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

Laura Duncanson; James R. Kellner; John Armston; Ralph Dubayah; David M. Minor; Steven Hancock; Sean P. Healey; Paul L. Patterson; Svetlana Saarela; Suzanne Marselis; Carlos E. Silva; Jamis Bruening; Scott J. Goetz; Hao Tang; Michelle Hofton; Bryan Blair; Scott Luthcke; Lola Fatoyinbo; Katharine Abernethy; Alfonso Alonso; Hans-erik Andersen; Paul Aplin; Timothy R. Baker; Nicolas Barbier; Jean Francois Bastin; Peter Biber; Pascal Boeckx; Jan Bogaert; Luigi Boschetti; Peter Brehm Boucher; Doreen S. Boyd; David F.r.p. Burslem; Sofia Calvo-rodriguez; Jérôme Chave; Robin L. Chazdon; David B. Clark; Deborah A. Clark; Warren B. Cohen; David A. Coomes; Piermaria Corona; K.c. Cushman; Mark E.j. Cutler; James W. Dalling; Michele Dalponte; Jonathan Dash; Sergio De-miguel; Songqiu Deng; Peter Woods Ellis; Barend Erasmus; Patrick A. Fekety; Alfredo Fernandez-landa; Antonio Ferraz; Rico Fischer; Adrian G. Fisher; Antonio García-abril; Terje Gobakken; Jorg M. Hacker; Marco Heurich; Ross A. Hill; Chris Hopkinson; Huabing Huang; Stephen P. Hubbell; Andrew T. Hudak; Andreas Huth; Benedikt Imbach; Kathryn J. Jeffery; Masato Katoh; Elizabeth Kearsley; David Kenfack; Natascha Kljun; Nikolai Knapp; Kamil Král; Martin Krůček; Nicolas Labrière; Simon L. Lewis; Marcos Longo; Richard M. Lucas; Russell Main; Jose A. 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 A. Orwig; Konstantinos Papathanassiou; Geoffrey Parker; Christopher Philipson; Oliver L. Phillips; Jan Pisek; John R. Poulsen; Hans Pretzsch; Christoph Rüdiger; Sassan Saatchi; Arturo Sanchez-azofeifa; Nuria Sanchez-lopez; Robert Scholes; Carlos A. Silva; Marc Simard; Andrew Skidmore; Krzysztof Stereńczak; Mihai Tanase; Chiara Torresan; Ruben Valbuena; Hans Verbeeck; Tomas Vrska; Konrad Wessels; Joanne C. White; Lee J.t. White; Eliakimu Zahabu; Carlo Zgraggen

doi:10.1016/j.rse.2021.112845

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

Laura Duncanson, James R. Kellner, John Armston, Ralph Dubayah, David M. Minor, Steven Hancock, Sean P. Healey, Paul L. Patterson, Svetlana Saarela, Suzanne Marselis, Carlos E. Silva, Jamis Bruening, Scott J. Goetz, Hao Tang, Michelle Hofton, Bryan Blair, Scott Luthcke, Lola Fatoyinbo, Katharine Abernethy, Alfonso AlonsoHans-erik Andersen, Paul Aplin, Timothy R. Baker, Nicolas Barbier, Jean Francois Bastin, Peter Biber, Pascal Boeckx, Jan Bogaert, Luigi Boschetti, Peter Brehm Boucher, Doreen S. Boyd, David F.r.p. Burslem, Sofia Calvo-rodriguez, Jérôme Chave, Robin L. Chazdon, David B. Clark, Deborah A. Clark, Warren B. Cohen, David A. Coomes, Piermaria Corona, K.c. Cushman, Mark E.j. Cutler, James W. Dalling, Michele Dalponte, Jonathan Dash, Sergio De-miguel, Songqiu Deng, Peter Woods Ellis, Barend Erasmus, Patrick A. Fekety, Alfredo Fernandez-landa, Antonio Ferraz, Rico Fischer, Adrian G. Fisher, Antonio García-abril, Terje Gobakken, Jorg M. Hacker, Marco Heurich, Ross A. Hill, Chris Hopkinson, Huabing Huang, Stephen P. Hubbell, Andrew T. Hudak, Andreas Huth, Benedikt Imbach, Kathryn J. Jeffery, Masato Katoh, Elizabeth Kearsley, David Kenfack, Natascha Kljun, Nikolai Knapp, Kamil Král, Martin Krůček, Nicolas Labrière, Simon L. Lewis, Marcos Longo, Richard M. Lucas, Russell Main, Jose A. 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 A. Orwig, Konstantinos Papathanassiou, Geoffrey Parker, Christopher Philipson, Oliver L. Phillips, Jan Pisek, John R. Poulsen, Hans Pretzsch, Christoph Rüdiger, Sassan Saatchi, Arturo Sanchez-azofeifa, Nuria Sanchez-lopez, Robert Scholes, Carlos A. Silva, Marc Simard, Andrew Skidmore, Krzysztof Stereńczak, Mihai Tanase, Chiara Torresan, Ruben Valbuena, Hans Verbeeck, Tomas Vrska, Konrad Wessels, Joanne C. White, Lee J.t. White, Eliakimu Zahabu, Carlo Zgraggen

