Prediction of stem attributes by combining airborne laser scanning and measurements from harvesters

Abstract

In this study, a new method was validated for the first time that predicts stem attributes for a forest area without any manual measurements of tree stems by combining harvester measurements and Airborne Laser Scanning (ALS) data. A new algorithm for automatic segmentation of tree crowns from ALS data based on tree crown models was developed. The test site was located in boreal forest (64°06’N, 19°10’E) dominated by Norway spruce (Picea abies) and Scots Pine (Pinus sylvestris).The trees were harvested on field plots, and each harvested tree was linked to the nearest tree crown segment derived from ALS data. In this way, a reference database was created with both stem data from the harvester and ALS derived features for linked tree crowns. To estimate stem attributes for a tree crown segment in parts of the forest where trees not yet have been harvested, tree stems are imputed from the most similar crown segment in the reference database according to features extracted from ALS data. The imputation of harvester data was validated on a sub-stand-level, i.e. 2–4 aggregated 10 m radius plots, and the obtained RMSE of stem volume, mean tree height, mean stem diameter, and stem density (stems per ha) estimates were 11%, 8%, 12%, and 19%, respectively. The imputation of stem data collected by harvesters could in the future be used for bucking simulations of not yet harvested forest stands in order to predict wood assortments.