Articles | Volume 11, issue 23
Biogeosciences, 11, 6827–6840, 2014
Biogeosciences, 11, 6827–6840, 2014

Research article 08 Dec 2014

Research article | 08 Dec 2014

Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

M. Réjou-Méchain1, H. C. Muller-Landau2, M. Detto2, S. C. Thomas3, T. Le Toan4, S. S. Saatchi5, J. S. Barreto-Silva6, N. A. Bourg7, S. Bunyavejchewin8, N. Butt10,9, W. Y. Brockelman11, M. Cao12, D. Cárdenas13, J.-M. Chiang14, G. B. Chuyong15, K. Clay16, R. Condit2, H. S. Dattaraja17, S. J. Davies18, A. Duque19, S. Esufali20, C. Ewango21, R. H. S. Fernando22, C. D. Fletcher23, I. A. U. N. Gunatilleke20, Z. Hao24, K. E. Harms25, T. B. Hart26, B. Hérault27, R. W. Howe28, S. P. Hubbell29,2, D. J. Johnson16, D. Kenfack30, A. J. Larson31, L. Lin12, Y. Lin14, J. A. Lutz32, J.-R. Makana33, Y. Malhi9, T. R. Marthews9, R. W. McEwan34, S. M. McMahon35, W. J. McShea7, R. Muscarella36, A. Nathalang11, N. S. M. Noor23, C. J. Nytch37, A. A. Oliveira38, R. P. Phillips16, N. Pongpattananurak39, R. Punchi-Manage40, R. Salim23, J. Schurman3, R. Sukumar17, H. S. Suresh17, U. Suwanvecho11, D. W. Thomas41, J. Thompson37,42, M. Uríarte36, R. Valencia43, A. Vicentini44, A. T. Wolf28, S. Yap45, Z. Yuan24, C. E. Zartman44, J. K. Zimmerman37, and J. Chave1 M. Réjou-Méchain et al.
  • 1Laboratoire Evolution et Diversité Biologique, UMR5174 CNRS, Université Paul Sabatier, 31062 Toulouse, France
  • 2Smithsonian Tropical Research Institute, Apartado Postal 0843-03092 Balboa, Ancon, Panama
  • 3University of Toronto, Faculty of Forestry, Toronto, Canada
  • 4Centre d'Etudes Spatiales de la Biosphère, UMR5126 CNRS, CNES, Université Paul Sabatier, IRD, 31401 Toulouse, France
  • 5Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
  • 6Instituto Amazónico de Investigaciones Científicas SINCHI, Avenida Vásquez Cobo entre calles 15 y 16, Leticia, Amazonas, Colombia
  • 7Conservation Ecology Center Smithsonian Conservation Biology Institute National Zoological Park 1500 Remount Rd., Front Royal, VA 22630, USA
  • 8National Parks, Wildlife and Plant Conservation Department, Research Office, Chatuchak, Bangkok 10900, Thailand
  • 9Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK
  • 10ARC Centre of Excellence for Environmental Decisions, School of Biological Sciences, The University of Queensland, St. Lucia, 4072, Australia
  • 11Ecology Lab, Bioresources Technology Unit, 113 Science Park, Paholyothin Road, Khlong 1, Khlongluang, Pathum Thani 12120, Thailand
  • 12Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming 650223, China
  • 13Instituto Amazónico de Investigaciones Científicas SINCHI, Calle 20 No. 5 -44. Bogotá, Colombia
  • 14Department of Life Science, Tunghai University, Taichung 40704, Taiwan
  • 15Department of Botany and Plant Physiology, University of Buea, PO Box 63, Buea, Cameroon
  • 16Department of Biology, Indiana University, Jordan Hall, 1001 East Third Street, Bloomington, IN 47405, USA
  • 17Center for Ecological Sciences, Indian Institute of Science, Bangalore 560012, India
  • 18Center for Tropical Forest Science, Smithsonian Institution Global Earth Observatory, Smithsonian Tropical Research Institute, P.O. Box 37012, Washington, DC 20012, USA
  • 19Departamento de Ciencias Forestales, Universidad Nacional de Colombia, Sede Medellín. Calle 59A No 63-20, Medellín, Colombia
  • 20Department of Botany, Faculty of Science, University of Peradeniya, Peradeniya, Sri Lanka
  • 21Centre de Formation et de Recherche en Conservation Forestière (CEFRECOF), Wildlife Conservation Society, Kinshasa, DR Congo
  • 22Royal Botanical Garden, Peradeniya, Sri Lanka
  • 23Forest Research Institute Malaysia (FRIM), 52109 Kepong, Selangor, Malaysia
  • 24State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110164, China
  • 25Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
  • 26Project TL2, Kinshasa, DR Congo
  • 27Cirad, UMR Ecologie des Forêts de Guyane (EcoFoG), Campus Agronomique, BP701, 97310 Kourou, French Guiana
  • 28Department of Natural and Applied Sciences, University of Wisconsin-Green Bay, Green Bay, WI 54311, USA
  • 29Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095, USA
  • 30CTFS-Arnold Arboretum Office, Harvard University, 22 Divinity Avenue, Cambridge, MA 02138, USA
  • 31Department of Forest Management, College of Forestry and Conservation, The University of Montana, Missoula, MT 59812, USA
  • 32Wildland Resources Department, Utah State University, 5230 Old Main Hill, Logan, UT 84322-5230, USA
  • 33Wildlife Conservation Society – DRC Program, Kinshasa, DR Congo
  • 34Department of Biology, University of Dayton, Dayton, OH 45469-2320, USA
  • 35Smithsonian Tropical Research Institute {&} Smithsonian Environmental Research Center, Edgewater, Maryland, USA
  • 36Department of Ecology, Evolution {&} Environmental Biology, Columbia University, New York, NY, USA
  • 37Department of Environmental Science, University of Puerto Rico, Box 70377, Rio Piedras, San Juan, 00936-8377, Puerto Rico
  • 38Departamento de Ecologia, Instituto de Biociências, Universidade de São Paulo, 04582050 São Paulo, Brazil
  • 39Department of Conservation, Faculty of Forestry, Kasetsart University, Bangkok, Thailand
  • 40Department of Ecosystem Modelling, University of Göttingen, Göttingen, Germany
  • 41Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, USA
  • 42Centre for Ecology {&} Hydrology, Edinburgh, Bush Estate, Penicuik, Midlothian, Scotland EH26 0QB, UK
  • 43Escuela de Ciencias Biológicas, Pontificia Universidad Católica del Ecuador, Apartado 17-01-2184, Quito, Ecuador
  • 44Instituto Nacional de Pesquisas da Amazônia – Manaus, AM, Brazil
  • 45Institute of Biology University of the Philippines Diliman, Quezon City 1101, Philippines

Abstract. Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8–50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mg ha–1) at spatial scales ranging from 5 to 250 m (0.025–6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20–400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial "dilution" bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.

Short summary
Forest carbon mapping may greatly reduce uncertainties in the global carbon budget. Accuracy of such maps depends however on the quality of field measurements. Using 30 large forest plots, we found large local spatial variability in biomass. When field calibration plots are smaller than the remote sensing pixels, this high local spatial variability results in an underestimation of the variance in biomass.
Final-revised paper