DYNAMICS OF LANDSCAPE PATTERNS AND THEIR IMPACTS ON URBAN THERMAL AND BIOMASS ENVIRONMENTS IN THE kUNMING METROPOLITAN AREA

CHAPTER ONE: INTRODUCTION 1.1 Land use change in the process of urbanization 1 CHAPTER TWO: LITERATURE REVIEW 2.1.1 Landscape ecology applied in the urban environment 8 2.1.2 Landscape

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ACKNOWLEDGEMENTS

First and foremost, I would like to express my earnest gratitude to my advisor, Dr Yi-Chen Wang for her patient guidance as I gradually got into my academic track, helpful instructions when I was bewildered, gentle criticisms and supportive encouragements when I did something wrong or right, and role model in terms of the commitment and enthusiasm in scientific research and scrutiny in reading and writing that will continually influence me in my future study and career It is her who taught me step by step from the basic academic writing, to critical thinking and conscientious working I am sincerely grateful for her supervision in the past short but meaningful two years

In addition, I would like to thank A/P Xixi Lu for his constant concerns and great help in my entire graduate studies Thanks also go to Dr Chen-Chieh Feng for his advices from GIS perspective and constructive suggestions in my study Also, I really appreciate A/P Alan Ziegler and Dr Jun Zhang and other lecturers in geography department for their suggestion and guiding

I have also received many helps from the administrative staff and technical staff, including Mr Lee Choon Yoong, Mr Yong Sock Ming and Ms Sakinah bte Yusof I am particularly grateful to Pauline for the timely and detailed answers for all administrative matters and joys we had together outside campus

Also, tremendous thanks go to all my friends whom I share my laughters, confusions and excitements with and I learned from, especially

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those lovely guys in the research clusters, Yu Liang, Yikang, Tzu-Yin, Yiqiong, Mr Huan and Valerie; the post-graduate fellows, Rana, Lina, Deborah, Jianjun, Swe Hlaing, Lishan, Serene, Fred, and Aidan; and my NUS friends, Quchen, Haigang, Yuqi and others

Most of all, I would like to thank my parents for their infinite support and encouragement Their continual help in every aspect is always accompanying me Thanks for being with me in my journey to the end of my master study!

Xiaolu Zhou

May 2010

CHAPTER ONE: INTRODUCTION

1.1 Land use change in the process of urbanization 1

CHAPTER TWO: LITERATURE REVIEW

2.1.1 Landscape ecology applied in the urban environment 8 2.1.2 Landscape metrics in sustainable development 9

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2.2.1 Impact of land use change on urban thermal environment 14 2.2.2 Measurements of the thermal environment 14 2.2.3 Relationship between LST and land use change 15

2.3.1 Importance to measure green biomass 18

CHAPTER THREE: THE STUDY AREA KUNMING, CHINA

CHAPTER FOUR: MATERIALS AND METHODS

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4.2.1 Preparations for planning maps and field survey data 32

4.2.4 Classification and accuracy assessment 35

4.4.2 Derivation of remote sensing indices 42

4.4.4 Modeling LST based on land use indicators 44

4.5.3 Linking vegetation indices with VD

4.5.4 VD change analysis in 2000, 2006 and 2009

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CHAPTER FIVE: LANDSCAPE PATTERN DYNAMICS IN

RESPONSE TO RAPID URBANIZATION AND GREENING POLICIES

5.1 Landscape analysis and change intensity analysis 50

5.2 Discussion of the green space dynamics analysis 61

5.2.1 Landscape change in response to urbanization and greening

5.2.3 Interpretation of landscape indices 63

CHAPTER SIX: THERMAL ENVIRONMENTAL IMPLICATIONS

OF THE LANDSCAPE PATTERN DYNAMICS

6.1 The change of the urban thermal environment 66 6.1.1 Impacts of landscape pattern change on LST 66 6.1.2 Modeling LST based on remote sensing indices 72 6.2 Discussion of the thermal environment analysis 77 6.2.1 Process of the thermal environmental change 77 6.2.2 Effects of green policies on thermal environment 78 6.2.3 Localized statistic in LST modeling 79

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CHAPTER SEVEN: GREEN BIOMASS IMPLICATIONS OF THE

LANDSCAPE PATTERN DYNAMICS

7.1 Green biomass dynamics of the study area 80 7.1.1 Comparison of vegetation density and vegetation cover 80

7.1.3 Estimated VD in 2000, 2006 and 2009 82 7.1.4 Distribution of the areas with VD variation 86 7.2 Discussion of the green biomass analysis 87 7.2.1 Comparison of vegetation indices in VD regression 87

