Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites

Edward T.A. Mitchard; Ted R. Feldpausch; Roel J.W. Brienen; Gabriela Lopez-Gonzalez; Abel Monteagudo; Timothy R. Baker; Simon L. Lewis; Jon Lloyd; Carlos A. Quesada; Manuel Gloor; Hans ter Steege; Patrick Meir; Esteban Alvarez; Alejandro Araujo-Murakami; Luiz E.O.C. Aragão; Luzmila Arroyo; Gerardo Aymard; Olaf Banki; Damien Bonal; Sandra Brown; Foster I. Brown; Carlos E. Cerón; Victor Chama Moscoso; Jerome Chave; James A. Comiskey; Fernando Cornejo; Massiel Corrales Medina; Lola Da Costa; Flavia R.C. Costa; Anthony Di Fiore; Tomas F. Domingues; Terry L. Erwin; Todd Frederickson; Niro Higuchi; Euridice N. Honorio Coronado; Tim J. Killeen; William F. Laurance; Carolina Levis; William E. Magnusson; Beatriz S. Marimon; Ben Hur Marimon Junior; Irina Mendoza Polo; Piyush Mishra; Marcelo T. Nascimento; David Neill; Mario P. Núñez Vargas; Walter A. Palacios; Alexander Parada; Guido Pardo Molina; Marielos Peña-Claros; Nigel Pitman; Carlos A. Peres; Lourens Poorter; Adriana Prieto; Hirma Ramirez-Angulo; Zorayda Restrepo Correa; Anand Roopsind; Katherine H. Roucoux; Agustin Rudas; Rafael P. Salomão; Juliana Schietti; Marcos Silveira; Priscila F. de Souza; Marc K. Steininger; Juliana Stropp; John Terborgh; Raquel Thomas; Marisol Toledo; Armando Torres-Lezama; Tinde R. Van Andel; Geertje M.F. van der Heijden; Ima C.G. Vieira; Simone Vieira; Emilio Vilanova-Torre; Vincent A. Vos; Ophelia Wang; Charles E. Zartman; Yadvinder Malhi; Oliver L. Phillips

doi:10.1111/geb.12168

Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites

Edward T.A. Mitchard^*, Ted R. Feldpausch, Roel J.W. Brienen, Gabriela Lopez-Gonzalez, Abel Monteagudo, Timothy R. Baker, Simon L. Lewis, Jon Lloyd, Carlos A. Quesada, Manuel Gloor, Hans ter Steege, Patrick Meir, Esteban Alvarez, Alejandro Araujo-Murakami, Luiz E.O.C. Aragão, Luzmila Arroyo, Gerardo Aymard, Olaf Banki, Damien Bonal, Sandra BrownFoster I. Brown, Carlos E. Cerón, Victor Chama Moscoso, Jerome Chave, James A. Comiskey, Fernando Cornejo, Massiel Corrales Medina, Lola Da Costa, Flavia R.C. Costa, Anthony Di Fiore, Tomas F. Domingues, Terry L. Erwin, Todd Frederickson, Niro Higuchi, Euridice N. Honorio Coronado, Tim J. Killeen, William F. Laurance, Carolina Levis, William E. Magnusson, Beatriz S. Marimon, Ben Hur Marimon Junior, Irina Mendoza Polo, Piyush Mishra, Marcelo T. Nascimento, David Neill, Mario P. Núñez Vargas, Walter A. Palacios, Alexander Parada, Guido Pardo Molina, Marielos Peña-Claros, Nigel Pitman, Carlos A. Peres, Lourens Poorter, Adriana Prieto, Hirma Ramirez-Angulo, Zorayda Restrepo Correa, Anand Roopsind, Katherine H. Roucoux, Agustin Rudas, Rafael P. Salomão, Juliana Schietti, Marcos Silveira, Priscila F. de Souza, Marc K. Steininger, Juliana Stropp, John Terborgh, Raquel Thomas, Marisol Toledo, Armando Torres-Lezama, Tinde R. Van Andel, Geertje M.F. van der Heijden, Ima C.G. Vieira, Simone Vieira, Emilio Vilanova-Torre, Vincent A. Vos, Ophelia Wang, Charles E. Zartman, Yadvinder Malhi, Oliver L. Phillips

