Eco-Engineering, 18（2）, 65-70, 2006

Original Paper

Estimation of Plant Abundance and Distributionof Miscanthus sacchariflorus and Phragmites australis
Using Matched Filtering of Hyperspetral Image
Shan Lu, Sho Funakoshi, Yo Shimizu, Jun Ishii,
Alejandro M. de Asis, Miho Ajima, Izumi Washitani and Kenji Omasa

Graduate School of Agricultural and Life Sciences, The University of Tokyo
1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan(Received December 5, 2005; Accepted January 31, 2006)

ABSTRACT
More than 40 endangered plants listed in the national red list are growing in moist tall grasslands of Watarase
wetland, the largest lowland wetland, in Japan. The dominant plants in grasslands are Miscanthus sacchariflorus andPhragmites australis, and each endangered species is associated with the habitats where either grass species ispredominate, or both are mixed more evenly. Therefore, estimation of the relative dominance of M. sacchariflorus or P.
australis may help in evaluation of the potential habitat area of the individual endangered species. In this study, matchedfiltering (MF), a specialized type of spectral mixture analysis, was used to estimate the abundance and distribution of M.
sacchariflorus and P. australis from Airborne Imaging Spectrometer for Applications (AISA) data. Correlation analysisof the MF results and ground truth data was conducted to determine the accuracy of the estimates. Overall performanceof MF for estimating amount of M. sacchariflorus was good with correlation coefficient of 0.89 for shoot density, and0.78 for total stem volume. However, a poor estimate was obtained for P. australis with correlation coefficient of 0.43 forshoot density and 0.58 for total stem volume. Possible reasons for the difference in the accuracy estimates were discussed
in this paper.

Key words : Matched filtering, Miscanthus sacchariflorus, Phragmites australis,
Shoot density, Total stem volume

both are mixed more evenly. In recent years, however, rapid

1. Introduction

environmental change by human interventions is suggested toConservation of endangered plant species in wetlands hasalter the growth, quantity and distribution of M. sacchariflorus

been proposed as one of the important components ofand P. australis, and thus the potential habitat area of theenvironmental conservation worldwide. In Watarase wetland, theendangered plants growing. Therefore, estimation of the relative

biggest lowland wetland located in central Japan, more than 40

dominance of M. sacchariflorus or P. australis may help inendangered vegetation species listed in the national red list areevaluation of the potential habitat area of the endangered species.

growing in moist tall grasslands. The dominant plants in the

A cost-effective, large-scale, and long-term documentation

moist tall grasslands are Miscanthus sacchariflorus andand monitoring of the dominant plants population is very

Phragmites australis. Each endangered plants are associated with

important for assessing the plant biodiversity in wetland such asa certain habitat where either grass species is predominate, ormoist tall grassland. Ground survey work in large areas is© The Society of Eco-Engineering（21）65

prohibitively expensive and time-consuming (Everitt et al.,October and bear fruits in November and December. The ground1995). Moreover, data generated from ground survey work areleaves and stems wither in winter.

quickly outdated as vegetations change due to abiotic and /or

2.2 Acquisition of hyperspectral imagery

biotic factors and control measures. Hence, a method which canHyperspectral sensors, i.e., imaging spectrometers whichquickly and efficiently collect data over a large area on a routineacquire digital imagery of earth material in many narrowbasis is needed for efficient monitoring of major characteristics

contiguous spectral bands can be used to detect small spectralof vegation such as coverage or abundance of dominant plants

features. In this study, we hypothesize that because of thein wetlands.

narrow spectral channels of hyperspectral sensors, it can detect

Using remotely sensed data to map M. sacchariflorus and P.

relatively dominant plants at a species-level, which is not

australis could provide a valuable tool for documenting their

possible using multi-spectral scanner. AISA (Airborne Imaging

abundance or distribution. Imaging spectrometers, orSpectrometer for Applications) is operated by the SPECIM LTD

hyperspectral sensors, provide high spectral resolution data and(Spectral Imaging LTD) and collects data in the spectral range

continuous spectrum for every pixel. By using spectral mixtureof 398 to 993 nm sampled by 68 spectral channels with aanalysis to model each pixel spectrum as a linear combination of

nominal 10 nm sampling. It also has very high radiometrica finite number of spectrally distinct signatures orresolution (16 bit), resulting in an increased ability to“endmembers”, subpixel estimates of endmember abundance candistinguish fine differences in reflectance values among pixels.

