Landscape and Urban Planning 79 (2007) 96–109
Spatial and temporal dynamics of urban sprawl along two
urban–rural transects: A case study of Guangzhou, China
Xi Jun Yu ∗ , Cho Nam Ng 1
Department of Geography, the University of Hong Kong, Hui Oi Chow Science Building, Pokfulam Road, Hong Kong, China
Received 20 September 2005; received in revised form 21 March 2006; accepted 21 March 2006
Available online 27 June 2006
Abstract
Detailed understanding of landscape changes along the urban–rural gradient provides a useful tool to compare the structural and functional
differences of landscape patches at different orientations. Although several case studies have been conducted confirming the efficacy of this
approach, integrating temporal data with gradient analysis is still rarely used in practice. In this study, a combination of remote sensing images,
landscape metrics and gradient analysis are employed to analyze and compare both the spatial and temporal dynamics of urban sprawl in Guangzhou,
China. The results show that landscape change in Guangzhou exhibits distinctive spatial differences from the urban center to rural areas, with
higher fragmentation at urban fringes or in new urbanizing areas. Due to the complexity of top-down constraints and local interactions, Guangzhou
exhibits a more complex, dynamic, multidimensional configuration of urban sprawl that is different from other cities in China. Urban area expanded
towards the north and south areas due to the increased population and rapid economic development. Property market forces and government policy
led to the rapid growth towards the southern areas. The study also confirms the hypothesis of diffusion-coalescence urban dynamics model in the
process of urbanization. It demonstrates that in order to reveal the complexity of landscape pattern, temporal data are needed to capture the baseline
as well as the spatio-temporal dynamics of landscape changes along the gradient. Combining temporal data with gradient analysis can characterize
the complex spatial pattern of urbanization in Guangzhou well.
© 2006 Elsevier B.V. All rights reserved.
Keywords: Urbanization; Landscape metrics; Landscape pattern; Gradient analysis; Spatio-temporal analysis; Guangzhou, China
1. Introduction
During the past century, the world’s population had been
rapidly congregating in urban areas. The urban population in
the world was estimated at 2.4 billion in 1995 and a doubling
is expected at about the year 2025 (Antrop, 2000). Increasing
population and urbanization result in the most complex process
of land use and land cover changes from local to global scale.
This process, in turn, has profoundly disrupted the structure
and function of ecosystems. For example, although urban areas
account for only 2% of Earth’s land surface, they produce 78%
of greenhouse gases, thus contributing significantly to global climate changes (Grimm et al., 2000). Increasing urbanization is
also thought to be an important cause of species extinction and
∗
Corresponding author. Tel.: +852 22415970; fax: +852 25598994.
E-mail addresses: [email protected] (X.J. Yu),
[email protected] (C.N. Ng).
1 Tel.: +852 28597025; fax: +852 25598994.
0169-2046/$ – see front matter © 2006 Elsevier B.V. All rights reserved.
doi:10.1016/j.landurbplan.2006.03.008
biotic homogenization (McKinney, 2006), hydrological alterations (Paul and Meyer, 2001), and rapid loss of cropland (Lin
and Ho, 2003). Thus, the relationships between urbanization and
ecological effects are gaining increasing attention in recent studies (Sui and Zeng, 2001; Carsjens and Van Lier, 2002; Herold
et al., 2003; Antrop, 2004).
The morphology and evolution of cities caused by urban
sprawl have long been hot topics in geography and other disciplines, and several classic theories have been developed, such
as the Concentric Zone Theory, the Sector Theory, and the Multiple Nuclei Theory (Luck and Wu, 2002; Zhang et al., 2004).
These theories tend to focus on economical and social issues
and the urban hierarchy that cannot fully address environmental and ecological parameters. Furthermore, the spatio-temporal
characteristics of urban expansion have long been neglected
or remained hypothetical due to the difficulties of data assemblage (Batty, 2002). For example, a hypothetical framework of
spatio-temporal urban expansion in the form of alternating processes of diffusion and coalescence was postulated by Dietzel
et al. (2005) (Fig. 1). In fact, urbanization processes are com-
X.J. Yu, C.N. Ng / Landscape and Urban Planning 79 (2007) 96–109
97
Fig. 1. A hypothesis of diffusion-coalescence urban process and metrics behaviors.
plex and varied, and so are the landscape ecological aspects
involved (Antrop, 2000). Thus, decisions on urban planning and
land use management in any urban landscape must consider the
ecological, physical and social components of the whole system
(Zipperer et al., 2000; Leitao and Ahern, 2002). The introduction of the concept of landscape ecology into urban studies can
facilitate this effort. The rapid development of remote sensing
technology, together with the proliferation of landscape metrics, also provides a potential means for understanding how
urban patterns evolve and change over time (Herold et al., 2002,
2003, 2005). However, these studies seldom link temporal data
to gradient analysis to investigate the specific process-related
differences.