School of Geosciences

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	112845
Journal	Remote Sensing of Environment
Volume	270
Early online date	7 Jan 2022
DOIs	https://doi.org/10.1016/j.rse.2021.112845
Publication status	Published - 1 Mar 2022

Keywords / Materials (for Non-textual outputs)

Aboveground biomass
Forest
GEDI
LiDAR
Modeling
Waveform

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10.1016/j.rse.2021.112845Licence: Creative Commons: Attribution (CC-BY)

Duncanson et al.
© 2021 The Authors. Published by Elsevier Inc.
Final published version, 3.63 MBLicence: Creative Commons: Attribution (CC-BY)

https://www.sciencedirect.com/science/article/pii/S0034425721005654Licence: Creative Commons: Attribution (CC-BY)

Cite this

Duncanson, L., Kellner, J. R., Armston, J., Dubayah, R., Minor, D. M., Hancock, S., Healey, S. P., Patterson, P. L., Saarela, S., Marselis, S., Silva, C. E., Bruening, J., Goetz, S. J., Tang, H., Hofton, M., Blair, B., Luthcke, S., Fatoyinbo, L., Abernethy, K., ... Zgraggen, C. (2022). Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission. Remote Sensing of Environment, 270, Article 112845. https://doi.org/10.1016/j.rse.2021.112845

@article{4b495d2e14a14868ba36a08c796d0c0c,

title = "Aboveground biomass density models for NASA{\textquoteright}s Global Ecosystem Dynamics Investigation (GEDI) lidar mission",

abstract = "NASA{\textquoteright}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{\textquoteright}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{\textquoteright}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.",

