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Rough Pasture testing sample 1
(pasture)
Mean pixel
deviation
95
Standard
deviation
Red 131.413 10.813
Green 167.157 9.494
Blue 102.256 12.591
Rough Pasture testing sample 2
(water)
Mean pixel
deviation
Standard
deviation
Red 35.191 4.374
Green 68.865 6.877
Blue 83.6 12.964
Rough Pasture testing sample 1
(road)
Mean pixel
deviation
Standard
deviation
Red 218.867 7.405
Green 240.2 9.321
Blue 197.533 10.802
Table 26: Rough pasture test sample values
The first testing sample for comparison to rough pasture was pasture –this
contrasts well with rough pasture and is a useful benchmark in the algorithm. As
with the similar (in terms of pixel values) area of mixed forestry, rough pasture
has high levels of standard deviation from a relatively low mean pixel value for
the red colour band (app. 70 on the converted greyscale). This contrasts well with
the mean red pixel value expected for pasture (almost double), something which
was borne out in the pasture sample. The level of standard deviation is similarly
low (app. One third of the value found in rough pasture for the red and green
colour bands). The fact that pasture is often adjacent to rough pasture makes this
also a very useful comparative measurement. It should also be noted that in terms
of the vector spatial data this is also useful as the same boundary polyline will flag
both areas and could be used to refine the algorithm. This is helpful to automated
image interpretation as it reduces the possible value set by allowing a reduced set
of values to be applied over the first iteration of the analysis. In this way an initial
analysis by polygon can apply the spectral values to a smaller subset of possible
neighbouring polygons and save time, opening up the possibility for the software