miles knight - bachelor thesis - environmental impact assessment

Geography Thesis (F8038) May 2015

Mapping and understanding urban transformation in Wuhan, China, since 2000

Candidate number: 106443

Wuhan Greenland Center, construction started: 2010, proposed completion: 2017

(http://www.constructionweekonline.com/article-22865-chinas-top-10-tallest-towers-in-the-making/9/)


Abstract

Urban transformation is happening at unprecedented speeds across the globe, with China being at the forefront of this intensification. This particularly applies to the central region of the country which is experiencing a surge in urban transformation. Despite this, since the turn of the millennium there has been very little research examining urban transformation and investigating its drivers at city based levels in the central region of China.

This thesis maps and examines the spatio-temporal changes of urban transformation in Wuhan, Hubei province [central China], using Landsat satellite imagery from 2001, 2007, and 2013. It then examines these changes in greater detail using georeferenced Google Earth Imagery whilst investigating the drivers behind the process of urban transformation through temporal analysis of government policy and socio-economic data from the year 2000 till present.

The results show that urban land has increased in size by 15.57% between 2001 and 2013 and bare earth landscapes (an indicator of construction sites) by 11.73%. These increases have been at the expense of decreases in wetlands by 17.9%, Agricultural land by 10.27%, and water bodies by 2.6%. In parallel to these changes the introduction of the 2004 Rise of Central China Plan has enhanced the key drivers accelerating urban transformation in Wuhan. The population has risen by 9.7%, fixed asset investment by ¥55 million, and foreign direct investment by $1750 million (Huang & Wei, 2014).

Acknowledgements

I would like to thank the following people for sparing their time to help me produce this project.

Dr Daniel Haberly – Lecturer in Human Geography

Thesis supervisor and mentor for this project, Dr Haberly helped to inspire many of the ideas for this project and provided his critical understanding and knowledge of Wuhan and China.

Dr Alexander Antonarakis – Lecturer in Global Change and Ecology

Remote Sensing tutor and advisor for this project, Dr Antonarakis taught me how to use ENVI for land classification and provided vital advice on how to make optimal use of Landsat data.

Prof Mick Dunford – Emeritus Professor of Economic Geography

Advisor in finding official Chinese statistics, Prof Mick Dunford helped me to find free access to the NBSC statistics.

Mr David Guest – Senior, Information Delivery Manager

Assisted in obtaining Chinese statistics from conventionally inaccessible Chinese websites; Mr Guest taught me how to use emulators on internet explorer so that I could access out dated web pages used by the NBSC. This ultimately allowed me to download the required NBSC statistics.


Contents

1. Abbreviations – 1

2. Introduction – 1

2.1 Research Questions - 3

2.2 Objectives - 3

3. Literature Review – 4

4. Methodology – 9

4.1 Study area – 9

4.2 Landsat Image Processing – 10

4.3 Land Cover Characterization - 13

4.4 Socio-economic Data Analysis – 13

5. Results: Maps – 14

6. Results: Tables – 17

7. Analysis and Discussion – 18

7.1 Research Question 1 – 18



8. Research Limitations -34

9. Conclusion – 36

10. Reference List - 38


1

1. Abbreviations

FDI – Foreign Direct Investment

NBSC – National Bureau of Statistics of China

RS – Remote Sensing

UT – Urban Transformation (the process of urbanization and urban renewal)

WUA – Wuhan Urban Agglomeration (The 1+8 zone, Wuhan (1) with (8) supporting and surrounding

cities)

WEHDZ – Wuhan East Lake High-Tech Zone (Est. 1988; referred to as ‘Optics Valley of China’, optical,

information, biology and telecommunications zone)

WEDZ - Wuhan Economic and Technological Development Zone (Est. 1991; the automotive industrial

zone)

Wujiashan ETDZ - Wujiashan Economic and Technological Development Zone (Est. 2010; base for

high technology electromechanical products, production of biotechnological food, import and export

logistics and trade centre)

2. Introduction

Urban transformation for the purpose of this thesis is the combination of urbanization and urban

renewal. Urbanization is the process of transforming natural landscapes into man-made impervious

surfaces composed of cement, asphalt, metals or chemical materials (Carlson, Dodd, Benjamin, &

Cooper, 1981; Owen, Carlson, & Gillies,1998). Urban renewal is the process of reshaping urban

landscapes that often have problematic socio-economic issues, through demolition of run-down

areas for new construction projects, or gentrification (Gregory et al., 2009).

Urban transformation has been illustrated as a “double edged sword” with irreversible

environmental impacts (Wang et al., 2012:2802) (Wenhui, 2012). Positively it has the capacity to

generate considerable socio-economic, technological and logistical benefits for society, nonetheless

it brings with it an array of negative impacts. Environmental pollution, food security, overcrowding,

traffic jams, acceleration in the spread of diseases, increases of surface runoff and radiation

reflection, and the placement of huge stress upon natural resources and surrounding ecological


2

systems (Wang et al., 2012) (Niebergall, Loew, & Mauser, 2007) (Schneider & Mertes, 2014).

Currently over half of the world’s building materials are consumed for construction in China, a trend

that is set to continue until 2030 (Wang et al., 2012). In addition 70% of anthropogenic Carbon

Dioxide emissions and 70% of global energy consumption originate from urban areas, generating

enormous strain upon environmental systems whilst also contributing to climate change (Pandey,

Joshi, & Seto, 2013). The list of negative impacts posed by UT is seemingly limitless

1978 was the year China introduced market-oriented economic liberalization reforms. From

then onwards the country has undergone a rapid transformation spearheaded by unprecedented

levels of urbanization which has been fed by a GDP growth of almost 10% per annum up until 2010,

combined with a high rate or rural to urban migration (Yao et al. 2014) (Tan el al., 2014). Today

China is still following a course of developmental reforms; urbanization is therefore set to continue

well into the future globally, but in China in particular the scale of this change will be the most

significant. Presently, 758 million Chinese live in urban areas, 19.5% of the total global urban

population making it the largest. This is expected to grow by a further 292 million people by 2030

(Quan et al., 2013) (United Nation, 2014). Whilst cities are doubling in population across the country,

they are tripling in physical size at the expense of the natural and agricultural landscapes (Schneider

& Mertes, 2014). Vast population growth will without doubt continue to enlarge the demand for

urban land; increasing urban transformation whilst enhancing the negative and positive impacts of

this process. Therefore the topic of urban transformation is pressing, it is imperative to monitor the

spatio-temporal changes of this process and examine the complex range of drivers behind it (Tan el

al., 2014).

Wuhan, Hubei province, China is a city that requires particular attention by researchers. Not

only does it host a burgeoning population of 8.2 million people, but under the national government’s

Rise of Central China Plan of 2004, Wuhan is planned to become a world leading megacity and

economic powerhouse for central China. Since the national reforms of 1978, Wuhan has embarked

upon a shift away from being an under-developed industrial city into becoming a regional catalyst

for growth. However despite this activity, very little research has monitored or analysed Wuhan’s

vast urban transformation (NBSC, 2014) (Lu et al., 2014) (Huang & Wei, 2014). The result of this UT

will drastically impact the lives of residents across the whole Wuhan Urban Agglomeration and

Central China.


3

2.1 Research Questions

1. How fast and large has Wuhan’s urban land grown spatially since 2000 and at what expense?

2. Where is urban transformation happening around Wuhan; how is the city evolving?

3. What are the driving factors behind urban transformation across Wuhan?

2.2 Objectives

The first objective is to map spatio-temporal changes in land classifications for Wuhan city between

2001 and 2013. This will be carried out using ENVI to process Landsat 7 and 8 imagery. The results

will be a set of medium resolution land classification maps with supporting quantitative data on each

land classifications size and any changes to it. This remotely sensed data will be used to answer

questions one and two.

The next objective will be to characterize the urban transformation that is happening around

Wuhan, to find out where UT is happening in the city, what is being built upon, and for what

purpose. To answer question two and characterize UT in the process, remotely sensed data will be

uniquely combined with georeferenced Google Earth historical imagery and secondary sources of

academic research on Wuhan.

The final objective is to investigate and determine the critical social, economic and political

driving factors behind Wuhan’s urban transformation. To determine the answer to question three,

Chinese national statistics will be analysed alongside UT data on a temporal scale to find any

parallels between the sets of data. In conjunction with this, national and provincial economic and

development policy will also be reviewed to complete this analysis.

The ultimate aim of this research is to provide a detailed profile of urban transformation and

its drivers in Wuhan where there is little previous research. This could provide a platform for policy

makers or developers to review the rapid changes that are taking place and assess the sustainability

of the current situation. It also aims to take a renewed approach towards urbanization studies using

a unique combination of remote sensing for city wide analysis of urban land and construction sites,

with a deeper neighbourhood/zonal analysis of UT using Google Earth Imagery.


