Bark beetle outbreaks are a significant cause of loss of vegetation cover, for which accurate monitoring of forest areas is required to detect and control bark beetle outbreaks as early as possible. A tool that processes aerial imagery from an unmanned aerial vehicle to automatically detect levels of damage caused by bark beetle outbreaks is proposed and evaluated. The true-color RGB flight imagery is combined into orthomosaics, enhanced, and then analyzed in reference to manually annotated samples to identify thresholding rules for training classifiers based on a cellular automaton that assigns to each nonbackground pixel in the image a class label corresponding to the estimated stage of infestation at the location—healthy (green), early-stage (yellow), late-stage (red), and dead (leafless). Also samples corresponding to the ground (nontrees) are annotated and processed. The resulting classifications are on average over 89% accurate over five flights and often near flawless; the view from above does not fully substitute a ground-based assessment for intermediate stages of the infestation.