Unpiloted aerial system (UAS) flight campaigns were conducted at two rangeland sites in Southwestern Montana during the 2018 growing season to classify vegetation and landcover types. A total of nine flights were conducted at the Argenta site and seven at the Virginia City site. To align images in space and time, we used four-dimensional structure from motion (4D SfM) and continued with processing for each flight date based on the full suite of images aligned for the entire growing season. We created dense point clouds, digital terrain models (bare earth), digital elevation models (including vegetation), and orthorectified images for each flight date at each site. We used the orthoimages to calculate the Normalized Difference Vegetation Index (NDVI) for each flight and used the flight at the peak of the growing season to calculate vegetation height and texture. We then used vegetation height and texture, along with different sets of flights as inputs into an Iterative Self-Organized (ISO) unsupervised data analysis algorithm to classify landcover types. We tested four flight frequencies: a single flight, a limited set, spring flights, and biweekly flights using different sets (or subsets) of the flight campaign. For each scenario, we classified the image to identify six functional groups: bare ground, litter, sparse, medium, and dense herbaceous, and sagebrush. For classifications based on multiple flights we tried to further identify subcategories of classes to reflect differences in phenology (timing of green-up and/or senescence).