be obtained (Williams and Hunt, 2002). The main goal of thisAISA imagery was acquired over the study area on May 21,

research was to map area based abundance of M. sacchariflorus

2004. The phenology of the two dominant plants of M.

and P. australis using hyperspectral data, in which the accuracysacchariflorus and P. australis indicates that they are in their mid

was evaluated by comparison with the ground estimates of shootgrowing period when we acquire the imagery and conducted fielddensity and total stem volume. The total stem volume refers tosurvey. Because P. australis grows a little faster than M.

the combined measured stem diameter and plant heights in asacchariflorus, the two plants show the biggest differences ingiven area by the following expression, which gives anheight and plant form. They will become more and more similar toindication of the growth condition of the plants.

each other and make the discrimination more difficult when they

Total stem volume = (d/2) 2 × h × π(1)

Where, d is the diameter of the stem and h is the plant height.

reach maximum maturity.
The image was acquired from aircraft flown at an altitude of
1,438 m with each pixel representing a ground area ofapproximately 1.5 m × 1.5 m. Four scenes (each approximately

2. Method
2.1 Study area

10 km × 1 km) were selected that covered the study area. EachAISA scene was radiometrically corrected and was thenThe study area is around 2,500 ha moist tall grasslands areas

atmospherically corrected to apparent surface reflectance using

in Watarase wetland which is located at latitudes 36º11´ to

the Fiber Optic Downwelling Irradiance System (FODIS)

36º17´ north and longitudes 139º40´ to 139º42´ east.

incorporated with the AISA sensor. The FODIS allows for thePrecipitation at Koga station near the study area averages toconcurrent measurement of downwelling and upwelling

1,197 mm annually from 1977 to 2004 year. The study area has aradiance by the AISA sensor head. A diffuse collector installed

relatively hot-wet summer and cold-dry winter because the areaon the top of the plane is connected to the AISA head via fiber

lies in the inland part of Japan. There are nearly 600 species of

optic cable and collects downwelling irradiance in the same

wetland plants (Ohwada and Ogura, 1996), and dominant plantbandwidth configurations as the areas being imaged. The

species are the Miscanthus sacchariflorus and Phragmites

calibration of the FODIS coupled with the AISA sensor allows

australis. M. sacchariflorus and P. australis in Watarase wetland

for the calculation of apparent at-platform reflectance.

start to grow after the wetland is burned usually in March. They

2.3 Ground calibration

grow fast and reach their maximum height around 4 meters inExtensive ground data collection was conducted on May 22

July and August. P. australis grows a little faster than M.

to June 14, 2004. Nine 5 × 5 square meter plots were placed tosacchariflorus. The plants reach maturity in during September todetermine the M. sacchariflorus abundance, while fifteen plots,

66（22）

Eco-EngineeringP. australis abundance. These plots included pure stands

suitable algorithm to extract the information of one or two given

predominated by either the species, and some mixed stands. The

plant species such as M. sacchariflorus and P. australis.

specific location of the four corners of each plot was recorded

2.4.2 Image processing and analysis

using Trimble GPS Pathfinder ProXR. These locations wereIn order to successfully employ MF, endmember of interest

transferred onto the AISA imagery with an estimated positionalmust be selected accurately. In the study plots, the selection oferror of less than 1 pixel. Shoot densities and total stem volume

M. sacchariflorus and P. australis endmembers from the imageof the plots were estimated from the averages obtained fromdata is possible because they occur in dense stands which arethree 1 × 1 square meter subplots arbitrarily sampled. The

more or less covering most of the area.

average values of the shoot density and total stem volume wereA minimum noise fraction (MNF) transformation (Green etcalculated and regarded as the estimates of the shoot density andal., 1988; Lee, Woodyatt and Berman, 1990) of AISA image

total stem volume for the whole plot.

