1. science
2. ecology

Landscape and Urban Planning 69 (2004) 1–16
A GIS-based gradient analysis of urban landscape
pattern of Shanghai metropolitan area, China
Liquan Zhang a,∗ , Jianping Wu b , Yu Zhen a , Jiong Shu b
a
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200062, China
b Open Laboratory of Geographical Information Science of Chinese Education Ministry,
East China Normal University, Shanghai 200062, China
Received 5 April 2003; received in revised form 1 August 2003; accepted 11 August 2003
Abstract
Quantifying landscape pattern and its change is essential for the monitoring and assessment of ecological consequences of
urbanization. As the largest city in the country, metropolitan Shanghai is now the fastest growing area among all major Chinese
cities with more than 13 million residents. Using the GIS-based land use data set of the year 1994 and combining gradient
analysis with landscape metrics, we attempted to quantify the spatial pattern of urbanization in the Shanghai metropolitan
area. The results of transect analysis with class-level metrics showed that the spatial pattern of urbanization could be quantified
reliably using landscape metrics and different land use types exhibited distinctive, but not necessarily unique, spatial signatures.
The results of transect analysis with landscape-level metrics showed that urbanization in the metropolitan Shanghai region
has resulted in dramatic increases in patch density (PD), edge density (ED), and patch and landscape shape complexity, and
sharp decreases in the largest and mean patch size (MPS), agriculture land use type, and landscape connectivity. The general
pattern of urbanization was that the increasingly urbanized landscape became compositionally more diverse, geometrically
more complex, and ecologically more fragmented. In addition, our results supported the hypotheses that, with increasing
urbanization, patch density increases while patch size and landscape connectivity decrease. However, our results on patch
shape seemed to reject the hypothesis that patch shape becomes more regular as human modification to landscapes intensifies.
© 2003 Elsevier B.V. All rights reserved.
Keywords: Urbanization; Gradient analysis; Landscape metrics; Landscape pattern; Metropolitan Shanghai
1. Introduction
Urbanization has been an important component of
land use and land cover change, and its significance
will undoubtedly continue to increase with the majority of the world’s population swarming into cities
(Breuste et al., 1998; Pickett et al., 2001; Whitford
et al., 2001). The world’s urban population was only
∗ Corresponding author. Tel.: +86-21-6223-2599;
fax: +86-21-6254-6441.
E-mail address: [email protected] (L. Zhang).
0169-2046/$20.00 © 2003 Elsevier B.V. All rights reserved.
doi:10.1016/j.landurbplan.2003.08.006
about 3% of the global population in the 1800s, but
increased to nearly 30% in 1950, and reached 50%
in 2000. It has been projected that by 2025, 60% of
the world population will live in urban areas, with a
dozen megacities that will be crowded with 20 million or more people (United Nations, 2000). In China,
the urban population was 37.7% in 2001 and, according to a recent Chinese official report (The Chinese
Mayor’s Association, 2002), it has been projected that
the urbanization in China will reach 75% by 2050.
Urban morphology and its evolution have long
been studied in geography, landscape planning and
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L. Zhang et al. / Landscape and Urban Planning 69 (2004) 1–16
architecture, and sociology. For example, as early
as in 1825, the German economist von Thünen
(1825) asserted that the urban morphology of an
isolated city would be characterized by concentric
economic rings (e.g., business, residential, industrial, agriculture), as dictated by simple cost-benefit
relations (the principle of marginal spatial utility;
cf. Portugali, 2000). von Thünen’s work laid an
important foundation for the development of urban
development theories, including the concentric zone
theory (Burgess, 1925) and the central place theory (Christaller, 1933) which depict cities as more
or less concentric or symmetric structures with one
or more central business districts. Different from
the concentric-ring models, the sector theory (Hoyt,
1939) allows for corridors or wedges of industrialization due to the influence of transportation
networks, while the multiple nuclei theory (Harris
and Ullmann, 1945) recognizes the multiple centers
of specialized activities (e.g., finance, industry, commerce, residence) that result in an asymmetric patch
mosaic pattern. These theories of urban forms were
developed based primarily on studies of American
cities (Chicago, San Francisco, and Boston, respectively) several decades ago, and thus may be less
applicable to cities in other countries or to many
modern and young cities (Thio, 1989; Wu et al.,
2003).
