Ground cover is a key indicator of rangeland condition and influences rangeland management decisions, yet there have been few advances in ground-cover measurement methods. The advent of digital photography and automated image processing promise a revolution in the way ground cover is measured. To assess the potential for automation we compared conventional and automated methods for measuring ground cover against known artificial populations. The known populations were created from 20 nadir images of a Wyoming big sagebrush (Artemisia tridentata Nutt. ssp. wyomingensis Beetle & Young) vegetation type acquired with a 5-megapixel Olympus E20 digital single lens reflex camera mounted on an aluminum camera frame at 2 m above ground level. The images were converted to color, 2-dimensional images that no longer represented real-world conditions but had known cover values and conserved a simplified form of the pattern and spatial context of the plant community. These images were then printed at 1:1 scale to a 1 × 1-m poster. Posters were evaluated for color cover under laboratory conditions using the conventional techniques of steel-point frame, laser-point frame, line-point intercept, ocular estimation, and line intercept. Photographs of the posters were measured for color cover using standard and custom-created algorithms within the VegMeasure image analysis framework, and using the Digital Grid Overlay method. Results indicate that conventional techniques had significantly greater correlation (≥ 92% agreement of measured to known) than measurements from the algorithms used in the VegMeasure analysis (70%). The critical factor influencing accuracy of point-sampling methods was the area of the contact point for the given method. These findings provide an important measure of relative accuracy among methods for land managers and for researchers seeking to improve rangeland monitoring methods.