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

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