Current issue: 56(2)
Under compilation: 56(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.
Silver birch (Betula pendula Roth.) is one of the main pioneer tree species occupying large areas of abandoned agricultural lands under natural succession in Estonia. We estimated aboveground biomass (AGB) dynamics during 17 growing seasons, and analysed soil nitrogen (N) and carbon (C) dynamics for 10 year period in a silver birch stand growing on former arable land. Main N fluxes were estimated and nitrogen budget for 10-year-old stand was compiled. The leafless AGB and stem mass of the stand at the age of 17-years were 94 and 76 Mg ha–1 respectively. The current annual increment (CAI) of stemwood fluctuated, peaking at 10 Mg ha–1 yr–1 at the age of 15 years; the mean annual increment (MAI) fluctuated at around 4–5 Mg ha–1. The annual leaf mass of the stand stabilised at around 3 Mg ha–1 yr–1. The stand density decreased from 11600 to 2700 trees ha–1 in the 8- and 17-year-old stand, respectively. The largest fluxes in N budget were net nitrogen mineralization and gaseous N2-N emission. The estimated fluxes of N2O and N2 were 0.12 and 83 kg ha–1 yr–1, respectively; N leaching was negligible. Nitrogen retranslocation from senescing leaves was approximately 45 kg ha–1, N was mainly retranslocated into stembark. The N content in the upper 0–10 cm soil layer increased significantly (145 kg ha–1) from 2004 to 2014; soil C content remained stable. Both the woody biomass dynamics and the N cycling of the stand witness the potential for bioenergetics of such ecosystems.
Species-specific allometric equations for shrubs and small trees are relatively scarce, thus limiting the precise quantification of aboveground biomass (AGB) in both shrubby vegetation and forests. Fourteen shrub and small tree species in Eastern China were selected to develop species-specific and multispecies allometric biomass equations. Biometric variables, including the diameter of the longest stem (D), height (H), wet basic density (BD), and crown area and shape were measured for each individual plant. We measured the AGB through a non-destructive method, and validated these measurements using the dry mass of the sampled plant components. The AGB was related to biometric variables using regression analysis. The species-specific allometric models, with D and H as predictors (D-H models) accounted for 70% to 99% of the variation in the AGB of shrubs and small trees. A multispecies allometric D-H model accounted for 71% of the variation in the AGB. Although BD, as an additional predictor, improved the fit of most models, the D-H models were adequate for predicting the AGB for shrubs and small trees in subtropical China without BD data.