D. M. Bhat and N. H. Ravindranath, Above-ground standing biomass and carbon stock dynamics under a varied degree of anthropogenic pressure in tropical rain forests of Uttara Kannada District, Taiwania, vol.56, pp.85-96, 2011.

C. Beer, M. Reichstein, E. Tomelleri, P. Ciais, M. Jung et al., Terrestrial gross carbon dioxide uptake: Global distribution and covariation with climate, Science, vol.329, pp.834-838, 2010.
URL : https://hal.archives-ouvertes.fr/cea-00819125

R. K. Dixon, S. Brown, R. A. Houghton, A. M. Solomon, M. C. Trexler et al., Carbon pools and flux of global forest ecosystems, Science, vol.263, pp.185-190, 1994.

G. R. Van-der-werf, D. C. Morton, R. S. Defries, L. Giglio, J. T. Randerson et al., Estimates of fire emissions from an active deforestation region in the southern Amazon based on satellite data and biogeochemical modelling, Biogeosciences, vol.6, pp.235-249, 2009.

. Unfcc and . Kyoto, Protocol Reference Manual on Accounting of Emissions Assigned Amount, p.15, 2008.

G. M. Devagiri, S. Money, S. Singh, V. K. Dadhawal, P. Patil et al., Assessment of above ground biomass and carbon pool in different vegetation types of south western part of Karnataka, India using spectral modeling, Trop. Ecol, vol.54, pp.149-165, 2013.

R. A. Houghton, Aboveground forest biomass and the global carbon balance, Glob. Change Biol, vol.11, pp.945-958, 2005.

R. B. Myneni, J. Dong, C. J. Tucker, R. K. Kaufmann, P. E. Kauppi et al., A large carbon sink in the woody biomass of northern forests, Proc. Natl. Acad. Sci, vol.98, pp.14784-14789, 2001.

P. Couteron, N. Barbier, C. Proisy, R. Pé-lissier, and G. Vincent, Linking remote-sensing information to tropical forest structure: The crucial role of modelling

H. K. Gibbs, S. Brown, J. O. Niles, and J. A. Foley, Monitoring and estimating tropical forest carbon stocks: Making REDD a reality, Environ. Res. Lett, vol.2, p.45023, 2007.

, Global Observations of Forest and Land Cover Dynamics (GOFC-GOLD), Proceedings of COMIFAC Regional Workshop, pp.2-4, 2010.

S. S. Saatchi, N. L. Harris, S. Brown, M. Lefsky, E. T. Mitchard et al., Benchmark map of forest carbon stocks in tropical regions across three continents, Proc. Natl. Acad. Sci, vol.108, pp.9899-9904, 2011.

G. P. Asner, Tropical forest carbon assessment: Integrating satellite and airborne mapping approaches, Environ. Res. Lett, p.34009, 2009.

S. J. Goetz, A. Baccini, N. T. Laporte, T. Johns, W. Walker et al., Mapping and monitoring carbon stocks with satellite observations: A comparison of methods, Carbon Balanc. Manag, 2002.

D. Lu, The potential and challenge of remote sensing based biomass estimation, Int. J. Remote Sens, vol.27, pp.1297-1328, 2006.

D. Sy, V. Herold, M. Achard, F. Asner, G. P. Held et al., Synergies of multiple remote sensing data sources for REDD+ monitoring, Curr. Opin. Environ. Sustain, vol.4, pp.696-706, 2012.

G. Hurtt, X. Xiao, M. Keller, M. Palace, G. P. Asner et al., IKONOS imagery for the large scale biosphere-atmosphere experiment in Amazonia (LBA), Remote Sens. Environ, vol.88, pp.111-127, 2003.

C. Proisy, N. Barbier, M. Gué-roult, R. Pé-lissier, J. Gastellu-etchegorry et al., Biomass Prediction in Tropical Forests: The Canopy Grain Approach, p.13, 2015.
URL : https://hal.archives-ouvertes.fr/ird-00658600

P. Couteron, R. Pé-lissier, E. A. Nicolini, and D. Paget, Predicting tropical forest stand structure parameters from Fourier transform of very high-resolution remotely sensed canopy images, J. Appl. Ecol, vol.42, pp.1121-1128, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00016130

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 Sens. Environ, vol.109, pp.379-392, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00164854

L. Coulibaly, P. Migolet, G. H. Adegbidi, R. Fournier, and E. Hervet, Mapping aboveground forest biomass from IKONOS satellite image and multi-source geospatial data using neural networks and a Kriging interpolation, Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS'08), pp.298-301, 2008.