keywords = "Aboveground biomass, Forest, GEDI, LiDAR, Modeling, Waveform",

author = "Laura Duncanson and Kellner, {James R.} and John Armston and Ralph Dubayah and Minor, {David M.} and Steven Hancock and Healey, {Sean P.} and Patterson, {Paul L.} and Svetlana Saarela and Suzanne Marselis and Silva, {Carlos E.} and Jamis Bruening and Goetz, {Scott J.} and Hao Tang and Michelle Hofton and Bryan Blair and Scott Luthcke and Lola Fatoyinbo and Katharine Abernethy and Alfonso Alonso and Hans-erik Andersen and Paul Aplin and Baker, {Timothy R.} and Nicolas Barbier and Bastin, {Jean Francois} and Peter Biber and Pascal Boeckx and Jan Bogaert and Luigi Boschetti and Boucher, {Peter Brehm} and Boyd, {Doreen S.} and Burslem, {David F.r.p.} and Sofia Calvo-rodriguez and J{\'e}r{\^o}me Chave and Chazdon, {Robin L.} and Clark, {David B.} and Clark, {Deborah A.} and Cohen, {Warren B.} and Coomes, {David A.} and Piermaria Corona and K.c. Cushman and Cutler, {Mark E.j.} and Dalling, {James W.} and Michele Dalponte and Jonathan Dash and Sergio De-miguel and Songqiu Deng and Ellis, {Peter Woods} and Barend Erasmus and Fekety, {Patrick A.} and Alfredo Fernandez-landa and Antonio Ferraz and Rico Fischer and Fisher, {Adrian G.} and Antonio Garc{\'i}a-abril and Terje Gobakken and Hacker, {Jorg M.} and Marco Heurich and Hill, {Ross A.} and Chris Hopkinson and Huabing Huang and Hubbell, {Stephen P.} and Hudak, {Andrew T.} and Andreas Huth and Benedikt Imbach and Jeffery, {Kathryn J.} and Masato Katoh and Elizabeth Kearsley and David Kenfack and Natascha Kljun and Nikolai Knapp and Kamil Kr{\'a}l and Martin Krů{\v c}ek and Nicolas Labri{\`e}re and Lewis, {Simon L.} and Marcos Longo and Lucas, {Richard M.} and Russell Main and Manzanera, {Jose A.} and Mart{\'i}nez, {Rodolfo V{\'a}squez} and Renaud Mathieu and Herve Memiaghe and Victoria Meyer and Mendoza, {Abel Monteagudo} and Alessandra Monerris and Paul Montesano and Felix Morsdorf and Erik N{\ae}sset and Laven Naidoo and Reuben Nilus and Michael O{\textquoteright}brien and Orwig, {David A.} and Konstantinos Papathanassiou and Geoffrey Parker and Christopher Philipson and Phillips, {Oliver L.} and Jan Pisek and Poulsen, {John R.} and Hans Pretzsch and Christoph R{\"u}diger and Sassan Saatchi and Arturo Sanchez-azofeifa and Nuria Sanchez-lopez and Robert Scholes and Silva, {Carlos A.} and Marc Simard and Andrew Skidmore and Krzysztof Stere{\'n}czak and Mihai Tanase and Chiara Torresan and Ruben Valbuena and Hans Verbeeck and Tomas Vrska and Konrad Wessels and White, {Joanne C.} and White, {Lee J.t.} and Eliakimu Zahabu and Carlo Zgraggen",

