Mapping tree species at the single-tree level is an active field of research linking ecology and remote sensing. However, the discrimination of tree species requires the selection of the relevant spectral features derived from imagery. We can extract an extensive number of image parameters even from images with a low spectral resolution, such as Red-Green-Blue (RGB) or near-infrared (NIR) images. Hence, identifying the most relevant image parameters for tree species discrimination is still an issue. We generated 42 parameters from very high resolution images acquired by Unmanned Aerial Vehicles (UAV), such as chromatic coordinates, spectral indices, texture measures and a canopy height model (CHM). The aim of this study was to compare the relevance of these components for classifying savannah tree species. We obtained very high (5 cm) pixel resolution RGB-NIR imagery with a delta-wing UAV in a thorn bush savannah landscape in central Namibia in April 2016. Simultaneously, we gathered ground truth data on the location of 478 individual trees and large shrubs belonging to 16 species. We then used a Random Forest classifier on single and combined thematic sets of image data, e.g. RGB, NIR, texture and in combination with CHM. The best average overall accuracy was 0.77 and the best Cohen´s Kappa value was 0.63 for a combination of RGB imagery and the CHM. Our results are comparable to other studies using hyperspectral data and LiDAR information. We further found that the abundance of the tree species is crucial for successful mapping, with only species with a high abundance being classified satisfactorily. Diverse ecosystems such as savannahs could therefore be a challenge for future tree mapping projects. Nevertheless, this study indicates that UAV-borne RGB imagery seems promising for detailed mapping of tree species.