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

Authors :
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
Department of Natural Resources
UT-I-ITC-FORAGES
Faculty of Geo-Information Science and Earth Observation
University of Maryland [College Park]
University of Maryland System
Brown University
University of Edinburgh
USDA Forest Service Rocky Mountain Forest and Range Experiment Station
United States Department of Agriculture (USDA)
Swedish University of Agricultural Sciences (SLU)
Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP)
Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Gembloux Agro-Bio Tech [Gembloux]
Université de Liège
Source :
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, Alonso, A, Andersen, H, Aplin, P, Baker, T R, Barbier, N, Bastin, J F, Biber, P, Boeckx, P, Bogaert, J, Boschetti, L, Boucher, P B, Boyd, D S, Burslem, D F R P, Calvo-rodriguez, S, Chave, J, Chazdon, R L, Clark, D B, Clark, D A, Cohen, W B, Coomes, D A, Corona, P, Cushman, K C, Cutler, M E J, Dalling, J W, Dalponte, M, Dash, J, De-miguel, S, Deng, S, Ellis, P W, Erasmus, B, Fekety, P A, Fernandez-landa, A, Ferraz, A, Fischer, R, Fisher, A G, García-abril, A, Gobakken, T, Hacker, J M, Heurich, M, Hill, R A, Hopkinson, C, Huang, H, Hubbell, S P, Hudak, A T, Huth, A, Imbach, B, Jeffery, K J, Katoh, M, Kearsley, E, Kenfack, D, Kljun, N, Knapp, N, Král, K, Krůček, M, Labrière, N, Lewis, S L, Longo, M, Lucas, R M, Main, R, Manzanera, J A, Martínez, R V, Mathieu, R, Memiaghe, H, Meyer, V, Mendoza, A M, Monerris, A, Montesano, P, Morsdorf, F, Næsset, E, Naidoo, L, Nilus, R, O’brien, M, Orwig, D A, Papathanassiou, K, Parker, G, Philipson, C, Phillips, O L, Pisek, J, Poulsen, J R, Pretzsch, H, Rüdiger, C, Saatchi, S, Sanchez-azofeifa, A, Sanchez-lopez, N, Scholes, R, Silva, C A, Simard, M, Skidmore, A, Stereńczak, K, Tanase, M, Torresan, C, Valbuena, R, Verbeeck, H, Vrska, T, Wessels, K, White, J C, White, L J T, 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, Remote Sensing of Environment, 270, Remote sensing of environment, 270:112845, 1-20. Elsevier, Remote Sensing of Environment, Remote Sensing of Environment, 2022, 270, pp.112845. ⟨10.1016/j.rse.2021.112845⟩, Remote sensing of environment 270 (2022): Article number 112845. doi:10.1016/j.rse.2021.112845, info:cnr-pdr/source/autori: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 E.C., Bruening J., Goetz J.S., Tang H., Hofton M., Blair B., Luthcke S., Fatoyinbo L., Abernethy K., Alonso A., Andersen H.E., Aplin P., Baker R.T., Barbier N., Bastin J.F., Biber P., Boeckx P., Bogaert J., Boschetti L., Brehm Boucher P., Boyd S.D., Burslem F.R.P.D., Calvo-Rodriguez S., Chave J., Chazdon L.R., Clark B.D., Clark A.D., Cohen B.W., Coomes A.D., Corona P., Cushman K.C., Cutler E.J.M., Dalling W.J., Dalponte M., Dash J., de-Miguel S., Deng S., Woods Ellis P., Erasmus B., Fekety A.P., Fernandez-Landa A., Ferraz A., Fischer R., Fisher G.A., García-Abril A., Gobakken T., Hacker M.J., Heurich M., Hill A.R., Hopkinson C., Huang H., Hubbell P.S., Hudak T.A., Huth A., Imbach B., Jeffery J.K., Katoh M., Kearsley E., Kenfack D., Kljun N., Knapp N., Král K., Kr??ek M., Labrière N., Lewis L.S., Longo M., Lucas M.R., Main R., Manzanera A.J., Martínez V.R., Mathieu R., Memiaghe H., Meyer V., Monteagudo Mendoza A., Monerris A., Montesano P., Morsdorf F., Næsset E., Naidoo L., Nilus R., O'Brien M., Orwig A.D., Papathanassiou K., Parker G., Philipson C., Phillips L.O., Pisek J., Poulsen R.J., Pretzsch h., Rüdiger C., Saatchi S., Sanchez-Azofeifa A., Sanchez-Lopez N., Scholes R., Silva A.C., Simard M., Skidmore A., Stere?czak K., Tanase M., Torresan C., Valbuena R., Verbeeck H., Vrska T., Wessels K., White C.J., White J.T.L., Zahabu E., Zgraggen C./titolo:Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission/doi:10.1016%2Fj.rse.2021.112845/rivista:Remote sensing of environment/anno:2022/pagina_da:Article number 112845/pagina_a:/intervallo_pagine:Article number 112845/volume:270, REMOTE SENSING OF ENVIRONMENT
Publication Year :
2022

Abstract

© 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). 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.

