  Diameter at breast height (DBH) distributions offer valuable information for
operational and strategic forest management decisions. We predicted DBH
distributions using Norwegian national forest inventory and airborne laser
scanning data and compared the predictive performances of linear mixed-effects
(PPM), generalized linear-mixed (GLM) and k nearest neighbor (NN) models. While
GLM resulted in smaller prediction errors than PPM, both were clearly
outperformed by NN. We therefore studied the ability of the NN model to improve
the precision of stem frequency estimates by DBH classes in the 8.7 Mha study
area using a model-assisted (MA) estimator suitable for systematic sampling. MA
estimates yielded greater than or approximately equal efficiencies as direct
estimates using field data only. The relative efficiencies (REs) associated
with the MA estimates ranged between 0.95-1.47 and 0.96-1.67 for 2 and 6 cm DBH
class widths, respectively, when dominant tree species were assumed to be
known. The use of a predicted tree species map, instead of the observed
information, decreased the REs by up to 10%.