4

3. Literature Review

Urbanisation is a pressing topic in China, one that has been examined by a great number of

researchers in the coastal region since the market-oriented reforms of 1978 using a broad range of

methods and approaches to document and analyse the issue. Up until the 1990’s research has

shown how China’s central government actively worked to control the UT of large and medium cities

in order to focus on development of towns and rural settlements (Quan, 1991) (Lin, 2002). City

growth in China was also being limited by some of the remaining economic and political structures

still in place from the Maoist period along with continued reliance on old heavy industry (Hsu, 1996)

(Lin, 2002).

Since the 1990’s China has been singled out by the United Nations as one of the biggest

contributors to world urbanisation. China alone is expected to shift 300 million rural residents into

urban areas by 2050, adding in the process one more mega city to its six existing ones, and another

six large to its current ten (United Nations, 2014). This is seen as the final shift in the urbanisation of

the world alongside India and Africa. Urbanisation is a topic which is agreed within the discipline of

urban studies to be a threat to China’s own future. The neglect of formulating a sustainable urban

UT policy endangers the future of China’s developmental process, even if it is the world’s second

largest economic powerhouse (Atkinson & Thielen, 2008).

Detailed urban studies outline the multifactorial effects of urbanisation both upon public

health and the environment - for example air pollution, disease, depression amongst migrant rural

workers, rationing of water supplies due to drought, increasing strain upon municipal supplies and

declining water quality (Kamal-Chaoui et al., 2009) (Gong et al. 2012). Air pollution alone is linked to

400,000 premature deaths a year across China in part caused by urban motor vehicle and industrial

pollution (Gong et al. 2012). Pollution of air and also water is starting to spread into rural areas

through environmental transport; increasing habitat loss and soil erosion whilst leaking into the

atmosphere, hydrosphere and pedoshpere (Gong et al. 2012) (Huiyi et al. 2004) (Xinhu et al., 2012).

The effects UT on energy consumption have also been well researched with studies showing

that the 1% annual rise in urbanization since 1978 has led to a significant overall increase in energy

consumption. Production and industrial energy consumption grew by 16.21% between 2001 and

2005 whilst it grew to 9% between 2002 and 2011 for residential energy consumption. This is a

major cause for concern that requires the Chinese to re-evaluate their impact upon natural resource

consumption and ultimately climate change through anthropogenic urban activities (Wang, 2014).


5

It must be remembered despite these negative impacts that studies show urban transformation

does have strong positive developmental implications for the vast majority of poor working class

Chinese. Urban villages are providing affordable housing for millions of rural migrant workers who

need the vital access these villages provide to urban industries. They form the critical foundations of

all cities that are aspiring to develop across China; providing low cost labour to feed economic

growth and in turn, development (Chen, 2012) (Wang, Wang & Wu, 2009).

The extent and impacts of UT in China have been researched and documented widely since

the reforms of 1978, as expressed in the previous selection of examples which help to contextualise

the topic of urban transformation as one that requires urgent attention. The following studies

analyse urbanisation in closer relation to this thesis’ research question through investigating spatio-

temporal urban land use change to determine its size, speed and drivers across China. Once again a

range of methods and approaches have been used across the subject of urban studies from GIS

analysis to structural equation analysis, however remote sensing is the overwhelming tool of choice

due to its powerful ability to process and analyse landscapes systematically on vast regional scales

(Schneider & Mertes, 2014) (Hadjimitsis, 2010).

Schneider & Mertes study of 2014 gives one of the most comprehensive and up to date

assessment of urbanisation across China. They compare the trends in urbanisation and population

growth of 142 cities and 17 agglomerations between 1978-2010 using Landsat imagery combined

with NBSC statistics. Their results have shown how cities of all sizes have on average tripled in size

whilst their populations have doubled, with urban agglomerations such as WUA showing the largest

consumption of natural land (Schneider & Mertes, 2014). Schneider & Mertes study is unique in that

it surveys spatio-temporal land use change and its drivers across multiple cities, when the majority

focus on a single city.

This thesis also falls under the category of single city research, where it differs from the

general trend of research in this area in its focus on central China [Wuhan specifically]. More than

85% of the over 150 research papers focusing on mapping Chinese cities to date investigate coastal

regions. They do not provide analysis of the rapid changes in Central China that have been occurring

since turn of the millennium, meaning there is a research gap which this thesis aims to contribute

towards filling (Schneider & Mertes, 2014).

Schneider & Mertes study highlights how the most significant regional growth in

urbanisation has been seen in coastal cities such Shanghai which has witnessed satellite cities [E.g.

Hangzhou] grow at an average rate of 16% annually (Schneider & Mertes, 2014). Results like these


6

generally share the same conclusion across the spectrum of RS urban studies. (Wang et al. 2012) for

example similarly analysed multiple cities using RS between 1990 and 2010 to conclude that

croplands were the main land classification being converted to urban landscapes. The speed at

which each city has been expanding over the 20 year period varies greatly; Jinjiang is one of 9 cities

that have expanded twentyfold, 18 more times than the national average (Schneider & Mertes,

2014).

GIS is another powerful tool that has been combined to affect with many studies to illustrate

the extent of urbanisation across China. It is being used to build a long-term urban information

system by combining RS data with socio-economic data to produce large scale urbanisation maps for

the whole of China. This combination also allows for the examination of spatial data sets on a more

acute scale (Chen et al. 2000) (Quan et al. 2013) (Schneider & Mertes, 2014) (Wenhui, 2012) (Yansui

et al. 2008) (Yu et al. 2011). GIS has been used in one case to overlay RS land use maps to

ingeniously calculate how much agricultural land is being lost to construction land. Between 1996

and 2005 results show that 34.03% of agricultural lands in eastern coastal China had been

encroached upon by construction sites. Much of this change was attributed to political incentives to

attract FDI which has created a surge in industrial developments followed by an ensuing demand for

new residential developments (Yansui et al. 2008). Currently there is little literature exploring

patterns of land use change and construction site growth despite the fact that construction sites

could serve as a proxy for measuring future UT. This gap in the literature has inspired the demand

for this thesis to explore the patterns of land use change and construction sites through RS detection

of bare earth land cover. Bare earth sites are more often than not areas of land that have been

cleared for construction, they have the potential to sketch the future boundaries of cities and

provide a new insight on how cities are developing. No research to date explores this issue in

Wuhan, this thesis will.

To calibrate the remotely sensed imagery used in many studies, Google Earth has been used

to train sites for improved accuracy (Quan et al. 2013) (Schneider & Mertes, 2014) (Wang et al.

2012). This thesis however intends to utilise Google Earth as a tool for spatio-temporal land use

change analysis on a finer level. Historical RS imagery from Google Earth will be used to highlight and

compare specific areas of land use change (particularly sites of bare earth) on local scales around

Wuhan. This is so that specific case studies of UT around Wuhan can be visualised and compared

using georeferenced RS imagery combined with photography to ground truth each case study.

Research has previously analysed spatial structural changes to the interior of cities such as Beijing.

(Wenhui, 2012) is the closest piece of research to this thesis in that it sets itself apart from most


7

other literature by examining UT on a district level. It dissects UT itself to map not only what land is

being expanded or renewed, but also how this land is currently being used, for example industrial,

residential or vacant land. The significance of this is that that the spatial evolution of a city such as

Beijing can be mapped, and new patterns of UT can be documented or even modelled. Sustainable

urban development policies can in turn be prepared to target specific neighbourhoods based upon

their individual land uses (Wenhui, 2012). This is yet to be carried out for Wuhan making the

objectives of this thesis unique.

As revealed in (Schneider & Mertes, 2014) and (Wang et al. 2012) there are an abundance of

papers that examine spatio-temporal urban land use change across China. Despite this there is a

clear lack of studies that examine this issue in Central China and in particular Wuhan, at a time when

the city is expected to experience a revolutionary stage urban transformation in its history. The

following studies are a selection of the few that have analysed UT and development in Wuhan.

(Lu et al. 2014) shares the most similarities with aims of this thesis in that it investigates

patterns of spatio-temporal land use change around the Wuhan Urban Agglomeration between 1980

and 2010 using Landsat imagery. The results of their study state that urban land had increased by

574.93km2 between 2000 and 2010 over a similar period that this thesis intends to record. Socio-

economic data from both the NBSC and the Hubei Statistical Yearbook were also incorporated into

the study to search for the drivers behind urbanisation in the WUA. A multitude of factors were

noted including explosive population growth, rises in FDI and fixed asset investment, and

implementation of governmental policy to join Wuhan city with its 8 surrounding other cities to

create the WUA. This policy rapidly increased the financial support being received for the whole

Hubei region (Lu et al. 2014). The core difference between their study and this thesis is the scale at

which the research is carried out; rather than surveying the whole WUA this thesis focuses upon

Wuhan city itself and processes of UT within Wuhan city alone for a more intricate examination.

