Walia Special Edition on the Bale Mountains (2011) - Zoologische ...

The ground-truthing points made up the training areas around which polygons for each
classification were drawn. These training areas were then used to define the digital signature used
for the supervised classification (Hord 1982). The training process was repeated independently for
each image mosaic.
Supervised classifications of all images were carried out using the Maximum Likelihood
Classifier Technique (Equation 1) (Hord 1982) in ERDAS Imagine software version 8.6 (ERDAS
1992). This method is the most preferred as it takes into account the most variables by using a
covariance matrix (Hord 1982).
Accuracy assessment
Once images were classified by the supervised method then a further 320 random points were
generated and data collected as above, in order to verify and assess the accuracy of the supervised
classification. Accuracy was determined by superimposing the points on the classified image and
obtaining the kappa coefficient, (which expresses the proportion of reduced error as generated by
the classification process compared with the error of a completely random classification, hence a
value of one indicates an accuracy of 100% or zero error) and the overall percentage accuracy, using
ERDAS Imagine. Images were reclassified by altering the training areas, until the accuracy met the
minimum requirement of 85% as set by Anderson et al. (1990).
Land cover change
The three images were compared to detect land cover change in two phases, 1973-2000 and 2000-
2005. Classified images were compared using the post classification image comparison technique
(Singh 1989) in ERDAS Imagine. This technique creates one image based on the difference of the
two comparative images from each year, which provides a summary table of the overall changes per
class; positive values denote an increase whereas negative values imply a decrease.
Patchiness
The patchiness of the land cover classes were also examined for each year. The number of patches
in each class, their areas and perimeters were all determined for each classified image using Spatial
statistical tools of the Geoprocessing tools sets in ArcGIS 9.1 software. Then, by comparing
the number and average area of patches in each of the considered years relative patchiness was
determined.
Results
Classification
Figure 3 shows static land cover classes in the study area for 1973, 2000 and 2005 respectively.
The maps clearly show persistent dominance of montane forest throughout the study periods
which covers 39.2-44.6 % of the total area and is entirely found in the southern part of the park. In
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