Statistically defensible information on vegetation conditions is needed to guide rangelandmanagement decisions following disturbances such as wildfire, often for heterogeneous pastures. Here we evaluate sampling effort needed to achieve a robust statistical threshold using >2 000 plots sampled on the 2015 Soda Fire that burned across 75 pastures and 113 000 ha in Idaho and Oregon. We predicted that the number of plots required to generate a threshold of standard error/mean ≤ 0.2 (TSR, threshold sampling requirement) for plant cover within pasture units would vary between sampling methods (rapid ocular versus grid-point intercept) and among plot sizes (1, 6, or 531 m²), as well as relative to topography, elevation, pasture size, spatial complexity of soils, vegetation treatments (herbicide or seeding), and dominance by exotic annual or perennial grasses. Sampling was adequate for determining exotic annual and perennial grass cover in about half of the pastures. A tradeoff in number versus size of plots sampledwas apparent,whereby TSRwas attainable with less area searched using smaller plot sizes (1 compared with 531 m²) in spite of less variability between larger plots. TSR for both grass types decreased as their dominance increased (0.5–1.5 plots per % cover increment). TSR decreased for perennial grass but increased for exotic annual grass with higher elevations. TSR increased with standard deviation of elevation for perennial grass sampled with grid-point intercept. Sampling effort could be more reliably predicted from landscape variables for the grid-point compared with the ocular sampling method. These findings suggest that adjusting the number and size of sample plots within a pasture or burn area using easily determined landscape variables could increase monitoring efficiency and effectiveness.