H. E. Andersen, S. E. Reutebuch, and R. J. Mcgaughey, A rigorous assessment of tree height measurements obtained using airborne lidar and conventional field methods, Canadian Journal of Remote Sensing, vol.102, issue.4, pp.355-36610, 2006.
DOI : 10.1080/02827580410019436

S. Attarchi and R. Gloaguen, Improving the Estimation of Above Ground Biomass Using Dual Polarimetric PALSAR and ETM+ Data in the Hyrcanian Mountain Forest (Iran), Remote Sensing, vol.5, issue.5, pp.3693-371510, 2014.
DOI : 10.3390/rs5073611

N. Baghdadi, G. Maire, I. Fayad, J. S. Bailly, Y. Nouvellon et al., Testing Different Methods of Forest Height and Aboveground Biomass Estimations From ICESat/GLAS Data in Eucalyptus Plantations in Brazil, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.7, issue.1, pp.290-299, 2014.
DOI : 10.1109/JSTARS.2013.2261978

URL : https://hal.archives-ouvertes.fr/hal-01522004

D. L. Bailey and D. Thompson, Developing neural network applications, pp.34-41, 1990.

L. Breiman, Bagging predictors, Machine Learning, vol.10, issue.2, 1994.
DOI : 10.2307/1403680

L. Breiman, Random forests, Machine Learning, pp.5-321010933404324, 2001.

M. Cairns, J. Barker, R. Shea, and P. Haggerty, Carbon dynamics of Mexican tropical evergreen forests: Influence of forestry mitigation options and refinement of carbon-flux estimates, Interciencia, issue.6, pp.20-401, 1995.

G. Chen and G. J. Hay, A Support Vector Regression Approach to Estimate Forest Biophysical Parameters at the Object Level Using Airborne Lidar Transects and QuickBird Data, Photogrammetric Engineering & Remote Sensing, vol.77, issue.7, pp.733-741, 2011.
DOI : 10.14358/PERS.77.7.733

Q. Chen, Retrieving vegetation height of forests and woodlands over mountainous areas in the Pacific Coast region using satellite laser altimetry. Remote Sensing of Environment, pp.1610-1627, 2010.

J. B. Drake, R. O. Dubayah, D. B. Clark, R. G. Knox, J. B. Blair et al., Estimation of tropical forest structural characteristics using large-footprint lidar, Remote Sensing of Environment, vol.79, issue.2-3, pp.305-319, 2002.
DOI : 10.1016/S0034-4257(01)00281-4

L. I. Duncanson, K. O. Niemann, and M. A. Wulder, Estimating forest canopy height and terrain relief from GLAS waveform metrics, Remote Sensing of Environment, vol.114, issue.1, pp.138-154, 2010.
DOI : 10.1016/j.rse.2009.08.018

E. Hajj, M. Baghdadi, N. Fayad, I. Vieilledent, G. Bailly et al., Interest of integrating spaceborne LiDAR data to derive accurate biomass estimates in high biomass forested areas. Remote Sensing Comparison of ordinary kriging, regression kriging, and cokriging techniques to estimate soil salinity using LANDSAT images, Journal of Irrigation and Drainage Engineering, vol.9, issue.1366, pp.1-19, 2010.

I. Fayad, N. Baghdadi, J. S. Bailly, N. Barbier, V. Gond et al., Canopy Height Estimation in French Guiana with LiDAR ICESat/GLAS Data Using Principal Component Analysis and??Random Forest Regressions, Remote Sensing, vol.35, issue.12, pp.11883-1191410, 2014.
DOI : 10.1016/j.rse.2011.01.024

URL : https://hal.archives-ouvertes.fr/hal-01128698

I. Fayad, N. Baghdadi, J. Bailly, N. Barbier, V. Gond et al., Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne lidar data: Application on French Guiana Do we need hundreds of classifiers to solve real world classification problems?, Remote Sensing The Journal of Machine Learning Research, vol.8, issue.151, pp.1-18, 2014.

S. R. Freitas, M. C. Mello, and C. B. Cruz, Relationships between forest structure and vegetation indices in Atlantic rainforest. Forest Ecology and Management, pp.353-362, 2005.
DOI : 10.1016/j.foreco.2005.08.036

A. Fu, S. Guoqing, and G. Zhifeng, Estimating forest biomass with GLAS samples and MODIS imagery in northeastern China, Proceedings of SPIE, vol.749812, pp.1-8, 2009.
DOI : 10.1117/12.833596

Y. Ge, J. A. Thomasson, R. Sui, and J. Wooten, Regression-kriging for characterizing soils with remote sensing data, Frontiers of Earth Science, vol.5, issue.3, pp.239-244, 2011.
DOI : 10.1007/s11707-011-0174-1

P. Goovaerts, Geostatistics for natural resources evaluation, 1997.

U. Grömping, Variable importance assessment in regression: Linear regression versus random forest. The American Statistician, p.8199, 2009.

