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A single a priori chosen linear regression model with two alternative error structures is proposed for model-assisted (MA) and model-dependent (MD) estimation of state and change in aboveground tree biomass (AGB, Mg·ha−1) in three forest strata in the Våler forest in southeastern Norway. Field data of tree height and stem diameter were collected in 145 permanent 200 m2 circular plots. Concurrent LiDAR data were collected for the entire forest. The regression model includes two LiDAR-based explanatory variables: the mean of canopy height raised to a power of 1.5 and the standard deviation of canopy heights. A nearest-neighbour thinning of the 2010 LiDAR data to the density of the 1999 data was implemented to counter density effects in the explanatory variables. Estimates of change based on a single regression model were more accurate than estimating change from year-specific models (and no data thinning). A canopy height dependent correlated error structure was preferred over a partitioning of the error to temporary and 'permanent' plot effects. For point estimates of AGB in 1999 and 2010, MA and MD estimates of errors were numerically comparable, but MD errors of change were much smaller than corresponding MA errors. [ABSTRACT FROM AUTHOR]