CHAPTER EIGHT: DISCUSSION

8.1.1 Impacts of the processes on landscape patterns 90 8.1.2 Impacts of landscape patterns on processes 91 8.2 Relationship between thermal environment and biomass amount 92 8.3 Environmental planning strategies

CHAPTER NINE: CONCLUSION AND FUTURE WORK

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SUMMARY

Urban expansion is occurring at an unprecedented rate in most countries worldwide with no exception in China The conversion of natural land into impervious areas has resulted in many environmental consequences Having realized the important role of green space in urban ecosystems, many municipal governments in China have set out a series of policies to introduce green elements into urban areas Insights into how urban landscape pattern changes in response to urbanization and greening policies and to what extent land use transformation affects local environment are essential for guiding sustainable urban development

This study investigated urban landscape pattern change in response to the rapid urbanization and greening policies in the Kunming metropolitan areas, China Urban thermal environment and green biomass were investigated

in the context of landscape pattern change The concentric and directional landscape analyses along with landscape metrics were first used to characterize landscape patterns Change intensities of the landscape patterns were then calculated for the study area as a whole, the concentric belts, and the directional transects to examine the variation of the green space change rate in the city Next, the study used land surface temperature (LST), derived from remotely sensed images, to characterize the thermal environment of the study area and to associate the LST with the changing landscape patterns Global and local models were performed to explore the impacts of different land use types on LST variations Urban green biomass was represented by vegetation

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density (VD) to evaluate the urban green space conditions and wildlife habitats VD was derived from remote sensing indices and its spatial change was analyzed using Geographical Information Systems

Results revealed that both rapid urbanization and greening policies accounted for the process of landscape pattern change Among different green space types, agriculture land was largely encroached and fragmented by urban sprawl, especially in the outer belts of the city Forest land was also impacted but encountered a relatively moderate loss rate compared to agriculture land Conversely, greening policies contributed to the recovery of grass land in the last decade Land use transformation largely altered the local thermal environment and green biomass A remarkable LST increase was detected in the urban fringe when natural land cover was replaced by impervious surface Green space was confirmed to be important in mitigating urban heat As for green biomass, low vegetated areas encountered a substantial biomass loss, mainly due to the rapid shrinkage of the agriculture land Densely vegetated areas maintained a relatively stable biomass status, suggesting forest areas remained less impacted by human disturbances

This study unveiled the processes of landscape pattern change in the presence of two seemingly contradicting driving forces, i.e urbanization and greening policies, providing insights into the mechanisms of urban land use change and the subsequent environmental implications Based on the results, several planning strategies were put forward to ensure a sustainable urban development

Keywords: Green biomass; Kunming; Landscape pattern; Urban sustainable

development; Urban thermal environment;

indices in 1992 and 2006

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Table 6.4: Comparison of the diagnostics for OLS and GWR modeling 75

Table 6.5: Parameter coefficients of the multivariate models based on

OLS and GWR methods

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LIST OF FIGURES

Page Figure 1.1: Process of landscape pattern change 5

Figure 4.2: Elaboration of data used in this study 30 Figure 4.3: The processes of data preparation 32 Figure 4.4: Flow chart of the landscape pattern analysis 36 Figure 4.5: The layout of concentric belts and directional cells 39 Figure 4.6: Flow chart of the thermal environment analysis 40 Figure 4.7: Flow chart of the green biomass analysis 46 Figure 5.1: Land use of the study area in 1992, 2000 and 2009 51 Figure 5.2: Synoptic landscape pattern change quantified by landscape

Figure 6.2: Remote sensing indices of the study area in 1992 and 2006 67 Figure 6.3: Spatial correspondence between changes in land use and

LST

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Figure 7.1: Computation of VC and VD

Figure 7.2: VD spatial distribution in 2000, 2006 and 2009

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Figure 8.1: Relationship between landscape patterns and processes

reflected by this study

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ETM+ Enhanced Thematic Mapper Plus

FLAASH Fast Line-of-sight Atmospheric Analysis of Spectral

Hypercubes GDP Gross Domestic Product

GIS Geographical Information Systems

GPS Global Positioning Systems

GWR Geographically Weighted Regression

LAI Leave Area Index

LPI Largest Patch Index

LSI Landscape Shape Index

LST Land Surface Temperature

LUCC Land Use/Cover Change

MNDWI Modified Normalized Difference Water Index

NDBI Normalized Difference Built-up Index

NDVI Normalized Difference Vegetation Index

NDWI Normalized Difference Water Index

OLS Ordinary Least Square

PLAND Percentage of Landscape

SHDI Shannon’s Diversity Index

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SPOT Systeme Probatoire d’Observation dela Tarre

