Using Remote Sensing of Land Cover Change in Coastal ... - BioOne

Journal of Coastal Research

28

4

930–944

West Palm Beach, Florida

July 2012

Using Remote Sensing of Land Cover Change in Coastal Watersheds to Predict Downstream Water Quality Jinliang Huang{ and Victor Klemas{* { Coastal and Ocean Management Institute Xiamen University Xiamen 361005, Fujian, P.R. China

{ School of Marine Science and Policy University of Delaware Newark, DE 19716, U.S.A. [email protected]

www.cerf-jcr.org

ABSTRACT Huang, J. and Klemas, V., 2012. Using remote sensing of land cover change in coastal watersheds to predict downstream water quality. Journal of Coastal Research, 28(4), 930–944. West Palm Beach (Florida), ISSN 0749-0208. Land cover and land use data are important for watershed assessment and runoff modeling. Satellite and airborne remote sensors can map land cover/use effectively. Whenever a strong linkage exists between land cover/use and runoff water quality, remotely sensed land cover trends can help predict long-term changes in water and habitat quality of downstream estuaries and bays. This paper reviews practical remote sensing techniques for land cover change monitoring and presents a case study that relates land cover/use, landscape patterns, and temporal scales to the water quality of runoff from a coastal watershed in SE China. The results of the case study show that the percentage of built-up land was a good predictor for downstream water quality and that the linkage among NHz 4 -N, CODMn, and landscape variables during wet precipitation years was stronger than during dry precipitation years.

ADDITIONAL INDEX WORDS:

Remote sensing, landscape patterns, land use, runoff linkage, water quality, coastal

watersheds.

INTRODUCTION The type and extent of land cover and the land use in a watershed impact the quantity and quality of the water running off into downstream estuaries and bays (Baker, 2003; Baker, Weller, and Jordan, 2006; Haith and Duffany, 2007). For instance, densely vegetated areas have lower erosion rates than those with bare soils. Urban areas and impervious surfaces discharge rainfall more rapidly and completely, while swamps trap large amounts of particulates and filter out pollutants as the runoff waters move through the swamp. Environmental managers and researchers use various sensors on boats and on fixed and portable stations to measure a range of runoff water parameters, including flow volume, nutrients, particulate organic and inorganic substances, bacteria, herbicides, and heavy metals. Land cover can be effectively mapped and monitored by airborne and satellite remote sensors over large coastal watersheds. When a strong link exists between land cover/use and runoff water pollutants, it may be possible to predict longterm water quality trends in coastal estuaries and bays using remotely sensed land cover/use change data in the respective watersheds (Goetz et al., 2004; Harwell et al., 2008; King and Balogh, 2001; Mustafa et al., 2005; Santillan, Makinano, and Paringit, 2011; Owers, Albanese, and Litts, 2012; Thanapura et al., 2006; Ucuncuoglu, Arli, and Eronat, 2006; Withers, Jarvie, and Stoate, 2011). Pollutants considered here might

DOI: 10.2112/JCOASTRES-D-11-00176.1 received 22 September 2011; accepted in revision 24 January 2011. ’ Coastal Education & Research Foundation 2012

include suspended sediment, which smothers coral reefs; high concentration of nutrients (nitrate and ammonium), which cause algal blooms; and dissolved organics (Craft, Vymazal, and Richardson, 1995). The objectives of this paper are to provide an overview of costeffective remote sensing techniques for land cover mapping and to review and illustrate the linkage between changes in land cover/use and water quality through a case study.

REMOTE SENSING OF LAND COVER IN COASTAL WATERSHEDS To study the impact of land runoff on estuarine and coastal ecosystems, a combination of models is frequently used, including watershed models, hydrodynamic models, and water quality models (Baker, Weller, and Jordan, 2006; Fedorko et al., 2005; Leon et al., 2000; Li et al., 2007; Linker et al., 1993). The watershed models usually estimate the downstream flow rates and nutrient/pollutant loads. The hydrodynamic models determine the circulation and sediment/nutrient transport patterns within a bay or estuary. The water quality model predicts the impact of nutrient, pollutant, and sediment loads on geological and living resources (Mayer, 2005; Wazniak et al., 2007; Yasuhara et al., 2007). Most coastal watershed models require land cover and land use data that, together with other inputs like slope and precipitation, can be used in attempts to predict the amount and type of runoff into rivers, estuaries, and bays and how their ecosystems will be affected (Jensen, 2007; Li et al., 2007; Mehaffay et al., 2005; Tu et al., 2007; Wilson and Weng, 2010). For instance, some models predict that severe degradation in stream water quality will occur when the

Remote Sensing of Land Cover to Predict Downstream Water Quality

Table 1.

Water and pollutant flows into the Chesapeake Bay.

Period (year)

TN (millions of lb.)

TP (millions of lb.)

Total Sediment (millions of lb.)

Flow (billions of gal/d)

2002 2003 L.t. ave 1986

130.5 353.6 207.0 364.4

6.0 30.0 12.2 30.9

1644.1 18,169.9 7875.7 28,659.2

37.7 86.7 50.1 87.5

* Loads are from only the nontidal portions of the tributaries(USGS).

