@article{fdi:010084343,
  title = {{A}boveground biomass density models for {NASA}'s {G}lobal {E}cosystem {D}ynamics {I}nvestigation ({GEDI}) lidar mission},
  author = {{D}uncanson, {L}. and {K}ellner, {J}. {R}. and {A}rmston, {J}. and {D}ubayah, {R}. and {M}inor, {D}. {M}. and {H}ancock, {S}. and {H}ealey, {S}. {P}. and {P}atterson, {P}. {L}. and {S}aarela, {S}. and {M}arselis, {S}. and {S}ilva, {C}. {E}. and {B}ruening, {J}. and {G}oetz, {S}. {J}. and {T}ang, {H}. and {H}ofton, {M}. and {B}lair, {B}. and {L}uthcke, {S}. and {F}atoyinbo, {L}. and {A}bernethy, {K}. and {A}lonso, {A}. and {A}ndersen, {H}. {E}. and {A}plin, {P}. and {B}aker, {T}. {R}. and {B}arbier, {N}icolas and {B}astin, {J}. {F}. and {B}iber, {P}. and {B}oeckx, {P}. and {B}ogaert, {J}. and {B}oschetti, {L}. and {B}oucher, {P}. {B}. and {B}oyd, {D}. {S}. and {B}urslem, {D}frp and {C}alvo-{R}odriguez, {S}. and {C}have, {J}. and {C}hazdon, {R}. {L}. and {C}lark, {D}. {B}. and {C}lark, {D}. {A}. and {C}ohen, {W}. {B}. and {C}oomes, {D}. {A}. and {C}orona, {P}. and {C}ushman, {K}. {C}. and {C}utler, {M}. {E}. {J}. and {D}alling, {J}. {W}. and {D}alponte, {M}. and {D}ash, {J}. and de-{M}iguel, {S}. and {D}eng, {S}. {Q}. and {E}llis, {P}. {W}. and {E}rasmus, {B}. and {F}ekety, {P}. {A}. and {F}ernandez-{L}anda, {A}. and {F}erraz, {A}. and {F}ischer, {R}. and {F}isher, {A}. {G}. and {G}arcia-{A}bril, {A}. and {G}obakken, {T}. and {H}acker, {J}. {M}. and {H}eurich, {M}. and {H}ill, {R}. {A}. and {H}opkinson, {C}. and {H}uang, {H}. {B}. and {H}ubbell, {S}. {P}. and {H}udak, {A}. {T}. and {H}uth, {A}. and {I}mbach, {B}. and {J}effery, {K}. {J}. and {K}atoh, {M}. and {K}earsley, {E}. and {K}enfack, {D}. and {K}ljun, {N}. and {K}napp, {N}. and {K}ral, {K}. and {K}rucek, {M}. and {L}abriere, {N}. and {L}ewis, {S}. {L}. and {L}ongo, {M}. and {L}ucas, {R}. {M}. and {M}ain, {R}. and {M}anzanera, {J}. {A}. and {M}artinez, {R}. {V}. and {M}athieu, {R}. and {M}emiaghe, {H}. and {M}eyer, {V}. and {M}endoza, {A}. {M}. and {M}onerris, {A}. and {M}ontesano, {P}. and {M}orsdorf, {F}. and {N}aesset, {E}. and {N}aidoo, {L}. and {N}ilus, {R}. and {O}'{B}rien, {M}. and {O}rwig, {D}. {A}. and {P}apathanassiou, {K}. and {P}arker, {G}. and {P}hilipson, {C}. and {P}hillips, {O}. {L}. and {P}isek, {J}. and {P}oulsen, {J}. {R}. and {P}retzsch, {H}. and {R}udiger, {C}. and {S}aatchi, {S}. and {S}anchez-{A}zofeifa, {A}. and {S}anchez-{L}opez, {N}. and {S}choles, {R}. and {S}ilva, {C}. {A}. and {S}imard, {M}. and {S}kidmore, {A}. and {S}terenczak, {K}. and {T}anase, {M}. and {T}orresan, {C}. and {V}albuena, {R}. and {V}erbeeck, {H}. and {V}rska, {T}. and {W}essels, {K}. and {W}hite, {J}. {C}. and {W}hite, {L}. {J}. {T}. and {Z}ahabu, {E}. and {Z}graggen, {C}.},
  editor = {},
  language = {{ENG}},
  abstract = {{NASA}'s {G}lobal {E}cosystem {D}ynamics {I}nvestigation ({GEDI}) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density ({AGBD}). {T}his paper presents the development of the models used to create {GEDI}'s footprint-level (similar to 25 m) {AGBD} ({GEDI}04_{A}) product, including a description of the datasets used and the procedure for final model selection. {T}he data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated {GEDI}-like waveforms from airborne lidar to build a calibration database. {W}e used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by {P}lant {F}unctional {T}ype ({PFT}) and six geographic regions. {GEDI}'s waveform-to-biomass models take the form of parametric {O}rdinary {L}east {S}quares ({OLS}) models with simulated {R}elative {H}eight ({RH}) metrics as predictor variables. {F}rom an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the {GEDI} domain to produce a set of comprehensive predictive footprint-level models. {W}e found that model selection frequently favored combinations of {RH} metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles ({RH}98, {RH}90, {RH}50, and {RH}10, respectively), but that inclusion of lower {RH} metrics (e.g. {RH}10) did not markedly improve model performance. {S}econd, forced inclusion of {RH}98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. {T}hird, stratification by geographic domain ({PFT}, geographic region) improved model performance in comparison to global models without stratification. {F}ourth, for the vast majority of strata, the best performing models were fit using square root transformation of field {AGBD} and/or height metrics. {T}here was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high {AGBD} values had the poorest performance. {T}hese models are used to produce global predictions of {AGBD}, but will be improved in the future as more and better training data become available.},
  keywords = {{L}i{DAR} ; {GEDI} ; {W}aveform ; {F}orest ; {A}boveground biomass ; {M}odeling},
  booktitle = {},
  journal = {{R}emote {S}ensing of {E}nvironment},
  volume = {270},
  numero = {},
  pages = {112845 [20 p.]},
  ISSN = {0034-4257},
  year = {2022},
  DOI = {10.1016/j.rse.2021.112845},
  URL = {https://www.documentation.ird.fr/hor/fdi:010084343},
}