Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites
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 direct...
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oai:repositorio:1-17776 Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites 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. 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 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.. 2020-06-15T21:49:13Z 2020-06-15T21:49:13Z 2014 Artigo https://repositorio.inpa.gov.br/handle/1/17776 10.1111/geb.12168 en Volume 23, Número 8, Pags. 935-946 Restrito Global Ecology and Biogeography |
| institution |
Instituto Nacional de Pesquisas da Amazônia - Repositório Institucional |
| collection |
INPA-RI |
| language |
English |
| topic |
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 |
| spellingShingle |
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 Mitchard, Edward T.A. Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites |
| topic_facet |
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 |
| description |
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.. |
| format |
Artigo |
| author |
Mitchard, Edward T.A. |
| author2 |
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. |
| author2Str |
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. |
| title |
Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites |
| title_short |
Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites |
| title_full |
Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites |
| title_fullStr |
Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites |
| title_full_unstemmed |
Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites |
| title_sort |
markedly divergent estimates of amazon forest carbon density from ground plots and satellites |
| publisher |
Global Ecology and Biogeography |
| publishDate |
2020 |
| url |
https://repositorio.inpa.gov.br/handle/1/17776 |
| _version_ |
1787141780766982144 |
| score |
11.678145 |