@article{fdi:010073610,
  title = {{U}sing volume-weighted average wood specific gravity of trees reduces bias in aboveground biomass predictions from forest volume data},
  author = {{S}agang, {L}. {B}. {T}. and {M}omo, {S}. {T}. and {L}ibalah, {M}. {B}. and {R}ossi, {V}. and {F}onton, {N}. and {M}ofack, {G}. {I}. and {K}amdem, {N}. {G}. and {N}guetsop, {V}. {F}. and {S}onke, {B}. and {P}ierre, {P}. and {B}arbier, {N}icolas},
  editor = {},
  language = {{ENG}},
  abstract = {{W}ith the improvement of remote sensing techniques for forest inventory application such as terrestrial {L}i{DAR}, tree volume can now be measured directly, without resorting to allometric equations. {H}owever, wood specific gravity ({WSG}) remains a crucial factor for converting these precise volume measurements into unbiased biomass estimates. {I}n addition to this {WSG} values obtained from samples collected at the base of the tree ({WSG}({B}ase)) or from global repositories such as {D}ryad ({WSG}({D}ryad)) can be substantially biased relative to the overall tree value. {O}ur aim was to assess and mitigate error propagation at tree and stand level using a pragmatic approach that could be generalized to {N}ational {F}orest {I}nventories or other carbon assessment efforts based on measured volumetric data. {I}n the semi-deciduous forests of {E}astern {C}ameroon, we destructively sampled 130 trees belonging to 15 species mostly represented by large trees (up to 45 {M}g). {W}e 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. {A}s {WWSG} cannot be assessed non-destructively, linear models were adjusted to predict field {WWSG} and revealed that a combination of {WSG}({D}ryad), diameter at breast height ({DBH}) and species stem morphology ({S}-m) were significant predictors explaining together 72% of {WWSG} variation. {A}t tree level, estimating tree aboveground biomass using {WSG}({B}ase) and {WSG}({D}ryad) yielded overestimations of 10% and 7% respectively whereas predicted {WWSG} only produced an underestimation of less than 1%. {A}t stand-level, {WSG}({B}ase) and {WSG}({D}ryad) 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). {W}e also observed that the stand-level bias obtained with {WSG}({B}ase) and {WSG}({D}ryad) decreased with total plot size and plot area. {T}he systematic bias induced by {WSG}({B}ase) and {WSG}({D}ryad) 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.},
  keywords = {{W}ood specific gravity ; {T}errestrial {L}i{DAR} ; {A}boveground biomass ; {L}inear model ; {E}rror propagation ; {C}ameroon eastern forest ; {R}emote sensing ; {CAMEROUN}},
  booktitle = {},
  journal = {{F}orest {E}cology and {M}anagement},
  volume = {424},
  numero = {},
  pages = {519--528},
  ISSN = {0378-1127},
  year = {2018},
  DOI = {10.1016/j.foreco.2018.04.054},
  URL = {https://www.documentation.ird.fr/hor/fdi:010073610},
}