TY - JOUR

T1 - Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission

AU - Duncanson, Laura

AU - Kellner, James R.

AU - Armston, John

AU - Dubayah, Ralph

AU - Minor, David M.

AU - Hancock, Steven

AU - Healey, Sean P.

AU - Patterson, Paul L.

AU - Saarela, Svetlana

AU - Marselis, Suzanne

AU - Silva, Carlos E.

AU - Bruening, Jamis

AU - Goetz, Scott J.

AU - Tang, Hao

AU - Hofton, Michelle

AU - Blair, Bryan

AU - Luthcke, Scott

AU - Fatoyinbo, Lola

AU - Abernethy, Katharine

AU - Alonso, Alfonso

AU - Andersen, Hans-Erik

AU - Aplin, Paul

AU - Baker, Timothy R.

AU - Barbier, Nicolas

AU - Bastin, Jean Francois

AU - Biber, Peter

AU - Boeckx, Pascal

AU - Bogaert, Jan

AU - Boschetti, Luigi

AU - Boucher, Peter Brehm

AU - Boyd, Doreen S.

AU - Burslem, David F.R.P.

AU - Calvo-Rodriguez, Sofia

AU - Chave, Jérôme

AU - Chazdon, Robin L.

AU - Clark, David B.

AU - Clark, Deborah A.

AU - Cohen, Warren B.

AU - Coomes, David A.

AU - Corona, Piermaria

AU - Cushman, K.C.

AU - Cutler, Mark E.J.

AU - Dalling, James W.

AU - Dalponte, Michele

AU - Dash, Jonathan

AU - de-Miguel, Sergio

AU - Deng, Songqiu

AU - Ellis, Peter Woods

AU - Erasmus, Barend

AU - Fekety, Patrick A.

AU - Fernandez-Landa, Alfredo

AU - Ferraz, Antonio

AU - Fischer, Rico

AU - Fisher, Adrian G.

AU - García-Abril, Antonio

AU - Gobakken, Terje

AU - Hacker, Jorg M.

AU - Heurich, Marco

AU - Hill, Ross A.

AU - Hopkinson, Chris

AU - Huang, Huabing

AU - Hubbell, Stephen P.

AU - Hudak, Andrew T.

AU - Huth, Andreas

AU - Imbach, Benedikt

AU - Jeffery, Kathryn J.

AU - Katoh, Masato

AU - Kearsley, Elizabeth

AU - Kenfack, David

AU - Kljun, Natascha

AU - Knapp, Nikolai

AU - Král, Kamil

AU - Krůček, Martin

AU - Labrière, Nicolas

AU - Lewis, Simon L.

AU - Longo, Marcos

AU - Lucas, Richard M.

AU - Main, Russell

AU - Manzanera, Jose A.

AU - Martínez, Rodolfo Vásquez

AU - Mathieu, Renaud

AU - Memiaghe, Herve

AU - Meyer, Victoria

AU - Mendoza, Abel Monteagudo

AU - Monerris, Alessandra

AU - Montesano, Paul

AU - Morsdorf, Felix

AU - Næsset, Erik

AU - Naidoo, Laven

AU - Nilus, Reuben

AU - O’Brien, Michael

AU - Orwig, David A.

AU - Papathanassiou, Konstantinos

AU - Parker, Geoffrey

AU - Philipson, Christopher

AU - Phillips, Oliver L.

AU - Pisek, Jan

AU - Poulsen, John R.

AU - Pretzsch, Hans

AU - Rüdiger, Christoph

AU - Saatchi, Sassan

AU - Sanchez-Azofeifa, Arturo

AU - Sanchez-Lopez, Nuria

AU - Scholes, Robert

AU - Silva, Carlos A.

AU - Simard, Marc

AU - Skidmore, Andrew

AU - Stereńczak, Krzysztof

AU - Tanase, Mihai

AU - Torresan, Chiara

AU - Valbuena, Ruben

AU - Verbeeck, Hans

AU - Vrska, Tomas

AU - Wessels, Konrad

AU - White, Joanne C.

AU - White, Lee J.T.

AU - Zahabu, Eliakimu

AU - Zgraggen, Carlo

PY - 2022

Y1 - 2022

N2 - NASA’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-to-biomass 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.

AB - NASA’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-to-biomass 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.

KW - LiDAR

KW - GEDI

KW - Waveform

KW - Forest

KW - Aboveground biomass

KW - Modeling

U2 - 10.1016/j.rse.2021.112845

DO - 10.1016/j.rse.2021.112845

M3 - Article

VL - 270

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

ER -