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photography) but was less useful for vegetation, leaving an “artificial looking
hue” (Knudsen, P.2691).
The final part of the paper sets out a method of modifying the first algorithm to
improve its value in creating data for interpretation. The steps are to restore
black/grey/white, vegetation-covered and red/yellow-reddish areas lost in
preprocessing, to re-whiten very bright objects and to amplify the pixels (enhance
the colour saturation). The author provides reference to a more detailed technical
implementation (using information from previous papers he published) but these
are less relevant to this thesis as the result would not be suitable for identifying
hard ground.
One area where there is considerable information to be gained is in the area of
forestry, particularly in capturing the spread of disease or invasive species in a
plantation. One such study was undertaken by M.Martin, S.Newman, J.Aber and
R.Congalton in 1998. I have included it here as I think it is a good example of
what appears to be a standard remotely sensed image analysis. In this study the
authors set out to obtain remote data relating to tree species in an area called
Prospect Hill in central Massachusetts. Their target data was species identified by
11 forest cover types. To do this they used a maximum likelihood algorithm
assigning all pixels in the aerial image to one of the 11 categories they were
searching. The survey was validated using field data (taken from a database of
species type). They note that at that time (late 19990’s) spectral data had already
been used to identify categories of forest cover. These prior surveys had been
successful in discriminating between coniferous and deciduous cover (the authors
cite the examples of Nelson et al., 1985, Shen et al., 1985 and Landthrop et al.,
1994). The primary goal was identification of species composition from the forest
canopy and in this the authors had reasonable success –a random selection of
pixels yielded an overall classification accuracy of 75% (Martin et al 1998). The
study used photographic tiles of 10*10km with a high spectral resolution. The
authors suggest further improvements in accuracy could be made by identifying
the (deciduous) species with both their leaves on and off which would allow for a
foliar biomass calculation to be made.
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