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Detecting spatiotemporal change of land useand landscape pattern in a coastal gulf region,southeast of China
Jinliang Huang Æ Jie Lin Æ Zhenshun Tu
Received: 29 August 2008 / Accepted: 19 November 2008� Springer Science+Business Media B.V. 2008
Abstract Geographic information system (GIS), remote sensing (RS), gradient analysis,
and landscape pattern metrics were coupled to quantitatively characterize the spatiotem-
poral change of land use and landscape pattern over the period 1988–2007 in a coastal gulf
region, southeast China. The results obtained show an increase in cropland, buildup land,
and aquiculture area and decrease in orchard, woodland, and beach area during 1988–2007.
Landscape fragmented processes were strengthened and landscape pattern structure
became more complicated in the last two decades in Luoyuan gulf region. The dynamics
intensity of landscape pattern is stronger during 2002–2007 than that during 1988–2002.
Spatial difference of urban–rural landscape pattern can be detected distinctively in two
transects in terms of landscape metrics. Urbanization processes and the policy developed to
transfer seawater into buildup land are two driving forces leading to the spatiotemporal
change of landscape pattern in Luoyuan gulf region in the last two decades.
Keywords Landscape pattern � Landscape metric � Gradient analysis �Coastal gulf region
1 Introduction
In the context of accelerated urbanization through the world, especially in developing
countries, the landscape has changed significantly (Xiao et al. 2006). For example, large
amounts of agricultural lands have been transformed into buildup land or urban land use.
At the same time, the spatial pattern of land use can reflect the underlying human
activities including urbanization processes and policies developed for social and
Readers should send their comments on this paper to: [email protected] within 3 months of publicationof this issue.
J. Huang (&) � J. Lin � Z. TuEnvironmental Science Research Center, Xiamen University, Xiamen 361005, Fujian Province,People’s Republic of Chinae-mail: [email protected]
123
Environ Dev SustainDOI 10.1007/s10668-008-9178-8
economic development at local to region scales (Redman 1999). Human activities can
modify the environment, which tends to increase landscape fragmentation by generating
more and smaller patches (Luck and Wu 2002).
The quantification of landscape pattern can compliment the identification and evalu-
ation of temporal changes, and for the study of the effects of pattern on ecological
processes (Turner 1989). Landscape pattern metrics are important tools for evaluating the
ecological processes and effects of land use (Wu 2000; Gautam et al. 2003). A large
collection of indices have been developed to describe landscape patterns and these
indices have proved useful for the description of landscape structure and its spatial–
temporal dynamics (Turner 1989; O’Neill et al. 1988; Riitters et al. 1995). Additionally,
gradient analysis has also proved to be a useful tool for studying the ecological con-
sequences of urbanization, since the direction and magnitude of landscape change could
be different when urbanization processes occur (Foresman et al. 1997; Li and Yeh 2004;
Yu and Ng 2007). In recent years, the method of integrating landscape pattern metrics
with gradient analysis was widely used to characterize the land use dynamics and
landscape pattern change on urban areas (Luck and Wu 2002; Kong et al. 2005; Xie
et al. 2006; Yu and Ng 2007).
As a developing but booming country, accelerated urbanization processes have taken
place in many places of China and a series of policies have been formulated, which
exerts great influence on the land use dynamics and landscape pattern change (Liu et al.
2003; Xie et al. 2006). In China, research has reported on the urbanization and conse-
quential land use change of some large cities such as Guangzhou (Seto and Fragkias
2005; Yu and Ng 2007), Shenzhen (Seto and Fragkias, 2005), Jinan (Kong et al. 2005),
Suzhou (Xie et al. 2006), Shijiazhuang (Xiao et al. 2006). However, less attention has
been paid to the economic development area within a coastal region in southeast China.
As we know, coastal area including the gulf region has suffered from intensive human
activities including urbanization process and so become one of the ecologically vul-
nerable regions. Human activities including urbanization processes become the major
driving force greatly modifying the shape of the coastal area and its ecologic environ-
ment (Townend 2002).
This study chose a typical coastal gulf region in southeast China, and analyzed quan-
titatively spatiotemporal change of landscape pattern over the period 1988–2007. The
objective of this study is to explore and explain quantitatively the spatiotemporal char-
acteristics of landscape pattern changes in a typical coastal gulf region in southeast China
in the last two decades.
