Using remote sensing of land cover change to predict downstream water quality

www.cerf-jcr.org

Using Remote Sensing of Land Cover Change in CoastalWatersheds to Predict Downstream Water Quality

Jinliang Huang{ and Victor Klemas{*

{Coastal and Ocean Management InstituteXiamen UniversityXiamen 361005, Fujian, P.R. China

{School of Marine Science and PolicyUniversity of DelawareNewark, DE 19716, [email protected]

ABSTRACT

Huang, J. and Klemas, V., 2012. Using remote sensing of land cover change in coastal watersheds to predict downstreamwater 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 airborneremote sensors can map land cover/use effectively. Whenever a strong linkage exists between land cover/use and runoffwater quality, remotely sensed land cover trends can help predict long-term changes in water and habitat quality ofdownstream estuaries and bays. This paper reviews practical remote sensing techniques for land cover changemonitoring and presents a case study that relates land cover/use, landscape patterns, and temporal scales to the waterquality of runoff from a coastal watershed in SE China. The results of the case study show that the percentage of built-upland was a good predictor for downstream water quality and that the linkage among NHz

4 -N, CODMn, and landscapevariables during wet precipitation years was stronger than during dry precipitation years.

ADDITIONAL INDEX WORDS: Remote sensing, landscape patterns, land use, runoff linkage, water quality, coastalwatersheds.

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, herbi-

cides, 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 long-

term 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

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 cost-

effective 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 INCOASTAL WATERSHEDS

To study the impact of land runoff on estuarine and coastal

ecosystems, a combination of models is frequently used, in-

cluding 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

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

Journal of Coastal Research 28 4 930–944 West Palm Beach, Florida July 2012

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 biochem-

ical 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 Systeme Proba-

toire 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, re-

spectively. 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 Map-

per 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

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, re-

sulting 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 radio-

metrically and geometrically corrected. The radiometric cor-

rection 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 unsuper-

vised classification procedures. Supervised classification re-

quires 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 classifica-

tion 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 high-

resolution satellite systems (Adam, Mutanga, and Rugege,

2010; Klemas, 2011). Airborne georeferenced digital cameras

providing color and color-infrared digital imagery are partic-

ularly 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

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 130.5 6.0 1644.1 37.7

2003 353.6 30.0 18,169.9 86.7

L.t. ave 207.0 12.2 7875.7 50.1

1986 364.4 30.9 28,659.2 87.5

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

Remote Sensing of Land Cover to Predict Downstream Water Quality 931

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

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 color-

infrared (green–red–NIR wavelengths) imagery. For a multiple-

camera 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 Quick-

Bird (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 im-

age into a series of closed objects that coincide with the actual

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).

932 Huang and Klemas


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 sen-

sed 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 vari-

able, 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 regis-

tration, geometric correction, mosaicking, subsetting, and

masking out clouds and irrelevant features. In the preprocess-

ing 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 radio-

metrically corrected images from different dates can be

accomplished by employing one of several techniques, includ-

ing postclassification comparison and spectral image differenc-

ing (SID). In postclassification comparison, two images from

different dates are independently classified. The two classified

Table 2. High-resolution satellite parameters and spectral bands.*

Sponsor

IKONOS QuickBird OrbView-3 WorldView-1 GeoEye-1 WorldView-2

Space Imaging DigitalGlobe Orbimage DigitalGlobe GeoEye DigitalGlobe

Launched Sept. 1999 Oct. 2001 June 2003 Sept. 2007 Sept. 2008 Oct. 2009

Spatial resolution (m)

Panchromatic 1.0 0.61 1.0 0.5 0.41 0.5

Multispectral 4.0 2.44 4.0 n/a 1.65 2

Spectral range (nm)

Panchromatic 525–928 450–900 450–900 400–900 450–800 450–800

Coastal blue NA NA NA NA NA 400–450

Blue 450–520 450–520 450–520 NA 450–510 450–510

Green 510–600 520–600 520–600 NA 510–580 510–580

Yellow NA NA NA NA NA 585–625

Red 630–690 630–690 625–695 NA 655–690 630–690

Red edge NA NA NA NA NA 705–745

NIR 760–850 760–890 760–900 NA 780–920 770–1040

Swath width (km) 11.3 16.5 8 17.6 15.2 16.4

Off nadir pointing 626u 630u 645u 645u 630u 645uRevisit time (d) 2.3–3.4 1–3.5 1.5–3 1.7–3.8 2.1–8.3 1.1–2.7

Orbital altitude (km) 681 450 470 496 681 770

* From DigitalGlobe (2003), Orbimage (2003), Parkinson (2003), and Space Imaging (2003).

NA 5 not applicable.



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 radio-

metric 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

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 COVERMAPPING PROGRAMS

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, require-

ments 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 Characteris-

tics 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.

