the processing of MODIS data by converting the raw

data into raster form. This is then inserted into a pre-designed model that processes the raster data into

a final LST map.

A map depicting the average LST for Dublin city from the MODIS raster data needed to be created.

To do this the cell statistics function was used to perform a query on the data that averaged the LST

data for the entire January dataset for 2007-2014. A similar process was conducted for the June

dataset. The resulting maps displayed the mean LST data for Dublin city for the respective months

during the seven year study period. This mean LST map served as a source to compare the individual

LST years with to discover which years had higher LST readings and which fell below the average

LST data.

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3.2 Data ProcessingUsing the ArcGIS Model builder tool, the MODIS scene data was subsetted and clipped using a shape

file of the Dublin city study area (Figure 13). This tailored the data to represent only the LST data

found within the study area. To remove individual days from the LST data, the clipped data were then

passed into a second model which averaged the LST across the entire month for a year (30 scenes per

year). This provided a more accurate estimate of a locations LST since individual days were not

considered (Figure 14). This process was done again to get the mean LST for the entire period (30

scenes per year x 8 years = 240 scenes). “No-data” arising from poor synoptic conditions such as high

cloud cover or wet surface conditions during the time of the satellite orbit over Dublin were not

included in the averaging of the pixels LST values. Thus, if a particular pixel had only 12 days with

LST values for a given month, the average was based on 12 days rather than 30. In doing this,

contaminated pixels were systematically removed from the dataset leaving behind only the scenes that

displayed accurate mean LST data per pixel.

Figure 13: Overview of the Methodology 1 (Read from left to Right) MODIS scenes are subdivided into years, the user specifies to the model (under “MODIS data”) which year to process. The sub-dataset desired is also specified (e.g. 0 for Daytime LST or 4 for Nighttime LST). The Subset of the data are then clipped to the study area shape and passed into the second model (Figure 14)

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MODIS data

Figure 14: Overview of Methodology 2 (As with Figure 13 read from left to right) The values of daily LST for each month (separated by year) are collected together into a single data set discounting NODATA pixels. Then the MEAN LST value for each pixel is computed. This process is done for each month (2) for each year (8) and for each subset of LST (2, i.e. 32 runs). Additionally the monthly LST for each year are then passed through to get a 8-year average for LST for each month.

LST values were displayed in DN (radiance) For analysis the DN value was converted into degrees

Celsius using the following function on each of the completed LST datasets:

(DN x 0.02) - 273 (5)

where DN is the digital number of each pixel. By scaling the data (0.02) DNs are converted into

Kelvin. By subtracting 273, data are converted into Degrees Celsius (°C) which conforms to the unit

used in other UHI research in Dublin.

3.3 Analysis ProcedureTo analyse the SUHI of Dublin in January and June, LST in °C were examined across the study area.

As per the research questions (see Chapter 1 section 1.3) the analysis focused on differences in LST

(ΔT) and LST statistics. Each year is considered; subsequently the 8-year mean LST maps for January

and June are statistically compared. The analysis is split by month, but focused on daytime and

nighttime LST values.

To better understand the relationship with land cover (see Chapter 2 section 2.1.3) Zonal Statistics

were used to calculate LST values based on the LCZ map for Dublin (Alexander & Mills, 2014). All

analysis comparing pixels were carried out in Arc GIS, using the spatial analysis toolset. All statistical

analysis was carried out in Excel.

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Clipped MODIS

data

Daily LST

values

Monthly LST Map per year

3.4 SUHI intensityAs with previous research in Dublin, the main purpose was to investigate the intensity of the SUHI

across the city. To do this, the following approach was taken:

- The LST values across the 8-year mean maps were average (separated by month and time of

day).

- This average value was then subtracted from the LST values for each map thus illustrating

each pixels tendency to be above or below the mean for the entire study area

- The SUHI intensity was then defined as the range between the lowest below average value

and highest above average value

- These area were then highlighted on each map to illustrate their spatial location.

The results arising from the methodology outlined above are presented in the next Chapter.

Chapter 4 – Results

In this chapter the results from the analysis of the June and January daytime and night-time 8-year

mean maps are presented.The magnitude of the SUHI found for both months during the daytime and

night-time will be investigated. Significant features such as warm islands and cold islands will be

identified. Finally, the spatial relationship between Local Climate Zones and warm/cool island

existence is presented.

