The mountain pine beetle (MPB) (Dendroctonus ponderosae) has killed millions of acres of pines throughout the western United States in recent years. Active control of the beetle has been hindered by the inability to detect infested trees prior to obvious visual signs. With sponsorship from the Forest Service, U.S. Department of Agriculture (USFS) Remote Sensing Steering Committee, the Remote Sensing Applications Center in collaboration with the Black Hills National Forest (NF), conducted a study to determine the feasibility of pre-visual detection of MPB-infested ponderosa pine by analyzing a time series of 26 high-resolution, 8-band, multispectral WorldView-2 (WV2) images of the Black Hills NF. Based on spectral and trend analyses of the time series data, the first sign of detection occurred around mid-March to April. Unfortunately, the newly attacked trees could not be detected early enough for the management goals of the Black Hills NF. Therefore, the emphasis of the project shifted to developing a semi-automated method to map the red-attack stage of the trees from high-resolution aerial photography and SPOT 6 imagery. The red-attack trees were mapped from the aerial photography using eCognition software across an area of approximately one million acres. In many areas, the results were exceptional, with very few errors; however, in areas of sparse or thinned forest, significant errors of omission and commission occurred. Despite the errors, the semi-automated process may still provide sufficient benefit to reduce the time and cost of mapping the trees as compared to traditional manual delineation methods.