Figure 2. Land cover change detection approach (Klemas, 2011).



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 Europe-

an 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 dis-

tributes 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–Bio-

sphere 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 ANDRUNOFF 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 industrializa-

tion, 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 be-

tween 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–

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 onWater Quality

Significant relationships between land use/landscape pat-

tern 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 character-

istics, 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

NHz4 -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 conse-

quences 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



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

NHz4 -N, whereas the cropland located on slopes of less than

15u and greater than 25u are positively correlated with CODMn

and NHz4 -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 NHz4 -N

at the watershed scale in spring, summer, and fall. Zhao (2008)

drew a similar conclusion that agriculture was negatively

correlated with TP and NHz4 -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

woodland had negative relationships with NO{3 , NHz

4 -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 un-

derstanding 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 com-

monly 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,

Table 3. Commonly used LPMs for delineating landscape patterns.

LPMs Description Computing Equation

Contagion Degree to which landscape is divided into many

small patches vs. a few large patches

Contagion ~ 1zXmk ~ 1

Xml ~ 1

Pið ÞgikXm

k ~ 1

gik

0BBBB@

1CCCCA

266664

377775 ln Pið Þ

gikXmk ~ 1

gik

0BBBB@

1CCCCA

266664

377775

2 ln mð Þ

2666666666666664

3777777777777775

|100

SHDI Equals, minus the sum across all patch types, the

proportional abundance of each patch type

multiplied by that proportion

SHDI ~ {X

pi ln pið Þ½ �

PD Equals the number of patches of the corresponding

patch type divided by the total landscape

area (in m2), multiplied by 10,000 and 1000

PD ~ni

A10,000ð Þ 100ð Þ

LPI Equals the percentage of landscape that the largest

patch comprisesLPI ~

Max a1,a2, . . . ,anð ÞA

|100

Mean shape index

(SHMN)

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 patchesSHMN ~

Xmi ~ 1

Xn

j ~ 1

0:25Pijffiffiffiffiffiffiaijp

!

N

Edge density (ED) 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 ED ~

Xmk ~ 1

eik

A10,000ð Þ

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.



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 distur-

bance, 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 de-

graded 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 parame-

ters. For example, Uuemaa, Roosaare, and Mander (2007)

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 deten-

tion 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 sig-

nificant relationships between wetland metrics and nutrient-

related variables, especially N variables, and gave the ex-

planation 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, NHz4 -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 ofLandscape 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 interan-

nual 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)



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 NHz4 -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 NHz4 -N,

CODMn, and landscape variables during a wet precipitation

year are stronger than those during a dry precipitation year.

Buffer vs. Catchment Landscape Influence onWater 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 variabil-

ity 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.

CASE STUDY: PREDICTING RUNOFF WATERQUALITY FROM WATERSHED LAND COVER/USE

IN A SUBTROPICAL COASTAL WATERSHEDOF 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

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 man-

agement. Specifically, the research objective of this study was

to detect the dynamic linkage between landscape characteris-

tics and water quality in the JRW during both dry and wet

years.

Description of the Study Site

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, Zhang-

zhou, 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 3. Location of the JRW.



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.

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 low-

flow 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 (depen-

dent) variables, and the landscape metrics were used as

independent variables. Backward stepwise regression was

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.

Results and Discussion

The %BL was consistently entered into the multiple linear

regression models and was positively correlated with NHz4 -N

and CODMn at three points in time (Table 4), which means that

the greater the %BL, the more NHz4 -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 NHz4 -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

NHz4 -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, im-

plying 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 NHz4 -N in this study, which is similar to the findings

by Uuemaa, Roosaare, and Mander (2007) and Johnson et al.

(1997).

Figure 4. Land use map of the JRW in 1996, 2002, and 2007 (Huang et al., 2012).



In this study, land use and land cover change explained more

of the variance in NHz4 -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 NHz4 -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 be-

tween 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 single-

date 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 compar-

ison 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., iden-

tify 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 contermi-

nous 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

Table 4. Multiple linear regression results of the effect of landscape on water quality at three points in time.{{

LULC\LPMs

1996 2002 2007

NHz4 -N CODMn NHz

4 -N CODMn NHz4 -N CODMn

PD 20.580**

SHDI 0.695*

%AGR 20.905**

%NA

%BL 0.834** 0.661* 0.675* 0.940** 0.741**

R2 0.712 0.437 0.455 0.438 0.892 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.



coupling effects on a spatial and temporal scale, as well as

natural and anthropogenic disturbances at different water-

sheds, 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 NHz4 -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

NHz4 -N, CODMn, and landscape variables during the wet

precipitation year were stronger than during the dry precip-

itation 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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Using remote sensing of land cover change to predict downstream water quality

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