UHI Urban Heat Island

UTM Universal Transverse Mercator

VD Vegetation Density

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Chapter 1: Introduction

1.1 Land use change in the process of urbanization

In the past several decades, population has been growing worldwide The explosion of population brings about urban expansion occurring at an unprecedented rate worldwide with 65% of the population expected to reside

in urban areas by 2025 (Schell & Ulijaszek, 1999) The rate in some developing countries is more remarkable due to the pursuit of fast development (Li et al., 2009) In China, the launch of economic reforms in the late 1970s largely pushed forward the urbanization process in the past several decades (Luo & Wei, 2009) The “Open Door” Policy initiated in 1978 and the land reform regulation launched in 1987 markedly expedited the urbanization rate (Cheng and Masser, 2003; Luo and Wei, 2009) The urbanization level is predicted to reach 50% with 1.5 billion urban residents by the end of 2020 (Tian et al., 2005)

In the process of urbanization, the most remarkable land use change is the loss of green space The impervious land sprawls unrestrainedly, causing tremendous suburban green areas to be swallowed by the overwhelming urban growth For example, in China, large areas of arable land were encroached by urban expansion (Tan et al., 2005) Forest land shrank tremendously in the suburban areas (Li et al, 2006; Fan et al., 2007) Concomitant with green space loss in the suburbs is the reduction of the semi-natural green space within urban areas (Tan, 2006) Due to the intense competition for the limited

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urban land, green elements in the most urbanized area are substituted for developments, further aggravating the rigidness of cities

1.2 Implications of green space loss

The loss of urban green space gives rise to many environmental problems In the urban context, drastic reduction of green space tends to elongate green patch distance and decrease habitat size, which tremendously degrades the quality and lessens the quantity of habitats, causing a sharp decrease of wildlife (Bender et al., 2003) The removal of vegetated areas changes land surface properties, such as moisture and optical characteristics, largely modifies the urban thermal environment (Owen et al., 1998) The increasing emission of greenhouse gas in urban areas and the lowered photosynthesis process induce the heat island effect in cities and increase the potential risk of global warming (Nowak & Crane, 2002; Guan & Chen, 2003)

In addition, urban green space can purify air and contain water The reduction

of green space aggravates air pollution, lessens the recharging of groundwater and results in polluted surface runoff (Chen & Jim, 2008) The degradation of urban green space also significantly affects the energy flow and the nutrient cycling of the ecosystem, causing the degradation of the functions of a life-supporting environment and lead to unsustainable development (Grimm, 2000; Whitford et al., 2001; Yeh & Huang, 2009) Moreover, since urban green space provides citizens recreational opportunities (Fleischer & Tsur, 2003), renders aesthetic enjoyments (Chen & Jim, 2008), promotes physical health (Hartig, 2003) and adjusts psychological well being (Milligan et al.,

in Australia (Low et al, 2005) Despite the severe land use competition for developments of high economic yields, Singapore embarked upon the implementation of an island-wide network of greenways to link urban parks to natural areas This policy significantly ameliorated the urban environment, distinguishing Singapore as a famous garden city worldwide (Tan, 2006) Cities in the United States witnessed an upsurge in “greening”, such as green roofs, new parks and tree plantings (Daniels, 2008) In China, several greening policies were also implemented recently, such as the comprehensive green space planning strategies for promoting sustainable development in the Beijing metropolitan area (Li et al., 2005) Other examples included a greening plan based on landscape ecology in Nanjing (Jim & Chen, 2003) and

a greenway augment plan in Xiamen Island (Zhang & Wang, 2006)

1.4 Process of landscape pattern change

Understanding the process of landscape pattern change requires the examinations of the driving forces and the subsequent implications There are

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complicated interactions among different driving forces, such as climate change, socioeconomic development and municipal regulations, which jointly determine the direction of the landscape pattern change (Verburg et al., 2004)

In the urban context, urbanization and urban greening are two conflicting driving forces that simultaneously modify the green space pattern It is thus important to explore the change of urban landscape patterns under these two driving forces collectively

As mentioned in Section 1.2, the change of landscape patterns will give rise to many environmental consequences In the urban areas, two environmental implications are closely related to land use change, i.e urban thermal environment and green biomass, which were also used by prior studies

to evaluate the urban environment qualities (Nichol & Wong, 2005) In this study, these two implications were thus investigated under the rapid urbanization and greening policies Figure 1.1 illustrates the framework of this study

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Figure 1.1: Process of landscape pattern change Urbanization and greening policies serve as driving forces to modify landscape patterns The change of landscape patterns results in a variety of implications, such as thermal environment and green biomass dynamics

