Landsat Imaginery Monitoring Exotic Plant in Estuarine Wedland

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Linear spectral mixture analysis of Landsat TM data for monitoring

invasive exotic plants in estuarine wetlands

MEIMEI HE, BIN ZHAO*, ZUTAO OUYANG, YANER YAN and BO LI

Coastal Ecosystems Research Station of the Yangtze River Estuary, Ministry of

Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institute

of Biodiversity Science, Fudan University, Shanghai 200433, PR China

(Received 2 February 2008; in final form 21 January 2009)

This study assessed the feasibility of spectral mixture analysis (SMA) of Landsat

thematic mapper (TM) data for monitoring estuarine vegetation at species level.SMA modelling was evaluated, using w2 test, by comparing SMA fraction images

with a precisely classified QuickBird image that has a higher spatial resolution. To

clearly understand the strengths and weaknesses of SMA, eight SMA models with

different endmember combinations were assessed. When the TM data dimension

for SMA and the endmember number required were balanced, a model with three

endmembers representing water and two vegetation types was most accurate,

whereas a model with five endmembers approximated the actual surface situation

and generated a relatively accurate result. Our results indicate that an SMA model

with appropriate endmembers had relatively satisfactory accuracy in monitoring

vegetation. However, errors might occur in SMA fraction images, especially in

models with an inappropriate endmember combination, and the errors weremainly distributed in areas filled with water or near water. Therefore, short

vegetation usually submerged during high tide tended to be poorly predicted by

SMA models. These results strongly suggest that tide water has a great influence on

SMA modelling, especially for short vegetation.

1. Introduction

Estuarine wetlands, where terrestrial and aquatic species live together, are productive

ecosystems that are ecologically important and provide various ecosystem services

(Pennings and Callaway 1992, Phinn et al. 1996, Klemas 2001, Zhao et al. 2004).

These precious wetlands are subjected to considerable loss that is caused by natural

and anthropogenic factors (Yue et al. 2003, Niemi et al. 2004, Levinson 2005). Among

these factors, the invasions by exotic plants have become extremely serious because

invasive plants can change the vegetation structure and functions of wetland ecosys-

tems through excluding native plants (Pimentel et al. 2000, Underwood et al. 2006).

Nowadays, many coastal and estuarine marshes are under serious threat from inva-

sions by exotic plants (Grosholz 2002). A better understanding of spatial dynamics of

invasive species in estuarine wetlands will facilitate well-targeted and proactive efforts

in controlling spread of invasive species. However, it is hard to investigate vegetation

distribution in estuarine wetlands using traditional approaches, such as field sampling

and survey, because these approaches are time-consuming, cost inefficient, and

*Corresponding author. Email: [email protected]

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online# 2010 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160903252343

International Journal of Remote Sensing

Vol. 31, No. 16, 20 August 2010, 43194333
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sometimes impractical (e.g. the areas are inaccessible) (Ozesmi and Bauer 2002).

Fortunately, remote sensing has the potential to update surveys by repeatedly cover-

ing large areas (Ozesmi and Bauer 2002, Rosso et al. 2005, Shabanov et al. 2005, Aplin

2006), especially by surveying areas that are difficult to access. Therefore, great

progress has been made over the past decades with the application of remote sensing

to wetlands (Ramachandran et al. 1998, Klemas 2001, Ozesmi and Bauer 2002).Among the remote sensing techniques, the Landsat platform has provided scientists

with medium-resolution satellite imagery for over 30 years, and has undoubtedly

become a popular platform for wetland researches.

