Multiview stereo images acquired by uninhabited / unmanned aerial vehicles (UAVs) in combination with structure from motion (SfM) photogrammetry have created new capacity to develop high-resolution geospatial data, but vertical error is typically higher in vegetated areas because the ground surface is not visible in stereo. Miniaturized LiDAR systems for UAVs have potential to overcome this limitation, but their vertical accuracy in different vegetation types is not well documented. This thesis evaluated the accuracy of UAV-LiDAR and UAV-SfM in six vegetation types: grasses (short and tall), shrubs (short and tall), and trees (deciduous and coniferous). Results indicate UAV-LiDAR was more accurate in estimating ground elevation in all types, while vegetation height accuracy was higher for some types with UAV-SfM. UAV-LiDAR consistently sampled sub-canopy tree structure, while UAV-SfM only captured tree tops. Several factors are proposed to explain these differences and direct future research.