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Réjou-Méchain, M.
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Muller-Landau, H. C.
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Detto, M.
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Thomas, S. C.
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Toan, T. Le
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Saatchi, S. S.
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Barreto-Silva, J. S.
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Bourg, N. A.
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Bunyavejchewin, S.
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Butt, N.
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Brockelman, W. Y.
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Cao, M.
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Cárdenas, D.
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Chiang, J.-M.
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Chuyong, G. B.
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Clay, K.
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Condit, R.
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Dattaraja, H. S.
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Davies, S. J.
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Duque, A.
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Esufali, S.
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Ewango, C.
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Fernando, R. H. S.
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Fletcher, C. D.
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Gunatilleke, I. A. U. N.
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Hao, Z.
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Harms, K. E.
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Hart, T. B.
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Hérault, B.
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Howe, R. W.
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Hubbell, S. P.
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Johnson, D. J.
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Kenfack, D.
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Larson, A. J.
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Lin, L.
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Lin, Y.
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Lutz, J. A.
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Makana, J.-R.
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Malhi, Y.
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Marthews, T. R.
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McEwan, R. W.
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McMahon, S. M.
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McShea, W. J.
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Muscarella, R.
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Nathalang, A.
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Noor, N. S. M.
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Nytch, C. J.
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Oliveira, A. A.
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Phillips, R. P.
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Pongpattananurak, N.
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Punchi-Manage, R.
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Salim, R.
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Schurman, J.
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Sukumar, R.
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Suresh, H. S.
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Suwanvecho, U.
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Thomas, D. W.
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Thompson, J.
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Uríarte, M.
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Valencia, R.
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Vicentini, A.
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Wolf, A. T.
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Yap, S.
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Yuan, Z.
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Zartman, C. E.
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Zimmerman, J. K.
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Chave, J.
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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 REDDC (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 Mgha⁻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.
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- Réjou-Méchain, M., Muller-Landau, H. C., Detto, M., Thomas, S. C., Le Toan, T., Saatchi, S. S., ... & Chave, J. (2014). Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks. Biogeosciences, 11(23), 6827-6840. doi:10.5194/bg-11-6827-2014
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Funding Statement (additional comments about funding) |
- We are also grateful to all the people, institutions, foundations, and funding bodies that have contributed to the collection of the large plot data sets (http://www.ctfs.si.edu/group/Partners/Forest+Plot+Institutions), including the staff members and central office of the Amacayacu National Natural Park of Colombia, NSF support for the LuquilloLTER program and EU FP7 support through the ROBIN project for Jill Thompson. Financial support for the analysis presented here was provided by the CNES (postdoctoral grant to M. Réjou-Méchain), the National Science Foundation (DEB#1046113), and two Investissement d’Avenir grants managed by Agence Nationale de la Recherche (CEBA: ANR-10-LABX-25-01;TULIP: ANR-10-LABX-0041).
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