(Tan et al. 2014) likewise produced similar results stating that urban land has grown at an

annual rate of 46.75% between 1988 and 2011 across the WUA, with much of this growth being

attributed to the same drivers. However this research incorporated spatial regression for a more

advanced analysis of urban transformations spatial determinants. Results from this test stated that

the construction of road networks had a substantial effect upon the size, density and shape of UT

whereas railroads and highways had no noticeable effects. This theme of spatial radiation has

however been referred to before in the context of Wuhan. (Heiduck & Pohl, 2001) notes how the

establishment of two national economic and technological development zones in Wuhan [with a

third since their article was published] has led to the diffusion of UT outside of the development


8

zones borders as new businesses attempt to set up near the special trade areas and take advantage

of the economic leverage they have to offer. A strong recurring theme throughout most of the

papers based on Wuhan is the increase in FDI attraction due to the creation of economic

development zones and the Rise of Central China Plan. Another is how FDI is one of urban

transformations principal drivers in the WUA (Huang & Wei, 2014) (Heiduck & Pohl, 2001)

(Miaolong, 1998) (Tan et al. 2014). Detailed maps have been created using GIS to georeference

locations of FDI across Wuhan city, these can aid the investigation of this thesis combined with RS to

locate and examine spots of FDI around the city then determine using Google Earth what types of UT

developments are happening [e.g. construction projects or newly built buildings]. This will help to

understand how Wuhan’s urban environment is evolving and to what effect.


9

4. Methodology

4.1 Study Area

Wuhan provincial capital lies to the east of Hubei province, it is the largest and most densely

populated city in central China (Mialong, 1998) (Han & Wu, 2004). Wuhan has now been combined

by the provincial government with eight other surrounding cities [Huangshi, Ezhou, Xiaogan,

Huanggang, Xianning, Xiantao, Qianjiang, and Tianmen] to form the Wuhan Urban Agglomeration.

However, for the purpose of this thesis, only Wuhan city will be under investigation [fig. 1]. The

location of the city is of paramount importance for connecting the entire country together. It is

conveniently placed within 1200km of the countries six other urban agglomerations Beijing, Tianjin,

Shanghai, Guangzhou, Xi'an, and Chongqing. Furthermore it is situated along the middle of the

Yangtze River linking Chongqing to Shanghai, whilst the Jingguang railway connecting Beijing in the

north to Guangzhou in the South intersects the city (Tan et al. 2014). Wuhan also has an

international airport and two ports, increasing its merit as a transportation hub.

[Figure 1: Map of Wuhan’s three main territorial divisions and development zones (Huang & Wei,

2014)]


10

Due to the cities strategic location Wuhan has enjoyed a “glorious” past and has now

become the largest rail and road transportation hub in China; presenting it with a favourable future

under the Rise of Central China plan (Han & Wu, 2004; 1) (Xiong & Liu, 2013). It has become the

economic, industrial, logistical, transportation and informatics centre of the WUA and central China

(Huang & Wei, 2014) (Xiong & Liu, 2013) (Tan et al. 2014). In conjunction with this Wuhan still serves

as one of the prime agricultural production and processing bases for the country, providing grain as

its main product. The climatic profile of the region drives this; subtropical monsoons, high humidity,

satisfactory levels of sunshine, ample rainfall and nutrient rich soils (Lu et al. 2014).

4.2 Landsat Image Processing

In this thesis remote sensing was the tool of choice to run a spatio-temporal analysis of Wuhan’s

urban transformation. Landsat ETM+/OLI images were collected from the United States Geological

survey [USGS] in the summer season; July 2001 [ETM+], August 2007 [ETM+ SCL-off], and August

2013 [OLI] (UGSG, 2015). All images have a spatial resolution of 15-30m and are projected using the

Universal Transverse Mercator. Following this the scenes were processed in ENVI and accuracy

tested using Google Earth to produce a time series of land classification maps for Wuhan.

NASA operated Landsat 7 [ETM+/SLC-off] and Landsat 8 [OLI] satellites were chosen for

selection for the following critical reasons. Firstly access to their data is free via the USGS, this is

their foremost advantage because the majority of other satellites remain under commercial control

where data access is expensive. This alone has helped to make Landsat the most widely used

Satellite family.

Secondly Landsat 7’s enhanced thematic mapper sensor - ETM+ has a favourable spatial

resolution of 30m whilst Landsat 8’s Operational Land Manager sensor –OLI has a markedly

improved spatial resolution of 15-30m (Satellite Imaging Corporation, 2014). For the purpose of this

thesis they provide sufficient levels of detail to delineate urban landscape from natural landscapes,

the OLI sensor of Landsat 8 has particularly high detail as it sits at the top end of the low resolution

sensor range (Bhatta, 2013). There are ‘extremely high resolution’ sensors which reach as low as

0.34m in with the GeoEye-2 Satellite. GeoEye-2 and other ‘extremely high resolution’ sensors

provide much higher detail favourable for neighbourhood level UT studies such as shadow pixels,

and horizontal layover of tall buildings, they are not however freely accessible which denied their

availability for this study (Bhatta, 2013) (Taha, 2014) )(Song, Du, Feng, & Guo, 2014).


11

The third and final reason for choosing Landsat is that it boasts extensive coverage of every

location on earth with 16 day repeat cycles and a swath of 168km (Satellite Imaging Corporation,

2014) (Zeng et al. 2013). This systematic coverage allowed for a comparative analysis of the same

georeferenced location in the same range of months across different years.

The Year and month of the image scenes used in the study were chosen for specific reasons.

The range of years had to be between 2000 and 2015 to provide an up to data investigation of

Wuhan. All scenes had to be taken within the warm season of May to September when chlorophyll

levels in plants are peaking. This increases the spectral contrast between natural landscapes and

urban landscapes, thus making the delineation of bare earth/urban sites from plant covered

landscapes much simpler. The 22nd July 2001 [fig.2] and 16th August 2013 [fig.4] scenes were

selected because they displayed clear skies which increase the validity of the land classification test

by reducing the amount of hidden pixels under clouds (Bhatta, 2013). They also had an

approximately equal gap of 12 years between the scenes meaning that the 24th August 2007 [fig.3]

scene provides a halfway reference point from which UT can be examined in comparison with 2001

[six years prior] and 2013 [six years onwards]. However, the August 2007 scene can only be used for

spatial reference and not quantitative reference; this is due to the failure of Landsat 7’s scan line

corrector [SLC] on 31st May 2003 which compensates for the satellites forward motion. As a result of

this even with the Landsat 7 SLC turned off, scenes between 31st May 2003 and present are

tarnished by large parallel strips of missing spectral data 1 -14 pixels wide across each scene, these

impact upon the land classification process with results being skewed by a 22-25% loss (USGS, 2013)

(Wijedasa et al. 2012) (Zeng et al. 2013) (Zhu & Liu, 2014). Quantitative outputs of each land

classification map from SLC-off scenes provide inaccurate results therefore only the 2001 and 2013

scenes could be compared with high accuracy and validity.

Before land classification began, the urban land cover and bare earth classes in particular

had been defined for the purpose of consistent training and subsequent analysis and discussion.

Urban land in this thesis is defined as all buildings, roads, man-made impermeable infrastructure or

surfaces. Bare earth is defined literally as exposed earth that is free from cover by impermeable

surfaces, plant cover or water. There is little natural bare earth cover around Wuhan due to the

absence of dry seasons in the region, the majority of bare earth cover is created artificially through

the demolition of buildings and unearthing of natural landscapes to generate land free for

construction. Therefore bare earth can be used with a sufficient level of confidence to map

construction sites and predict future growth of the city.


12

To begin the land classification process the selected Landsat scenes were stacked and

processed using ENVI. Once the stacked image had been produced, training sites were created for

each land classification [agricultural land, bare earth, cloud, forest, urban land, water bodies and

wetlands]. Through trial and error the combination of bands 4, 5 and 7 were found to produce the

most functional RGB colour image for training of sites. This combination excelled particularly at

delineating urban landscapes from natural landscapes; the prime objective of this land classification

mission. The training sites themselves were selected by eye where there was deemed to be enough

pixels to form a prominent sized polygon to encompass each land classification site. To verify that

each training site was of the correct land classification, Google Earth historical imagery was used to

ground truth each site location. This is carried out by entering the co-ordinates of each site into

Google Earth so they can be cross referenced with 2.5m high resolution SPOT 5 imagery and aerial

photography over the same location. This technique was carried out in the laboratory and removed

the need for fieldwork which is what has made it popular with many researchers (Schneider &

Mertes, 2014) (Quan et al. 2013) (Wang et al. 2012). The supervised maximum likelihood classifier in

ENVI was employed to extrapolate the spectral signatures of each training class to produce the land

classification maps, it was chosen for its wide endorsement by researchers as a statistical method

used for digital classification (Taha, 2014).

The next step was to calibrate and authenticate each map with accuracy tests. The minimum

overall accuracy target for all maps was set at 85%; a sufficient representation of the real landscape

based upon (Janssen & Van der Wel, 1994; Landis & Koch, 1977). To test for accuracy each land

classification was ground truthed using Google Earth, with the same approach as previously

mentioned, to verify training sites for each land cover class. Finally a confusion matrix test was run

to determine the accuracy of the 2001 and 2013 maps. The 2007 map however, was not accuracy

tested as it was only created for spatial reference; SLC-off scenes theoretically cannot obtain

accuracies higher than 78% based upon the estimated 22-25% amount of missing data (Wijedasa et

al. 2012).

