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Background:When auxiliary information in the form of airbe laser scanning (ALS) is used to assist in estimating the population parameters of interest, the benefits of prior information from previous inventories are not self-evident. In a simulation study, we compared three different approaches:1) using only current data, 2) using non-updated old data and current data in a composite estimator and 3) using updated old data and current data with a Kalman filter. We also tested three different estimators, namely i) Horwitz-Thompson for a case of no auxiliary information, ii) model-assisted estimation and iii) model-based estimation. We compared these methods in terms of bias, precision and accuracy, as estimators utilizing prior information are not guaranteed to be unbiased. Results:The largest standard errors were obtained when neither prior information nor auxiliary information were used. If a growth model was not applied to update the old data, the resulting composite estimators were biased. Largest RMSEs were obtained using non-updated prior information in a composite estimator. Using the ALS data as auxiliary information produced smaller RMSE than using prior information from the old inventory. The smallest RMSEs were obtained when both the auxiliary data and updated old data were used. With growth updating the bias can be substantially reduced, although design-unbiasedness of the estimator cannot be guaranteed. Conclusions:Prior information from old inventory data can be useful also when combined with highly accurate auxiliary information, when both data sources are efficiently used. The benefits obtained from using the old data will increase if the past harvests can be detected without errors from changes in the auxiliary data instead of being predicted with models.