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International Journal of Remote
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A land cover distribution composite image from coarse
spatial resolution images using an unmixing method

T. M. Uenishi a; K. Oki a; K. Omasa a; M. Tamura aaGraduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1
Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
Online Publication Date: 01 March 2005
To cite this Article: Uenishi, T. M., Oki, K., Omasa, K. and Tamura, M. (2005) 'A
land cover distribution composite image from coarse spatial resolution images using
an unmixing method', International Journal of Remote Sensing, 26:5, 871 - 886
To link to this article: DOI: 10.1080/01431160412331269760
URL: http://dx.doi.org/10.1080/01431160412331269760

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International Journal of Remote Sensing
Vol. 26, No. 5, 10 March 2005, 871–886

A land cover distribution composite image from coarse spatial resolution
images using an unmixing method
T. M. UENISHI{, K. OKI*{, K. OMASA{ and M. TAMURA{{Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1
Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan{National Institute for Environmental Studies Japan, 16-2 Onogawa, Tsukuba, Ibaraki
305-8506, Japan(Received 13 January 2003; in final form 27 April 2004 )A method of sub-pixel land cover estimation including an algorithm forminimizing missing data due to cloud cover was proposed for the purpose ofevaluating and monitoring the environment of wide areas. A pair of Landsat
Thematic Mapper (TM) scenes over coincident multitemporal National Oceanicand Atmospheric Administration (NOAA) Advanced Very High Resolution
Radiometer (AVHRR) time-series of directional hemispherical reflectance wereused to develop a fine-scale land cover map using either eight or three categories
and to estimate the endmembers of the AVHRR image using a positive
constrained linear least-squares method. Furthermore, three approaches were
evaluated for compositing sub-pixel estimates over cloudy areas in the AVHRR
image. Finally, from validation tests made for unmixing and compositing
methods, the results suggest that these methods may be generally useful forcomparing multispectral images in space and time.

1.

Introduction

At present, it has been shown that for environmental assessment, fine spatial
resolution images such as Landsat Thematic Mapper (TM) (30 m630 m) have beeneffective in measuring heterogeneous land cover distribution in regions where theobjective area is comparatively narrow (Oleson et al. 1995). However, when an
extensive area such as Asia becomes the object of research, it is difficult to measure
the land cover distribution of the entire area with Landsat TM, which can only
observe narrow areas. Furthermore, in many areas cloud-free data at fine spatial
resolutions are unavailable (Laporte et al. 1995). The repeat coverage being every 16
days can also hinder the assessment of vegetation dynamics even in relatively cloudfree areas (Hall et al. 1991). This shows that it has been difficult to obtain consistentcategories from images taken at different times, due to variability in atmosphericeffects, instrumental response, and coarse temporal resolution.
Consequently, the utilization of coarse resolution images, which are useful inmonitoring land cover distribution and vegetation transformations at a global scale,can be considered. For example, the Advanced Very High Resolution Radiometer(AVHRR) has been used to produce multitemporal profiles of the Normalized
Difference Vegetation Index (NDVI) in order to measure and assess changes in*Corresponding author. Email: agrioki@mail.ecc.u-tokyo.ac.jp
International Journal of Remote Sensing
ISSN 0143-1161 print/ISSN 1366-5901 online # 2005 Taylor & Francis Ltd
http://www.tandf.co.uk/journals
DOI: 10.1080/01431160412331269760

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T. M. Uenishi et al.

