Abstract
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.
Highlights
Cut-to-length (CTL) is the dominating harvesting method in Scandinavian forestry
This study examines the operational application of estimating forest attributes and stem diameter distribution at stand level in Sweden, using tree data measured and collected from harvesters and the national airborne laser scanning (ALS) data, based on an area-based estimation method
In selection of variables for the canonical correlation model in k-Most Similar Neighbour (MSN), the stepwise regression model of V, H and D based on spruce, pine, p95, cc, d04, lnd01 and lnd04, showed to be the most significant and were selected for constructing the canonical correlation models used in k-MSN
Summary
Using CTL harvesters, tree stems are measured and cut into separate timber assortments at the time of felling, and optimisation of bucking and the subsequent flow of timber to industries is crucial in maximising revenues. Using airborne laser scanning (ALS) data, a planned harvesting area is matched to previous harvests of similar forests, and existing harvester data are used to create a very detailed representation of the forest planned for cutting. This enables a completely new level of harvest optimisation using simulations of bucking strategies and harvesting systems (Möller et al 2015)
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