@article{fdi:010086303,
  title = {{N}on-destructive estimation of individual tree biomass : allometric models, terrestrial and {UAV} laser scanning},
  author = {{B}rede, {B}. and {T}erryn, {L}. and {B}arbier, {N}icolas and {B}artholomeus, {H}. {M}. and {B}artolo, {R}. and {C}alders, {K}. and {D}erroire, {G}. and {M}oorthy, {S}. {M}. {K}. and {L}au, {A}. and {L}evick, {S}. {R}. and {R}aumonen, {P}. and {V}erbeeck, {H}. and {W}ang, {D}. and {W}hiteside, {T}. and van der {Z}ee, {J}. and {H}erold, {M}.},
  editor = {},
  language = {{ENG}},
  abstract = {{C}alibration and validation of aboveground biomass ({AGB}) ({AGB}) products retrieved from satellite-borne sensors require accurate {AGB} estimates across hectare scales (1 to 100 ha). {R}ecent studies recommend making use of non-destructive terrestrial laser scanning ({TLS}) based techniques for individual tree {AGB} estimation that provide unbiased {AGB} predictors. {H}owever, applying these techniques across large sites and landscapes remains logis-tically challenging. {U}noccupied aerial vehicle laser scanning ({UAV}-{LS}) has the potential to address this through the collection of high density point clouds across many hectares, but estimation of individual tree {AGB} based on these data has been challenging so far, especially in dense tropical canopies. {I}n this study, we investigated how {TLS} and {UAV}-{LS} can be used for this purpose by testing different modelling strategies with data availability and modelling framework requirements. {T}he study included data from four forested sites across three biomes: temperate, wet tropical, and tropical savanna. {A}t each site, coincident {TLS} and {UAV}-{LS} campaigns were con-ducted. {D}iameter at breast height ({DBH}) and tree height were estimated from {TLS} point clouds. {I}ndividual tree {AGB} was estimated for & {GE};170 trees per site based on {TLS} tree point clouds and quantitative structure modelling ({QSM}), and treated as the best available, non-destructive estimate of {AGB} in the absence of direct, destructive measurements. {I}ndividual trees were automatically segmented from the {UAV}-{LS} point clouds using a shortest-path algorithm on the full 3{D} point cloud. {P}redictions were evaluated in terms of individual tree root mean square error ({RMSE}) and population bias, the latter being the absolute difference between total tree sample population {TLS} {QSM} estimated {AGB} and predicted {AGB}. {T}he application of global allometric scaling models ({ASM}) at local scale and across data modalities, i.e., field-inventory and light detection and ranging {L}i{DAR} metrics, resulted in individual tree prediction errors in the range of reported studies, but relatively high popu-lation bias. {T}he use of adjustment factors should be considered to translate between data modalities. {W}hen calibrating local models, {DBH} was confirmed as a strong predictor of {AGB}, and useful when scaling {AGB} esti-mates with field inventories. {T}he combination of {UAV}-{LS} derived tree metrics with non-parametric modelling generally produced high individual tree {RMSE}, but very low population bias of & {LE};5% across sites starting from 55 training samples. {UAV}-{LS} has the potential to scale {AGB} estimates across hectares with reduced fieldwork time. {O}verall, this study contributes to the exploitation of {TLS} and {UAV}-{LS} for hectare scale, non-destructive {AGB} estimation relevant for the calibration and validation of space-borne missions targeting {AGB} estimation.},
  keywords = {{T}errestrial laser scanning ({TLS}) ; {U}noccupied aerial vehicle laser ; scanning ({UAVLS}) ; {Q}uantitative structure modelling ({QSM}) ; {F}orest ; {A}boveground biomass ({AGB}) ; {A}llometric scaling model ({ASM})},
  booktitle = {},
  journal = {{R}emote {S}ensing of {E}nvironment},
  volume = {280},
  numero = {},
  pages = {113180 [20 p.]},
  ISSN = {0034-4257},
  year = {2022},
  DOI = {10.1016/j.rse.2022.113180},
  URL = {https://www.documentation.ird.fr/hor/fdi:010086303},
}