agricultural land use in watersheds exceeds 50% or the urban land use exceeds 20% (Tiner et al., 2002). Before discussing remote sensing of long-term changes of land cover and related runoff, it is important to acknowledge that there will be short-term fluctuations in runoff caused mainly by variations in precipitation and storm frequency, rather than land cover/use changes. The importance of precipitation data is illustrated in Table 1, which shows the large difference between a ‘‘dry’’ year (2002) and a ‘‘wet’’ year (2003) in the amount of nitrogen, phosphorus, and sediment that ran off into the Chesapeake Bay. The units are millions of pounds and billions of gallons. If the flow and precipitation data had not been recorded, we might wrongly conclude that the land cover/use had dramatically changed for the worse between those two years. Monitoring the actual quantity and biochemical content of runoff water using remote sensors is difficult and has been studied by other researchers (Cannizzaro and Carder, 2006; Chipman et al., 2004; Keith, 2010; Kutser, 2009; Miller et al., 2006; Ritchie, Zimba, and Everitt, 2003; Schalles et al., 1998; Simis, 2005). Two of the more common medium-resolution satellites for mapping watershed land cover on a regional scale are the U.S. Land Satellite (Landsat) and French Syste`me Probatoire d’Observation de la Terre (SPOT). The satellites have multispectral scanners that provide spatial resolutions of 30 and 10 m and cover swaths 185 and 60 km wide, respectively. The Landsat Thematic Mapper (TM) with its 30-m resolution has for decades provided reliable data for monitoring land cover changes in large coastal watersheds, such as the Chesapeake Bay (Lunetta and Balogh, 1999). Figure 1 shows a land cover map of the Chesapeake Bay watershed derived from Landsat Enhanced Thematic Mapper Plus (ETM+) imagery. Thirteen land cover classes are mapped in Figure 1, including two wetland classes. Similar satellites with medium-resolution imagers can also be used. Before performing image analysis for thematic land cover or vegetation mapping, we must choose or develop a classification system that meets the needs of the problem to be addressed (Klemas, 2005). One of the most commonly used land cover classification systems is the U.S. Geological Survey (USGS) Land Use and Land Cover Classification System for use with remote sensor data (Anderson et al., 1976). Most projects use the top classes of the Anderson scheme and define lower classes based on the needs of the specific project. The top-level classes of the Anderson system represent land cover, such as agriculture, forest, and urban, and can usually be mapped with medium-resolution satellite sensors, such as Landsat TM and SPOT. The more detailed levels include land use, such as residential, commercial, and industrial, and require

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high-resolution imagery and additional inputs, such as aerial photos and field data (Jensen, 2007; Lu, Hetrick, and Moran, 2010; Purkis and Klemas, 2011). There are other classification schemes in use by programs such as the U.S. Fish and Wildlife Service National Wetlands Inventory, the USGS Gap Analysis Program (GAP), and the National Oceanic and Atmospheric Administration (NOAA) Coastwatch Change Analysis Program (Cowardin, 1978; Cowardin et al., 1979; Jensen, 2007; Klemas et al., 1993; Wilen and Bates, 1995). The most recent effort to standardize vegetation inventory procedures in the United States has been conducted by the USGS and the National Park Service, resulting in the Standardized National Vegetation Classification (Nature Conservancy, 1994). In a typical digital image analysis approach for classifying land cover, the multispectral imagery must first be radiometrically and geometrically corrected. The radiometric correction reduces the influence of haze and other atmospheric scattering particles and any sensor calibration anomalies. The geometric correction compensates for Earth’s rotation and for variations in the position and attitude of the satellite. Image segmentation simplifies the analysis by first dividing the image into homogeneous patches or ecologically distinct areas. The classification of each pixel in the image is often performed by alternating between supervised and unsupervised classification procedures. Supervised classification requires the analyst to select training samples from the data that represent the themes to be classified. The training sites are ground areas previously identified using field visits or other data, such as aerial photographs. The spectral reflectance of these training sites is used to develop spectral ‘‘signatures,’’ which are then employed to assign each pixel in the image to a thematic class (Jensen, 1996; Lillesand and Kiefer, 1994). Unsupervised classifications may be performed to identify variations in the image not contained in the training sites. In unsupervised classification, the computer automatically identifies the spectral clusters (in multidimensional color space) representing all features on the ground. Training site spectral clusters and unsupervised spectral classes are then compared and analyzed using cluster analysis to develop an optimum set of spectral signatures. Final image classification is then performed to match the classified themes with the project requirements (Jensen, 1996). Throughout the process, ancillary data are used whenever available (e.g., aerial photos, maps, and field samples). Ancillary data are also used to improve the accuracy of classification, especially for those land cover categories that do not meet the required 85% accuracy. For instance, the class of forest is sometimes confused with that of forested wetland. Similarly, the grassland class may be difficult to discriminate from the cultivated land class. When studying small watersheds, we can use aircraft or highresolution satellite systems (Adam, Mutanga, and Rugege, 2010; Klemas, 2011). Airborne georeferenced digital cameras providing color and color-infrared digital imagery are particularly suitable for accurate mapping or interpreting satellite data. Most digital cameras are capable of recording reflected visible to near-infrared (NIR) light. A filter is placed over the

Journal of Coastal Research, Vol. 28, No. 4, 2012

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Huang and Klemas

Figure 1. Map of Chesapeake Bay watershed land cover produced from multitemporal Landsat ETM+ imagery for 2000 (modified with permission from Goetz et al., 2004).

lens that transmits only selected portions of the wavelength spectrum. For a single-camera operation, a filter is chosen that generates natural color (blue–green–red wavelengths) or colorinfrared (green–red–NIR wavelengths) imagery. For a multiplecamera operation, filters that transmit narrower bands are chosen. For example, a four-camera system may be configured so that each camera filter passes a band matching a specific satellite imaging band, e.g., blue, green, red, and NIR bands matching the bands of the IKONOS satellite multispectral sensor (Ellis and Dodd, 2000; Klemas, 2011). Digital camera imagery can be integrated with global positioning system position information and used as layers in a geographic information system (GIS) for a range of modeling applications (Lyon and McCarthy, 1995). Small aircraft flown

at low altitudes (e.g., 500 m) can be used with digital cameras to supplement field data. High-resolution imagery (0.6–4 m) can also be obtained from satellites, such as IKONOS and QuickBird (Table 2). However, cost becomes excessive if the site is larger than a few hundred square kilometers, and in that case, medium-resolution sensors, such as Landsat TM (30 m) and SPOT (20 m), become more cost effective. High-resolution imagery is more sensitive to within-class spectral variance, making separation of spectrally mixed land cover types more difficult than when using medium-resolution imagery. Therefore, pixel-based techniques are sometimes replaced by object-based methods, which incorporate spatial neighborhood properties, by segmenting/partitioning the image into a series of closed objects that coincide with the actual



Table 2.