Land use change is a dynamic process, and the direction and
magnitude of landscape change could be different (Li and Yeh,
2004). From an ecological perspective, the urban–rural gradient
represents the structural and functional differences of transitional patches in the temporal and spatial contexts, which can
capture the spatio-temporal complexity of urban dynamics. By
integrating ecological, social, and physical variables in different disciplines, the gradient paradigm has proved to be a useful
tool for studying the ecological consequences of urbanization
(McDonnell and Pickett, 1990; Medley et al., 1995; Foresman
et al., 1997). Studies have demonstrated that habitat diversity and
life-support conditions vary greatly across the urban–rural gradient (Bryant, 2006). Other environmental variables have also been
investigated along the urban–rural gradient (Zhu and Carreiro,
1999; Honnay et al., 2003; Weng and Yang, 2004). Since landscape metrics can describe the spatio-temporal changes of landscape pattern, they thus provide an alternative method for measuring urban–rural change. Combining landscape metrics with
the gradient paradigm, the spatial properties of land use changes
along the specific transect have been systematically investigated
in recent studies (Luck and Wu, 2002; Zhang et al., 2004). However, these pioneering studies can only address the static spatial
characteristics of the urban–rural gradient because only one single data set was employed, so they cannot adequately reflect
the temporal differences of intra-city urban structure. A recent
study has addressed this issue by evaluating a time series of
gradients along multiple transects in Chengdu, a mid-sized city
in central and western China (Schneider et al., 2005). Due to
the complexity of urban morphology and the diversity of driving forces during the transition from a centrally planned to a
market-oriented economy system, more comparative studies of
the spatio-temporal characteristics of urban structure are needed
in order to formulate a more general theoretical framework of
dynamic urban expansion.
Urbanization has also been a prominent phenomenon in
China’s economic development since the country adopted the
‘reform and openness’ policy in 1978. The urban built-up area
in 1996 had reached 1.388 million hectares which was 2.6 times
that in 1949 (Lin and Ho, 2003). The speed of urban sprawl has
been most prominent in the Pearl River Delta region during the
past two decades. For example, urban areas have increased by
more than 300% between 1988 and 1996 (Seto et al., 2002), and
the growth exhibited the characteristics of spatial dependency
in different parts of the region (Weng, 2002; Li and Yeh, 2004).
With the power devolving gradually from the central government
to the municipalities, patterns and processes of China’s cities
have undergone fundamental transformation (Gaubatz, 1999).
Large cities, such as Beijing, Shanghai, and Guangzhou, are
changing from the concentric zone cities of the industrial age
to the decentralized multi-nuclei cities of today. This change
is also an indicator of the transition of Chinese cities to more
post-industrial forms, such as those seen in the USA, Canada,
Australia, and Europe (Schneider et al., 2005).
As one of the largest cities in China, Guangzhou has a
development history of almost 2000 years and has undergone
rapid economic development and urbanization over the past two
decades. In 2000, the Guangzhou government put forward a
new development strategy with the aim of building itself into a
world class metropolis by 2010 (Weng and Yang, 2003). Considering the rapid transformations of land use requirements, it is
interesting, for both theoretical and practical reasons, to study
the landscape change of Guangzhou at the strategic level over
recent decades. The findings will also be useful for analyzing
other Chinese cities. By combining gradient analysis with landscape metrics, this study addresses the process of urbanization in
both spatial and temporal contexts, and investigates its dynamics
using a more quantitative approach.
In this study, four sets of Landsat TM images were used and
two transects were selected to analyze urban development at different orientations and its effects on landscape pattern. The transects extend from the core of Guangzhou city to the boundaries
that border with other cities. The study focused on comparing
the differences of urban expansion over time to investigate the
driving forces. It addressed the following research questions: (1)
What are the general temporal and spatial trends of landscape
changes along the two transects? (2) Is there any difference
in the rates of landscape changes through time by comparing
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X.J. Yu, C.N. Ng / Landscape and Urban Planning 79 (2007) 96–109
four remote sensing data sets? (3) What are the driving forces
that cause the differences in land use and their implications for
planning? (4) Does Guangzhou exhibit a diffusion-coalescence
process with a multi-nucleated urban pattern?
became two administrative districts under Guangzhou’s jurisdiction. This now means that Guangzhou consists of 10 administrative districts and two cities at the county level. This study
covered 10 administrative districts, with special emphasis on the
urban built-up area.
2. Study area
Guangzhou is located in south China between 112◦ 57 E to
114◦ 3 E and 22◦ 26 N to 23◦ 56 N, with an area of appropriate 3567 km2 (Fig. 2). It is the geometric center of the Pearl
River Delta area, bounded by the Pearl River to the east and
south, Zhongshan city and Foshan city to the west, and Qinyuan
city to the north. Guangzhou is located in the piedmont and
coastal plain physiographic regions, declining from the mountain areas in the north to sea level at the confluence of the Pearl
River in the south. The annual precipitation in this area is about
1689.3–1876.5 mm. Being at the central location of the Pearl
River Delta areas, Guangzhou was historically an intensive agricultural area embedded with a dike-pond system (Ruddle and
Zhong, 1988).
