Informed forest management requires accurate, up-to-date information. Ground-based forest inventory is commonly conducted to generate estimates of forest characteristics with a predetermined level of statistical confidence. As the importance of monitoring forest resources has increased, budgetary and logistical constraints often limit the resources needed for precise estimates. In this research, the incorporation of ancillary information in planted loblolly pine (Pinus taeda L.) forest inventory was investigated. Additionally, a simulation study using synthetic populations provided the basis for investigating the effects of plot and stand-level inventory aggregations on predictions and projections of future forest conditions. Forest regeneration surveys are important for assessing conditions immediately after plantation establishment. An unmanned aircraft system was evaluated for its ability to capture imagery that could be used to automate seedling counting using two computer vision approaches. The imagery was found to be unreliable for consistent detection in the conditions evaluated. Following establishment, conditions are assessed throughout the lifespan of forest plantations. Using small area estimation (SAE) methods, the incorporation of light detection and ranging (lidar) and thinning status improved the precision of inventory estimates compared with ground data alone. Further investigation found that reduced density lidar point clouds and lower resolution elevation models could be used to generate estimates with similar increases in precision. Individual tree detection estimates of stand density were found to provide minimal improvements in estimation precision when incorporated into the SAE models. Plot and stand level inventory aggregations were found to provide similar estimates of future conditions in simulated stands without high levels of spatial heterogeneity. Significant differences were noted when spatial heterogeneity was high. Model form was found to have a more significant effect on the observed differences than plot size or thinning status. The results of this research are of interest to forest managers who regularly conduct forest inventories and generate estimates of future stand conditions. The incorporation of auxiliary data in mid-rotation stands using SAE techniques improved estimate precision in most cases. Further, guidance on strategies for using this information for predicting future conditions is provided.