Details

Language :
English
ISSN :
00344257 and 18790704
Database :
OpenAIRE
Journal :
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, Alonso, A, Andersen, H, Aplin, P, Baker, T R, Barbier, N, Bastin, J F, Biber, P, Boeckx, P, Bogaert, J, Boschetti, L, Boucher, P B, Boyd, D S, Burslem, D F R P, Calvo-rodriguez, S, Chave, J, Chazdon, R L, Clark, D B, Clark, D A, Cohen, W B, Coomes, D A, Corona, P, Cushman, K C, Cutler, M E J, Dalling, J W, Dalponte, M, Dash, J, De-miguel, S, Deng, S, Ellis, P W, Erasmus, B, Fekety, P A, Fernandez-landa, A, Ferraz, A, Fischer, R, Fisher, A G, García-abril, A, Gobakken, T, Hacker, J M, Heurich, M, Hill, R A, Hopkinson, C, Huang, H, Hubbell, S P, Hudak, A T, Huth, A, Imbach, B, Jeffery, K J, Katoh, M, Kearsley, E, Kenfack, D, Kljun, N, Knapp, N, Král, K, Krůček, M, Labrière, N, Lewis, S L, Longo, M, Lucas, R M, Main, R, Manzanera, J A, Martínez, R V, Mathieu, R, Memiaghe, H, Meyer, V, Mendoza, A M, Monerris, A, Montesano, P, Morsdorf, F, Næsset, E, Naidoo, L, Nilus, R, O’brien, M, Orwig, D A, Papathanassiou, K, Parker, G, Philipson, C, Phillips, O L, Pisek, J, Poulsen, J R, Pretzsch, H, Rüdiger, C, Saatchi, S, Sanchez-azofeifa, A, Sanchez-lopez, N, Scholes, R, Silva, C A, Simard, M, Skidmore, A, Stereńczak, K, Tanase, M, Torresan, C, Valbuena, R, Verbeeck, H, Vrska, T, Wessels, K, White, J C, White, L J T, 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, Remote Sensing of Environment, 270, Remote sensing of environment, 270:112845, 1-20. Elsevier, Remote Sensing of Environment, Remote Sensing of Environment, 2022, 270, pp.112845. ⟨10.1016/j.rse.2021.112845⟩, Remote sensing of environment 270 (2022): Article number 112845. doi:10.1016/j.rse.2021.112845, info:cnr-pdr/source/autori: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 E.C., Bruening J., Goetz J.S., Tang H., Hofton M., Blair B., Luthcke S., Fatoyinbo L., Abernethy K., Alonso A., Andersen H.E., Aplin P., Baker R.T., Barbier N., Bastin J.F., Biber P., Boeckx P., Bogaert J., Boschetti L., Brehm Boucher P., Boyd S.D., Burslem F.R.P.D., Calvo-Rodriguez S., Chave J., Chazdon L.R., Clark B.D., Clark A.D., Cohen B.W., Coomes A.D., Corona P., Cushman K.C., Cutler E.J.M., Dalling W.J., Dalponte M., Dash J., de-Miguel S., Deng S., Woods Ellis P., Erasmus B., Fekety A.P., Fernandez-Landa A., Ferraz A., Fischer R., Fisher G.A., García-Abril A., Gobakken T., Hacker M.J., Heurich M., Hill A.R., Hopkinson C., Huang H., Hubbell P.S., Hudak T.A., Huth A., Imbach B., Jeffery J.K., Katoh M., Kearsley E., Kenfack D., Kljun N., Knapp N., Král K., Kr??ek M., Labrière N., Lewis L.S., Longo M., Lucas M.R., Main R., Manzanera A.J., Martínez V.R., Mathieu R., Memiaghe H., Meyer V., Monteagudo Mendoza A., Monerris A., Montesano P., Morsdorf F., Næsset E., Naidoo L., Nilus R., O'Brien M., Orwig A.D., Papathanassiou K., Parker G., Philipson C., Phillips L.O., Pisek J., Poulsen R.J., Pretzsch h., Rüdiger C., Saatchi S., Sanchez-Azofeifa A., Sanchez-Lopez N., Scholes R., Silva A.C., Simard M., Skidmore A., Stere?czak K., Tanase M., Torresan C., Valbuena R., Verbeeck H., Vrska T., Wessels K., White C.J., White J.T.L., Zahabu E., Zgraggen C./titolo:Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission/doi:10.1016%2Fj.rse.2021.112845/rivista:Remote sensing of environment/anno:2022/pagina_da:Article number 112845/pagina_a:/intervallo_pagine:Article number 112845/volume:270, REMOTE SENSING OF ENVIRONMENT
Accession number :
edsair.doi.dedup.....19d0576946c561982b642f75920f5935
Full Text :
https://doi.org/10.1016/j.rse.2021.112845