2 Description of study area
As one of the largest sixth gulf in Fujian province, Luoyuan gulf region, covered about
860 km2, is located in northeast of Fuzhou (119�3604200–119�5001200E, 26�1900500–26�2805000N) (Fig. 1). Luoyuan gulf region belongs to the subtropical region and owns a
typical monsoon climate characteristics, with annual precipitation about 1,650 mm.
Luoyuan gulf region is surrounded by mountains, with the mean elevation 215 m. Original
vegetation was destroyed by human activities and now pine tree is dominant in the veg-
etation cover. Administratively, Luoyuan gulf region is comprised Luoyuan county and
Lianjiang county. The population of such region is about 0.39 million. Baishui and
Songshan, covering 7.6 and 21.8 km2, respectively, are two typical areas where the land
use is converted from seawater into buildup land (Fig. 1).
J. Huang et al.
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3 Materials and methods
3.1 Data sources and processing
The main data used in the research included a Landsat Thematic Mapper satellite image
from 1988, 2002, and 2007 (resolution 30 m, seven bands). Subsets of satellite images
including TM were rectified first for their inherent geometric errors using digital topo-
graphic maps in Modified Universal Transverse Mercator coordinate system obtained as
above as the reference materials. TM image was first registered to the digital topographic
maps using distinctive features such as road intersections and stream confluences that are
also clearly visible in the image. The TM images were registered to the already registered
image through image-to-image registration technique with rectification errors of pixels,
respectively.
In such study, unsupervised classification method and manual on-screen digitizing and
recoding method were coupled to use for the classification of all the images. Landsat data
were separated into 30 classes using unsupervised classification technique in ERDAS and
then eight classes were further classified, namely, cropland, orchard, woodland, buildup land,
beach, aquiculture, water body, and unused land. Croplands include paddy and dry farming
land. Woodlands include forest, shrub, and grasses. Buildup land includes urban area, rural
settlements, and roads. Water bodies include stream and rivers, reservoir, ponds, and sea
water. Unused land includes bare soil and bare rock. Based on the primary result by unsu-
pervised classification, land use map by the result map based on on-the-spot survey in 1988,
high resolution image i.e. SPOT with pixel of 2.5 m 9 2.5 m in 2002 and field survey in 2007
were then individually to check and verify the result and to obtain the land use in Luoyuan gulf
region in 1988, 2002, and 2007. As a result, the classification accuracy assessment was
performed for the images in 1988, 2002, and 2007 with Kappa index of 0.74, 0.84, and 0.93,
respectively. It seems to meet the requirements according to the study results from Lecas et al.
(1994). But it is worthwhile to carry out the accuracy assessment by the method proposed by
Pontius and Lippitt (2006), namely, performing the sensitivity analysis in order to state
clearly the minimum level of accuracy the data would need to have in order for us to be certain
that the observed differences among the maps indicate real change on the ground.
Fig. 1 Location of study region
Detecting spatiotemporal change of land use and landscape pattern
123
After selectively combining classes, classified images were sieved, clumped, and
filtered before producing final output. All activities were performed in Image ERDAS
version 8.7 (Leica, USA). Classified images were then exported to ARCGIS version 9.0
(ESRI, Redlands, USA) from ERDAS and the rest of the analyses was performed in GIS
environments. The images were converted to grid with cell size of 30 m 9 30 m in
ARCGIS.
3.2 Detection of land use changes
The land use polygon themes for 1988, 2002, and 2007, obtained from the digital classi-
fication of satellite data and subsequent GIS analyses using the method described above
were overlaid two at a time in ARCGIS and the area converted from each of the classes to
any of the other classes was computed, resulting from a land use transform matrix.
3.3 Study of landscape pattern change
In order to explore landscape pattern change, 11 landscape metrics were chosen for such
study in the following: number of patches (NP), patch density (PD), class percent of
landscape (PLAND), largest patch index (LPI), mean patch size (MPS), landscape shape
index (LSI), area-weighted mean patch fractal dimension (FRAC_AW), Shannon’s
diversity index (SHDI), contagion index (CONT), patch cohesion index (COHES), and
splitting index (SPLIT). These landscape metrics were chosen on the basis of literature
review and the relevance of this research theme. According to the former studies, the
landscape metrics should have direct influence on landscape pattern, ecosystem function,
and habitat quality. Eleven landscape metrics were computed by landscape pattern analysis
package, FRAGSTATS (Version 3.3) at both the class and landscape level in this study to
quantify and examine spatiotemporal changes of landscape compositions and landscape
configurations for the whole gulf region (McGarigal and Marks 2002).
