Résumé
1. Calibration of local, regional or global allometric equations to
estimate biomass at the tree level constitutes a significant burden on
projects aiming at reducing Carbon emissions from forest degradation and
deforestation. The objective of this contribution is to assess the
precision and accuracy of Terrestrial Laser Scanning (TLS) for estimating
volumes and above-ground biomass (AGB) of the woody parts of tropical
trees, and for the calibration of allometric models. 2. We used a
destructive dataset of 61 trees, with diameters and AGB of up to 186.6 cm
and 60 Mg respectively, which were scanned, felled and weighed in the
semi-deciduous forests of eastern Cameroon. We present an operational
approach based on available software allowing the retrieving of TLS volume
with low bias and high accuracy for large tropical trees. Edition of the
obtained models proved necessary, mainly to account for the complexity of
buttressed parts of tree trunks, which were separately modelled through a
meshing approach, and to bring a few corrections in the topology and
geometry of branches, thanks to the amapstudio-scan software. 3. Over the
entire dataset, TLS-derived volumes proved highly reliable for branches
larger than 5 cm in diameter. The volumes of the remaining woody parts
estimated for stumps, stems and crowns as well as for the whole tree
proved very accurate (RMSE below 2.81% and R² above of .98) and unbiased.
Once converted into AGB using mean local-specific wood density values, TLS
estimates allowed calibrating a biomass allometric model with coefficients
statistically undistinguishable from those of a model based on destructive
data. The Unedited Quantitative Structure Model (QSM) however leads to
systematic overestimations of woody volumes and subsequently to
significantly different allometric parameters. 4. We can therefore
conclude that a non-destructive TLS approach can now be used as an
operational alternative to traditional destructive sampling to build the
allometric equations, although attention must be paid to the quality of
QSM model adjustments to avoid systematic bias.