was first performed in this study. By examining the eigenvalues

2.4 Image processing and analysis

and the spatial information contained in the output MNF-

2.4.1 Matched filtering analysis

transformed images, the first 15 MNF images (with large

The measured reflectance of a pixel represents the integration

eigenvalues) were chosen for further analysis. This is becauseof all reflectance from all the surface material within the ground

only the bands with large eigenvalues contain data, while bands

instantaneous field of view (GIFOV). When the size of the pixel

with eigenvalues near 1.0 contain only noise. The MNF

includes more than one type of terrain cover, mixed pixels are

eigenvalue plot is shown in Figure 1.

generated. Spectral mixture analysis decomposes each image
pixel into a linear combination of reference spectra, referred toas “endmembers” that characterize the various spectrally uniquecomponents of landscape (McGwire, Minor and Fenstermaker,
2000). Spectral mixture analysis utilizes the high dimensionalityof the AISA imagery to produce a suite of abundance or fraction
images for each endmember. Each fraction image shows a
subpixel estimate of endmember relative abundance as well as
the spatial distribution of the endmember (Williams and Hunt,
2002).
Matched filtering (MF), a special type of spectral mixture
analysis, is based on well-known signal processing

Fig. 1 Minimum Noise Fraction (MNF) transformation analysis

methodologies (Harsanyi and Chang, 1994). MF filters the input
image for good matches to the chosen target spectrum by

The M. sacchariflorus endmember and P. australis

maximizing the response of the target spectrum within the data

endmember derived from the AISA image by groundand suppressing the response of everything else (which isinvestigation were used as the endmember in the MF analysis.

treated as a composite unknown background to the target). The

The relationships between MF estimates of subpixel M.

background material’s data histogram is centered around 0.0,

sacchariflorus and P. australis and ground estimates of theirand the target data distribution occurs in the upper tail of theshoot density and total stem volume were examined using

histogram (Research Shystem, Inc. 2003). Like Complete

simple linear correlation analysis for all sites.

Unmixing, a pixel value in the output image is proportional to
the fraction of the pixel that contains the target material. (where
1.0 is a perfect match). One major advantage of the method is

3. Results and discussion
3.1 Spectra of two plantsthat MF does not require finding the signatures for the other

From appereance, Miscanthus sacchariflorus, differs fromendmembers that occur in the scene (Boardman, Kruse andPhragmites australis in Watarase wetland due to the presence ofGreen, 1995). In Watarase wetland, a large number of plant

white veining in the center of the leaf. In P. australis this white

species coexist in the same stand, MF method is possibly aveining does not exist. In addition, the leaves of M.

Eco-Engineering（23）67

sacchariflorus are light green, while the leaves of P. australis
are relatively darker green. These differences should yield
different spectra, which can help discriminate the grasses fromeach other in the imagery.
In remote sensing imagery, the reflectance spectra of the twoplants differed from each other in some wavelengths (Figure2(a)). M. sacchariflorus was consistently brighter than P.
australis in the range of 500-650 nm. The reflectance of M.
sacchariflorus also appeared higher in the NIR (from 750 nm to920 nm) than P. australis. Figure 2(b) shows the ratio of spectraof M. sacchariflorus and P. australis indicating the difference inspectral shape between the two grasses.

Fig. 3 (a) False color composite of the AISA scene in Watarase
wetland. (b) MF fraction image for M. sacchariflorus. (c) MF
fraction image for P. australis.
The top image points to the north. The bands used in thefalse color composite are blue-band 23 (585 nm), green-band
31 (657 nm), and red-band 52 (847 nm).

The output from the MF analysis was a fraction image withvalues for each pixel representing the relative abundance of M.
sacchariflorus and P. australis as illustrated in Figure 3(b) and(c). Pixel with a high fraction value means high abundance ofeach plant in it. In MF, a threshold of 0.1 was used because it
was determined to be the most appropriate values for detecting
materials in hyperspectral imagery (Boardman, 1998). The
performance of the MF for estimating shoot density and total
Fig. 2 (a) Spectra of M. sacchariflorus and P. australis,(b) Reflectance ratio of M. sacchariflorus to P. australis

stem volume for M. sacchariflorus was relatively good asshown by a significant correlation between the results ofmatched filtering and ground data. The correlation coefficient of

3.2 Matched filtering fraction images

MF fraction values against shoot density of M. sacchariflorus

Figure 3(a) shows the false color composite of AISA scene.

