The recent introduction of low cost small unmanned aircraft systems (sUAS) to remote sensing has provided a significant improvement in the quantity and quality of high resolution imagery. The purpose of this research was to describe the acquisition of very high resolution imagery using sUAS (drones) and assess the effectiveness of spectral-based classification for distinguishing vegetation (species, total cover), percent bare ground, litter, and rock from this data. Images were obtained from a semiarid rangeland site in central Nevada, USA. Flight missions were flown 15 m above ground level using automated flight paths, and individual images were processed into orthomosaics using the Pix4D software. Features were classified using a spectral unsupervised classification. Ground-based measurements were collected in the field to compare rangeland structure with generated classification output. Results indicate that very high resolution imagery can be effectively used to assess rangeland ecosystems that can aid in rangeland assessment and monitoring. The ability to use sUAS to monitor ecosystem structure and condition can be an important resource for rangeland managers, as they improve their ability to access high quality data for making informed management decisions within and across multiple years.