4.1 January and June 8-year mean SUHI intensity

Referring to table 3 (below), it is seen that the SUHI in Dublin is most intense during the daytime

(DT) in June when it has a SUHI of 12.7°C. It was found that June 2009 had the most intense SUHI

during the day with urban-non-urban LST differences of 17.1°C. By using the 8-year mean LST maps

(see Figure 15) it is noted that the pixels containing the highest daytime LST values (26.9-28.8°C) for

June are found around the urban core of Dublin city while the lowest daytime LST values (16.1-18.3)

for June are found at the edge of the city’s boundaries . Section 4.3 will identify the correlation

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between the location of the most intense SUHI sections and the land cover found for that same

location.

In June, Night-time (NT) SUHI intensity is 6.7°C cooler than the daytime SUHI. This highlights the

relationship between the absorption of energy and the intensity of the daytime SUHI. As mentioned in

section 2.1, the DT SUHI tends to be stronger due to the high absorption rate of solar energy in urban

areas. NT SUHI emissivity rates tend to be higher and so energy is lost at a faster rate than the

emissivity of non-urban surfaces. Hence, the NT SUHI is less intense as the difference between the

urban and non-urban LST falls due to the slower emissivity rates of non-urban land surfaces.

The January DT SUHI is far less intense compared to June at only 4.96°C. This may be as a direct

result of the lesser quantity of solar energy being absorbed by the surface during the winter month of

January. However, the NT SUHI is found to be 5.58°C – this is 0.62°C more intense than the DT

January SUHI which is an unusual finding – this is discussed further in section 5.1.

Table 3: Maximum urban LST value (Turban) compared to minimum LST value (TNon-Urban) for June and January Day and night. Also shown is the SUHI which is the difference (Δ) between the maximum and minimum LST value. Below each month is the difference (Δ) between daytime (dt) and night-time (nt) SUHI.

Month Time TUrban (°C) TNon-Urban (°C) SUHI (ΔT Urban-Non Urban) (°C)June 8-year mean Daytime 28.8 16.1 12.7June 8-year mean Night-time 12.8 6.8 6ΔTdt-nt 6.7

January 8-Year mean

Daytime 5.13 0.165 4.96

January 8-year mean

Night-time 2.4 -3.18 5.58

ΔTdt-nt -0.62

It can be noted therefore, that the daytime SUHI for June represents the most intense SUHI for both

months. There appears however, to be no obvious similarities between the SUHI intensity experienced

in June and January. In fact, for this study’s results, DT SUHI is found to be most intense in June

while the NT SUHI represents the most intense SUHI in January – Figure 15.

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Figure 15: January and June LST 8-year mean for both DT AND NT

4.2 Correlation with LCZAs was identified in both the DT and NT June maps, certain LST features were present.

Warm islands exist within the overall SUHI and were found to be the areas of greatest LST values

for both DT and NT June LST observations. Applying the LCZ classification system first applied to

Dublin city by (Alexander & Mills, 2014), it can be noted that the spatial distribution of the warm

island is present in areas if Dublin were urbanisation is most intense. The LCZ below the warm island

can be identified as a mix of compact low-rise and compact mid-rise buildings. These LCZ’s create

suitable conditions for the formation of SUHI (see section 2.2). Therefore, the LCZ’s help explain

why a warm island can be found along these locations within the study area. (Cheval & Dumitrescu,

2008) found similar results when studying the location of warm islands in Bucharest. They also found

that the greater the compactness of urban features, the greater the SUHI intensity would be at that

location.

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Cool Islands are also found within the overall SUHI. A cool island was consistently present between

both the June DT and NT maps. Applying the LCZ classification map it is found that the cool island

occupies parkland. The Phoenix Park is the largest open space (7.07km2) in Dublin city. It has an

LCZ classification of scattered trees and low plants. This area is therefore representative of a non-

urban area and will have a mitigating effect on the SUHI formation (see section 2.5.1 ). Due to the

LCZ found for the Park, LST values do not reach the same intensity as the dense urban inner city

areas. This has led to the formation of a cool island at the Parks location where LST values are far

lower than the surrounding areas. (Chapman, Tomlinson, Thornes, & Baker, 2012)had similar

findings for their study. They too found a significant cool island which corresponded with Sutton Park

Nature Reserve- the largest green space area in Birmingham. The coolest LST temperatures found for

the 8-year mean maps are located on the outskirts of the study area. Applying the LCZ system to this

area reveals the locations to be classified as low plants to the south and West of the study area with

the north and north east having a combination of low plants and dense trees LCZ’s. These natural land

surface types may help explain why low LST are found for these regions (see section 2.5.1 for more

on this).