note = "Funding Information: Armston, Kellner, Hancock, and Dubayah were supported by NASA Contract #NNL 15AA03C to the University of Maryland for the development and execution of the GEDI mission. Duncanson and Minor were supported by a NASA GEDI Science Team Grant NNH20ZDA001N and a NASA Post Doctoral Program fellowship. Saarela was supported through NASA Carbon Monitoring System Grant 80HQTR18T0016 , and Healey and Patterson were funded by the GEDI mission through Interagency Agreement RPO201523 . We thank the NASA Terrestrial Ecology program for continued support of the GEDI mission, and the University of Maryland for providing independent financial support of the GEDI mission. We also thank NASA for contributing to several lidar data collections used in this study, including from the NASA Carbon Monitoring System (Grant number NNH13AW621 , to PI Cohen at the USFS Service). We also gratefully acknowledge the collection and provision of field and airborne data from a wide variety of other sources, including by the Sustainable Landscapes Brazil project supported by the Brazilian Agricultural Research Corporation (EMBRAPA) , the US Forest Service, the National Science Foundation ( DEB 0939907 ), Smithsonian Tropical Research Institute, USAID, and the US Department of State, among others. Additional data were acquired from the Terrestrial Ecosystem Research Network (TERN), an Australian Government NCRIS-enabled research infrastructure project, for provision of data used in this analysis, and from the National Ecological Observatory Network (NEON), a program sponsored by the National Science Foundation and operated under cooperative agreement by Battelle. We also thank the National Science and Engineering Research Council of Canada (NSERC), Discovery Grant Program (PI Sanchez-Azofeifa). We also thank the Spanish institutions and programs Instituto Geogr{\'a}fico Nacional, Organismo Aut{\'o}nomo de Parques Nacionales and Inventario Forestal Nacional for supporting this science with open data. The Council for Scientific and Industrial Research (CSIR) project {"}National Woody Vegetation Monitoring System for Ecosystem and Value-added Services{"} contributed to the collection of South African ALS and field data. We also thank the Sabie Sand Wildtuin, South African National Parks (SANPARKS), the Wits Rural Knowledge Hub and the Bushbuckridge Municipality in South Africa, for support in the South African field data collection. Additional Australian data were collected as part of the SMAPEx project funded by an Australian Research Council Discovery Project ( DP0984586 ). We thank Shell Gabon and the Smithsonian Conservation Biology Institute for funding the Rabi plot in Gabon, which is contribution No. 204 of the Gabon Biodiversity Program. We also acknowledge funding in French Guiana from CNES and {"}Investissement d'Avenir{"} grants managed by Agence Nationale de la Recherche (CEBA, ref. ANR-10-LABX-25-01). We thank the Project LIFE+ ForBioSensing PL “Comprehensive monitoring of stand dynamics in Bia{\l}owie{\.z}a Forest supported with remote sensing techniques{"} co-funded by Life Plus (contract number LIFE13 ENV/PL/000048) and Poland{\textquoteright}s National Fund for Environmental Protection and Water Management (contract number 485/2014/WN10/OP-NM-LF/D) for funding the collection of the Polish data, and Rafa{\l} Sadkowski for helping with data preparation from the ForBioSensing project. We also thank The Silva Tarouca Research Institute (Czech Republic) for collecting and providing field reference data under an INTER-ACTION project ( LTAUSA18200 ). We also thank the former NERC Airborne Research Facility for their support with airborne data collection, and funding for airborne Lidar data provided by the Australian Department of Agriculture, Fisheries, and Forestry (DAFF). We also thank the Norwegian Agency for Development Cooperation (Norad), although the views expressed in this publication do not necessarily reflect the views of Norad. We also acknowledge DfID and UK Natural Environment Research Council ( NE/P004806/1 ) for collection of field data. The Tanzanian field work for this study was carried out as part of the project “Enhancing the measuring, reporting and verification (MRV) of forests in Tanzania through the application of advanced remote sensing techniques”, funded by the Royal Norwegian Embassy in Tanzania as part of the Norwegian International Climate and Forest Initiative. Finally, data from RAINFOR plots were supported by the Moore Foundation , and SERNANP (Peru) granted research permissions. Funding Information: Armston, Kellner, Hancock, and Dubayah were supported by NASA Contract #NNL 15AA03C to the University of Maryland for the development and execution of the GEDI mission. Duncanson and Minor were supported by a NASA GEDI Science Team Grant NNH20ZDA001N and a NASA Post Doctoral Program fellowship. Saarela was supported through NASA Carbon Monitoring System Grant 80HQTR18T0016, and Healey and Patterson were funded by the GEDI mission through Interagency Agreement RPO201523. We thank the NASA Terrestrial Ecology program for continued support of the GEDI mission, and the University of Maryland for providing independent financial support of the GEDI mission. We also thank NASA for contributing to several lidar data collections used in this study, including from the NASA Carbon Monitoring System (Grant number NNH13AW621, to PI Cohen at the USFS Service). We also gratefully acknowledge the collection and provision of field and airborne data from a wide variety of other sources, including by the Sustainable Landscapes Brazil project supported by the Brazilian Agricultural Research Corporation (EMBRAPA), the US Forest Service, the National Science Foundation (DEB 0939907), Smithsonian Tropical Research Institute, USAID, and the US Department of State, among others. Additional data were acquired from the Terrestrial Ecosystem Research Network (TERN), an Australian Government NCRIS-enabled research infrastructure project, for provision of data used in this analysis, and from the National Ecological Observatory Network (NEON), a program sponsored by the National Science Foundation and operated under cooperative agreement by Battelle. We also thank the National Science and Engineering Research Council of Canada (NSERC), Discovery Grant Program (PI Sanchez-Azofeifa). We also thank the Spanish institutions and programs Instituto Geogr?fico Nacional, Organismo Aut?nomo de Parques Nacionales and Inventario Forestal Nacional for supporting this science with open data. The Council for Scientific and Industrial Research (CSIR) project ?National Woody Vegetation Monitoring System for Ecosystem and Value-added Services? contributed to the collection of South African ALS and field data. We also thank the Sabie Sand Wildtuin, South African National Parks (SANPARKS), the Wits Rural Knowledge Hub and the Bushbuckridge Municipality in South Africa, for support in the South African field data collection. Additional Australian data were collected as part of the SMAPEx project funded by an Australian Research Council Discovery Project (DP0984586). We thank Shell Gabon and the Smithsonian Conservation Biology Institute for funding the Rabi plot in Gabon, which is contribution No. 204 of the Gabon Biodiversity Program. We also acknowledge funding in French Guiana from CNES and ?Investissement d'Avenir? grants managed by Agence Nationale de la Recherche (CEBA, ref. ANR-10-LABX-25-01). We thank the Project LIFE+ ForBioSensing PL ?Comprehensive monitoring of stand dynamics in Bia?owie?a Forest supported with remote sensing techniques{"} co-funded by Life Plus (contract number LIFE13 ENV/PL/000048) and Poland's National Fund for Environmental Protection and Water Management (contract number 485/2014/WN10/OP-NM-LF/D) for funding the collection of the Polish data, and Rafa? Sadkowski for helping with data preparation from the ForBioSensing project. We also thank The Silva Tarouca Research Institute (Czech Republic) for collecting and providing field reference data under an INTER-ACTION project (LTAUSA18200). We also thank the former NERC Airborne Research Facility for their support with airborne data collection, and funding for airborne Lidar data provided by the Australian Department of Agriculture, Fisheries, and Forestry (DAFF). We also thank the Norwegian Agency for Development Cooperation (Norad), although the views expressed in this publication do not necessarily reflect the views of Norad. We also acknowledge DfID and UK Natural Environment Research Council (NE/P004806/1) for collection of field data. The Tanzanian field work for this study was carried out as part of the project ?Enhancing the measuring, reporting and verification (MRV) of forests in Tanzania through the application of advanced remote sensing techniques?, funded by the Royal Norwegian Embassy in Tanzania as part of the Norwegian International Climate and Forest Initiative. Finally, data from RAINFOR plots were supported by the Moore Foundation, and SERNANP (Peru) granted research permissions. Publisher Copyright: {\textcopyright} 2021 The Authors",

