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| Titel |
Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks |
| VerfasserIn |
M. Réjou-Méchain, H. C. Muller-Landau, M. Detto, S. C. Thomas, T. Le Toan, S. S. Saatchi, J. S. Barreto-Silva, N. A. Bourg, S. Bunyavejchewin, N. Butt, W. Y. Brockelman, M. Cao, D. Cárdenas, J.-M. Chiang, G. B. Chuyong, K. Clay, R. Condit, H. S. Dattaraja, S. J. Davies, Á. Duque, S. Esufali, C. Ewango, R. H. S. Fernando, C. D. Fletcher, I. A. U. N. Gunatilleke, Z. Hao, K. E. Harms, T. B. Hart, B. Hérault, R. W. Howe, S. P. Hubbell, D. J. Johnson, D. Kenfack, A. J. Larson, L. Lin, Y. Lin, J. A. Lutz, J.-R. Makana, Y. Malhi, T. R. Marthews, R. W. McEwan, S. M. McMahon, W. J. McShea, R. Muscarella, A. Nathalang, N. S. M. Noor, C. J. Nytch, A. A. Oliveira, R. P. Phillips, N. Pongpattananurak, R. Punchi-Manage, R. Salim, J. Schurman, R. Sukumar, H. S. Suresh, U. Suwanvecho, D. W. Thomas, J. Thompson, M. Uriarte, R. Valencia, A. Vicentini, A. T. Wolf, S. Yap, Z. Yuan, C. E. Zartman, J. K. Zimmerman, J. Chave |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 11, no. 23 ; Nr. 11, no. 23 (2014-12-08), S.6827-6840 |
| Datensatznummer |
250117719
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| Publikation (Nr.) |
 copernicus.org/bg-11-6827-2014.pdf |
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| Zusammenfassung |
| 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. |
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