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

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 levels forest industry chain also planning when combined with remote sensing data. The aim this study was to examine operational application predicting merchantable stem volume, basal area, area-weighted mean height, diameter distribution stand level airborne laser scanning harvester final felling operations. area-based approach using k-MSN estimation evaluated six variants spatial partitioning. results were predictions relative root square errors 11-14%, 10-15%, 3-4% 6-7% height diameter, respectively. Predictions distributions resulted in error indices 0.13-0.14. demonstrate that forests may serve as ground truth provide accurate estimates level. predicted could useful improving yield bucking simulations.