A major goal of urban ecology is to understand
the relationship between the spatial pattern of urbanization and ecological processes (Sukopp, 1990,
1998; Loucks, 1994; Breuste et al., 1998; Pauleit
and Duhme, 2000; Wu and David, 2002). Although
urban ecology is not new, a resurgence of interest in the ecology of urban environments is evident
in recent years (e.g., Pickett et al., 1997; Breuste
et al., 1998; Kent et al., 1999; Collins et al., 2000;
McIntyre et al., 2001). The spatial pattern of land
use reflects underlying human processes and influences the ecology of urban environments (Redman,
1999; Bastian, 2000). Humans have the ability to
greatly modify their environment, which tends to increase landscape fragmentation by generating more
and smaller patches. Forman and Godron (1986)
(also see Godron and Forman, 1983) postulated that
patch characteristics exhibit generally predictable
patterns along a landscape modification gradient
(i.e.,
natural—managed—cultivated—suburban—
urban). Four specific hypotheses can be stated as
follows: (1) patch density (PD) increases exponentially (Fig. 1A); (2) the regularity of patch shape
increases (Fig. 1B); (3) both the mean and variance
of patch size decrease, but the latter decreases faster
(Fig. 1C); and (4) landscape connectivity decreases
(Fig. 1D).
To test the theories of urban morphology and to
relate the spatial pattern of urbanization to ecological processes adequately, quantitative spatial analysis methods are needed. Among others, gradient
analysis and landscape pattern analysis seem appropriate for such studies (Luck and Wu, 2002).
Gradient analysis, developed in the context of vegetation analysis (Whittaker, 1975), has been used
to investigate the effects of urbanization on plant
distribution (e.g., Kowarik, 1990; Sukopp, 1998)
and ecosystem properties (Pouyat and McDonnell,
1991; Pouyat et al., 1995; Zhu and Carreiro, 1999).
McDonnell and Pickett (1990) indicated that the
‘urban–rural’ gradient, similar to the ‘city–country
gradient’ used by European urban (plant) ecologists
(e.g., Kowarik, 1990), provides an unplanned field
experiment in which study plots can be arranged in
a transect along the gradient of urbanization. On the
other hand, landscape ecology, focusing on the interactions between spatial pattern and ecological processes, has brought about a number of new methods
which can be conveniently used for studying urban
systems (O’Neill et al., 1988; Turner, 1989; Frohn,
1998; Wu et al., 2000), but their use in urban ecology
is yet to be fully explored.
In this study, we integrated gradient analysis with
landscape pattern metrics to quantitatively characterize the urbanization pattern of the metropolitan area
of Shanghai, the biggest city in China. We aimed to
address several questions: (1) how do different land
use types change with distance away from the urban
center? (2) do different land use types have their own
unique spatial signatures (e.g. the shape of the change
curve along an urban–rural transect)? (3) can urbanization gradients be detected using landscape pattern
analysis? (4) to test the hypotheses on landscape structural responses along a human modification gradient
postulated by Forman and Godron. Identifying these
gradients or patterns is an important first step to relate
urban morphology to ecological and socioeconomic
processes.
L. Zhang et al. / Landscape and Urban Planning 69 (2004) 1–16
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Fig. 1. Landscape modification gradient hypothesis (redrawn from Godron and Forman, 1983 and Forman and Godron, 1986).
2. Study area
The Shanghai metropolitan region is located on the
eastern coast of China, the Yangtse River estuary is to
the north of it, East Sea to the east of it and Hangzhou
Bay to the south of it, between 30◦ 23 –31◦ 37 N and
120◦ 50 –121◦ 45 E (Fig. 2). Shanghai has a northern subtropical monsoon climate, with an average
annual temperature 16 ◦ C, summer temperatures average 28 ◦ C while winters are cold with an average
temperature of 4 ◦ C. Average annual precipitation is
approximately 1200 mm, with 60% of rainfall occurring during May–September. The native vegetation
is characterized by subtropical evergreen broadleaved
forests dominated by Castanopsis sclerophylla, Cyclobalanopsis glauca, Machilus thunbergii, Schima
superba and Cinnamomum japonicum, and along the
coast vast areas of wetland dominated by Phragmites australis, Scirpus mariqueter and Spartina
alterniflora.
The study area of this research contains the entire
Shanghai metropolitan region and covers approximately 6340 km2 , with a maximum north–south
length of about 120 km and east–west distance of
about 100 km. (Fig. 2). Historically, the city of Shanghai was established in the Yuan Dynasty (1292 A.D.).
The late 1800s marked a major turning point in the
history of Shanghai because of the development of
industry and trade port and since then, Shanghai has
become the largest city in China. Shanghai is now the
fastest growing area among all major Chinese cities
with the population from 5.2 million in 1949 to 13.5
million in 2001 and the urban area from 91.5 km2
in 1947 to more than 800 km2 in 2000 (Shanghai
Municipal Statistics Bureau, 2001). Since the beginning of the 1990s Shanghai has experienced a
tremendous land transformation from an agricultural
to urban area and the urban fringe has constantly
advanced outward into the surrounding agricultural
land.