To detect the dynamic change of landscape pattern in Luoyuan gulf region, two tran-
sects were selected that cut across the almost entire region from the west to east (transect1)
and from the northwest to southeast (transect2), respectively. The transect1 is composed of
14 2 km 9 2 km blocks and the transect2 is composed of 15 blocks (Fig. 2). The orien-
tation was chosen to cover the distinctive land use change, especially the region of being
transferred seawater into buildup land.
Fig. 2 Landscape of transect1 (West to East) for gradient analysis in Luoyuan gulf region
J. Huang et al.
123
3.4 Annual urban grow
For further analyze, the spatiotemporal pattern due to two driving forces, namely,
urbanization processes and policy of Transferring seawater/beach into buildup land, one
indicator named annual urban growth rate (AGR) for buildup land was adopted to evaluate
the spatial distribution of urban expansion intensity (Xiao et al. 2006; Yu and Ng 2007).
AGR is defined as the following equation:
AGR ¼ UAnþi � UAi
n;
where AGR is the annual urban growth rate, ha/year; UAn?i and UAi are the built-up areas
in the target blocks at time i ? 1 and i, respectively; n is the interval of the calculating
period (in years).
4 Results and analysis
Three years of Landsat Thematic Mapper images in 1988, 2002, and 2007 were interpreted
to obtain land-use datasets using ERDAS and ARCGIS software (Fig. 3).
4.1 Land use dynamic change in Luoyuan gulf region
Land use transfer matrix is the necessary way to quantify and examine the land use
dynamic change (Pontius et al. 2004). Tables 1 and 2 showed the general level of infor-
mation regarding the land use change over the period 1988–2002, and 2002–2007 in
Luoyuan gulf region.
As shown in Tables 1 and 2, area of cropland, buildup land, and aquiculture tended to
increase during 1988–2002 and 2002–2007. On the contrary, area of orchard, woodland,
and beach shrank during 1988–2002 and 2002–2007. Water body decreased during 1988–
2002 but increased during 2002–2007.
It should be mentioned that it is possible that some unused land such as barren rock area
was misclassified as woodland, and beach is misclassified as water body in different time
of satellite observation, which can make area of the unused land area, water body, and
beach fluctuate during the study period.
4.2 General trend of landscape pattern in Luoyuan gulf region
Based on the three period time of land use/land cover, FRAGSTAT3.3 software was
further used to calculate the landscape pattern metrics at landscape level in 1988, 2002, and
2007 (Table 3).
Table 3 shows the change of landscape pattern metrics at landscape level of Luoyuan
gulf region during 1988–2007. NP increased from 1364 in 1988 to 1686 in 2007; PD
increased from 1.56 in 1988 to 1.96 in 2007; SHDI increased from 1.27 in 1988 to 1.54 in
2007; CONT reduced from 63.3% in 1988 to 54.12% in 2007. Wu (2000) found CONT
decreased accompanied with the urbanization processes accelerated. LPI substantially
decreased from 55.13 in 1988 to 15.97 in 2007. The landscape pattern metrics mentioned
changed during the study period underlies the information that fragmentation of landscape
tends to strengthen and spatial variability of landscape becomes greater in Luoyuan gulf
region. Moreover, the landscape pattern structure become more complicated which can be
Detecting spatiotemporal change of land use and landscape pattern
123
Fig. 3 Land use change in Luoyuan gulf region in 1988, 2002, and 2007
J. Huang et al.
123
discerned by the following landscape metric change over the period 1988–2007. LSI
increased from 31.1 in 1988 to 46.67 in 2007. FRAC_AW showed a very slight increase in
the first period (1988–2002) and substantially increased in the second period (2002–2007).
The shape complexity has become more irregular over time and the landscape fragmented
processes were strengthened.
