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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

SUBMITTER: Mitchard ET 

PROVIDER: S-EPMC4579864 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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

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

Global ecology and biogeography : a journal of macroecology 20140422 8


<h4>Aim</h4>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.<h4>Location</h4>Tropica  ...[more]

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