Clear wood content in standing trees predicted from branch scar measurements with terrestrial LiDAR and verified with X-ray computed tomography

Abstract

Knowledge about the wood quality of standing trees is crucial in that it serves as an excellent means for nearly all stages of the wood-supply chain. Better information about internal wood characteristics can be derived from the outside appearance by establishing a correlation between the bark characteristics of a stem and its internal quality. This paper presents an approach where the quality determination of standing trees using a terrestrial light detection and ranging (LiDAR) system is combined with the information about internal quality of logs using X-ray computed tomography (CT). Results show a high accuracy for branch scar measurements with terrestrial LiDAR and knot measurement with CT. A strong correlation between scar seal quotient and the amount of clear wood could be confirmed using European beech (Fagus sylvatica L.) as an example. Quality grading of virtually segmented logs using terrestrial LiDAR and CT showed moderate correlation; 62.5% of the segments were allocated to the same grade by both approaches. In conclusion, terrestrial LiDAR in forest inventory could be used as an instrument to predict inner wood quality in greater detail by gathering data on the outer appearance and branch scars of standing trees. This additional knowledge has the potential to improve forest planning, bucking instructions, and a roundwood allocation that meets industry demand.