Once the 2001 and 2013 maps were accuracy tested, all land cover classes could be spatially

quantified to provide an output table citing the size of each class and its proportion of map

coverage. Using this data both maps could be compared with each other quantitatively to determine

how much each land cover class has changed over time.


13

4.3 Land Cover Characterization

For a deeper analysis of urban transformation at a scale below that of Landsat, Google Earth will be

employed to characterize spots of UT around Wuhan. Land classification maps from this thesis will

be used to locate patterns of substantial urban growth/renewal or increased bare earth coverage;

the co-ordinates from these sites will be entered into Google Earth to pinpoint their location.

Following this the historical imagery tool will be used to study the time series of aerial photography

and SPOT 5 imagery, this will provide a street by street high resolution insight into how each location

has transformed and for what purpose. Sites of interest can also be measured in size using Google

Earth’s measurement tools. In conjunction with this, crowd sourced georeferenced photography

that has been uploaded to Google Earth will be utilised to provide further assistance in

characterizing UT. These photos can to an extent replace the additional support that fieldwork

would otherwise provide by removing the need to travel to and investigate each the location. This

can be a powerful tool that will provide a unique insight into UT that has not yet been used for

Wuhan or any other city.

4.4 Socio-economic Data Analysis

To investigate and determine the drivers behind urban transformation in Wuhan between 2000 and

the present, socio-economic data has been collected for analysis from the National Bureau of

Statistics of China. This data is collected through the annual national survey and published with free

access online in the national statistical yearbooks (NBSC, 2014). The data sets were downloaded and

entered into spreadsheets to create time series graphs for comparison against the land classification

map statistical data to determine patterns between UT and socio-economic indicators. The data

obtained from the NBSC for this thesis includes Wuhan’s annual population, total investment in fixed

assets, gross domestic product and budgetary revenue. Whilst data provided by the national

statistical office was freely accessible, data from Wuhan’s statistical office was not freely available

from the UK. This means that for data on annual foreign direct investment for Wuhan which [not

included in the national statistical yearbook] will have to be cited from (Huang & Wei, 2014) who

have obtained access to the Wuhan statistical yearbooks. This method of combining RS with national

and regional statistical data has been well utilised by a number of researchers across China to study

UT (Han & Wu, 2004) (Huang & Wei, 2014) (Schneider & Mertes, 2014) (Wenhui, 2012).

Can

did

ate n

um

ber: 1

06

44

3

5. R

esu

lts: Map

s

[Figure 2

: ETM+ Lan

d classificatio

n m

ap o

f Wu

han

metro

po

litan area 2

2n

d July 2

001

, created

usin

g ENV

I.]

14

Can

did

ate n

um

ber: 1

06

44

3

[Figure 3

: ETM+

SLC-o

ff Land

classification

map

of W

uh

an m

etrop

olitan

area 24th A

ugu

st 200

7, create

d u

sing EN

VI.]

15

Can

did

ate n

um

ber: 1

06

44

3

[Figure 4

: OLI Lan

d classificatio

n m

ap o

f Wu

han

metro

po

litan area 1

6th A

ugu

st 201

3, create

d u

sing EN

VI.]

16


17

Results - Tables

[Table 1: Land cover proportion and ratio change; derived from Landsat land cover classification]

Land cover classification

Land cover proportion in 2001 (%)

Land cover proportion in 2013 (%)

Change in land cover proportion between 2001 & 2013 (%)

Agricultural land 33.69 23.42 -10.27

Bare earth 13.86 25.59 11.73

Cloud 0.05 0.70 0.65

Forest 5.52 7.64 2.12

Urban land 7.68 23.25 15.57

Water bodies 13.72 11.12 -2.60

Wetlands 25.48 8.29 -17.90

[Table 2: Confusion matrix accuracy test results]

Land cover classification Ground truth percentage for 2001

Ground truth percentage for 2013

Agricultural land 92.46 98.81

Bare earth 91.13 97.75

Cloud N/A 97.80

Forest 95.74 95.93

Urban land 93.01 99.41

Water bodies 97.94 86.13

Wetlands 77.46 72.09

Combined land cover classes 94.54 88.63

[Table 3: Socio-economic statistics for Wuhan from the National Bureau of Statistics of China]

Year Total population at year-end

Total investment in fixed assets (Chinese Yuan)

Gross domestic product (current prices) (Chinese Yuan)

Budgetary revenue of local governments (Chinese Yuan)

2000 7491900 46192650000 N/A 6977110000

2001 7582300 48550270000 134780270000 8615860000

2002 7681000 54932950000 149274350000 8582870000

2003 7811900 62282150000 166217970000 8043680000

2004 7859000 79665380000 195600000000 10402180000

2005 8013600 105518080000 223800000000 13881690000

2006 8190000 132528270000 259075690000 17860210000

2007 8282100 173278950000 314190480000 22167550000

2008 8330000 225205240000 396010000000 27731760000

2009 8360000 300110450000 462100000000 31607160000

2010 8367300 375316820000 556593000000 39018660000

2011 8270000 425516210000 676220000000 67326000000

2012 8220000 503124880000 800380000000 82858460000

2013 8220000 597452720000 905130000000 173065430000


18

7. Analysis and Discussion

7.1 Research question 1: How fast and large has Wuhan’s urban land grown spatially since

2000 and at what expense?

Since the beginning of the millennium Wuhan has experienced rapid urban transformation. Its

borders have extended greatly whilst internally buildings are being demolished or regenerated and

land is being re-shaped for new construction projects. This has been proven and displayed through

the implementation of remote sensing and land classification as follows.

In the year 2001 the proportion of urban land cover in Wuhan stood at 7.68%, by 2013 this

figure had already risen to 23.25% [table 1]. This is a 15.57% increase in the relative proportion of

urban land cover from 300km2 to 910km2 over a 12 year period [fig. 2] [fig. 4]. The proportion of

bare earth land cover also increased from 13.86% to 25.59%, this is an 11.73% rise in the relative

proportion of bare earth cover across the same period between 2001 and 2013 [table 1]. Bare earth

for the purpose of this study holds great significance as it represents land that has been cleared for

construction. This means that not only did Wuhan increase in size by 609,379km2 by 2013, it has

marked out and cleared more natural land increasing the city’s capacity to grow up to 459km2 more

over the coming years; under the assumption that all new bare earth that has been cleared for

construction will be utilised for new urban projects. This would expand the cities size from 910km2 in

2013 to 1,369 km2 in the near future. The current leap in the land coverage proportion of urban

land/bare earth is clearly displayed in [fig. 5].

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

2001 2013

Pe

rce

nta

ge o

f la

nd

cla

ss c

ove

rage

Year of land classification

Temporal change in Wuhan land classification coverage

Wetlands

Water bodies

Urban land

Forest

Cloud

Bare earth

Agricultural land

[Figure 5: Land classification coverage area graph.]


19

Whilst bare earth land has risen by an additional 11.73% in relative coverage it must be

noted that the true rise in construction sites cannot be accurately derived from this figure; bare

earth serves as a measure for the maximum potential number of construction sites. For example a

handful of bare earth sites such as those visible in 2001 have since been converted into forest lands

or agricultural land rather than urban usage [fig.6]. This is also evident in the statistical output of the

land classification maps which state that forest lands have seen a 2.6% rise in relative coverage.

However, the majority of bare earth sites have in actuality been verified using Google Earth as sites

cleared for construction. Approximately 78.79% of Wuhan’s bare earth coverage is being used for

construction. This is because bare earth coverage in the rural perimeter of Wuhan is not being used

for construction, instead bare earth here represents areas of agricultural land that are yet to be

planted or have been harvested. This was calculated by working out the ratio of bare earth to

agricultural lands in the rural perimeter to the north, south and west of Wuhan, then upscaling this

figure for all bare earth to agricultural land across Wuhan. The result of this is a figure for estimated

bare earth that is not being used for construction that can be subtracted from the bare earth

coverage figure for the entire map to produce the estimated percentage f construction sites

amongst bare earth.

[Figure 6: Land cover change from bare earth in red colour to forest in green colour at the grid

refernce ‘830000 - 360000’ – left side image from [fig.2] in 2001, right side image from [fig.4] in

2013.]


20

One of the more startling characteristics of Wuhan’s UT is that whilst the city’s population

has increased by 9.71% from 2000 [table 3], the rate at which new urban land is being created is

much higher. This is proven using NBSC statistics and the quantitative output of each land

classification map to calculate population density. In 2001 Wuhan had a population density of 17.2

people per km2, however, this dropped to 8.6 people per km2 by 2013. Urban land is therefore

growing much larger in Wuhan whilst the population is becoming sparser. This prompts questions

over how sustainable UT in Wuhan is, and whether urban land usage needs to become more

efficient to restrain rapid urban expansion into natural landscapes.

