@article{8733219,
  abstract     = {{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 (similar to 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.}},
  articleno    = {{112845}},
  author       = {{Duncanson, Laura and Kellner, James R. and Armston, John and Dubayah, Ralph and Minor, David M. and Hancock, Steven and Healey, Sean P. and Patterson, Paul L. and Saarela, Svetlana and Marselis, Suzanne and Silva, Carlos E. and Bruening, Jamis and Goetz, Scott J. and Tang, Hao and Hofton, Michelle and Blair, Bryan and Luthcke, Scott and Fatoyinbo, Lola and Abernethy, Katharine and Alonso, Alfonso and Andersen, Hans-Erik and Aplin, Paul and Baker, Timothy R. and Barbier, Nicolas and Bastin, Jean Francois and Biber, Peter and Boeckx, Pascal and Bogaert, Jan and Boschetti, Luigi and Boucher, Peter Brehm and Boyd, Doreen S. and Burslem, David F.R.P. and Calvo-Rodriguez, Sofia and Chave, Jérôme and Chazdon, Robin L. and Clark, David B. and Clark, Deborah A. and Cohen, Warren B. and Coomes, David A. and Corona, Piermaria and Cushman, K.C. and Cutler, Mark E.J. and Dalling, James W. and Dalponte, Michele and Dash, Jonathan and de-Miguel, Sergio and Deng, Songqiu and Ellis, Peter Woods and Erasmus, Barend and Fekety, Patrick A. and Fernandez-Landa, Alfredo and Ferraz, Antonio and Fischer, Rico and Fisher, Adrian G. and García-Abril, Antonio and Gobakken, Terje and Hacker, Jorg M. and Heurich, Marco and Hill, Ross A. and Hopkinson, Chris and Huang, Huabing and Hubbell, Stephen P. and Hudak, Andrew T. and Huth, Andreas and Imbach, Benedikt and Jeffery, Kathryn J. and Katoh, Masato and Kearsley, Elizabeth and Kenfack, David and Kljun, Natascha and Knapp, Nikolai and Král, Kamil and Krůček, Martin and Labrière, Nicolas and Lewis, Simon L. and Longo, Marcos and Lucas, Richard M. and Main, Russell and Manzanera, Jose A. and Martínez, Rodolfo Vásquez and Mathieu, Renaud and Memiaghe, Herve and Meyer, Victoria and Mendoza, Abel Monteagudo and Monerris, Alessandra and Montesano, Paul and Morsdorf, Felix and Næsset, Erik and Naidoo, Laven and Nilus, Reuben and O’Brien, Michael and Orwig, David A. and Papathanassiou, Konstantinos and Parker, Geoffrey and Philipson, Christopher and Phillips, Oliver L. and Pisek, Jan and Poulsen, John R. and Pretzsch, Hans and Rüdiger, Christoph and Saatchi, Sassan and Sanchez-Azofeifa, Arturo and Sanchez-Lopez, Nuria and Scholes, Robert and Silva, Carlos A. and Simard, Marc and Skidmore, Andrew and Stereńczak, Krzysztof and Tanase, Mihai and Torresan, Chiara and Valbuena, Ruben and Verbeeck, Hans and Vrska, Tomas and Wessels, Konrad and White, Joanne C. and White, Lee J.T. and Zahabu, Eliakimu and Zgraggen, Carlo}},
  issn         = {{0034-4257}},
  journal      = {{REMOTE SENSING OF ENVIRONMENT}},
  keywords     = {{Computers in Earth Sciences,Geology,Soil Science,cavelab,LiDAR,GEDI,Waveform,Forest,Aboveground biomass,Modeling,TROPICAL FOREST BIOMASS,CANOPY STRUCTURE,GROUND BIOMASS,AIRBORNE LIDAR,BOREAL FOREST,CARBON,VEGETATION,REGIONS,HEIGHT,INVERSION}},
  language     = {{eng}},
  pages        = {{20}},
  title        = {{Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission}},
  url          = {{http://doi.org/10.1016/j.rse.2021.112845}},
  volume       = {{270}},
  year         = {{2022}},
}

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