Understanding the distribution of cheatgrass (Bromus tectorum) is key to planning and executing strategies to protect sensitive wildlife and ecosystems. This presentation will look at the construction of a mapping workflow and classification model based on a suite of biophysical variables and independent physical measurements to predict cheatgrass occurrences across the western United States’ sagebrush country. Learn about Woolpert’s combination of four-band imagery and co-collected lidar data into a robust data cube with derivatives such as slope and canopy height, as well as how field measurement values were used to derive biophysical variables (while removing highly correlated data). Finally, explore how the workflow yielded a predictive model that accurately determines the probability of cheatgrass cover in the study area.