vegetation phenology and conditions (Maselli et al. 1998) caused by events such as
deforestation. It can also be utilized to perform land-cover type classification at
global scales (Tucker et al. 1985). Moreover, the frequent repeat cycle of the satellite
providing fine temporal data of at least one sample per day depending on latitude,is necessary for monitoring vegetation dynamics. However, its coarse spatial
resolution imposes limitations on spectral and spatial information (Cross et al. 1991,
Malingreau and Belward 1992, Quarmby et al. 1992, DeFries et al. 1997). In thisway, even though coarse resolution satellites can observe the Earth over a shortperiod, many categories are intermingled within each pixel of the observed image. It
becomes difficult to determine the categories and to calculate the land coverage with
sufficient precision.
To overcome these problems, numerous studies have shown that mixture
modelling can be used to estimate vegetation cover (Mackin et al. 1990, Smithet al. 1990, Roberts et al. 1993), and Garcia-Haro et al. (1999) have shown that
mixture modelling is less sensitive than NDVI to soil background effects. However,in traditional unmixing studies with AVHRR data, it is assumed that only a few
endmembers exist throughout an entire image (Robinson et al. 2000). For example,
Oleson et al. (1995) and Oki et al. (2002) have retrieved spectral reflectance for eachcover type in coarse resolution images using multiple linear regression analysis
dealing with only two to four endmembers. In other words, manifold categories
have not been dealt with.
Therefore, in this study, a method to calculate the distribution of land cover types
around those areas in northeastern Asia within AVHHR mixed pixels was
attempted by integrating multitemporal data in a mixture analysis to increase thenumber of categories that can be analysed.
Moreover, because conventional unmixing studies have not dealt with cloudcovered areas, a method to synthesize a composite image of the unmixed site, for theuse of land cover assessment of northeastern Asia was evaluated to eliminate theproblem of clouds. To illustrate, Oki et al. (2004), have unmixed and validated theirresults for a wide area; however, only a faveolate map could be produced due toclouds and assessment of the area is difficult. Thus, because atmospheric conditions
can hinder the assessment of land coverage, we propose to produce composite
images.
Furthermore, a validation test was carried out in order to evaluate the accuracy ofthe estimated land cover ratio results produced in the composite land cover map.
Classified Landsat TM data, which were verified with ground data, were used as thecriteria to assess the accuracy of the estimated results.
2.

Study site

Because the environment, which is delicate, is easily affected by humandevelopment, a method in which the land cover distribution is measured precisely
to assess environmental change is necessary. It is impossible to cover expansive areas
with tumultuous land cover types in ground data surveys. In order to show that byusing remote sensing technology, specific land coverage maps can be synthesized forthe use of different environmental research, we have chosen northeastern Asia,
shown in figure 1, as a study site to monitor specific land cover distribution due to its
vast and diverse land cover types (wetlands, grasslands, evergreen trees, deciduoustrees, agricultural areas, urban areas, deserts, rivers, lakes, etc.).

873

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A land cover distribution image

Figure 1. Location of study area in northeastern Asia (upper left coordinate 52u N, 121u E/
lower right coordinate 42u N, 142u E). The image located in the centre is the Landsat TM
image (path 116/row 26) used to calculate endmembers and to evaluate results.

In this study, we used Landsat TM (satellite image overlapped onto map infigure 1) and National Oceanic and Atmospheric Administration (NOAA) AVHRR(figure 2) data. We selected this area, because the area within an AVHRR image has
the same land cover distribution as a single TM image. Furthermore, the Landsat
TM area (path 116/row 26) shown in figure 1 was chosen. This is because a fairly
accurate classification map could be produced through verification with land cover
data acquired from field surveys on foot and by helicopter using a global positioning
system (GPS) digital camera. Landsat TM images (path 116/row 26) from 16 Juneand 18 July 1997 were selected. Eight NOAA AVHRR images with an upper leftcoordinate of 52u N, 121u E and lower right coordinate of 42u N, 142u E shown infigure 1, from June–September 1997 were chosen.
3.

Algorithm of unmixing method

Generally, when considering coarse resolution images, pixels, which containmultiple categories, can be called mixed pixels. On the other hand, pixels that are
made up of a single category are called pure pixels. Spectra of mixtures can beanalysed with linear spectral mixture analysis (LSMA), which models each spectrum
in a spectral dataset (not necessarily an image) as a linear combination of a finite
number of spectrally distinct signatures, referred to as endmembers, with coefficients or fractional abundances between zero and one and summing to one (Adams
et al. 1986, Smith et al. 1990).

T. M. Uenishi et al.

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874

Figure 2. Sample NOAA AVHRR image of a study site projected in Albers Conic Equal
Area and resampled at a resolution of 1.1 km using a nearest neighbour interpolation method.
(Upper left coordinate 52u N, 121u E/lower right coordinate 42u N, 142u E.)