^*Corresponding author for this work

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

244 Citations (Scopus)

Abstract

Aim: The accurate mapping of forest carbon stocks is essential for understanding the global carbon cycle, for assessing emissions from deforestation, and for rational land-use planning. Remote sensing (RS) is currently the key tool for this purpose, but RS does not estimate vegetation biomass directly, and thus may miss significant spatial variations in forest structure. We test the stated accuracy of pantropical carbon maps using a large independent field dataset. Location: Tropical forests of the Amazon basin. The permanent archive of the field plot data can be accessed at: http://dx.doi.org/10.5521/FORESTPLOTS.NET/2014_1 Methods: Two recent pantropical RS maps of vegetation carbon are compared to a unique ground-plot dataset, involving tree measurements in 413 large inventory plots located in nine countries. The RS maps were compared directly to field plots, and kriging of the field data was used to allow area-based comparisons. Results: The two RS carbon maps fail to capture the main gradient in Amazon forest carbon detected using 413 ground plots, from the densely wooded tall forests of the north-east, to the light-wooded, shorter forests of the south-west. The differences between plots and RS maps far exceed the uncertainties given in these studies, with whole regions over- or under-estimated by >25%, whereas regional uncertainties for the maps were reported to be <5%. Main conclusions: Pantropical biomass maps are widely used by governments and by projects aiming to reduce deforestation using carbon offsets, but may have significant regional biases. Carbon-mapping techniques must be revised to account for the known ecological variation in tree wood density and allometry to create maps suitable for carbon accounting. The use of single relationships between tree canopy height and above-ground biomass inevitably yields large, spatially correlated errors. This presents a significant challenge to both the forest conservation and remote sensing communities, because neither wood density nor species assemblages can be reliably mapped from space.

Original language	English
Pages (from-to)	935-946
Number of pages	12
Journal	Global Ecology and Biogeography
Volume	23
Issue number	8
DOIs	https://doi.org/10.1111/geb.12168
Publication status	Published - Aug 2014

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Mitchard, E. T. A., Feldpausch, T. R., Brienen, R. J. W., Lopez-Gonzalez, G., Monteagudo, A., Baker, T. R., Lewis, S. L., Lloyd, J., Quesada, C. A., Gloor, M., ter Steege, H., Meir, P., Alvarez, E., Araujo-Murakami, A., Aragão, L. E. O. C., Arroyo, L., Aymard, G., Banki, O., Bonal, D., ... Phillips, O. L. (2014). Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites. Global Ecology and Biogeography, 23(8), 935-946. https://doi.org/10.1111/geb.12168

@article{60a744396f8c4dd1b470211d1b0dffa3,

title = "Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites",

abstract = "Aim: The accurate mapping of forest carbon stocks is essential for understanding the global carbon cycle, for assessing emissions from deforestation, and for rational land-use planning. Remote sensing (RS) is currently the key tool for this purpose, but RS does not estimate vegetation biomass directly, and thus may miss significant spatial variations in forest structure. We test the stated accuracy of pantropical carbon maps using a large independent field dataset. Location: Tropical forests of the Amazon basin. The permanent archive of the field plot data can be accessed at: http://dx.doi.org/10.5521/FORESTPLOTS.NET/2014_1 Methods: Two recent pantropical RS maps of vegetation carbon are compared to a unique ground-plot dataset, involving tree measurements in 413 large inventory plots located in nine countries. The RS maps were compared directly to field plots, and kriging of the field data was used to allow area-based comparisons. Results: The two RS carbon maps fail to capture the main gradient in Amazon forest carbon detected using 413 ground plots, from the densely wooded tall forests of the north-east, to the light-wooded, shorter forests of the south-west. The differences between plots and RS maps far exceed the uncertainties given in these studies, with whole regions over- or under-estimated by >25%, whereas regional uncertainties for the maps were reported to be <5%. Main conclusions: Pantropical biomass maps are widely used by governments and by projects aiming to reduce deforestation using carbon offsets, but may have significant regional biases. Carbon-mapping techniques must be revised to account for the known ecological variation in tree wood density and allometry to create maps suitable for carbon accounting. The use of single relationships between tree canopy height and above-ground biomass inevitably yields large, spatially correlated errors. This presents a significant challenge to both the forest conservation and remote sensing communities, because neither wood density nor species assemblages can be reliably mapped from space.",

