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

We derived class descriptions from the quantitative surveys of substrate, cover, vegetation
height and plant species composition, supplemented by photos, qualitative descriptions of vegetation
structure, and familiarity with easily identifiable plant communities.
Results
Landsat PCA
Principal components (PCs) one to three captured over 95% of the variation in the Landsat pixel
values, Table 1; see Tallents (2007) for factor loadings. PC1 isolated the factors which most strongly
influenced reflectance in all wavelengths, contrasting water bodies and densely vegetated land with
highly reflective bare soil. It was positively correlated with illumination (r = 0.44), separating
heavily shaded from sun-facing slopes. PC2 had high values in cool, damp areas with high plant
productivity such as drainage lines, and was lower in the arid Tullu Deemtu rain shadow and welldrained
lava slopes. PC3 correlated strongly with altitude (r = -0.65), contrasting the Helichrysumdominated
high plateaux with the lower-altitude grassy valleys.
Table 1. Eigenvalues and variability captured by the Landsat image principal components.
PC Eigenvalue Cumulative %
1 4.99 62.39
2 1.47 80.79
3 1.14 95.06
4 0.27 98.39
5 0.06 99.16
6 0.04 99.65
7 0.02 99.95
8 < 0.01 100.00
PC8 only described differences between the two highly correlated thermal bands, and
appeared as spatially random speckle, with some striping characteristic of the scanning path. PC7
isolated speckle probably associated with the resampling undertaken during orthoregistration, and
was discarded along with PC8. The remaining six PCS captured over 99% of the variability in the
data (Table 1), and formed the input for the classification. PCs 4 to 6 were retained despite their
relatively low information content because they highlighted features of interest and it was expected
that they would allow discrimination between broadly similar land cover classes. The fact that
components have low eigenvalues can simply indicate that the variability they encapsulate is very
localized (Eastman and Fulk 1993), so they can help to distinguish rare habitat types or those which
are spectrally similar despite being floristically distinct (Townshend and Justice 1980).
Classification
Several classifications fulfilled the criterion of spectral separability based on non-overlapping 80%
ellipses. The one with 23 classes (here labelled A to W) was chosen because it adequately represented
the full range of land cover types, and clearly delineated habitats of interest, such as wetlands, grass
versus herbaceous pasture, lava flows and high-altitude Helichrysum meadows, Figure 1.
<strong>Walia</strong>-<strong>Special</strong> <strong>Edition</strong> on the Bale Mountains 102