Being the capital and the largest city in Guangdong province
and South China with a population of 7.38 million in 2004,
Guangzhou has experienced rapid economic development over
the past decades. The value of gross domestic production was
49.7 billion US$ in 2004, double that of what it was in 1999
(Guangzhou Statistical Bureau, 2005). Since June 2000, Panyu
and Huadu, which were formerly cities at the county level,
3. Data processing and methodology
Four sets of Landsat TM images were used in this study
(1988/1993/1998/2002, resolution 30 m, seven bands). They
were processed using ERDAS IMAGINE software, which
involves geometric correction, unsupervised classification and
supervised classification, and GIS reclassification. Since unsystematic geometric errors remain in commercially available
remote sensing data, geometric rectification was therefore
needed to reduce the error before land use classification. The
images were rectified to Gauss-Krüger projection based on
1:50,000 scale topographic maps. Second order polynomial geometric model and cubic convolution algorithm were used during
this process. The root mean squared errors for all four images
were 0.68, 0.47, 0.48, and 0.33 pixels, respectively.
Different land use types were then categorized by using both
unsupervised classification and supervised classification algorithms. Land use classification system of land use survey was
chosen and referred to form the classification system for this
study, which include cultivated land, orchard, forest, urban builtup area, new development area, and water area (Fig. 3). New
Fig. 2. The study area in the Pearl River Delta.
X.J. Yu, C.N. Ng / Landscape and Urban Planning 79 (2007) 96–109
99
Fig. 3. Land use changes in Guangzhou in 1988 and 2002.
development area is defined as an area that has recently been
bulldozed to make way for construction, and thus has a high
contrast characteristic in remotely sensing images. It is therefore
classified separately to capture land use dynamics. ISODATA
algorithm was first used to perform unsupervised classification
to obtain different land use clusters. This preliminary interpretation can maximally reduce the artificial errors and select the
most appropriate clusters for further processing. The Maximum
Likelihood algorithm was then used to improve the accuracy
of land use classification. As a road network is very important
for calculating landscape metrics (Herzog and Lausch, 2001;
Lausch and Herzog, 2002), a vector road layer was merged with
the above classified images. Thus, the final land use classes consist of cultivated land, orchard, forest, urban built-up area, new
development area, road, and water area.
Accuracy assessment is critical for a map generated from
any remote sensing data (Congalton and Green, 1999; Lunetta
and Lyon, 2004). A confusion matrix was used for this purpose
in this study. In order to ensure that the dates of the referenced data were obtained from the same periods, three kinds of
data were employed to conduct an accuracy assessment. For the
1988 and 1993 images, classification accuracies were evaluated
using color aerial photography taken in the corresponding years
and the overall accuracy was found to be 92.67% and 93.00%,
respectively. For the 1998 image, a 1:10,000 topographic map
was used to select the referenced data and the overall accuracy
was found to be 93.45%. The ground truth method has been
carried out for the 2002 image and the overall accuracy was
found to be 91.10%. Because land use types are closely related
to geographical information such as altitude and grade, and this
information has some specific distribution rules, a GIS model
programme was finally used to reduce the errors with the help of
a digital elevation model, which was collected from a 1:50,000
topographic map.
Land use misclassification will have a direct influence on land
use composition, which is reflected by the statistical variation of
each land use class. This is difficult to overcome due to the data
resolution and the accuracy of classification. However, study
has demonstrated that bias in landscape metrics did not appear
to be amplified by land cover misclassification (Wichham et al.,
1997). Moreover, the objective of this study was to characterize
the overall dynamic trend of landscape change. Thus, the data
obtained can accordingly reflect the observed land use statistical
change and its spatial configuration.
To detect the dynamics of landscape pattern in Guangzhou,
two transects were selected that cut across the entire Guangzhou
city from the northwest to the southeast and from the west to
the east, respectively (Fig. 3). The northwest–southeast transect
is composed of twenty 5 km × 5 km blocks and the west–east
transect six blocks. The orientation was chosen to cover the dis-
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Table 1
Landscape metrics selected in this study
Name
Description
Justification
Number of patches
Mean patch size (ha)
Largest patch index (%)
Total number of patches in the landscape
Average size of patches
The area of the largest patch in the landscape divided by total
landscape area, multiplied by 100
Shape index adjusted for size
Shape complexity weighted by the area of patches
Equals minus the sum, across all patch types, of the proportional
abundance of each patch type multiplied by that proportion
0 < CONTAG ≤ 100. Approaches 0 when the patch types are
maximally disaggregated and interspersed
0 ≤ COHESION < 100. Approaches 0 as the proportion of the
landscape comprised of the focal class decreases and becomes
increasingly subdivided and less physically connected
Fragmentation index
Fragmentation index
Dominance index
Area weighted mean shape index
Area weighted mean patch fractal dimension
Shannon’s diversity index
Contagion index (%)
Patch cohesion index
Fragmentation index
Fragmentation index
Diversity index
Fragmentation index
A measure of physical connectedness
Note: The details of the parameters can be found in McGarigal et al. (2002).
tinctive land use changes and to avoid the influence of Baiyun
Mountain Reserve that located 7.5 km north of Guangzhou.