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L. Zhang et al. / Landscape and Urban Planning 69 (2004) 1–16
Fig. 2. 1994 Land use map of the Shanghai metropolitan area, China. The transects were west–east and south–north, and 64 km long and
6 km wide and 66 km long and 6 km wide, respectively.
3. Data and methods
The analysis of structural characteristics of the
Shanghai metropolitan landscape was based on the
1994 Shanghai land use data set produced by the Open
Laboratory of Geographical Information Science
of Chinese Education Ministry, East China Normal
University. A set of 154 aerial color infrared photographs (taken in 1994) was used to create the land
use map, while a topographic map of 1984 created
from the Shanghai Institute of Surveying and Drawing (1:25,000) was imported as a background and
auxiliary information from adjacent years was also
incorporated to make up data gaps. The land use map
L. Zhang et al. / Landscape and Urban Planning 69 (2004) 1–16
of the year 1994 was created by manual interpretation
of the aerial color infrared photographs combining
with necessary field survey and validation. This land
use map was then compiled in 1997 using Arc/Info
(Fig. 2, Mei et al., unpublished data). The land use
data set was reclassified from 15 patch types into 7
patch types (or classes): residence, industry, agriculture, roads, water, land for construction and other
urban land. This simplified land use classification
scheme was justified and effective for the purpose of
assessing the spatial characteristics of the major human alterations to natural or semi-natural landscapes,
and for understanding some of the general patterns of
their interactions.
To capture some of the synoptic features of the landscape, several landscape-level metrics were calculated
using the raster version of FRAGSTATS (McGarigal
and Marks, 1995). The vector data (ArcView Shape
files) were converted to raster format (ArcView Grids)
with the same extent (6340 km2 ) and the same geographic coordinates at the pixel size of 10 m × 10 m
using ESRIs ArcView Spatial Analyst.
To detect the urbanization gradient of landscape pattern, we conducted a series of analyses along west–east
and south–north transects cutting across the urban center (the People’s Square) of Shanghai metropolitan
area (Fig. 2). Both transects had three adjacent rows
5
each composed of 32 and 33 2 km × 2 km blocks (i.e.,
the west–east transect was 64 km long and 6 km wide,
and the south–north transect 66 km long and 6 km
wide, respectively). A series of moving windows each
sized 3×3 blocks (i.e., 6 km ×6 km, Fig. 3) were sampled across these two transects from west to east and
south to north, each time moving one block (i.e., 2 km),
thus the west–east transect was composed of 32 and
the south–north transect 33 overlapping moving windows, respectively. The vector data of the moving windows were then converted to raster format at the pixel
size of 10 m ×10 m and both class and landscape-level
metrics were computed using with FRAGSTATS. This
procedure, similar to the derivation of moving averages for a time series, smoothed out much of the noise
caused by fine-scale and local variations.
The redundancy and overlap among landscape metrics have been investigated previously (e.g., O’Neill
et al., 1999). Not all the landscape metrics were needed
to capture the changes in the composition and configuration of the landscape. We have tried to choose a
small set of the metrics both sensitive to changes and
numerically reliable (i.e., showing consistent trends)
for depicting landscape patterns. Sixteen landscape
metrics were used to quantify the landscape pattern of
the Shanghai metropolitan region (Table 1). As in most
cases in the existing literature, the term “landscape
Fig. 3. The moving window located at the city center along the transects, size 3 × 3 blocks (i.e., 6 km × 6 km).
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L. Zhang et al. / Landscape and Urban Planning 69 (2004) 1–16
Table 1
Landscape metrics used to quantify the spatial pattern of urbanization in metropolitan Shanghai, China (based on McGarigal and Marks,
1995)
Landscape metrics
Abbreviation
Description
Compositional measures
Percent coverage
PLAND
The proportion of total area occupied by a particular patch type; a measure
of dominance of patch types
The number of patches of per 100 ha
The total length of all edge segments per hectare for the class or landscape
of consideration
Patch richness The number of patch types in the landscape; a measure of
diversity of patch types
A measure of patch diversity in a landscape, which is determined by both
the number of different patch types and the proportional distribution of area
among patch types
The ratio of the actual value of the Shannon diversity to its maximum value
that is achieved when all patch types each occupy the same proportion of
the landscape
The ratio of the area of the largest patch to the total area of the landscape
The average area of all patches in the landscape
The standard deviation of patch size in the entire landscape
The standard deviation of patch size divided by mean patch size for the
entire landscape
Patch density
Edge density (m/ha)
PD
ED
Patch richness
PR
Shannon diversity index
SHDI
Shannon evenness index
SHEI
Largest patch index (%)
Mean patch size (ha)
Patch size standard deviation (ha)
Patch size coefficient of variation (%)
LPI
MPS
PSSD
PSCV
Configurational measures
Landscape shape index
Mean patch shape index
Area-weighted mean shape index
Mean patch fractal dimension
Area-weighted mean fractal dimension
Contagion
LSI
MSI
AWMSI
MPFD
AWMFD
CONT
The total length of patch edges within the landscape divided by the total
area, adjusted by a constant for a square standard
A patch-level shape index averaged over all patches in the landscape
Mean patch shape index weighted by relative patch size
The average fractal dimension of individual patches in the landscape
The patch fractal dimension weighted by relative patch area
An information theory-based index that measures the extent to which
patches are spatially aggregated in the landscape
pattern” here includes both the non-spatial composition (e.g., the number and relative abundance of patch
types, patch size, and other related non-spatial measures) and spatial configuration (e.g., patch shape,
juxtaposition, contrast, and boundary characteristics).
