C. Antin, G. Pélissier, P. Vincent, and . Couteron, Crown allometries are less responsive than stem allometry to tree size and habitat variations in an Indian monsoon forest, Trees, vol.92, issue.5, pp.1485-1495, 2013.
DOI : 10.1007/s00468-013-0896-7

N. Barbier, P. Couteron, J. Gastelly-etchegorry, and C. Proisy, Linking canopy images to forest structural parameters: potential of a modeling framework, Annals of Forest Science, vol.112, issue.2, pp.305-311, 2012.
DOI : 10.1007/s13595-011-0116-9
URL : https://hal.archives-ouvertes.fr/ird-00657316

J. Bastin, N. Barbier, P. Couteron, A. Adams, J. Shapiro et al., Aboveground biomass mapping of African forest mosaics using canopy texture analysis: toward a regional approach, Ecological Applications, vol.24, issue.8
DOI : 10.1890/13-1574.1.sm

L. Breiman, Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.
DOI : 10.1023/A:1010933404324

P. Couteron, Quantifying change in patterned semi-arid vegetation by Fourier analysis of digitized aerial photographs, International Journal of Remote Sensing, vol.4, issue.17, pp.3407-3425, 2002.
DOI : 10.2307/2258335

P. Couteron, R. Pelissier, E. Nicolini, and D. Paget, Predicting tropical forest stand structure parameters from Fourier transform of very high-resolution remotely sensed canopy images, Journal of Applied Ecology, vol.269, issue.3, pp.1121-1128, 2005.
DOI : 10.1111/j.1365-2664.2005.01097.x
URL : https://hal.archives-ouvertes.fr/hal-00016130

R. Defries, F. Achard, S. Brown, M. Herold, D. Murdiyarso et al., Earth observations for estimating greenhouse gas emissions from deforestation in developing countries, Environmental Science & Policy, vol.10, issue.4, pp.385-394, 2007.
DOI : 10.1016/j.envsci.2007.01.010

T. G. Dietterich, Ensemble Methods in Machine Learning, Proceedings of the First International Workshop on Multiple Classifier Systems, MCS '00, pp.1-15, 2000.
DOI : 10.1007/3-540-45014-9_1

N. R. Draper and H. Smith, Applied Regression Analysis, 1998.
DOI : 10.1002/9781118625590

A. Hatem, . Fayed, F. Amir, and . Atiya, A novel template reduction approach for the k-nearest neighbor method, Trans. Neur. Netw, vol.20, issue.5, pp.890-896, 2009.

J. P. Gastellu-etchegorry, 3D modeling of satellite spectral images, radiation budget and energy budget of urban landscapes. Meteorology and Atmospheric Physics, pp.187-207, 2008.
URL : https://hal.archives-ouvertes.fr/ird-00405362

M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann et al., The WEKA data mining software, ACM SIGKDD Explorations Newsletter, vol.11, issue.1, pp.10-18, 2009.
DOI : 10.1145/1656274.1656278

N. L. Harris, S. Brown, S. C. Hagen, S. S. Saatchi, S. Petrova et al., Baseline Map of Carbon Emissions from Deforestation in Tropical Regions, Science, vol.336, issue.6088, pp.1573-1576, 2012.
DOI : 10.1126/science.1217962

P. E. Hart, The condensed nearest neighbor rule (Corresp.), IEEE Transactions on Information Theory, vol.14, issue.3, pp.515-516, 1968.
DOI : 10.1109/TIT.1968.1054155

T. Hastie, R. Tibshirani, J. Friedman, . Hastie, R. Friedman et al., The elements of statistical learning, 2009.

R. A. Houghton, F. Hall, and S. J. Goetz, Importance of biomass in the global carbon cycle, Journal of Geophysical Research: Biogeosciences, vol.35, issue.43, 2009.
DOI : 10.1029/2008GL035746

D. Li, S. Simske, D. Li, and S. Simske, Training Set Compression by Incremental Clustering, Journal of Pattern Recognition Research, vol.6, issue.1, 2011.
DOI : 10.13176/11.254

D. Maniatis and D. Mollicone, Options for sampling and stratification for national forest inventories to implement REDD+ under the UNFCCC, Carbon Balance and Management, vol.5, issue.1, 2010.
DOI : 10.1186/1750-0680-5-9

M. Palace, M. Keller, G. P. Asner, S. Hagen, and B. Braswell, Amazon Forest Structure from IKONOS Satellite Data and the Automated Characterization of Forest Canopy Properties, Biotropica, vol.73, issue.2, pp.141-150, 2008.
DOI : 10.1111/j.1744-7429.2007.00353.x

P. Ploton, R. Pélissier, C. Proisy, T. Flavenot, N. Barbier et al., Assessing aboveground tropical forest biomass using Google Earth canopy images, Ecological Applications, vol.22, issue.3, pp.993-1003, 2012.
DOI : 10.1016/S0034-4257(97)00169-7

C. Proisy, N. Barbier, M. Guéroult, R. Pélissier, J. P. Gastellu-etchegorry et al., Biomass Prediction in Tropical Forests: The Canopy Grain Approach, Remote Sensing of Biomass -Principles and Applications. Temilola Fatoyinbo, 2012.
DOI : 10.5772/17185
URL : https://hal.archives-ouvertes.fr/ird-00658600

C. Proisy, P. Couteron, and F. Fromard, Predicting and mapping mangrove biomass from canopy grain analysis using Fourier-based textural ordination of IKONOS images, Remote Sensing of Environment, vol.109, issue.3, pp.379-392, 2007.
DOI : 10.1016/j.rse.2007.01.009
URL : https://hal.archives-ouvertes.fr/hal-00164854

R. Shapire, Y. Freund, P. Bartlett, and W. Lee, Boosting the margin: a new explanation for the effectiveness of voting methods, The Annals of Statistics, vol.26, issue.5, pp.1651-1686, 1998.
DOI : 10.1214/aos/1024691352

M. Singh, Y. Malhi, and S. Bhagwat, Biomass estimation of mixed forest landscape using a Fourier transform texture-based approach on very-high-resolution optical satellite imagery, International Journal of Remote Sensing, vol.88, issue.1, pp.3331-3349, 2014.
DOI : 10.1038/nclimate1601

F. Thomas, . Stocker, G. Dahe, and . Plattner, Climate change 2013: The physical science basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, p.2013

S. Weisberg, Applied linear regression, 2014.
DOI : 10.1002/0471704091

H. Ian, E. Witten, and . Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2005.