Roads were converted to raster format and then merged with the
categorical data with a resolution of 30 m × 30 m using ERDAS
IMAGINE. Landscape metrics were then calculated for each
block at the class and landscape levels using the raster version
of FRAGSTATS program (Version 3.3) (McGarigal et al., 2002).
Eight metrics were selected in this study (Table 1), which can
fully reflect their conceptual basis and reduce correlation and
redundancy.
4. Result
4.1. General trend of land use change in Guangzhou
In 1988 Guangzhou was dominated by cultivated land,
orchard, and forest land, which together accounted for 80.8% of
the total area (Table 2). In contrast, the urban built-up area and
new development area covered only 30,507 ha, a mere 8.5% of
the total area. This indicated that Guangzhou was still an agricultural dominated area, whereas urban development was still in its
initial stages at that time. From 1988 to 2002, urban sprawl was
maintained at a rapid speed and a total of 46,008 ha urban land
were added (including new development areas). The increase
was mainly due to the conversion from cultivated land and forest,
a loss of 30.2% of the total area during the study period. In 2002
the urban built-up area and new development areas amounted
to 76,515 ha, more than twice that in 1988. The increases of the
urban built-up area were 6938, 20,049, and 19,021 ha, respectively during the three intervals. These changes, together with the
Table 2
Land use statistics in Guangzhou between 1988 and 2002 (ha)
Cultivated land
Orchard
Forest land
Urban built-up area
New development area
1988
1993
1998
2002
127514
45008
115670
29036
1471
117664
47308
105203
33319
4126
111535
49388
88213
53877
3617
85252
50908
84556
74643
1872
Note: Data calculated from the whole Guangzhou images.
growth of new development area, indicate that the rapid industrialization and urbanization mainly took place between 1993 and
2002. This process accompanied the determination and commitment of the Chinese government towards a market economy
system since 1992.
4.2. Landscape metrics at class level along the transects
In general, the west–east transect shows a similar but more
simple pattern compared with that of the northwest–southeast
transect. Thus, only the results of the northwest–southeast transect at class level are present here. Cultivated land has a low
number of patches at the urban center and gradually reaches their
peak values at 15 km in the northwest and 15 km in the southeast
(Fig. 4). But the peak values of the largest patch index and mean
patch size appear at 25 km in the northwest. In the southern part
of the transect, the peaks extend southward from 20 km in 1988
and 1993 to 35 km in 1998 and 2002. As the largest patch index
is a simple measure of dominance (McGarigal et al., 2002),
the variation reflects the low dominance of cultivated land at the
urban center and the gradual loss towards the rural areas through
time. Area weighted mean shape index and area weighted mean
patch fractal dimension increase from the urban center and reach
peak values at 25 km in the northwest and around 20 km in the
southeast. This implies the high shape complexity of cultivated
land in these areas. In contrast with the relatively stable peak values at one distance in the northwest, the peaks of area weighted
mean shape index and area weighted mean patch fractal dimension extend southward from 20 to 35 km over time, suggesting a
higher level of human activities in these areas. With increasing
patch cohesion index values towards the ends of the transect, cultivated land patches became more aggregately distributed among
other types patches. When comparing though time, variation of
number of patches is erratic and inconsistent, but the patch size,
shape complexity, and patch connectivity are decreasing at the
urban center.
The number of patches of orchard has a similar pattern to that
of cultivated land. It increases from the urban center towards the
urban fringe, and subsequently decreases at both ends of the
transect (Fig. 5). Two prominent peaks of largest patch index
X.J. Yu, C.N. Ng / Landscape and Urban Planning 79 (2007) 96–109
101
Fig. 4. Variations in landscape metrics for cultivated land along the transect.
and mean patch size appear at 5 and 55 km in the southeast, suggesting the high dominance of orchards at these distances. Their
values generally decrease over time, suggesting the declining
trend of dominance during the study period. Several peak values of shape indices are observed at rural areas or on the urban
fringes. The declines of area weighted mean shape index and
area weighted mean patch fractal dimension from 1988 to 2002
suggest that the shape complexity has become more regular over
time. Low patch cohesion index values are observed at the urban
center and at 20 km distance in the southeast, indicating orchard
is highly subdivided by other type patches at these distances.
From 1988 to 2002, the number of patches decreases between
25 km in the northwest and 10 km in the southeast, and increases
towards both ends of the transect. The declining patch cohesion
values over time suggest increasing subdivision and less physical connection of orchard among other patch types.
The number of patches of forest is low at the urban center
and increases along the transect until 15 km in the northwest
and 20 km in the southeast, with subsequent decline towards the
ends of the transect (Fig. 6). The largest patch index shows a consistent multi-peaked pattern along the transect, with relatively
constant values over the study period. However, both the number of patches and mean patch size values decrease from 1988
to 2002, together suggesting the process of the loss of forest.