Li and Reynolds (1994) defined spatial heterogeneity in a similar way. Specifically, we considered 10
indices as compositional measures: percent coverage
(PLAND), patch density, edge density (ED), patch
richness (PR), diversity (SHDI), evenness (SHEI),
largest patch index (LPI), mean patch size (MPS),
patch size standard deviation (PSSD), and patch size
coefficient of variation (PSCV), and the remaining 6
as configurational measures: landscape shape index
(LSI), mean patch shape index (MSI), area-weighted
mean shape index (AWMSI), mean patch fractal
dimension (MPFD), area-weighted mean fractal dimension (AWMFD), and contagion (CONT). This
dichotomy of compositional versus configurational indices is apparently an oversimplification, and many of
these indices reflect both aspects of landscape pattern
to varying degrees (McGarigal and Marks, 1995). We
adopt this simple classification scheme so as to facilitate the organization of results and their interpretation.
4. Results
4.1. Synoptic characteristics of the Shanghai
metropolitan area landscape
In 1994, the level of urbanization of the entire
Shanghai metropolitan region was rather high, with
agriculture occupying 80.8% of the area, while residence, industry, land for construction, other urban
land, roads and water accounted for 2.5, 3.1, 1, 5.6, 1
L. Zhang et al. / Landscape and Urban Planning 69 (2004) 1–16
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Fig. 4. Synoptic landscape characteristics for the Shanghai metropolitan area (the legend of land uses: A: residence, B: industry, C:
agriculture, D: road, E: water, F: land for construction, and G: other urban land): (a) patch type percent cover, (b) patch density (patch
number/km2 ), (c) largest patch index (% total area), and (d) mean patch size (ha).
and 5.9%, respectively (Fig. 4a). The mean patch size
for the entire landscape was approximately 103 ha,
and the mean patch density was 0.97 patches/km2 . PD
increased progressively from road, agriculture to the
urban land uses (i.e., residence, industry, land for construction and other urban land), but other urban land
had the highest patch density over the landscape defined by the boundary of Shanghai metropolitan area
(Fig. 4b). Similar to the pattern of the relative area
of patch types (Fig. 4a), agriculture had the highest
values of largest patch index and MPS, and from road
and water to the urban land uses (residence, industry
and other urban land) both LPI and MPS continued
to decrease (Fig. 4c and d). Overall, the urban land
uses had the smallest values for MPS and LPI and the
largest PD, together suggesting a high degree of fragmentation. The agriculture patch type had the highest
proportion of cover and the highest values of LPI and
MPS, indicating its dominant and least fragmented status outside the urban core and within the metropolitan
boundary. However, the largest PD value of the other
urban land patch type (i.e., square and parking, storehouse, science and education, green land, commerce,
public utility, sport, culture and recreation and hospital) suggested a high degree of fragmentation that
mostly occurred within the urban area. This interpretation was corroborated by transect analysis presented
below.
4.2. Transect analysis with class-level metrics
The two linear types, road and water as well as land
for construction patch type (comprised a total of less
than 1% of the total area) were not presented in this
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L. Zhang et al. / Landscape and Urban Planning 69 (2004) 1–16
section for the purpose of an effective interpretation.
In general, the west–east transect and the south–north
transect showed similar patterns, therefore, we present
here only the result of the west–east transect for
class-level metrics. The percent coverage for the land
use types of residence, industry, agriculture and other
urban land varied with distance from the west end
of the landscape transect eastward (Fig. 5a) and, in
general, the relative dominance of land use types
showed a somewhat symmetric pattern along the
west–east transect: shifting from agriculture to industry/residence and then back to agriculture. Undoubtedly, the distances (between −15 and 5 km, while
0 km indicated the urban center) where the urban
Fig. 5. Changes in landscape pattern along the west–east transect by patch type: (a) patch type percent cover, (b) patch density, (c) mean
patch size, (d) patch size coefficient of variation, (e) landscape shape index, and (f) area-weighted mean shape index. The values were
averages obtained using a 3 × 3 overlapping moving window.
