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

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 (~...

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Veröffentlicht in:	Remote sensing of environment Jg. 270; S. 112845
Hauptverfasser:	Duncanson, Laura, Kellner, James R., Dubayah, Ralph, Minor, David M., Hancock, Steven, Healey, Sean P., Patterson, Paul L., Saarela, Svetlana, Marselis, Suzanne, Silva, Carlos E., Bruening, Jamis, Goetz, Scott J., Tang, Hao, Hofton, Michelle, Blair, Bryan, Luthcke, Scott, Fatoyinbo, Lola, Alonso, Alfonso, Andersen, Hans-Erik, Aplin, Paul, Baker, Timothy R., Barbier, Nicolas, Bastin, Jean Francois, Biber, Peter, Boeckx, Pascal, Bogaert, Jan, Boschetti, Luigi, Boucher, Peter Brehm, Boyd, Doreen S., Calvo-Rodriguez, Sofia, Chazdon, Robin L., Coomes, David A., Corona, Piermaria, Cushman, K.C., Cutler, Mark E.J., Dalling, James W., Dash, Jonathan, de-Miguel, Sergio, Deng, Songqiu, Ellis, Peter Woods, Erasmus, Barend, Fekety, Patrick A., Fernandez-Landa, Alfredo, Ferraz, Antonio, Fisher, Adrian G., García-Abril, Antonio, Hacker, Jorg M., Heurich, Marco, Hill, Ross A., Hopkinson, Chris, Huang, Huabing, Hubbell, Stephen P., Hudak, Andrew T., Huth, Andreas, Imbach, Benedikt, Jeffery, Kathryn J., Katoh, Masato, Kearsley, Elizabeth, Kenfack, David, Kljun, Natascha, Král, Kamil, Krůček, Martin, Labrière, Nicolas, Lewis, Simon L., Longo, Marcos, Lucas, Richard M., Main, Russell, Martínez, Rodolfo Vásquez, Mathieu, Renaud, Memiaghe, Herve, Meyer, Victoria, Monerris, Alessandra, Montesano, Paul, Morsdorf, Felix, Næsset, Erik, Naidoo, Laven, Nilus, Reuben, O’Brien, Michael, Orwig, David A., Papathanassiou, Konstantinos, Poulsen, John R., Pretzsch, Hans, Saatchi, Sassan, Sanchez-Azofeifa, Arturo, Sanchez-Lopez, Nuria, Scholes, Robert, Silva, Carlos A., Simard, Marc, Skidmore, Andrew, Stereńczak, Krzysztof, Tanase, Mihai, Torresan, Chiara, Valbuena, Ruben, Verbeeck, Hans, Vrska, Tomas, Wessels, Konrad, White, Joanne C., White, Lee J.T., Zahabu, Eliakimu, Zgraggen, Carlo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Elsevier Inc 01.03.2022 Elsevier BV Elsevier
Schlagworte:	Aboveground biomass Biodiversity Biodiversity and Ecology Biomass Biomass density Botanics Computers in Earth Sciences data collection Datasets Density Domains Earth and Related Environmental Sciences Ecology, environment Ecosystem dynamics Ecosystems environment Environment models ENVIRONMENTAL SCIENCES Environmental sciences & ecology Fjärranalysteknik Forest Forest biomass forests GEDI Geographics Geology Geovetenskap och relaterad miljövetenskap Global ecosystem dynamic investigation LiDAR Life Sciences Mathematical models Meteorologi och atmosfärsvetenskap Meteorology and Atmospheric Sciences model validation Modeling Modeling performance Modelling Natural Sciences Naturgeografi Naturvetenskap Performance degradation Physical Geography Remote Sensing Sciences de l’environnement & écologie Sciences du vivant Soil Science Strata Stratification Systematics, Phylogenetics and taxonomy Training Transformations (mathematics) Vegetal Biology Waveform Waveforms Waveform Forest GEDI Modeling LiDAR Aboveground biomass
ISSN:	0034-4257, 1879-0704, 1879-0704
Online-Zugang:	Volltext
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Zusammenfassung:	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. •NASA’s GEDI collects spaceborne lidar data used for mapping aboveground biomass.•A global database of field and airborne lidar was compiled.•Models stratified by Plant Functional Type and geographic region outperform a global model.•GEDI04_A models are OLS models predicting biomass as a function of RH metrics.•Maximum forest height is an important predictor of biomass across geographic domains.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 scopus-id:2-s2.0-85123289336 National Aeronautics and Space Administration (NASA) USDOE Office of Science (SC), High Energy Physics (HEP) National Science Foundation (NSF) AC02-05CH11231; NNL 15AA03C; NNH20ZDA001N; 80HQTR18T0016; RPO201523; NNH13AW621; DEB 0939907
ISSN:	0034-4257 1879-0704 1879-0704
DOI:	10.1016/j.rse.2021.112845