Variations of shape indices appear to be low in the urban center,
suggesting that the forest patches have become more regular in
urban areas. This can also be reflected by their declines over time
with the process of urbanization. The physical connectedness of
forest patches is also low at urban areas, which is characterized by low values of the patch cohesion index. The constant
decline of the patch cohesion index over time indicates that forest is increasingly disaggregated with urbanization. High values
appear at 40 km in the southeast because this area is mainly
covered by hilly forest that has reduced the impact of human
activities.
The number of patches of urban built-up area is low at
the urban center and increases until 15 km in the northwest
and around 20 km in the southeast, and subsequently decreases
towards the ends of the transect (Fig. 7). The largest patch index
has a multi-peaked pattern along the transect, indicating the
dominant status of urban patches at these distances. The relatively higher values appearing at 10 km in the northwest and 5 km
in the southeast, instead of at the urban center, may be due to the
separation of the urban center into two parts by the Pearl River.
The mean patch size values can also reflect this character. The
high complexity of urban patches appears at the urban center, 20
and 50 km in the southeast, respectively, which are revealed by
the high values of area weighted mean shape index and area
weighted mean patch fractal dimension. The patch cohesion
index is high at 5 km in the northwest and 40 km in the southeast,
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Fig. 5. Variations in landscape metrics for orchard along the transect.
which indicates the high connectedness of urban patches. However, the highest value appears at 30 and 40 km in the northwest.
This may be due to the relatively low urban area aggregating
in a small area that makes urban patches more clumped in its
own distribution. It has increased from 1988 to 2002 at each
block, suggesting more aggregation of urban patches with the
process of urbanization. But an obvious increase is observed at
20, 40, and 50 km in the southeast, revealing the higher degree
of urbanization at these distances.
4.3. Landscape metrics at landscape level
Along the west–east transect, the landscape generally showed
a similar but simpler character compared with that of the
northwest–southeast transect. Thus, the results are not presented
here. Along the northwest–southeast transect the number of
patches shows several peaks at different distances. It increases
rapidly away from the urban center and reaches the peaks at
15 km in the northwest and 15 km in the southeast (Fig. 8). The
other peak values appear at 30 km in the northwest and 40 km
in the southeast, respectively. The high values at these distances
indicate an obvious increase in the degree of landscape fragmentation. The patches become increasingly disaggregated and
irregular from the urban center to the peripheral areas, which
can be characterized by the increase of area weighted mean
patch fractal dimension. A direct result of disaggregation is the
low level physical connectedness revealed by the patch cohesion
index. This finding reveals the behavior of the patch cohesion
index over time in a rapidly urbanized area, which has not been
reported in current studies. Human activities, such as urbanization, will first result in more fragmented patches and then
disconnect them from each other, which will decrease the value
of the patch cohesion index. This process can be confirmed by
its variations at 20 and 40 km in the southeast, around two new
development areas. However, it will gradually increase over time
when urbanization reaches to a certain degree, which corresponds to the percolation threshold at the class level (McGarigal
et al., 2002). The gradual extension and final connection among
urban patches might contribute to this point. Variations in the
contagion index can further confirm this inference. Although it is
erratic and inconsistent along the transect, temporal variations
in Shannon’s diversity index decline around the urban center
whereas they increase at the ends of the transect. Thus, the landscape has become more unevenly distributed and is dominated
by an urban built-up area at the urban center. On the contrary, the
landscape at the ends of the transect become more equitably and
evenly distributed when urban areas gradually became embedded in these areas.
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103
Fig. 6. Variations in landscape metrics for forest along the transect.
5. Discussion
5.1. General trends of landscape changes
The above examination of landscape metrics along the transects of Guangzhou reveals that urban areas gradually extended
from the city center to the peripheral rural areas. This is consistent with rural–urban–rural gradient in Shanghai as reported by
Zhang et al. (2004). However, a multi-peaked pattern is identified in some metrics such as largest patch index, area weighted
mean shape index, and patch cohesion index along the southwest portion of the northwest–southeast transect. These findings
reveal the distinctive multi-center pattern of urban sprawl in
Guangzhou, which is different from that of other Chinese cities
(Ding, 2004; Zhang et al., 2004). There was only one urban
center in Guangzhou between 15 km in the northwest and 5 km
in the southeast in 1988. Significant changes in some suburban areas are observed during the study period and two rapid
development areas gradually appear, one at 20 km and another
at 40 km in the southeast. They can clearly be identified in 2002,
although the extent and degree of urbanization are still lower
than that of the old center. The details of it will be presented in
the following section. Among the four land use types, cultivated
land and orchard have shown relatively inconsistent, yet distinctive patterns compared with forest and urban built-up area.
This character might be explained by two points. First, cultivated land and orchard have become more changeable due to
the high impact of human activities, and therefore show relatively erratic and unruly behaviors. Second, a recent study by
Yu and Ng (2006) shows that agrotype have the character of
rotatable crops between cultivated land and orchard, which will
also result in an unstable landscape pattern.
