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The ability to expand the use of predictive Airborne Laser Scanning (ALS)-derived Forest Resource Inventory (FRI) models to broader regional scales is crucial for supporting large scale sustainable forest management. This research examined the transferability of ALS-based FRI attributes between two forest estates located in the eastern and western boreal forest regions of Canada. The sites were structurally diverse due to a strong east-to-west gradient in climate conditions and disturbance regimes. We first examined the ALS–FRI attribute relationships between the sites. Second, we applied Ordinary Least Squares regressions and Random Forest, to predict four FRI attributes. Third, we tested if the inclusion of calibration data from the target location improved the performance of the transferred models. As the sites were located on opposing sides of a bioclimatic gradient, inclusion of target calibration data improved transferred model performance. However, attribute prediction accuracy varied with modeling approach, attribute, and site. The best transferability models fell within a ± 5% relative RMSE of the local predictive models but increased up to 10% in relative bias. These results have implications for forest researchers and managers on both the number, and location, of FRI plots when considering undertaking forest inventories over large disparate areas.