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As an alternative to ground-cover data collection by conventional and expensive sampling techniques, we compared measurements obtained from very large scale aerial (VLSA) imagery for calibrating moderate resolution Landsat data. Using a grid-based sampling scheme, 162 VLSA images were acquired at 100 m above ground level. The percent vegetation cover in each photo was derived using SamplePoint (a manual inventory method) and VegMeasure (a reflectance based, automated method). Approximately two-thirds of the VLSA images were used for calibrating Landsat data while the remainder was used for validation. Regression models with Landsat bands accounted for 55% of the VegMeasure-based measurements of vegetation, whereas models that included both Landsat bands and elevation data accounted for 67%. The relationship between the Landsat bands and the percent vegetation cover measured by SamplePoint was lower (R2 = 20%),highlighting the differences between the inventory and reflectance based protocols. Results from the model validation indicated that the model’s predictive power was lower when the vegetation cover was either 20% or 55%. Additional work is needed in these ecosystems to improve the calibration techniques for sites with low and high vegetation cover; however, these results demonstrate the VLSA imagery could be used for calibrating Landsat data and deriving rangeland vegetation cover. By adopting such methodologies the US Federal land management agencies can increase the efficiency of the monitoring programs in Wyoming and in other western states of the US.(author abstract)