Urban transformation has taken a huge toll on the land coverage proportion. This is

overwhelmingly concentrated on two classes, wetlands and agricultural lands. Wetlands have

plummeted by 17.9% in their share of the land cover whilst agricultural lands also lost a 10.27%

share of the overall coverage at the expense of urban land and bare earth [table 1]. Evidence of this

is highlighted in [fig.7]. Whereas in 2001 the landscape comprised of agricultural lands, wetlands and

forested patches, by 2013 most of the grid square ‘790000 - 370000’ had been transformed into

urban land cover and bare earth. This bare earth is now being used to enable the construction of

new industrial buildings and roads in the Wujiashan ETDZ as displayed in [fig.8].

[Figure 7: Urban land in black and bare earth in red consuming natural lands at the grid reference

‘790000 - 370000’. Left side image 2001 [fig.2], right side image 2013 [fig.4].]


21

[Figure 8: Wujiashan ETDZ, Hanyang region of Wuhan, 30°27'27.19"N - 114° 5'34.50"E. Left side

image taken 20/05/2008, right side image taken, 21/01/2015 (Google Earth, 2015).]

Wuhan is an important grain producer for Hubei and China as a whole, however, its capacity

to produce grain is being diminished as urban areas encroach agricultural lands and wetlands which

are currently used for cultivating rice. Whilst policies were put in place in 1998 such as ‘The Land

Management Act’ and the ‘New Jiben Nongtian Baohu Tiaoli’ to ensure 80% of agricultural land and

wetlands used for grain cultivation remain untouched by construction, evidently UT is still an

imminent threat as has just been highlighted (Boyang et al. 2014) (Lu et al. 2014) (Tan et al. 2014).

Wetlands are not only used for grain cultivation but they also form an integral part of

Wuhan’s urban lake ecosystem (Dai et al. 2011). Urban transformation is threatening Wuhan’s urban

lakes reducing their surface area as more wetlands and water bodies are reclaimed for urban land

use. [Fig.9] displays how Shahu Lake has shrunken in size as its banks have been re-shaped following

the removal of wetlands to create urban residential projects on re-claimed land, Neishahu park in

the south east of the lake, and a bridge has also been built across the lake urbanizing the area

further. The example of Shashu Lake shrinking due to UT is one that has been happening in tandem

with numerous other lakes across the city from Tangsun Lake in the East to Mushui Lake in the West.


22

[Figure 9: Shahu Lake, Wuchang region of Wuhan, 30°34'4.24"N - 114°19'45.71"E. Left side image

taken before land was re-claimed -23/12/2000. Right side image taken after land was re-claimed

21/01/2015 (Google Earth, 2015). Red ring marks the lakes banks in 2015.]

A reduction in wetlands due to UT has brought rapid ecosystem loss leading to a decline in

biodiversity, water pollution, declining water supplies and consequent losses in viable land for grain

cultivation. Furthermore a reduction in lake capacity will increase the risk of inner city flooding

throughout the monsoon season when water levels are at their highest (Dai et al. 2011).


23

7.2 Research Question 2: Where is urban transformation happening around Wuhan; how

is the city evolving?

As discussed in the last section of this study, Wuhan is growing at break-neck speed and swallowing

up natural landscapes to do so. It is important to understand not only how fast the city is growing

and what land is being consumed, but also where it is happening and how the city is evolving so that

the future of Wuhan can be planned and managed sustainably. To do this systematically each of the

three major regions of Wuhan [fig.1] has been analysed on a spatio-temporal scale using Google

Earth historical imagery and georeferenced photography.

Wuchang

Wuchang is the city’s educational and cultural hub, home to the first national development zone in

the region; Wuhan East Lake High-Tech Zone. WEHDZ [as it is referred to] was established in 1988

and is known as the ‘Optics Valley of China’, specialising in optics, information technology, bio-

technology and telecommunications (Heiduck & Pohl, 2001) (Huang & Wei, 2014) (WEHDZ

Administrative Committee, 2015). WEHDZ has experienced some of the largest urban growth in the

city over the study period with new urban land and bare earth stretching 16km east from the centre

of the development zone, and to Tangsun Lake in the South, as illustrated by [Fig.10].

[Figure 10: Wuhan East Lake High-Tech Zone, Wuchang, Wuhan. Grid reference for bottom left

square in both images ‘820000 – 370000’. Left side image July 2001 [fig.2], right side image August

2013 [fig.4]. Areas in black – urban land, and red – bare earth]


24

Much of the area that existed prior to this massive UT consisted of agricultural lands,

wetlands and patches of forest. Huge investment has poured into the WEHDZ since its opening as

the central government bids to expose Wuhan to the global market to attract foreign investment

and business interest. As a result the zone has markedly more freedom to plan and build new

projects to attract business from across China and the World (Heiduck & Pohl, 2001) (Huang & Wei,

2014). Whilst there has already been a giant expansion of new urban land up till 2013, bare earth

coverage highlights how the future of the zone will look. This is backed up by plans for further

construction to fill to the development zone which can be seen in [fig. 11]. Much of this

development is already under way such as the construction of a new site called Bio Park to attract

national and international leading biopharmaceutical companies [fig.12] (WEHDZ Administrative

Committee, 2015).

A more extreme example of urban transformation in Wuchang can be seen in the north of

[fig.10], between two of the lakes. This is the site of Wuhan Railway Station on the Guangzhou –

Beijing high speed railroad which was constructed in less than seven months; the progress of which

can be viewed on Google Earth. The station plays a pivotal role in making Wuhan the transportation

hub of Central China. The construction of the station is attracting FDI to the city as foreign

companies are choosing to locate their businesses in the lucrative development zones where they

can connect with other major agglomerations and provinces via the railroad (Huang & Wei, 2014)

(WEHDZ Administrative Committee, 2015). The attraction of FDI in turn drives ongoing or increased

UT as discussed the analysis for research question 3.

[Figure 11: WEHDZ plans (WEHDZ

Administrative Committee, 2015).]

[Figure 12: Bio Park, Longshancun, WEHDZ,

30°29'34.37"N - 114°32'1.84"E (Google Earth, 2015).]


25

Hanyang

Hanyang is the primary industrial centre of Wuhan. Whilst Wuchang is home to the longstanding

Wuhan Iron and Steel Corporation, Hanyang has the highest proportion of urban industrial land

across all three major divisions of the city (Huang & Wei, 2014). It is also the location of the Wuhan

Economic and Technological Development Zone [also referred to as WEDZ]; set up in 1991 as the

regions automotive industrial centre, it successfully met its completed aim of attracting FDI and

joint ventures from international automotive companies such as Honda and Peugeot (WEDZ

Administrative Commission, 2015). This development zone has seen heavy urban transformation just

like its predecessor the WEHDZ. Vast areas of agricultural land have been stripped back to bare earth

for construction projects [fig.13]. Much of the area has already been converted into factories,

automotive test centres and residential areas that fill the fringes of the development zone. [Fig.14]

highlights the extent of UT surrounding the WEDZ; clusters of new residential areas have been

constructed around Houguan Lake, the largest of which stretches 5km in length. These new

construction projects are part of the spatial radiation effect that development zones often have in

which the influx of new business opportunities and investment diffuse into neighbouring areas with

the creation of new homes, infrastructure and universities (Heiduck & Pohl, 2001).

[Figure 13: Wuhan Economic and Technological Development Zone, Hanyang, Wuhan. Grid

reference for bottom left square in both images ‘780000 – 360000’. Left side image August 2007

[fig.3], right side image August 2013 [fig.4]. Areas in black – urban land, and red – bare earth]


26

[Figure 14: Marked in red - new residential areas created since 2007 surrounding the WEDZ,

Hanyang, Wuhan, ‘30°29'50.65"N - 114° 6'59.33"E’, image taken 21/01/2015 (Google Earth, 2015).]

Hankou

Hankou is the third and final region of the city to be analysed in this study, it is the largest of the

three urban settlements which all date back 3500 years in existence (Han & Wu, 2004). Hankou is

undergoing an ambitious plan of urban renewal that is part of Mayor Tang Liangzhi’s vision to

transform over 200km2 of land by 2020. 1.9 trillion Chinese yuan will be spent on this plan over the

next five years alone (Peston , 2014). Whilst Hankou’s UT looks deceptively small through low

resolution landsat imagery, when using Google Earth’s very high resolution Spot 5 imagery we you

can begin to observe large scale projects of urban renewal reshaping the profile of the city, both

spatially and economically.

One of the centre piece projects of Wuhan’s development plan is the new central business

district in the Wangjiadun area of Hankou. It is a perfect example of how land use cover is changing

not only at the periphery of Wuhan but also internally as the old international airport was

demolished and converted into the “new heart” of the city; the CBD [fig.15] (Wuhan CBD Investment

& Development, 2015). This new 7.41km2 area will incorporate Wuhan’s new financial sector, global

conference centres, hotels, international residential areas and a 438m tall skyscraper, this is in order


27

to boost Wuhan onto the international stage in a bid to become a globally recognised city in the

manner of Shanghai (Peston , 2014) (Wuhan CBD Investment & Development, 2015). With the

placement of the CBD in between the major development zones, the international airport, ports and

rail station; this ease of access is once again attracting more FDI to the city and driving further UT

(Huang & Wei, 2014).