However, a practical difficulty facing us is that the diameters of new patches of

invasive plants in coastal wetlands may be less than those covered by the maximum

average resolution 30 m of Landsat Thematic Mapper (TM), meaning that the vegeta-

tion information acquired by TM is mixed pixel, and often contains several plant

species. To overcome this difficulty, many efforts have been made to identify vegetation

patches at the sub-pixel level (Levinson 2005, Shanmugam et al. 2006). Some studies

have shown that sub-pixel classification techniques, such as spectral mixture analysis(SMA), are likely to give more accurate results than the traditional per-pixel classifica-

tion techniques (i.e. iterative self-organizing data analysis, ISODATA; and maximum

likelihood classification, MLC) (Foody and Cox 1994, Bateson and Curtiss 1996, Pu

et al. 2003, Shanmugam et al. 2006). The results of SMA are a set of fraction maps

providing not only spatial but also abundance information for each vegetation type

under investigation (Settle and Drake 1993, Schmidtlein and Sassin 2004). For this

reason, SMA has been used to investigate wetland vegetation for many purposes, such

as mapping non-photosynthetic vegetation (NPV) (Roberts et al. 1993) and studying

mature stands of chaparral (Roberts et al. 1998). Previous studies have treated all plants

as one category (presented as vegetation), without identifying the plant communitiesat species level, which is obviously inadequate for monitoring invasive plants, especially

at the early phase of invasion when the species have only a small invading populations

and patches. Some recent studies have started to consider vegetation classification at

species level. For example, Li et al. (2005) and Rosso et al. (2005) have investigated the

spatial distributions of several marsh plant species using SMA technique on Airborne

Visible Infrared Imaging Spectrometer (AVIRIS) data at San Pablo Bay and San

Francisco Bay, respectively. Their results have shown that the species abundance

maps derived with SMA were in good agreement with their field observations.

To classify vegetation at species level, many pioneering efforts have focused on

hyperspectral data (Schmidtlein and Sassin 2004, Rosso et al. 2005, Underwood et al.

2006). Because hyperspectral data have large numbers of contiguous bands, more

class types could be contained in SMA, which may be limited by using multispectral

data. However, these studies have some disadvantages in monitoring vegetation

because such images are usually obtained with aerial platforms that depend

on occasional and often irregularly timed flights. Therefore, it is practically

difficult to monitor the whole invasion process within a particular time frame, because

of the lack of historical data (Aplin 2006) and the inadequacy of the repetitive

observations.

In this respect, our work was an attempt to apply SMA to Landsat TM images, and

to study the potential use of TM images to determine the distributions of invasive plants

at species level. It should be noted that it is not easy to apply SMA in an estuarinewetland because the complex hydrological conditions make the surface soil highly

variable. In estuarine wetlands, various plant communities may occur in heterogeneous

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mosaics, with bare soil and open water. A critical step in SMA is to select the proper

components, or endmembers. The complex surface conditions increase the difficulty of

endmember selection, especially under the inherent mathematical limitations of SMA,

i.e. the number of endmembers cannot exceed the number of spectral bands used.

Therefore, another purpose of our work was to determine how endmember schemas

affect mapping accuracy, and how the moist background influences SMA modelling.Moreover, our work provided a cross-validation technique for comparing the fractional

images derived from SMA of TM data with high-spatial-resolution imagery.

2. Materials and methods

2.1 Study site

Our study area is part of brackish marshes in the Yangtze River Estuary, China

(figure 1), which typically consists of marshy land that is growing rapidly (Zhao et al.

2008). It is characterized by continuous expansion because of the large amounts ofsediment brought down by the Yangtze River (Ma et al. 2003). The tidal fluctuations

are regular and semi-diurnal, and there are two distinct periods of ebb and flood tides

during the day. The width of the tideland uncovered by tidewater is about 1.5 km

during the neap tide, whereas almost all the tideland is submerged by tidewater during

the spring tide (Sun and Cai 2001). The vegetation is dominated by Phragmites

australis (hereafter Phragmites), Scirpus mariqueter (hereafter Scirpus) and Spartina

alterniflora (smooth cordgrass, hereafter Spartina). Phragmites and Spartina usually

Figure 1. Location of the study area.

Linear spectral mixture analysis of Landsat TM data 4321


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grow up to 1.52.5 m, whereas Scirpus only reaches a height of 0.30.7 m (Zhao et al.

