Monitoring of large conservation areas has to be accomplished to fulfil the reporting commitment of the European FFH Directive. Aim of this project was to develop a new monitoring approach for area-wide mapping on a stand level. This approach was based on the combination of numerical methods in vegetation ecology with imaging spectroscopy.

The study took place in the FFH conservation area Murnauer Moos, Upper Bavaria. The imagery had been gathered using the imaging spectrometer HyMap™. In order to develop maps that include spatial information on vegetation types as well as on transitions, crisp field and image classifications were combined with fuzzy methods in field and image data analysis. With Non-metric Multidimensional Scaling (NMS) ordination technique for the pre-processing of vegetation data and Partial Least Squares (PLS) regression for extrapolation, we took account of occurring mixed stands and gradual vegetation transitions.

In contrast, crisp supervised image classifications are suited to assign clear categories, which are also needed in management practice. Certain emphasis was given to the different possibilities of ground data classification and endmember selection. Different applications of endmember determination to Spectral Angle Mapper (SAM) classification and Multiple Endmember Spectral Mixture Analysis (MESMA) were compared.

Synthesis maps for monitoring were produced that deliver two-fold information on pixel basis: vegetation type membership on the one side, stand position in the context of the continuous field of the vegetation on the other. Hence, ecotones can be monitored within habitats. This study shows that with the use of high spatial and spectral resolution of the imagery, this information is given in the same spatial detail for a large area, and the quality of the given details is measurable.