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A method to forecast forest inventory variables derived from light detection and ranging (LiDAR) would increase the usefulness of such data in future forest management. We evaluated the accuracy of forecasted inventory from imputed tree lists for LiDAR grid cells (20 × 20 m) in spruce (Picea sp.) plantations and tree growth predicted using a locally calibrated tree-list growth model. Tree lists were imputed by matching measurements from a library of sample plots with grid cells based on planted species and the smallest sum of squared difference between six inventory variables. Total and merchantable basal area, total and merchantable volume, Lorey’s height, and quadratic mean diameter increments predicted using imputed tree lists were highly correlated (0.75–0.86) with those from measured tree lists in 98 validation plots. Percent root mean squared error ranged from 12.8–49.0% but was much lower (4.9–13.5%) for plots with ≤10% LiDAR-derived error for all plot-matched variables. When compared with volumes from 15 blocks harvested 3–5 years after LiDAR acquisition, average forecasted volume differed by only 1.5%. To demonstrate the novel application of this method for operational management decisions, annual commercial thinning was planned at grid-cell resolution from 2018–2020 using forecasted inventory variables and commercial thinning eligibility rules.

Forecasting Forest Inventory Using Imputed Tree Lists for LiDAR Grid Cells and a Tree-List Growth Model