Using ancillary data to improve classification of degraded Mediterranean vegetation with HyMap spectroscopic images

Accurate land cover maps based on remote sensing observations are required for a.o. the evaluation of vegetation change models. In this study, where we investigate the intensification and extensification of land use in an area in southern France, purely spectrally based classification accuracy proved not to be sufficient. Therefore, we present a method to classify Mediterranean vegetation communities by integrating environmental and ecological information into a spatio-temporal image classification model: the Ancillary Data Classification Model (ADCM). Compared to a traditional Spectral Angle Mapper classification with 14 classes, the new proposed ADCM yields an increase of overall accuracy from 51 to 69 %. We anticipate that the use of additional environmental factors will further improve the classification results.