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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| Published in: | Remote sensing of environment Vol. 270; p. 112845 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
Elsevier Inc
01.03.2022
Elsevier BV Elsevier |
| Subjects: | |
| ISSN: | 0034-4257, 1879-0704, 1879-0704 |
| Online Access: | Get full text |
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| 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.
•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. |
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| AbstractList | 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 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. 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. 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 considerablevariability 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. |
| ArticleNumber | 112845 |
| Author | Fekety, Patrick A. Saatchi, Sassan Bastin, Jean Francois Huang, Huabing Kljun, Natascha Philipson, Christopher Goetz, Scott J. Imbach, Benedikt Lewis, Simon L. Blair, Bryan Chave, Jérôme Marselis, Suzanne Heurich, Marco Král, Kamil Skidmore, Andrew Hofton, Michelle Fisher, Adrian G. Luthcke, Scott Boeckx, Pascal Bogaert, Jan Naidoo, Laven Minor, David M. de-Miguel, Sergio Clark, David B. Næsset, Erik Fischer, Rico Ellis, Peter Woods Patterson, Paul L. Kenfack, David Hudak, Andrew T. Manzanera, Jose A. Nilus, Reuben Lucas, Richard M. Fernandez-Landa, Alfredo Torresan, Chiara White, Lee J.T. Scholes, Robert Bruening, Jamis Andersen, Hans-Erik Tanase, Mihai García-Abril, Antonio Kellner, James R. Simard, Marc Dubayah, Ralph Healey, Sean P. Aplin, Paul Hopkinson, Chris Monerris, Alessandra Pisek, Jan Fatoyinbo, Lola Duncanson, Laura Boyd, Doreen S. Silva, Carlos A. Corona, Piermaria Dash, Jonathan Rüdiger, Christoph Stereńczak, Krzysztof Ferraz, Antonio Alonso, Alfonso Longo, Marcos Verbeeck, Hans Labrière, Nicolas Wessels, Konrad Jeffery, Kathryn J. A |
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| Copyright | 2021 The Authors Copyright Elsevier BV Mar 1, 2022 Attribution |
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| CorporateAuthor | Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States) MERGE: ModElling the Regional and Global Earth system Strategiska forskningsområden (SFO) Strategic research areas (SRA) Profile areas and other strong research environments Lunds universitet Naturvetenskapliga fakulteten BECC: Biodiversity and Ecosystem services in a Changing Climate Faculty of Science Lund University Profilområden och andra starka forskningsmiljöer Centre for Environmental and Climate Science (CEC) Centrum för miljö- och klimatvetenskap (CEC) Sveriges lantbruksuniversitet |
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| DOI | 10.1016/j.rse.2021.112845 |
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| Keywords | Waveform Forest GEDI Modeling LiDAR Aboveground biomass |
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359 Sun (10.1016/j.rse.2021.112845_bb0865) 2011 Næsset (10.1016/j.rse.2021.112845_bb0675) 2008; 112 Lucas (10.1016/j.rse.2021.112845_bb0590) 2008 Saatchi (10.1016/j.rse.2021.112845_bb0785) 2011 Simard (10.1016/j.rse.2021.112845_bb0820) 2018; 12 Lang (10.1016/j.rse.2021.112845_bb0530) 2019; 233 Réjou-Méchain (10.1016/j.rse.2021.112845_bb0765) 2019 Andersen (10.1016/j.rse.2021.112845_bb0020) 2011; 37 Meyer (10.1016/j.rse.2021.112845_bb0640) 2018; 15 Swatantran (10.1016/j.rse.2021.112845_bb0870) 2011 Foody (10.1016/j.rse.2021.112845_bb0340) 2003 Margolis (10.1016/j.rse.2021.112845_bb0610) 2015; 45 Zhao (10.1016/j.rse.2021.112845_bb0945) 2009 Baccini (10.1016/j.rse.2021.112845_bb0045) 2008 Auscover (10.1016/j.rse.2021.112845_bb0035) 2016 Valbuena (10.1016/j.rse.2021.112845_bb0905) 2017; 366 Powell (10.1016/j.rse.2021.112845_bb0755) 2010; 114 Duncanson (10.1016/j.rse.2021.112845_bb0250) 2010 Baccini (10.1016/j.rse.2021.112845_bb0050) 2012 10.1016/j.rse.2021.112845_bb0560 Næsset (10.1016/j.rse.2021.112845_bb0685) 2013 Le Toan (10.1016/j.rse.2021.112845_bb0550) 1992; 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| SubjectTerms | 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 |
| Title | Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission |
| URI | https://dx.doi.org/10.1016/j.rse.2021.112845 https://www.proquest.com/docview/2639033249 https://www.proquest.com/docview/2636398667 https://hal.inrae.fr/hal-03516285 https://orbi.uliege.be/handle/2268/288263 https://www.osti.gov/servlets/purl/1893116 https://res.slu.se/id/publ/116407 |
| Volume | 270 |
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