Current issue: 57(2)
Under compilation: 57(3)
Terrestrial laser scanning (TLS) has been applied to estimate forest wood volume based on detailed 3D tree reconstructions from point cloud data. However, sources of uncertainties in the point cloud data (alignment and scattering errors, occlusion, foliage...) and the reconstruction algorithm type and parameterisation are known to affect the reconstruction, especially around finer branches. To better understand the impacts of these uncertainties on the accuracy of TLS-derived woody volume, high-quality TLS scans were collected in leaf-off conditions prior to destructive harvesting of two forest-grown common ash trees (Fraxinus excelsior L.; diameter at breast height ~28 cm, woody volume of 732 and 868 L). We manually measured branch diameters at 265 locations in these trees. Estimates of branch diameters and tree volume from Quantitative Structure Models (QSM) were compared with these manual measurements. The accuracy of QSM branch diameter estimates decreased with smaller branch diameters. Tree woody volume was overestimated (+336 L and +392 L) in both trees. Branches measuring < 5 cm in diameter accounted for 80% and 83% of this overestimation respectively. Filtering for scattering errors or improved coregistration approximately halved the overestimation. Range filtering and modified scanning layouts had mixed effects. The small branch overestimations originated primarily in limitations in scanner characteristics and coregistration errors rather than suboptimal QSM parameterisation. For TLS-derived estimates of tree volume, a higher quality point cloud allows smaller branches to be accurately reconstructed. Additional experiments need to elucidate if these results can be generalised beyond the setup of this study.
We introduce a general procedure to match a stochastic functional-structural tree model (here LIGNUM augmented with stochastic rules) with real tree structures depicted by quantitative structure models (QSMs) based on terrestrial laser scanning. The matching is done by iteratively finding the maximum correspondence between the measured tree structure and the stochastic choices of the algorithm. First, we analyze the match to synthetic data (generated by the model itself), where the target values of the parameters to be estimated are known in advance, and show that the algorithm converges properly. We then carry out the procedure on real data obtaining a realistic model. We thus conclude that the proposed stochastic structure model (SSM) approach is a viable solution for formulating realistic plant models based on data and accounting for the stochastic influences.