Table 1 Land use transfer matrix over the period 1988–2002 in Luoyuan gulf region (km2)
Land use Cropland Orchard Woodland Builduparea
Beach Aquiculture Waterbody
Unusedland
Loss
Cropland 44.93 0.14 41.66 13.94 0.33 4.10 1.30 2.44 63.93
Orchard 3.78 0.04 4.71 0.43 0.00 0.03 0.17 0.16 9.29
Woodland 69.52 6.81 387.42 8.68 0.54 3.40 2.70 10.29 101.94
Buildup area 4.05 0.00 0.73 3.16 0.08 1.21 0.31 0.08 6.46
Beach 9.53 0.00 0.82 6.94 16.87 28.58 5.11 0.06 51.05
Aquiculture 0.07 0.00 0.00 0.15 0.07 7.83 0.10 0.00 0.40
Water body 2.60 0.00 2.09 2.49 23.80 16.84 113.50 0.14 47.97
Unused land 0.58 0.06 3.26 0.03 0.01 0.06 0.16 0.32 4.17
Gain 90.13 7.01 53.28 32.69 24.85 54.22 9.85 13.18
Change inarea
26.20 -2.28 -48.66 26.22 -26.21 53.82 -38.12 9.01
Note: The columns and rows contain data for 1988 and 2002, respectively
Table 2 Land use transfer matrix over the period 2002–2007 in Luoyuan gulf region (km2)
Land use Cropland Orchard Woodland Builduparea
Beach Aquiculture Waterbody
Unusedland
Loss
Cropland 75.55 0.11 27.17 24.51 0.02 4.76 1.27 1.73 59.58
Orchard 0.70 0.03 6.18 0.08 0.00 0.00 0.01 0.04 7.01
Woodland 95.64 2.09 324.68 7.07 0.05 0.71 1.34 8.57 115.46
Buildup area 7.75 0.00 1.53 20.53 0.12 4.26 1.25 0.43 15.34
Beach 0.08 0.00 0.05 1.31 10.36 9.93 20.00 0.02 31.39
Aquiculture 1.49 0.00 0.21 5.93 0.88 50.45 2.96 0.13 11.60
Water body 1.47 0.01 0.68 3.86 0.85 10.84 105.63 0.03 17.73
Unused land 4.38 0.00 6.78 0.52 0.02 0.34 0.03 1.42 12.08
Gain 111.55 2.22 43.10 43.29 1.94 30.96 26.95 10.98
Change inarea
51.97 -4.79 -72.37 27.96 -29.45 19.36 9.22 -1.09
Note: The columns and rows contain data for 2002 and 2007, respectively
Table 3 Landscape metrics at landscape level over the period 1988–2007 in Luoyuan gulf region
Year NP PD LPI LSI FRAC_AM CONT COHES SPLIT SHDI
1988 1346 1.56 55.13 31.10 1.26 63.22 99.72 2.97 1.27
2002 1647 1.91 28.80 43.93 1.28 55.99 99.60 7.10 1.48
2007 1686 1.96 15.97 46.67 1.58 54.12 99.37 12.57 1.54
Detecting spatiotemporal change of land use and landscape pattern
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Patch cohesion index showed a slight reduction over the period 1988–2007. This is
greatly related to unchanged main component of landscape, namely, woodland and water
body, due to the mountainous and hilly topographic feature and the gulf region charac-
teristics in Luoyuan gulf region. Additionally, SPLIT metrics substantially increased from
2.97 in 1988 to 12.57 in 2007 and the increasing rate in the second period (2002–2007) is
higher than that in the first period (1988–2007), which suggests the ecological processes
become more strengthened due to human disturbance in recent years.
4.3 Gradient analysis for Luoyuan gulf region
Two transects were designed to detect the dynamics of landscape pattern in Luoyuan gulf
region. Using FRAGSTAT3.3 software, landscape metrics at landscape level and class
level were calculated and analyzed spatiotemporally to see the change of landscape pattern
(Figs. 4, 5, 6).
4.3.1 Landscape metrics at landscape level along the transects
The landscape along transect2 generally showed similar characteristics compared with that
of the transect1. Thus, the results along transect2 are not presented here. Along transect1,
the peak value of PD over the period 1988–2007 appears around the urban center and then
quickly decreases to the end of two sides of the transect1. The PD value in 2002 and 2007
shows another peak value appearing in the region from 12 to 16 km in the east, and region
from 8 to 16 km in the east, respectively. This phenomenon reflects the fragmentation
status in the urban center resulting from accelerated urbanization; on the other hand, it can
be found that the development area expanded spatially with time. The newly expanded area
of PD peak value is due to the land use conversion, namely, Baishui where the land
converted from sea water and beach because of policy formulated. Spatial variability of
LSI shows a similar tendency as PD, all suggesting a similar landscape fragmented pro-
cesses. Additionally, the least value of COHES along the transect1 shows the similar
region as the peak value of PD and LSI. Intensive fragmented processes naturally lead to
low patches connectivity. The peak value of COHES appears at 4 km in the west and large
region from 6 to 20 km in the east in 1988, whereas spatial variability is great along the
transect1 due to human activities in 2002 and 2007 (Fig. 4).
