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ORIGINAL ARTICLE
Analyzing coastal wetland change in the Yancheng National
Nature Reserve, China
Chang-Qing Ke •Dong Zhang •Fu-Qiang Wang •
Shu-Xing Chen •Christance Schmullius •
Wolfgang-Martin Boerner •Hui Wang
Received: 1 January 2010 / Accepted: 21 May 2010 / Published online: 5 June 2010
ÓSpringer-Verlag 2010
Abstract Coastal zones provide habitat cores and corri-
dors that maintain the diversity of entire landscapes, and
they can form the cornerstone elements of regional con-
servation strategies. Natural environmental driving factors
and excessive anthropogenic activities play important roles
in coastal wetland change. Many studies have used remote
sensing images to map and assess coastal wetland change
on local or regional scales. This paper aims to provide
insight into coastal wetland change in the Yancheng
National Nature Reserve (YNNR) using remote sensing
technology and landscape metrics analysis. The results
reveal that grass flat and reed areas have significantly
decreased, whereas agriculture fields, aquaculture ponds
and built-up areas have continuously increased from 1988
to 2006. The spatial pattern of the coastal landscape has
become fragmented and heterogeneous under great pres-
sure from rapid economic development and population
growth. The wetland changes have important impacts on
natural habitat of the red-crowned cranes. The results of
this study provide basic information that is required for
developing measures toward a sustainable management
and conservation of the YNNR.
Keywords Coastal wetland Land use change
Remote sensing Landscape metrics Driving forces
The Yancheng National Nature Reserve (YNNR)
Abbreviations
YNNR The Yancheng National Nature Reserve
NBSC National Bureau of Statistics of China
Introduction
Coastal zones are important boundaries, forming transition
areas between terrestrial and marine environments. With
approximately 41% of the world’s human population living
within 100 km of the coast (Martinez et al. 2007), the
coastal zones and issues of sustainability are paramount.
Coastal zones play an important role in land-based socio-
economic development such as agriculture, industry and
tourism (Ramachandran et al. 2005). Coastal zones are
environments with high risk of hydrogeological hazards
and are seriously affected by coastal erosion, saltwater
C.-Q. Ke (&)D. Zhang F.-Q. Wang S.-X. Chen
Department of Geographic Information Science,
Nanjing University, No.22 Hankou Road, 210093 Nanjing,
People’s Republic of China
e-mail: kecq@nju.edu.cn
D. Zhang
e-mail: zhangdongazx@163.com
F.-Q. Wang
e-mail: fuqwsun@126.com
S.-X. Chen
e-mail: webgis2006@163.com
C. Schmullius
Institute of Geography, Jena University, Grietgasse 6,
07743 Jena, Germany
e-mail: c.schmullius@uni-jena.de
W.-M. Boerner
Communications, Sensing & Navigation Laboratory,
Illinois University at Chicago, Chicago, IL 60607-7018, USA
e-mail: wmb1uic@yahoo.com
H. Wang
Administration of Yancheng National Natural Reserve,
224002 Yanhceng, People’s Republic of China
e-mail: ycwangh@yahoo.com.cn
123
Reg Environ Change (2011) 11:161–173
DOI 10.1007/s10113-010-0130-8
encroachment in the phreatic aquifer and sea-level rise (Cai
et al. 2009; Ryu et al. 2008; Zhang et al. 2002). Thus,
coastal environments are currently affected by natural
environmental changes, anthropogenic activities and syn-
ergistic combinations of the two (Burak et al. 2004; Fan
et al. 2006; Long and Skewes 1996; Ramessur 2002). The
expected accelerated rise in global mean sea levels during
the 21st century may endanger coastal human populations
and infrastructure and threaten many coastal ecosystems
(Lewsey et al. 2004; Thanh et al. 2004). Natural habitats
have been lost due to the reclamation of land for urban and
industrial development, agriculture, aquaculture and mari-
culture (LeDee et al. 2008; Walker et al. 2008). The large
and growing extent of human activity in coastal zones has
caused or enhanced a variety of environmental problems
(Alonso-Perez et al. 2003; Chen et al. 2006; Zhang 2002).
Coastal wetlands have wet, spongy soils and are located
in the transition zone between terrestrial land and ocean,
and they include freshwater, saltwater and mixed areas.
Due to these characteristics, wetlands contain a diverse
variety of wildlife and plants (Gibbes et al. 2009). Coastal
wetlands are a vital element of coastal zones and are valued
for a wide range of ecological, economic and cultural
reasons (Mackay et al. 2009). Coastal wetlands are
remarkable and crucial ecosystems in terms of environ-
mental health, distinctive geomorphologic features, typical
vegetation and faunal associations, and the human activi-
ties related to their singular environment (Jones et al. 2009;
Turner et al. 2004). Wetlands play a key role in supporting
the diversity and abundance of plants and animals of entire
landscape, and they provide habitats and refuges for many
migratory, rare or threatened species (Gan et al. 2009).
