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For the track/ hard cover the difference in the values for mean number of pixels<br />
converted to greyscale for the red colour band was significantly higher. This was<br />
also the case for the mean of the green colour band where the variation was<br />
upwards of 40% for the pasture which had not been cut. It should be noted,<br />
however, that the mean pixel in the green colour band was higher for cut pasture<br />
and this variation brought it closer to the value of hard ground –the difference in<br />
the mean of the blue colour band between the test sample and all the samples of<br />
pasture (including the cut pasture samples) was consistently 40% higher. This<br />
differential (in conjunction with a low standard deviation and a mean 10% to 40%<br />
lower than hard ground in the red and green colour bands) could be used to<br />
determine the presence of pasture.<br />
The two forestry samples showed a mean pixel value in the red colour band of<br />
approximately 50% less than pasture –with an increased standard deviation for<br />
both. In terms of distinguishing between coniferous and mixed; the standard<br />
deviation was one third higher for the red and green colour bands in the mixed<br />
forestry. The mean value for the green colour band in both of the forestry types<br />
was 40% lower than the value of the mean in the pasture, giving another relative<br />
indicator to calibrate pasture values from. Small areas of forestry are found close<br />
to most semi urban areas and the fact that they are coded and measured means<br />
they are useful in training an algorithm to match land use to neighbouring<br />
polygons.<br />
The aim of this sampling is to try and achieve a number of probability factors that<br />
will allow an automated process to determine land use or coverage types based on<br />
the available data. With this in mind a broad variety of samples were taken and the<br />
relative proportions between them assessed. In the above example the pasture area<br />
was compared against other known area types. The increased amount of cross<br />
checking allows the elimination of areas which conform to a particular type. In<br />
this way the image is gradually broken down until every polygon is referenced.<br />
The value of this is not necessarily in the ability to classify field use (although this<br />
has some merit in that it adds value to available mapping) but in the identified<br />
process. This enables a piece of software to be developed which can be reused<br />
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