Data for: UAS and high-resolution satellite imagery improve the accuracy of cheatgrass detection across an invaded Yellowstone landscape

We mapped cheatgrass at different scales in the Greater Yellowstone Ecosystem using 10-m Sentinel-2 imagery, 3-m PlanetScope, and 10-cm Unoccupied Aerial Systems (UAS) imagery. We compared these maps to field-collected data to address 1) variation in seasonal phenological signals of native and cheatgrass patches, 2) the influence of scale on detectability and map accuracy across our study area. Model accuracy to predict cheatgrass presence increased with imagery resolution and reached 94% with the integration of PlanetScope and UAS imagery. While there was spatial agreement across models, UAS could best detect small cheatgrass patches required for early management intervention. Our novel use of different data sources in the classification of cheatgrass capitalizes on the senescence of cheatgrass during peak summer periods where cloud free imagery is more prevalent. Our satellite and UAS-based models of varying scale could be used in a multistage effort to discover where cheatgrass infestations exist, then pinpoint their precise location and extent for eradication and vegetation management.