  Cut-to-length harvesters collect useful information for modeling
relationships between forest attributes and airborne laser scanning (ALS) data.
However, harvesters operate in mature forests, which may introduce selection
biases that can result in systematic errors in harvester data-based forest
attribute maps. We fitted regression models (harvester models) for volume (V),
height (HL), stem frequency (N), above-ground biomass, basal area, and
quadratic mean diameter (QMD) using harvester and ALS data. Performances of the
harvester models were evaluated using national forest inventory plots in an 8.7
Mha study area. We estimated biases of large-area synthetic estimators and
compared efficiencies of model-assisted (MA) estimators with field data-based
direct estimators. The harvester models performed better in productive than
unproductive forests, but systematic errors occurred in both. The use of MA
estimators resulted in efficiency gains that were largest for HL (relative
efficiency, RE=6.0) and smallest for QMD (RE=1.5). The bias of the synthetic
estimator was largest for N (39%) and smallest for V (1%). The latter was due
to an overestimation of deciduous and an underestimation of spruce forests that
by chance balanced. We conclude that a probability sample of reference
observations may be required to ensure the unbiasedness of estimators utilizing
harvester data.
