Full text: Proceedings of the Symposium on Global and Environmental Monitoring (Pt. 1)

daily GVI images are registered exactly, the 
probability that a real area of a daily GVI pixel 
in one image overlaps the one in another image is 
low. Because the ratio of a real area of GVI pixel 
to an area represented by the pixel is very low and 
the location of a real area of GVI pixel in the 
area represented by the pixel depends on images. 
In other words, pixels of so-called multi-temporal 
GVI data do not actually coincide geographically. 
Therefore, in a strict sense, seasonal vegetation 
dynamics by 12 monthly GVI data are an apparent one 
produced from GVI data of twelve different small 
areas, that is approximately 1.1km by 4.4km( in the 
case of nadir observation), within approximately 
20km by 20km. This problem is called as 
'multi-temporal sampling problem’ in this study. 
Though clouds contamination in daily GVI data 
is reduced considerably by the above processing 
(2), weekly GVI data still have clouds in some 
2.2 Other Data 
(1) Wilson's global land cover data 
Wilson et al produced data sets which archive 53 
categories of land cover types with a resolution of 
1° latitude by 1° longitude from a number of 
existing maps. The data set represents a land 
based survey of surface cover roughly appropriate 
to the period of 1950-1970. In their paper(Wilson 
et al 1985), they described a method to derive 24 
vegetation types (listed in Table 2) from 53 
categories. Authors produced images of each 
vegetation type in Polar Stereographic projection 
where a pixel value represents a percentage of 
coverage of a particular vegetation type in a unit 
area of 1° by 1°. 
(2) Global DEM 
This is a product of NOAA/NGDC(National Geophysical 
Data Center) which is called 'ET0P05'. It includes 
global altimetric and bathymetric data at grid 
points of 5 minutes latitude by 5 minutes 
(3) Snow cover data 
The official name of this data is 'Satellite 
Observation of Variations in Northern Hemisphere 
Seasonal Snow Cover data’ which is a product of 
NOAA/NESDIS (National Environmental Satellite, 
Data, and Information Service). The data indicate 
the existence of snow in a unit area by binary 
value (0 or 1). 89 by 89 unit areas cover Northern 
hemisphere in Polar Stereographic projection. This 
data were produced from NO A A AVHRR visible images 
by human interpretation. 
(1) Extraction of Asian region from GVI image 
The size of GVI images in Polar Stereographic 
projection is 1024 pixels by 1024 lines on Northern 
or Southern hemisphere. A part of the image with 
512 pixels by 400 lines which covers almost whole 
Asia were extracted from weekly GVI images of 
Northern hemisphere. 
(2) Production of monthly GVI image 
In order to reduce remaining clouds and eliminate 
noises in weekly GVI images, a monthly GVI image 
were produced from four or five weekly GVI images. 
If a pixel has a remarkably low SNVI value among 
the other values in a month, it is regarded as a 
noise and ignored in the next processing. Since 
pixels of clouds have high SNVI(low NVI) values, 
lowest SNVI(highest NVI) value is selected to 
produce monthly GVI image. 
Almost all noises can be removed by the above 
processing but it is impossible to produce a 
completely cloud free image. 
(3) Extraction of land area 
Global DEM were used to extract land area from 
monthly GVI images in order to eliminate remaining 
clouds over the sea. 
12 monthly GVI images show the seasonal vegetation 
dynamics. One method to display the change of the 
seasonal vegetation dynamics is to produce color 
composite images from selected three monthly GVI 
Figure 2 is a color composite image of 
April(red), August(green) and December(blue) in 
1987. Green region in Siberia shows less 
vegetation in April and December. Red regions from 
Mediterranean Sea to Persian Gulf and along Hindu 
Kush Mountains and Tien Shan Mountains show high 
vegetation in April. These areas correspond to the 
type C s (Warm temperate rain climates with dry 
summer season) of Koppen's classification of 
climate. Yellow region in Great plain of China has 
high vegetation in April and August. This region 
is a typical two-crop area in China. There is 
wheat in April and rice in August. 
Figure 3 is another color composite image of 
consecutive three months, May(red), June(green) and 
July(blue) in 1987. Red region in Myanma shows 
high vegetation in May and purple region along 
Western Gharts in India and In Great Plain in China 
has less vegetation in June compared with May and 
July. This figure explains the monthly vegetation 
changes in three months. 
Principal component analysis was applied to 12 
monthly GVI data of land area in 1987 in order to 
visualize seasonal vegetation dynamics throughout 
a year. 
Figure 4 shows eigen vectors for the 1st 
principal component(PC) to the 6th PC. The meaning 
of the 1st PC is the sum of NVI in a year and the 
2nd PC corresponds to the difference between 
summer(July and August) and winter(November to 
March). The 3rd PC explains the difference between 
April and July to January. The 4th PC shows the 
difference between May, October, November and 
February, July. The 5th PC is the difference 
between July, December and April, September. The 
6th PC is the difference between April, July and 
May, June. 
Figure 5 and 6 are color composite images of 
the 1st to the 3rd PC and the 4th to the 6th PC 
respectively. Each color shows the degree of the 
meaning of each principal component. For example, 
red color of Kalimantan in Figure 5 shows high 
vegetation throughout the year. Blue region in 
northern India in Figure 6 has high values of the 
6th PC which means that there is more vegetation in 
May and June than April and July. 
Since land covers with different vegetation types 
have different seasonal vegetation dynamics, land 
cover can be classified using 12 monthly GVI data. 
In this study land cover in Asia were classified by 
cluster analysis using 12 monthly GVI data in 1983. 
Applied algorithm of cluster analysis is K-means 
method with merger of clusters. 
Figure 7 shows classified 16 clusters in Asia. 
In order to investigate the similarity of each

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