004 
ture 
Istin 
ger- 
'ixel 
f an 
y of 
tion. 
4. A 
rvey 
Map 
tern 
ects 
xtely 
002. 
Itern 
th 
que 
995. 
eural 
stics. 
, pp. 
199% 
ig an 
mote 
terns 
back 
Sing, 
uracy 
. The 
"sive 
1990. 
nimal 
vised 
omote 
Yeural 
multi- 
nsing, 
^nces: 
057. 
with 
[ssues 
ks. In: 
nnual 
metry 
G., in 
tial of 
gy & 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
Miller, D.M., Kaminsky, E.J. and Rana, S., 1995, Neural 
Network classification of remote-sensing data. Computers & 
Geosciences, 21(3), pp. 377-386. 
Openshaw, S. and Openshaw, C., 1997. Artificial intelligence in 
geography. John Wiley &Sons, Chichester, UK. 
RSPB, 2000. Royal Society for the Protection of Birds. 
http://www. rspb.org.uk/wildlife/default.asp (accessed 19th Feb 
2002). 
Slater, J. and Brown, R., 2000. Changing Landscapes: 
Monitoring Environmentally Sensitive Areas using Satellite 
Imagery. International Journal of Remote Sensing, 21, pp. 
2753-2767. 
Tallis, J.H., 1985. Erosion of blanket peat in the southern 
Pennines: new light on an old problem. In: R.H. Johnson 
(Editor), The geomorphology of North-West England. 
Manchester University Press, Manchester, pp. 313-336. 
915 
Taylor, J.C., Bird, A.C. and Keech, M.A., 1991. Landscape 
changes in the national parks of England, application of satellite 
remote sensing. Report 14, Silsoe College, Bedford. 
Wilkinson, G.G., 1997. Open Question in Neurocomputing for 
Earth Observation. In: J. Austin (Editor), Veurocomputation in 
Remote Sensing. Springer-Verlag, Berlin, pp. 3-13. 
6. ACKNOWLEDGEMENTS 
The authors of this paper like to thank the UK Ministry of 
Defence and the Northumberland National Park Authority, in 
particular Gillian Thompson, for their support and assistance. 
The author would also like to thank the Remote Sensing and 
Photogrammetry Society (UK) for financially supporting the 
participation of this congress.