L. Zhang et al. / Landscape and Urban Planning 69 (2004) 1–16
land use types of residence, industry and other urban
land peaked indicated the location of the urbanization
center in the Shanghai metropolitan region. Agriculture was dominant at distances less than −20 km and
again greater than 10 km, forming a ‘U’ shape. Industrial land use exhibited two peaks on both sides
of the urbanization center. In contrast, residential
and other urban land use types increased gradually
towards, and peaked prominently at the urbanization
center. The area coverage of residence was consistently greater than that of other urban land in this
area.
The patch density of all land use types was low
from the west end of the west–east transect to −10 km,
with residence/other urban land reaching a maximum
around 0 km (Fig. 5b). PD of industry showed a similar pattern to its percent cover, with high values at
−10 km in the west and 5 km in the east, and low
values at and around the urban center. PD of all urban land use types decreased to the east of the urban
center. At distances less than −15 km, the mean patch
size of agriculture was high. It then decreased to low
values or zero at the urban center until 10 km and
increased again rapidly beyond that distance. MPS of
the three urban land use types was consistently small
along the west–east transect. The general pattern of
MPS for agriculture mimicked the pattern of its percent cover (compare Fig. 5a and c). The variations
of the mean patch size for the urban land use types
were less conspicuous along the west–east transect
partly because they were composed of relatively small
patches.
Patch size coefficient of variation showed that all
land use types, except agriculture, were relatively
less variable, while agriculture showed a more complex pattern with multiple peaks at different distances
(Fig. 5d). Landscape shape index and area-weighted
mean patch shape index of agriculture decreased at
the distance of −10 km and then increased eastward
from the distance of 5 km (Fig. 5e and f). Landscape
shape index of residence increased from the distance
of −15 km, reached its peak in the urban center and
then decreased eastward, while industry and other urban land reached their peaks in the vicinity of the urban center (Fig. 5e). Area-weighted mean patch shape
indices of all urban land use types (i.e., residence,
industry and other urban land) were relatively less
variable.
9
From Fig. 5, an urbanization center identified quantitatively by these landscape metrics, existed between
distances −15 and 10 km. The south–north transect
(not shown in this section) displayed a similar pattern
as the west–east transect, and in the south to north
direction an urbanization center could be identified
between distances −10 and 15 km. Within this urban
core area, the rankings of agriculture (A), residence
(R), industry (I) and other urban land (U) in terms of
their relative dominance were different among landscape metrics. The rankings were R > U > I > A for
percent cover and patch density; R > I > U > A for
mean patch size; U > R > I > A for patch size coefficient of variation; R > U > I > A for landscape
shape index; and U > R > I > A for area-weighted
mean patch shape index. These results seemed to characterize the urban core of the Shanghai metropolitan
area accurately and precisely: agriculture patches were
abundant and less fragmented; urban land use types
were extensive, having many small patches and highly
fragmented.
4.3. Transect analysis with landscape-level metrics
In this section, we first present the compositional
and then the configurational landscape-level metrics,
combining all the seven land use types, to quantify the
spatial pattern of urbanization along the west–east and
south–north transects.
The curves of compositional landscape-level metrics along the west–east and south–north transects
exhibited qualitatively similar patterns (Fig. 6). The
density of both patches (PD) and their edges (ED) per
moving window (6 km×6 km) were low from the west
and the south ends of the transects, then increased and
reached peaks at the urban center, thereafter decreased
to the east and the north of the urban center (Fig. 6a
and b). An increase in PD and ED apparently occurred
between −20 and 10 km along the west–east transect
and between −30 and 10 km along the south–north
transect, indicating dramatic increase in the degree of
landscape fragmentation—on an average, the contiguous agriculture landscape has increasingly become a
patchwork of different urban land use types. Patch
richness, the number of land use types, was low outside the urban area and increased within the urban
area (Fig. 6c). While this information itself is rather
straightforward, PR as well as Shannon evenness
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L. Zhang et al. / Landscape and Urban Planning 69 (2004) 1–16
Fig. 6. Changes in landscape-level compositional metrics along the transects (west–east transect is solid line and south–north transect is
dashed line): (a) patch density (patch number/km2 ), (b) edge density (m/ha), (c) patch richness, (d) Shannon diversity index, (e) Shannon
evenness index, (f) largest patch index (%), (g) mean patch size (ha), and (h) patch size standard deviation (ha).
