The development of new multispectral sensors with unique band settings is critical for mapping the spatial distribution of increaser vegetation species in disturbed rangelands. The objective of this study was to evaluate the potential of WorldView-2 imagery for spectral classification of four increaser species, namely Hyparrhenia hirta, Eragrostis curvula, Sporobolus africanus, and Aristida diffusa, in the Okhombe communal rangelands of South Africa. The 8-bands were extracted from the WorldView-2 image, and 24 of the most widely used vegetation indices in estimating grassland biophysical parameters were calculated. The random forest algorithm and forward variable method were applied to identify the optimal variables (WorldView-2 spectral bands, vegetation indices, and a combination of bands and indices) for classifying the species. The random forest algorithm could classify species with an overall accuracy of 82.6% () using six of the WorldView-2 spectral bands and an overall accuracy of 90% () using a subset of vegetation indices (). Three bands selected were located at the new WorldView-2 spectral regions of coastal blue, yellow, and the red-edge. There was no significant improvement in increaser species classification by using a combination of bands and indices. Overall, the study demonstrated the potential of the WorldView-2 data for improving increaser separability at species level.