Operational prediction of forest attributes using standardised harvester data and airborne laser scanning data in Sweden

Abstract

With cut-to-length harvesters, tree stems are measured and cut into different timber assortments at the time of felling. These measurement data collected from harvested trees can be used for decision-support at different levels of the forest industry chain and also for forest planning when combined with remote sensing data. The aim of this study was to examine the operational application for predicting merchantable stem volume, basal area, basal area-weighted mean tree height, basal area-weighted mean stem diameter and diameter distribution at stand level with airborne laser scanning data and harvester data from final felling operations. The area-based approach using k-MSN estimation was evaluated for six different variants of spatial partitioning. The results were stand level predictions with relative root mean square errors of 11–14%, 10–15%, 3–4% and 6–7% for merchantable stem volume, basal area, basal area-weighted mean tree height and basal area-weighted mean stem diameter, respectively. Predictions of stem diameter distributions resulted in error indices of 0.13–0.14. The results demonstrate that harvester data from cut forests may serve as ground truth to airborne laser scanning data and provide accurate forest estimates at stand level. The predicted diameter distributions could be useful for improving yield estimates and bucking simulations.