2009). Spartina was intentionally introduced into Dongtan in 2001 for land reclama-

tion, because of its capacity to increase sediment accretion (Chen et al. 2004). After a

burst of growth and expansion for several years, Spartina has become a dominant

species, and a rapid succession has occurred. The invasion of Spartina has a tremen-

dous impact on the ecosystems, seriously threatening the native ecosystem and coastalaquaculture. The native species, Phragmites and Scirpus, are being rapidly replaced by

Spartina, which modifies the structure of the marsh dramatically (Li et al. 2009). The

native species Scirpus usually grows in tidal zones with an elevation between 2.0 and

2.9 m, whereas Phragmites dominates higher zones close to the sea wall, and forms a

mosaic with Spartina. The distributions of these species have produced a zonal pattern

of plant communities (Wang et al. 2009), providing more information for the classi-

fication of the vegetation by remote sensing. The phenologies of Phragmites and

Spartina are different. Phragmites begins to germinate in early April, has its rapid

growth stage spanning from June to mid August, flowers in mid October, and senesces

in late November. In contrast, Spartina emerges in May, has its rapid vegetativegrowth from June to early September, flowers in late September, and dies away in

late December. These differences provide important information for the classification

of remotely sensed imageries.

2.2 Data

2.2.1 Remotely sensed data. Two different sets of remotely sensed data, including

TM and QuickBird, were used in this study. The Landsat TM data were acquired on 20

April 2006, under clear atmospheric conditions. Six bands with 30 m resolution (TM-1:

0.450.52 mm; TM-2: 0.520.60 mm; TM-3: 0.600.69 mm; TM-4: 0.760.90 mm; TM-5:

1.551.75mm and TM-7: 2.082.35 mm) were extracted from the TM scene. The originaldigital number (DN) data were calibrated and transformed into reflectance values with

a processing module in the software package ENVI version 4.1. The QuickBird data

were obtained on 10 May 2006, and comprised five bands: (1) panchromatic band

(450900 nm) with a spatial resolution of 0.61 m; (2) blue band (450520 nm); (3) green

band (520600 nm); (4) red band (630690 nm); and (5) infrared band (760900 nm).

Bands 25 have a spatial resolution of 2.44 m. An ultimate image composed of the last

four bands, with a spatial resolution of 0.61 m, was derived after data fusion.

Geometrical calibration and atmospheric calibration were performed first, followed

by object-oriented classification using eCognition. Because the spatial resolution of

QuickBird (0.61 m) is high enough to identify small plant patches, with some preparedclassification by field validation, we found that the QuickBird could achieve a high level

of classification accuracy (above 90%). It thus approximated the real surface cover, and

could be considered a good proxy to assess the accuracy of SMA modelling. The

accuracy of QuickBird images was assessed with a confusion matrix using 150 field

points, with ground cover situation and global positioning system (GPS) information

used as validation. In order to reduce the disturbances caused by tide water, both the

TM imagery and QuickBird imagery were obtained under similar tidal period, which

was 147 cm when QuickBird image was taken and 140 cm when TM image was taken,

and were further carefully cross-registered and geo-referenced, with root mean square

error (RMSE) of less than 0.5 TM pixel.2.2.2 Field data and spectral characteristics of the main components. To assess the

spectral distinction of the dominant plant species, soil and water, and to estimate the

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feasibility of remotely sensed SMA, an Analytical Spectral Device (ASD) FieldSpec

Handheld (ASD, Inc., Boulder, CO), with a detection region of 3501050 nm and a

spectral resolution of 3 nm, was used for the field measurements. Considering the

phenological characteristics of the plants, the spectral data were collected on 10 May

2007, coinciding with the season when the remotely sensed data were obtained. The

field spectral data were acquired at 0.51.0 m above the canopy with a field of view of25. All data were collected between 10:00 and 14:00 on cloud-free days with neap

tide. Samples were designated at separate monoculture patches and nine spectrum

measurements were made and averaged for each patch. Meanwhile, plant spectral

data used in this paper were all collected in sites without water coverage.