Endmembers are the features in a scene that are meaningful for an observer, andconstitute abstractions of real objects that can be regarded as having uniform
properties (Strahler et al. 1986). Unless the endmember of a particular category is
known, it is difficult to decompose these linear mixture models to calculate thecategory ratio within a mixed pixel. Because the endmember of each category
covered within a pixel must be precisely known before carrying out the unmixing
method (Cross et al. 1991, Quarmby et al. 1992, Foody and Cox 1994), the methods
in which these endmembers are determined become vital for LSMA.
It is difficult to identify and estimate the spectral signature of a pure component
or endmembers which form the scene, since they vary with the scale and purpose of
the study. Several methods have been proposed for estimating the endmembers foreach category (Oleson et al. 1995, Thompkins et al. 1997, Bateson et al. 2000, Oki
et al. 2002). In this research endmembers are estimated by overlapping classified
Landsat TM images over AVHRR images and using a positive constrained linear
least-squares estimation. Oleson et al. (1995) and Oki et al. (2002) have also
retrieved spectral reflectance for each cover type in coarse resolution images in asimilar way using multiple linear regression analysis. However, they only deal with
two to four endmembers; in other words, only a few categories can be studied.
Moreover, only a limited area is analysed. Therefore, we propose to increase
categories and assess a wider range.

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875

First, the total radiance of a coarse resolution mixed pixel can be assumed as alinear sum of the products of the cover type radiance and the cover type weight as alinear spectral mixture. Although pure pixels do not exist in any sort of digital
images, we also set the fine spatial resolution pixels as pure such that the cover typeradiances are invariant in order to extract details of AVHHR data up to Landsat
TM level. Furthermore, for temporal data, we define that the distribution of covertypes does not change.
Here, a linear spectral mixture model is a model where the observed radiance of aparticular pixel is given as P by endmembers m which include each category withfractional cover ratio a of k types of categories as in the following equation.
P~kX

aj m jð1Þ

j~1

where,
aj §0

ð2Þ

and
k
X

aj ~1

ð3Þ

j~1

First, the coarse resolution image is geocorrected with the high spatial resolution
image as a basis. Next, we assume that a pixel within the high spatial resolution
image covers all uniform categories and classify it into k types of categories. By
assuming this, we unmix AVHRR images to Landsat TM-level resolution. Here,land coverage a1 of each pixel in a portion of the coarse resolution image iscalculated by overlapping land cover distribution, which is evaluated from a highspatial resolution image, onto the coarse resolution image. Furthermore, theendmember of each category in the coarse resolution image is estimated for eachband. If the unknown endmember for the coarse resolution image is m1, theobserved radiance of each pixel within a coarse resolution image p1 is:
pl ~

k
X

ajl mjl

ð4Þ

j~1

where
mjl §0ð5Þ

In this study, under the constraint of equation (5), we estimated the unknown
endmember m1 of each category in equation (4) using a non-negative least square
method.
With the estimated endmembers, the entire coarse resolution image is unmixedusing a non-negative least square method for each band. Thus, the land coverage afor each pixel per category throughout the entire coarse resolution image is estimated.
Simple (traditional) unmixing assumes that a set number of endmembers (three orfour is a typical number) exists throughout the entire scene, and attempts to findDownloaded By: [University of Tokyo/TOKYO DAIGAKU] At: 06:04 3 March 2008

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T. M. Uenishi et al.

(a)Figure 3. Fine scale land cover map of a multitemporal Landsat TM image (path 116/row
26). The Landsat TM image was classified into (a) eight categories and (b) three categories
using the ISODATA method and then separated into two images. The bottom left image wasused to estimate endmembers. The bottom right image was used for validation purposes.

fractions for these endmembers in every pixel (Robinson et al. 2000). In this study,
we have applied this unmixing methodology to unmix the AVHRR images into amaximum of eight categories. It was possible to calculate up to eight endmembers bycreating multitemporal AVHRR data.

4.

Process and results

4.1

Atmospheric calibration considerations

Time-of-flight system and atmospheric calibration data were not available forLandsat or AVHHR images of the study area, nor was it possible to calibrate
empirically to ground targets at the time of each overpass of the satellite. A nominal
calibration to radiance using preflight instrument calibration data and a standard
atmospheric model was not made, because the accuracy of these corrections could
not have been verified and any errors in calibration might have propagatedundetected into the classification (Sabol et al. 1992a, b).

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877

(b)

Figure 3.

4.2(Continued.)

