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With the improvement of remote sensing techniques for forest inventory application such as terrestrial LiDAR, tree volume can now be measured directly, without resorting to allometric equations. However, wood specific gravity ( WSG ) remains a crucial factor for converting these precise volume measurements into unbiased biomass estimates. In addition to this WSG values obtained from samples collected at the base of the tree ( WSG Base ) or from global repositories such as Dryad ( WSG Dryad ) can be substantially biased relative to the overall tree value. Our aim was to assess and mitigate error propagation at tree and stand level using a pragmatic approach that could be generalized to National Forest Inventories or other carbon assessment efforts based on measured volumetric data. In the semi-deciduous forests of Eastern Cameroon, we destructively sampled 130 trees belonging to 15 species mostly represented by large trees (up to 45 Mg). We also used stand-level dendrometric parameters from 21 1-ha plots inventoried in the same area to propagate the tree-level bias at the plot level. A new descriptor, volume average-weighted WSG ( WWSG ) of the tree was computed by weighting the WSG of tree compartments by their relative volume prior to summing at tree level. As WWSG cannot be assessed non-destructively, linear models were adjusted to predict field WWSG and revealed that a combination of WSG Dryad , diameter at breast height ( DBH ) and species stem morphology ( S m ) were significant predictors explaining together 72% of WWSG variation. At tree level, estimating tree aboveground biomass using WSG Base and WSG Dryad yielded overestimations of 10% and 7% respectively whereas predicted WWSG only produced an underestimation of less than 1%. At stand-level, WSG Base and WSG Dryad gave an average simulated bias of 9% (S.D. = ±7) and 3% (S.D. = ±7) respectively whereas predicted WWSG reduced the bias by up to 0.1% (S.D. = ±8). We also observed that the stand-level bias obtained with WSG Base and WSG Dryad decreased with total plot size and plot area. The systematic bias induced by WSG Base and WSG Dryad for biomass estimations using measured volumes are clearly not negligible but yet generally overlooked. A simple corrective approach such as the one proposed with our predictive WWSG model is liable to improve the precision of remote sensing-based approaches for broader scale biomass estimations. [ABSTRACT FROM AUTHOR]

Precise accounting of carbon stocks and fluxes in tropical vegetation using remote sensing approaches remains a challenging exercise, as both signal saturation and ground sampling limitations contribute to inaccurate extrapolations. Airborne LiDAR Scanning (ALS) data can be used as an intermediate level to radically increase sampling and enhance model calibration. Here we tested the potential of using ALS data for upscaling vegetation aboveground biomass (AGB) from field plots to a forest-savanna transitional landscape in the Guineo–Congolian region in Cameroon, using either a design-based approach or a model-based approach leveraging multispectral satellite imagery. Two sets of reference data were used: (1) AGB values collected from 62 0.16-ha plots distributed both in forests and savannas; and (2) an AGB map generated form ALS data. In the model-based approach, we trained Random Forest models using predictors from recent sensors of varying spectral and spatial resolutions (Spot 6/7, Landsat 8, and Sentinel 2), along with biophysical predictors derived after pre-processing into the Overland processing chain, following a forward variable selection procedure with a spatial 4-folds cross validation. The models calibrated with field plots lead to a systematic overestimation in AGB density estimates and a root mean squared prediction error (RMSPE) of up to 65 Mg.ha−1 (90%), whereas calibration with ALS lead to low bias and a drop of ~30% in RMSPE (down to 43 Mg.ha−1, 58%) with little effect of the satellite sensor used. Decomposing bias along the AGB density range, we show that multispectral images can (in some specific cases) be used for unbiased prediction at landscape scale on the basis of ALS-calibrated statistical models. However, our results also confirm that, whatever the spectral indices used and attention paid to sensor quality and pre-processing, the signal is not sufficient to warrant accurate pixelwise predictions, because of large relative RMSPE, especially above (200–250 t/ha). The design-based approach, for which average AGB density values were attributed to mapped land cover classes, proved to be a simple and reliable alternative (for landscape to region level estimations), when trained with dense ALS samples. [ABSTRACT FROM AUTHOR]