L. Zhang et al. / Landscape and Urban Planning 69 (2004) 1–16
index (SHEI), is needed to interpret the change in
Shannon diversity index (SHDI) adequately because
SHDI is determined by both of them. It is clear from
Fig. 6c-e, the increasing diversity of land use types
with urbanization has been attributable mainly to the
increasing uneven areal distribution of the seven land
use types. Comparing Fig. 6c-e with Fig. 5a reveals
that the constant increase in SHEI corresponded to
11
both the declining dominance status of agriculture
and the increasing proportions of the urban land use
types. That is, towards the urban center, the Shanghai metropolitan landscape became more fragmented
and more evenly distributed among land use types.
The largest patch within the landscape and the mean
patch size continued to decrease towards the urban
center, as expected, but the drastically faster decline
Fig. 7. Changes in landscape-level configurational metrics along the transects (west–east transect is solid line and south–north transect is
dashed line): (a) landscape shape index, (b) mean patch fractal dimension, (c) area-weighted mean patch fractal dimension, (d) contagion,
(e) mean patch shape index, and (f) area-weighted mean patch shape index.
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L. Zhang et al. / Landscape and Urban Planning 69 (2004) 1–16
in the urban area is noteworthy (Fig. 6f and g). The
absolute variation of patch size among all land use
types across the landscape (patch size standard deviation, PSSD) decreased dramatically within the
urban area (Fig. 6h). This was a direct result of the
rapid decrease in patch size along both transects
towards the urban center (Fig. 6f and g). The compositional aspect of the Shanghai landscape pattern
corroborated quite well the interpretation of the results of class-level metrics presented in the previous
section.
The configurational landscape-level metrics showed
both similar and different patterns as compared to
the compositional landscape-level metrics along the
west–east and south–north transects in the Shanghai
metropolitan landscape. Landscape shape index, a
form of patch perimeter–area ratio at the landscape
level, showed a similar pattern as the compositional
landscape-level metrics of PD and ED (Figs. 6a and
b and 7a). This pattern suggested that the shape of
the landscape as a patch mosaic became more irregular with urbanization. The results of the mean
patch fractal dimension and area-weighted mean
fractal dimension did not show consistent patterns
(Fig. 7b and c) because of the insufficient number of
patches (within each moving window, 6 km × 6 km)
to calculate these metrics and no information on
statistical significance was available. While contagion, measuring the degree of clumping of patches
that pertains to both the compositional and configurational aspects of landscape pattern, declined in
the urban area (Fig. 7d). Its behavior showed a similar pattern as that of MPS (Figs. 6g and 7d). In
general, these landscape-level indices showed that
the landscape shape complexity and the degree of
fragmentation (conversely related to CONT) of the
Shanghai metropolitan region increased considerably
towards the urban center. How did the shape of individual patches change towards the urban center, and
how did it relate to the landscape-level shape complexity? Fig. 7e and f depicts the average changes
in patch shape with two indices. Mean patch shape
index remained relatively unchanged and did not
show any obvious trend along the west–east and
south–north transects, while area-weighted mean
shape index increased considerably in the urban area
(Fig. 7e and f), resembling the pattern shown by LSI
(Fig. 7a).
5. Discussion and conclusions
5.1. General urbanization patterns in the
metropolitan Shanghai region
Our study has demonstrated that the center and
spatial pattern of urbanization can be quantified using a combination of landscape metrics and gradient
analysis. The results from our study can adequately
address the research questions we defined earlier in
Section 1: how do different land use types change
with distance away from the urban center? Do different land use types have their own unique spatial
signatures? Can urbanization gradients be detected
using landscape pattern analysis? While the answer to
these questions is generally affirmative, more details
are discussed below.
The different land use types exhibited distinctive,
but not necessarily unique, spatial signatures that were
dependent on specific landscape metrics. For example,
for patch type percent coverage, patch density, patch
size coefficient of variation, landscape shape index,
and area-weighted mean patch shape index, the residence and other urban land displayed similar patterns
along the transects from west and south ends to the
urban center, and then eastwards and northwards—a
largely monotonic gradient with its peak at the urban
core. Industry showed a similar pattern, while with
two peaks in the vicinity of urban area. For all the six
class-level measures, agriculture displayed a very different, yet unique, multiple-peaked pattern. Therefore,
different land use types may indeed show distinctive
“spatial signatures” as distance based “landscape pattern profiles” which may be used to compare urban
developmental patterns between cities among different regions and dynamics of the same city over time
(Wu et al., 2003).
