LAUSR

Many volume functions use diameter and height as input variables. Measurement and prediction errors in heights propagate into the error of the wood volume estimate for a tree or a plot. We quantified these errors (and bias) with census data of species, heights, and stem diameters in a 1.44 ha hardwood stand in Lower Saxony (GER) dominated by beech ( Fagus sylvatica L.) and with an understorey of hardwood species. We simulated simple random sampling with fixed area and variable radius plots and distorted the actual height measurements with three magnitudes of random Gaussian errors. The inventory protocol for height measurement results in a selection of three to four trees per plot, while heights for the remaining trees were predicted via two alternative models. Tree and plot level RMSE% of wood volume with a mix of measured and predicted tree heights were—across the three magnitudes of measurement errors, 18–81% greater than root-mean-squared errors obtained when all trees were measured for heights with errors. The results from this study contribute the statistical side of recommendations on the number of height measurements per forest inventory plot, where inferences on optimal numbers of height measurements will also depend on the costs.