Bark stripping of pine trees by baboons (Papio ursinus) is challenging the sustainability of the forestry industry in the Mpumalanga province of South Africa. To date, the drivers of this behavior are not clear and likely to be related to a combination of environmental factors and troop-specific acquired behavior. Understanding the extent of the damage and monitoring changes over time are key components of an effective management strategy. The use of satellite imagery for mapping and monitoring forest health is a cost-effective alternative to field surveys and an integral component of forest monitoring systems worldwide. This study aims to develop a spatially based surveillance system using the Landsat 8 sensor to remotely detect and map damage in Mpumalanga pine plantations. An ecological risk model was also developed by relating environmental predictors to the presence and absence of baboon damage and then combined with the remote sensing map for accurate detection. Damage maps produced using Landsat 8 imagery for 2014 and 2015 displayed an overall accuracy of 75% and 83% respectively, with vegetation indices providing a more important contribution to the mapping exercise than individual spectral bands. The ecological risk model was also successful in predicting damage occurrence (AUC = 0.96). Variable predictors that contributed the most to the risk model classification accuracy, were mainly related to pine stand characteristics, with age of trees being the most important predictor, followed by species, and Site Index (SI). The routine mapping system and risk model developed in this study attempt to address the lack of scientific-based information necessary for the development of a successful integrated management strategy, by quantifying the extent of baboon damage and its changes over time, enabling the evaluation of the effectiveness of management intervention and highlighting areas where intervention is needed.