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Shade Sample 1 Mean Pixel Value Standard Deviation<br />
Red 33.9264 5.428<br />
Green 71.9221 7.446<br />
Blue 80.2165 14.322<br />
Shade Sample 2 Mean Pixel Value Standard Deviation<br />
Red 41 5.577<br />
Green 81.361 8.077<br />
Blue 78.381 11.5439<br />
Shade Sample 3 Mean Pixel Value Standard Deviation<br />
Red 42.813 6.943<br />
Green 83.186 7.878<br />
Blue 84.505 12.848<br />
Shade Sample 4 Mean Pixel Value Standard Deviation<br />
Red 45.333 15<br />
Green 85.854 17.921<br />
Blue 85.75 17.118<br />
Table 15: Shade sample values<br />
This is something which has been attempted in previous studies (Martin et al,<br />
1998), however, as with the thesis in general, I believe a hybrid method<br />
incorporating aspects of vector mapping and spectral analysis produces the best<br />
results. This is because in certain situations removing shade based on pixel values<br />
alone might bias the analysis (mixed forestry could produce a signature similar to<br />
marsh once lower pixel values are removed). A better method might be to<br />
manually take four or five samples per square kilometre and determine a mean<br />
percentage of shade based on areas of shade within those samples –this can be<br />
done relatively quickly using any vector manipulation software and the sample<br />
areas recorded for reference. Any further automated analysis would not eliminate<br />
pixels matching the shade signature above the sample percentages.<br />
Note: In future the addition of lidar data might allow the calculation of shade<br />
based on the height of the irregular tree canopy/ roof pitches etc. to be calculated<br />
and incorporated into the automated image analysis algorithm. This data is not<br />
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