Some wetlands provide coastal protection against destruc-
tive natural events such as cyclones and storm tide inun-
dations (Rebelo et al. 2009). Moreover, coastal wetlands
can form the cornerstone elements of regional conservation
strategies (Weber 2004).
Land use maps are regarded to be fundamental for the
purpose of assessment and management planning of coastal
environments (Baban 1997; Chen et al. 2005; Sarma et al.
2008). Accurate coastal land use maps are essential for
monitoring changes over time, for assessing habitat con-
dition and for investigating their links with other ecological
system components that rely directly or indirectly on them
(Carreno et al. 2008). Remote sensing could play an
important and effective role in the coastal land use map-
ping and environmental monitoring (Mas 2004; White and
Asmar 1999) and can provide data in support of decision
making for the management of coastal resource and envi-
ronment, including in the context of international protocols
(Seto and Fragkias 2007). Kumar et al. (2007) studied land
use changes on Sagar Island, India, using Indian Remote
Sensing Satellite 1C (IRS IC) data from 1998 and 1999.
Barducci et al. (2009) studied coastal wetland change in the
San Rossore Natural Park with hyperspectral imaging
sensors. These coastal land use studies have helped assess
and monitor the status of wetland resources by detecting
changes on spatial and temporal scales, as well as pre-
dicting potential future trends.
Analyzing the changes in landscape patterns helps
identifying some of the most critical implications of
complex interactions between natural environmental
changes and anthropogenic activities (Forman 1997;
Turner et al. 2001; Yue et al. 2003) and, therefore, plays an
important role in guiding the planning and management
efforts. Landscape metrics can be used to assess the eco-
logical integrity of landscapes or as variables for models
that support planning actions (Yang and Liu 1995). Land
use transformation stages (Forman 1997) such as frag-
mentation, shrinkage and attrition (disappearance) can
easily be detected by landscape metrics. Landscape metrics
can also be used to analyze habitat change (Liu et al. 2003;
Fletcher et al. 2009), especially habitat fragmentation
(Gibbes et al. 2009).
Habitat fragmentation and natural vegetation loss have
been recognized as a major threat to ecosystems (Laurance
1999; Noss 2001). These two processes may have negative
effects on biodiversity, by increasing isolation of habitats
and putting at risk the viability of resident species popu-
lations (Debinski and Holt 2000), endangering species as
their habitat disappears (Armenteras et al. 2003) and
modifying species population dynamics (Watson et al.
2004). Fragmentation may also have negative effects on
species richness by reducing the probability of successful
dispersal and establishment (Gigord et al. 1999) as well as
by reducing the capacity of a patch of habitat to sustain a
resident population (Iida and Nakashizuka 1995). There-
fore, an understanding of the relationship between land-
scape patterns and the ecological processes influencing
distribution of species is required by resource managers to
provide a basis for making land use decisions (Turner et al.
2001).
The Yancheng National Nature Reserve (YNNR) was
established in 1983 with the major aim of protecting an
endangered bird species, the red-crowned cranes (Grus
japonensis), and its habitats. In 1992, the YNNR was
approved as an international biosphere reserve under
UNESCO’s Man and the Biosphere Programme (MAB),
and in 2002, it was included in the Ramsar Convention List
of Wetlands of International Importance. It is one of the
world’s major winter habitats for red-crowned cranes.
Every November to March, about two-thirds of the world
population of red-crowned cranes winter in the reserve.
The YNNR is also a stop-over site for over 300 species of
migratory birds from Northeast Asia and Australia (Zhu
et al. 2004). The YNNR is a critical area for the rescue of
162 C.-Q. Ke et al.
123
threatened species including the red-crowned crane, the
black-mouthed gull, etc. It is a vital element of China’s
conservation of both coastal wetland ecosystem and
biodiversity.
Due to human population growth and economic devel-
opment, the YNNR is subjected to a multiple resource use
conflict, overexploitation of coastal resources and envi-
ronmental degradation (Ou et al. 2004). Therefore, a large
part of the original coastal wetland has been developed
since the late 1980s, and some natural coastal wetlands
have been transformed into other land types, such as fish
ponds and agricultural fields (Zuo et al. 2004). This has
resulted in a change to the coastal landscape and a reduc-
tion and fragmentation of the red-crowned crane habitat. In
order to better protect coastal wetland ecosystem and bio-
diversity in the YNNR, periodic mapping of land use and
coastal habitats should be performed to observe trends and
changes.
However, very few studies have been conducted to
identify coastal wetland and habitat change in the YNNR.