The peak value of LPI for 1988, 2002, and 2007 all exhibits the peak value at 6 and
20 km in the east, which is related to the existence of woodland in such areas. But there
some spatially variability occurred during 1988–2007. For example, the place where the
region at 4 km in the west exhibits the peak value in 1988 but tends to low value in terms
of LPI in 2002 and 2007. Obviously, these regions mentioned suffered from intensive
human activities in recent years, since the LPI is a simple measure of dominance
(McGarigal and Marks 2002). The spatial gradient of CONT is similar to that of the LPI,
which to some extent indicates that the main component of landscape in Luoyuan gulf
region, namely, woodland, is difficult to change with time due to the topographic feature.
During 1988–2007, the least value of LPI and COHES appeared at 2 km around the
urban center, indicating such region is undergone strong human activities and landscape
fragmented processes. Such phenomenon is further validated by the SPLIT index as its
peak value also appears at 2 km in the east. Moreover, the peak value of SPLIT became
higher and another peak value of SPLIT appears at the region from 8 to 12 km in the east
in 2002 and 2007. The information underlined that fragmented processes in recent years is
strengthened temporally and spatially.
J. Huang et al.
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There are two peak values of SHDI over study period 1988–2007, namely, 2 km around
the urban center, and the region extending from 8 to 12 km at the east of the urban center.
But the value of SHDI in 2002 and 2007 is higher than that in 1988. The information
underlined is similar to the PD and LSI, all indicating that fragmented processes was
strengthen and diversity of landscape become more in Luoyuan gulf region.
The peak value of COHES extends from urban center to 4 km in the east in 1988. In
contrast, there are three peak values of FRAC_AW in 2002, namely, 2 km in the west,
4 km and 14–16 km in the east, respectively, indicating the cultivated land patches in these
areas became more aggregately distributed among other types of patches.
As for FRAC_AW, the spatial tendency along the transect1 is similar to that of LSI,
SHDI. The least value of FRAC_AW is the same as the least value of LSI and SHDI,
Fig. 4 Variations in landscape metrics at landscape level along the transect1
Detecting spatiotemporal change of land use and landscape pattern
123
although there is little difference. It is postulated that such a difference may be result from
the uncertainty for describing the empirical analysis of the landscape pattern using
FRAC_AW (Wu 2000).
4.3.2 Landscape metrics at class level along the transects
Buildup land in the transect1 and water body in the transect2 was chosen for quantitatively
describing the spatiotemporal change of landscape pattern (see Figs. 5, 6).
Just as shown in Fig. 5, LPI reaches its peak value at urban center over the study period.
While the value of LPI in 2002 and 2007 is higher than that in 1988, which reflects the
urban land expanded spatially in recent years. There is only one place of peak value of LPI
in 1988. But for 2002 and 2007, another peak value of LPI appears at 8 km in the east in
2002 and 12 km in the east in 2007 (Baishui), which directly related to the policy
developed called ‘‘Transferring seawater into buildup land.’’
The peak value of MPS of buildup land in 1988 appears at 2 km in the west away from
the urban center, and then change to place of urban center in 2002 and 2007. And the
amount of MPS in 2002 and 2007 is higher than that in 1988. Accordingly, the peak value
of COHES in 1988 appears from urban center to 2 km in the west, and then transferred to
urban center and 8 km in the east in 2002, transferred to around 2 km and the region from
8 km to 12 km in the east away from urban center in 2007. The spatiotemporal pattern in
Fig. 5 Variation in landscape metrics for buildup land along transect1 during 1988–2007
J. Huang et al.
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terms of COHES is characterized by the aggregated and expanded build up land at the
urban center and another place at another place.
Figure 6 shows the spatiotemporal characteristics along the transect2. Over the period
1988–2007, peak value of LSI of water body appears at urban center in 1988, and then
transferred to southeast ward, and then appears at Songshan, located at the region from 4 to
8 km in the southeast in 2002 and 2007. It should be mentioned that Songshan was developed
since 1992 (Committee for Compiling Luoyuan County Annals 1998). Additionally, in the
transect2, the metric value of water body including PD, LPI, MPS, and COHES in 1988 is
almost higher than that in 2002 and 2007, especially in the region from 2 to 4 km in the east
(area with land use transferred from seawater into buildup land called Songshan). The largest
project ‘‘Songshan’’ occurred during 1992–1995, resulted in water body shrank, fragmented
processes strengthen, and complex (Committee For Compiling Luoyuan County Annals 1998).