Processing of high spatial resolution image

In this study, a pair of Landsat TM scenes over the same region is used to develop afine scale land cover map (eight and three categories) as shown in figure 3(a) and (b).
The Landsat TM image was classified into eight and three categories, using theISODATA method, assuming that the high spatial resolution image was composedof pure pixels.
Before classifying, the thermal infrared layer (band 6) was removed because of itsdifference in spatial resolution; thus, a total of six bands using visible and nearinfrared images was used. Furthermore, in order to improve the classification
accuracy, a multitemporal image dataset was produced from areas with no clouds
using two scenes, 16 June and 18 July 1997, producing a dataset with a total of 12
bands. We produced a multidate cloud-free Landsat TM composite dataset in order
to investigate various types of land cover that show seasonal characteristics. Thus,
depending on the study site, not all sites used in estimating endmembers will needmultidate data.
Using an unsupervised classification, the Landsat TM image was ultimately
classified into eight categories: wetland 1, wetland 2, grassland, evergreen, deciduoustrees, agricultural area, urban area/bare soil, and water (figure 3(a)). Furthermore, athree classification (vegetation, urban area/bare soil and water) thematic dataset(figure 3(b)) was also created for comparison with the eight category result. Table 1

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Table 1. Definitions of nomenclature used in this study.

(a) Eight categories
Category
number
1
2
3
4
5
6
7
8

Category type

Description

Wetland 1
Wetland 2
GrasslandEvergreen
Deciduous trees
Agricultural area
Urban area/bare soil
Water

Reeds constitute this category; water level is highSedges consitute this category; water level is low
Grasses constitute this category; dry area
Evergreens constitute this category; dry area
Deciduous trees constitute this category; dry area
Areas where agriculture is in activity
Buildings, roads, areas where agriculture is inactive
Rivers’ lakes, and sea(b) Three categories
Category
number1
2
3

Category type

Description

Vegetation
Urban area/bare soil
Water

Wetlands, forests, agricultural areas constitute this area
Buildings, roads, areas where agriculture is inactive
Rivers’ lakes, and sea

shows the definitions for nomenclature of categories used in this study. The
classified image was confirmed with ground data produced from field surveys ofnortheastern Asia.
4.3

Processing of coarse spatial resolution

The NOAA AVHHR image was preprocessed by geocorrection at a resolution of
1.1 km. Each NOAA image was corrected geometrically using Landsat TM images
as a datum with manual control points. A nearest-neighbour interpolation method
was used to prevent the original pixel values from being annulled. As a result, theRoot Mean Square Error (RMSE) of the superposed NOAA image was within one
pixel.
Next, areas that included clouds in each scene were removed based on a thresholddetermined for bands 3, 4, and 5 of the AVHHR data. After confirming the removalof cloud pixels common in each image, a multi-temporal image was produced. In
this study, full cloud effects are assumed to be composited out. All five bands foreach dataset were used; thus, 10 band datasets were produced.
Four multitemporal image datasets, each with a total of 10 bands, were produced
combining images from June, July, or September 1997. Four areas that did not havesignificant amounts of cloud were selected to produce a final composite map. Eachdataset was used to compensate another for the unidentified areas. Dataset 1 was a
combination of 14 June and 18 July 1997. Dataset 2 was a combination of 16 Juneand 1 September 1997. Dataset 3 was a combination of 29 June and 5 September
1997. Dataset 4 was a combination of 23 July and 30 September 1997. By producing
multitemporal datasets, a more accurate endmember estimation can be calculated.
4.4

Determining endmembers of coarse resolution images

After preprocessing each fine and coarse resolution image, endmembers were
determined. First, the bottom left image in figure 3(a) and (b) of the classified

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879

Landsat TM image was overlapped onto each multitemporal NOAA image and theland coverage from each mixed pixel of the overlapped area was calculated. Finally,
endmember m1 in equation (4) was estimated using a least-squares method under
non-negative constraints. This method was applied for both eight categories andthree categories.
4.5