The field spectroscopy was used to estimate the spectral distinctions among the

major marsh components. According to our field spectral analysis, the spectra of

the three dominant species had distinct characteristics (figure 2). The reflectance

curve ofScirpus showed differences in the magnitude of reflectance, but the overall

shape of the curve was similar to that of Phragmites. The reflectance curve of

Spartina was different from those ofPhragmites and Scirpus in both magnitude andshape. Because the spectral difference around 600700 nm resulted from the

chlorophyll content of the plant leaves (Hunt et al. 2004), the spectral shapes of

the three dominant species indicated that Phragmites had the highest chlorophyll

content, and Spartina had the lowest. This result was consistent with the field

observation that Spartina just started to emerge and its ramets were still under its

own standing litter in early May. The spectral differences among the three species

provided information on endmember selection for the next step in SMA modelling.

Figure 2 also shows that dry mud had higher reflectance than washy mud at all

wavelengths, which prompted us to pre-set an endmember for washy mud in the

SMA model.Moreover, because spectroscopy was only used to validate the differences

between categories, and the spectral properties of the same plant at a certain

phonological stage do not basically change from year to year, the spectra collected

one year after the satellite images had been taken should be reliable in evaluating

spectral separability. In other respects, our fieldwork supported the field observa-

tions and validated the point selection as well as located the pure patches for

categories.

360 400 440 480 520 560 600 640 680 720 760 800 840 880 920 960

Dry mud

Washy mud

Wavelength (nm)

360 400 440 480 520 560 600 640 680 720 760 800 840 880 920 960

Reflectance(%)

0

10

20

30

40

50

60(a) (

b)

Phragmites

Spartina

Scirpus

Figure 2. Field reflectance spectra of major marsh components.



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2.2.3 Reference preparation for accuracy evaluation. To assess the accuracy of the

SMA unmixing results, the synchronous QuickBird image was first classified as a

reference for the subsequent evaluation of the accuracy of the TM fraction maps. As

our concern was to identify a particular species, especially the invasive Spartina, the

final classification was divided into six classes: Phragmites, Spartina, Scirpus, washy

mud, water and road. To ensure high classification accuracy of reflecting the realsituation, a total of 150 ground data points were collected in the field, and then a

Training and Test Area (TTA) was established for the assessment of the confusion

matrix. After the initial classification, some minor errors were carefully corrected to

ensure that the final results were as accurate as possible.

2.3 Endmember selection and spectral mixture analysis

The flow chart in figure 3 illustrates the framework of the SMA and accuracy

assessment in this study, in which one of the critical steps was endmember selection.

The criteria for a good set of endmembers include linear independency, spectralrepresentativity and spatial generality (Li et al. 2005). Generally, the appropriate

endmembers were acquired with two approaches: (1) using the field- or laboratory-

measured spectra as endmembers; and (2) deriving the representative endmembers

Calibrated

QuickBird imagery

Endmembers

selection

Pure pixel index

(PPI) calculation

N-dimensional

visualization

Spectral mixture

analysis (SMA)

Endmember fraction

derivation

Resolution matching(30 m 30 m)

Abundance

calculation

Correlation

analysis

NDVI

extraction

Calibrated

TM imagery

Classification

Minimum noise

fraction (MNF)

transformation

Normalized

difference index

transformation

Accuracy

assessment

Figure 3. Flow chart illustrating the framework of SMA and accuracy assessment in this study.

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directly from the image spectra (Small 2003). Laboratory- and field-measured spectra

are standardized but they do not usually relate well to the remotely sensed images.

However, the image-derived spectra as endmembers are more representative. Because

some researchers believed that image-derived spectra are the optimal choice for SMA

applications (Peddle and Smith 2005, Chen and Vierling 2006), we used image-derived

spectra as the endmembers in this study. The endmembers were selected based on thepure pixels index (PPI) calculation and N-dimensional visualization of transformed

data as the following procedures.

