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Accuracy of land cover area estimated from coarse
spatial resolution images using an unmixing method

K. Oki a; T. M. Uenishi a; K. Omasa a; M. Tamura baGraduate School of Agricultural and Life Sciences, The University of Tokyo,
Bunkyo-ku, Tokyo 113-8657, Japanb
National Institute for Environmental Studies Japan, Tsukuba, Ibaraki 305-8506,
Japan
Online Publication Date: 01 May 2004
To cite this Article: Oki, K., Uenishi, T. M., Omasa, K. and Tamura, M. (2004)'Accuracy of land cover area estimated from coarse spatial resolution images using
an unmixing method', International Journal of Remote Sensing, 25:9, 1673 - 1683
To link to this article: DOI: 10.1080/0143116031000139854
URL: http://dx.doi.org/10.1080/0143116031000139854

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INT. J. REMOTE SENSING,

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10 MAY, 2004,
25, NO. 9, 1673–1683

Technical Note
Accuracy of land cover area estimated from coarse spatial resolution
images using an unmixing method
K. OKI*, T. M. UENISHI, K. OMASA
Graduate School of Agricultural and Life Sciences, The University of Tokyo,
1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japanand M. TAMURA
National Institute for Environmental Studies Japan, 16-2 Onogawa, Tsukuba,
Ibaraki 305-8506, Japan(Received 17 May 2002; in final form 8 April 2003 )Abstract. Many techniques intended to estimate land coverage of multiple
categories occupied within each pixel from such coarse resolution data have been
proposed. However, in traditional unmixing studies with coarse resolution
imagery such as Advanced Very High Resolution Radiometer (AVHRR) data, it
is assumed that only a few endmembers exist throughout an entire image.
Therefore, it is essential to evaluate how well an unmixing method would workfor various categories within pixels of coarse resolution images. In this study, theland coverage of eight classes in National Oceanic and Atmospheric
Administration (NOAA) AVHRR imagery by using finer resolution Landsat
Thematic Mapper (TM) imagery was estimated, and the accuracy of theseestimated classes was evaluated. The results suggest that this method may begenerally useful for comparing multi-spectral images in space and time.

1.

Introduction
The utilization of coarse resolution images, which easily cover a wide area, canbe considered. For example, the Advanced Very High Resolution Radiometer(AVHRR) has been utilized to study vegetation development cycles (Achard andBlasco 1990), perform land cover type classification (Tucker et al. 1985) on
continental and global scales, and monitor vegetation transformations such as
tropical deforestation (Nelson and Holben 1986). The AVHRR provides fine
temporal solution of a given land target. Nevertheless, even though coarse
resolution satellites can observe the Earth in a short period, many categories areintermingled within each pixel of the observed image. It is necessary to determine
the categories and to calculate the land coverage of each with precision.
*Corresponding author; tel: z81-3-5841-8101; fax: z81-3-5841-8175; e-mail: agrioki@
mail.ecc.u-tokyo.ac.jp
International Journal of Remote Sensing
ISSN 0143-1161 print/ISSN 1366-5901 online # 2004 Taylor & Francis Ltd
http://www.tandf.co.uk/journals
DOI: 10.1080/0143116031000139854

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There are several unmixing methods (Adams et al. 1986, Inamura 1988, Smithet al. 1990, Settle and Drake 1993, Huguenin et al. 1997, Settle and Campbell 1998)
that allow a user to estimate the subpixel coverage within each category using linear
mixture models. However, the spectral radiance (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). Several
methods have been proposed for estimating the endmembers for each category(Oleson et al. 1995, Tompkins et al. 1997, Bateson et al. 2000, Defries et al. 2000,
Oki et al. 2002). For example, Oleson et al. (1995) and Oki et al. (2002) haveretrieved spectral reflectance for each land cover type in coarse resolution imagery
using fine spatial resolution imagery. However, in traditional unmixing studies withcoarse resolution imagery such as AVHRR data, it is assumed that only few
endmembers exist throughout an entire image (Defries et al. 2000, Robinson et al.
2000). Therefore, it is essential to evaluate how well an unmixing method would
work for various categories within pixels of coarse resolution images such as
AVHRR.
In this study, the land coverage of eight classes in AVHRR imagery by using
higher resolution Landsat Thematic Mapper (TM) imagery was estimated, and theaccuracy of the estimated classes was evaluated. The land coverage of three classes
was also estimated.
2.