1.5 Scope and aims of this study

This study investigated the dynamics of landscape patterns and the associated environmental implications in a city with rapid urbanization, Kunming, China In Kunming City, rapid urban growth encroached much green space on the urban fringe and suburbs, while the intensive urban development took place in many semi-natural places within the urban areas in the past several decades To ameliorate the urban environment and to enhance citizens’ quality of life, the municipal government has implemented several urban greening policies since 2000

To examine the effects of driving forces on landscape pattern change and the subsequent environmental implications, this study consisted of three analyses The first part focused on the landscape dynamics in response to the rapid urbanization and greening policies Integrated approaches were used to

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characterize the changing patterns and intensities of green space change in Kunming Spatial variations of landscape patterns were derived through concentric and directional landscape analyses integrated with landscape metrics Change intensities were calculated for the study area as a whole, the concentric belts, and the directional transects to examine the variation of the green space change rate in the city

The second and the third analyses focused on the environmental implications of the changing landscape patterns The second part concerned with urban thermal environment change Integrated methods of Geographical Information Systems (GIS) and remote sensing were used to investigate the impact of land use change on the dynamics of land surface temperature (LST) Remote sensing indices were used to quantify land use types and also as explanatory variables in LST modeling

The third part explored the urban green biomass in Kunming City in the past decade Vegetation density (VD) was used as an indicator of green biomass VD, derived from remote sensing indices, reflected the vegetation condition and the relative habitat abundance in the study area Therefore, the aim of this study is to address the following research questions: (1) How does the landscape pattern change in response to the rapid urbanization process and the greening policies? (2) To what extent do landscape pattern changes influence the local thermal environment? (3) How does the green biomass vary in the changing landscape pattern?

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1 6 Outline of the thesis

Following this brief introduction on the general rationale of the research, Chapter Two reviews a series of prior studies conducted in each of the three aspects of this study Chapter Three describes the Kunming City in terms of the geography, climate, vegetation, economy and the urbanization process Chapter Four unveils the research methods employed in this study, comprising the concentric and directional landscape metric analysis and change intensity analysis, derivation of LST and its relationship with land use change, and the measurement of urban green biomass Chapter Five presents the results and discusses the dynamics of the urban green space pattern in response to the urbanization and greening policies Chapter Six displays and discusses findings of the thermal environment change due to landscape alterations Chapter Seven reveals the results of the amount of green biomass derived from VD and discusses its temporal change Chapter Eight discusses the relationship between landscape patterns and processes as well as provides suggestions for the potential environmental planning strategies for the study area Chapter Nine concludes this study by summarizing the major findings and illustrating possible directions for future research

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Chapter 2: Literature Review

2.1 Landscape pattern change analysis

Urban sprawl has caused tremendous land use alteration This trend raises many alerts and a number of studies have been carried out to investigate the changing landscape patterns Landscape ecology is a science which provides concepts and methods to study land use/cover change (LUCC) The following part reviews the state of the art research in landscape ecology and its applications

2.1.1 Landscape ecology applied in the urban environment

Landscape ecology is a science contributing to improve the understanding of the relationship between spatial patterns and ecological processes on different landscape scales and organizational levels (Forman & Godron, 1986; Wu, 2006) It has been widely used in investigating LUCC, inferring ecological flows and guiding sustainable planning (Wu & Hobbs, 2002) While landscape ecology emerged in Europe half a century ago (Naveh

& Lieberman 1984), the past two decades witnessed a rapid development due

to the growing awareness of the environmental problems (Wu & Hobbs, 2002)

Landscape ecology has been introduced into urban land use study for

at least three reasons First, since landscape ecology is capable to look simultaneously at a broad scale (the scale of the entire landscape) and a local scale (the scale of neighborhood), it is particularly useful in the urban

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environment to depict overall urban morphology and complicated heterogeneous patches (Low et al., 2005) Second, landscape ecology seeks to explain the fundamental landscape structure based on three spatial concepts, i.e matrix, patch and corridor, which fit the elements in the urban ecological context (Jim & Chen, 2003) Patches correspond to urban parks, grass land, and open space with different sizes and shapes, clustering, dispersion or isolation in urban areas Corridors can be reckoned as linearly arranged elements, such as riparian green belts, green avenues with different length, width and shapes Matrix is associated with uniform and dominant areas over space, such as urbanized areas, with different heterogeneous extents By depicting the urban environment using these basic structural elements, complicated urban landscape can be better understood (Dramstad et al., 1996) Third, Landscape ecology examines landscape composition and configuration which can provide insights into urban ecological process Concepts of landscape ecology can be incorporated into sustainable urban planning, maximizing the ecological functions of the limited green elements in the urban areas (Opdam et al., 2002; Leitão et al., 2006; Roy et al., 2009)

