Airborne laser scanning (ALS) is widely used to predict the total volume of trees in a forest stand. However, in operational forestry, it is usually not sufficient to consider the total volume only, because the various tree species and timber assortments are priced differently. As tree quality strongly affects how harvested logs are assigned to different timber assortments, tree quality information prior to harvesting, for example, would improve the planning of harvesting operations. The main aim of this thesis was to test different methods to predict tree quality, especially sawlog volume, by means of ALS.
The three sub-studies of this thesis were implemented using datasets from eastern Finland (3 datasets) and south-eastern Norway (1 dataset). All the study forests were dominated by Scots pine (Pinus sylvestris L.) or Norway spruce (Picea abies (L.) Karst.). The first study focused on the effects of transferring tree-level models between inventory areas. In the second study, various methods to predict plot-level (30 m × 30 m) sawlog volume were tested. The third study focused on the field-calibrations of stand-level merchantable and sawlog volumes by using basal area measurements. All the ALS-based predictions were made with either linear mixed-effects models or k-nearest neighbor imputations at the tree or plot-levels (15 m × 15 m).
The results showed that there is only weak correlation between the ALS metrics and tree quality. Nevertheless, sawlog volume predictions with relative root mean squared error values between 20–30 % were obtained after aggregations to the 30 m × 30 m and stand-levels. Moreover, the study-specific results showed that a notable decrease in accuracy can be expected when tree-level models are transferred between inventory areas, and that basal area information is not generally useful to increase the accuracy of sawlog volume predictions in Norway spruce dominated stands.