The present study provides a new dimension to understand
this. Through field investigations, the present study, by
adopting remote sensing data and landscape metrics anal-
ysis, examines coastal wetland change and its impact on
the red-crowned cranes habitat during the past few decades
and analyzes the underlying causes, which are also the
specific purposes of this study.
Study area
The YNNR is located on the east coast of Jiangsu Province,
China, from 32°200Nto34°370N and from 119°290Eto
121°160E (Fig. 1). The YNNR spans the five counties of
Yancheng City: Xiangshui County, Binhai County, Shey-
ang County, Dafeng City and Dongtai County. The
northern border of the reserve is the Guan River in
Xiangshui County; the southern border is the Xingang dam;
the western border is the Yellow Sea Road; and the
reserve’s eastern border is the Yellow Sea. It includes
582 km of coastline and many sand dunes of the conti-
nental shelf (Xu et al. 2005). It is normally divided into
three zones: the core zones of 175 km
2
located in Sheyang
County, the buffer zone and the experimental zone. Above
0 m bathymetric, the total area is about 57,033 ha. It is an
alluvial plain and beach area, and a typical intertidal
mudflat coast. There are many small rivers and lakes in the
area. Elevation in the YNNR varies between 0 and 4 m,
with an average slope of less than 5 degrees. The YNNR
lies in the transition belt between the warm temperate and
northern subtropical zones. As a result, the reserve’s cli-
mate is governed by seasonality, with a dry, cold winter
and a hot, rainy summer (Ma et al. 1998).
The YNNR is a coastal wetland typical of the Jiangsu
coastline. Its original landscape comprises coastal salt
marsh, so the variety of vegetation is poor and dominated
by salt tolerant plants. The plant community had a typical
landward succession sere type (Wan et al. 2001): (1) the
pioneer species Spartina alterniflora dominates the ele-
vated part of the intertidal zones; (2) a Suaeda salsa and
Suaeda glauca community is dominant in the hightidal
zones; and (3) in the supratidal zone, Aeluropus littoralis,
Phragmites australis, Imperata cylindrical, Scripus karu-
izawensis and Zoysiam jacrostachys are prevalent. The
original vegetation of the YNNR was comprised of Suaeda
salsa (L. Pall.) and common reed (Phragmites communis
Fig. 1 Location of study area in
China
Analyzing coastal wetland change 163
123
Trin). In 1963 and 1979, common cordgrass (Spartina
anglica C.E. Hubbard) and smooth cordgrass (Spartina
alterniflora Loisel) were introduced from England and the
United States, respectively, and after the 1990s, they
became the two dominant plants of the intertidal zone in
the YNNR (Li et al. 2005). The main land use types in the
YNNR are reeds, grass flats, ponds, agriculture fields,
rivers, salt fields and developed areas.
Data and methods
Data
In this study, three remote sensing images were used to
examine coastal wetland change in the YNNR. Landsat
images (Table 1) were purchased from China Remote
Sensing Satellite Ground Station. Late spring time images
were selected because, for this area, main plants flourish
and have different height and density in late spring, which
reduced the spectral confusion between reeds and grass
flats during land use interpretation from the images. The
images, which have six bands (except for the thermal band)
and 30-m spatial resolution, are predominantly cloud-free.
Ancillary data include 1: 50,000 digital topographical
maps, aerial photographs, land use maps, administrative
maps and social-economic statistical data from local
authorities. The YNNR boundary was delineated using
administrative maps. The aerial photos and land use maps
were used as a reference for satellite images interpretation
and land use mapping.
Land use mapping
The raw images used for this study were georeferenced
based on the digital topographical maps. After georefer-
ence, the images had a Gaussian–Krueger projection and a
Root Mean Squared Error (RMS error) of less than one
pixel. The images were cut to include only the study area in
order to create a multi-temporal image data set. The
empirical method referred to by Hall et al. (1991)as
‘radiometric rectification’ was used for radiometric cor-
rection. Image enhancement techniques (Bajjouk et al.
1998) were applied to the data in order to optimize the
information for visual interpretation and digitalization.
After this processing, image statistics and histograms from
the three periods were found to be similar and comparable
for the study area.
Classification of land use based on multi-spectral or
multi-temporal remote sensing images has been the main
approach for detecting wetland change (Franklin et al.
2001). But visual interpretation is also a popular method,
and it is particularly suitable for small areas (Liu et al.
2005). Although visual interpretation is a time-consuming
and difficult method, it can more accurately provide land
use maps compared with automatic classification (Liu et al.
2005). In order to obtain high quality land use change
information on the basis of Landsat TM image in 2006, we
developed a land use database at a spatial scale of 1:
100,000 through visual interpretation and digitalization
with technical support from ArcGIS software (ESRI 1999).