2.1.2 Landscape metrics in sustainable development

Investigating urban landscape space often requires researchers to first quantify the urban landscape composition and configuration so that a sound understanding of the distribution of urban landscape pattern can be attained (Forman & Godron, 1986) One of the milestones of landscape ecology was the extensive use of landscape metrics in spatial pattern analysis due to the increased availability of spatial data (Gustafson, 1998; Turner et al., 2001; Li

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& Wu, 2004) Most of the landscape metrics were originally derived from percolation theory, fractal theory and information theory (Cardille & Turner, 2002), devoted to investigating landscape characteristics, such as dominance, fragmentation, connectivity, diversity, and complexity (McGarigal et al., 2002)

Landscape metrics were widely used in natural landscape analysis and monitoring (Herzog et al., 2001) Landscape metrics have been used to guide conservation and restoration planning Metrics were proved to be an effective approach when performing conservation planning (Sundell-Turner & Rodewald, 2008) These metrics also can be used to model alternative planning and management scenarios and thereafter guide ecologically sound planning (Lasanta et al., 2006) Biodiversity can be reflected or monitored by landscape metrics (Bailey et al., 2007) To maintain biodiversity and monitor animal migration, animal movement was attempted to be modeled by different patch isolation metrics (Bender et al., 2003) To curb deforestation, forest pattern was also monitored and analyzed by landscape metrics (Frohn & Hao, 2006)

In addition, there has been a growing awareness of the usefulness of landscape metrics in metropolitan environmental planning (Jim & Chen, 2003;

Li et al., 2005), such as using them to optimize the benefit of green space in urban sustainable development (Uy & Nakagoshi, 2008) Landscape metrics can also provide foundations for assessing, evaluating and visualizing biodiversity in response to urbanization (Mörtberg et al., 2007) Moreover, landscape metrics were used to examine the landscape connectivity and green

on a variety of forms, selecting representative and understandable metrics shows great significance (McGarigal et al., 2002)

2.1.3 Landscape gradient analysis

Quantifying gradient using landscape metrics is an important breakthrough because it attempts to reflect the spatial landscape variations instead of describing only the overall patterns (McDonnell & Hahs, 2008) The most intensive urban development generally takes place in the city centre, while developments are less densely distributed in the urban fringe This variation cannot be revealed by the conventional synoptic metrics analysis The advent of “urban-rural” gradient largely unveils spatial heterogeneity, bringing insights into the directional landscape variations (Luck & Wu, 2002) The layout of spatial gradients commonly originates from the city centre and radiates to different orientations Prior studies used transects in different directions, such as two directions (Luck & Wu, 2002), four directions (Hahs & McDonnell, 2006; Yu & Ng, 2007; Yeh & Huang, 2009), and eight directions (Kong & Nakagoshi, 2006)

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The spatial patterns and variations obtained from the gradient analysis

in conjunction with landscape metrics, however, often depend on the positions and the orientations of transects (Hahs & McDonnell, 2006; Yeh & Huang, 2009) Linear gradient analysis examines patterns along a predefined direction which may be limited in capturing the spatial variation of land use patterns Urban morphology suggests that there are several urban development forms, such as linear, concentric, sector and multiple nuclei forms (Yu & Ng, 2007) For cities of which development patterns are other than linear form, linear gradient analysis may be limited in capturing the landscape patterns (Yeh & Huang, 2009) Scant research, however, examines the spatial variation of green space in cities whose developments conform to a concentric form, which

is also a common development model in many Chinese cities (Jim & Chen, 2003; Tian et al., 2010) This study thus proposes concentric and directional landscape analyses with landscape pattern metrics to characterize the changing land use patterns The proposed method compensates the deficiency of the linear transect method in capturing the landscape feature in different concentric belts

2.1.4 Change intensity analysis

Apart from understanding the spatial variation of land use patterns, insights into the rate of land use change is desirable because such information

is vital for land use monitoring and prediction (Xiao et al., 2006) The availability of multi-temporal remotely sensed data and their increased spatial resolution and coverage have enabled more extensive monitoring of land use change (Yang et al., 2003) Timely and accurate change detection of land use

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can build up concrete premise for a better understanding of land surface features and is also useful in quantifying the land use change intensity (Lu et al., 2004)

Change intensity is an important indicator to evaluate the urban land use dynamics (Yu & Ng, 2007) Areas which urbanize at a fast rate are mostly involved with an intensive loss of natural land and severe environmental degradations This indicator can be used directly as planning guide to monitor and control urban expansion In addition, change intensity is a critical factor for predicting land use conditions (Liu et al., 2003)

