Authors

M. Rejou-Mechain, Universite Paul Sabatier (Toulouse III)
H. C. Muller-Landau, Smithsonian Tropical Resource Institute
M. Detto, Smithsonian Tropical Resource Institute
S. C. Thomas, University of Toronto
T. Le Toan, Universite Paul Sabatier (Toulouse III)
S. S. Saatchi, California Institute of TechnologyFollow
J. S. Barreto-Silvia, Instituto Amazonico de Investigaciones Cientificas
N. A. Bourg, Smithsonian Conservation Biology Institute
S. Bunyavejchewin, National Parks - Thailand
N. Butt, University of Queensland
W. Y. Brockelman, Bioresources Technology - ThailandFollow
M. Cao, Chinese Academy of Sciences
D. Cardenas, Instituto Amazonico de Investigaciones Cientificas
J.-M. Chiang, Tunghai University
G. B. Chuyong, University of Buea
K. Clay, Indiana University - Bloomington
R. Condit, Smithsonian Tropical Resource Institute
H. S. Dattaraja, Indian Institute of Science, Bangalore
S. J. Davies, Smithsonian Tropical Research Institute
A. Duque, Universidad Nacional de Colombia
S. Esufali, University of Peradeniya
C. Ewango, Wildlife Conservation Society - Kinshasa
R.H.S. Fernando, Royal Botanical Garden - Peradeniya
C. D. Fletcher, Forest Research Institute - Malaysia
I.A.U.N. Gunatilleke, University of Peradeniya
Z. Hao, Chinese Academy of Sciences
K. E. Harms, Louisiana State University
T. B. Hart, Project TL2
B. Herault, Ecologie des Forets de Guyane
R. W. Howe, University of Wisconsin - Green Bay
S. P. Hubbell, University of California - Los Angeles
D. J. Johnson, Indiana University - Bloomington
D. Kenfack, Harvard University
A. J. Larson, University of Montana - MissoulaFollow
L. Lin, Chinese Academy of Sciences
Y. Lin, Tunghai University
J. A. Lutz, Utah State UniversityFollow
J.-R. Makana, Wildlife Conservation Society
Y. Malhi, University of OxfordFollow
T. R. Marthews, University of Oxford
R. W. McEwan, University of Dayton
S. M. McMahon, Smithsonian Tropical Research Institute
W. J. McShea, Smithsonian Conservation Biology Institute
R. Muscarella, Columbia University
A. Nathalang, Bioresources Technology - Thailand
N.S.M. Noor, Forest Research Institute - Malaysia
C. J. Nytch, University of Puerto Rico - Rio Piedras Campus
A. A. Oliveira, Universidade de Sao Paulo
R. P. Phillips, Indiana University - Bloomington
N. Pongpattananurak, Kasetsart University
R. Punchi-Manage, University of Gottingen
R. Salim, Forest Research Institute - Malaysia
J. Schurman, University of Toronto
R. Sukumar, Indian Institute of Science, Bangalore
H. S. Suresh, Indian Institute of Science, Bangalore
U. Suwanvecho, Bioresources Technology - Thailand
D. W. Thomas, Oregon State University
J. Thompson, Columbia University
M. Uriarte, Columbia University
R. Valencia, Universidad Catolica del Ecuador
A. Vicentini, Instituto Nacional de Pesquisas da Amazonia
A. T. Wolf, University of Wisconsin - Green Bay
S. Yap, University of the Philippines Diliman
Z. Yuan, Chinese Academy of Sciences
C. E. Zartman, Instituto Nacional de Pesquisas da Amazonia
J. K. Zimmerman, University of Puerto Rico - Rio Piedras Campus
J. Chave, Universite Paul Sabatier (Toulouse III)

Document Type

Article

Publication Title

Biogeosciences Discussions

Publisher

Copernicus Publications on behalf of the European Geosciences Union

Publication Date

4-22-2014

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+. 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 (AGB) at spatial grains ranging from 5 to 250m (0.025–6.25 ha), and we evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that the spatial sampling error in AGB is large for standard plot sizes, averaging 46.3% for 0.1 ha subplots and 16.6% for 1 ha subplots. Topographically heterogeneous sites showed positive spatial autocorrelation in AGB at scales of 100m and above; at smaller scales, most study sites showed negative or nonexistent spatial autocorrelation in AGB. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGB leads to a substantial “dilution” bias in calibration parameters, a bias that cannot be removed with current statistical methods. Overall, 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.

DOI

10.5194/bgd-11-5711-2014

Rights

© 2014 the authors

Creative Commons License

Creative Commons Attribution 3.0 License
This work is licensed under a Creative Commons Attribution 3.0 License.

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