The actual urbanization tends to cause an increased fragmentation and gradual uniformity of landscapes and cities
(Antrop, 2000). The sums of number of urban patches for the
northwest–southeast and west–east transects are 1702 and 592 in
1988, and 2024 and 346 in 2002, respectively. It had increased
by 19% along the northwest–southeast transect but decreased
by 42% along the west–east transect. At the landscape level, the
sums of number of patches along the northwest–southeast transect had increased from 11,058 in 1988 to 12,883 in 2002. On the
contrary, it had decreased from 2839 to 2804 along the west–east
transect during the study period. Thus, the northwest–southeast
transect exhibited a process of fragmentation while the west–east
transect became more aggregated over time, which represents
two different stages of urban sprawl. The results also show
that the number of patches is low in the old urban center and
has decreased over time. In contrast, it is high and increases
over time in the new urbanization areas, for example, at 20 and
40 km distance in the southeast. These findings reveal that the
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Fig. 7. Variations in landscape metrics for urban built-up area along the transect.
distinctive impacts of urbanization on landscape pattern vary
over time. At first, the landscape becomes more fragmented due
to the initiation of urbanization. When the degree of urbanization increases to a threshold, the mean patch size will increase
and the landscape will become more aggregated. In fact, this
process might be accompanied by a process of landscape matrix
conversion. When comparing the number of urban patches over
time, the decline in the urban center and the increase in new
urbanized areas agree with the findings of Antrop (2004) that
the urban fringe or suburban landscapes are expressed in a complex, diverse and highly fragmented morphology.
5.2. Rates and driving forces of landscape changes over
time
Characterizing urban sprawl involves quantifying its pattern
and analyzing the driving forces behind it. Landscape changes
along transects are profound due to the history of urban development, the physical geographic setting, and the characteristics of
the urban core (Medley et al., 1995). It is important to compare
them among different transects in order to capture the dynamics of landscape change. The amount of urban built-up area is
generally considered as the parameter for this purpose (Li and
Yeh, 2004; Sudhira et al., 2004). The growth rate of urban area
can be used to evaluate the spatial distribution of urban expan-
sion intensity (Xiao et al., 2006). In this study variations of urban
built-up area and new development area are employed to characterize the urban sprawl of Guangzhou. As the area of each block
is constant in this study, the annual urban growth, as defined
as follow, of urban built-up area can be used to compare urban
growth at different distances along the transects:
annual urban growth =
UAi+n − UAi
n
where n is the interval of the calculating period (in years); UAi+n
and UAi are the urban built-up areas in the target blocks at time
i + 1 and i, respectively. The results are illustrated in Fig. 9.
It is clear from Fig. 9 that the amounts and rates of urban
growth differ strongly between transects and among locations.
The southeast and east portions of the transects witnessed the
fastest growth during the study period. This demonstrated the
spatio-temporal differences of urban dynamics of the intra-city
areas. In particularly, high ratio of change is observed since
1993, which can be characterized by both higher annual urban
growth and larger area of new development area. Investigating
this difference requires the integration of land use policies at
the national and provincial levels and the responses of the local
economy and society. In Guangzhou, urban sprawl to the west is
limited due to the influences of the border between Guangzhou
and Foshan. In the northwest, it is also restricted by the Baiyun
X.J. Yu, C.N. Ng / Landscape and Urban Planning 79 (2007) 96–109
105
Fig. 8. Variations in landscape metrics at landscape level along the northwest–southeast transect.
Mountain Reserve and Baiyun International airport, and hence
only a slight increase occurred at 10 km in the north between
1993 and 1998. The growth towards the east reflects the process
of inherent evolution from the inner city to the outer initiated by
population growth and economic development since the ‘reform
and openness’ policy in the late 1970s.
With the increasing degree of urbanization in Guangzhou,
urban expansion to the southeast has gradually replaced the tra-
Fig. 9. Variations in urban built-up area and new development area.
106
X.J. Yu, C.N. Ng / Landscape and Urban Planning 79 (2007) 96–109
ditional eastward and northward expansion. Along the southeast
portion of the northwest–southeast transect, two new urban centers successively established: one located closer to Guangzhou
center (hereafter called the northern part of Panyu) and the
other further south (hereafter called the southern part of Panyu).
Although there are social and economic causes, two different
driving forces are identified by further investigation. Since the
announcement of ‘the Provisional Regulation of the Land Rights
over State-owned Land in Cities and Towns’ in 1991, land price
mechanisms began to have tangible effects on the land market
development in China, especially in some large cities (Ding,
2004). The landscape in the northern part of Panyu clearly
reflects this process of market orientation in the real estate market. Due to low land price, it has attracted much interest from
real estate developers and residents from the Guangzhou urban
core. For example, there were a total of 116 real estate projects
in Panyu, amounting to 5127 ha floor spaces at June 2001
(Guangzhou Statistical Bureau, 2002). Most of these projects
were located close to Guangzhou in the northern part of Panyu
at Nancun (1243.8 ha), Dashi (935.6 ha), Zhongcun (522.0 ha),
Shawan (396.5 ha), and Shiqiao (174.1 ha). As compared with
the inherent evolution towards the east, low land price could
serve as a precursor for the recent urbanization in this area (Burgi
et al., 2004).