Unmixing and validation

After endmembers were estimated for each land cover type from equation (4) under
the constraint of equation (5), they were input into equation (1) and the land coverarea of each category was estimated using a least-squares method under theconstraints of equations (2) and (3). All four multitemporal AVHRR datasets were
each unmixed separately with endmembers determined accordingly. Because thestudy site consists of the same categories as in the classified map, it was possible tounmix the entire AVHRR data.
Furthermore after unmixing, land cover estimates of each unmixed image were
evaluated for accuracy of the unmixing method for both eight categories and three
categories. The RMSEs, shown in equation (6), for each category were calculated bycomparing estimated results and classified Landsat TM data as a reference of anarea that was not used in determining endmembers (bottom right image shown infigure 3(a) and (b)). Here we defined that the classified Landsat TM image was true.
sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
n
.
X
RMSE~
ðâi {aih Þ2 n
ð6Þ
i~1

where â is each land coverage of a certain category for each pixel estimated byunmixing the AVHRR image; ah is the true land coverage of the same category forthe same pixel in the AVHRR image, calculated by overlapping the land cover
distribution that is evaluated from the Landsat TM image on to the AVHRR image;and n is the number of pixels in the validation area of the AVHRR image. Accuracy
results of the estimated land coverage for eight and three categories are shown intable 2.
Several conclusions can be drawn about the results from table 2. For eightcategories, although the unmixing accuracy was different according to land covertype, it showed that certain categories could be analysed more affectively thanothers. For example, in this study, the results for wetland 1 and water were
estimated with relatively good accuracy. The best RMSE for wetland 1 was 5.2%,
and water was 9.6%. Thus, it was shown that if the subject of one’s research wereabout reeds or bodies of water they could be estimated from a coarse resolution
image at a relatively good accuracy. On the other hand, categories other thanwetland 1 and water, such as deciduous trees, showed poorer results relatively.
However, traditionally only approximate land cover types, such as vegetation/soil/
water could be distinguished using coarse resolution images. Nevertheless, by using
the unmixing method in this study, the analysis of certain land cover types, evendeciduous trees, in an extensive area becomes possible. In other words, it is possible
to evaluate land cover changes of multiple categories within a pixel with thistechnique.
The unmixing accuracy was also relatively good for the three category case as
shown in table 2, making it possible to evaluate the land cover changes from NOAA
images when assessing only three categories.

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Table 2. RMSEs of land cover estimates per category of four unmixed AVHRR datasets, (a)eight categories and (b) three categories.
(a) Eight categories
Dataset 1
RMSE(%)

Dataset 2
RMSE(%)

Dataset 3
RMSE(%)

Dataset 4
RMSE(%)

5.2
21.4
21.7
32.9
34.6
15.8
12.1
26.7

9.3
19.4
27.2
16.0
34.1
36.6
9.9
9.6

8.2
24.1
23.8
31.5
37.6
15.5
6.5
18.0

9.6
20.1
25.8
34.8
39.2
13.8
7.4
20.5

Dataset 1
RMSE(%)

Dataset 2
RMSE(%)

Dataset 3
RMSE(%)

Dataset 4
RMSE(%)

29.2
12.1
27.6

13.6
9.1
11.3

21.9
5.9
21.2

25.3
8.4
24.6

Wetland 1
Wetland 2
GrasslandEvergreen
Deciduous trees
Agricultural area
Urban area/bare soil
Water(b) Three categories

Vegetation
Urban area/bare soil
Water

4.6

Compositing method

The unmixing method used in this study was relatively useful for determining theland cover distribution; however, the presence of clouds prevented the entire sitefrom being evaluated (figure 2). Therefore, a compositing method was proposed toeliminate the issue of clouds and produce a land cover map of a wide area.
When synthesizing a composite image with multiple datasets, areas that overlap
geographically become a problem. Theoretically the results for overlapping areas
should be the same; however, in reality there is a slight variation in the results. Threeapproaches were evaluated for compositing the sub-pixel estimates over cloudy
areas. The three methods were tested to see which method was optimal in creating acomposite image from a number of results. Land cover estimates for eachoverlapping area were calculated for each category separately.
The first method for determining the pixel value of an overlapping area was tocompare RMSE results of the unmixed images. Instead of calculating an RMSE foreach category, one RMSE value was calculated for each dataset by using all of theestimated values as one result. The image with the lowest error was selected torepresent the overlapping area. Thus, the estimated value for each category wasprovided by the ‘better’ image. However, in this case the result depended on thenumber of pixels in each category.
The second method was to compare the actual estimated results of each category
for each overlapping area and simply selecting the better-estimated result for the
pixel value of the particular overlapping area in each category.
Finally, rather than comparing, the third method was to calculate the mean of theestimated values of each category for each overlapping area and represent the meanvalue as the pixel value for each overlapping area in each category.
Furthermore, after producing a composite image for each method, a validation
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Table 3. RMSEs of three different compositing methods per category within the composite
image for eight categories and three categories.
(a) Method 1
Category (8)Wetland 1
Wetland 2
GrasslandEvergreen
Deciduous trees
Agricultural area
Urban/soil
Water

