Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission

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dc.contributor.authorDuncanson, Laura
dc.contributor.authorKellner, James R.
dc.contributor.authorArmston, John
dc.contributor.authorDubayah, Ralph
dc.contributor.authorMinor, David M.
dc.contributor.authorHancock, Steven
dc.contributor.authorHealey, Sean P.
dc.contributor.authorPatterson, Paul L.
dc.contributor.authorSaarela, Svetlana
dc.contributor.authorMarselis, Suzanne
dc.contributor.authorSilva, Carlos E.
dc.contributor.authorBruening, Jamis
dc.contributor.authorGoetz, Scott J.
dc.contributor.authorTang, Hao
dc.contributor.authorHofton, Michelle
dc.contributor.authorBlair, Bryan
dc.contributor.authorLuthcke, Scott
dc.contributor.authorFatoyinbo, Lola
dc.contributor.authorAbernethy, Katharine
dc.contributor.authorAlonso, Alfonso
dc.contributor.authorAndersen, Hans-Erik
dc.contributor.authorAplin, Paul
dc.contributor.authorBaker, Timothy R.
dc.contributor.authorBarbier, Nicolas
dc.contributor.authorBastin, Jean-Francois
dc.contributor.authorBiber, Peter
dc.contributor.authorBoeckx, Pascal
dc.contributor.authorBogaert, Jan
dc.contributor.authorBoschetti, Luigi
dc.contributor.authorBrehm Boucher, Peter
dc.contributor.authorBoyd, Doreen S.
dc.contributor.authorBurslem, David
dc.contributor.authorCalvo-Rodriguez, Sofia
dc.contributor.authorChave, Jérôme
dc.contributor.authorChazdon, Robin L.
dc.contributor.authorClark, David B.
dc.contributor.authorClark, Deborah A.
dc.contributor.authorCohen, Warren B.
dc.contributor.authorCoomes, David A.
dc.contributor.authorCorona, Piermaria
dc.contributor.authorCushman, Katherine C.
dc.contributor.authorCutler, Mark E.J.
dc.contributor.authorDalling, James W.
dc.contributor.authorDalponte, Michele
dc.contributor.authorDash, Jonathan
dc.contributor.authorMiguel Magaña, Sergio de
dc.contributor.authorDeng, Songqiu
dc.contributor.authorWoods Ellis, Peter
dc.contributor.authorErasmus, Barend
dc.contributor.authorFekety, Patrick A.
dc.contributor.authorFernandez-Landa, Alfredo
dc.contributor.authorFerraz, Antonio
dc.contributor.authorFischer, Rico
dc.contributor.authorFisher, Adrian G.
dc.contributor.authorGarcía-Abril, Antonio
dc.contributor.authorGobakken, Terje
dc.contributor.authorHacker, Jorg M.
dc.contributor.authorHeurich, Marco
dc.contributor.authorHill, Ross A.
dc.contributor.authorHopkinson, Chris
dc.contributor.authorHuang, Huabing
dc.contributor.authorHubbell, Stephen P.
dc.contributor.authorHudak, Andrew T.
dc.contributor.authorHuth, Andreas
dc.contributor.authorImbach, Benedikt
dc.contributor.authorJeffery, Kathryn J.
dc.contributor.authorKatoh, Masato
dc.contributor.authorKearsley, Elizabeth
dc.contributor.authorKenfack, David
dc.contributor.authorKljun, Natascha
dc.contributor.authorKnapp, Nikolai
dc.contributor.authorKrál, Kamil
dc.contributor.authorKrůček, Martin
dc.contributor.authorLabrière, Nicolas
dc.contributor.authorLewis, Simon L.
dc.contributor.authorLongo, Marcos
dc.contributor.authorLucas, Richard M.
dc.contributor.authorMain, Russell
dc.contributor.authorManzanera, Jose A.
dc.contributor.authorVásquez Martínez, Rodolfo
dc.contributor.authorMathieu, Renaud
dc.contributor.authorMemiaghe, Herve
dc.contributor.authorMeyer, Victoria
dc.contributor.authorMonteagudo Mendoza, Abel
dc.contributor.authorMonerris, Alessandra
dc.contributor.authorMontesano, Paul
dc.contributor.authorMorsdorf, Felix
dc.contributor.authorNæsset, Erik
dc.contributor.authorNaidoo, Laven
dc.contributor.authorNilus, Reuben
dc.contributor.authorO’Brien, Michael
dc.contributor.authorOrwig, David A.
dc.contributor.authorPapathanassiou, Konstantinos
dc.contributor.authorParker, Geoffrey
dc.contributor.authorPhilipson, Christopher
dc.contributor.authorPhillips, Oliver L.
dc.contributor.authorPisek, Jan
dc.contributor.authorPoulsen, John R.
dc.contributor.authorPretzsch, Hans
dc.contributor.authorRüdiger, Christoph
dc.contributor.authorSaatchi, Sassan S.
dc.contributor.authorSanchez-Azofeifa, Arturo
dc.contributor.authorSanchez-Lopez, Nuria
dc.contributor.authorScholes, Robert
dc.contributor.authorSilva, Carlos Alberto
dc.contributor.authorSimard, Marc
dc.contributor.authorSkidmore, Andrew
dc.contributor.authorStereńczak, Krzysztof
dc.contributor.authorTanase, Mihai
dc.contributor.authorTorresan, Chiara
dc.contributor.authorValbuena, Ruben
dc.contributor.authorVerbeeck, Hans
dc.contributor.authorVrska, Tomas
dc.contributor.authorWessels, Konrad
dc.contributor.authorWhite, Joanne C.
dc.contributor.authorWhite, Lee J.T.
dc.contributor.authorZahabu, Eliakimu
dc.contributor.authorZgraggen, Carlo
dc.date.accessioned2022-12-18T14:39:16Z
dc.date.available2022-12-18T14:39:16Z
dc.date.issued2022
dc.description.abstractNASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI’s footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The 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. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI’s waveform-tobiomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From 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. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.ca_ES
dc.identifier.doihttps://doi.org/10.1016/j.rse.2021.112845
dc.identifier.issn0034-4257
dc.identifier.urihttp://hdl.handle.net/10459.1/84891
dc.language.isoengca_ES
dc.publisherElsevierca_ES
dc.relation.isformatofReproducció del document publicat a https://doi.org/10.1016/j.rse.2021.112845ca_ES
dc.relation.ispartofRemote Sensing of Environment, 2022, vol. 270, art. 112845.ca_ES
dc.rightscc-by (c) Laura Duncanson et. al., 2022.ca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectLiDARca_ES
dc.subjectGEDIca_ES
dc.subjectWaveformca_ES
dc.subjectForestca_ES
dc.subjectAboveground biomassca_ES
dc.subjectModelingca_ES
dc.titleAboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar missionca_ES
dc.typeinfo:eu-repo/semantics/articleca_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_ES
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