[Figure 15: Wuhan Central Business District, Wangjiadun , Hankou, ‘30°35'54.56"N - 114°14'30.22"E’.

The red ring marks the catchment area of the CBD defined by (Wuhan CBD Investment &

Development, 2015). Left side image taken before demolition of Wangjiadun Airport - 17/12/2006.

Right side image taken during construction of the new CBD - 21/01/2015 (Google Earth, 2015).]

To the west of Hankou the periphery of the city has expanded outwards over the three years

prior to 2013 with the establishment of the Wujiashan Economic and Technological Development

Zone. Wujiashan ETDZ is the latest of the economic zones to be set up in 2010 as a centre for food

technology and logistics (Huang & Wei, 2014) (Hubei Provincial Government, 2012). Once again

much of this growth can be attributed to FDI since the state has encouraged Taiwan to invest a large

stake in the zone with special incentives for businesses from Taiwan (Hubei Provincial Government,

2012). As a result agricultural lands have yet again become the victim of UT in the bid to develop

Wuhan [fig. 16].


28

[Figure 16: Wujiashan Economic and Technological Development Zone, Hankou, Wuhan. Grid

reference for top middle square in both images ‘790000 – 390000’. Left side image August 2007

[fig.3], right side image August 2013 [fig.4].]

Finally, to the North of Hankou, on the outskirts of Wuhan are a cluster of new major

infrastructure and residential developments. With the closure of Wangjiadun Airport for

construction of the new CBD, Wuhan Tianhe International Airport in Hankou has undergone a 7km2

expansion whilst to the east of the airport a brand new 6.5 k long freight train station has been built

on the Beijing-Guangzhou line. Wuhan North Marshalling Station is the largest of its kind in Asia, as a

result of this ground-breaking investment in the rail system, UT is escalating as material flows

increase, and the aeas surrounding each station are opened up through regional and national access

(Lue et al. 2014) (WEHDZ Administrative Committee, 2015). Further south of Tianhe Airport, off

Houho, lake lies one of Wuhan’s largest residential developments of approximately 8km in length.

This site emphasizes the rate at which Wuhan has been consuming natural landscapes; [fig. 17]

shows how in less than 11 years this location has become a satellite town on the outskirts of Wuhan

complete with schools, an athletics track, and a university [fig. 18].


29

[Figure 17: Location south of Houho lake prior to new town construction, Hankou, Wuhan,

‘30°42'47.85"N - 114°15'56.90"E’, image taken 19/02/2003 (Google Earth, 2015).]

[Figure 18: Location south of Houho lake after construction of new town, Hankou, Wuhan,

‘30°42'47.85"N - 114°15'56.90"E’, image taken 21/01/2015 (Google Earth, 2015).]


30

7.3 Research Question 3: What are the driving factors behind urban transformation across

Wuhan?

Population growth is one, if not the most important, of the drivers behind urbanisation in China;

Wuhan is no exception to this (Tan et al. 2014). At the beginning of the study period in the year 2000

Wuhan had a population of 7.49 million, by 2013 the city had experienced a 9.71% rise to 8.22

million people [table 3] [fig. 3]. Whilst there has been an observable dip in population in Wuhan

since its peak in of 8.37 million in 2010 [table 4], it is still predicted that Wuhan’s population will

continue to rise to 9.44 million by 2030, this would rank it as the 47th largest city in the world by

population (United Nations, 2014). The significance of this in relation to UT is that changes in the

urban population have sequential effects upon land use change and land use intensity (Lu et al.

2014). Rural to urban migration coupled with suburbanisation of former farming villages is creating

new urban economies at the periphery of cities (Kamal-Chaoui, 2009). These suburban areas require

new municipal infrastructure, transport and housing whilst the low cost labour there new suburban

economies provide help to fuel the construction of these new projects, thus enhancing the effects of

UT (Lu e al. 2014). This is evident in the size and scale of the urban expansion that can be seen in

[fig.14] and [fig. 18] which represent just a few of the new suburban residential areas being

constructed to house Wuhan’s burgeoning population.

Economic development, just like population growth, has played a fundamental role in

driving and enhancing urban transformation (Lu et al. 2014). During the study period of this thesis

Wuhan’s regional and national economic power has been overhauled since it has been under the

scope of the 2004 ‘Rise of Central China Plan’. As a part of this plan Wuhan was merged with 8 other

cities to form the WUA so creating a nationally and internationally competitive economic region

(Miaolong, 1998) (Lu et al. 2014) (Tan et al. 2014) (Xiong & Liu, 2013). Preferential policies were put

in place to improve and strengthen the industrial competitiveness of the city’s automotive and high

tech industries. The economic development zones that these industries were situated in were also

given preferential treatment with fiscal aid and tax incentives, and low price land guaranteed to be

cheap enough to attract new tenants (Huang & Wei, 2014). Furthermore projects in economic

development zones costing under ¥185 million no longer required approval via the central

government granting more flexibility for state level administrators, planners and those investing

(Heiduck & Pohl, 2001). Remodelling Wuhan as a new high-tech industrial based city through

boosting its development zones and giving its government more autonomy has triggered a growth

burst in the city’s economy (Huang & Wei, 2014).


31

Grand economic development plans like the ‘Rise of Central China Plan’ have translated into

blistering economic growth across the study period in Wuhan. [Fig. 20] shows how the cities GDP

had risen by 571.56% between 2001 and 2013 whilst the budgetary revenue increased by 2380.47%,

based on raw figures from [table 3], from ¥7 billion in 2000 to ¥173 billion in 2013 [fig.21]. These

results run in parallel with the rise in UT across Wuhan; as the cities GDP increases so does its

budgetary revenue which the local government uses to invest back into infrastructure projects and

its national development zones. This not only drives UT itself but also attracts more private investors

from across China and internationally who help fuel this process further (Tan et al. 2014).

Since major plans were laid out in 1996 to invest in and develop Wuhan until 2020, the

attraction of the city to foreign investors has continued as it transforms into a regional powerhouse.

FDI is another key player in driving UT. This has continued to increase in conjunction with the spatial

growth of the city [fig. 22]. Whilst not all FDI in Wuhan is being placed into expanding or renewing

urban areas, government initiatives to attract FDI and foreign businesses are however leading to the

construction of new industrial/business parks, economic development zones, the new CBD and

updated transport infrastructure. Despite this FDI does still have direct impacts upon UT; foreign

companies are now allowed to acquire land from farmers for construction in China using the ‘Land

Acquisition Act’ and in 2008 17.6% of all FDI was invested in real estate meaning that a high amount

of FDI may be directly influencing UT around Wuhan (Huang & Wei, 2014). This is certainly evident in

the economic development zones where both Hong-Kong and Taiwan have been two of the largest

real estate and infrastructure investors sine the 1990’s (Heiduck & Pohl, 2001) (Miaolong, 1998)

(Hubei Provincial Government, 2012). Hong-Kong in particular provided funding for some of the

city’s largest urban projects such as the Wuhan Tianhe International Airport, Yanglui Port’s container

terminal, the second Changiiang River Bridge, regeneration of the old town, construction of high

tech industrial parks, and real estate developments across the city (Miaolong, 1998).

The final leading driver of UT, and perhaps the most overt, is investment in fixed assets.

Investment in fixed assets is defined by the NSBC as investment in construction projects, real estate

development and defence projects (NBSC, 2014). Whilst investment in defence projects may not

relate to UT, investment in construction and real estate can directly influence an increase in urban

land and bare earth coverage. Over the study period fixed asset investment has increased by

1194.17% from ¥46 billion to ¥597 billon as displayed in [fig.23], this is in part due to the China

development bank providing discounted loans for infrastructure projects (Huang & Wei, 2014). Just

like the rises in population, GDP, budgetary revenue of the government and FDI, these have taken

place in parallel with the increase of UT in Wuhan. When examining closer the breakdown of where


32

this investment has been placed, in 2002 17.25% was spent on housing. However of the 5.23 million

m2 of housing floor space only 3.3 million m2 was sold that year (Han & Wu, 2004). This helps to

explain why it is that Wuhan’s urban land has expanded by 15.57% yet its population density has

fallen from 17.2 people per km2 in 2000, to 8.6 people per km2 by 2013 based on united nation

population figures. Not only has fixed asset investment increased amongst other drivers, the

efficiency of its use in preserving the amount natural land used has decreased significantly.

[Figure 19: Wuhan population line graph; data acquired from the NBSC can be viewed in [table 3].]

[Figure 20: Line graph of Wuhan’s GDP growth, 2001 – 2013. Data acquired from the NBSC can be

viewed in [table 3].]

7000000

7200000

7400000

7600000

7800000

8000000

8200000

8400000

8600000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Nu

mb

er

of

pe

op

le a

t ye

ar e

nd

Year

Wuhan population

0

100,000,000,000

200,000,000,000

300,000,000,000

400,000,000,000

500,000,000,000

600,000,000,000

700,000,000,000

800,000,000,000

900,000,000,000

1,000,000,000,000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

¥Y

uan

Year

Gross domestic product of Wuhan


33

[Figure 21: Line graph of Wuhan’s budgetary revenue growth, 2000 – 2013. Data acquired from the

NBSC can be viewed in [table 3].]