Unmixing method for coarse spatial resolution image
Many recent studies have demonstrated the potential of linear mixture models(Adams et al. 1986, Inamura 1988, Smith et al. 1990, Settle and Drake 1993)
wherein the spectral radiance varies linearly with the relative proportions of thecategories within the subpixel mixtures of various land cover type. However, unlessthe spectral radiance (endmember) of a particular category is known, it is
impossible to decompose these linear mixture models to calculate the category ratio
within a mixed pixel. It is difficult to identify the spectral signature of purecomponents or endmembers which form the scene.
In this study, endmembers are estimated from coarse resolution images byoverlapping a classified TM image over an AVHRR image based on a linear
mixture model. In the AVHRR image, a linear spectral mixture model is a model
where the observed radiance of a particular pixel is given as P by endmember mwhich represents each category when cover ratio a of k types of categories exist, asin the following equation:P~kX

aj m jð1Þ

j~1

where,
aj ¢0,

k
X

aj ~1

ð2Þ

j~1

and
mj ¢0

ð3Þ

If the endmember m is given, the unknown ratio of area within a pixel can be
estimated from the observed radiance P. However, in the actual image, it is difficult
to determine the endmember m for each category.

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In this study, a TM image, which overlaps part of the observed area from an
AVHRR image, was used in order to calculate the endmember of each category of
the AVHRR image. First, a TM image is used to geo-correct an AVHRR image.
Next, we classify the TM image into k types of categories. Here, land coverage a ofeach pixel in the AVHRR image is calculated by overlapping land cover
distribution, which is evaluated from the TM image, on to the AVHRR image.
Furthermore, the endmember m of each category in the AVHRR image isestimated. In this study, under the condition of equation (3), a quadratic
programming method (Inamura 1988) was used in order to estimate the unknown
endmember m of each category in equation (1).
Using the estimated endmembers, the entire AVHRR image is unmixed. The
land coverage a for each unknown category in equation (1) is estimated byunmixing the entire AVHRR image under the condition of equation (2).
Conventional unmixing methods assume that the number of endmembers are
three or four in an entire scene, and attempt to find fractions for these endmembers
in every pixel (Defries et al. 2000, Robinson et al. 2000). In this study, we haveapplied the proposed unmixing methodology by unmixing the AVHRR image into
eight categories using multi-temporal AVHRR images. The land coverage of threeclasses was also estimated.
3.

Processing of Landsat TM
In this study, we selected Siberia’s wetland area as the study site. as shown infigure 1. In the study area, the land cover types have been investigated by the
National Institute for Environmental Studies, Japan, and we made classification
imagery with multi-temporal Landsat TM data as a high spatial resolution image
based on the ground data. The multi-temporal TM image was classified into threeand eight categories by the ISODATA method using two data (16 June and 18 July1997) in order to improve the classification accuracy, especially the wetland area.
The thermal infrared band (band 6) was not used in the classification. The eight

Figure 1.

Location of the study site. (Upper left coordinate: 52‡N, 121‡E; lower right
coordinate: 42‡N, 142‡E.)

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categories were classified as wetland 1, wetland 2, grassland, evergreen, deciduoustrees, agricultural area, urban area/bare soil and water, as shown in figure 2.
Furthermore, figure 3 shows three classification (vegetation, urban area/bare soil
and water) images that were created in order to compare with the eight category
results. Table 1 shows the definitions for nomenclature used in this study. In
figures 2 and 3, the left classified TM image is used to estimate the endmembers of
an AVHRR image, and the right image is used to evaluate the accuracy of theunmixing method.
4.

Processing of NOAA AVHHR
Figure 4 shows a multi-temporal National Oceanic and Atmospheric Administration (NOAA) AVHRR image dataset produced by combining three images
from 13 June, 27 July and 1 September 1997 at the location shown in figure 1. In
this study, all five bands for each image were used; thus, a 15 band dataset was
produced. By using a multi-temporal image dataset (15 band images), it is possible
to unmix the AVHRR image into three or eight categories. Then, cloud areas were
removed in each image. In figure 4, the black colour expresses cloud and sea area.
In this study, each AVHRR image was corrected geometrically using TM
images as a datum. A nearest neighbour interpolation method was used to preventthe original pixel values from being annulled. As a result, the error of superposed
AVHRR image was less than 1.0 pixel.
5.

Results and discussion
The endmembers of AVHHR images estimated from equation (1) using eachland coverage calculated by overlapping classified TM imagery are shown in figure 5
and figure 6, respectively for eight categories and three categories. Furthermore,using the estimated endmembers, results of the AVHHR images unmixed fromequation (1) under the condition of equation (2) are shown in figure 7 and figure 8,
respectively, for eight categories and three categories.
In figures 7 and 8, the black colour expresses cloud and sea area, which were notused for analysis in this study. Using the rms error (RMSE) as shown inequation (4), the accuracy of this unmixing method was evaluated for both three
categories and eight categories cases by comparing the results with the respectiveclassified TM image made using the ground truth information, assuming that theclassified image was true. RMSE is expressed asvﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
uPu nu ð^ai {aih Þ2t
RMSE~ i~1ð4Þnwhere â is each land coverage of each pixel estimated by unmixing an AVHRR
image, ah is the true land coverage of each pixel in the AVHRR image calculated byoverlapping land cover distribution, which is evaluated from the TM image, on to
the AVHRR image, and n is number of pixels in the validation area of the AVHRR
image. Results of the RMSE are shown in table 2 for eight categories and three
categories.
Several conclusions can be drawn from these results. For both eight and three
categories, although the unmixing accuracy was different according to land covertype, it was found that RMSE of each land cover was less than 50%. Therefore, it isDownloaded By: [University of Tokyo/TOKYO DAIGAKU] At: 06:01 3 March 2008

Figure 2.