keywords = "Above-ground biomass, Allometry, Carbon cycle, Remote sensing, Satellite mapping, Wood density",

author = "Mitchard, {Edward T.A.} and Feldpausch, {Ted R.} and Brienen, {Roel J.W.} and Gabriela Lopez-Gonzalez and Abel Monteagudo and Baker, {Timothy R.} and Lewis, {Simon L.} and Jon Lloyd and Quesada, {Carlos A.} and Manuel Gloor and {ter Steege}, Hans and Patrick Meir and Esteban Alvarez and Alejandro Araujo-Murakami and Arag{\~a}o, {Luiz E.O.C.} and Luzmila Arroyo and Gerardo Aymard and Olaf Banki and Damien Bonal and Sandra Brown and Brown, {Foster I.} and Cer{\'o}n, {Carlos E.} and {Chama Moscoso}, Victor and Jerome Chave and Comiskey, {James A.} and Fernando Cornejo and {Corrales Medina}, Massiel and {Da Costa}, Lola and Costa, {Flavia R.C.} and {Di Fiore}, Anthony and Domingues, {Tomas F.} and Erwin, {Terry L.} and Todd Frederickson and Niro Higuchi and {Honorio Coronado}, {Euridice N.} and Killeen, {Tim J.} and Laurance, {William F.} and Carolina Levis and Magnusson, {William E.} and Marimon, {Beatriz S.} and {Marimon Junior}, {Ben Hur} and {Mendoza Polo}, Irina and Piyush Mishra and Nascimento, {Marcelo T.} and David Neill and {N{\'u}{\~n}ez Vargas}, {Mario P.} and Palacios, {Walter A.} and Alexander Parada and {Pardo Molina}, Guido and Marielos Pe{\~n}a-Claros and Nigel Pitman and Peres, {Carlos A.} and Lourens Poorter and Adriana Prieto and Hirma Ramirez-Angulo and {Restrepo Correa}, Zorayda and Anand Roopsind and Roucoux, {Katherine H.} and Agustin Rudas and Salom{\~a}o, {Rafael P.} and Juliana Schietti and Marcos Silveira and {de Souza}, {Priscila F.} and Steininger, {Marc K.} and Juliana Stropp and John Terborgh and Raquel Thomas and Marisol Toledo and Armando Torres-Lezama and {Van Andel}, {Tinde R.} and {van der Heijden}, {Geertje M.F.} and Vieira, {Ima C.G.} and Simone Vieira and Emilio Vilanova-Torre and Vos, {Vincent A.} and Ophelia Wang and Zartman, {Charles E.} and Yadvinder Malhi and Phillips, {Oliver L.}",