In contrast, landscape change in the southern part of Panyu
shows a different character that is mainly influenced by government policy and direct foreign investments. The area was
underdeveloped and remote before the early 1990s. However,
there have been rapid changes since the setting up of the Nansha Economic and Technological Development Zone in 1993.
From then on, several new policies were enacted to facilitate
economic development and foreign investment. By 2005, there
were already over 200 foreign companies established in Nansha and that had also boosted the economic development in the
surrounding areas (Guangzhou Statistical Bureau, 2005). For
example, the total number of industrial enterprises in Panyu had
increased about 54% from 4859 in 1995 to 7540 in 2003. Most
of them are established in Nansha Economic and Technological Development Zone. The inflow of direct foreign investments
has resulted in an obvious landscape change that can be clearly
seen by comparing the 1988 and 2002 images. This process was
mainly due to the decision and development strategy from local
government. This kind of driving factor was defined as ‘initiator’
by Antrop (2000).
5.3. Implications for land use planning and urban
management
Changes in landscape pattern along the transects may have
important ecological implications to land management and land
use planning. Studies have demonstrated that remnant forest
fragments, particularly larger ones, are critical for providing
habitat and sustaining ecosystem functions (Christian et al.,
1998; Lindenmayer et al., 1999). There is also evidence that
disconnected urban areas converge towards a pattern of contiguous urban fabric (Seto and Fragkias, 2005). Along the
northwest–southeast transect, the number of patches and mean
Fig. 10. Annual variation in forest areas during three intervals.
patch size of forest declined between 1988 and 1998. The rapid
decline occurred in the urban core or in newly urbanized areas,
for example between 20 km in the northwest and 30 km in the
southeast (Fig. 10). Thus, how to maintain the pre-urban natural
remnants that connect urban cores will be most important in any
effort to mitigate the potential impacts of urbanization. Linking
gradient analysis with urban dynamics can help to detect such
spatially explicit urban patterns, and improve the ability of planners to integrate ecological considerations in urban planning. For
example, urban green space planning in Guangzhou has recognized the impact of urban expansion on the environment, and
hence important green corridors and nodes are proposed among
urban centers (Guangzhou Landscape Bureau, 2002). A significant effort has been made by the Guangzhou Government to
establish two ‘green forest rings’ with a total area of 86 km2 that
encircle the urban cores of the city, and which can also help to
mitigate environmental pollution problems due to a high degree
of urban coalescence.
Conservation of cultivated land is of great significance in
China because of the scarce land resources per capita in comparison to the world average (Lin and Ho, 2003). Thus, to maintain
the ‘dynamic equilibrium of farmland’ is proposed as one of the
fundamental objectives of land management of the country. In
order to achieve this goal, ‘principle agricultural protecting area’
is zoned and strictly controlled according to the ‘Ordinance for
the Protection of Primary Agricultural Land’, which was enacted
in 1994, as well as other local regulations. Once the land is protected as ‘principle agricultural protecting area’, it will be strictly
restricted from occupation by non-agricultural purposes. However, this study reveals that the transition between cultivated land
and orchard has been rapid, especially along the main urbanized gradients, where the urban–rural fringe is shifting rapidly
through time. For example, along the northwest–southeast transect, only 57.9%, 51.9%, and 48.3% of the total cultivated land
remained as the same type during three interval periods (Table 3).
Also, conversion from cultivated land to other types is much
higher along the transect than in the whole Guangzhou area. This
observation indicates that the ‘dynamic equilibrium of farmland’
policy is ineffective in preserving farmland in Guangzhou. Thus,
it is necessary to consider the spatial characters of landscape
dynamics when reexamining land use policies.
5.4. Testing the hypotheses and detecting stages of urban
sprawl
By combining gradient analysis with time series data, two
hypotheses postulated to investigate urban dynamics were tested
X.J. Yu, C.N. Ng / Landscape and Urban Planning 79 (2007) 96–109
107
Table 3
Rates of change from cultivated land to other types
Interval
1988–1993
1993–1998
1998–2002
Cultivated land to cultivated land
Cultivated land to orchard
Guangzhou (%)
NS transect (%)
Guangzhou (%)
NS transect (%)
62.0
59.9
48.9
57.9
51.9
48.3
12.0
17.0
22.9
15.5
17.9
18.1
in this study. Seto and Fragkias (2005) hypothesized that the
area weighted mean patch fractal dimension will increase during the early periods of urban land use change when new urban
nuclei and expansion of existing urban space creates irregularly
shaped landscape pattern. The results of this study confirmed
this character in new development areas, for example at 20 and
40 km in the southeast of the northwest–southeast transect. In the
old urban core, however, a decline was revealed after the initial
increase from 1988 to 1993. This distinct process confirmed the
hypothesis proposed by Dietzel et al. (2005) that urban expansion exhibits an alternating process of diffusion and coalescence,
and the behaviors of landscape metrics vary accordingly during
this process. The spatial evolution of cities can be described as a
two-step process, starting with the expansion of an urban seed or
core area, and beginning to coalesce when the diffusion reaches
a certain point.