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High-resolution satellite parameters and spectral bands.* IKONOS

QuickBird

OrbView-3

WorldView-1

Sponsor

Space Imaging

Launched

Sept. 1999

GeoEye-1

WorldView-2

DigitalGlobe

Orbimage

Oct. 2001

June 2003

DigitalGlobe

GeoEye

DigitalGlobe

Sept. 2007

Sept. 2008

Oct. 2009

1.0 4.0

0.61 2.44

1.0 4.0

0.5 n/a

0.41 1.65

525–928 NA 450–520 510–600 NA 630–690 NA 760–850 11.3 626u 2.3–3.4 681

450–900 NA 450–520 520–600 NA 630–690 NA 760–890 16.5 630u 1–3.5 450

450–900 NA 450–520 520–600 NA 625–695 NA 760–900 8 645u 1.5–3 470

400–900 NA NA NA NA NA NA NA 17.6 645u 1.7–3.8 496

450–800 NA 450–510 510–580 NA 655–690 NA 780–920 15.2 630u 2.1–8.3 681

Spatial resolution (m) Panchromatic Multispectral

0.5 2

Spectral range (nm) Panchromatic Coastal blue Blue Green Yellow Red Red edge NIR Swath width (km) Off nadir pointing Revisit time (d) Orbital altitude (km)

450–800 400–450 450–510 510–580 585–625 630–690 705–745 770–1040 16.4 645u 1.1–2.7 770

* From DigitalGlobe (2003), Orbimage (2003), Parkinson (2003), and Space Imaging (2003). NA 5 not applicable.

spatial pattern, and then proceed to classify the image. ‘‘Region growing’’ is among the most commonly used segmentation methods. This procedure starts with the generation of seed points over the whole scene, followed by grouping neighboring pixels into an object under a specific homogeneity criterion. Thus, the object keeps growing until its spectral closeness metric exceeds a predefined break-off value (Kelly and Tuxen, 2009; Shan and Hussain, 2010; Wang, Sousa, and Gong, 2004). Identifying land use practices and wetland species with remote sensors is difficult; however, some progress is being made using hyperspectral imagers (Jensen et al., 2007; Klemas, 2009; Porter et al., 2006; Schmidt et al., 2004; Yang et al., 2009). Hyperspectral imagers may provide several hundred spectral bands as compared to multispectral imagers, which use less than a dozen bands.

REMOTE SENSING OF LAND COVER CHANGE To identify long-term trends of land cover change, researchers need to analyze the time series of remotely sensed imagery. The acquisition and analysis of time series of multispectral imagery is a difficult task. The imagery must be acquired under similar environmental conditions (same time of year, same sun angle, etc.) and in similar spectral bands. There will be changes in both time and spectral content (Green, Kempka, and Lackey, 1994). One way to approach this problem is to reduce the spectral information to a single index, reducing the multispectral imagery into a single field of the index for each time step. In this way, the problem is simplified to the analysis of the time series of a single variable, one for each pixel of the images. The most common index used is the Normalized Difference Vegetation Index (NDVI), which is expressed as the difference between the red and the NIR reflectances divided by their sum. These two spectral bands represent the most detectable spectral characteristic of green plants. This is because the red

radiation is absorbed by the chlorophyll in the surface layers of the plant (Palisade parenchyma) and the NIR is reflected from the inner leaf cell structure (Spongy mesophyll) as it penetrates several leaf layers in a canopy. Thus, the NDVI can also be related to plant biomass or stress, because the NIR reflectance depends on the abundance of plant tissue and the red reflectance indicates the surface condition of the plant. It has been shown by researchers that time series of remote sensing data can be used effectively to identify long-term trends and subtle changes of NDVI by means of principal component analysis (Jensen, 2007; Young and Wang, 2001). The preprocessing of multidate sensor imagery, when absolute comparisons between different dates are to be carried out, is more demanding than the single-date case. It requires a sequence of operations, including calibration to radiance or at-satellite reflectance, atmospheric correction, image registration, geometric correction, mosaicking, subsetting, and masking out clouds and irrelevant features. In the preprocessing of multidate images, the most critical steps are the registration of the multidate images and their radiometric rectification. To minimize errors, registration accuracies of a fraction of a pixel must be attained. The second critical requirement for change detection is attaining a common radiometric response for the quantitative analysis for one or more of the image pairs acquired on different dates. This means that variations in solar illumination, atmospheric scattering and absorption, and detector performance must be normalized; i.e., the radiometric properties of each image must be adjusted to those of a reference image (Coppin et al., 2004; Lunetta and Elvidge, 1998). Detecting the changes between two registered and radiometrically corrected images from different dates can be accomplished by employing one of several techniques, including postclassification comparison and spectral image differencing (SID). In postclassification comparison, two images from different dates are independently classified. The two classified


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used to identify areas of significant spectral change. Then postclassification comparison can be applied within areas where spectral changes were detected to obtain class-to-class change information. Change analysis results can be further improved by including probability filtering that allows only certain changes and forbids others (e.g., urban to forest). A detailed, step-by-step procedure for performing change detection was developed by the NOAA Coastal Change Analysis Program and is described in Dobson et al. (1995) and Klemas et al. (1993).

REGIONAL AND GLOBAL LAND COVER MAPPING PROGRAMS

Figure 2.

Land cover change detection approach (Klemas, 2011).

maps are then compared pixel by pixel (Figure 2). This avoids the difficulties in change detection associated with the analysis of images acquired at different times of the year or day or by different sensors, thereby minimizing the problem of radiometric calibration among dates. One disadvantage is that every error in the individual date classification maps is also present in the final change detection map (Dobson et al., 1995; Jensen, 1996; Lunetta and Elvidge, 1998). The SID algorithm is the most widely applied change detection algorithm. Techniques for SID rely on the principle that land cover changes result in changes in the spectral signature of the affected land surface. This involves the transformation of two original images to a new single- or multiband image in which the areas of spectral change are highlighted. This is accomplished by subtracting one date of raw or transformed (e.g., vegetation indices or albedo) imagery from a second date, which has been precisely registered to the image of the first date. Pixel difference values exceeding a selected threshold are considered changed. A change/no change binary mask is overlaid onto the second date image, and only the pixels labeled as having changed are classified in the second date image. While the unchanged pixels remain in the same classes as in the first date imagery, the spectrally changed pixels must be further processed by other methods, such as a classifier, to produce a labeled land cover change map. The band difference or ‘‘change’’ image is clustered and then aggregated into change and no change spectral classes. This approach eliminates the need to identify land cover changes in areas where no significant spectral change has occurred between the two dates of imagery (Coppin et al., 2004; Jensen, 1996; Lunetta and Elvidge, 1998; Yuan, Elvidge, and Lunetta, 1998). However, to obtain accurate results, radiometric normalization must be applied to one date of imagery to match the radiometric condition of the two dates of data before image subtraction. A comparison of the SID and the postclassification comparison change detection algorithms is provided by Macleod and Congalton (1998). The SID method and the post-classification–based method are often combined in a hybrid approach. For instance, SID can be

