Aquatic vegetation species at the genus level in an oxbow lake were identified in
Hungary based on a multispectral Uncrewed Aerial System (UAS ) survey within an elongated
oxbow lake area of the Tisza River under continental climate. Seven and 13 classes
were discriminated using three different classification methods (Support Vector Machine
[SVM], Random Forest [RF] , and Multivariate Adaptive Regression Splines [MARS]) using
different input data in ten combinations: original spectral bands , spectral indices,
Digital Surface Model (DSM) , and Haralick texture indices. We achieved a high (97.1%)
overall accuracies (OAs) by applying the SVM classifier, but the RF performed only
<1% worse, as it was represented in the first places of the classification rank before
the MARS. The highest classification accuracies (>84% OA) were obtained using the
most important variables derived by the Recursive Feature Elimination (RFE) method
. The best classification required DSM as an input variable. The poorest classification
performance belonged to the model that used only texture indices or spectral indices.
On the class level, Stratoites aloides exhibit the lowest degree of separability compared
to the other classes. Accordingly, we recommend using supplementary input data for
the classifications beside s the original spectral bands, for example , DSM, spectral
, and texture indices, as these variables significantly improve the classification
accuracies in the proper combinations of the input variables .