Therefore, it can be expressed that there is indeed a correlation between the LCZ and SUHI intensity.

Warm islands, where LST values are present, are characterized by dense urban land surfaces. Cool

islands, where LST values are lowest, are characterized by a non-urban LCZ classification. This

study’s results mirror the findings of other studies into the spatial patterns of warm and cool islands

(Cheval & Dumitrescu, 2008) (Cheng, Zhao, & Li, 2006) and (Chapman, Tomlinson, Thornes, &

Baker, 2012).

4.3 SUHI IntensityIt was discovered that the June SUHI was greatest during the DT. In light of this finding, the spatial

location of the warmest and coolest area in each year for June DT were examined, this is presented in

Figure 16. Without exception, the warmest areas are always found within Dublin city boundary,

whereas the location of the coldest pixel varies consistently between the South and East of the study

area, but always well beyond the urban area. It is possible a topographic effect is being detected here

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(compare Figure 16 with topography shown in Figure 12). The NT SUHI was far less intense which

reflects the evidence of various studies conducted into the SUHI phenomenon (Peng, et al., 2011)

(Chapman, Tomlinson, Thornes, & Baker, 2012) and (Cheval & Dumitrescu, 2008).

The January SUHI presents a different story. While the DT SUHI is indeed present, it is in fact, the

NT SUHI that is most intense. A mere 0.62°C separates the two intensities but nevertheless the NT

SUHI is greater. This is contrary to the findings of previous studies and may be occurring as a result

of not enough LST data samples for January.

Figure 16: The co-location of the warmest and coolest pixels, against which the SUHI is derived for 2007 (Top left) to 2014 (Bottom Right)The following results have been highlighted in this chapter:

(1) June was found to have the most intense SUHI’s. This was particularly evident in June 2009

when the DT SUHI reached 17.1°C.

(2) The June DT SUHI was found to be more intense than the night time SUHI. This was

particularly evident in June 2009 when the DT SUHI reached 17.1°C.

(3) No obvious similarities were found between the January DT and NT SUHI and the June DT

and NT SUHI.

(4) Indeed, a finding contrary to previous studies was found that suggested that the SUHI was

more intense at night in January – this may be as a result of not enough available samples to

determine an accurate SUHI measurement for January.

(5) The SUHI was found to be largely influenced by the underlying surface classified by LCZ.

(6) Warm and cool islands were found to exist within the overall SUHI.

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(7) Warm islands had the greatest LST values and were found in areas of dense urban design.

(8) Cool islands were found within the SUHI and in areas of non-urban LCZs on the outskirts of

the study area. They were found to contain temperatures far lower than the surrounding

SUHI.

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Chapter 5 – Discussion and Conclusions

5.1 Number of Data Acquisitions

The number of data acquisitions was severely limited by the MODIS sensors ability to penetrate cloud

cover. As mentioned in section 2.2, cloud cover prevents the MODIS sensor from obtaining accurate

LST data. As a result, many LST scenes were discarded as they were corrupted by partial or full cloud

cover and so were not accurate representations of the LST data. It was found that a larger proportion

of MODIS scenes were contaminated by cloud cover for the month of January than June data. This

would suggest that the poorer synoptic conditions often experienced in January (compared to June)

resulted in less clear, dry days and ultimately prevented successful MODIS scene collection for many

of the 8-years of data. While MODIS scenes were indeed recorded for January, they were far less

numerous than the quantity of scenes collected for June. This may have influenced the unusual

finding that indicated a warmer NT SUHI for January. Had an increased number of samples been

available a more accurate measurement of the January DT and NT SUHI may have been possible.

This could present far different findings from those found for January in this study

The synoptic conditions experienced in June are far more favourable for the capturing of LST data. As

a result, there were far more accurate LST scenes available for examination. The greater number of

LST data scenes available for June made the averaged LST values increasingly accurate as there was

a larger body of samples to work with. January data, on the other hand was far less abundant and so

the mean values may not be as accurate as if we had a larger body of scenes to draw upon. For some

years only 6-12 days of accurate LST data was obtainable for the entire month of January, while it

was not uncommon for 20 or more days of accurate LST data to be available for June. The question of

how future studies may improve data acquisitions, particularly for January, will be examined in the

next section.