It was clear, though not surprising, from this study
that the degree of human impact on the Shanghai
landscape depended on the distance from the urban
center. An urbanization center was clearly identifiable with the landscape metrics when plotted along
both transects. Specifically, both the class-level and
landscape-level metrics indicated dramatic changes
in landscape pattern at −20 and 10 km along the
west–east transect and at −30 and 10 km along the
south–north transect, marking the urbanizing front
of the Shanghai metropolitan area in the west–east
L. Zhang et al. / Landscape and Urban Planning 69 (2004) 1–16
and south–north directions. While the landscape-level
metrics were able to characterize the center of urbanization as having the smallest mean patch size and the
highest patch richness, patch density, edge density,
patch size standard deviation, landscape shape index,
and area-weighted mean shape index, the class-level
indices provided more detailed information on the relative contributions of individual land use types. The
high degrees of fragmentation and spatial complexity
of the urbanization center, while not new findings,
were able to be quantified in relation to distance
and individual land use types. Processes and factors
responsible for urbanization such as socioeconomic
activities, accessibility to transportation and service,
natural conditions, and urban planning resulted in the
heterogeneous arrangement of land uses in the Shanghai metropolitan area (Mei et al., unpublished data).
Although the distance-dependent trends of land use as
represented by different landscape metrics were not
exactly linear or symmetrical, some general patterns
did emerge.
Urbanization in the Shanghai metropolitan region
has resulted in dramatic increases in patch density,
edge density, and patch and landscape shape complexity, and sharp decreases in the largest and mean patch
size, agriculture habit area, and landscape connectivity (Figs. 6 and 7). The general pattern of urbanization
revealed here was that the increasingly urbanized
landscape became compositionally more diverse, geometrically more complex, and ecologically more
fragmented.
5.2. Testing landscape modification gradient
hypotheses
The results of our study can also adequately address the last research question we defined earlier in
Section 1: to test the hypotheses on landscape structural responses along a human modification gradient
postulated by Forman and Godron (1986): the patch
density will increase with urbanization, the regularity of patch shape will increase with urbanization,
both the mean and variance of patch size decrease
with urbanization and landscape connectivity will
decrease with urbanization. Our results can be used
to test these hypotheses because the landscape transect analysis data set for the Shanghai metropolitan
region represents a landscape transition from largely
13
semi-natural agriculture, agricultural urban mix, to
highly urbanized city core along the transects.
The results from our analysis showed that, with
increasing urbanization: (1) patch densities indeed
increased exponentially (Fig. 6a); (2) the shape complexity of individual patches seemed lower at the
agriculture-dominated area, but became more complex in general (Fig. 7b and f); (3) both patch size and
its variance decreased similarly (Fig. 6g and h); and
(4) landscape connectivity, if assumed to be positively
related to contagion or negatively to patch (or edge)
density, decreased in an accelerating manner (Figs. 6a
and b and 7d). Thus, our results were in complete
agreement with Hypothesis 1 (patch density), and
supported Hypotheses 3 (patch size) and 4 (connectivity) in general. However, the results on patch shape
seemed apparently at odds with the hypothesized pattern. Our interpretation is that, when the degree of
urbanization is high, not only the density, but also
the shape complexity, of patches will increase. Future
studies are needed to further confirm these findings.
5.3. Testing the existing theories of urban
morphology
Although this study was not designed to test the
existing theories of urban development, the results
of this rapidly growing metropolitan Shanghai may
provide some interesting perspectives on urban morphology. First of all, both 64 and 66 km long and
15 km wide west–east and south–north transects in
this study provided an excellent opportunity for characterizing broad-scale landscape pattern change along
a rural–urban–rural environment gradient, which has
not been reported in any Chinese city. The urbanization gradient revealed by our transect analysis
seemed to suggest that different land use zones were
distinguishable: agriculture—industry—other urban
land/residence—industry—agriculture (see Fig. 5a).
The classic concentric zone theory seems to be able
to account adequately for the land use pattern of the
Shanghai metropolitan region up to the year 1994,
and this is partly because these classic theories of
urban morphology were developed based primarily
on studies of old and well-established cities (i.e.,
Chicago, San Francisco, and Boston).
In recent decades, a variety of new theories and
methods have been developed for describing urban
14
L. Zhang et al. / Landscape and Urban Planning 69 (2004) 1–16
forms and dynamics. These include catastrophe
theory (Wilson, 1976), chaos theory (Wong and
Fotheringham, 1990), dissipative structure theory
(Allen and Sanglier, 1979), fractals (Batty and
Longley, 1989 cellular automata (Batty, 1997),
and theory of self-organization (Schweitzer, 1997;
Portugali, 2000). These new approaches emphasize the
dynamics of urban landscape pattern and its relation
to local-scale generating processes. Also in sharp contrast with the traditional views, these new approaches
are based on non-equilibrium and nonlinear dynamics
perspectives. In addition, from fractals to cellular automata and to self-organization, bottom-up and local
interactions are viewed essential for the formation of
urban systems. As Shanghai was growing rapidly in
the 1990s and the beginning of the 21st century, which
may be indicative of a fast growing city, or urban
development among Chinese coastal cities, or more
likely both, these new theories and methods seem more
appropriate for understanding the urbanization development of Shanghai metropolitan region in the near
future.
