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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004 
  
  
Digital Elevation Model 
  
  
Gradient 
  
  
  
Figure 2. DEM and Gradient image. The higher 
elevation/gradient, the darker a pixel is. The elevation is in the 
range (0, ) m. 
2.0 Data 
The A.S.T.E.R. image captured on 22/09/2001 with ID pg- 
PR1A0000-2001092201 019 057 was used. It covers the area 
geographic coordinates (38.3235, 20.5307), (38.2266, 21.2362), 
(37.6733, 21.0737), (37.7694, 20.3734). The bands 01 (green), 
02 (red), 03 (near infrared) of the V.N.I.R. sensor were used 
with spatial resolution 15 m (Fujisada, 1998; Kahle et al., 
1991). The radiometric conditions (gains) for it's band during 
the data acquisition was high gain for bands 01 and 02, normal 
for band 03 (Abrams and Hook, 2002; Chavez, 1996) 
R->03, 6->02, B->01 
  
Figure 3. Color composite image 
Additionally a digital elevation model (DEM) was used (figure 
2). Contour lines were digitised from a topographic map 
produced by the Hellenic Military Service. The map scale was 
1:50000 and the contour interval was 20 m. A DEM with grid 
size 30 m was derived by interpolating the contour lines. The 
gradient is in the range (0, ) degrees (figure 2). 
495 
2.3 Pre-processing of A.S.T.E.R. Imagery 
First radiometric correction was implemented. Each band was 
distriped by using the histogram matching technique. Then 
digital values were converted to radiance on the basis of the 
corresponding band gain. Finally path radiance was removed on 
the basis of linear regression of green and red band to the near 
infrared band (Eastman, 1999). 
Then non parametric geometric correction was implemented by 
the use of a second degree polynomial and ground control 
points (gcps) derived by field survey with a hand-held Garmin 
G.P.S. The root mean square error of the transformation was 
16.47 m for 14 gcps. The images were resampled to 30 m. A 
color composite image of the corrected bands is given in figure 
  
Figure 4. Cluter Map 
2.4 Cluter Map 
Training areas were selected for the major thematic classes 
occurring in the study area (Table 1). 
  
  
  
  
  
  
  
  
  
  
  
  
  
ID | Thematic Class Occurrence 
(number of pixels) 
1 Fir Forest 9,660 
2 Barren 24,023 
3 Cultivated 1 22,757 
4 Cultivated 2 21,714 
5 Cultivated 3 20,609 
6 Cultivated 4 3,307 
7 Lake 329 
8 Mixed Forest 86,242 
  
  
Table 1. The thematic classes and their occurrence 
in the cluter map. 
Maximum likelihood classification (Mather, 1987) defined the 
landcover classes of the study area. The cluter map derived is 
given in figure 4 while the occurrence of it's class is given in 
table 1 and figure 5. 
2.5 Geomorphometric description of cluter map 
Each thematic class was described on the basis of mean 
maximum and standard deviation of both elevation and gradient 
(table 2 and table 3) (Evans, 1980; Florinsky, 1998; Mark, 
1975).