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(Phynn et al, 2002). In this study all roads and tracks have been captured and the
purpose of analyzing the spectral qualities of these is in an effort to train an
algorithm to recognise the specific properties of hard ground/ impermeable
surface area within small area polygons. The results revealed distinctive qualities
thanks mostly to the high reflective quality of these types of surface for red and
green colour bands.
As is mentioned in other sections of this study, the initial part of the algorithm
involves extracting the roads (and water, and forestry, known marsh etc.) to leave
a smaller number of polygons for analysis. The next step would be the removal
(classification) of areas with relatively unique spectral values such as all the
pasture polygons. Following this, the urban polygons would be analyzed, having
been identified by the presence of building polygons within them –these would
then be classified according to the nature of the building (as this data is only
available in some instances the first iteration of the loop would include all
buildings). The nature of the spectral values would then be compared to the values
sampled here, as the low level of standard deviation among the colour bands
associated with them enables classification with a degree of certainty.
This type of survey has particular benefit in flood mapping and can help the
development of models factoring in runoff rates during times of high rainfall. The
sample area used here is just outside an urban area and as such has a useful mix of
all the possible land cover types –ranging from dirt track to paved yards to
forestry to pasture to dwelling houses within small polygons. Specific searches of
urban developments could expect to find more homogenous ranges within each
polygon. The values taken from this sampling could serve as the baseline for one
of these surveys. The user would then select known areas of the target land cover
being analyzed (via a mobile GPS unit or selected from the vector mapping draped
over the aerial photography). A combination of the standard expected values and
the entered key values could then be used to calculate the percentages across a
wide area. The fact that for each area analysis the pixel variations are confined to
a small area polygon means that there is less chance of a gradual distortion biasing
the results, as each separate polygon is calculated based on its own features (i.e.
the values of neighbouring polygons and presence of buildings mentioned above).
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