Monitoring of forage utilization typically occurs at sample locations, or key areas, selected for their presumed potential to represent utilization across pastures. However, utilization can vary greatly across landscapes and may not be well represented by traditional ground-based sampling without great effort. Remote sensing from satellite and manned airborne platforms offers spatial coverage at landscape scale, but their poor spatial resolution (satellite) and cost (manned airborne) may limit their use in monitoring forage utilization. High-resolution photogrammetric point clouds obtained from small unmanned aerial systems (sUAS) represent an appealing alternative. We developed a method to estimate utilization by observing the height reduction of herbaceous plants represented by 3-dimensional point clouds. We tested our method in a semiarid savanna in southern Arizona by comparing utilization estimates with ground-based methods after a month-long grazing duration. In six plots, we found strong correlation between imagery and ground-based estimates (r2 = 0.78) and similar average estimate of utilization of across all plots (ground-based = 18%, imagery = 20%). With a few workflow and technological improvements, we think it is feasible to estimate point cloud utilization over the entire pasture (150 ha) and potentially even larger areas. These improvements include optimizing the number of images collected and used, equipping drones with more accurate global navigation satellite systems (e.g., Global Positioning System), and processing images with cloud-based parallel processing. We show proof of concept to provide confident estimates of forage utilization patterns over large pastures and landscapes, at levels of spatial precision that are consistent with ground-based methods. The adoption of drone-based monitoring of utilization of forage on rangelands could follow the paradigm shift already demonstrated by Global Positioning Systems and Geographic information systems technologies, where the initial high computing costs were reduced, use became the norm, and the availability of more precise spatial patterns was applied to prescribe and evaluate management practices.