RMSE (%)

Category (3)RMSE(%)

7.8
20.5
25.7
20.6
32.5
29.2
9.4
12.4

Vegetation
Urban/soil
Water

16.8
9.1
15.2

RMSE(%)

Category (3)RMSE(%)

8.0
19.5
26.0
17.3
34.3
28.2
10.1
10.3

Vegetation
Urban/soil
Water

14.5
9.5
12.3

RMSE(%)

Category (3)RMSE(%)

9.5
19.5
27.3
17.3
34.3
36.0
9.9
10.3

Vegetation
Urban/soil
Water

14.5
9.2
12.3

(b) Method 2
Category (8)Wetland 1
Wetland 2
GrasslandEvergreen
Deciduous trees
Agricultural area
Urban/soil
Water(c) Method 3
Category (8)Wetland 1
Wetland 2
GrasslandEvergreen
Deciduous trees
Agricultural area
Urban/soilWatermethod. Validation tests were made for each compositing method by calculating the
RMSE in equation (6) using an area of the composite image with that of theclassified Landsat TM image, which was not used in determining endmembers(bottom right image shown in figure 3(a) and (b)). Validation results of the estimatedland coverage for eight and three categories are shown in table 3.
From validation tests, the optimum method to create an accurate land coverage
map was to simply select the best estimation of a given area from unmixed results asshown in method 2 of table 3. By synthesizing composite images, the accuracy of theestimated values was able to be improved. It was possible to improve estimation ofland cover area significantly, by comparing both eight and three category accuracy
results of single multitemporal datasets, shown in table 2, with the accuracy resultsfrom the second compositing method, shown in table 3. This could be explained by
the fact that by increasing datasets, estimated values could converge towards theprobable value. Therefore, by producing composite images, the environment can bemonitored for land cover distribution and vegetation dynamic studies without theDownloaded By: [University of Tokyo/TOKYO DAIGAKU] At: 06:04 3 March 2008

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Figure 4. Composite images of eight categories. The top four categories are (a) wetland 1,(b) wetland 2, (c) grassland, (d) evergreen, from left to right. The bottom four categories are(e) deciduous trees, (f) agricultural land, (g) urgan area/bare land (h) water from left to right.
The colour bar on the right is an index to show the area percentage of a category in a certain
pixel. Black regions along the perimeter of the colour image were not used in the calculations(e.g. ocean, distinct land cover distribution), and the black regions within the image could not
be embedded with cloud-free data.

Figure 5. Composite image of three categories. The categories are (a) vegetation, (b) urban
area/bare land, (c) water from left to right. The colour bar on the right is an index to show thearea percentage of a category in a certain pixel. Black regions along the perimeter of thecolour image were not used in the calculations (e.g. ocean, distinct land cover distribution),and the black regions within the image could not be embedded with cloud-free data.

affects of clouds. The final land distribution composite maps using method 2 foreach category are shown in figure 4 and figure 5 for eight categories and three
categories respectively. The composite map shows the area ratio of a category withina pixel accordingly with the colour bar. The black regions along the perimeter of thecolour image are areas that were not used in the calculations (e.g. ocean, distinct
land cover distribution), and the black regions within the image are areas that could
not be embedded with cloud-free data.
5.

Discussion

In this way, by increasing datasets and using the proposed composite method,
estimated values can be ameliorated while clouds can be totally eliminated at thesame time.
Although the error did exceed 30% for some categories as shown in tables 2 and 3,
this unmixing and compositing method is considerably useful for quantifying theamount of categories, such as wetland 1. To illustrate, conventionally for NOAA
images, the NDVI was calculated to evaluate the quantity of vegetation (Elmore
et al. 2000). However, only a relative amount of vegetation could be tracked.
Because of this fact, it is advantageous that the proposed technique can estimate thequantity of a category.