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5.2 Limitations of the Study and Alternative Methods

As mentioned in section 5.1, data acquisition, particularly for January was largely influenced by

synoptic conditions. This resulted in a smaller sample of MODIS scenes. In order to increase the

accuracy of any study, a general rule of; the larger the sample the more accurate the results, applies.

Therefore, in order to increase data acquisition alternative methods should be explored. (Z., 2008) has

expressed the possibility to combine MODIS data collection with other modelling or microwave

remote sensing methods. By doing so, it may be possible to achieve high spatial LST data without the

effect of cloud cover limitations experienced by infrared sensors such as the MODIS sensors.

(Chapman, Tomlinson, Thornes, & Baker, 2012) have suggested comparing the MODIS dataset with

Landsat ETM+ data (higher spatial resolution but lower temporal resolution). By utilising these two

systems it may be possible to examine temperature changes at a finer scale. For this study, the pixel

sizes (as mentioned in section 2.2) are quite large at 1 km2. By having a finer scale, it will be possible

to detect the LST values for an area more accurately.

5.3 Comparison with Other Works

The findings of (Cheval & Dumitrescu, 2008) study into the SUHI of Bucharest correlated urban

fabric with SUHI intensity. They found that the magnitude of the SUHI was significantly larger when

the urban materials in that area where of dense construction. This was also found for the areas of most

intense SUHI temperatures in Dublin. Compact mid-rise and low-rise buildings created a LCZ that

created the conditions needed for intense SUHI temperatures. These were represented in the map as

warm islands.

Likewise, (Chapman, Tomlinson, Thornes, & Baker, 2012) found that the urban fabric had an

important role in producing cool islands within the overall SUHI. The Birmingham city SUHI study

correlated the presence of a cool island with the location of a large green park. This non-urban land

cover type will have a mitigating effect on the LST resulting in the LST being lower here than

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elsewhere in the SUHI. For Dublin, similar results are found. The Phoenix Park is a large non-urban

space found near the urban core of Dublin city. A significant cool island is found here that persists

throughout all the June and January LST maps. Therefore, in a similar fashion to the study conducted

in Birmingham, the cool island is formed as a direct result of the non-urban LCZ park land

Evidence from this study of Dublin city indicates that the SUHI is most intense overall during the

daytime. This is complimentary to the findings of (Peng, et al., 2011) who studied the SUHI of 419

large global cities and discovered that the daytime SUHI was stronger than the night-time the reasons

for which are explained in section 2.2. However it should be noted there is likely a topographic

element influencing the cold LST values. Future work could attempt to remove this effect by

examining the correlation between LST and elevation, and using this relationship to correct for

differences in elevation. Also in January NT mean lst seems to exhibit some coastal influences – these

would also need to be removed in order to truly gauge the urban effect on LST.

5.4 Conclusion

This project attempted to quantify the LST values of Dublin city in order to construct the

SUHI of the city and discover the magnitude of this heat island under the contrasting synoptic

conditions experienced for January and June under a wide range of weather conditions. It was

also sought to discover the contrast in SUHI magnitude between day and night. This study

used Land Surface Temperature (LST) data derived from the MODIS satellite and used the

ArcGIS mapping programme to process this data into visual maps of the SUHI and its

magnitude within the study area. This study found that LST values represent a SUHI that is

more intense during the day. The maximum magnitude of the SUHI was found to be greatest

during the month of June with a much smaller maximum magnitude found for January. Warm

and cold islands within the SUHI were identified and a correlation between the Local Climate

Zone (LCZ) and the warm/cool islands was shown to be in agreement with previous research.

Future work could focus on breaching the knowledge gap of the relationship between LST

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and air now that values are recorded for both factors. (Chapman, Tomlinson, Thornes, &

Baker, 2012) advise the use of an increased number of temperature sensors in order to

calculate the empirical relationship between the two factors. To conclude, I found that Dublin

city’s SUHI is most intense during the day and in June. Its magnitude is influenced largely by

the underlying surface or LCZ and the SUHI will be at its greatest magnitude during ideal

synoptic conditions of clear skies and dry days in June.

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