Many prior studies identified the transitions among different land use types as an overall change rate (Chen et al., 2005; Xiao et al., 2006; Yu & Ng, 2007) Localized change intensity analysis to reveal the spatially varied land use change intensities would therefore be desirable Therefore, this study calculates the change intensity of the study area as a whole, the concentric belts, and the directional transects to examine the local variation of land use change rate in the city

2.2 Urban thermal environment analysis

Incessant urbanization process causes a major alteration to the earth surface, with much of the natural land cover being replaced by impervious materials Both the increase of impervious surface and the loss of green space bring about a series of negative environmental impacts Of these impacts, urban heat island (UHI), a phenomenon whereby surface and air temperature

in the urban core is higher than that in the suburban areas (Voogt & Oke, 2003), is commonly associated with cities (Weng, 2001) This part reviews

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prior studies on the urban thermal environmental dynamics caused by the change of land use

2.2.1 Impact of land use change on urban thermal environment

Since each land use type has its unique thermal, moisture, and optical spectral properties, the change of land use affects local thermal environment (Oke, 1982) The expansion of impervious surface alters the heat capacity and radiative properties of land surface (Streutker, 2002) The removal of vegetated areas alters the surface energy balance, largely increasing the sensible heat flux at the cost of the latent heat flux (Owen et al., 1998) The evapotranspiration of vegetation is substantially reduced

The warming thermal environment may induce many negative influences, such as the rise of urban pollution and the modification of precipitation patterns (Yuan & Bauer, 2007) Intense heat in the urban environment will bring much discomfort, and in most severe cases even cause mortality (Chang et al., 2007; Johnson et al., 2009) Hence, there is a growing interest concerning the urban thermal environment and its driving forces (Nichol, 2005; Buyantuyev & Wu, 2010)

2.2.2 Measurements of the thermal environment

The thermal environment can be represented by atmospheric heat and surface heat (Yuan & Bauer, 2007) Atmospheric heat can be measured in the urban canopy layer, the layer extending from the earth surface upwards to approximately mean building height, and the urban boundary layer, the layer above the urban canopy layer, which is influenced by the earth surface (Voogt

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& Oke, 2003) Previously, studies of urban thermal environment are usually

based on the air temperature obtained from in-situ weather stations or automobile transects (Li et al., 2009) Although in-situ data accurately record

the local temperature, they suffer from some limitations They are normally costly and subject to governmental restrictions when conducting regional scale field studies (Owen et al., 1998) Furthermore, data collected from several meteorological stations are discrete points in a continuous space, which can hardly reflect the spatial temperature variation caused by different land use types

An alternative to measuring urban thermal environment is land surface temperature (LST) because it is able to modulate the air temperature of the layer immediately above the earth surface and is a major parameter that closely associates with surface radiation and energy exchange (Voogt & Oke, 1998; Weng, 2009) With the advent of thermal airborne sensors, satellite and aircraft platforms provide considerable opportunities to observe surface temperature with high spatial detail and temporal frequency (Jensen & Cowen, 1999) A variety of sensors can be used to retrieve thermal infrared data, such

as Landsat TM/ETM+, MODIS, ASTER and AVHRR, facilitating the monitoring and investigation of surface thermal properties (Weng, 2009)

2.2.3 Relationship between LST and land use change

Many studies have been conducted to investigate the LST pattern and its relationship to land use change (Chen et al., 2006; Xiao & Weng, 2007) Prior studies on this subject mainly used two approaches: comparison between

In order to quantify the relationship between LST and LUCC, some remote sensing indices have been used to model LST instead of using the categorical land use types (Dousset & Gourmelon, 2003) A series of remote sensing indices have been used to reflect the relationship between land use types and surface temperature Vegetation indices, such as the normalized difference vegetation index (NDVI), were used to validate the important role played by green space in mitigating UHI (Yuan & Bauer, 2007) Other indices, such as the normalized difference built-up index (NDBI) (Zha et al., 2003) and the normalized difference water index (NDWI) (Gao, 1996), can be also used

to represent water and urban areas quantitatively Prior studies have adopted these indices to model LST (Chen et al 2006), but few of them compared the modeling results among different stages of urbanization This study thus

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compares the LST in two different stages of urbanization, aiming at investigating the impacts of urban expansion on the local thermal environment

2.2.4 Spatial variation in LST modeling

Prior studies used different global models, such as linear, nonlinear, multivariate models to estimate LST based on different explanatory variables (Price, 1990; Weng et al., 2004; Chen et al., 2006) These global regressions assumed that relationship between LST and explanatory variables were spatially constant In other words, the estimated parameters remained as constants across space (Bagheri et al., 2009) However, in most cases, the relationship might vary spatially Since different land use types are not evenly distributed across the space and the impact intensity of land use on LST may differ from place to place, the conventional models may overlook the spatial variation in LST modeling