year = "2022",

month = mar,

day = "1",

doi = "10.1016/j.rse.2021.112845",

language = "English",

volume = "270",

journal = "Remote Sensing of Environment",

issn = "0034-4257",

publisher = "Elsevier",

}

Duncanson, L, Kellner, JR, Armston, J, Dubayah, R, Minor, DM, Hancock, S, Healey, SP, Patterson, PL, Saarela, S, Marselis, S, Silva, CE, Bruening, J, Goetz, SJ, Tang, H, Hofton, M, Blair, B, Luthcke, S, Fatoyinbo, L, Abernethy, K, Alonso, A, Andersen, H, Aplin, P, Baker, TR, Barbier, N, Bastin, JF, Biber, P, Boeckx, P, Bogaert, J, Boschetti, L, Boucher, PB, Boyd, DS, Burslem, DFRP, Calvo-rodriguez, S, Chave, J, Chazdon, RL, Clark, DB, Clark, DA, Cohen, WB, Coomes, DA, Corona, P, Cushman, KC, Cutler, MEJ, Dalling, JW, Dalponte, M, Dash, J, De-miguel, S, Deng, S, Ellis, PW, Erasmus, B, Fekety, PA, Fernandez-landa, A, Ferraz, A, Fischer, R, Fisher, AG, García-abril, A, Gobakken, T, Hacker, JM, Heurich, M, Hill, RA, Hopkinson, C, Huang, H, Hubbell, SP, Hudak, AT, Huth, A, Imbach, B, Jeffery, KJ, Katoh, M, Kearsley, E, Kenfack, D, Kljun, N, Knapp, N, Král, K, Krůček, M, Labrière, N, Lewis, SL, Longo, M, Lucas, RM, Main, R, Manzanera, JA, Martínez, RV, Mathieu, R, Memiaghe, H, Meyer, V, Mendoza, AM, Monerris, A, Montesano, P, Morsdorf, F, Næsset, E, Naidoo, L, Nilus, R, O’brien, M, Orwig, DA, Papathanassiou, K, Parker, G, Philipson, C, Phillips, OL, Pisek, J, Poulsen, JR, Pretzsch, H, Rüdiger, C, Saatchi, S, Sanchez-azofeifa, A, Sanchez-lopez, N, Scholes, R, Silva, CA, Simard, M, Skidmore, A, Stereńczak, K, Tanase, M, Torresan, C, Valbuena, R, Verbeeck, H, Vrska, T, Wessels, K, White, JC, White, LJT, Zahabu, E & Zgraggen, C 2022, 'Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission', Remote Sensing of Environment, vol. 270, 112845. https://doi.org/10.1016/j.rse.2021.112845

