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Classification of peatland vegetation types using in situ hyperspectral measurements

This study aims at evaluating two classes of methods to discriminate 13 peatland vegetation types using reflectance data from hyperspectral in situ measurements. These vegetation types were empirically defined according to their composition, strata and biodiversity richness. We suppose that specific biophysical properties/components may help discriminating vegetation types applying supervised classification such as Random Forest (RF), Support Vector Machines (SVM), Regularized Logistic Regression (RLR), Partial Least Squares-Discriminant Analysis (PLS-DA). Biophysical components can be used in a local way considering vegetation spectral indices or in a global way considering spectral ranges which characterize specific biophysical properties. and transformed spectral signatures enhancing absorption features. The results of this study suggest that RLR classifier is promising to map the different vegetation types with high ecological values despite vegetation heterogeneity and mixture.