Information on landscape-scale patterns in species distributions and community types is vital for ecological science and effective conservation assessment and planning. However, detailed maps of plant community structure at landscape scales seldom exist due to the inability of field-based inventories to map a sufficient number of individuals over large areas. The Carnegie Airborne Observatory (CAO) collected hyperspectral and lidar data over Kruger National Park, South Africa, and these data were used to remotely identify >500 000 tree and shrub crowns over a 144-km2 landscape using stacked support vector machines. Maps of community compositional variation were produced by ordination and clustering, and the importance of hillslope-scale topo-edaphic variation in shaping community structure was evaluated with redundancy analysis. This remote species identification approach revealed spatially complex patterns in woody plant communities throughout the landscape that could not be directly observed using field-based methods alone. We estimated that topo-edaphic variables representing catenal sequences explained 21% of species compositional variation, while we also uncovered important community patterns that were unrelated to catenas, indicating a large role for other soil-related factors in shaping the savanna community. Our results demonstrate the ability of airborne species identification techniques to map biodiversity for the evaluation of ecological controls on community composition over large landscapes.