Before the interpretation began, fieldwork was conducted
in March 2007 covering the entire study area. Therefore,
we had a priori knowledge of the study area as a whole,
including landform, soil, vegetation, ponds, rivers, salt
fields, agriculture fields and built-up areas.
Interpreters used ArcGIS software to identify land use
types based on their understanding on the object’s spectral
reflectance, structure and other ancillary information.
Then, they drew boundaries and added the attribute labels
to the polygons to produce the digital map. The smallest
patch of land use we selected was not less than 25 pixels
(2.25 ha), and the shortest edge was longer than 3 pixels
(90 m). After the preliminary interpretation, an inventory
was conducted of the areas, which had not been definitely
delineated or identified. A second round of field surveys,
conducted on April–May 2007, had two targets: areas
where ground surveys were the only solution and areas
where it sufficed to study aerial photographs. The field
data were statistically analyzed. The checked cases were
reviewed based on the field verification and the photo-
graphs; if the results of the verifications were unsatisfac-
tory, some or all of the interpretation process was
repeated. The final vector land use maps, which form the
core of the spatial database, were edited and compiled by
comparing the results of visual interpretation and field-
work with the help of land use maps from local land
agencies.
A system of land use classification was established in
which land use was grouped into 7 categories: river, reed,
grass flat, agriculture field, built-up area, salt field and
pond. Reed areas were defined as areas covered with reed
beds (Phragmites communis Trin). Grass flat areas included
areas covered with Spartina alterniflora,Suaeda salsa, etc.
Agriculture field areas included paddy and dry farming
land. Built-up areas included urban areas, rural settlements
and others areas such as roads. Areas used for salt
Table 1 List of satellite images used in this study
Platform Sensor Path/row Resolution
(m)
Acquisition
date
Landsat 5 Thematic Mapper 119-37 30 April 9, 1988
Landsat 5 Thematic Mapper 119-37 30 May 20, 1997
Landsat 5 Thematic Mapper 119-37 30 May 29, 2006
164 C.-Q. Ke et al.
123
production was classified as salt field areas. Pond areas
included lakes and artificial aquaculture areas.
In order to evaluate the accuracy of the land use maps
derived from remote sensing images covering the YNNR,
we conducted a third round of field surveys in June 2007,
covering a total survey length of 61 km across the study
area, 123 patches and more than 80 photos located with
GPS facilities. The overall accuracy of the land cover
classification was found to be 93.4% (Table 2). For the
reed and grass flat areas, the accuracy was 95.5 and 90.0%
respectively, based on the evaluation of 22 and 10 patches,
respectively. For pond areas, the accuracy was 94.7%
based on an evaluation of 19 patches. For agriculture land
areas, the accuracy was 93.5% based on an evaluation of 31
patches. The identification of built-up areas was 92.8%
accurate based on 14 patches. We collected 47 slices to
evaluate the accuracy of location during mapping process.
The results indicated that 96.7% of the polygon boundaries
show less than one pixel (30 m) shift from the real
boundary.
To obtain land use maps for 1988 and 1997, the inter-
preters drew the land use patches based on remote sensing
images from those years. The land use maps for 2006 were
used as supporting information to identify the types of land
use for each patch. We chose 54 and 110 land use patches
in 1988 and 1997, respectively, to make an accuracy
evaluation by interviewing YNNR administration staff and
longtime residents in the YNNR. We also referred to aerial
photos and historical land use maps obtained from the local
authorities. The results showed that the overall identifica-
tion accuracies were 96.2% in 1988 and 95.6% in 1997
(Table 2).
Quantifying landscape metrics
Time series landscape metrics can be used to quantify
coastal wetland structure and spatial configuration
(Seto and Fragkias 2005). Moreover, they can be used as
indicators of habitat quality and other environmental con-
cerns (Hansen et al. 2001; Hargis et al. 1999; Revenga
2005). Of interest to us is the coastal wetland change of the
YNNR as a whole; we analyze the coastal wetland change
and habitat fragmentation by examining the changing
landscape metrics in the last few decades.
Numerous landscape metrics have been proposed (For-
man and Godron 1986). Choices for appropriate landscape
metrics are dependent upon the scale of analysis and
objectives of the study (Turner and Gardner 1991; Forman
1995; Turner et al. 2001). For example, if landscape
fragmentation is to be examined, one will choose indicators
that relate to patch number, mean patch size, patch density,
etc. In the YNNR study area, we sought to identify those
indicators that best reflect the landscape’s temporal change
and habitat fragmentation. Five landscape metrics at the
class level were selected: NP, PD, MPS, SHAPE-AM and
IJI (Table 3). Another 5 landscape metrics at the landscape
level were chosen: NP, MPS, LPI, SHAPE-AM and IJI
(Table 3). Metrics computed at the class level are helpful
for the understanding of landscape development. Indicators
computed at the landscape level yield relatively general
information averaged over the entire landscape (unit) under
investigation.
