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

Laura Duncanson 1 James Kellner 2 John Armston 1 Ralph Dubayah 1 David Minor 1 Steven Hancock 3 Sean Healey 4 Paul Patterson 4 Svetlana Saarela 5 Suzanne Marselis 1 Carlos Silva 1 Jamis Bruening 1 Scott Goetz Hao Tang 1 Michelle Hofton 1 Bryan Blair Scott Luthcke Lola Fatoyinbo Katharine Abernethy Alfonso Alonso Hans-Erik Andersen Paul Aplin Timothy Baker Nicolas Barbier 6 Jean Francois Bastin 7 Peter Biber Pascal Boeckx Jan Bogaert Luigi Boschetti Peter Brehm Boucher Doreen Boyd David F.R.P. Burslem Sofia Calvo-Rodriguez Jérôme Chave Robin Chazdon David Clark Deborah Clark Warren Cohen David Coomes Piermaria Corona K.C. Cushman Mark E.J. Cutler James Dalling Michele Dalponte Jonathan Dash Sergio De-Miguel Songqiu Deng Peter Woods Ellis Barend Erasmus Patrick Fekety Alfredo Fernandez-Landa Antonio Ferraz Rico Fischer Adrian Fisher Antonio García-Abril Terje Gobakken Jorg Hacker Marco Heurich Ross Hill Chris Hopkinson Huabing Huang Stephen Hubbell Andrew Hudak Andreas Huth Benedikt Imbach Kathryn Jeffery Masato Katoh Elizabeth Kearsley David Kenfack Natascha Kljun Nikolai Knapp Kamil Král Martin Krůček Nicolas Labrière Simon Lewis Marcos Longo Richard Lucas Russell Main Jose Manzanera Rodolfo Vásquez Martínez Renaud Mathieu Herve Memiaghe Victoria Meyer Abel Monteagudo Mendoza Alessandra Monerris Paul Montesano Felix Morsdorf Erik Næsset Laven Naidoo Reuben Nilus Michael O’brien David Orwig Konstantinos Papathanassiou Geoffrey Parker Christopher Philipson Oliver Phillips Jan Pisek John Poulsen Hans Pretzsch Christoph Rüdiger Sassan Saatchi Arturo Sanchez-Azofeifa Nuria Sanchez-Lopez Robert Scholes Marc Simard Andrew Skidmore Krzysztof Stereńczak Mihai Tanase Chiara Torresan Ruben Valbuena Hans Verbeeck Tomas Vrska Konrad Wessels Joanne White Lee J.T. White Eliakimu Zahabu Carlo Zgraggen
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 (~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.
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Submitted on : Friday, January 7, 2022 - 10:20:28 AM
Last modification on : Saturday, January 8, 2022 - 3:40:30 AM


Distributed under a Creative Commons Attribution 4.0 International License

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Laura Duncanson, James Kellner, John Armston, Ralph Dubayah, David Minor, et al.. Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission. Remote Sensing of Environment, 2022, 270, pp.112845. ⟨10.1016/j.rse.2021.112845⟩. ⟨hal-03516285⟩



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