[Figure 22: FDI flows in Wuhan, 1990 – 2010. Graph from (Huang & Wei, 2014).]

0

20,000,000,000

40,000,000,000

60,000,000,000

80,000,000,000

100,000,000,000

120,000,000,000

140,000,000,000

160,000,000,000

180,000,000,000

200,000,000,000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

¥1

0 0

00

Year

Bugetary revenue of Wuhan government


34

[Figure 23: Line fixed investment growth in Wuhan’s, 2000 – 2013. Data acquired from the NBSC can

be viewed in [table 3].]

8. Research Limitations

During the research for this thesis there has been a scope of limitations that has had potential

affects upon the quality/type of the findings or the ability to interpret results to effectively answer

the research question. These limitations are discussed in order beginning with the highest potential

impacting limitation to the lowest.

The use of remote sensing for the period of 2000 till present presented a range of potentially

high impacting limitations that were eventually overcome. The most prominent of these was the

issue of the failed scan line corrector on board Landsat 7. This restricted the ability to create time

series of land use change as all Landsat 7 scenes from 31st May 2003 onwards suffer from a 22-25%

data loss as detailed in the methodology and data section of this thesis (Wijedasa et al. 2012) (Zeng

et al. 2013) (Zhu & Liu, 2014). Because of this ETM+ SLC-off scenes could not be compared with

ETM+ scenes prior to the 31st May 2003 or to Landsat 8 OLI scenes, both of which have working scan

line correctors. Using SLC-off scenes to record quantitative changes in land coverage would distort a

time series with the un-scanned sections of data thus providing an inaccurate set of results for

analysis. To overcome this potentially limiting factor the August 2007 ETM+SLC-off scene was still

utilised for spatio-temporal analysis but removed from the quantitative analysis of urban

transformation. There are methods of improving the accuracy of SLC-off scenes through the

Mosaicking technique or MAP-MRF based classifiers, these either fill the gaps by creating composite

images that use SLC-on scenes [Mosaicking], or by taking an average of the pixels surrounding the

0

100,000,000,000

200,000,000,000

300,000,000,000

400,000,000,000

500,000,000,000

600,000,000,000

700,000,000,000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

¥1

0 0

00

Year

Total investment in fixed assets in Wuhan


35

un-scanned pixel areas then filling that gap based upon the assumption that the surrounding pixels

will belong to the same class and share the same temporal patterns as the un-scanned area

(Wijedasa et al. 2012) (Zeng et al. 2013) (Zhu et al. 2013). However to keep within the time-scale

that was set for this thesis, these methods were not employed because of their added time

consumption. Ultimately this limitation did not prevent any of the research questions from be

answered, rather it lead to a change in the methods and approach of this thesis to resolve the issue.

The next limitation with regard to use of RS was the variation in weather conditions. A small

proportion of the dry season Landsat scenes suffered from adverse weather conditions that

obscured the earth’s surface as Landsat ETM+ and OLI sensors do not have cloud the penetrating

capabilities of RADAR (Bhatta, 2013). This became apparent whilst selecting Landsat scenes for land

classification, many scenes could not be used due to widespread cloud coverage [fig. 24].

[Figure 24: ETM+ scenes of Wuhan - left side scene 2/8/2000 displaying high cloud coverage, right

side 22/11/2001 displaying clear skies (USGS, 2014).]

This limitation meant that some years and months could not be used for land classification

such as the year 2000 which experienced high cloud coverage throughout the dry season. However

the impact of this was minimal for the following years where scenes were available with clear skies

from differing months in each dry season. The final limitation of RS was the impact of land cover

types potentially being miss-classified. Fortunately urban land and bare earth could be classified

with high accuracies of over 90% keeping the core land classifications and the overall map accuracy

above the 85% threshold set in the methodology; however, some classes such as wetlands showed

accuracies as low as 72.09% [Table 2]. Errors were kept to an absolute minimum through rigorous


36

ground truthing, this was to ensure that the most important land classifications [urban land and bare

earth] correctly represented the real world in order to answer the research questions.

The application of Google Earth as a tool for ground truthing and UT analysis showcased the

capabilities of Google’s RS database. However, historical imagery was limited in the earlier years of

this millennium; less Spot imagery and aerial photography was available over Wuhan between 2000

and 2006. This reduced the size of the study area until greater coverage of Wuhan could be accessed

from 2006 onwards.

The final set of limitations with the lowest potential to impact upon research or subsequent

analysis was related to the Chinese statistical surveys. Chinese National Statistical Yearbooks are

freely accessible and available online dating back to the year 1996, however Wuhan’s statistical

yearbooks are not published freely online for viewing in the UK; they can only be accessed through

purchasing them online. The Chinese National Statistical Yearbooks contain data on Wuhan that was

vital to this study, however where data was missing from these such as FDI investment for Wuhan,

other studies which had access to Wuhan’s Statistical Yearbooks were referenced to complete this

study. One factor that cannot be avoided in using the Chinese National Statistical Yearbooks is that

there lies the possibility that data could have been manipulated by the various different levels of

statistical bureaus that produce this data in favour of their political interests (Wang et al. 2012).

9. Conclusion

This study has determined, using land classification of Landsat imagery that between 2001 and 2013,

that there has been a 15.57% increase in the proportion of urban land to all other classes in Wuhan.

This means Wuhan has tripled in size in this time. Simultaneously bare earth has increased its

proportion of the land cover in Wuhan by 11.73%. From these results it can be inferred that the

city’s urban land cover may grow from 910km2 in 2013 up to 1,369 km2, evidenced by the fact that

much of the bare earth cover which was already located in the year 2001 has since been converted

into urban land use. This has been ground truthed using Google Earth. Natural landscapes were

impacted the most by this change, with Wetlands decreasing their relative proportion of the study

area by 17.9% between 2001 and 2013, whilst the proportion of agricultural lands in the study area

fell by 10.27%. These were almost directly as a result of urban transformation as proven by a range

of ground truthed examples using Google Earth. Water bodies and forests showed no overwhelming

changes as a result of UT however there were visible changes in the size of Wuhan’s urban lakes.


37

When analysing the drivers behind urbanisation in Wuhan it has been determined that the

population has risen by 728,100 people, a 9.71% increase, between 2000 and 2013. This is due to

numerous factors such as rural to urban migration and the associated swallowing of rural villages by

urbanisation. Population density in this time has fallen by 8.6 people per km2 meaning the efficiency

of land use is falling. Meanwhile national political reform and increased marketization coupled with

the ‘Rise of Central China Plan’ have favoured Wuhan. They have focused funding and development

planning on Wuhan whilst attracting FDI to modernise the city; evolving from a legacy of heavy

industry into a high-tech and automotive, logistics and informatics based economy. This has grown

the GDP of the city, boosted its budgetary revenue, and increased spending on fixed assets such as

infrastructure, housing and other real estate across the city’s development zones and urban fringes.

Most of the urban development that has been identified by Landsat has been at the periphery of the

city such as the expansion of the national development zones, expansion of Wuhan Tianhe

International Airport, construction of Wuhan’s marshalling and high speed rail stations, and the

growth of numerous residential areas. However internally Wuhan has seen projects like the new

CBD and numerous other real estate developments revolutionize the profile of the city; intensifying

the UT process by attracting new investors.

When assessing the level of urban transformation that has materialized in Wuhan, it is clear

that this city has experienced a gigantic expansion of its peripheral urban land, whilst internally it

has become more intensively developed. Its infrastructure has grown in size but its population

density has decreased. This leaves some vital questions over the sustainability of Wuhan’s urban

transformation process that policymakers and urban developers may need to address. The city’s

economy may be evolving and growing, but wetlands and agricultural lands are being threatened

whilst the population is forecast to rise within the city; issues such as food security may impact the

region if agricultural lands are not protected. Additionally, if all bare earth locations that had been

cleared and added by 2013 are utilised for construction around city, Wuhan’s urban land could

become 4.56 times the size it was in 2001 in the coming years. This could exacerbate the

environmental and social issues that the city may face.


