Numerous studies have been conducted that evaluate the utility of remote sensing for monitoring and assessing vegetation and ground cover to support land management decisions and complement ground measurements. However, few comparisons have been made that evaluate the utility of object-based image analysis (OBIA) to accurately classify a landscape where rule sets (models) have been developed at various scales. In this study, OBIA rule sets used to estimate land cover from high–spatial resolution imagery (0.06-m pixel) on Pinus L. (pinyon) and Juniperus L. (juniper) woodlands were developed using eCognition Developer at four scales with varying grains—1) individual plot, 2) individual sites, 3) regions (western juniper vs. Utah juniper sites), and 4) pinyon-juniper woodland network (all plots)—that were within the same study extent. Color-infrared imagery was acquired over five sites in Oregon, California, Nevada, and Utah with a Vexcel UltraCamX digital camera in June 2009. Ground cover measurements were also collected at study sites in 2009 on 80 0.1-ha plots. Correlations between OBIA and ground measurements were relatively high for individual plot and site rule sets (ranging from r = 0.52 to r = 0.98). Correlations for regional and network rule sets were lower (ranging from r = 0.24 to r = 0.63), which was expected due to radiance differences between the images as well as vegetation differences found at each site. All site and plot OBIA average cover percentage estimates for live trees, shrubs, perennial herbaceous vegetation, litter, and bare ground were within 5% of the ground measurements, and all region and network OBIA average cover percentage estimates were within 10%. The trade-off for decreased accuracy over a larger area (region and network rule sets) may be useful to prioritize management strategies but will unlikely capture subtle shifts in understory plant communities that site and plot rule sets often capture.