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

N1 - Funding Information: Armston, Kellner, Hancock, and Dubayah were supported by NASA Contract #NNL 15AA03C to the University of Maryland for the development and execution of the GEDI mission. Duncanson and Minor were supported by a NASA GEDI Science Team Grant NNH20ZDA001N and a NASA Post Doctoral Program fellowship. Saarela was supported through NASA Carbon Monitoring System Grant 80HQTR18T0016 , and Healey and Patterson were funded by the GEDI mission through Interagency Agreement RPO201523 . We thank the NASA Terrestrial Ecology program for continued support of the GEDI mission, and the University of Maryland for providing independent financial support of the GEDI mission. We also thank NASA for contributing to several lidar data collections used in this study, including from the NASA Carbon Monitoring System (Grant number NNH13AW621 , to PI Cohen at the USFS Service). We also gratefully acknowledge the collection and provision of field and airborne data from a wide variety of other sources, including by the Sustainable Landscapes Brazil project supported by the Brazilian Agricultural Research Corporation (EMBRAPA) , the US Forest Service, the National Science Foundation ( DEB 0939907 ), Smithsonian Tropical Research Institute, USAID, and the US Department of State, among others. Additional data were acquired from the Terrestrial Ecosystem Research Network (TERN), an Australian Government NCRIS-enabled research infrastructure project, for provision of data used in this analysis, and from the National Ecological Observatory Network (NEON), a program sponsored by the National Science Foundation and operated under cooperative agreement by Battelle. We also thank the National Science and Engineering Research Council of Canada (NSERC), Discovery Grant Program (PI Sanchez-Azofeifa). We also thank the Spanish institutions and programs Instituto Geográfico Nacional, Organismo Autónomo de Parques Nacionales and Inventario Forestal Nacional for supporting this science with open data. The Council for Scientific and Industrial Research (CSIR) project "National Woody Vegetation Monitoring System for Ecosystem and Value-added Services" contributed to the collection of South African ALS and field data. We also thank the Sabie Sand Wildtuin, South African National Parks (SANPARKS), the Wits Rural Knowledge Hub and the Bushbuckridge Municipality in South Africa, for support in the South African field data collection. Additional Australian data were collected as part of the SMAPEx project funded by an Australian Research Council Discovery Project ( DP0984586 ). We thank Shell Gabon and the Smithsonian Conservation Biology Institute for funding the Rabi plot in Gabon, which is contribution No. 204 of the Gabon Biodiversity Program. We also acknowledge funding in French Guiana from CNES and "Investissement d'Avenir" grants managed by Agence Nationale de la Recherche (CEBA, ref. ANR-10-LABX-25-01). We thank the Project LIFE+ ForBioSensing PL “Comprehensive monitoring of stand dynamics in Białowieża Forest supported with remote sensing techniques" co-funded by Life Plus (contract number LIFE13 ENV/PL/000048) and Poland’s National Fund for Environmental Protection and Water Management (contract number 485/2014/WN10/OP-NM-LF/D) for funding the collection of the Polish data, and Rafał Sadkowski for helping with data preparation from the ForBioSensing project. We also thank The Silva Tarouca Research Institute (Czech Republic) for collecting and providing field reference data under an INTER-ACTION project ( LTAUSA18200 ). We also thank the former NERC Airborne Research Facility for their support with airborne data collection, and funding for airborne Lidar data provided by the Australian Department of Agriculture, Fisheries, and Forestry (DAFF). We also thank the Norwegian Agency for Development Cooperation (Norad), although the views expressed in this publication do not necessarily reflect the views of Norad. We also acknowledge DfID and UK Natural Environment Research Council ( NE/P004806/1 ) for collection of field data. The Tanzanian field work for this study was carried out as part of the project “Enhancing the measuring, reporting and verification (MRV) of forests in Tanzania through the application of advanced remote sensing techniques”, funded by the Royal Norwegian Embassy in Tanzania as part of the Norwegian International Climate and Forest Initiative. Finally, data from RAINFOR plots were supported by the Moore Foundation , and SERNANP (Peru) granted research permissions. Funding Information: Armston, Kellner, Hancock, and Dubayah were supported by NASA Contract #NNL 15AA03C to the University of Maryland for the development and execution of the GEDI mission. Duncanson and Minor were