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From the above samples, samples 4 and 5 were of freshly cut fields, reflected in
the high mean pixel value for the red colour band (almost 50% higher than the
average for the image as a whole). This gives areas of freshly cut pasture a unique
trait in a high red colour band mean and low standard deviation for a given
polygon. This trait also distinguishes cut pasture as the red colour band mean
values are over 35% higher than those of pasture in general. The mean green
colour band pixel value for pasture is 25% more than that of the image as a whole;
and when matched to a low standard deviation gives another strong indication of
the type of land use.
The above data is useful for indicating the presence of earthworks and landscaped
areas within a small polygon. With a key calibrated to identify polygons
containing the above values it should be able to accurately calculate the
percentage of land area given to pasture; and also improve the accuracy of any
process ran on imagery (in conjunction with vector data) by reducing the number
of polygons for analysis.
Pasture also serves an important part in the algorithm this thesis is attempting to
map out. This is for two reasons. Its uniform property (in terms of standard
deviation of the pixels form mean values and also the relative proportional
difference between those mean values and the mean values for other land
coverage types) allows it to form the bases of keys generated at the beginning of
image processing to identify land use with. The second reason is that in an Irish
context it is by far the biggest form of land use and any algorithm processing
aerial photography (even in semi urban areas) will have successfully identified the
majority of the surface area by correctly flagging area polygons of pasture. Of the
remaining areas most can be identified by polyline coding from the vector data
leaving the study with a higher chance of success in obtaining useful information
about the remaining areas. It is useful to divide the classes of pasture into the two
categories identified tin the samples above –that is to create a separate category of
cut pasture for the purposes of executing a comparative survey of polygons in the
aerial photography. With this in mind the following test samples were compared
against the spectral values of both.
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