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

A preliminary unsupervised classification of land above 2900m discriminated two classes of
high-altitude vegetation. Lower-altitude forest and agricultural land was assigned to a third class,
which was then masked (e.g. Lauver and Whistler 1993). The reduction in spectral variability of the
image enhanced our ability to distinguish between the land cover types of interest.
A principal components analysis (PCA) on the eight non-panchromatic bands reduced
dimensionality of the dataset by partitioning the majority of the spectral variation into fewer bands.
This allowed us to remove spectral anomalies such as haze and banding (Jensen 1996) which were
separated into the last components and excluded from the classification.
Unsupervised classification
We set the minimum number of classes as the number of habitat types that could be distinguished
visually in false-colour composites by an observer familiar with the area. We examined exploratory
classifications using 20 to 50 classes to assess the representation of known land cover types and the
ability of the classification to distinguish between them. We determined the appropriate number
of classes by a combination of testing for statistical separation of classes, and expert knowledge
of the site. It is important to include expert knowledge in the classification process, particularly in
topographically complex areas (Shrestha and Zinck 2001), to ensure that the map represents the fine
distinctions needed by end users, and isolates land cover types of particular research or management
interest.
Clusters were initialised along the principal axis, using the automatic scaling range option.
The convergence threshold was set to 0.98, with 30 as the maximum number of iterations, and every
cell in the grid used as input for the clustering algorithm (Leica Geosystems 2003).
Our criterion for class separation was that ellipses plotted in full-dimensional feature space
showed no overlap at 1.28 standard deviations (SD). This allowed for 20% of the pixels to lie in the
extremes beyond the ellipse, potentially falling within the signature of another class, but incorporating
80% of the pixels in non-overlapping space. Separation of class means was assessed by Euclidean
distance, and spectral relationships between classes were illustrated using a dendrogram, based on
complete linkage agglomeration (Leica Geosystems 1991-2003).
Vegetation sampling
Clustered stratified random sampling was used to survey vegetation intensively within the areas of
primary interest in the Afroalpine zone: the Web Valley and Sanetti Plateau. Grid cells of 200 x
200m were chosen at random, and five locations coordinates were generated random within each
cell. See Tallents (2007) for further detail on the sampling design. Data from 442 vegetation
sampling locations and the central four points of 112 rodent trapping grids were included; 39 of the
latter were located outside the two sites of primary interest. Extensive surveys throughout other
areas of the Bale Mountains resulted in qualitative descriptions of the vegetation at a further 132
random locations, ensuring that all land cover classes were represented in the ground-truthing data.
<strong>Walia</strong>-<strong>Special</strong> <strong>Edition</strong> on the Bale Mountains 100