The national forest inventory conducted by the United States Forest Service Forest Inventory and Analysis (FIA) program provides information for strategic level decisions regarding national and regional management of forest ecosystem goods and services. However, the sampling intensity typically limits the application of traditional direct estimators to areas the size of a large county, if not larger. This dissertation describes methods for combining FIA data with auxiliary information to enhance its relevance for local forest management. Background information is provided on the way population estimates are currently produced, and how precision can be improved via satellite imagery. A study is described that uses features extracted from dense time series of Landsat imagery with a model-assisted direct estimator. The study examined the relative predictive power of land cover models incorporating extracted spectro-temporal features versus composite imagery alone. Non-parametric models were fitted for multiple attributes measured on FIA plots using all archived Landsat scenes for Minnesota from 2009-2013. The estimated coefficients developed by harmonic regression of the time series imagery were shown to be moderately to highly correlated with tree-level and land cover attributes. When comparing results for spectro-temporal features to monthly image composites, regression models had greater explained variance and classification models had greater overall and individual class accuracies. Finally, a study is presented that tested the performance of a proposed variant of the k-nearest neighbors algorithm for areas too small to use a direct estimator. Spectro-temporal features were extracted for one ecological unit in Minnesota. A simulated population of tree canopy cover was sampled at FIA plot locations. The proposed algorithm was used to fit a non-parametric model to predict tree canopy cover that incorporates the spectro-temporal features. The model was used to construct predictive intervals for spatial domains over a range of domain sizes, and the resultant tests showed the coverage probability approached the theoretical value for areas as small as 1200 hectares. The study suggests that, given good auxiliary data and models, the scale of valid inference using FIA data can approach what is needed for local decision makers.