The increasing availability of highly detailed three-dimensional remotely-sensed data depicting forests, including airborne laser scanning (ALS) and digital aerial photogrammetric (DAP) approaches, provides a means for improving stand dynamics information. The availability of data from ALS and DAP has stimulated attempts to link these datasets with conventional forestry growth and yield models. In this study, we demonstrated an approach whereby two three-dimensional point cloud datasets (one from ALS and one from DAP), acquired over the same forest stands, at two points in time (circa 2008 and 2015), were used to derive forest inventory information. The area-based approach (ABA) was used to predict top height (H), basal area (BA), total volume (V), and stem density (N) for Time 1 and Time 2 (T1, T2). We assigned individual yield curves to 20 × 20 m grid cells for two scenarios. The first scenario used T1 estimates only (approach 1, single date), while the second scenario combined T1 and T2 estimates (approach 2, multi-date). Yield curves were matched by comparing the predicted cell-level attributes with a yield curve template database generated using an existing growth simulator. Results indicated that the yield curves using the multi-date data of approach 2 were matched with slightly higher accuracy; however, projections derived using approach 1 and 2 were not significantly different. The accuracy of curve matching was dependent on the ABA prediction error. The relative root mean squared error of curve matching in approach 2 for H, BA, V, and N, was 18.4, 11.5, 25.6, and 27.53% for observed (plot) data, and 13.2, 44.6, 50.4 and 112.3% for predicted data, respectively. The approach presented in this study provides additional detail on sub-stand level growth projections that enhances the information available to inform long-term, sustainable forest planning and management. less detailed information than single-tree or size-class models [2]. The choice of growth simulator depends on its availability for the area of interest-e.g., in Canada growth simulators have been developed for each province separately, with whole-stand models being popular in British Columbia and Alberta. In the majority of cases, the core set of input attributes for stand-level growth simulators includes species composition, stand age, and top height or alternatively site index. Additional inputs can improve model accuracy and can include information on stand density, basal area, stocking, canopy cover, information on insect damage, and silvicultural practices such as thinning or fertilization [3,4]. Simulator output typically consists of a list of predicted stand attributes for a specified age sequence (e.g., 10, 20, 30 years in the future), stratified by species. Stand attributes include top height, basal area, merchantable and total volume, and stand density.Airborne laser scanning (ALS) has demonstrated capacity for characterizing a number of important forest stand attributes [5,6]. Three-dimensional point clouds acquired with ALS preci...