Before starting a new land cover mapping effort in a watershed, investigators should check local, state, and regional land cover mapping programs to determine whether the products are suitable for their own project. Intermediate-scale (10–30 m) land cover data are required by an increasing number of applications on local, state, and regional scales to support a range of management, monitoring, and modeling activities in such areas as agriculture, forestry, disease control, water quality, and wildlife (Wardlow and Egbert, 2003). Such applications require land cover data at fine spatial resolutions and with relatively detailed levels of classification, requirements satisfied by many state-level and several nationwide land cover mapping efforts. The USGS National Land Cover Data (NLCD) program and the Gap Analysis Program (GAP) provide intermediate-scale information to support a range of user projects (Fry et al., 2011). The data sets are comparable but have different objectives, classification systems, and analysis methodologies (Wardlow and Egbert, 2003). The GAP’s objective is to provide a land cover map to support ‘‘state-level’’ biodiversity-related research activities (i.e., identify gaps in the network of biodiversity management areas). Thus, the GAP data set is detailed from a classification standpoint (Scott et al., 1993). On the other hand, the NLCD’s objective was to provide a generalized, consistent, and seamless land cover data set for the conterminous United States (Homer et al., 2007). The NLCD’s generalized land cover classification system was based on a modified Anderson level II classification scheme, which specifies land use within each level I land cover class (Vogelmann, Sohl, and Howard, 1998; Vogelmann et al., 2001). This presents obvious limitations to applications requiring detailed land cover information but is appropriate for regional-scale applications (i.e., state or multistate), because of its continuous and seamless nature (Wardlow and Egbert, 2003). The NLCD products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics Consortium, which is a partnership of federal agencies. Previously, the NLCD consisted of data releases in 1992 and 2001, based on a 10-year cycle, including layers of thematic land cover, percent imperviousness, and percent tree canopy. Due to the rapid change of land cover in some areas, the NLCD moved to a 5-year cycle, producing a land cover product in 2006 (Fry et al., 2011). The new approach meets user needs for more frequent land cover monitoring and reduces the production time between image capture and product release.



There is also a research project, Land Cover Trends, that is focused on the rates, trends, causes, and consequences of contemporary U.S. land use and land cover change. That research project is supported by the USGS, Environmental Protection Agency, and National Aeronautics and Space Administration (NASA). On a global scale, land cover studies around the world vary greatly both temporally and spatially (Friedl, 2002; Latifovic et al., 2005; Lunetta and Elvidge, 1998). For example, in the Sahel region of West Africa, scientists are monitoring, mapping, and quantifying changes in natural resources through the use of land cover changes. The European Environmental Agency produced a land cover database—CORINE—for the 25 European Community (EC) member states and other European countries that includes 44 land cover and land use classes (USGS/LCI, 2010). The Global Land Cover Facility (GLCF), which is housed at the University of Maryland, also provides earth science data and products. The GLCF develops and distributes land cover data with emphasis on determining where, how much, and why land cover changes around the world (Hansen and Reed, 2000).The International Geosphere–Biosphere Program (IGBP) provides a quantitative understanding of Earth’s past climate and environment, while the Land Use and Land Cover Change (LUCC) Project is a program element of the IGBP.

LINKAGE BETWEEN LAND COVER/USE AND RUNOFF WATER QUALITY The water quality in the streams reflects the interactions between humans and nature (Baker, 2003; Novotny, 2002). Land use and landscape patterns can reflect underlying human activities and are helpful for evaluating ecological processes (Gautam et al., 2003; Huang, Tu, and Lin, 2009; Redman, 1999). Therefore, many anthropogenic influences, including urbanization, agricultural intensification, and industrialization, are part of the larger process of watershed land use and land cover change that can affect water quality of rivers (Baker, 2003; Fisher et al., 2006; Huang et al., 2011b; Roberts and Prince, 2010). It is vital to address how various land uses affect nonpoint source pollution (NPS) nutrient loading so that changes in water quality due to modified land use can be predicted and realistically modeled (Brett, Arhonditsis, and Mueller, 2005). Specifically, understanding the linkage between water quality and land use pattern is helpful for estimating water quality in rivers suffering from diffuse pollution and for predicting water quality in unmonitored catchments (Baker, 2003). The importance of the relationship between land cover/use change and water quality is reflected by the increased recognition of NPS as a major environmental concern (Griffith, 2002). Landscape-scale approaches have been commonly used for water quality–land use studies on a watershed scale, where watersheds are usually subdivided into various combinations of land use and land cover change so that the outflowing waters could be monitored. Increasing the feasibility of using remotely sensed data enables landscape–water quality studies to be more easily performed on local and regional scales (Griffith, 2002). In situ sampling and monitoring is also crucial, because the land use–

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water quality relationship is in part a function of the sampling strategy, including sampling locations and frequency (Baker, 2003). Regression analysis has been widely used to examine relationships between land cover/use change and water quality (Allan, Erickson, and Fay, 1997; Johnson et al., 1997; Sliva and Williams, 2001). Given that the land–water relationships often vary over space, geographically weighted regressions were also used to analyze the spatially varying relationships between land use and water quality (Tu, 2011; Tu and Xia, 2008).

Effect of Land Use and Land Cover Change on Water Quality Significant relationships between land use/landscape pattern and water quality have been found in watersheds around the world (Huang et al., 2011b; Tu, 2011; Yang, 2012). However, land–water studies are scale dependent and vary over time and space (Behrendt et al., 2002; Uuemaa, Roosaare, and Mander, 2007). As suggested by Wiens (2002), the relationships between terrestrial landscape and freshwater ecosystems that are apparent at one scale may disappear or be replaced by other relationships at other scales. Because each watershed has a unique combination of landscape characteristics, some mixed results or inconsistencies remain in the linkage between land cover/use change and water quality (Baker, 2003; Griffith, 2002; Sliva and Williams, 2001). The effects of some specific land use and land cover change types on water quality can be summarized as follows.