was 0.89 at significance level of 0.01 (Figure 4). For total stem

This is a subset image which shows a part of the Watarase

volume the correlation coefficient was 0.78, at 0.05 level ofwetland. The red area represents vegetation. The light red color

significance (Figure 5). On the other hand, the correlation

areas which lie between the water and road are relatively pure

coefficient of MF fraction values against shoot density of P.

stands of M. sacchariflorus, while the dark red areas are those of

australis was only 0.43 with no significance (Figure 6), and that

P. australis (Figure 3(a)). However, reliance on color alone

against total stem volume was 0.58 (Figure 7) at significancecaused some spectral confusion because of the presence oflevel of 0.05. The MF analysis for P. australis didn’t seem as

underlying plants.

effective as for M. sacchariflorus.

68（24）

Eco-EngineeringFig. 4 Relationship between MF fraction values andshoot density of M. sacchariflorus.

Fig. 6 Relationship between MF fraction values andshoot density of P. australis.

Fig. 5 Relationship between MF fraction values andtotal stem volume of M. sacchariflorus.

Fig. 7 Relationship between MF fraction values andtotal stem volume of P. australis.

3.3 Effect of plants typethe image based on field ground data. The endmember selected

Aside from the slight difference in color of the leaves of M.

for M. sacchariflorus represents the characteristics of this plant.

sacchariflorus and P. australis, there is also a little difference inOn the contrary, because of the characteristics of the leaves ofthe leaves orientation between the two grasses. The leaves of M.

P. australis, pure endmember was not selected properly

sacchariflorus tend to spread more horizontally because it has aresulting to poor performance of MF for distinguishing thisrelatively soft leaves. This makes the land surfaces andgrass.

underneath plants to be covered almost entirely by the leaves of

One of the limitations of MF method is the difficulty of

M. sacchariflorus. This enables the hyperspectral sensor to

distinguishing material of interest with almost similar

capture pure spectrum of M. sacchariflorus unaffected by thereflectance to the background (Oki, Funakoshi and Inamura,background materials such as soils, litters and short plants in the2000). However, in this study this limitation was not observed,

bottom layer. The leaves of P. australis, on the other hand, areparticularly for M. sacchariflorus. The quantity and distribution

relatively hard, erect and grow up straight that exposes the soil

was clearly known by the difference in reflectance between M.

and underneath plants. Thus hyperspectral sensor captures asacchariflorus and its background. That means to say (as thismixed spectrum of P. australis, underlying soil and short plants.

study found out) that the extent of difference between the grass

The physical difference between the two plants provides the key

and its background is enough to discriminate one from the other.

in the effectiveness of the MF fraction to estimate theIn the MF fraction map of M. sacchariflorus, almost all of the P.

abundance and distribution of M. sacchariflorus and P. australis

australis areas with high density appeared low fraction value. In

in the study area.

the same manner, good results can be expected if MF was

The selection of endmembers is a critical step in spectral
unmixing. In our study, the endmembers were extracted from

Eco-Engineering

applied using M. sacchariflorus as background to P. australis
providing that correct endmember was selected.

（25）69detecting and mapping leafy spurge (Euphorbia esula). Weed

4. Conclusions

Technology, 9, 599-609.

The study demonstrated the capability of high spectral andGreen, A. A., Berman, M., Switzer, P. and Craig, M. D., 1998 : A

spatial resolution hyperspectral imagery in estimating thetransformation for ordering multispectral data in terms of image

abundance and distribution of dominant tall grass species ofquality with implications for noise removal. IEEE Trans. Geosci.

wetland Miscanthus sacchariflorus and Phragmites australis.

Rem. Sens., 26, 65-74.

This study showed that M. sacchariflorus could be satisfactorily

Harsanyi, J. C. and Chang, C. I, 1994 : Hyperspectral image

estimated by MF spectral unmixing method using goundtruth-classification and dimensionality reduction: an orthogonal subspace

derived endmember. For P. australis, however, this method did

projection approach. IEEE Trans. Geosci. Rem. Sens., 32, 779-785.

not work well because of the difficulty of selecting pure pixel or

McGwire, K., Minor, T. and Fenstermaker, L., 2000 : Hyperspectral

endmember for it. Further study is needed to focus on the

mixture modeling for quantifying sparse vegetation cover in arid

endmember selection to get accurate estimation of P. australis.

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