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The second test sample looked at an area of mixed forestry for deviation (in terms
of mean pixel values across the colour bands) from those found to be present in
areas of marsh. The values had a relatively unique variation from marsh in that
while both the red and green colour band mean pixel values were a lot lower (35%
and 20% respectively) the blue colour band had a comparable level of standard
deviation of a mean which was within 10% of marsh, although this could be
attributed to the level of shade present in the forestry due to the tree canopy
varying in height across the sample. As is pointed out elsewhere in this study,
areas of mixed forestry did not give reliable enough data to calibrate other surface
areas form, based on spectral values alone. In the case of these types of areas there
is vector data coding present to uniquely identify the forestry, however,
knowledge of an expected proportional difference between the (known) forestry
and an area of marsh is a useful additional factor to include in the algorithm and
might increase the accuracy of any search for these types of areas (or at least help
to eliminate them from a search for other specific properties).
The third test sample took an area of hard cover (paving/ track) from a yard
between agricultural buildings. This type of cover is a part of this study which
revealed the most distinct values and presents a valuable calibration tool for the
algorithm. When compared to this third sample the mean pixel value (converted to
greyscale) for the red colour band found in the marsh samples was only 43% of
the hard cover, while similar disparity was found between the green and blue
mean pixel values in marsh and the green and blue mean pixel values in the hard
cover (with the marsh mean values at only 51% and 46% of the hard cover
respectively). These types of areas are well coded in the vector data. Some areas
of hard cover surrounding private dwellings and farm buildings may not be
captured and the automated identification of these types of areas through aerial
image processing is one of the aims of this thesis. It can be assumed, however,
that for any given area (excepting rural mapping covering mountains, which this
study is not addressing) the road polygons have been accurately captured and
there will be several sample polygons to calibrate a hard cover value from. In
terms of this part if the study, the relative proportional deviation of marsh values
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