5 Discussions
5.1 Driving forces analysis
During 1988–2007, aquiculture and buildup is the two land use types with the largest
increasing rate, mainly due to the very low value in 1988. Population increase and
Fig. 6 Variation in landscape metrics for Water body along transect2 during 1988–2007
Detecting spatiotemporal change of land use and landscape pattern
123
economic growth in this region may be the two major causes making buildup land area and
construction area increased.
Moreover, the policy formulated to transfer beach into construction land since 1992 is
surmised to be another important factor contributing to the increment of buildup land area
and aquiculture area (Committee for Compiling Luoyuan County Annals 1998). It can be
testified by Basishui and Songshan identified in Fig. 1. From Figs. 2 and 3, the land use in
such two regions mentioned above changed sharply from sea or beach into buildup from
1988 to 2007. It is surmised to be greatly related to the influence of policy.
As mentioned before, Luoyuan gulf region was surrounded by hill and mountains. With
population growth, economic development, and urbanization processes accelerated, it
seems no alternative but have to develop the policy of transferring seawater or beach into
buildup land. From the spatial pattern analysis with empirical landscape pattern metrics and
gradient analysis at the whole region and in the two typical transects, urbanization processes
around the urban center and the policy developed for transferring seawater or beach into
buildup land was found to be the two driving forces contributing to the spatiotemporal
landscape pattern change in Luoyuan gulf region in last two decades. This conclusion seems
similar as that of some research ever found (Marton 2000; Xie et al. 2006).
5.2 Spatiotemporal pattern analysis along both transects
Annual urban growth rate for buildup land was further adopted to evaluate the spatial
distribution of urban expansion intensity. The results are illustrated in Fig. 7.
As shown in Fig. 7, the rates of urban growth differ distinctly between transects and among
locations. The curve during 1988–2007 shows that the peak value of AGR exhibits at the
radium of 4 km around the urban center and then tended to zero at the end of sides of the rural
areas for both transects. However, the tendency between transect1 and transect2 shows a little
difference. Along the transect1, with the exception of urban center, another peak value exhibits
at the region extending from 8 to 12 km in the east. This place named Baishui was undergone
intensive development in recent years, driven by the policy of transferring seawater/beach into
buildup land. On the other hand, along the transect2, two other peak values appear at 10 and
14 km in the southeast of the urban center, respectively. This phenomenon is greatly related to
the urbanization processes accelerated in Mabi town nearby such two places.
6 Conclusions
GIS, RS, gradient analysis, and landscape pattern metrics were coupled to quantitatively
characterize the spatiotemporal change of land use and landscape pattern over the period
Fig. 7 Variations in buildup area during 1988–2007 for both transects in Luoyuan gulf region
J. Huang et al.
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1988–2007 in a coastal gulf region, southeast China. Three years of Landsat TM images in
1988, 2002, and 2007 were interpreted to obtain land-use datasets. The results obtained are
as follows.
There is an increase in cropland, buildup land, and aquiculture area and decrease in
orchard, woodland, and beach area during 1988–2007. Overall trend analysis of landscape
pattern over the period 1988–2007 shows landscape fragmentation were strengthened and
landscape structure tended to become more complicated. And the main component of
landscape in Luoyuan gulf region is woodland, water body, and cropland. The dynamic
intensity of landscape pattern is stronger during 2002–2007 than that during 1988–2002,
which reflects that driving forces including urbanization processes contributed greatly to
the change of landscape pattern in recent years.
Spatial gradient of urban–rural landscape pattern can be detected distinctively in both
transect in terms of landscape pattern metrics. Urbanization processes and the policy
developed to transferring seawater into buildup land is found to be the two driving forces
leading to the spatiotemporal change of landscape pattern in Luoyuan gulf region in the
last two decades.
Acknowledgments The authors gratefully acknowledge the two reviewers for their valuable comments onthe manuscript. Thanks would be given to the financial support from the Department of Science & Tech-nology, Fujian Province, as a Talented Youth in Fujian Province (No. 2007F3093).
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