2.3.1 Endmember selection through calculating the PPI. To reduce the noise in the

TM image, a forward minimum noise fraction (MNF) transformation was applied,

followed by the PPI calculation for each pixel. The MNF analysis of the TM image

showed that the first MNF band had an eigenvalue of 212.33 and the sixth band had

an eigenvalue of 2.36, at which many of marsh structural features disappeared. The

eigenvalue of the fourth band dropped sharply to 6.08 from a value of 12.11 for the

third band. This indicates the first three MNF bands contain the major data variance,while bands 4,6 contain less information. Accordingly, it suggests the intrinsic end-

members for TM data are 3,5.

Based on the result of MNF, the PPI introduced determined most of the spectrally

pure pixels in the image. Several pure patches of different categories had been

identified previously from field observations, and were geographically located on

the image with GPS data. The pixels with highest PPI values within the located pure

patches were then selected as the endmembers for each category. Five endmembers

were finally defined under the data dimension of the TM image. The endmembers and

their PPI values are listed in table 1.

2.3.2 Endmember selection through index transformation. Rogers and Kearney(2004) have also suggested that the SMA model can be greatly enhanced if the

variability of the inner-endmember category is reduced. To eliminate the influence

of water content on the spectra of the soil endmember, as well as some interfering

signatures, three normalized difference indices were used here:

1. Normalized difference water index (NDWI): (band 3 band 5) / (band 3 band 5);

2. Normalized difference soil index (NDSI): (band 5 band 4) / (band 5 band 4);

3. Normalized difference vegetation index (NDVI): (band 4 band 3) / (band 4

band 3).

Of these, NDWI is used to detect open-water areas because water is more reflective in

band 3 than that in band 5 of TM (McFeeters 1996); NDSI is calculated from the band

pair based on the representative spectra of soil and NDVI (Deering 1978) is well

established as the standard for vegetation indices (Shanmugam et al. 2006). If the

three indices are envisaged as axes and oriented vertically, water appears at the top of

Table 1. List of endmembers (EMs).

Phragmites Spartina Scirpus Water Washy mud

Abbreviation Ph Sp Sc W WmPPI value 3056 2035 2146 3722 2027



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the NDWI axis, vegetation at the top of the NDVI axis and most soil at the top of the

NDSI axis. Thus, these indices can gather pixels with similar spectra, and form a data

space with a triangular data cloud. The points inside the triangle can be seen as

mixtures, whereas the points around the vertices are supposed to be pure and

could be selected as endmembers. Using this approach, three endmembers,

Phragmites, Spartina and washy mud, were selected from the corners of the data

cloud for SMA modelling (figure 4).

After the endmember selection, the SMA was carried out with a processing module

in the software package ENVI version 4. To evaluate the influence of endmember

combinations on SMA accuracy, models were grouped into three-, four- and five-

endmember ones. Because the purpose of this study was to distinguish Spartina from

Phragmites, the endmembers representing Spartina and Phragmites were always

included in each model, whereas other endmembers were combined with these two.

Thus, a total of eight SMA models were analysed on the TM image. The endmember

combinations are listed in table 2.

2.4 SMA mapping accuracy assessmentTo match the spatial resolution of the TM image for later analysis, the classified

QuickBird image was regrouped to pixels of 30 m 30 m. The pixel coordinates

of TM were located on the QuickBird image, and then a 30 m 30 m grid was

generated with the Arc Info Workstation. The grids centre was the coordinates

of the TM data. In each grid the areas of the classified categories were counted

to represent the real percentage of each endmember. Thus, the calculated percen-

tage can be compared with the abundance ratio derived with the SMA models.

Then, w2 tests were pixel-by-pixel performed to compare the model results

(model-predicted fraction image from TM) with the real situation (percentages

counted from the classified QuickBird image). A significant difference betweenthe pixels in the tests implied the poor accuracy of the SMA model, and vice

versa.