year = "2014",

month = aug,

doi = "10.1111/geb.12168",

language = "English",

volume = "23",

pages = "935--946",

journal = "Global Ecology and Biogeography",

issn = "1466-822X",

publisher = "Wiley-Blackwell Publishing Ltd",

number = "8",

}

Mitchard, ETA, Feldpausch, TR, Brienen, RJW, Lopez-Gonzalez, G, Monteagudo, A, Baker, TR, Lewis, SL, Lloyd, J, Quesada, CA, Gloor, M, ter Steege, H, Meir, P, Alvarez, E, Araujo-Murakami, A, Aragão, LEOC, Arroyo, L, Aymard, G, Banki, O, Bonal, D, Brown, S, Brown, FI, Cerón, CE, Chama Moscoso, V, Chave, J, Comiskey, JA, Cornejo, F, Corrales Medina, M, Da Costa, L, Costa, FRC, Di Fiore, A, Domingues, TF, Erwin, TL, Frederickson, T, Higuchi, N, Honorio Coronado, EN, Killeen, TJ, Laurance, WF, Levis, C, Magnusson, WE, Marimon, BS, Marimon Junior, BH, Mendoza Polo, I, Mishra, P, Nascimento, MT, Neill, D, Núñez Vargas, MP, Palacios, WA, Parada, A, Pardo Molina, G, Peña-Claros, M, Pitman, N, Peres, CA, Poorter, L, Prieto, A, Ramirez-Angulo, H, Restrepo Correa, Z, Roopsind, A, Roucoux, KH, Rudas, A, Salomão, RP, Schietti, J, Silveira, M, de Souza, PF, Steininger, MK, Stropp, J, Terborgh, J, Thomas, R, Toledo, M, Torres-Lezama, A, Van Andel, TR, van der Heijden, GMF, Vieira, ICG, Vieira, S, Vilanova-Torre, E, Vos, VA, Wang, O, Zartman, CE, Malhi, Y & Phillips, OL 2014, 'Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites', Global Ecology and Biogeography, vol. 23, no. 8, pp. 935-946. https://doi.org/10.1111/geb.12168

TY - JOUR

T1 - Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites

AU - Mitchard, Edward T.A.

AU - Feldpausch, Ted R.

AU - Brienen, Roel J.W.

AU - Lopez-Gonzalez, Gabriela

AU - Monteagudo, Abel

AU - Baker, Timothy R.

AU - Lewis, Simon L.

AU - Lloyd, Jon

AU - Quesada, Carlos A.

AU - Gloor, Manuel

AU - ter Steege, Hans

AU - Meir, Patrick

AU - Alvarez, Esteban

AU - Araujo-Murakami, Alejandro

AU - Aragão, Luiz E.O.C.

AU - Arroyo, Luzmila

AU - Aymard, Gerardo

AU - Banki, Olaf

AU - Bonal, Damien

AU - Brown, Sandra

AU - Brown, Foster I.

AU - Cerón, Carlos E.

AU - Chama Moscoso, Victor

AU - Chave, Jerome

AU - Comiskey, James A.

AU - Cornejo, Fernando

AU - Corrales Medina, Massiel

AU - Da Costa, Lola

AU - Costa, Flavia R.C.

AU - Di Fiore, Anthony

AU - Domingues, Tomas F.

AU - Erwin, Terry L.

AU - Frederickson, Todd

AU - Higuchi, Niro

AU - Honorio Coronado, Euridice N.

AU - Killeen, Tim J.

AU - Laurance, William F.

AU - Levis, Carolina

AU - Magnusson, William E.

AU - Marimon, Beatriz S.

AU - Marimon Junior, Ben Hur

AU - Mendoza Polo, Irina

AU - Mishra, Piyush

AU - Nascimento, Marcelo T.

AU - Neill, David

AU - Núñez Vargas, Mario P.

AU - Palacios, Walter A.

AU - Parada, Alexander

AU - Pardo Molina, Guido

AU - Peña-Claros, Marielos

AU - Pitman, Nigel

AU - Peres, Carlos A.

AU - Poorter, Lourens

AU - Prieto, Adriana

AU - Ramirez-Angulo, Hirma

AU - Restrepo Correa, Zorayda

AU - Roopsind, Anand

AU - Roucoux, Katherine H.

AU - Rudas, Agustin

AU - Salomão, Rafael P.

AU - Schietti, Juliana

AU - Silveira, Marcos

AU - de Souza, Priscila F.

AU - Steininger, Marc K.

AU - Stropp, Juliana

AU - Terborgh, John

AU - Thomas, Raquel

AU - Toledo, Marisol

AU - Torres-Lezama, Armando

AU - Van Andel, Tinde R.

AU - van der Heijden, Geertje M.F.

AU - Vieira, Ima C.G.

AU - Vieira, Simone

AU - Vilanova-Torre, Emilio

AU - Vos, Vincent A.

AU - Wang, Ophelia

AU - Zartman, Charles E.

