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Mitchard ET, Feldpausch TR, Brienen RJ, et al. Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites. Glob Ecol Biogeogr. 2014;23(8):935-946doi: 10.1111/geb.12168.
Mitchard, E. T., Feldpausch, T. R., Brienen, R. J., Lopez-Gonzalez, G., Monteagudo, A., Baker, T. R., Lewis, S. L., Lloyd, J., Quesada, C. A., Gloor, M., Ter Steege, H., Meir, P., Alvarez, E., Araujo-Murakami, A., Aragão, L. E., Arroyo, L., Aymard, G., Banki, O., Bonal, D., Brown, S., Brown, F. I., Cerón, C. E., Chama Moscoso, V., Chave, J., Comiskey, J. A., Cornejo, F., Corrales Medina, M., Da Costa, L., Costa, F. R., Di Fiore, A., Domingues, T. F., Erwin, T. L., Frederickson, T., Higuchi, N., Honorio Coronado, E. N., Killeen, T. J., Laurance, W. F., Levis, C., Magnusson, W. E., Marimon, B. S., Marimon Junior, B. H., Mendoza Polo, I., Mishra, P., Nascimento, M. T., Neill, D., Núñez Vargas, M. P., Palacios, W. A., Parada, A., Pardo Molina, G., Peña-Claros, M., Pitman, N., Peres, C. A., Poorter, L., Prieto, A., Ramirez-Angulo, H., Restrepo Correa, Z., Roopsind, A., Roucoux, K. H., Rudas, A., Salomão, R. P., Schietti, J., Silveira, M., de Souza, P. F., Steininger, M. K., Stropp, J., Terborgh, J., Thomas, R., Toledo, M., Torres-Lezama, A., van Andel, T. R., van der Heijden, G. M., Vieira, I. C., Vieira, S., Vilanova-Torre, E., Vos, V. A., Wang, O., Zartman, C. E., Malhi, Y., & Phillips, O. L. (2014). Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites. Global ecology and biogeography : a journal of macroecology, 23(8), 935-946. https://doi.org/10.1111/geb.12168
Mitchard, Edward T A, et al. "Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites." Global ecology and biogeography : a journal of macroecology vol. 23,8 (2014): 935-946. doi: https://doi.org/10.1111/geb.12168
Mitchard ET, Feldpausch TR, Brienen RJ, Lopez-Gonzalez G, Monteagudo A, Baker TR, Lewis SL, Lloyd J, Quesada CA, Gloor M, Ter Steege H, Meir P, Alvarez E, Araujo-Murakami A, Aragão LE, Arroyo L, Aymard G, Banki O, Bonal D, Brown S, Brown FI, Cerón CE, Chama Moscoso V, Chave J, Comiskey JA, Cornejo F, Corrales Medina M, Da Costa L, Costa FR, Di Fiore A, Domingues TF, Erwin TL, Frederickson T, Higuchi N, Honorio Coronado EN, Killeen TJ, Laurance WF, Levis C, Magnusson WE, Marimon BS, Marimon Junior BH, Mendoza Polo I, Mishra P, Nascimento MT, Neill D, Núñez Vargas MP, Palacios WA, Parada A, Pardo Molina G, Peña-Claros M, Pitman N, Peres CA, Poorter L, Prieto A, Ramirez-Angulo H, Restrepo Correa Z, Roopsind A, Roucoux KH, Rudas A, Salomão RP, Schietti J, Silveira M, de Souza PF, Steininger MK, Stropp J, Terborgh J, Thomas R, Toledo M, Torres-Lezama A, van Andel TR, van der Heijden GM, Vieira IC, Vieira S, Vilanova-Torre E, Vos VA, Wang O, Zartman CE, Malhi Y, Phillips OL. Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites. Glob Ecol Biogeogr. 2014 Aug;23(8):935-946. doi: 10.1111/geb.12168. Epub 2014 Apr 22. PMID: 26430387; PMCID: PMC4579864.
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Glob Ecol Biogeogr. 2014 Aug;23(8):935-946. doi: 10.1111/geb.12168. Epub 2014 Apr 22.