Percentage of Built-up Land Many studies found that the percentage of built-up land (%BL) was positively correlated with degraded water quality and therefore represents a good predictor for water quality (Galbraith and Burns, 2007; Guo et al., 2010; Hertler et al., 2009; Huang et al., 2011b; Kang et al., 2010; Lee et al., 2009; Osborne and Wiley, 1988; Reimann et al., 2009; Sliva and Williams, 2001; Tran et al., 2010). The relationship between built-up land and water quality should consider the wastewater treatment condition in the study area. Ahearn et al. (2005) concluded that the insufficient wastewater treatment in the catchments results in a good relationship between total-N and built-up land. They also argued that using urban cover as a NPS for nutrients can give spurious results, because much of the cover in urban areas is impervious and the drainage is frequently routed to wastewater treatment plants (WWTPs) and then discharged to local rivers as point sources. Brett, Arhonditsis, and Mueller (2005) found that NHz 4 -N was not significantly correlated with urban land cover in their study area where WWTP or industrial effluent was not discharged into any of the streams.

Percentage of Agriculture Agricultural activities are commonly related to commercial fertilizer use and soil losses. One of the unintended consequences of the increased intensity of agricultural land use has been the contamination of shallow groundwater with nitrate (NO{ 3 ; Gutman et al., 2004). It is understandable that percentage of agriculture (%AGR) is positively correlated with the degraded water quality, such as NO{ 3 (Bahar, Ohmori, and


936

Huang and Klemas

Table 3.

Commonly used LPMs for delineating landscape patterns.

LPMs

Contagion

SHDI

PD

LPI Mean shape index (SHMN)

Edge density (ED)

Description

Degree to which landscape is divided into many small patches vs. a few large patches

Equals, minus the sum across all patch types, the proportional abundance of each patch type multiplied by that proportion Equals the number of patches of the corresponding patch type divided by the total landscape area (in m2), multiplied by 10,000 and 1000 Equals the percentage of landscape that the largest patch comprises Given by the sum of the patch perimeter divided by the square root of patch area for each patch in landscape, adjusted by a constant for a square standard, and divided by the number of patches Equals the sum of the lengths (in m) of all edge segments involving the corresponding patch type, divided by the total landscape area (in m2) and multiplied by 10,000

Computing Equation

2

2

0

132

0

133

B B C76 C77 6 6 B B C76 C7 6 6 6ðPi ÞB gik C76lnðPi ÞB gik C77 6 m m B B C C77 6 6 7 6 X X @ @ A A57 4 4 5 7 6 gik gik 7 6 m m XX 7 6 k ~ 1 k ~ 1 7|100 Contagion ~ 6 1z 7 6 ð Þ 2 ln m 7 6 k~1 l~1 7 6 7 6 7 6 7 6 5 4 SHDI ~ {

PD ~

X

½pi lnðpi Þ

ni ð10,000Þð100Þ A

Maxða1 ,a2 , . . . ,an Þ |100 A ! m X n X 0:25Pij pffiffiffiffiffiffi aij i~1 j~1 SHMN ~ N

LPI ~

m X

ED ~

k~1

A

eik ð10,000Þ

Pi is the proportion of the landscape occupied by the patch type (class) i; i 5 1, …, m is the number of patch types; gik is the number of adjacencies (joins) between pixels of patch types (classes) i and k based on the double count; m is the number of patch types (classes) present in the landscape, including the landscape border if present; ni is the total number of patches in the landscape occupied by the patch type (class) i; A is the total landscape area (in square meters); an is the area (in square meters) of patch n; Pij is the perimeter of patch ij (in meters); aij is the area of patch ij; i 5 1, …, m is the number of patch types; j 5 1, …, n is the number of patches; N is the total number of patches in the landscape; eik is the length (in meters) of the edge between pixels of patch types (classes) i and k based on the double count.

Yamamuro, 2008; Fedorko et al., 2005; Lee et al., 2009; Shen et al., 2011; Tong and Chen, 2002). Sun et al. (2011b) had an interesting finding that %AGR in the entire watershed is not significantly correlated with total phosphorus (TP) and NHz 4 -N, whereas the cropland located on slopes of less than 15u and greater than 25u are positively correlated with CODMn and NHz 4 -N, indicating the spatial pattern of cropland contributed greatly to the water quality. Comparatively, many studies showed that %AGR had an insignificant or even negative relationship with water quality. For example, Sun, Chen, and Chen (2011) concluded that the agricultural area (30% of the upstream regions of the Haihe River basin, China) is not significantly correlated with total N concentration due to little irrigated farmland and rainfall. Lee et al. (2009) found that agricultural land uses showed no significant relationships with water quality parameters, including biochemical oxygen demand (BOD), total nitrogen (TN), and TP. Sliva and Williams (2001) found the %AGR was negatively correlated with NHz 4 -N at the watershed scale in spring, summer, and fall. Zhao (2008) drew a similar conclusion that agriculture was negatively correlated with TP and NHz 4 -N. He surmised that agriculture is no longer the ‘‘source’’ but the ‘‘sink’’ for pollution under the context of rapid urbanization. Other studies showed the same results (Johnson et al., 1997; Lenat and Crawford, 1994).

Percentage of Woodland and Grassland The percentage of woodland and grassland had positive relationships with nutrients due to the general understanding that the concentration of nutrients in the surface runoff from

woodlands and grasslands can be reduced effectively. The woodland and grassland classes become sinks for such pollutants. Huang et al. (2011a) found that the percentage of z woodland had negative relationships with NO{ 3 , NH4 -N, and CODMn. Many studies had similar observations (Bahar, Ohmori, and Yamamuro, 2008; Lopez et al., 2008; Novotny, 2002; Osborne and Kovacic, 1993; Sliva and Williams, 2001). In addition, Galbraith and Burns (2007) found that the nutrient concentrations, suspended sediment, and water color showed a strong negative correlation with the percentage of grassland.