R. Hayashi, A. Weiskittel, S. Sader, C. Hilbert, and C. N. Schmullius, Assessing the feasibility of low-density lidar for stand inventory attribute predictions in complex and managed forests of northern Maine, USA. Forests Influence of surface topography on ICESat/GLAS forest height estimation and waveform shape Random Forests: An algorithm for image classification and generation of continuous fields data sets, International Conference on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences, pp.363-383, 2010.

I. Kaastra and M. Boyd, Designing a neural network for forecasting financial and economic time series, Neurocomputing, vol.10, issue.3, pp.215-23610, 1996.
DOI : 10.1016/0925-2312(95)00039-9

URL : http://www.geocities.com/francorbusetti/KaastraArticle.pdf

J. O. Katz, Developing neural network forecasters for trading. Technical Analysis for Stocks and Commodities, pp.160-168, 1992.

F. Kayitakire, C. Hamel, and P. Defourny, Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery, Remote Sensing of Environment, vol.102, issue.3-4, pp.390-401, 2006.
DOI : 10.1016/j.rse.2006.02.022

R. A. Khorrami, A. A. Darvishsefat, T. Kochaksaraei, M. Jouybari, and S. , Potential of LIDAR data for estimation of individual tree height of Acer velutinum and Carpinus betulus, Iranian Journal of Forest, vol.6, issue.2, pp.127-140, 2014.

K. Kumar and G. S. Thakur, Advanced Applications of Neural Networks and Artificial Intelligence: A Review, International Journal of Information Technology and Computer Science, vol.4, issue.6, pp.57-68, 2012.
DOI : 10.5815/ijitcs.2012.06.08

H. Latifi, A. Nothdurft, B. Koch, M. A. Lefsky, M. A. Lefsky et al., Nonparametric prediction and mapping of standing timber volume and biomass in a temperate forest: Application of multiple optical/LiDAR derived predictors Global forest canopy height map from the moderate resolution imaging spectroradiometer and the geoscience laser altimeter system. Geophysical Research Letters, 37. doi:10 Estimates of forest canopy height and aboveground biomass using lCESat Revised method for forest canopy height estimation from geoscience laser altimeter system waveform, Forestry Geophysical Research Letters Journal of Applied Remote Sensing, vol.83, issue.1, pp.395-407, 1029.

A. Liaw and M. Wiener, Classification and regression by random forest. R News, pp.18-22, 2002.

A. Liaw and M. Wiener, Package 'randomForest': Breiman and Cutler's random forests for classification and regression, pp.1-29, 2014.

E. Lindberg and M. Hollaus, Comparison of Methods for Estimation of Stem Volume, Stem Number and Basal Area from Airborne Laser Scanning Data in a Hemi-Boreal Forest, Remote Sensing, vol.19, issue.12, pp.1004-102310, 2012.
DOI : 10.1080/02827580410019454

M. Liu, M. Wang, J. Wang, and D. Li, Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar, Sensors and Actuators B: Chemical, vol.177, pp.970-980, 2013.
DOI : 10.1016/j.snb.2012.11.071

P. Maillard, Spectral-textural image classification in a semiarid environment, Proceedings of the ISPRS Commission VII Symposium 'Remote Sensing: From Pixels to Processes, 2006.

M. Namiranian, Measurement of tree and forest biometry, 2006.

R. Nelson, Model effects on GLAS-based regional estimates of forest biomass and carbon, International Journal of Remote Sensing, vol.79, issue.5, pp.1359-1372, 2010.
DOI : 10.1016/j.rse.2006.09.036

R. Nelson, K. J. Ranson, G. Sun, D. S. Kimes, V. Kharuk et al., Estimating Siberian timber volume using MODIS and ICESat/GLAS. Remote Sensing of Environment, pp.691-701, 2009.
DOI : 10.1016/j.rse.2008.11.010

J. E. Nichol and S. L. Md, Improved Biomass Estimation Using the Texture Parameters of Two High-Resolution Optical Sensors, IEEE Transactions on Geoscience and Remote Sensing, vol.49, issue.3, pp.930-948, 2010.
DOI : 10.1109/TGRS.2010.2068574

E. J. Olaya-marín, F. Martínez-capel, and P. Vezza, A comparison of artificial neural networks and random forests to predict native fish species richness in Mediterranean rivers. Knowledge and Management of Aquatic Ecosystems, pp.1-19, 2013.