Spartina

Phragmites

Washy mud

Figure 4. Distribution of pixels in the NDX transformation data spaces. The three axes in thefigure denote NDWI (axis 1), NDVI (axis 2) and NDSI (axis 3), respectively.

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3. Results and discussion

3.1 QuickBird classification

The classification accuracy of QuickBird is shown in table 3. The accuracy of

the QuickBird images was assessed with a confusion matrix using 150 field

points, with the ground cover situation and GPS information used as valida-

tion. These results indicate a highly accurate, stable and reliable classification

result, with an overall classification accuracy of 93.18% and an overall kappa

statistic of 0.9054. As mentioned above, some manual checks and corrections

were made to ensure that the classification reached a higher level of accuracy

after classification. Thus, the classified image represented the real land-cover

situation and could be used as a proxy to assess the accuracy of the SMA

unmixing results.

3.2 Unmixing results

3.2.1 c2 tests of pixel-by-pixel comparisons. w2 tests of pixel-by-pixel comparisons

between classified QuickBird image and SMA endmember fraction image weremade, and the significant results were counted and listed in table 2. Of all eight

Table 2. SMA models and their prediction accuracy.

Group Code Endmember

Number of pixelsdifferent from

QuickBird image(a 0.05)

Percentageof the

inaccuratepixels (%)

AverageRMSE

3EMs 3EMw Ph Sp W 553 12.14 0.0079563EMsc Ph Sp Sc 1241 27.25 0.0094853EMwm Ph Sp Wm 649 14.25 0.007360

3EMs (withNDXtransferreddata)

3EMnd Ph Sp Wm 718 15.77 0.000586

4EMs 4EMwsc Ph Sp Sc W 1023 22.46 0.0018334EMwwm Ph Sp W Wm 714 15.68 0.0018964EMwmsc Ph Sp Sc Wm 670 14.71 0.006429

5EMs 5EM Ph Sp Sc W

Wm

718 15.77 0.001440

Table 3. Accuracy assessment of object-oriented classification of QuickBird images. KIA,Kappa Index of Agreement.

Class Producer User KIA per class Overall accuracy KIA

Phragmites 0.9962 0.9910 0.9934 0.9319 0.9055Spartina 0.9778 0.8939 0.9692Scirpus 0.7914 0.9703 0.7681

Water 0.6563 1.0000 0.6373Washy mud 0.9430 0.9362 0.9360Road 0.8539 1.0000 0.8526



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SMA models, 3EMw had the highest accuracy and 3EMsc the lowest accuracy,

suggesting that PhragmitesSpartinawater was the best endmember combination

for SMA modelling, whereas PhragmitesSpartinaScirpus was the worst one. The

models 3EMsc and 4EMwsc were less accurate because they included the endmem-

ber Scirpus. Model 4EMwmsc also included Scirpus, but was reasonably accurate

because it included the endmember washy mud. This phenomenon is comprehen-sible because Scirpus only grows in the lower tidal areas, with a height of less than

0.7 m, thus a water background greatly influences the distinction of Scirpus.

However, Scirpus prefers to grow in a washy-mud habitat, so Scirpus could not be

distinguished perfectly, which could not be treated as a good representative end-

member in this study.

Data transformation is designed to reduce the variation within the endmember

types, but it cannot solve the problem of intra-endmember similarity. In our

study, a spectral similarity existed among the different plant communities,

Spartina with standing litter, and mud, so it could not be eliminated by data

transformation. Therefore, the accuracy of model 3EMnd was not obviouslyimproved by transforming the data into normalized difference indices. Rogers

and Kearney (2004) have reported that the transformation of normalized differ-

ence can reduce the spectral variability of soil, water and some principal vegeta-

tion components derived from TM data. However, this transformation did

not offer a significant advantage for our classification of vegetation at species

level.