5.4. Importance of quantifying spatial patterns
of urbanization
To understand the ecological and socioeconomic
consequences of urbanization, it is necessary to quantify the spatial patterns of urban landscapes. First, urban systems are spatially extended systems in which
physical, ecological, and socioeconomic processes
create, maintain, or destroy spatial patterns, and at
the same time spatial patterns facilitate, inhibit, or
neutralize these processes. The changes in landscape
pattern along the transects as revealed by our analysis may have important ecological implications. For
example, the elimination of large agriculture patches,
increased habitat fragmentation, and substantially
high patch density of human land use types may significantly affect the biogeochemical cycling and biota
of this area (e.g., Brady et al., 1979; Kent et al., 1999;
Baker et al., 2001; McIntyre et al., 2001). Thus, relating pattern to process is essential to understanding
urban ecosystems, and this cannot be done effectively
without first quantifying the spatial pattern of the
urbanized and urbanizing areas under consideration.
Second, prediction is one of the ultimate goals of any
science although the emerging science of complexity
suggests that complex self-organizing systems, such
as ecological and socioeconomic systems, are effectively unpredictable (Schweitzer, 1997; Portugali,
2000). Nevertheless, projections (scenario-based forecasts) of future urbanization and its ecological and socioeconomic impacts are often needed for a variety of
purposes including urban planning, resource management, and biodiversity conservation (e.g., Fairbanks
and Benn, 2000; Jensen et al., 2000; Pauleit and
Duhme, 2000; Veldkamp and Lambin, 2001). Urban
planning and decision-making for sustainable development urgently need data of high spatial resolution to
establish the relationship between the socioeconomic
performance of the urban system and its different
subunits (i.e., housing schemes, commercial and industrial developments, services) on the one hand and
their environmental impacts of these subunits on the
other (Pauleit and Duhme, 2000). Such projections
and designs of more environmentally efficient urban
development often require the quantification of spatial
patterns. Third, to understand urban systems adequately they must be studied as integrated landscapes
in which context and spatial relations are as important
as entities and processes. This landscape perspective
needs to emphasize heterogeneity, inter-disciplines,
holistic properties, and the integration among pattern, process, scale, and hierarchical linkages (Pickett
et al., 1997; Bastian, 2000; Collins et al., 2000;
Naveh, 2000; Portugali, 2000; Zipperer et al.,
2000).
The effects of the urbanization pattern on ecological conditions and processes are the focus of
urban landscape ecology and can help understand the
relationship between the landscape pattern and urban ecological processes (Hobbs, 1988; Cook, 1991;
Foresman et al., 1997; Pickett et al., 1997; Antrop
and Van Eetvelde, 2000; Zipperer et al., 2000). In particular, combining gradient analysis with landscape
metrics, as illustrated here, can help to identify quantitatively and characterize the gradients and complex
spatial pattern of urbanization, which can subsequently be related to ecological and socioeconomic
processes (McDonnell et al., 1997). This study was
only a first step towards understanding the structure
and functioning of the Shanghai urban landscape.
The extension of this study to understanding the
mechanisms involved in urban landscape pattern formation necessitates a more comprehensive framework
L. Zhang et al. / Landscape and Urban Planning 69 (2004) 1–16
that explicitly incorporates geographical, ecological,
socioeconomic, and political considerations.
Acknowledgements
We thank Prof. Martin Kent, Plymouth University,
UK for valuable comments and linguistic checking,
and Dr. Jianguo Wu, Arizona State University, USA
for helpful discussion. Dr. Denis Saunders and two
anonymous reviewers provided valuable comments
on earlier drafts of this paper were also appreciated. This research has been supported by grants
from Chinese National Science Foundation for Key
Projects (40131030), the Shanghai Priority Academic Discipline and the State’s 10th 5-year “211
Project”-supported key academic discipline program
of ECNU.
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Liquan Zhang is currently a professor of ecology of the State
Key Laboratory of Estuarine and Coastal Research at the East
China Normal University, China. He holds a BS in biology from
the East China Normal University in 1978 and a PhD in ecology
from the Uppsala University, Sweden in 1983. His research and
publications focus on landscape ecology, plant population ecology, community and ecosystem ecology and wetland ecology. His
recent work focuses on the relationship between the landscape patterns and the ecological processes, and how wetland vegetations
and ecosystems in Yangtze estuary respond to natural and anthropogenic disturbance. He has published about 40 peer-reviewed
articles over a wide range of topics.