Influence of Landscape Patterns on Water Quality Landscape patterns play an important role in water quality variation on the watershed scale. It is widely recognized that the spatial configuration of landscapes, including the extent, distribution, intensity, and frequency of human land uses, plays a critical role in determining natural habitats, hydrological processes, and nutrient cycles (Alberti et al., 2007; Grim et al., 2000; Lee et al., 2009). Alberti et al. (2007) further emphasized that the spatial configuration is an important factor in understanding the hydrological processes linking land uses and water quality in adjacent aquatic systems. Since the 1980s, landscape pattern metrics (LPMs) have been employed as a means to quantify the spatial heterogeneity and landscape structure, including composition and configuration. Some commonly used LPMs are shown in Table 3. LPMs are used to measure landscape patterns at the landscape and class level. At the landscape level, spatial configurations were measured as a whole, including multiple land use types. At the class level,



spatial configurations were measured separately only within single land use types, excluding other land use types (Lee et al., 2009). We summarize the linkages between several LPMs at the landscape scale and water quality as follows: (1) Shannon Diversity Index (SHDI)-water quality. The SHDI increases as the number of land use types increases (McGarigal and Marks, 1995). A high SHDI means high landscape fragmentation due to intensive human disturbance, thereby generating a high abundance and complex landscape structure of patches (Osborne and Wiley, 1988; Uuemaa, Roosaare, and Mander, 2005). Many researchers found that the SHDI was positively correlated with degraded water quality parameters (Guo et al., 2010; Lee et al., 2009; Uuemaa, Roosaare, and Mander, 2007). Huang et al. (2011b) also found that the SHDI is the important predictor for explaining the variance in CODMn. (2) Contagion-water quality. Contagion is associated with both dispersion and interspersion of land use types, and it is high when there are low levels of dispersion and interspersion of land use types. Contagion approaches 0 when land use types are maximally disaggregated and interspersed and approaches 100 when all land use types are maximally aggregated. Contagion was negatively related to degraded water quality. For example, Uuemaa, Roosaare, and Mander (2007) found contagion was the most important predictor for CODMn and had a negative relationship with CODMn. Xiao and Ji (2007) reported negative relationships of contagion with total Fe and total Zn in streams in watersheds with mines. Lee et al. (2009) found contagion was consistently and negatively related to water quality. (3) Largest patch index (LPI)-water quality. The LPI quantifies the percentage of the total watershed area comprising the largest land use patch and dominance of the single largest land use patch (McGarigal and Marks, 1995). The LPI is the important predictor for water quality. It negatively correlated with water quality parameters. With increase of landscape fragmentation, the LPI decreases (Wu, 2007). Conversely, a higher value of the LPI means a low intensity of the anthropogenic disturbance; therefore, water quality is relatively good. Lee et al. (2009) revealed an association between degraded water quality and dominance of urban land use as the largest patches. (4) Patch density (PD)-water quality. The PD measures the number of land use patches within watershed areas but does not provide information on the size and spatial distribution of land use (McGarigal and Marks, 1995). Thus, fragmented land uses might have negative impacts on water quality (Lee et al., 2009). For example, Uuemaa, Roosaare, and Mander (2005) and Lee et al. (2009) found the PD showing positive correlation with the chemical oxygen demand (COD). Richards, Johnson, and Host (1996) also found that PD can relate to water quality variables in some seasons. However, there are some mixed results on the linkage of PD with water quality parameters. For example, Uuemaa, Roosaare, and Mander (2007)

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found that PD was negatively correlated with BOD5 and CODMn; Johnson et al. (1997) also found that PD was negatively correlated with PD and phosphate (PO34 -P). Landscape classes such as riparian forests, wetlands, and sedimentation ponds influence pollutant transport and detention and, to some extent, could reduce the risk of developing NPS (Chen et al., 2002). Lee et al. (2009) pointed out that most previous studies focused on the composition of land use types and adopted LPMs to delineate patterns at the landscape level, making it difficult to apply the findings to landscape and land use planning. More recently, some attempts have been made to explore the linkage between landscape pattern at the class level (e.g., wetland, agriculture, or built-up) and water quality (Lee et al., 2009; Moreno-Mateos et al., 2008; Sun et al., 2011a, 2011b), which enables us to gain in-depth insight into land–water studies and facilitates the applications of research findings for landscape planning and land management. For example, Moreno-Mateos et al. (2008) found that the relative abundance of wetlands and the aggregation of its patches influence salinity variables at wetland. They also concluded there are no significant relationships between wetland metrics and nutrientrelated variables, especially N variables, and gave the explanation of such findings as ‘‘current existing wetlands are not functional enough in nutrient retention, as a consequence of its lack of design with this purpose.’’ Lee et al. (2009) revealed PD of urban land use shows positive correlation with COD. They also found that PD of agricultural land use and forest are positively correlated with TP in the fall. Sun et al. (2011a) found that the LPI and landscape shape index of built-up land are positively correlated to CODMn, NHz 4 -N, and TP, which further verified that the impacts of urban built-up land on water quality are influenced by not only urban built-up land areas but also their spatial patterns. Sun et al. (2011b) revealed that contagion of agricultural land use is positively correlated to TP, underlying the information that TP concentrations in the stream increased with farmland gathered in the study area.

Influence of the Temporal Scale on the Linkage of Landscape Characteristics and Water Quality Considering the role of the temporal scale, communities examined the linkage between land cover/use change and water quality based on seasonal and interannual variation. Since the late 1980s, researchers have addressed the issue of seasonality by linking water quality during multiple seasons to land use and land cover change for each season (Brett, Arhonditsis, and Mueller, 2005; Johnson et al., 1997; Lee et al., 2009; Osborne and Wiley, 1988; Sliva and Williams, 2001). In recent years, researchers have come to realize the important role of climate variability on such a linkage and thereby performed interannual analyses for land cover/use change and water quality (Ahearn et al., 2005; Kaushal et al., 2008; Rothenberger, Burkholder, and Brownie, 2009). The results indicate that land use and land cover change has a varying impact on water quality between dry and wet years. For example, Ahearn et al. (2005) found that dry years with little precipitation produce less effect on NO{ 3 -N than average years. Kaushal et al. (2008)


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found that NO{ 3 -N exports showed significant interannual variability, with declines during dry years and increases during wet years. Rothenberger, Burkholder, and Brownie (2009) concluded that NHz 4 -N concentrations were elevated after high precipitation. The results from Mehaffey, Nash, and Wade (2005) also show dynamics regarding landscape variables with respect to water quality represented by three regression models at three points in time. Huang et al. (2011b) concluded that climatic variability influences the linkage of water quality– landscape characteristics and the fit of empirical regression models. They found that the relationships among NHz 4 -N, CODMn, and landscape variables during a wet precipitation year are stronger than those during a dry precipitation year.