P. Ploton, R. Pé-lissier, C. Proisy, T. Flavenot, N. Barbier et al., Assessing aboveground tropical forest biomass using Google Earth canopy images, Ecol. Appl, vol.22, pp.993-1003, 2012.

N. C. Coops, T. Hilker, M. A. Wulder, B. St-onge, G. Newnham et al., Estimating canopy structure of Douglas-fir forest stands from discrete-return Lidar, Trees, vol.21, pp.295-310, 2007.

M. A. Lefsky, D. Harding, W. B. Cohen, G. Parker, and H. H. Shugart, Surface Lidar remote sensing of basal area and biomass in deciduous forests of eastern Maryland, USA. Remote Sens. Environ, vol.67, pp.83-98, 1999.

S. Magnussen and P. Boudewyn, Derivations of stand heights from airborne laser scanner data with canopy-based quantile estimators, Can. J. For. Res, vol.28, pp.1016-1031, 1998.

M. L. Clark, D. A. Roberts, J. J. Ewel, and D. B. Clark, Estimation of tropical rain forest aboveground biomass with small-footprint Lidar and hyperspectral sensors, Remote Sens. Environ, vol.115, pp.2931-2942, 2011.

L. Chasmer, C. Hopkinson, B. Smith, and P. Treitz, Examining the influence of changing laser pulse repetition frequencies on conifer forest canopy returns, Photogramm. Engin. Remote Sens, vol.72, pp.1359-1367, 2006.

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 Sens. Environ, vol.79, pp.305-319, 2002.

G. P. Asner, R. F. Hughes, J. Mascaro, A. L. Uowolo, D. E. Knapp et al., High-resolution carbon mapping on the million-hectare Island of Hawaii, Front. Ecol. Environ, vol.9, pp.434-439, 2011.

U. Vepakomma, B. St-onge, and D. Kneeshaw, Response of a boreal forest to canopy opening: assessing vertical and lateral tree growth with multi-temporal Lidar data, Ecol. Appl, vol.21, pp.99-121, 2011.

S. C. Popescu and K. Zhao, A voxel-based Lidar method for estimating crown base height for deciduous and pine trees, Remote Sens. Environ, vol.112, pp.767-781, 2008.

D. Riaño, E. Meier, B. Allgöwer, E. Chuvieco, and S. L. Ustin, Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling, Remote Sens. Environ, vol.86, pp.177-186, 2003.

W. Ni-meister, S. Lee, A. H. Strahler, C. E. Woodcock, C. Schaaf et al., Assessing general relationships between aboveground biomass and vegetation structure parameters for improved carbon estimate from Lidar remote sensing, J. Geophys. Res. Biogeo, pp.0-11, 2010.

A. Gonsamo, J. N. Walter, and P. Pellikka, Sampling gap fraction and size for estimating leaf area and clumping indices from hemispherical photographs, Can. J. Forest Res, vol.40, pp.1588-1603, 2010.

R. F. Nelson, P. Hyde, P. Johnson, B. Emessiene, M. L. Imhoff et al., Investigating RaDAR-LiDAR synergy in a North Carolina pine forest. Remote Sens. Environ, vol.110, pp.98-108, 2007.

K. Lim, P. Treitz, I. Baldwin, J. Morrisson, and J. Green, Lidar remote sensing of biophysical properties of northern tolerant hardwood forests, Can. J. Remote Sens, vol.29, pp.658-678, 2003.

R. Pé-lissier, J. Pascal, N. Ayyappan, B. R. Ramesh, S. Aravajy et al., Twenty years tree demography in an undisturbed Dipterocarp permanent sample plot at Uppangala, Western Ghats of India-Data Paper, Ecology, pp.92-1376, 2011.

J. Pascal and R. Pé-lissier, Structure and floristic composition of a tropical evergreen forest in South-West India, J. Trop. Ecol, vol.12, pp.191-214, 1996.

C. Antin, R. Pé-lissier, G. Vincent, and P. Couteron, Crown allometries are less responsive than stem allometry to tree size and habitat variations in an Indian monsoon forest, Trees, vol.27, pp.1485-1495, 2013.

J. Pascal, Wet Evergreen Forests of the Western Ghats of India: Ecology, Structure, Floristic Composition and Succession (Travaux de la Section Scientifique et Technique)

R. Pé-lissier, J. Pascal, F. Houllier, and H. Laborde, Impact of selective logging on the dynamics of a low elevation dense moist evergreen forest in the Western Ghats (South India), For. Ecol. Manag, vol.105, pp.107-119, 1998.

