Accurate assessment of woody species diversity using remote sensing can assist ecologists by providing timely information for ecosystem management. The increasing availability of remotely sensed data necessitates the investigation of accuracies of different sensors in classifying plant species, especially during the dry season when foliage amount is low. WorldView-2, SPOT-6, and Sentinel-2 images were compared in detecting woody species (n = 27) and three coexisting land cover types in a savanna environment during a dry period. Random Forest (RF) and Support Vector Machine (SVM) classifiers were applied to each imagery to make a strong case for the comparison. The overall classification accuracies ranged between 52% and 65% for all images, with the WorldView-2 image performing the best followed by Sentinel-2 and SPOT-6 images. These accuracy rankings were similar for both the RF and SVM classifiers, with the former faring better. Pairwise comparison of the images using McNemar’s test showed significant differences between images in their ability to correctly identify woody species. Analysis of band importance revealed better contributions to the classifications of infrared bands for all images. Overall, the findings showed the potential of optical imagery in classifying and monitoring woody species hotspots in savanna environments even during a low photosynthesis season.