Buffer vs. Catchment Landscape Influence on Water Quality Spatial pattern is the determinant factor for water quality and even for aquatic ecosystems (Alberti et al., 2007). Several researchers have addressed the issue of whether land use near streams and river is a better predictor of water quality than land use over the whole catchment (Guo et al., 2010; Johnson et al., 1997; Osborne and Wiley, 1988; Roberts and Prince, 2010; Sawyer et al., 2004; Sliva and Williams, 2001; Tran et al., 2010). In general, the significance of riparian landscape characteristics on water quality in a watershed is widely recognized. For example, Johnson et al. (1997) and Tran et al. (2010) found that the whole catchment-scale analysis explained slightly less of the water quality variability than their buffer-scale analysis. Guo et al. (2010) concluded that the impact of land use on water quality is stronger in the effective buffer than in the catchments. Robert and Prince (2010) demonstrated the significance of the riparian land use and land cover change and landscape metrics on water quality simulation in the Chesapeake Bay watershed and had the finding that the model with a 31-m riparian stream buffer width accounted for the highest variance of mean annual TN and TP yield, compared with the entire catchments and five other riparian stream buffer widths. Conversely, Silva and Williams (2001) concluded that water quality is correlated with a catchment-scale landscape more than with a buffer landscape.

Figure 3.

Location of the JRW.

human activities. Therefore, it is essential to understand the linkage of land cover/use change and water quality in the coastal watersheds of China. The Jiulong River watershed (JRW) is a typical medium-sized subtropical coastal watershed in China that has been suffering from drastic land use and land cover change and thereby water quality degradation in the last 20 years. It plays an important role in the region’s economic and ecological health. Investigating the linkage between land cover/use change and water quality in the JRW is therefore crucial for regional- and watershed-scale water quality management. Specifically, the research objective of this study was to detect the dynamic linkage between landscape characteristics and water quality in the JRW during both dry and wet years.

Description of the Study Site CASE STUDY: PREDICTING RUNOFF WATER QUALITY FROM WATERSHED LAND COVER/USE IN A SUBTROPICAL COASTAL WATERSHED OF CHINA Although many attempts have been made to examine the relationships between landscape characteristics and water quality in the scientific communities (Bahar, Ohmori, and Yamamuro, 2008; Fedorko et al., 2005; Griffith et al., 2002; Johnson et al., 1997; Kaushal et al., 2008; Lee et al., 2009; Rhodes, Newton, and Pufall, 2001; Sliva and Williams, 2001; Wilson and Weng, 2010; Yang, 2012), investigating the linkage between landscape and water quality is still in its infancy in China. Over the last three decades, China has undergone rapid anthropogenic changes. Land use and land cover change in coastal watersheds in China is driven especially by intensive

The JRW covers about 14,700 km2 in eastern coastal areas of China (116u469550 E–118u029170 E and 24u239530 N–25u539380 N) and consists mainly of eight counties/districts: Zhangzhou, Xinlou, Zhangping, Hua’an, Changtai, Pinghe, Longhai, and Nangjing (Figure 3). The watershed’s gross domestic product accounts for a quarter of the Fujian Province’s economic output. More than 5 million residents from Xiamen, Zhangzhou, and Longyan use the Jiulong River as their source of water for residential, industrial, and agricultural uses (Huang and Hong, 2010). The Zhangzhou plain, the Fujian province’s largest plain, located at the downstream end of the Jiulong River, constitutes one of China’s most developed regions in terms of agricultural production due to its subtropical monsoon climate and agricultural policies, which are influenced by its closeness to Taiwan (Huang et al., 2012). We chose this case



Figure 4.

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Land use map of the JRW in 1996, 2002, and 2007 (Huang et al., 2012).

study because it involves a critical estuary that is being affected by agricultural and urban development and because the study approach and results illustrate some of the key points of this paper.

then used to isolate a final model, with only the significant (p , 0.05) independent variables included. For each model, the initial fixed independent variables were five landscape variables: percentage of natural (%NA), %AGR, %BL, PD, and SHDI.

Methods and Materials Geospatial technology, including GIS and remote sensing, were used in this study to retrieve the land use and land cover change data and delineate the watersheds. This study used land use and land cover change maps with three categories, agriculture, natural, and built-up, produced for 1996, 2002, and 2007 (Figure 4; Huang et al., 2012). The study delineated 11 subwatersheds according to the location of 11 gauge stations set up for regular monitoring of water quality by the Environmental Protection Bureau of the Fujian province (Figure 3). Therefore, the existing water quality data for each subwatershed in 1996, 2002, and 2007 can be used for investigating linkages between landscape and water quality. The annual average daily flow at the Punan hydrological station (PN; Figure 3) for the 3 years 1996, 2002, and 2007 is 243, 205, and 270 m3/s, respectively. The mean daily flow over the period 1968–2007 at the PN station is 266 m3/s. Therefore, 1996 and 2002 belong to lowflow years (dry years), while 2007 belongs to a high-flow year (wet year). In this study, two LPMs were chosen: PD and SHDI. The Kolmogorov-Smirnov goodness of fit test was used to test for normality of the distribution of the individual water quality and landscape variables. All analyses were conducted on log-transformed water quality data. The log-transformed water quality indicators were treated as response (dependent) variables, and the landscape metrics were used as independent variables. Backward stepwise regression was

Results and Discussion The %BL was consistently entered into the multiple linear regression models and was positively correlated with NHz 4 -N and CODMn at three points in time (Table 4), which means that the greater the %BL, the more NHz 4 -N and CODMn present in the surface water. The results of the case study suggest that the %BL is the most important variable associated with NHz 4 -N and CODMn in the watershed studied, which is similar to other findings (Galbraith and Burns, 2007; Guo et al., 2010; Hertler et al., 2009; Kang et al., 2010; Lee et al., 2009; Osborne and Wiley, 1988; Reimann et al., 2009; Sliva and Williams, 2001; Tran et al., 2010). The significant relationship between %BL and NHz 4 -N, and between %BL and CODMn to some extent, shows problems with wastewater management in this watershed (Ahearn et al., 2005). The regression analysis results also suggest that LPMs were useful in predicting water quality (Table 4). For example, SHDI is significantly positively correlated with CODMn in 2002, implying that the higher the fragmentation processes, the higher the CODMn concentration. Many researchers also found that SHDI was positively correlated with degraded water quality parameters (Guo et al., 2010; Lee et al., 2009; Uuemaa, Roosaare, and Mander, 2007). PD was negatively correlated with NHz 4 -N in this study, which is similar to the findings by Uuemaa, Roosaare, and Mander (2007) and Johnson et al. (1997).


