Swedish case study on the prediction of detailed product recovery from individual stem profiles based on airborne laser scanning

• CONTEXT : Improved and cost-efficient predictions of detailed product recovery from logging operations may increase efficiency and improve value chains based on modern cut-to-length harvesting (CTL). • AIMS : The objective of this study was to investigate and evaluate the use of individual tree data estimates from two inventory techniques: (a) established airborne laser scanner inventory (ALS case) and (b) traditional field inventory (BAU case) for predicting product recovery in a Swedish case study. • METHODS : Statistics from previous harvester production files within the region were used to generate realistic levels of simulated stem defects. Bucking simulations were performed to optimise log products according to stem profiles, stem defects, and an operational price list expressing the demand of the industry customer. All simulation results at the stand level were compared to operational harvester production data that were used to provide an accurate measure of the ‘true’ product recovery. The total harvested area was 139 ha including 16 forest stands. Seven groups of log products were included in the analysis. The predicted versus real top diameter distributions of sawlogs were evaluated using an error index to express deviations. • RESULTS : At the stand level, the average error index values were 0.15 and 0.18 for the ALS and BAU approaches, respectively. As a consequence of an overall bias of the ALS tree lists the opposite was found at the total wood flow level, with the field-based data yielding a lower error index. • CONCLUSIONS : The volume predictions for different log product groups were slightly more accurate in the ALS case than in the BAU case.