Predicting Douglas-fir knot size in the stand: a random forest model based on CT and field measurements

Abstract

AbstractBranches are not only of vital importance to tree physiology and growth but are also one of the most influential features in wood quality. To improve the availability of data throughout the forest-to-industry production, information on internal quality (e.g. knots) of both felled and standing trees in the forest would be desirable. This study presents models for predicting the internal knot diameter of Douglas-fir logs based on characteristics measured in the field. The data were composed of 87 trees (aged from 32 to 78 years), collected from six trial sites in southwest Germany, and cut into 4–5 m logs on-site. The internal knot diameter was obtained by applying a knot detection algorithm to the CT images of the logs. Applying the Random Forest (RF) technique, two models were developed: (1) MBD: to predict the branch diameter (BD) at different radial positions within the stem, and (2) MBDmax: to predict the maximum internal branch diameter (BDmax). Both models presented a good performance, predicting BD with an RMSE of 4.26 mm (R2 = 0.84) and BDmax with an RMSE of 5.65 mm (R2 = 0.78). In this context, the innovative combination of CT technology and RF modelling technique showed promising potential to be used in future investigations, as it provided a good performance while being flexible in terms of input data structure and also allowing the inclusion of otherwise underexplored databases. This study showed a possibility to predict the internal diameter of branches from field measurements, introducing an advance towards connecting forest and sawmill.