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Table 4.

Huang and Klemas

Multiple linear regression results of the effect of landscape on water quality at three points in time.{{ 1996

LULC\LPMs

NHz 4 -N

PD SHDI %AGR %NA %BL R2

20.580**

2002 CODMn

NHz 4 -N

2007 CODMn

NHz 4 -N

CODMn

0.695* 20.905** 0.834** 0.712

0.661* 0.437

0.675* 0.455

0.438

0.940** 0.892

0.741** 0.549

* indicates p , 0.05; ** indicates p , 0.01. { The number of observations for the multiple regression analysis for all three years is 11. { From Huang et al. (2011b). LULC 5 land use and land cover change.

In this study, land use and land cover change explained more of the variance in NHz 4 -N and CODMn concentrations with the exception of CODMn in 2002, compared to LPMs such as PD and SHDI (Table 4), which is similar to research results by Johnson et al. (1997) and Richards, Johnson, and Host (1996). This study reveals that landscape influences water quality and that the influences vary over time (Table 4). Specifically, the relationships among NHz 4 -N, CODMn, and landscape variables during the wet precipitation year 2007 are stronger, with R2 values of 0.892, than those during the dry precipitation years 1996 and 2002, which had R2 values of 0.712 and 0.455, respectively. Such interannual variation of the linkage between landscape characteristics and water quality were also verified by other studies (Ahearn et al., 2005; Kaushal et al., 2008; Rothenberger, Burkholder, and Brownie, 2009).

SUMMARY AND CONCLUSIONS Land cover and land use in a watershed influence the quality of water running off into downstream water bodies. Land cover can be cost-effectively mapped and monitored by remote sensors on aircraft and satellites over large coastal watersheds. Whenever a strong linkage exists between land cover/use and runoff water quality, remotely sensed long-term land cover trends can help predict changes in the water quality of the downstream rivers, estuaries, and bays, and how their ecosystems will be affected. The Landsat TM has been a reliable source for land cover data because its 30-m resolution and spectral bands have proved suitable for observing land cover changes in large coastal watersheds (e.g., the Chesapeake Bay). When studying small watersheds, we can use aircraft or high-resolution satellite systems. Airborne georeferenced digital cameras providing color and color-infrared digital imagery are particularly suitable for accurate mapping or interpreting satellite data. High-resolution imagery (0.6–4 m) can also be obtained from satellites, such as IKONOS and QuickBird. However, cost becomes excessive if the site is larger than a few hundred square kilometers, and in that case, medium-resolution sensors, such as Landsat TM (30 m) and SPOT (20 m), become more cost effective. The preprocessing of multidate sensor imagery for detecting changes among different dates is more difficult than the singledate case. The most critical steps are the registration of the multidate images and their radiometric rectification. To

minimize errors, registration accuracies of a fraction of a pixel must be attained. Detecting changes between two registered and radiometrically corrected images can be accomplished by one of several techniques, including postclassification comparison and SID. In postclassification comparison, two images from different dates are independently classified and changed pixels are identified. In SID, two multidate images are transformed to a new single- or multiband image in which the areas of spectral change are highlighted. This is done by subtracting one date of raw or transformed (e.g., vegetation index or albedo) imagery from a second date. Pixel difference values exceeding a selected threshold are considered as changed. In a hybrid approach, SID can be used to identify areas of significant spectral change. Then postclassification comparison can be applied within areas where spectral changes were detected to obtain class-to-class change information. Intermediate-scale land cover data are required by an increasing number of applications to support a range of management, monitoring, and modeling activities. The USGS NLCD program and the GAP provide intermediate-scale information to support a considerable number of user projects. The GAP’s objective is to provide a land cover map to support ‘‘state-level’’ biodiversity-related research activities (i.e., identify gaps in the network of biodiversity management areas). Thus, the GAP data set is detailed from a classification standpoint. The NLCD’s objective was to provide a generalized, consistent, and seamless land cover data set for the conterminous United States. The NLCD consisted of data releases in 1992 and 2001, based on a 10-year cycle, including layers of thematic land cover, percent imperviousness, and percent tree canopy. Since then, the NLCD moved to a 5-year cycle, producing a land cover product in 2006. Globally, land cover studies vary greatly both temporally and spatially. The European Environmental Agency produced a land cover database—CORINE—for the 25 EC member states and other European countries that include 44 land cover and land use classifications. The GLCF at the University of Maryland develops and distributes data showing land cover changes around the world. The IGBP provides a quantitative understanding of Earth’s past climate and environment, while the Land Use and Land Cover Change Project is a program element of the IGBP. Although some inconsistencies remain in the linkage between land cover/use change and water quality due to the



coupling effects on a spatial and temporal scale, as well as natural and anthropogenic disturbances at different watersheds, some general understanding of the linkages between landscape characteristics and water quality has been attained. Study results show that the %BL is positively correlated with degraded water quality; the percentage of woodland is negatively correlated with degraded water quality; the linkage of land cover/use change and water quality is stronger in wet years than in dry years; the SHDI and contagion are the important predictors for explaining variances of water quality; and land use and land cover change type might be more important than LPMs in predicting stream water quality. The case study results illustrate the dynamic linkage between landscape characteristics and water quality in both dry and wet years in a subtropical coastal watershed in SE China. The %BL was a good predictor for NHz 4 -N and CODMn for the subwatersheds without WWTPs. This finding is meaningful for watershed-scale water quality management in the watersheds of China that have similar wastewater management and land use patterns. The relationships among NHz 4 -N, CODMn, and landscape variables during the wet precipitation year were stronger than during the dry precipitation years. Climate change should be recognized as an important factor influencing the linkage between landscape characteristic and water quality in similar spatial-scale watersheds.

ACKNOWLEDGMENTS This research was supported in part by the NOAA Sea Grant and by the NASA EPSCoR programs at the University of Delaware. This study was also supported by Natural National Science Foundation of China (Grant No. 40810069004, Grant No. 40901100) and Natural Science Foundation of the Fujian province, China (Grant No. 2009J01222).

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