Leafy spurge is a designated noxious weed. Accurate mapping and monitoring of this species are needed to understand leafy spurge's extent and spread. Current methods are based on ground crews who survey patches. Development of an affordable technique to map and monitor leafy spurge would contribute to the control of this species. High spatial, temporal, and spectral resolution imagery was used to classify the amount of leafy spurge present with ground and aerial-based imagery. A proof of concept study was performed in 2008 using ground-based images of an area infested with leafy spurge. This proof of concept project guided the development of the methods to be used for the 2009 aerial portion of the study. Thirty-five randomly selected reference points were selected in a range area in southwest Montana. These reference points were ground surveyed to record the density of leafy spurge in a 0.5-m radius area around the reference point. Images were captured approximately 108-m from the study area and classified using random forest classification. Multiple images were collected throughout the summer in order to determine at which time period leafy spurge is most easily detected. A classification using multiple image dates was also performed to determine if a time series of images improves classification. Single date accuracies were highest late in the summer with the highest single date classification achieving 83% accuracy. The multiple date classification significantly increased overall accuracy. Several aerial images were acquired in southwest Montana over the 2009 summer. Fifty randomly selected 2-m x 2-m reference areas were surveyed for percent cover of leafy spurge as well as several other variables. Aerial images were collected at flight elevations between 300-m to 460-m. Classifications were performed using random forest classifier, and both single date and multiple date classifications were performed. Leafy spurge was most accurately detected early and late in the growing season, and significant classification accuracy increases were observed with the multiple date classification. Single date accuracies achieved 90% accuracy in early June, while multiple date classifications achieved over 96% accuracy.