Localized modeling, on the other hand, allows the relationship between predictor and explanatory variables to alter over space, assessing the local influences on the dependent variable (Fotheringham et al., 2002) Localized modeling techniques have been used in spatial pattern modeling, such as the analysis of accessibility to facilities (Bagheri et al., 2009), driving forces of urban growth (Luo & Wei, 2009), and population pattern estimation (Luo & Wei, 2006) However, few studies documented the application of localized statistical models in analyzing spatially varied impacts of land use on LST patterns In this study, a localized modeling based on geographically weighted regression (GWR) will thus be applied to reveal the spatial variations

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2.3 Green biomass analysis

Another implication which comes along with the landscape pattern change is associated with the biophysical environment (Brovkin et al., 1999) Under the pressure of population growth and incessant urbanization, many anthropogenic activities have resulted in high green space loss rates worldwide

It was estimated that 30% to 50% of land surface has been transformed by human actions and a large portion of them is associated with deforestation (Brovkin et al., 2004) Biophysical impacts from urban expansion include the disorder in the interaction between the biosphere and atmosphere (Anaya et al., 2009) and the crisis in biodiversity conservation (Marsh & Grossa, 1996) It is imperative to investigate the biophysical impacts of rapid urbanization Aboveground green biomass is an important indicator for evaluating ecosystem functions and understanding carbon equilibrium, which has been used to represent biophysical characteristics (Li et al., 2009)

2.3.1 Importance to measure green biomass

Green space is the primary source of carbon conservation Green plants absorb solar radiation to manufacture organic subsistence which is the fundamental premise of vegetation Although less than 1% of the solar energy

is utilized in this process, it is essential to the entire system of life on the earth (Marsh & Grossa, 1996) Among all the green space, forest conserves more carbon than other terrestrial ecosystems, accounting for 90% of the annual carbon flux between the atmosphere and the earth surface (Winjum et al., 1993) The stored carbon in the forests emits to atmosphere through

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deforestation and forest fires, and this carbon emission is one of the most important sources of global warming (Lu, 2006) To monitor the forest carbon storage, particularly in support of a sustainable forest management and carbon accounting, biomass estimation and mapping become critical (Labrecque et al., 2006) Estimation of green biomass is an effective way to assess forest ecosystem productivity (Scott et al., 2010) Delineation of green biomass distribution helps in reducing the uncertainty of carbon sequestration and understanding its role in environmental processes and sustainability (Foody, 2003) Mapping temporal biomass change can detect areas where forests are endangered and areas where afforestation are needed

Another negative impact of the biomass loss is associated with the reduction in biodiversity Habitat loss has been regarded as one of the most important factors in the global biodiversity crisis (Sala et al., 2000) and as a significant predicator of the amount of the threatened species in certain hotspots (Brooks et al., 2002) Land use change often makes the green space become more fragmented, separating species in isolated patches This trend further impacts the local biodiversity, because species with small populations are prone to extinct since the relatively few individuals are more vulnerable to predators, and the population may be under the critical threshold for breeding (Marsh & Grossa, 1996)

More species tend to be found in habitats with dense vegetation cover where there may be fewer human disturbances (Leitão et al., 2006) Therefore, green space with different VD might have varied capacities in terms of biodiversity conservation Indeed, VD has been used to represent biomass (Nichol & Wong, 2005), an important indicator of biodiversity

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2.3.2 Approaches to measure biomass

Three approaches are commonly used to measure biomass (Lu, 2006) First, conventional techniques based on field measurement are generally the most accurate approach to derive green biomass (Brown, 2002; Lu, 2006) To derive green biomass, field measurements or spatial inventory data are combined with allometric equations to develop a function to estimate tree height and above ground biomass (Parresol, 1999; Fournier et al., 2003; Labrecque et al., 2006) The number of field sites and measurements must attain to a sufficient amount to achieve a high accuracy To meet this requirement, especially at a regional scale, it is normally time consuming and labor intensive Some remote places are even difficult to measure (Labrecque

et al., 2006; Lu, 2006) In addition, in-situ biomass measurement carried out at

a certain time may become useless if the landscape undergoes a major change (Hall et al., 2006)

The second approach is GIS based green biomass estimation Spatial interpolation and extrapolation are performed based on the known biomass samples and vegetation types to model the biomass in the entire study area (Houghton et al., 2001) Different geostatistical methods, such as Kriging, are widely used to model the green biomass (Sales et al., 2007) However, the accuracy of the interpolation method depends on the acquired data quality and

it will become less effective where green space distribution is largely heterogeneous GIS based biomass estimation also employs some ancillary data (e.g elevation, soil, slope and precipitation) combined with sampled green biomass to estimate regional green biomass (Brown & Gaston, 1995)

