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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, U.S.A.klemas@udel.edu
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
Journal of Coastal Research, Vol. 28, No. 4, 2012
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.
Remote Sensing of Land Cover to Predict Downstream Water Quality 933
Journal of Coastal Research, Vol. 28, No. 4, 2012
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).
934 Huang and Klemas
Journal of Coastal Research, Vol. 28, No. 4, 2012
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
Remote Sensing of Land Cover to Predict Downstream Water Quality 935
Journal of Coastal Research, Vol. 28, No. 4, 2012
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.
936 Huang and Klemas
Journal of Coastal Research, Vol. 28, No. 4, 2012
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)
Remote Sensing of Land Cover to Predict Downstream Water Quality 937
Journal of Coastal Research, Vol. 28, No. 4, 2012
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.
938 Huang and Klemas
Journal of Coastal Research, Vol. 28, No. 4, 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.
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).
Remote Sensing of Land Cover to Predict Downstream Water Quality 939
Journal of Coastal Research, Vol. 28, No. 4, 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.
940 Huang and Klemas
Journal of Coastal Research, Vol. 28, No. 4, 2012
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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