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polygons of uniform ground type. Further analysis of these polygons can be done<br />
once initial categorizations have been made and the level of analysis increased. To<br />
accurately determine the correct pixel proportion to flag requires a larger sample<br />
than being used in this study, which could be obtained from the data sets of<br />
polygons requiring further analysis returned from prolonged use of the algorithm.<br />
In other words this is something which would be developed later in the image<br />
analysis cycle because the nature of the sample (deliberately crossing outside the<br />
search polygons) makes it unlikely to be a feature of these types of images.<br />
The second sampling area involved taking a section of pasture for comparison<br />
with the coniferous samples. The values differed with an increase of<br />
approximately 40% for the mean of the red and green colour bands with a<br />
standard deviation 50% reduced on those found in coniferous forestry. This data is<br />
another useful reference in the identification of pasture as coniferous areas are<br />
coded and outlined in the vector data so can be automatically fed into a reference<br />
table during image analysis. As mentioned throughout this part of the study, the<br />
correct identification (and elimination) of areas of pasture from the image analysis<br />
is essential for the success of the suggested algorithm. Using spectral analysis for<br />
aerial image analysis (and remote sensing in general) is a specialized field of<br />
knowledge and studies tend to focus on a particular study area (Such as the<br />
analysis and classification undertaken by Coredo-Sancho and Adler in 2007).<br />
The focus of this thesis is to create a generic method for image analysis which<br />
makes use of captured vector data to filter the image, reduce the study area, and<br />
narrow the range of pixel variations that can be analyzed. In this way the study has<br />
focused on finding an algorithm that can be coded into an easy to use solution for<br />
this type of research. By necessity the current mapping methods are labour<br />
intensive and resources are not available to capture the type of secondary data that<br />
might be gained from automatic image analysis. In addition to this specific<br />
research (such as an inventory of impermeable surface in a region) could require<br />
specialist skills and methods –the algorithm suggested here is aimed to allow a<br />
user to filter through the current data by including their required target area into<br />
the process. For this reason the sampling has been generic (in that the image as a<br />
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