The definition and description of these landscape met-
rics (Table 3) in FRAGSTATS are given in the FRAG-
STATS user’s guide (McGarigal and Marks 1995).
FRAGSTATS, developed by the Forest Science Depart-
ment, Oregon State University, USA, is a program for
quantifying landscape structure (McGarigal and Marks
1995), and the vector version of this program was used to
calculate landscape metrics for each land use map in the
YNNR. The choice of these metrics seems appropriate,
because the utilized metrics either individually or in con-
junction reveal a distinct but complementary aspect of
complex processes such as fragmentation in a particular
land use class. Moreover, these metrics are in the core set
of landscape metrics indicated by Leitao and Ahern (2002).
Table 2 Accuracy assessment for the land use maps for 1988, 1997 and 2006
Year AF Pond BA SF GF Reed River Total
1988 SPN 6 8 5 2 6 15 12 54
CPN 6 8 5 2 6 14 11 52
ACC 100% 100% 100% 100% 100% 93.3% 91.7% 96.2%
1997 SPN 32 20 21 2 5 19 11 110
CPN 31 19 20 2 5 18 10 105
ACC 96.9% 95% 95.2% 100% 100% 94.7% 90.9% 95.8%
2006 SPN 31 19 14 1 10 22 26 123
CPN 29 18 13 1 9 21 24 115
ACC 93.5% 94.7% 92.8% 100% 90% 95.5% 92.3% 93.4%
AF agriculture land, BA built-up area, SF salt flat, GF grass flat, SPN sampling patch number, CPN correct patch number, ACC accuracy
Analyzing coastal wetland change 165
123
Results
Land use change
Land use changes are presented in Figs. 2and Fig. 3,
Tables 4,5and 6. The grass flat dominated the YNNR in
1988 and 1997, accounting for 44.04 and 36.55% of the
total area, respectively. It continuously decreased from
1988 to 2006, and 66.91% (16,843.50 ha) of the 1988 area
was lost by 2006. Moreover, as of 2006, only the central
region of the YNNR still remained as grass flat habitat,
with most of the decrease recorded in the north and south
regions. The grass flat area decreased most quickly
between 1997 and 2006. Reed area was the second largest
land use type in 1988, and it experienced the same change
trend as the grass flat, decreasing from 33.68% in 1988 to
14.15% in 2006. In terms of spatial distribution, the reed
area is adjacent to the grass flat area. The grass flat and reed
areas were the main vegetation cover type in the YNNR,
and they also constitute the habitat of the red-crowned
cranes. Salt flat, concentrated in the north part of the study
area, increased from 1988 to 1997 but significantly
decreased from 1997 to 2006.
Most other land use types increased during the study
period. Agriculture fields significantly increased from
1.46% in 1988 to 18.57% in 2006 and the annual mean
increase rate was 599.84 ha. The increase in agriculture
fields occurred on the north bank of Xinyanggang River,
i.e. the north and south part of the study area, replacing
both grass flat and reed cover. Pond area, another fast
increasing land use type, was distributed mainly in the
north and south parts and increased by 25,241.84 ha over
18 years. Its area in 2006 was 7 times greater than in 1988,
and its annual mean increase rate was 1,402.38 ha—the
highest rate of increase for any land type. Pond area
increased most quickly from 1997 to 2006, when its annual
mean increase rate reached 2,062.76 ha. The areas in which
pond increased were also the areas where grass flat and
reed cover decreased. Built-up areas increased fivefold
from 77.76 ha in 1988 to 468 ha in 2006 but still accounted
for only a small proportion of the total area. River area
(mainly canals, channels and ditches) showed a little
increase from 1988 to 2006.
Landscape change
The quantification of landscape pattern through landscape
metrics is a key element for studying landscape function
and change (Forman and Godron 1986; Turner et al. 2001).
Figure 4and Table 7compared changes in landscape
metrics at the class level. Grass flat areas were the fastest
decreasing landscape patch type: the NP of grass flat
decreased from 7 in 1988 to 5 in 1997 but then increased to
12 by 2006, and the MPS increased in the first 9 years but
quickly decreased in the second 9 years. PD and AWMSI
decreased in the first period and increased in the second
period. Reed areas also showed rapid loss over the 18-year
period. The NP and PD for reed areas continuously
increased during the study period but the MPS decreased.
The landscape metrics changes in grass flat and reed areas
reflect coastal wetland and vegetation ecosystem change,
and natural vegetation is disturbed and reduced.
Agriculture field area was the highest variable landscape
patch type: the NP dramatically increased at first before
decreasing slightly in the second period (Fig. 4; Table 7).
