The study explore an optimal phenological period to identify 9 tree species in mixed deciduous floodplain forest in the Czech Republic: Austrian oak (Quercus cerris L), wild apple (Malus L.), conker tree (Aesculus hippocastanum L.), hornbeam (Carpinus betulus L.), ash (Fraxinus angustifolia L.), maple (Acer canpestre L.), elm (Ulmus laevis L.), linden (Tilia europaea L.), willow (Salix carpea L.). Airborne hyperspectral data (spectral range of 380-1050 nm, spectral resolution of 9 nm, spatial resolution of 1 m) were acquired six times during a vegetation period: end of April, beginning of July, end of July, beginning of August, beginning of September and mid of October. Maximum Likelihood, Artificial neural network (ANN) and Support vector machine (SVM) methods were applied to classify tree species. The spectra analysis showed that the most important spectral variables to distinguish between 9 tree species were located in red (520..580 nm) and near infrared (740..1050 nm) spectral regions. Individual tree species accuracies ranged from 28.06% (wild apple) to 93.1% (conker tree). Maximum Likelihood achieved higher Kappa coefficient (K) in each phenological period than other methods, with that the Overall accuracy (OA) and Average accuracy (AA) were not high using Maximum Likelihood. SVM achieved a higher level of classification accuracy (OA = 89.4%, AA = 81%, K = 0.78) than other methods in the beginning of July, that was found as the optimal phenological period to identify 9 tree species in mixed deciduous forest.
The study was supported by the Ministry of Agriculture of the Czech Republic grant number QK1910150.