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

Global ecology and biogeography : a journal of macroecology

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

Affiliations

  1. School of GeoSciences, University of Edinburgh Edinburgh, EH9 3JN, UK.
  2. School of Geography, University of Leeds Leeds, LS2 9JT, UK ; Geography, College of Life and Environmental Sciences, University of Exeter Exeter, EX4 4RJ, UK.
  3. School of Geography, University of Leeds Leeds, LS2 9JT, UK.
  4. Jardín Botánico de Missouri Oxapampa, Peru.
  5. School of Geography, University of Leeds Leeds, LS2 9JT, UK ; Department of Geography, University College London UK.
  6. Faculty of Natural Sciences, Department of Life Sciences, Imperial College London, UK.
  7. Instituto Nacional de Pesquisas da Amazônia Manaus, Brazil.
  8. Naturalis Biodiversity Center Leiden, the Netherlands ; Institute of Environmental Biology, Utrecht University Utrecht, the Netherlands.
  9. School of GeoSciences, University of Edinburgh Edinburgh, EH9 3JN, UK ; Research School of Biology, Australian National University Canberra, ACT, 0200, Australia.
  10. Jardín Botánico de Medellín, Grupo de Investigación en Servicios Ecosistémicos y Cambio Climático Medellin, Colombia.
  11. Museo de Historia Natural Noel Kempff Mercado, Universidad Autónoma Gabriel René Moreno Casilla 2489, Av. Irala 565, Santa Cruz, Bolivia.
  12. Geography, College of Life and Environmental Sciences, University of Exeter Exeter, EX4 4RJ, UK ; Remote Sensing Division, National Institute for Space Research - INPE São José dos Campos, SP, Brazil.
  13. UNELLEZ-Guanare, Programa de Ciencias del Agro y el Mar, Herbario Universitario (PORT) Mesa de Cavacas, Estado Portuguesa, 3350, Venezuela.
  14. IBED, University of Amsterdam POSTBUS 94248, 1090 GE, Amsterdam, the Netherlands.
  15. L'Institut National de la Recherche Agronomique UMR 1137 EEF, 54280, Champenoux, France.
  16. Ecosystem Services Unit, Winrock International Arlington, VA, 22202, USA.
  17. Woods Hole Research Center Falmouth, MA, USA ; Universidade Federal do Acre, Centro de Ciências Biológicas e da Natureza Rio Branco, AC, 69910-900, Brazil.
  18. Herbario Alfredo Paredes (QAP), Universidad Central del Ecuador Quito, Ecuador.
  19. Université Paul Sabatier, Laboratoire EDB bâtiment 4R3, 31062, Toulouse, France.
  20. National Park Service Fredericksburg, VA, USA.
  21. Universidad Nacional Agraria La Molina, Facultad de Ciencias Forestales Lima, Peru.
  22. Universidad Nacional de San Agustín de Arequipa Arequipa, Peru.
  23. Geociencias, Universidade Federal de Para Belem, Brazil.
  24. Univeristy of Texas Austin, TX, USA.
  25. FFCLRP-USP, Department of Biology, Universidade de São Paulo 05508-090, Brazil.
  26. Department of Entomology, Smithsonian Institution P.O. Box 37012, MRC 187, Washington, DC, 20013-7012, USA.
  27. Ferrum College Ferum, Virginia, USA.
  28. School of Geography, University of Leeds Leeds, LS2 9JT, UK ; Instituto de Investigaciones de la Amazonía Peruana Av. José A. Quiñones km. 2.5, Iquitos, Peru.
  29. World Wildlife Fund 1250 24th Street, N.W., Washington, DC, 20037, USA.
  30. Centre for Tropical Environmental and Sustainability Science (TESS), School of Marine and Tropical Biology, James Cook University Cairns, Queensland, 4878, Australia.
  31. Universidade do Estado de Mato Grosso, Campus de Nova Xavantina Caixa Postal 08, CEP 78.690-000, Nova Xavantina, MT, Brazil.
  32. Department of Civil Engineering, Indian Institute of Technology Roorkee, Uttarakhand, 247667, India.
  33. Centro de Biociências e Biotecnologia, Universidade Estadual do Norte Fluminese Campos dos Goytacazes, RJ, Brasil.
  34. Puyo, Universidad Estatal Amazónica Paso lateral km 2½ via a Napo, Pastaza, Ecuador.
  35. Universidad Nacional de San Antonio Abad del Cusco Cusco, Peru.
  36. Escuela de Ingeniería Forestal, Universidad Técnica del Norte Ecuador.
  37. Universidad Autónoma del Beni Riberalta, Beni, Bolivia.
  38. Forest Ecology and Forest Management Group, Wageningen University P.O. Box 47, 6700 AA, Wageningen, the Netherlands ; Instituto Boliviano de Investigación Forestal Santa Cruz, Bolivia.
  39. Center for Tropical Conservation, Duke University Box 90381, Durham, NC, 27708, USA.
  40. Centre for Biodiversity Research, School of Environmental Sciences, University of East Anglia Norwich, NR4 7JT, UK.
  41. Forest Ecology and Forest Management Group, Wageningen University P.O. Box 47, 6700 AA, Wageningen, the Netherlands.
  42. Instituto de Ciencias Naturales, Universidad Nacional de Colombia Bogota, Colombia.
  43. Universidad de Los Andes Merida, Venezuela.
  44. Department of Biology, University of Florida P.O. 118526, 511 Bartram Hall, Gainesville, FL, 32611-8526, USA.
  45. Universidad Nacional de Colombia Leticia, Colombia.
  46. Museu Paraense Emilio Goeldi Av. Magalhães Barata, 376, São Braz, 66040-170, Belém, PA, Brazil.
  47. Universidade Federal do Acre, Centro de Ciências Biológicas e da Natureza Rio Branco, AC, 69910-900, Brazil.
  48. Center for Applied Biodiversity Science, Conservation International Washington, DC, USA.
  49. Institute for Environment and Sustainability, Joint Research Centre of the European Commission Via Enrico Fermi, 2748 TP 440, I-21027, Ispra, Italy.
  50. Nicholas School of the Environment, Duke University Box 90381, Durham, NC, 27708, USA.
  51. Iwokrama International Centre 77 High Street Kingston, Georgetown, Guyana.
  52. Instituto Boliviano de Investigación Forestal Santa Cruz, Bolivia ; Universidad Autónoma Gabriel René Moreno Santa Cruz, Bolivia.
  53. Naturalis Biodiversity Center Leiden, the Netherlands.
  54. University of Wisconsin-Milwaukee P.O Box 413, Milwaukee, WI, 53201, USA ; Smithsonian Tropical Research Institute Apartado, Postal 0843-03092, Panamá, Panama.
  55. Núcleo de Estudos e Pesquisas Ambientais, Universidade Estadual de Campinas Campinas, Brazil.
  56. Lab of Landscape Ecology and Conservation Biology, Northern Arizona University Flagstaff, AZ, USA.
  57. School of Geography and the Environment, University of Oxford Oxford, UK.

PMID: 26430387 PMCID: PMC4579864 DOI: 10.1111/geb.12168

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.

Keywords: Above-ground biomass; REDD+; allometry; carbon cycle; remote sensing; satellite mapping; wood density

References

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