Deconstructing and Ameliorating Woody Volume Estimation Errors Arising from Leaves in Quantitative Structure Models of Trees

A major challenge for accurate characterization of tree architecture with LiDAR is distinguishing leaves from woody parts. Leaves may block scans of woody parts, or be confused with them, when quantitative structure models (QSMs) are applied. Leaf–wood separation algorithms (LWSAs) can be used to simulate ‘leaf-off’ conditions before applying QSMs but need further study. We analyzed 110 deciduous trees of 25 species, scanned in leaf-on and leaf-off conditions, with a Faro Focus3D X330, to examine the influence of leaves-on errors in tree woody volume estimation from QSMs. TreeQSM (v2.4.1) was applied, with and without applying the GBSeparation LWSA, and Real Twig (rTwig) was used to create leaf-off woody tree ‘skeletons’ for reference. The results showed greatly overestimated volume with TreeQSM, because it constructed additional branches out of leaves and the noise generated by them, adding large amounts of non-existent branch volume. GBSeparation allowed for accurate total woody volume estimation, but via compensating errors; overallocation of volume to stems and lower-order branches and omission of smaller, higher-order ones. We tested a novel field-calibrated allometric model for predicting leaf-off tree volume from leaf-on tree volume, which showed promise as an accurate, time-efficient alternative pathway for processing leaf-on TLS data.