Pond area was the fastest increasing landscape patch type:
its NP increased in the first study period and then increased
by another patch from 1997 to 2006. In particular, MPS
increased very quickly from 1997 to 2006. Such growth
changes in agriculture fields and ponds resulted from the
high economic benefits of transforming grass flat, reed and
salt flat areas into agriculture fields and aquaculture ponds
from 1988 to 2006. Built-up area was another highly var-
iable patch type. NP continuously increased from 6 in 1988
to 21 in 1997 and to 33 in 2006. The expansion of built-up
area further shows the effects of human activities on the
red-crowned cranes habitat.
The above results reveal that spatial patterns in the
YNNR have become more heterogeneous and fragmented:
grass flat and reed areas consistently shrank, while agri-
culture field and pond area significantly grew, but the NP
for all these land use types increased from 1988 to 2006.
Changes in other landscape metrics also support this
inference. Overall, the change direction of the coastal
landscape has been toward increased heterogeneity and
fragmentation.
Comparison of the change of landscape metrics at the
landscape level is shown in Table 8. The NP continuously
Table 3 Landscape pattern metrics description
Index (unit) Level Description
NP Class Number of patches
PD Class Patch density
MPS (ha) Class Mean patch size
AWMSI Class Area-weighted mean shape index
IJI Class Interspersion and juxtaposition index
NP Landscape Number of patches
MPS (ha) Landscape Mean patch size
LPI Landscape Largest patch index
AWMSI Landscape Area-weighted mean shape index
ENN_MN (m) Landscape Mean Euclidean nearest neighbor
distance
Metrics calculated using Fragstats. Index and description definition
adapted from McGarigal and Marks (1995)
166 C.-Q. Ke et al.
123
increased from 69 in 1988 to 147 in 1997 and then 195 in
2006. Correspondingly, MPS steadily decreased from
828.40 to 426.62 ha and then 321.17 ha, showing that
some original patches were divided and that landscape
heterogeneity and fragmentation was rising. The spatial
context of the landscape patches also changed significantly.
For instance, the ENN-MN steadily shortened from
1,354 m in 1988 to 539 m in 1997 and then 509 m in 2006.
This illustrates that the spatial distribution of various pat-
ches in the YNNR became fragmented. Landscape metrics
helped in quantification of coastal landscape structures in
the YNNR and conveys the extent of changes and their
effects.
Discussions
Driving force analysis
The wetland changes revealed for the YNNR have occur-
red as a result of interactions of a number of socioeconomic
forces. Many studies indicate that population growth is an
important driving force of wetland change and also one of
the main factors in landscape change. The human popula-
tion of the coastal region in Yancheng city significantly
increased from 750 million in 1988 to 805 million in 2006
(Editing committee of ‘‘50 Years in Jiangsu’’ 1999; City
Social-Economic Investigation Department of NBSC 2008).
Population growth has resulted in the expansion of built-up
areas and also contributed to the reclamation of natural
grass flat and reed areas for agricultural purposes. Some
canals, channels and ditches were dug to meet growing
demands for irrigation. Increased agricultural land area
facilities the increased grain production necessary to feed a
growing population. Clearly, human activities have
disturbed the natural coastal wetland ecosystem and the
red-crowned cranes habitat.
Government policy plays an important role in the coastal
wetland change in the YNNR. The government strategy to
develop the marine zone in eastern Jiangsu, referred to in the
Province as ‘‘constructing marine eastern Jiangsu’’, has been
promoted since the early 1990s, and the coastal development
of Jiangsu has gradually entered a new era. Preferential
policies for coastal development in Jiangsu were imple-
mented in order to promote aquaculture development and
Fig. 2 Land use maps of the
YNNR in 1988, 1997 and 2006
Fig. 3 Land use of the YNNR in 1988, 1997 and 2006 as a percent of
the total area
Analyzing coastal wetland change 167
123
resulted in quick economic growth. Gross domestic product
(GDP) of Yancheng city was about 1.2 billion $ in 1988 and
167.8 billion $ in 2006, respectively (Editing committee of
‘‘50 Years in Jiangsu’’ 1999; City Social-Economic Inves-
tigation Department of NBSC 2008). In order to implement
government policy of economic growth, many grass flat and
reed areas were reclaimed and transformed into aquaculture
ponds to obtain greater economic benefits compared with the
modest economic benefits of agriculture. At the same time,
the salt industry, with low economic benefit, was replaced by
more profitable aquaculture. Artificial channels area
increase also showed the development of aquaculture. The
aquaculture output significantly increased from 132 thou-
sand ton in 1988 to 852 thousand ton in 2006 (Editing
committee of ‘‘50 Years in Jiangsu’’ 1999; City Social-
Economic Investigation Department of NBSC 2008). The
fast development of aquaculture led to significant increase of
ponds area and area reduction of reed, grass flat and salt flat.