S. N. Rai, Productivity of Tropical Rain Forests of Karnataka, 1981.

E. Naesset, Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data, Remote Sens. Environ, vol.80, pp.88-99, 2002.

J. E. Means, S. A. Acker, B. J. Fitt, M. Renslow, L. Emerson et al., Predicting forest stand characteristics with airborne scanning Lidar, Photogramm. Eng. Remote Sens, vol.66, pp.1367-1371, 2000.

N. Barbier, C. Proisy, C. Vé-ga, D. Sabatier, and P. Couteron, Bidirectional texture function of high resolution optical images of tropical forest: An approach using LiDAR hillshade simulations, Remote Sens. Environ, vol.115, pp.167-179, 2011.

H. Lu, X. Liu, and L. Bian, Terrain complexity: Definition, index, and DEM resolution, Proc. SPIE, vol.6753, p.675323, 2007.

R. M. O'brien, A caution regarding rules of thumb for variance inflation factors, Qual. Quant, vol.41, pp.673-690, 2007.

K. P. Burnham and D. R. Anderson, Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, p.488, 2002.

C. M. Hurvich and C. Tsai, Regression and time series model selection in small samples, Biometrika, vol.76, pp.297-307, 1989.

R. R. Picard and R. D. Cook, Cross-validation of regression models, J. Am. Stat. Assoc, vol.79, pp.575-583, 1984.

S. A. Hall, I. C. Burke, D. O. Box, M. R. Kaufmann, and J. M. Stoker, Estimating stand structure using discrete-return Lidar: An example from low density, fire prone ponderosa pine forests. For. Ecology Manag, vol.208, pp.189-209, 2005.

N. Barbier and P. Couteron, Attenuating the bidirectional texture variation of satellite images of tropical forests: No use to cry for a shadow! Remote Sens, 2015.

M. K. Jakubowski, Q. Guo, and M. Kelly, Tradeoffs between Lidar pulse density and forest measurement accuracy, Remote Sens. Environ, vol.130, pp.245-253, 2013.

N. Barbier, P. Couteron, C. Proisy, Y. Malhi, and J. Gastellu-etchegorry, The variation of apparent crown size and canopy heterogeneity across lowland Amazonian forests, Glob. Ecol. Biogeo, vol.19, pp.72-84, 2010.
URL : https://hal.archives-ouvertes.fr/halsde-00454134

A. Robert and M. Moravie, Topographic variation and stand structure heterogeneity in a wet evergreen forest of India, J. Trop. Ecol, vol.19, pp.697-707, 2003.

C. Vé-ga, A. Hamrouni, S. El-mokhtari, J. Morel, J. Bock et al., Trees: A point-based approach to forest tree extraction from Lidar data, Inter. J. Appl. Earth Observ. Geoinf, vol.33, pp.98-108, 2014.

V. Meyer, S. S. Saatchi, J. Chave, J. W. Dalling, S. Bohlman et al., Detecting tropical forest biomass dynamics from repeated airborne Lidar measurements, Biogeosciences, vol.10, pp.5421-5438, 2013.

J. Chave, C. Andalo, S. Brown, M. Cairns, J. Chambers et al., Tree allometry and improved estimation of carbon stocks and balance in tropical forests, Oecologia, vol.145, pp.87-99, 2005.

T. Gobakken and E. Naesset, Assessing effects of positioning errors and sample plot size on biophysical stand properties derived from airborne laser scanner data, Can. J. For. Res, vol.39, pp.1036-1052, 2009.

G. W. Frazer, S. Magnussen, M. A. Wulder, and K. O. Niemann, Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of Lidar-derived estimates of forest stand biomass, Remote Sens. Environ, vol.115, pp.636-649, 2011.

L. A. Ruiz, T. Hermosilla, F. Mauro, and M. Godino, Analysis of the influence of plot size and Lidar density on forest structure attribute estimates, vol.5, pp.936-951, 2014.

A. Khosravipour, A. K. Skidmore, T. Wang, M. Isenburg, and K. Khoshelham, Effect of slope on treetop detection using a Lidar canopy height model, ISPRS J. Photogramm. Remote Sens, vol.104, pp.44-52, 2015.

C. Vé-ga, J. P. Renaud, S. Durrieu, and M. Bouvier, On the interest of penetration depth, canopy area and volume metrics to improve Lidar-based models of forest parameters