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In order to improve the accuracy of land cover estimates in a wide area for futurestudies, several points were considered for improvement. To begin with, the spectral
signature of the categories in northeastern Asia resembled each other well, so it wasdifficult to discriminate between them and estimate endmembers. For example, thespectra of deciduous tree and agricultural field resemble each other. McGwire et al.
(2000) also found that this may occur because two spectral endmembers are sosimilar, creating non-unique solutions to the mixture equations where noise plays alarge factor in pushing the weights to one endmember or the other. Thus, themethod can be expected to perform optimally when cover types are spectrally
separated and well represented in the area of interest.
Secondly, the accuracy of the classification Landsat TM data, which are assumed
to be composed of pure pixels, may not be as high due to current classification
methods that are subjective; thus, in the future, a classification method which does
not intervene human judgment needs to be developed. Here, the ISODATA method
was used to classify the Landsat TM image. This requires human intervention tojudge the categories. In this study, a 30 m630 m spatial resolution surface imagery
was used, so it is possible to unmix the NOAA images at a higher accuracy if higher
resolution data were used instead.
Moreover, in the future surface radiant exitance-related errors need to be dealt
with. Here we assumed that the measured coarse spatial resolution radiance
represents the true radiance of the target area. However, errors can be made due toatmospheric attenuation of the signal, solar and viewing geometry (bidirectional
effects), subpixel cloud effects, topographical variation, and instrument noise. Thus
these problems need to be taken into consideration.
Finally, there was the problem of error caused from the geometric correction of
the AVHHR imagery. The conversion RMSE was within one pixel, which equals
less than one kilometre difference in the AVHHR imagery. It is difficult to estimate
an accurate endmember due to this deviation caused by geometric correction.
Currently there are limits to geolocation accuracy for existing fine and coarse spatial
resolution satellites due to the fact that the fine and coarse spatial resolution
instruments currently orbit different platforms. Puyou-Lascassies et al. (1994) also
found that the ability to unmix coarse spatial resolution data using fine resolution
data is adversely affected by current limitations on coregistration accuracies.
However, according to Oleson et al. (1995), recently developed techniques for
optimally determining spacecraft position and attitude based on high resolution
surface maps have demonstrated potential for significantly increasing georegistration accuracies for coarse spatial resolution instruments. In future studies, inaddition to taking BRDF effects into account, georegistration techniques may
improve the proposed method.
6.

Conclusions

A practical method for unmixing mixed pixels of coarse resolution images andsynthesizing a land cover area composite image to eliminate the influence of clouds
has been proposed. AVHRR radiance measurements were unmixed using co-locatedLandsat TM land cover for calibration. From this, the land cover area at the
subpixel level of a coarse resolution image can be estimated. In this study, the actual
land cover distribution within each mixed pixel was measured from AVHRR imagesof northeastern Asia.

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T. M. Uenishi et al.

In addition, the verification of the unmixing accuracy was carried out with
optimistic results. Over a single scene processed the land cover was estimated with,20% accuracy for three classes and ,30% accuracy for eight classes. From the
RMSE results, it was shown that the land coverage could be estimated at a relatively
acceptable accuracy. Although the unmixing accuracy was different according toland cover type, this unmixing method was shown to be effective according tocertain objectives, from both results of eight categories and three categories.
Moreover, the proposed techniques are a considerable improvement over estimating
relative quantity of a category.
Furthermore, a compositing method was considered to be able to evaluate
uniform land cover areas to remove the influence of clouds. From validation tests,
the best method to create an accurate land coverage map was simply to select thebest estimation of a given area from unmixed results. By synthesizing composite
images, the accuracy of the estimated values could also be improved. By producing
composite images land cover distribution can be monitored without the affects ofclouds.
Accumulated data from the past can be used for environmental assessment at a
global scale. By using this technique, it is possible to evaluate yearly change of thedetailed land cover using coarse resolution images. Furthermore, this study showsthat integration techniques that exploit the unique characteristics of multiple
instruments are preferable over methods that are compromised by the inherent
disadvantages of a single dataset. Thus, by using multiple instruments with theproposed method, the affects of clouds can be removed and information on thestatus and condition of the existing land types as well as any change occurring in
the land type condition over time can be obtained to assess detailed land cover
distribution at a global scale for research such as improvement of land management
practices on lands susceptible to desertification.

Acknowledgments
The authors would like to thank the National Space Development Agency of Japan(NASDA) for providing Landsat TM data for this research.
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