Table 5 Change matrix for 1988 and 1997 (area in ha)
AF Pond BA SF GF River Reed 1988
Total
AF 186.96 8.28 10.44 0.00 0.00 0.03 59.37 265.08
Pond 32.69 2932.54 10.75 224.73 0.00 0.32 54.75 3255.78
BA 41.15 55.41 53.09 0.00 0.00 0.00 29.07 178.72
SF 0.00 0.00 0.00 7341.96 0.00 0.00 42.53 7384.49
GF 4325.46 3952.34 12.93 218.81 22580.30 3.88 510.58 31604.30
River 1.33 73.06 0.00 0.00 0.14 930.01 62.86 1067.40
Reed 825.11 2757.16 703.84 523.52 2112.24 18.08 6338.20 13278.15
1997 Total 5412.70 9778.79 791.05 8309.02 24692.68 952.32 7097.36 57033.92
AF agriculture land, BA built-up area, SF salt flat, GF grass flat
Table 6 Change matrix for 1997 and 2006 (area in ha)
AF Pond BA SF GF River Reed 1997
Total
AF 4330.13 761.06 101.23 0.00 0.00 121.60 98.69 5412.71
Pond 732.23 8468.18 79.03 1.43 9.69 52.67 435.56 9778.79
BA 66.87 398.68 106.08 0.00 0.00 12.55 206.98 791.16
SF 0.00 4894.02 0.02 3409.03 0.00 5.94 0.00 8309.01
GF 4699.34 10089.28 28.80 0.00 8462.19 63.35 1349.58 24692.54
River 42.87 93.12 7.83 0.60 5.18 671.52 131.21 952.33
Reed 1331.10 1809.50 146.22 0.00 194.99 67.55 3548.02 7097.38
2006 Total 11202.54 26513.84 469.21 3411.06 8672.05 995.18 5770.04 57033.92
AF agriculture land, BA built-up area, SF salt flat, GF grass flat
Table 4 Percentage and rate of land use changes for 1988, 1997 and 2006
Land use types AF Pond BA SF GF River Reed
1988–1997
CP (%) 599.71 187.08 390.39 6.43 -8.96 19.65 -25.08
AMCR (ha/a) 556.77 742.00 33.73 52.73 -250.49 18.80 -536.53
1997–2006
CP (%) 98.97 181.16 22.73 -56.60 -63.66 7.64 -38.56
AMCR (ha/a) 642.91 2062.76 9.63 -494.35 -1621.01 8.74 -618.08
1988–2006
CP (%) 1292.20 707.15 501.85 -53.81 -66.91 28.79 -53.96
AMCR (ha/a) 599.84 1402.38 21.68 -220.81 -935.75 13.77 -577.31
CP change percentage, AMCR annual mean change rate
168 C.-Q. Ke et al.
123
Impacts on habitat
The changes in land use and landscape metrics discussed in
Sect. 4provide verification for the habitat change of red-
crowned cranes. Correspondingly, the natural coastal wetland
consistently decreased from 1988 to 2006 and became frag-
mented and heterogeneous, with significant decrease in grass
flat and reed areas, and thus, the habitat gradually diminished,
and even the core area of the YNNR was threatened. At the
same time, this variation trends also indicated that the natural
coastal landscape and wetland ecosystem in the YNNR
deteriorated during the entire study period.
Birds are an indicator species for wetland environment
(Foster et al. 2009; Robledano et al. 2010), and the red-
crowned cranes can be a symbol of wetland ecosystem (Ma
et al. 1999), and bird number and species can reflect the
health status of wetland ecosystem. The changing trend of
the red-crowned cranes number from 1988 to 2010 also
showed the deteriorated habitat and wetland ecosystem of
the YNNR (Fig. 5). If conservation measures are not
adopted, the critical habitat will cease to exist and red-
crowned cranes may disappear from the YNNR. This
species is already considered globally threatened and los-
ing its habitat, especially such an important wintering area
will likely put it at the brink of extinction or drive it to
extinction altogether. Habitat loss of the YNNR would
mean a great loss of a unique ecosystem and of the
diversity of species, which will be unrecoverable.
Certainly, the habitat changes have negative impacts on the
global ecosystem and biodiversity because the YNNR is
important to the red-crowned cranes population and other
migratory birds.
