Mediterranean forest mapping using hyper-spectral satellite imagery

heterogeneity that is associated with Mediterranean
climate, floristic biodiversity and topographic
variability. Satellite remote sensing can be an effective tool
for characterizing and monitoring forest vegetation distribution
within these fragmented Mediterranean landscapes. The
heterogeneity of Mediterranean vegetation, however, often
exceeds the resolution typical of most satellite sensors.
Hyper-spectral remote sensing technology demonstrates
the capacity for accurate vegetation identification. The objective
of this research is to determine to what extent forest
types can be discriminated using different image analysis
techniques and spectral band combinations of Hyperion
satellite imagery. This research mapped forest types using
a pixel-based Spectral Angle Mapper (SAM), nearest neighbour
and membership function classifiers of the objectoriented
classification. Hyperion classification was done
after reducing Hyperion data using nine selected band combinations.
Results indicate that the selection of band combination
while reducing the Hyperion dataset improves
classification results for both the overall and the individual
forest type accuracy, in particular for the selected optimum
Hyperion band combination. One shortcoming is that the performance of the best selected band combination was
superior in terms of both overall and individual forest type
accuracy when applying the membership classifier of the
object-oriented method compared to SAM and nearest
neighbour classifiers. However, all techniques seemed to
suffer from a number of problems, such as spectral similarity
among forest types, overall low energy response of the Hyperion
sensor, Hyperion medium spatial resolution and spatiotemporal
and spectral heterogeneity of the Mediterranean
ecosystem at multiple scales.