Over the past two decades, severe mountain pine beetle (MPB) outbreaks have affected several million hectares of forest in western North America. The extensive ecological and economic damage caused by widespread insect infestations make understanding the development and spread of MPB outbreaks critical. This study uses a time series of Landsat5 TM and Landsat7 ETM + images to map the spread of mortality due to MPB infestation in Arapaho–Roosevelt National Forest, Colorado, between 2003 and 2010. The Normalized Difference Vegetation Index (NDVI) and change in the Normalized Difference Moisture Index (NDMI) were used to classify red attack and non-red attack stands based on a maximum likelihood algorithm with manually selected training classes. The classification was validated by comparison with independent interpretations of aerial photography and high-resolution satellite imagery. The classification had good agreement (84.5–90.5% total accuracy). Cluster analysis for time series showed infestations originating in several different locations on the landscape early in the time series and subsequent infestations likely represent a combination of dispersal from outbreak populations and independent population growth. Analysis using conditional inference trees suggested that a combination of forest composition, topography, and dispersal predicted the distribution of MPB infestation on the landscape and that the importance of these variables changed as the outbreak developed. In early years, red attack was associated with forest and topographic characteristics known to influence susceptibility to MPB. Over time, beetle pressure became an increasingly important predictor of red attack, but in later years host tree availability played an important role in outbreak spread. If this pattern occurs consistently in MPB outbreaks, knowledge of these patterns could aid managers in targeting their efforts to reduce damage resulting from MPB outbreaks.