The Significance and Sustainability of Charcoal Production in the ...

process as he/she utilises a priori knowledge about the area of interest to select samples of
pixels, known as training sites, which represent homogenous examples for every land cover
type thought by the analyst to be present in the study area. The spectral characteristics of
these training sites are used to derive statistics for each land cover class, which are then used
for a classification algorithm to assign every pixel in an image to the class for which its
multispectral properties are most similar. The a priori knowledge is derived through a
combination of fieldwork, interpretation of maps, high-resolution imagery and the original
imagery, and also personal experience (Clark 2007: 9). Following operations were performed
in ERDAS IMAGINE: defining training signatures based on field observation from October
2010, evaluating training signatures, processing a supervised classification, and conducting an
accuracy assessment.
Training data was assumed to have a normal distribution, for this reason parametric rule was
chosen for the signatures. About 10 signature were collected with AOI tools from each land
cover class: 1) Water, 2) Woodland closed (brachystegia), 3) Woodland open (brachystegia),
4) Woody vegetation, 5) Thicket (cynometra), 6) Bushland, 7) Agriculture 1, 8) Agriculture 2,
9) Grassland, and 10) Bare soil, in order to get the spectral variance of each class before final
merging of signatures. Before performing the actual classification, an evaluation of the
separability between signatures was undertaken. The Signature Separability utility computes
the statistical distance between signatures, which can be used to determine how distinct the
signatures are from one another besides determining the best band combination to use in the
classification. A transformed divergence value of 2000 suggests excellent between class
separations, whilst a value above 1900 indicates good separation, and values below 1700
indicate confusion (Clark 2007: 15). Separability figures can be seen from Appendix 2. In the
2005 image minor incoherence was in 2:3 and 2:4 class pairs and in the 2006 image 2:3, 2:8,
3:9 and 3:7 pairs. Below 1700 values were approved because the incoherence was mainly
between the woodland closed and woodland open classes that both are formed of
Brachystegia woodland. In the 2011a image, all but the class pair 2:3 where distinct, the low
separability figure is due to similar reflectance properties between open and closed woodland.
In the 2011b image there is a bit more variance: class pairs 2:3, 7:9, and 2:7 have low
separability values. The second pair may have low separability due to similar reflectance
properties of agricultural fields and grassland, and soil properties may influence the third class
pair result: Brachystegia woodland gives soil reflectance through which is why it may be
confusing to agricultural fields with similar soil properties.
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