Rangeland monitoring is used by land managers for assessing multiple-use management practices on western rangelands. Managers benefit from improved monitoring methods that provide rapid, accurate, cost-effective, and robust measures of rangeland health and ecological trend. In this study, we used a supervised classification image analysis approach to estimate plant cover and bare ground by functional group that can be used to monitor and assess rangeland structure. High-resolution color infrared imagery taken of 40 research plots was acquired with a UltraCam X (UCX) digital camera during summer 2011. Ground estimates of cover were simultaneously collected by the Utah Division of Wildlife Resources' Range Trend Project field crew within these same areas. Image analysis was conducted using supervised classification to determine percent cover from Red, Green, Blue and infrared images. Classification accuracy and mean difference between cover estimates from remote sensed imagery and those obtained from the ground were compared using an accuracy assessment with Kappa statistic and a t-test analysis, respectively. Percent cover estimates from remote sensing ranged from underestimating the surface class (rock, pavement, and bare ground) by 27% to overestimating shrubs by less than 1% when compared to field-based measurements. Overall accuracy of the supervised classification was 91% with a kappa statistic of 0.88. The highest accuracy was observed when classifying surface values (bare ground, rock) which had a user's and producer's accuracy of 92% and 93%, respectively. Although surface cover varied significantly from field-based estimates, plant cover varied only slightly, giving managers an option to assess plant cover effectively and efficiently on greater temporal and spatial extents.