Urban expansion in Guangzhou clearly demonstrated a
diffusion-coalescence process with a multi-nucleated urban pattern. A shift from diffusion to coalescence was revealed in old
urban centers in 1993 when the landscape became more aggregated and compacted, which was characterized by the decrease
of number of urban patches and an increase of mean patch size,
decline in shape complexity, and increase in contagion and connectivity. On the contrary, the shift appeared later in the new
urbanized areas, for example at 20 and 40 km in the southeast in
1998. In fact, this pattern of urbanization can reflect the history
and strategy of urban development in Guangzhou. Before Panyu
was designated as a district of Guangzhou in 2000, several urbanization centers were coexisting, but their rates of growth differed.
This was the result of the establishment of targeted development
zones and other specialized centers, which reflect the economic
and social changes in China (Gaubatz, 1999). Variations in landscape metrics values can detect the conversion process from
diffusion of new urban center to coalescence towards a saturated urban landscape (Dietzel et al., 2005). This distinct process
demonstrates that different stages of urban growth and hierarchical network might be more appropriate to explain the urban
sprawl process in Guangzhou. It provides a useful method to
capture, quantify, and understand the spatio-temporal patterns
of urban growth, and to link them to social processes.
ining landscape metrics variations that usually extended from
the city center, through the suburbs, to the rural outskirts or
urban fringes. This study also provides new findings about the
spatial and temporal dynamics of urban sprawl in Guangzhou
that are different from that of other cities in China. Urbanization
in Guangzhou does not show a simple urban–rural gradient. This
is because the changes or disturbances associated with urbanization show a complex spatial pattern not clearly related to
urban–rural distance alone (Medley et al., 1995; McDonnell et
al., 1997). The classical urban theories, which are mainly based
on social and economic rules, cannot capture local changes in
land use pattern. A diffusion-coalescence process with multinucleated urban pattern might well explain the spatio-temporal
dynamics of urban sprawl and its relation to local-scale generating processes. This character can be revealed by combining
gradient analysis with time series data sets. The study also
reveals that Chinese cities may exhibit different spatial patterns of urbanization due to the complexity of local history
and driving forces. The study provides some new interesting
perspectives on urban morphology, especially from a comparative viewpoint. China is a highly centralized nation and land
use policies have played a vital role in shaping the landscape
before the reforms of the late 1970s. Thus, the strict hierarchy
of the land use planning system and the restricted population
migration to cities resulted in relatively undifferentiated urban
pattern (Ng and Tang, 1999; Seto and Fragkias, 2005). The emergence of multiple centers recently, which are driven by land use
policy changes, local economic development and direct foreign
investments, characterizes the restructuring of the urban agglomerations from a central planned system to a more market oriented
system in China. Bottom-up forces and local interactions begin
to play more and more important roles in forming this new urban
pattern. The interactions of local, regional, national, and international forces have resulted in the complexity of centrifugal and
centripetal forces at play in the economic environment (Seto and
Fragkias, 2005). Thus, analysis of both top-down (constraints)
and bottom-up (local interactions) mechanisms are essential for
characterizing the complex, dynamic, multidimensional configuration of urban pattern in China. This study demonstrates that
dynamics analysis and comparison of landscape metrics along
the urban–rural transects can serve this purpose.
5.5. Comparative analysis of urban sprawl along the
transects
Acknowledgments
The combination of gradient analysis and landscape metrics
can characterize the complex spatial pattern of urbanization in
Guangzhou well. Most of the changes can be detected by exam-
We would like to thank Prof. Li Xiuzhen, Institute of Applied
Ecology, Chinese Academy of Sciences, China and Dr. Shen
Weijun, South China Institute of Botany, Chinese Academy
108
X.J. Yu, C.N. Ng / Landscape and Urban Planning 79 (2007) 96–109
of Sciences, China for their helpful discussion and suggestion in the revision of this manuscript. We are also grateful to
Dr. W.L. Kyle, the University of Hong Kong for proof reading. Special thanks to Dr. Denis Saunders and two anonymous
reviewers for their valuable comments on the earlier draft of this
paper.
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Xi jun Yu is currently a PhD candidate in the Department of Geography at
the University of Hong Kong. He received a BS in geography from Shandong
Normal University and MS in environmental science from Zhongshan Uni-
109
versity. His research focuses on landscape ecology assessment and strategic
environmental assessment (SEA), with special emphasis on applying landscape ecology principles into SEA on spatial plan. He had also been involved
in several programs in marine functional zonation and integrated coastal
zone management when he worked as an assistant researcher in Guangdong
Center for Marine Resource Research and Development between 1999 and
2002.
Cho Nam Ng is an associate professor of the Department of Geography at the
University of Hong Kong. His research focuses on methodology for environmental modelling and impact assessment, with special emphasis on applying
spatial and time-series analysis methods in environmental systems. He teaches
courses on environmental management and impact assessment, and sustainable
development. He is the chairman of the Environmental Impact Assessment Subcommittee in the Advisory Council on the Environment of the Hong Kong SAR
Government. He received a BSc and PhD in Environmental Science from the
University of Lancaster, UK.