*2.14. Classification Trials*

All the classification trials are summarized in Figure 5. First, we classified all species with more than three samples using SVM and RF classifiers with the following feature sets: (1) reflectance; (2) NVI; (3) MNF; (4) ALS; (5) reflectance+ALS; (6) NVI+ALS; and (7) MNF + ALS features. Next, we run VSURF feature selection on all feature sets and repeated the classifications. Then, we evaluated the impact of feature selection on classification accuracy and identified important features. We selected the classifier and feature set that produced the highest Kappa values for later analysis of different approaches for grouping the species. In the final step, we tested different approaches for grouping species with fewer samples and tested the impact of up-sampling on the classification results.

The statistical significance between the different grouping approaches was not evaluated as the number of classes varied. Instead, we used 3-fold cross validation that was repeated 10 times to evaluate the stability of the classifier and interpreted the precision, recall and F1-scores. We tested three different approaches to group species. First, we grouped all species with less than 20 samples together to group "other" and classified the rest of the species separately as done earlier in a similar setting [10]. Next, we classified each of the species separately against a mixed group of all other species. The last approach was to use JM distance to combine species together, while species with high F1-scores and low variability were classified individually.

**Figure 5.** Workflow for the classification trials.
