The use of hyperspectral data in coastal zone vegetation monitoring. European Association of Remote Sensing Laboratories (EARSeL) eProceedings

Vegetation monitoring is an important tool in the evaluation of nature management in the coastal zone of The Netherlands. Remote sensing images are valuable in order to investigate spatio-temporal changes in the vegetation. A classification method has been developed based on air-borne hyperspectral data, which were acquired using the GER EPS-A scanner. A supervised clas-sification method has been used applying the Spectral Angle Mapper, in combination with an ex-pert system. The SAM algorithm determines the similarity between spectra of different vegetation types. The expert system adds extra information about environmental conditions to the classifica-tion in order to improve the discrimination of vegetation types, which are otherwise spectrally diffi-cult to identify. In our case, this method gives the opportunity for rapid classification with an overall accuracy of 60-70%