Accuracy of land cover area estimation

Class 1: wetland 1
Class 2: wetland 2
Class 3: grassland
Class 4: evergreen
Class 5: deciduous trees
Class 6: agricultural area
Class 7: urban area / bare soil
Class 8: water
Eight classifications map using multitemporal Landsat TM imagery. (Path 116/row 26.)

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K. Oki et al.
Class 1: vegetation
Class 2: urban area / bare soil
Class 3: water

Figure 3.

Three classifications map using multitemporal Landsat TM imagery. (Path 116/row 26.)

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Accuracy of land cover area estimation
Table 1.

1679

Definitions of nomenclature used in this study.

Category No.

Category type

Eight category
1
2
3
4
5
6

wetland 1
wetland 2
grassland
evergreen
deciduous trees
agricultural area

7
8
Three category1urban area/bare soil
water

2

urban area/bare soil

3

water

vegetation

Description
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 inactive
buildings, roads, areas where agriculture isinactiverivers, lakes and sea
wetlands, forests, agricultural areas constitutethis areabuildings, roads, areas where agriculture isinactiverivers, lakes and sea

possible to quantitatively estimate each land cover area within a pixel by the
proposed method.
For eight categories, wetland 1 and water were estimated with relatively good
accuracy. The RMSE for wetland 1 was 13.3%, and for water was 16.4%. Thus, it
was shown that if the subject of one’s research were reed or water they could be
estimated from coarse resolution images with good accuracy. Traditionally, only
approximate vegetations could be distinguished through coarse resolution images;
however, by using the unmixing method in this study, the analysis of certain
vegetation types, such as reeds, in an extensive area became easier.
Because the unmixing accuracy was relatively good for the three categories case,similarly it was possible to evaluate the land cover changes from the AVHRR

Figure 4. Multi-temporal AVHRR image data produced by combining three images from13 June, 27 July and 1 September 1997. (Upper left coordinate: 52‡N, 121‡E; lower
right coordinate: 42‡N, 142‡E.)

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K. Oki et al.

wetland 1
wetland 2
grassland
evergreen
deciduous trees
agriculture
urban
water

Figure 5.

Estimated endmembers of AVHHR images for eight categories.

image. The conventional method for evaluating vegetation was to calculate NDVI(Normalized Difference Vegetation Index) from the AVHRR image (Elmore et al.
2000). However, this left the problem that only a relative amount of vegetation
could be known. For this reason, it is advantageous that this technique canquantitatively estimate the area of each category.
The unmixing error exceeded 30% for some categories. The following points
were considered as an explanation.

vegetation
urban
water

Figure 6.

Estimated endmembers of AVHHR images for three categories.

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Accuracy of land cover area estimation

Figure 7.

Distribution of each land coverage estimated by unmixing method.

(1) The spectrum values of each vegetation type resembled each other well, so it
was difficult to discriminate each type and estimate endmembers.
(2) The classified image, which is assumed to be composed of pure pixels, always
includes errors. So, the unmixing error of AVHRR image depends on the error of
the classified TM image.
(3) Moreover, there was the problem of error caused by the geometric correctionof the AVHHR imagery. The conversion error was less than 1.0 pixel. Due to thisdeviation caused by geometric correction, it is difficult to estimate an accurate
endmember.

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Figure 8.
Table 2.

Distribution of each land coverage estimated by unmixing method.

RMSE of unmixed AVHRR image for eight categories and three categories.

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

RMSE (%)

Three category

RMSE (%)

13.3
25.2
26.6
19.6
36.9
36.7
18.5
16.4

Vegetation
Urban area/bare soil
Water

20.5
17.3
15.7

6.

Conclusions
A practical unmixing method for mixed pixels of coarse resolution images has
been proposed. This method utilized high-resolution images, such as Landsat TM
images, in order to determine endmembers for mixed pixels in coarse resolution
images, such as NOAA AVHHR images.
Furthermore, the evaluation of the unmixing accuracy was carried out when
there are various categories of interest in coarse spatial resolution imagery. From
the RMSE results, it was shown that the land coverage could be estimated with arelatively good accuracy. Although the unmixing accuracy was different according
to land cover type, it was found that RMSE of each land cover was less than 50%.
Therefore, it was possible to quantitatively estimate each land cover area within apixel with the proposed method.
Acknowledgment
The authors would like to thank the National Space Development Agency ofJapan (NASDA) for providing Landsat TM data for this research.

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