@article{fdi:010080409,
  title = {{A} simulation method to infer tree allometry and forest structure from airborne laser scanning and forest inventories},
  author = {{F}ischer, {F}. {J}. and {L}abriere, {N}. and {V}incent, {G}r{\'e}goire and {H}erault, {B}. and {A}lonso, {A}. and {M}emiaghe, {H}. and {B}issiengou, {P}. and {K}enfack, {D}. and {S}aatchi, {S}. and {C}have, {J}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}ropical forests are characterized by large carbon stocks and high biodiversity, but they are increasingly threatened by human activities. {S}ince structure strongly influences the functioning and resilience of forest communities and ecosystems, it is important to quantify it at fine spatial scales. {H}ere, we propose a new simulation-based approach, the "{C}anopy {C}onstructor", with which we quantified forest structure and biomass at two tropical forest sites, one in {F}rench {G}uiana, the other in {G}abon. {I}n a first step, the {C}anopy {C}onstructor combines field inventories and airborne lidar scans to create virtual 3{D} representations of forest canopies that best fit the data. {F}rom those, it infers the forests' structure, including crown packing densities and allometric scaling relationships between tree dimensions. {I}n a second step, the results of the first step are extrapolated to create virtual tree inventories over the whole lidar-scanned area. {A}cross the {F}rench {G}uiana and {G}abon plots, we reconstructed empirical canopies with a mean absolute error of 3.98 m [95% credibility interval: 3.02, 4.98], or 14.4%, and a small upwards bias of 0.66 m [-0.41, 1.8], or 2.7%. {H}eight-stem diameter allometries were inferred with more precision than crown-stem diameter allometries, with generally larger heights at the {A}mazonian than the {A}frican site, but similar crown-stem diameter allometries. {P}lot-based aboveground biomass was inferred to be larger in {F}rench {G}uiana with 400.8 t ha(-1) [366.2-437.9], compared to 302.2 t ha(-1) in {G}abon [267.8-336.8] and decreased to 299.8 t ha(-1) [275.9-333.9] and 251.8 t ha(-1) [206.7-291.7] at the landscape scale, respectively. {P}redictive accuracy of the extrapolation procedure had an {RMSE} of 53.7 t ha(-1) (14.9%) at the 1 ha scale and 87.6 t ha(-1) (24.2%) at the 0.25 ha scale, with a bias of -17.1 t ha(-1) (-4.7%). {T}his accuracy was similar to regression-based approaches, but the {C}anopy {C}onstructor improved the representation of natural heterogeneity considerably, with its range of biomass estimates larger by 54% than regression-based estimates. {T}he {C}anopy {C}onstructor is a comprehensive inference procedure that provides fine-scale and individual-based reconstructions even in dense tropical forests. {I}t may thus prove vital in the assessment and monitoring of those forests, and has the potential for a wider applicability, for example in the exploration of ecological and physiological relationships in space or the initialisation and calibration of forest growth models.},
  keywords = {{V}egetation structure ; {T}ropical forest ; {I}ndividual-based modeling ; {A}irborne lidar ; {A}pproximate {B}ayesian {C}omputation ; {A}llometry ; {B}iomass ; {C}anopy space filling ; {ZONE} {TROPICALE}},
  booktitle = {},
  journal = {{R}emote {S}ensing of {E}nvironment},
  volume = {251},
  numero = {},
  pages = {112056 [16 ]},
  ISSN = {0034-4257},
  year = {2020},
  URL = {https://www.documentation.ird.fr/hor/fdi:010080409},
}