38

10. Reference list

Atkinson, A., Thielen, K. (2008) ‘Modernisation’ and Urbanisation in China, City, Vol. 12, No. 2, Pp. 154-160

Bhatta, B. (2013) Research Methods in Remote Sensing, Springer, New York London, Pp. 43-74

Boyang, G., Weidong, L., Dunford, M. (2014) State Land Policy, Land Markets and Geographies of Manufacturing: The Case of Beijing, China, Land Use Policy, No. 36, Pp. 1-12

Carlson, T. N., Dodd, J. K., Benjamin, S. G., & Cooper, J. N. (1981) cited in Song, J., Du, S. Feng, X. Guo, L. (2014) The relationships between landscape compositions and land surface temperature: Quantifying their resolution sensitivity with spatial regression models, Landscape and Urban Planning, No.123, Pp.145-157

Chen, G. (2012) Housing the Urban Poor in Post-Reform China: Some Empirical Evidence From the City of Nanjing, Cities, No. 29, Pp. 252-263

Chen, S., Zeng, S., Xie, C. (2000) Remote Sensing and GIS for Urban Growth analysis in China, Photogrammetric Engineering & Remote Sensing, Vol. 66, No. 5, Pp. 593-598

Dai, J., He, M., Chen, C., Wang, J. (2011) Urban Lake Evolution and Ecological Protection of Wuhan City Based on GIS, 2011 19th International Conference on Geoinformatics, Pp. 1-4

Gong, P., Liang, S., Carlton, E., Jiang, Q., Wu, J., Wang, L., Remais, J. (2012) Urbanisation and Health in China, Lancet, No. 379, Pp. 843-852

Google Earth (2015) Google Earth 7.0, 22/04/2015

Gregory, D., Johnston, R., Pratt, G., Watts, M., Whatmore, S. (2009) The Dictionary of Human Geography, Pp. 790-794

Hadjimitsis, D. (2010) Determination of urban growth in catchment areas in Cyprus using multi-temporal remotely sensed data: risk assessment study, Natural Hazards and Earth System Sciences, No.10, Pp.2235-2240

Han, S., Wu, X. (2004) City Profile: Wuhan, Cities, Vol. 21, No. 4 pp. 349-362

Heiduk, G. & Pohl, N. (2001) Empirical Evidence From Wuhan’s Sate Level Economic Development Zones, Journal of the Asia Pacific Economy, Vol. 6, No. 2, Pp. 244-260

Huang, H. & Wei, Y. (2014) Intra-metropolitan location of foreign direct investment in Wuhan, China: Institution, urban structure, and accessibility, Applied Geography, No. 47, Pp. 78-88

Hubei Provincial Government (2012) http://hubei.chinadaily.com.cn/gov/2010-06/21/content_14584079.htm, 12/03/2015

Huiyi, Z., Changhe, L., Xiubin, L. (2004) Land Use Studies in China, Journal of Geographical Sciences, Vol. 14, No. Z, Pp. 69-73


39

Hsu, M. (1996) China’s Urban Development: A Case Study of Luoyang and Guiyang, Urban Studies, Vol. 33, No.6, Pp. 895-910

Janssen, L., Van der Wel, F. (1994) & Landis, J., Koch, G. (1977) cited in Tan, R., Liu, Y., Liu, Y., He, Q., Ming, L., Tang, S. (2014) Urban growth and its determinants across the Wuhan urban agglomeration, central China, Habitat International, No. 44, Pp. 268-281

Kamal-Chaoui, L., Leman, E., Rufei, Z. (2009) Urban Trends and Policy in China, OECD Regional Development Working Papers 2009, No.1, Pp. 1-69

Lin, G. (2002) The Growth and Structural Change of Chinese Cities: A Contextual and Geographic Analysis, Cities, Vol. 19, No. 9, Pp. 299-316

Lu, S., Guan, X., He, C., Zhang, J. (2014) Spatio-Temporal Patterns and Policy Implications of Urban Land Expansion in Metropolitan Areas: A Case Study of Wuhan Urban Agglomeration, Central China, Sustainability, No. 6, Pp. 4723-4748

Miaolong, L. (1998) The Position and Role of Wuhan City in the Regional Economic Macro-Strategies of China, Chinese Geographical Science, Vol. 8, No. 2, Pp. 106-116

National Bureau of Statistics of China (2014) China Statistical Yearbook, State Statistical Press, Beijing, 2001-2014

Niebergall, S., Loew, A., & Mauser, W. (2007) Object-Oriented Analysis of Very High-Resolution QuickBird Data for Mega City Research in Delhi/India, Urban Remote Sensing Joint Event, Pp.1-8

Pandey, B., Joshi, P., Seto, K. (2013) Monitoring urbanization dynamics in India using DMSP/OLS night time lights and SPOT-VGT data, International Journal of Applied Earth Observation and Geoinformation, No.23, Pp.49-61

Peston (2014) How China Fooled The World With Robert Peston, Wuhan Mayor Tang Liangzhi Interview, BBC, 21/02/2014

Quan, B., Xiao, Z., Römkens, M. Bai, Y., Lei, S. (2013) Spatiotemporal Urban Land Use Changes in the Changzhutan Region of Hunan Province in China, Journal of Geographic Information System, No. 5, Pp.136-147

Quan, Z. (1991) Urbanisation in China, Urban Studies, Vol. 28, No. 1, Pp. 41-51

Satellite Imaging Corporation (2014) http://www.satimagingcorp.com/satellite-sensors/other-satellite-sensors/landsat-8/, 17/04/2015

Schneider, A., & Mertes, C. (2014) Expansion and growth in Chinese cities, 1978-2010, Environmental Research Letters, No.9, Pp.1-11

Song, J., Du, S., Feng, X., Guo, L. (2014) The relationships between landscape compositions and land surface temperature: Quantifying their resolution sensitivity with spatial regression models, Landscape and Urban Planning, No.123, Pp.145-157


40

Taha, L. (2014) Assessment of urbanization encroachment over Al-Monib island using fuzzy post classification comparison and urbanization metrics, The Egyptian Journal of Remote Sensing and Space Sciences, No.17, Pp.135-147

Tan, R., Liu, Y., Liu, Y., He, Q., Ming, L., Tang, S. (2014) Urban growth and its determinants across the Wuhan urban agglomeration, central China, Habitat International, No. 44, Pp. 268-281

United Nations, Department of Economic and Social Affairs, Population Division. (2014). World urbanization prospects: The 2014 Revision, Pp. 1-27

Unites States Geological Survey (2013) http://landsat.usgs.gov/products_slcoffbackground.php, 14/03/2015

Unites States Geological Survey (2013) http://landsat.usgs.gov/sci_an.php#4 , 29/11/2014

Unites States Geological Survey (2014) http://earthexplorer.usgs.gov/, 1/10/2014

Wang, L., Li, C., Wing, Q., Cheng, X., Wang, X., Li, X., Hu, L., Liang, L., Yu, L., Huang, H., & Gong, P. (2012) China’s urban expansion from 1990 to 2010 determined with satellite remote sensing, Chinese Science Bulletin, Vol.57, No.22, Pp.2802-2812

Wang, Q. (2014) Effects of Urbanisation on Energy Consumption in China, Energy Policy, No. 65, Pp. 332-339

Wang, Y., Wang, Y., Wu, J. (2009) Urbanisation and Informal Development in China: Urban Villages in Shenzen, International Journal of Urban and Regional Research, Vol. 33, No. 4, Pp. 957-73

Wenhui, K. (2012) Spatio-temporal Patterns of Intra-urban Land Use Change in Beijing Between 1984 and 2008, Chinese Geographical Science, Vol. 22, No. 2, Pp. 210-220

Widjedesa, L., Sloan, S., Mickelakis, D., Clements, G. (2012) Overcoming Limitations with Landsat Imagery for Mapping of Peat Swamp Forests in Sundaland, Remote Sensing, No. 4, Pp. 2595-2618

Wuhan East Lake High-Tech Zone Government Administrative Committee (2015) http://en.wehdz.gov.cn/, 12/03/2015

Wuhan Economic and Technological Development Zone Administrative Commission (2015) http://www.wedz.gov.cn/theme/foreignlanguage/en/1-1.html, 12/03/2015

Wuhan Investment and Development Co Ltd (2015) http://www.whcbd.com/en_us/index.shtml, 13/03/2015

Xiong, Y., Liu, X. (2013) Empirical Research on the Urban Development Level of Wuhan Urban Circle, Research Journal of Applied Sciences, Engineering and Technology, Vol. 5, No. 10, Pp. 2951-2956

Xinhu, L., Wang, C., Zhang. (2012) Urbanisation and Human Health in China: Spatial Features and a System Perspective, Environmental Science, Vol. 19, No. 5, Pp. 1375-1384

Yansui, L., Lijuan, W., Hualo, L. (2008) Spatio-temporal Analyses of Land Use Conversion in the Eastern Coastal China during 1996-2005, No. 18, Pp. 274-282


41

Yao, S., Luo, D., Wang, J. (2013) Housing Development and Urbanisation in China, The World Economy, Vol. 37, No. 3, Pp. 481-500

Yu, W., Zang, S., Wu, C., Liu, W., Na, X. (2011) Analysing and Modelling Land Use Land Cover Change (LUCC) in the Daqing City, China, Applied Geography, Vol. 31, No. 2, Pp. 600-608

Zeng, C., Shen, H., Zhang, L. (2013) Recovering missing pixels of Landsat ETM+ SLC-off imagery using multi-temporal regression analysis and a regularization method, Remote Sensing of Environment, Vol. 131, Pp. 182-194

Zhu, X., Liu, D. (2014) MAP-MRF Approach to Landsat ETM+ SLC-Off Image Classification, IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 2, Pp. 1131-1141

miles knight - bachelor thesis - environmental impact assessment

Documents