Because in the classification of model 5EM the ground cover included

Phragmites, Spartina, Scirpus, washy mud and water which approximated the

real conditions of the area, it should be highly accurate. However, our data

dimension, as described above, was less than four, and thus this model was onlymoderately accurate.

It should be noted that, in the three-endmember group, model 3EMsc had a much

higher RMSE than others because it included the component Scirpus. Similarly,

model 4EMwmsc had the highest RMSE in the four-endmember group because it

included Scirpus. This result suggested that an SMA model including a wrong or

inappropriate endmember would greatly increase RMSE.

Conversely, model accuracy does not always increase as RMSE decreases. For

example, 3EMw had the highest accuracy but a fairly high RMSE, whereas 3EMnd

had moderate accuracy with the lowest RMSE. This is because extra information

from multiple bands helped increase the robustness of the SMA least-squares solu-

tion. Thus, RMSE does not seem to be an adequate criterion for evaluating the

performance of the SMA model.

To analyse the spatial variation of the SMA model that is influenced by

environmental factors, the significant results from pixel-by-pixel w2 tests and

their values were mapped (figures 5 and 6). As the distance increased from the

sea wall to the sea, the cell numbers with high accuracy decreased (figure 5). In

particular, in the water-filled areas or areas near water, the endmember fractions

were poorly predicted in all SMA models. In comparison, the areas covered by

dense vegetation seemed to be predicted well with the SMA models, except model

3EMsc including Scirpus (figure 6). This result indicates that tide water had a

great influence on SMA modelling.

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4. Conclusions

The special emphasis of this study was placed on the applicability of SMA with TM

data to the detection of invasive plants in estuarine wetlands. w2

tests of comparisonsbetween an SMA-model-predicted fraction and a QuickBird classification image

provide a measurement to better understand the reliability, the strengths and the

0

20

40

60

80

0

20

40

60

80

0

20

40

60

80

0 300 600 900 1200 1500 1800 2100

0

20

40

60

80

0 300 600 900 1200 1500 1800 2100

Percentageofpointswithsignificantd

ifference

Distance from the sea wall (m)

3EMnd5EM

4EMwwm

4EMwsc

4EMwmsc

3EMwm

3EMsc

3EMw

Figure 5. Distance from the sea wall and the number of inaccurate pixels.



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weaknesses of SMA models applied to such detection. Several SMA models with

various endmember combinations were analysed and their results were evaluated. A

proper endmember combination generated satisfactory SMA results, and had a

relatively good capacity to predict vegetation.

However, the assessment of accuracy showed that certain errors occurred in the

SMA fraction images. Most of these inaccurate predictions were distributed in areas

filled with water or near water, indicating that tidal water had a great effect on SMA

modelling. Moreover, transforming the TM data to reduce the endmember spectral

variation did not actually improve the accuracy of the SMA modelling, which may

not be adequate for the detection of invasive plants.It is noteworthy that the multiple endmember spectral mixture (MESMA) (Roberts

et al. 1998) assumes that individual pixels contain limited endmembers, although the

whole image may contain many spectrally distinct components, which allow the

optimization of each pixel. Therefore, MESMA has a great potential for vegetation

monitoring in estuarine wetlands, and further work needs to be done using this

technique to monitor invasive plants more efficiently.

Acknowledgements

This work was supported by the National Basic Research Program of China (No.

2006CB403305), the Science and Technology Commission of Shanghai (No.07DZ12038-2), the National Natural Science Foundation of China (No. 30870409

and 40471087) and the Program for New Century Excellent Talents in University

Figure 6. Occurrence of inaccurate pixels in the image for each of SMA models. Pixels in redare inaccurate, while blue ones are accurate, assessed by chi-square. A darker colour representsa higher chi-square value.

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(NCET-06-0364) funded by the Ministry of Education of China. We thank

Chongming Dongtan National Natural Reserve and the students in our laboratories

for their assistance in field sampling.

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