Produção Nacional
Revisado por pares
Markedly divergent estimates of A mazon forest carbon density from ground plots and satellites
2014; Wiley; Volume: 23; Issue: 8 Linguagem: Inglês
10.1111/geb.12168
ISSN1466-8238
AutoresEdward T. A. Mitchard, Ted R. Feldpausch, Roel J. W. Brienen, Gabriela López‐González, Abel Monteagudo, Timothy R. Baker, Simon L. Lewis, Jon Lloyd, Carlos Alberto Quesada, Manuel Gloor, Hans ter Steege, Patrick Meir, Esteban Álvarez‐Dávila, Alejandro Araujo‐Murakami, Luiz E. O. C. Aragão, Luzmila Arroyo, Gerardo A. Aymard C., Olaf Bánki, Damien Bonal, Sandra Brown, Foster Brown, Carlos Cerón, Víctor Chama Moscoso, Jérôme Chave, James A. Comiskey, Fernando Cornejo Valverde, Massiel Corrales Medina, Lola da Costa, Flávia R. C. Costa, Anthony Di Fiore, Tomas F. Domingues, Terry L. Erwin, Todd Frederickson, Níro Higuchi, Eurídice N. Honorio Coronado, Tim J. Killeen, Susan G. W. Laurance, Carolina Levis, William E. Magnusson, Beatriz Schwantes Marimon, Ben Hur Marimon, Irina Polo, Piyush Mishra, Marcelo Trindade Nascimento, David Neill, Mario Percy Núñez Vargas, Walter A. Palacios, Alexander Parada, Guido Pardo Molina, Marielos Peña‐Claros, Nigel C. A. Pitman, Carlos A. Peres, Lourens Poorter, Adriana Prieto, Hirma Ramírez‐Angulo, Zorayda Restrepo, Anand Roopsind, Katherine H. Roucoux, Agustín Rudas, Rafael P. Salomão, Juliana Schietti, Marcos Silveira, Priscila Figueira de Souza, Marc K. Steininger, Juliana Stropp, John Terborgh, Raquel Thomas, Marisol Toledo, Armando Torres‐Lezama, Tinde van Andel, Geertje van der Heijden, Ima Célia Guimarães Vieira, Simone Aparecida Vieira, Emilio Vilanova, Vincent Antoine Vos, Ophelia Wang, Charles E. Zartman, Yadvinder Malhi, Oliver L. Phillips,
Tópico(s)Remote Sensing in Agriculture
ResumoThe 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.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.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.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%.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.
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