Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks. Réjou-Méchain, M., Muller-Landau, H. C., Detto, M., Thomas, S. C., Le Toan, T., Saatchi, S. S., Barreto-Silva, J. S., Bourg, N. A., Bunyavejchewin, S., Butt, N., Brockelman, W. Y., Cao, M., Cárdenas, D., Chiang, J. M., Chuyong, G. B., Clay, K., Condit, R., Dattaraja, H. S., Davies, S. J., Duque, A., Esufali, S., Ewango, C., Fernando, R. H. S., Fletcher, C. D., Gunatilleke, I. A. U. N., Hao, Z., Harms, K. E., Hart, T. B., Hérault, B., Howe, R. W., Hubbell, S. P., Johnson, D. J., Kenfack, D., Larson, A. J., Lin, L., Lin, Y., Lutz, J. A., Makana, J. R., Malhi, Y., Marthews, T. R., McEwan, R. W., McMahon, S. M., McShea, W. J., Muscarella, R., Nathalang, A., Noor, N. S. M., Nytch, C. J., Oliveira, A. A., Phillips, R. P., Pongpattananurak, N., Punchi-Manage, R., Salim, R., Schurman, J., Sukumar, R., Suresh, H. S., Suwanvecho, U., Thomas, D. W., Thompson, J., Ur$\$'ıarte, M., Valencia, R., Vicentini, A., Wolf, A. T., Yap, S., Yuan, Z., Zartman, C. E., Zimmerman, J. K., & Chave, J. Biogeosciences Discussions, 11(4):5711–5742, 2014.
Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks [link]Paper  doi  abstract   bibtex   
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 250 m (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 100 m 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 \textquotedbldilution\textquotedbl 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.
@article{RejouMechain2014Local,
 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 250 m (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 100 m 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 {\textquotedbl}dilution{\textquotedbl} 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.},
 author = {R{\'e}jou-M{\'e}chain, M. and Muller-Landau, H. C. and Detto, M. and Thomas, S. C. and {Le Toan}, T. and Saatchi, S. S. and Barreto-Silva, J. S. and Bourg, N. A. and Bunyavejchewin, S. and Butt, N. and Brockelman, W. Y. and Cao, M. and C{\'a}rdenas, D. and Chiang, J. M. and Chuyong, G. B. and Clay, K. and Condit, R. and Dattaraja, H. S. and Davies, S. J. and Duque, A. and Esufali, S. and Ewango, C. and Fernando, R. H. S. and Fletcher, C. D. and {Gunatilleke, I. A. U. N.} and Hao, Z. and Harms, K. E. and Hart, T. B. and H{\'e}rault, B. and Howe, R. W. and Hubbell, S. P. and Johnson, D. J. and Kenfack, D. and Larson, A. J. and Lin, L. and Lin, Y. and Lutz, J. A. and Makana, J. R. and Malhi, Y. and Marthews, T. R. and McEwan, R. W. and McMahon, S. M. and McShea, W. J. and Muscarella, R. and Nathalang, A. and Noor, N. S. M. and Nytch, C. J. and Oliveira, A. A. and Phillips, R. P. and Pongpattananurak, N. and Punchi-Manage, Ruwan and Salim, R. and Schurman, J. and Sukumar, R. and Suresh, H. S. and Suwanvecho, U. and Thomas, D. W. and Thompson, J. and Ur$\backslash$'{\i}arte, M. and Valencia, R. and Vicentini, A. and Wolf, A. T. and Yap, S. and Yuan, Z. and Zartman, C. E. and Zimmerman, J. K. and Chave, J.},
 year = {2014},
 title = {Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks},
 url = {http://dx.doi.org/10.5194/bgd-11-5711-2014},
 keywords = {ecol;phd},
 pages = {5711--5742},
 volume = {11},
 number = {4},
 journal = {Biogeosciences Discussions},
 doi = {10.5194/bgd-11-5711-2014},
 howpublished = {refereed}
}
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