The study suggested that protecting the natural coastal
wetland habitats and reducing human interventions are
urgent and crucial problems for the YNNR. Local activity
related to land use change should be carefully managed and
designed to develop a more suitable environment for the
bird species. The essential wintering habitat should be
identified and protected from future development, and
other key areas should also be restored.
a
0
10
20
30
40
50
60
70
Year
NP
c
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Year
MPS(ha)
e
0
10
20
30
40
50
60
70
80
90
100
Year
IJI
b
0
0.02
0.04
0.06
0.08
0.1
0.12
Year
PD
d
0
1
2
3
4
5
6
7
8
1988 1997 2006
1988 1997 2006
1988 19 97 2006
1988 1997 2006
1988 1997 2006
Year
AWMSI
Fig. 4 Change of landscape metrics at class level for 1988, 1997 and 2006 (aNP, bPD, cMPS, dAWMSI, eIJI)
Analyzing coastal wetland change 169
123
National nature reserve planning and management are
presently insufficient for the preservation of the red-
crowned cranes habitat. As a functional unit, a national
nature reserve is only one of many interrelated ecological
chains crossing a range of scales. Comprehensive conser-
vation strategies should also consider the interrelationships
among the complicated ecological and social processes at
various scales.
Conclusions
We applied remote sensing technology and landscape pat-
tern metrics to examine the coastal wetland change in the
YNNR. The results revealed that grass flat and reed areas
have significantly decreased, whereas agriculture fields,
aquaculture ponds and built-up areas have continuously
increased from 1988 to 2006, and thus, the red-crowned
cranes habitat gradually diminished, and even the core area
of the YNNR is threatened. The overall patch number
increased, resulting in a reduced mean patch size. The spatial
pattern of wetland landscape had become fragmented and
heterogeneous. The changes in both landscape metrics and
the red-crowned cranes number showed that the natural
coastal landscape and wetland ecosystem deteriorated dur-
ing the entire study period. Anthropogenic driving forces
influencing wetland change include population growth and
Table 7 Metrics comparison at class level for 1988, 1997 and 2006
Land use types AF Pond BA SF GF River Reed
1988
NP 6166271418
PD 0.011 0.028 0.011 0.004 0.012 0.025 0.032
MPS (ha) 139.26 223.10 12.96 3693.00 3596.20 61.50 1069.80
AWMSI 2.01 1.83 1.54 1.61 2.56 6.80 3.04
IJI 78.82 85.35 82.69 55.54 58.99 60.75 77.74
1997
NP 60 25 21 2 5 11 23
PD 0.096 0.040 0.034 0.003 0.008 0.018 0.037
MPS (ha) 97.44 409.91 18.16 3930.26 4583.79 93.65 627.29
AWMSI 2.07 1.77 2.32 1.41 2.34 6.94 2.81
IJI 86.98 81.87 71.09 77.11 64.87 71.44 90.23
2006
NP 53 26 33 1 12 31 39
PD 0.085 0.042 0.053 0.002 0.019 0.050 0.062
MPS (ha) 219.48 1108.17 14.18 3411.36 694.16 35.77 227.31
AWMSI 2.03 3.62 1.86 1.34 3.14 6.39 3.11
IJI 55.87 83.80 76.92 6.27 45.06 71.91 75.54
AF agriculture land, BA built-up area, SF salt flat, GF grass flat, NP number of patches, PD patch density, MPS mean patch size, AWMSI area-
weighted mean shape index, IJI interspersion and juxtaposition index
Table 8 Metrics comparison at landscape level in the YNNR for
1988, 1997 and 2006
Year NP MPS (ha) LPI AWMSI ENN_MN (m)
1988 69 828.40 24.49 2.61 1354.80
1997 147 426.62 14.05 2.28 539.26
2006 195 321.17 22.67 3.10 509.44
NP number of patches, MPS mean patch size, LPI largest patch index,
AWMSI area-weighted mean shape index, ENN_MN mean Euclidean
nearest neighbor distance
y = -6.4595x + 13633
R2 = 0.0723
400
600
800
1000
1200
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Year
red-crowned crane number
Fig. 5 Red-crowned cranes number from 1988 to 2010
170 C.-Q. Ke et al.
123
coastal development policies of aquaculture promotion. It is
expected that the trend toward fragmentation and hetero-
geneity will continue if driving forces are not altered.
To protect the red-crowned cranes habitat, conservation
measures must be strengthened. From this study, we can see
that coastal zone development is leading to the natural
wetland decrease and may threaten the survival of the
red-crowned cranes and other migratory birds in the YNNR.
In any policy development, we should give special consid-
eration to the protection of the coastal wetland ecosystem to
seek both economic and environmental benefits.
Acknowledgments Thanks are extended to the anonymous
reviewers and to the subject editor Prof. Ruth DeFries and editor-in-
chief Prof. Wolfgang Cramer for their excellent reviews and con-
structive comments. This research is financially supported by the
National Natural Science Foundation of China (NSFC Grant No.
40730635 and 40971044), Hydrologic public benefit project of Water
Resource Ministry of China (Grant No. 200701024), Program for
New Century Excellent Talents in University (NCET-08-0276) and
Deutscher Akademischer Austauschdienst (DAAD) scholarship.
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