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

Edward T. A. Mitchard 1 Ted R. Feldpausch 2, 3 Roel J. W. Brienen 3 Gabriela Lopez-Gonzalez 3 Abel Monteagudo 4 Timothy R. Baker 3 Simon L. Lewis 5, 3 Jon Lloyd 6 Carlos A. Quesada 7 Manuel Gloor 3 Hans Ter Steege 8, 9 Patrick Meir 10, 1 Esteban Alvarez Alejandro Araujo-Murakami 11, 12 Luiz E. O. C. Aragao 13, 2 Luzmila Arroyo 12, 11 Gerardo Aymard 14 Olaf Banki 15 Damien Bonal 16 Sandra Brown 17 Foster I. Brown 18, 19 Carlos E. Ceron 20 Victor Chama Moscoso 4 Jerome Chave 21 James A. Comiskey Fernando Cornejo 22 Massiel Corrales Medina 23 Lola Da Costa 24 Flavia R. C. Costa 7 Anthony Di Fiore 25 Tomas F. Domingues 26 Terry L. Erwin 27 Todd Frederickson Niro Higuchi 7 Euridice N. Honorio Coronado 28, 3 Tim J. Killeen 29 William F. Laurance 30 Carolina Levis 7 William E. Magnusson 7 Beatriz S. Marimon 31 Ben Hur Marimon Junior 31 Irina Mendoza Polo Piyush Mishra 32 Marcelo T. Nascimento 33 David Neill 34 Mario P. Nunez Vargas 35 Walter A. Palacios Alexander Parada 12, 11 Guido Pardo Molina 36 Marielos Pena-Claros 37, 38 Nigel Pitman 39 Carlos A. Peres 40 Lourens Poorter 37 Adriana Prieto 41 Hirma Ramirez-Angulo 42 Zorayda Restrepo Correa Anand Roopsind 43 Katherine H. Roucoux 3 Agustin Rudas 41 Emilio Salomao 28 Juliana Schietti 7 Marcos Silveira 18 Priscila F. De Souza 3 Marc K. Steininger Juliana Stropp John Terborgh Raquel Thomas Marisol Toledo 11, 38 Armando Torres-Lezama 42 Tinde R. Van Andel 8 Geertje M. F. Van Der Heijden 44, 45 Ima Vieira 28 Simone Vieira 46 Emilio Vilanova-Torre 42 Vincent A. Vos Ophelia Wang 47 Charles E. Zartman 7 Yadvinder Malhi 48 Oliver L. Phillips 3
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: 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.
Type de document :
Article dans une revue
Global Ecology and Biogeography, Wiley, 2014, 23 (8), pp.935 - 946. 〈10.1111/geb.12168〉
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Edward T. A. Mitchard, Ted R. Feldpausch, Roel J. W. Brienen, Gabriela Lopez-Gonzalez, Abel Monteagudo, et al.. Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites. Global Ecology and Biogeography, Wiley, 2014, 23 (8), pp.935 - 946. 〈10.1111/geb.12168〉. 〈hal-01555979〉



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