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This approach has its own limitation as well because high quality ancillary data are required and more importantly, the indirect relationship between biomass and the ancillary data weakens the reliability of the estimated results (Lu, 2006)

The third approach is remote sensing based biomass estimation Several characteristics, such as the multi-temporal imageries, fine resolution and high correlations with vegetation parameters, make remote sensing the best way to estimate green biomass at a regional scale when field data are relatively scarce (Anaya et al., 2009) It provides efficient and timely estimation of green biomass based on repetitive, comprehensive observations

at different scales (Patenaude et al, 2005; Scott et al., 2010) Remote sensing based regional biomass estimation have been used in, for example, India (Roy

& Ravan, 1996), Malaysia (Phua & Saito, 2003), and Wisconsin, USA (Zheng

et al., 2004) In addition, a variety of optical sensors with different spatial resolutions have been used (Lu, 2006), such as the fine spatial resolution IKONOS and Quick Bird images (Lévesque & King, 2003), the medium spatial resolution TM/ETM+ images (Labrecque et al., 2006), and the coarse resolution AVHRR (Barbosa et al., 1999)

For remote sensing based biomass estimation, three pathways are generally used to derive green biomass The first is based on the direct correlations between different spectral values and biomass (Asner & Heidebrecht, 2002) The spectral values can be satellite radiance, reflectance and vegetation indices (Labrecque et al., 2006) Other methods such as spectral mixture analysis and canopy reflectance are also employed to estimate green biomass (Asner et al., 2003)

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The second pathway is using biomass conversion tables to link with land use classification, and then calculate the biomass for each land use type (Labrecque et al., 2006) The level of accuracy depends on how detail the land use classification and the corresponding conversion tables are

The third pathway is to use other indicators to represent green biomass, such as canopy texture index and VD Canopy texture index was proposed by Couteron et al (2005) as an alternative to measure physical attributes of tree crowns In some cases relative abundance of greenness may suffice, and the accurate biomass value is not required This is particularly true in the urban areas In this context, the absolute biomass value is represented by some other indicators with explicit meanings VD is one indicator which is devised to coarsely represent the amount of urban green biomass and explicitly describe the complicated vegetation structures (Nichol & Lee, 2005) Due to its simplicity and representativeness, VD is calculated to represent urban green biomass and model urban environmental quality (Nichol & Wong, 2005) However, only a few studies used this indicator to reveal the temporal differences in urban green biomass Thus, this study examines the biomass dynamics in response to urbanization and greening policies using VD, which

is an appropriate substitute of absolute biomass value for urban vegetation measurement

2.4 Urban expansion in Chinese cities

A number of studies have been conducted in Chinese cities to examine the process of land use change in recent decades Cities like Hangzhou,

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Jiaxing and Huzhou exhibited a remarkable urban expansion from 1994 to

2003 (Su et al., 2010) Driven by economic growth and population increase, urbanization was accelerated, resulting in the degradation, isolation and fragmentation of agricultural land in many parts of China (Su et al., 2010) For example, the built-up area in Guangzhou City expanded 325.5 km2 from 1979

to 2002, replacing much of the vegetation areas (Ma & Xu, 2010) In addition, many coastal cities in the eastern China face the decline and destruction of farmland and wetland For example, wetland in Lianyungang was found to shrink 54.4 km2 from 2000 to 2006 (Li et al., 2010) Cities in central China also face huge challenges of urban expansion The encroachment of green space and arable land in Wuhan city from 1993 to 2000 is documented (Cheng

& Masser, 2003) Apart from these, 13 Chinese mega cities were reported to

be experiencing rapid urbanization, largely driven by demographical change, economic growth, and land use regulations (Liu et al., 2005) Almost all the urban expansion in those mega cities accompanies a reduction of natural space The process of urbanization has posed many problems to the landscape and affected ecosystem functioning of the cities and their surrounding areas (Xian et al., 2007) Finding certain countermeasures to curb the rate of urban expansion and evaluating the influence of those countermeasures are of great significance Additionally, most case studies are conducted in economically developed cities, such as Beijing, Shanghai, Guangzhou, and Hangzhou, while cities in southwestern China are somewhat neglected (Cheng & Masser, 2003) Therefore, this study uses Kunming City in the southwest China as an empirical case to investigate the process of urban expansion Moreover, this study attempts to evaluate the effect of greening policies as a countermeasure

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to urban expansion in Kunming City The following chapter will detail the study area and describe the greening policies

Figure 3.1: Location of the study area (a) Yunnan Province in China; (b) Kunming City in Yunnan Province; (c) Kunming metropolitan area with newly constructed urban parks and ecological wedges labeled