89 
Lstics of the 
tion and range 
rded under the 
the training 
1 B7+B4 
B7-B4 
56 
39.39 
59.24 
57 
5.29 
8.14 
21 
32-48 
50-70 
29 
84.31 
11.26 
56 
2.23 
3.97 
2 
76-92 
6-16 
19 
66.58 
22.69 
51 
2.23 
3.66 
1 
63-69 
19-26 
58 
79.57 
30.OC 
47 
4.65 
3.53 
4 
72-87 
24-35 
32 
36.41 
15.35 
28 
4.19 
4.65 
7 
31-41 
10-24 
41 
78.00 
18.16 
71 
4.89 
3.39 
3 
72-85 
13-21 
60 
8.90 
17.50 
48 
2.66 
4.76 
50 
6-14 
8-24 
idsat MSS band 
ieir values 
50-70) 
ess biased by 
nined using the 
of 3 bands, 
also examined 
>hich were used 
were: addition 
7 with band 4 
>and 7 (B7-B5), 
getation index 
rd deviations 
;d for each of 
ling to each of 
e 2 shows the 
classes. 
ower Jurassics 
bands 5 and 4, 
in the middle 
ion and shadow 
le histogram, 
i. Subtraction 
4 from band 7 
aestone in the 
e histogram 
tures tend to 
(b) 
Figure 1 Vegetation discrimination. a)Subtraction 
of (B7-B5) of the September image- White and yellow 
colours representing vegetated areas- b) Vegetation 
Index (B7-B5)/(B7+B5) of the September image- The 
lowest values represent sunfacing unvegetated areas, 
while high values correspond to shadow and 
vegetation. 
(b) 
Figure 2 subtractions of bands 4 and 5 from band 7 
in the September and July images, a) subtraction of 
band 4 from band 7 of the September image (high 
values, white and yellow, correspond to vegetation), 
b) subtraction of band 4 from band 7 of the July 
image ( also high values represent vegetation). 
Addition of band 7 to band 5 reveals more detail and 
permits shadow and coarse lava to be classified 
across the range of the histogram. However, other 
features still tend to coincide on the upper and 
middle part of the histogram- As was pointed out 
previously, shadow and vegetation overlap in the 
vegetation index for most of the upper range of the 
histogram, whilst landslide and scree ( usually 
associated with each other on the ground) can be 
delineated from the lower part of the data range. 
The vegetaion index therefore appears to be less 
suitable for classifying vegetation in a 
mountainous region (such as the study area) than for 
a plain terrain where there is such little or no 
shadow. Table 3 shows the results achieved using 
the approach described above. With all seven 
classes classified using three spectral bands 
together with the classifications obtained by the 
five combination of the three bands. 
A detailed study of each of the features listed in 
Tables 2 and 3 provides a useful insight into the 
nature of image processing. For example one can see 
how, for all three bands, low pixel values in 
shadowed areas produce a very high range of values 
on the vegetation index. While in the vegetated 
areas a moderate response of the spectral signatures 
in the three bands gives moderately high values 
using the same combination. 
Taken as a package, the various combinations of 
bands used in this research provide a comprehensive 
and concise method of classification. Each 
band/combination of bands yields additional, unique 
information on at least one of the seven features. 
The number of bands/combinations of bands used 
represents the smallest number that will allow all 
the features to be properly classified. The 
classification procedure is illustrated 
diagrammatically in Figure 4. 
As a general point, this section has demonstrated 
that without an understanding of how an image 
processing system works, the use of sophisticated 
computer equipment to analyse images will produce 
spurious results. A thorough understanding of the 
principles underlying image processing is therefore 
essential, in order to both choose the appropriate 
computer routines and to usefully interpret the 
results. 
3 LIMITATIONS 
During this study the main aim was to search for a 
method to locate unstable areas in mountainous 
region. Several limitations of MSS data for this 
kind of discrimination were revealed. The main 
shortcoming was the size of the ground cell 
resolution (79x79 m). A second limitation in this 
particular area was the shadow effects of the 
dramatic relief. The other limitations can be listed 
as vegetation cover, and atmospheric effects. 
4.1 Ground Cell Resolution 
The smallest picture element (pixel) in MSS imagery 
is 79 x 79 m. Due to the technical characteristics 
of the equipment on boardthe Landsat satellite, an 
overlap occurs along the latitudinal scan lines. 
This makes each pixel 79 m long in N - S direction 
and 57 m in E - W direction and prevents features 
smaller than 79 m from being distinguished (except 
for very highly reflective features such as water 
bodies and disturbed construction sites). 
Despite the narrowness of the roads and channels, 
these can be detected on the MSS images. However, 
the high reflectivity of small areas of disturbed 
land, combined with the reflectivity of surrounding 
features produces a mixed pixel response and makes 
interpretation difficult.