Y. Pang, M. Lefsky, G. Sun, M. E. Miller, and Z. Li, Temperate forest height estimation performance using ICESat GLAS data from different observation periods, pp.777-782, 2008.

A. K. Parmar, A neuro-genetic approach for rapid assessment of above ground bioass: An improved tool for monitoring the impact of forest degradation (Master's thesis). Faculty of geo-information science and earth observation of, 2012.

C. Pascual, A. García-abril, W. B. Cohen, and S. Martín-fernández, Relationship between LiDAR-derived forest canopy height and Landsat images, International Journal of Remote Sensing, vol.29, issue.5, pp.1261-128010, 1080.
DOI : 10.1016/S0034-4257(03)00139-1

B. Peterson, K. J. Nelson, M. Pourrahmati, N. Baghdadi, A. A. Darvishsefat et al., Mapping forest height in Alaska using GLAS, Landsat composites, and airborne lidar doi:10.3390 Capability of GLAS/ICESat data to estimate forest canopy height and volume in mountainous forests of Iran, Remote Sensing IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.6, issue.11, pp.12409-12426, 2014.
DOI : 10.3390/rs61212409

URL : http://www.mdpi.com/2072-4292/6/12/12409/pdf

J. W. Roberts, S. Tesfamichael, M. Gebreslasie, J. Van-aardt, and F. B. Ahmed, Forest structural assessment using remote sensing technologies: an overview of the current state of the art, Southern Hemisphere Forestry Journal, vol.69, issue.3, pp.183-203, 2007.
DOI : 10.2989/SHFJ.2007.

G. P. Robertson, Geostatistics in Ecology: Interpolating With Known Variance, Ecology, vol.68, issue.3, pp.744-748, 1987.
DOI : 10.2307/1938482

J. A. Rosette, P. R. North, and J. C. Suárez, Vegetation height estimates for a mixed temperate forest using satellite laser altimetry, International Journal of Remote Sensing, vol.29, issue.5, pp.1475-149310, 1080.
DOI : 10.1016/S0264-3707(02)00042-X

M. Simard, P. Naiara, J. B. Fisher, and A. Baccini, Mapping forest canopy height globally with spaceborne lidar, Journal of Geophysical Research, vol.34, issue.53, pp.4021-4031, 2011.
DOI : 10.1016/S0264-3707(02)00042-X

H. Su, Y. Sheng, P. Du, C. Ch, and K. Liu, Hyperspectral image classification based on volumetric texture and dimensionality reduction, Frontiers of Earth Science, vol.4, issue.1, pp.225-236, 2015.
DOI : 10.1117/1.3491192

G. Sun, K. J. Ranson, J. Masek, A. Fu, and D. Wang, Predicting tree height and biomass from GLAS data, International symposium on physical measurements and signatures in remote sensing, 2007.

T. Takahashi, Y. Awaya, Y. Hirata, N. Furuya, T. Sakai et al., ) plantations, International Journal of Remote Sensing, vol.36, issue.5, pp.31-1281, 1080.
DOI : 10.1016/S0034-4257(03)00139-1

Y. Ting, W. Cheng, L. Guicai, L. Shezhou, X. Xiaohuan et al., Forest canopy height mapping over China using GLAS and MODIS data, Science ChinaEarth Sciences), vol.58, issue.1, pp.96-105, 2015.

J. C. Trinder, A. Shamsoddini, and R. Turner, Relating worldview-2 data to pine plantation lidar metrics, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp.11-13, 2013.

X. Wang, X. Cheng, P. Gong, H. Huang, L. Zh et al., Earth science applications of ICESat/GLAS: a review, International Journal of Remote Sensing, vol.34, issue.471, pp.8837-8864, 2011.
DOI : doi: 10.1029/2006JC003978

K. Were, D. T. Bui, Ø. B. Dick, and B. R. Singh, A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape, Ecological Indicators, vol.52, pp.394-403, 2015.
DOI : 10.1016/j.ecolind.2014.12.028

K. Y. Yeung and W. L. Ruzzo, Principal component analysis for clustering gene expression data, Bioinformatics, vol.17, issue.9, 2001.
DOI : 10.1093/bioinformatics/17.9.763

G. Zhang, B. E. Patuw, and M. Y. Hu, Forecasting with artificial neural networks:, International Journal of Forecasting, vol.14, issue.1, pp.35-62, 1998.
DOI : 10.1016/S0169-2070(97)00044-7

G. P. Zhang, Neural networks for classification: a survey, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol.30, issue.4, pp.451-462, 2000.
DOI : 10.1109/5326.897072

URL : https://cours.etsmtl.ca/sys843/pdf/PZhang2000.pdf