AU - Malhi, Yadvinder

AU - Phillips, Oliver L.

PY - 2014/8

Y1 - 2014/8

N2 - Aim: The accurate mapping of forest carbon stocks is essential for understanding the global carbon cycle, for assessing emissions from deforestation, and for rational land-use planning. Remote sensing (RS) is currently the key tool for this purpose, but RS does not estimate vegetation biomass directly, and thus may miss significant spatial variations in forest structure. We test the stated accuracy of pantropical carbon maps using a large independent field dataset. Location: Tropical forests of the Amazon basin. The permanent archive of the field plot data can be accessed at: http://dx.doi.org/10.5521/FORESTPLOTS.NET/2014_1 Methods: Two recent pantropical RS maps of vegetation carbon are compared to a unique ground-plot dataset, involving tree measurements in 413 large inventory plots located in nine countries. The RS maps were compared directly to field plots, and kriging of the field data was used to allow area-based comparisons. Results: The two RS carbon maps fail to capture the main gradient in Amazon forest carbon detected using 413 ground plots, from the densely wooded tall forests of the north-east, to the light-wooded, shorter forests of the south-west. The differences between plots and RS maps far exceed the uncertainties given in these studies, with whole regions over- or under-estimated by >25%, whereas regional uncertainties for the maps were reported to be <5%. Main conclusions: Pantropical biomass maps are widely used by governments and by projects aiming to reduce deforestation using carbon offsets, but may have significant regional biases. Carbon-mapping techniques must be revised to account for the known ecological variation in tree wood density and allometry to create maps suitable for carbon accounting. The use of single relationships between tree canopy height and above-ground biomass inevitably yields large, spatially correlated errors. This presents a significant challenge to both the forest conservation and remote sensing communities, because neither wood density nor species assemblages can be reliably mapped from space.

AB - Aim: The accurate mapping of forest carbon stocks is essential for understanding the global carbon cycle, for assessing emissions from deforestation, and for rational land-use planning. Remote sensing (RS) is currently the key tool for this purpose, but RS does not estimate vegetation biomass directly, and thus may miss significant spatial variations in forest structure. We test the stated accuracy of pantropical carbon maps using a large independent field dataset. Location: Tropical forests of the Amazon basin. The permanent archive of the field plot data can be accessed at: http://dx.doi.org/10.5521/FORESTPLOTS.NET/2014_1 Methods: Two recent pantropical RS maps of vegetation carbon are compared to a unique ground-plot dataset, involving tree measurements in 413 large inventory plots located in nine countries. The RS maps were compared directly to field plots, and kriging of the field data was used to allow area-based comparisons. Results: The two RS carbon maps fail to capture the main gradient in Amazon forest carbon detected using 413 ground plots, from the densely wooded tall forests of the north-east, to the light-wooded, shorter forests of the south-west. The differences between plots and RS maps far exceed the uncertainties given in these studies, with whole regions over- or under-estimated by >25%, whereas regional uncertainties for the maps were reported to be <5%. Main conclusions: Pantropical biomass maps are widely used by governments and by projects aiming to reduce deforestation using carbon offsets, but may have significant regional biases. Carbon-mapping techniques must be revised to account for the known ecological variation in tree wood density and allometry to create maps suitable for carbon accounting. The use of single relationships between tree canopy height and above-ground biomass inevitably yields large, spatially correlated errors. This presents a significant challenge to both the forest conservation and remote sensing communities, because neither wood density nor species assemblages can be reliably mapped from space.

KW - Above-ground biomass

KW - Allometry

KW - Carbon cycle

KW - Remote sensing

KW - Satellite mapping

KW - Wood density

UR - http://www.scopus.com/inward/record.url?scp=84903817503&partnerID=8YFLogxK

U2 - 10.1111/geb.12168

DO - 10.1111/geb.12168

M3 - Article

SN - 1466-822X

VL - 23

SP - 935

EP - 946

JO - Global Ecology and Biogeography

JF - Global Ecology and Biogeography

IS - 8

ER -

Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites

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