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Diameter at breast height (DBH) distributions offer valuable information foroperational and strategic forest management decisions. We predicted DBHdistributions using Norwegian national forest inventory and airborne laserscanning data and compared the predictive performances of linear mixed-effects(PPM), generalized linear-mixed (GLM) and k nearest neighbor (NN) models. WhileGLM resulted in smaller prediction errors than PPM, both were clearlyoutperformed by NN. We therefore studied the ability of the NN model to improvethe precision of stem frequency estimates by DBH classes in the 8.7 Mha studyarea using a model-assisted (MA) estimator suitable for systematic sampling. MAestimates yielded greater than or approximately equal efficiencies as directestimates using field data only. The relative efficiencies (REs) associatedwith the MA estimates ranged between 0.95-1.47 and 0.96-1.67 for 2 and 6 cm DBHclass widths, respectively, when dominant tree species were assumed to beknown. The use of a predicted tree species map, instead of the observedinformation, decreased the REs by up to 10%.

Prediction and model-assisted estimation of diameter distributions using Norwegian national forest inventory and airborne laser scanning data