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

Table 1. Characteristics of satellite images used in this study
Image type Date of acquisition
Spectral composition
(Number of Bands)
Spatial resolution
Land Sat MSS 30/01/1973 4 58m
Land Sat ETM+ 28/11/2000 8 30m
Aster 19/12/2005 6 30m
Prior to analyses the satellite images were preprocessed in order to enhance efficiency,
harmonize data and to reduce error (Jensen 1996). Firstly, the satellite images were stacked to
produce a multispectral image from each of the panchromatic bands provided per image (see Table
1). Single images did not necessarily cover the entire study area, so adjacent images were stacked
independently and merged by mosaic operation. Secondly, the satellite images were geo-rectified to
reduce positional inaccuracies which stem from geometric instability of the satellites during the data
acquisition (Jensen 1996). Both of the geo-rectification strategies were used here, these were the
geometric registration strategy, where images are rectified with ground features, and the image to
image registration strategy, where geometrically distorted images are corrected based on a reference
image. The ASTER 2005 image was rectified by the geometric method, as it had better spatial
resolution to check with ground features such as road and river intersections and the Ground Control
Point data collected from the field. All other images were rectified by image to image registration
using Aster 2005 as a reference.
And lastly, due to the mountainous topography settings of the study area, images were
distorted radiometrically (Hodgson and Shelley 1994). As a result, images were corrected for
their radiometric errors using Topographic Normalization Technique (Colby 1991). In addition we
transformed the Images by Principal Component Analysis (PCA) method in order to reduce the
dimensionality of the image (Jensen 1996). This produces a more visually interpretable image and
assists the classification. Images were then clipped using the park boundaries.
Image classification
The following eight land cover types were investigated: shrubland-grassland-pasture (SHR-
GL-PAS), open woodlands (OWL), Erica shrub (ESH), Erica forest and closed woodland (EF),
pasture (PAS), Helichrysum (HEL), montane forest (MF) and glade (GLA). These classes were
defined by unsupervised classification combined with local knowledge of the study area. Eight
classes were selected as these were easily distinguishable visually in the filed and when carrying
out supervise classifications. We generated 900 random points distributed evenly across the study
site. Field assistants sub-sampled these locations based on accessibility, practicality, locality and
logistical feasibility, resulting in 320 ground truthing points. This number of points is well above
the recommended number for the Maximum Likelihood Classifier Technique, which is a minimum
of 250 points for total study area, irrespective of the number of categories used (Congalton 1991).
Ground truthing points were located by hand-held GPS Garmin 12 and the vegetation
type, altitude, slope and aspect were recorded. Photographs were also taken to provide a means of
verifying consistency in the classification data collected by field assistants.
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