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infrared signatures to identify the presence of natural deciduous species at the
borders of area polygons but this would be prohibitively costly and beyond the
budget of any potential environmental survey. A cheaper solution might become
possible in future by identifying patterns in lidar data at polygon boundaries –the
focus of this study, however, is on spectral values and although unique in
comparison to the image average –the sample returned values very close to those
of rough pasture.
Areas of this type of surface cover, comprising of a mixture of coniferous and
natural woodland are already captured in Ireland and the study took a section of
land from one of these (present in the study area) and compared the spectral
values to those of the image as a whole.
Mixed Forestry Sample Mean Pixel Value Standard Deviation
Red 70.246 33.337
Green 104.33 32.726
Blue 85.974 19.626
Table 11: Mixed forestry sample values
As was expected the results showed a similar disparity with the values for the
image as a whole to rough pasture. As with rough pasture the standard deviation in
pixel values for the red and green colour bands was high, with a similar large
difference in values for those bands (37% and 23% lower respectively). This is
also an indication that areas of rough pasture can contain similar coverage to
mixed forestry –in that rough pasture is often overgrown and contains some tree
cover. Once areas without buildings and roads with the comparative variation
between the whole image and sample polygons which match those above (and in
the rough pasture survey) it should be possible to apply the rough pasture attribute.
The known mixed forestry polygon set (which is taken from the vector data
coding) can then be subtracted from this to give a percentage of rough pasture for
a target area.
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