279 
lability of multiband remotely sensed data, and can 
lead to extraction of information not apparent on the 
original imagery. One such technique is principal 
component analysis (PCA), especially useful if the 
bands are highly correlated. New images are produced 
by calculating new, uncorrelated principal components 
using a series of linear weights, called eigenvectors, 
which are applied to the origional band values. 
These principal components represent more efficient 
ly the variance of the data. 
A study of the eigenvalues for the El Guettar scene 
indicates that the data are just about three dimensi 
onal in structure with 99 per cent of the variance 
being explained by the first three components (Table 
4). A study of the eigenector weights attached to 
six principal component images, derived for the study 
area, using the six reflective TM bands. PCI is an 
overall summary of the albedo while PC2 is the diff 
erence between the visible/near IR and the mid-IR. 
This explains the brightness of the image, the extra 
ction of the areas of fan deposition, variation with 
in the playa and structure in the mountains. PC3 
(Fig. 8a) has a high band 4 weight which produces an 
image similar to the 3/4 ratio displaying vegetation 
while PC4 (Fig. 8b) is the difference between the two 
mid-IR bands and picks out the structure in the solid 
geology, fans and variation in the playa very clearly. 
Too much noise in the remaining components prevent 
any useful analysis. 
Many remote sensing studies have used supervised 
classification whereby the image is classified by the 
user who defines training areas where the surface 
properties are known. This allows the computer to 
classify the image on the basis of the information 
held in the training areas. This works very well in 
vegetation studies (Townshend 1981) but in geomorph 
ology it is very'- difficult to obtain unique spectral 
responses for landforms. Therefore, supervised class 
ification is not very suitable and the potential of 
unsupervised classification has been tested. This 
applies a clustering algorithm to the data and requi 
res no previous knowledge of the area. In the algor 
ithm used, the data was clustered according to a max 
imum likelihood classification, and the only user in 
volvement was to stipulate the starting number of 
classes, the maximum and minimum percentage of the 
image contained in any one class and the number of 
iterations for clustering. The resulting image was 
colour-coded, density sliced, and class statistics 
were derived allowing the display of individual class 
es and the proportion of the image contained in each 
class. 
Figure 9. Example of one cluster derived from an un 
supervised classification of El Guettar using TM bands 
3,4 and 5. This class relates to areas of solid 
geology. 
The test area was subjected to an unsupervised class 
ification on a three band image (3, 4 and 5) which 
produced 10 classes and 99 per cent of the image 
classified. The results are very engouraging. Some 
of the features which have been classified are areas 
of solid geology, variation within the fans, playa, 
alluvial plain, agriculture and the large ephemeral 
channel but a number of meaningless classes were also 
produced. The level of separation is visible in fig 
ure 9 which shows how one class corresponds to areas 
of solid lithology and oasis vegetation. Only rarely 
did a unique class occur, usually a number of featu 
res could be identified in the same cluster. This is 
due to similarity of pixel values of different units 
which leads to confusion within the feature space. 
However, the technique shows great potential for geo- 
morphological mapping especially if the user wishes 
to extract the main geomorphic units in a new area of 
interest. 
Further information reqarding the imaae nrocessina 
techniques discussed here can be found in Moik (1980) 
Schowengerdt (1983) and Gillespie (1980). 
6 CONCLUSIONS 
It has been shown that although single band Thematic 
Mapper imagery and standard false-colour composites 
show excellent geomorphological detail, it is worth 
noting that when dealing with bare, unvegetated sur 
faces, typical of arid/semi-arid areas, the degree of 
correlation between the bands is so high that most of 
the bands are redundant. Much more information can be 
extracted using digital image processing. Effective 
techniques range from simple contrast stretching to 
more complex multivariate techniques such as unsuper 
vised classification and principal component analysis. 
However, no single image processing technique is suit 
able for all landforms. Best results are obtained 
when there exists a specific relationship between 
spectral response and the physical properties of the 
phenomena under investigation. This relationship can 
then be exploited by selecting the optimum image pro 
cessing technique. 
The resulting processed imagery are an invaluable 
asset to anyone contemplating geomorphological mapp 
ing especially in difficult terrain. Digitally enhan 
ced satellite Thematic Mapper imagery is a powerful 
tool for geomorphologists for studying processes and 
for mapping. Not only does it afford a new perspect 
ive from which to observe the Earth's surface but it 
allows the development of new ideas regarding geomorph 
ological processes, the origins and modifications of 
landforms. 
ACKNOWLEDGEMENTS 
I wish to thank Dr. J.R.G. Townshend for his construe 
tive comments during the preparation of this paper and 
to the cartographic and photographic units of the 
Geography Department, University of Reading. Arwyn 
Jones is a NERC postgraduate research student GT4/ 
83/GS/87. 
REFERENCES 
Bailey, G.,J.Dwyer & Francica 1982. Evaluation of ima 
ge processing of Landsat data for geologic interpre 
tation of the Qaidam Basin, China. Second Thematic 
Conf., Remote Sensing for Exploration Geology, Fort 
Worth, Texas, p.555-577. 
Brunsden, D.,J.Doornkamp & D.Jones 1979. The Bahrain 
Surface Materials Resources Survey and its applicat 
ion to planning. Geogr. Jour. 145:1-35. 
Colwell, R. 1983. Manual of Remote Sensing, Vol I & II 
American Soc. of Photogram., Falls Church, USA. 2nd 
Edition. 
Doehring, D. 1980. Geomorphology in arid regions. All 
en & Unwin, London, p. 272.