supported by a NASA GEDI Science Team Grant NNH20ZDA001N and a NASA Post Doctoral Program fellowship. Saarela was supported through NASA Carbon Monitoring System Grant 80HQTR18T0016, and Healey and Patterson were funded by the GEDI mission through Interagency Agreement RPO201523. We thank the NASA Terrestrial Ecology program for continued support of the GEDI mission, and the University of Maryland for providing independent financial support of the GEDI mission. We also thank NASA for contributing to several lidar data collections used in this study, including from the NASA Carbon Monitoring System (Grant number NNH13AW621, to PI Cohen at the USFS Service). We also gratefully acknowledge the collection and provision of field and airborne data from a wide variety of other sources, including by the Sustainable Landscapes Brazil project supported by the Brazilian Agricultural Research Corporation (EMBRAPA), the US Forest Service, the National Science Foundation (DEB 0939907), Smithsonian Tropical Research Institute, USAID, and the US Department of State, among others. Additional data were acquired from the Terrestrial Ecosystem Research Network (TERN), an Australian Government NCRIS-enabled research infrastructure project, for provision of data used in this analysis, and from the National Ecological Observatory Network (NEON), a program sponsored by the National Science Foundation and operated under cooperative agreement by Battelle. We also thank the National Science and Engineering Research Council of Canada (NSERC), Discovery Grant Program (PI Sanchez-Azofeifa). We also thank the Spanish institutions and programs Instituto Geogr?fico Nacional, Organismo Aut?nomo de Parques Nacionales and Inventario Forestal Nacional for supporting this science with open data. The Council for Scientific and Industrial Research (CSIR) project ?National Woody Vegetation Monitoring System for Ecosystem and Value-added Services? contributed to the collection of South African ALS and field data. We also thank the Sabie Sand Wildtuin, South African National Parks (SANPARKS), the Wits Rural Knowledge Hub and the Bushbuckridge Municipality in South Africa, for support in the South African field data collection. Additional Australian data were collected as part of the SMAPEx project funded by an Australian Research Council Discovery Project (DP0984586). We thank Shell Gabon and the Smithsonian Conservation Biology Institute for funding the Rabi plot in Gabon, which is contribution No. 204 of the Gabon Biodiversity Program. We also acknowledge funding in French Guiana from CNES and ?Investissement d'Avenir? grants managed by Agence Nationale de la Recherche (CEBA, ref. ANR-10-LABX-25-01). We thank the Project LIFE+ ForBioSensing PL ?Comprehensive monitoring of stand dynamics in Bia?owie?a Forest supported with remote sensing techniques" co-funded by Life Plus (contract number LIFE13 ENV/PL/000048) and Poland's National Fund for Environmental Protection and Water Management (contract number 485/2014/WN10/OP-NM-LF/D) for funding the collection of the Polish data, and Rafa? Sadkowski for helping with data preparation from the ForBioSensing project. We also thank The Silva Tarouca Research Institute (Czech Republic) for collecting and providing field reference data under an INTER-ACTION project (LTAUSA18200). We also thank the former NERC Airborne Research Facility for their support with airborne data collection, and funding for airborne Lidar data provided by the Australian Department of Agriculture, Fisheries, and Forestry (DAFF). We also thank the Norwegian Agency for Development Cooperation (Norad), although the views expressed in this publication do not necessarily reflect the views of Norad. We also acknowledge DfID and UK Natural Environment Research Council (NE/P004806/1) for collection of field data. The Tanzanian field work for this study was carried out as part of the project ?Enhancing the measuring, reporting and verification (MRV) of forests in Tanzania through the application of advanced remote sensing techniques?, funded by the Royal Norwegian Embassy in Tanzania as part of the Norwegian International Climate and Forest Initiative. Finally, data from RAINFOR plots were supported by the Moore Foundation, and SERNANP (Peru) granted research permissions. Publisher Copyright: © 2021 The Authors

PY - 2022/3/1

Y1 - 2022/3/1

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 - Aboveground biomass

KW - Forest

KW - GEDI

KW - LiDAR

KW - Modeling

KW - Waveform

U2 - 10.1016/j.rse.2021.112845

DO - 10.1016/j.rse.2021.112845

M3 - Article

SN - 0034-4257

VL - 270

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

M1 - 112845

ER -

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

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