Airborne laser scanning may provide a means for assessing local forest biomass resources. In this study, national
forest inventory (NFI) data was used as reference data for modeling forest basal area, volume, aboveground
biomass, and total biomass from laser scanning data obtained in a countrywide scanning survey. Data
covered a wide range of forest ecotypes, stand treatments, tree species, and tree species mixtures. The four
forest characteristics were modeled using nonlinear regression and generalized method-of-moments estimation
to avoid biased and inefficient estimates. The coefficient of determination was 68% for the basal area
model and 77–78% for the volume and biomass models. Despite the wide range of forest types model accuracy
was comparable to similar studies. Model predictions were unbiased across the range of predicted values
and crown cover percentages but positively biased for deciduous forest and negatively biased for coniferous
forest. Species type specific (coniferous, deciduous, or mixed forest) models reduced root mean squared error
by 3–12% and removed the bias. In application, model predictions will be improved by stratification into deciduous
and coniferous forest using e.g. infrared orthophotos or satellite images.