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If the sample does not match the above criteria but had a mean pixel value for the
red and green colour bands close to half that of roads, and displayed a level of
standard deviation three times that of roads and the red and green values represent
close to double the value of those in the water polygon (taken from the image key)
then the polygon is coded as rough pasture.
If the sample has red values around 30% lower than mixed forestry, and 20%
lower in green for the same known polygon type and the standard deviation
remains within 10% of the mixed forestry then the polygon can be coded as marsh
(usually a symbol under the level is present in within the polygon in the vector
dataset, but not always).
If the sample does not match those criteria outlined so far but has a low standard
deviation across all three colour bands and contains a decrease in the mean value
of over 30% for all colour bands when compared to the known road values then
the sample is tested for area size, if it is above the maximum value for pasture then
it is coded as bog.
The remaining polygons after this step in the algorithm fall into two categories –
those surrounding buildings or with mixed use and those with a high level of
shade present. Further analysis is required to step through the remainder to
identify areas with a homogenous pixel value but have a higher level of standard
deviation due to levels of shade, and those of mixed use. The output from this part
of the process are six further area sets; pasture, rough pasture, marsh and bog,
areas with high standard deviation for further analysis and areas containing
building polygons (automatically flagged through the vector data).
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