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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
station from Thailand, and process and stores the data.
DMSP/OLS data come from National Geophysical Data Center
/NOAA using network system such as Asia Pacific Advanced
Network (APAN), we get NOAA/AVHRR and DMSP/OLS
data on semi-real time.
The NOAA/AVHRR data, which are processed at the
Computer Center for Agriculture, Forestry and Fisheries
Research, Japan, named as Satellite Image Database System in
Agriculture, Forestry and Fisheries (SIDaB) (Kodama et al.,
2000). In this system, the user can search and download the
satellite images freely using WWW and FTP
(http://www.affre.go.jp/agropedia/).The NOAA/AVHRR data
of this system are receiving at three stations of Japan,
Shiogama, Yokohama and Ishigaki, and one station of
Thailand, Bangkok belonged to Asian Institute of Technology
(AIT). These data are transmitted using the high-speed network
system, and processed at the Computer Center for Agriculture,
Forestry and Fisheries Research, Japan. The geometric
correction and mosaic are doing in short time using TeraScan
software and EWS. The daily, weekly, ten-days and monthly
NDVI, MCSST and other datasets are supplied automatically.
3. RESULTS
3.1 Creating the standard NDVI image
Firstly, we select the suitable period NOAAA data for creating
the standard NDVI images. On thinking of climatically
periodicity, it seems that the longer time series data are suitable
for creating the standard NDVI image. The NOAA/AVHRR
data in SIDaB are useful from 1994. However, it is known that
clouds and other atmosphere constituents obscure the land
surface, reducing NDVI considerably (Goward et al, 1991;
Goward et al, 1993; Gutman 1991; Twonshend 1994; Los et al.
1994; Justice and Townshend 1994). Due to a volcanic
eruption, the NDVI was depressed for a long time (Kogan et al.
1994). On the other hand, on thinking of agriculture, since there
are the annual changes in the vegetation, the too old data are
impossible for explanation of vegetation changes. Therefore,
considering both two factors above, because the atmospheric
state is almost stable since 1997, the NOAA/AVHRR data in
1997-1999 are selected for creating the standard NDVI images.
In order to minimize the cloud effects, the ten-days NDVI
images are created using the maximum value of every pixel
firstly. Then, the ten-days NDVI images are masked because
there are also cloudy pixels even in this method. The cloudy
pixels (NDVI < 0) are substituted as the special value areas so
that these pixels are eliminated when the averages of NDVI
data are calculated. The average values of NDVI data in1997-
1999 are that of standard NDVI images. Examples of the
standard NDVI images are shown in fig. 2.
3.2 Agriculture Monitoring Using up-to-date (2000 year)
NDVI image
The up-to-date NDVI is calculated just after the ten days and
cloud pixels are masked for elimination. Similar to the reason
described above, there are also the cloudy pixels in up-to-date
ten-days NDVI image even using the maximum value of each
pixel for the daily and ten-days images. For these areas, it is
impossible to monitor the change of vegetation. Therefore,
these pixels (NDVI < 0) are substituted as the special value
area. In the NDVI image, these areas are shown in gray areas
(fig. 3). When the NDVI image is used, these areas will be
eliminated.
The difference NDVI images between the standard and up-to-
date ten-days NDVI images are calculated, and the drought risk
665
maps are created using this difference NDVI. The bigger
difference NDVI pixels are listed as the drought risk areas, and
the values of this difference NDVI show the intensities of
drought effects. Considering the change of vegetation growth,
the drought risk maps are created using the following two
calculation methods. The first method is using the value of the
difference NDVI between the standard and up-to-date directly
(fig. 3). There are 3 classes, >0.1, 0.1~-0.1, <-0.1. Considering
the general condition of vegetation growth, < -0.1 classes is
listed as poor growth of crops as usual.
3.3 Detecting and monitoring drought in China
China is essentially an agricultural country with about 80
percent of its total population engaged in agriculture. It is the
biggest developing country, with the one fifth of the world’s
population. Anything is bound to affect all over world,
especially, the food problem direct affected by agriculture. It is
important to detect and monitor the changes of the agriculture
in China. In last year (2000), there were heavy droughts in
China. In this research, using the early detection system
described above, the drought area and intensity are successfully
detected and monitored.
Using the agriculture monitoring system described above, the
drought risk maps on each ten-days interval in 2000 are
created. In this paper, the NDVI difference and intensity in the
last ten-days of June and July are described fig. 3. In early
June, there were drought possibility areas at Inner Mongolia in
China and Mongolia, and then the area extends all direction (fig.
4). The standard, up-to-date, difference and ratio of difference
and standard NDVI profiles are created (fig. 5) in the average
NDVI values at 10 pixels of every plot (fig. 5). According to
these NDVI profiles. Forest Steppe (A and B) has a big peak in
July and Desert Steppe (C) has a little high plateau from June
to September. We can easily understand to the effects of
drought at these plots. Two crops system area in China (D) has
two peaks that are winter wheat and rice. At the D of fig. 5, it is
almost same 2000 year and average of three years.
In fig. 6, Beijing City was included the drought possibility area,
and we checked the precipitation at Beijing. The averages of
precipitation in 1997-1999 are calculated, and then compared
with the precipitation in 2000, the changes of average, up-to-
date and the difference precipitation are shown in fig. 4. From
fig. 4, the precipitation in 2000 was less than the average
precipitation at Beijing city before July. Due to the little
precipitation, the negative difference between precipitation and
potential evaporation occurred to drought.
4. CONCLUSIONS
In this research, using the changes of up-to-date and normal
NDVI from NOAA/AVHRR data, agriculture monitoring
system is developed. The standard ten-days NDVI images are
created using the averages of ten-days cloud free NDVI data in
1997-1999. Using this system, we can make the drought risk
map. The map is created using the differences between the up-
to-date ten-days NDVI images and the standard NDVI images.
The bigger negative difference pixels (difference NDVI < -0.1)
are listed as the drought risk areas. The drought risk maps in
China last year are discussed as the example. Compiling the
analyses of characteristics of NDVI profiles and meteorological
precipitation data, the drought effects on agriculture in 2000 are
detected and monitored successful. It is shown that this system
is possible and useful to detect the drought area and intensity
on agriculture
