Please use this identifier to cite or link to this item: https://repositorio.inpa.gov.br/handle/1/17776
Title: Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites
Authors: Mitchard, Edward T.A.
Feldpausch, Ted R.
Brienen, Roel J.W.
Lopez-Gonzalez, Gabriela
Monteagudo, Abel Lorenzo
Baker, Timothy R.
Lewis, Simon L.
Lloyd, Jon
Quesada, Carlos Alberto
Gloor, Manuel E.
ter Steege, H.
Meir, Patrick W.
Alvarez, Esteban
Araujo-Murakami, Alejandro
Aragao, L. E.O.C.
Arroyo, Luzmila P.
Aymard, Gerardo Antonio C.
Bánki, Olaf S.
Bonal, Damien
Brown, Sandra L.
Brown, Foster I.
Cerón, Carlos E.
Chama Moscoso, Victor
Chave, Jérôme
Comiskey, James A.
Cornejo, Fernando H.
Corrales Medina, Massiel
Costa, Lola da
Costa, Flávia Regina Capellotto
Di Fiore, Anthony
null, Tomas
Erwin, Terry L.
Frederickson, Todd
Higuchi, Niro
Honorio Coronado, Euridice N.
Killeen, Timothy J.
Laurance, William F.
Levis, Carolina
Magnusson, William Ernest
Marimon, Beatriz Schwantes
Marimon Júnior, Ben Hur
Mendoza Polo, Irina
Mishra, Piyush
Nascimento, Marcelo Trindade
Neill, David A.
Núñez-Vargas, Mario Percy
Palacios, Walter A.
Parada, Alexander G.
Pardo-Molina, Guido
Pena-Claros, Marielos
Pitman, Nigel C.A.
Peres, Carlos A.
Poorter, L.
Prieto, Adriana
Ramírez-Angulo, Hirma
Restrepo-Correa, Zorayda
Roopsind, Anand
Roucoux, Katherine H.
Rudas, Agustín
Salomão, Rafael Paiva
Schietti, Juliana
Silveira, Marcos
Souza, Priscila Figueira de
Steininger, Marc K.
Stropp, Juliana
Terborgh, John W.
Thomas, Raquel S.
Toledo, Marisol
Torres-Lezama, Armando
van Andel, Tinde
Van Der Heijden, Geertje M.F.
Guimarães Vieira, Ima Cèlia
Vieira, Simone Aparecida
Vilanova, Emilio
Vos, Vincent A.
Wang, Ophelia
Zartman, Charles Eugene
Malhi, Yadvinder Singh
Phillips, Oliver L.
Keywords: Accuracy Assessment
Allometry
Carbon Cycle
Conservation Management
Deforestation
Emission Control
Environmental Degradation
Forest Management
Mapping Method
Remote Sensing
Satellite Imagery
Tropical Forest
Wood
Amazon Basin
Issue Date: 2014
metadata.dc.publisher.journal: Global Ecology and Biogeography
metadata.dc.relation.ispartof: Volume 23, Número 8, Pags. 935-946
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. © 2014 The Authors. Global Ecology and Biogeography published by John Wiley & Sons Ltd..
URI: https://repositorio.inpa.gov.br/handle/1/17776
metadata.dc.identifier.doi: 10.1111/geb.12168
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