query and retrieve data from the database and to perform 
analysis on these data. 
SATELLITE IMAGE ANALYSIS 
Using satellite image to detect land use changes has the 
advantages of lower cost and shorter processing time 
over aerial photograph interpretation and field 
investigation. 
Another advantage of using satellite image over field 
survey for Der-Chi area is that satellite images can be 
received and processed without the interference of local 
interest groups. 
Image Source 
The satellite receiving facilities in Center of Space and 
Remote Sensing Research at Central University receives 
SPOT, LANDSAT and ERS data regularly. This assured 
the data availability for the research and for future 
monitoring. SPOT images were preferred than 
LANDSAT images because SPOT had better spatial 
resolution. Two images were chosen for the study, one 
was taken on December 30; 1993 and the other was 
taken on January;7 1995. 
Preprocessing 
ERDAS's imagine software was used in this research to 
process satellite images. The images were geocoded to 
Transverse Mercator projection. The RMS error for the 
registration was 1 to 2 pixels, or 20 to 30 meters. 40m 
DTM was used to produce ortho Satellite images. 
Land Use Classification 
This research was interested in distinguishing between 
natural vegetation and different types of framing and in 
identifying landslides. 
First, unsupervised classification were used to uncover 
the nature categories in the images. The results indicated 
that the rugged terrain of the research area caused large 
area within shadow. The shadow regions' spectral 
characteristics were very different from those of the 
unshaded regions. Several research had proposed 
methods to resolve this problem (Golby 1991; Hodgson 
and Shelley 1993, Michael 1993). The band ratio method 
was chosen for this research. To further increase the 
accuracy of classification, two sets of training samples 
are chosen for the shadow regions and unshaded regions 
respectively for performing supervised classification. 
In order to reduce the effect of shadow and distinguish 
different types of vegetation, a variety of vegetation 
Indices, such as IR-R, IR/R and (IR-R)(IR R) were 
tested. The IR-R and IR/R indices were used to classified 
the 1993 and 1995 image respectively. Because the 
correlation between the G and the R channel was very 
high (0.99), the R channel was not used. Vegetation 
index, G and IR channels were used for supervised 
classification. 
The classification scheme must be reliable and met the 
requirements for policy makers. After several trail 
classifications, a classification scheme was derived for 
the research (figure 2). 
The 1993 image was taken during a serious drought 
period. Large area of river beds and river banks was 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
SPOT 
image 
| 
| | | 
water shadow unshaded 
region region 
| 
| | | | | | 
vegetation buildup barrean land vegetation buildup barrean land cloud 
area and area and 
landslide forest 
| | 
p | | | | | 
forest farm grass forest farm grass 
land 
dense sparse orchard vegetable tea dense sparse orchard vegetable tea 
forest forest farm garden forest forest farm garden 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Figure 2 Classifiction Scheme 
132 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B7. Vienna 1996