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: BhaskarNath@aol.com 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: jlhuang@xmu.edu.cn

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.

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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

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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

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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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