Mapping ash tree colonization in an agricultural mountain landscape: investigating the potential of hyperspectral imagery
Résumé
In this contribution, we evaluate the potential of hyperspectral imagery for identifying ash tree and other dominant species in encroached mountain grasslands. The method is based on a supervised approach using Support Vector Machines in which kernel parameters are fixed by kernel alignment. We present the application of the method and the first results obtained. The statistical measures derived from the confusion matrix show that tree species are well discriminated with accuracies > 90%. These results confirm the possibility of detecting tree species with this data and the performance of the SVM classifier.