P. Ciais, A. Bombelli, M. Williams, S. L. Piao, J. Chave et al., The carbon balance of Africa: Synthesis of recent research studies, Philos. Trans. R. Soc. A Math. Phys. Eng. Sci, vol.369, pp.2038-2057, 2011.

P. Ciais, S. Piao, P. Cadule, P. Friedlingstein, and A. Chedin, Variability and recent trends in the African terrestrial carbon balance, Biogeosciences, vol.6, pp.1935-1948, 2009.

R. Valentini, A. Arneth, A. Bombelli, S. Castaldi, R. C. Gatti et al., A full greenhouse gases budget of Africa: Synthesis, uncertainties, and vulnerabilities, vol.11, pp.381-407, 2014.

C. Williams, N. Hanan, J. Neff, R. J. Scholes, J. Berry et al., Africa and the global carbon cycle. Carbon Balance Manag, vol.2, 2007.

, Remote Sens, vol.12, p.1637, 2020.

A. Richard, E. Pekka, A. Werner, L. Oliver, L. Simon et al., A large and persistent carbon sink in the world's forests. Larg. Persistent Carbon Sink World For, vol.333, pp.988-993, 2011.

V. Avitabile and A. Camia, An assessment of forest biomass maps in Europe using harmonized national statistics and inventory plots, For. Ecol. Manag, vol.409, pp.489-498, 2018.

M. Réjou-méchain, N. Barbier, P. Couteron, P. Ploton, G. Vincent et al., Upscaling Forest Biomass from Field to Satellite Measurements: Sources of Errors and Ways to Reduce Them, Surv. Geophys, vol.40, pp.881-911, 2019.

M. D. King and S. Platnick, Spatial and Temporal Distribution of Tropospheric Clouds observed by MODIS onboard the Terra and Aqua Satellites, Four. Transform. Spectrosc. Hyperspec. Imaging Sound. Env, vol.51, pp.3826-3852, 2005.

D. C. Morton, J. Nagol, C. C. Carabajal, J. Rosette, M. Palace et al., Amazon forests maintain consistent canopy structure and greenness during the dry season, Nature, vol.506, pp.221-224, 2014.

C. Song, C. E. Woodcock, K. C. Seto, M. P. Lenney, and S. A. Macomber, Classification and Change Detection Using Landsat TM Data, Remote. Sens. Environ, vol.75, pp.230-244, 2001.

A. Baccini and G. P. Asner, Improving pantropical forest carbon maps with airborne LiDAR sampling. Carbon Manag, vol.4, pp.591-600, 2013.

G. P. Asner and J. Mascaró, Mapping tropical forest carbon: Calibrating plot estimates to a simple LiDAR metric, Remote. Sens. Environ, vol.140, pp.614-624, 2014.

L. Xu, S. S. Saatchi, A. Shapiro, V. Meyer, A. Ferraz et al., Spatial Distribution of Carbon Stored in Forests of the Democratic Republic of Congo

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

N. Jha, N. K. Tripathi, W. Chanthorn, W. Brockelman, A. Nathalang et al., Forest aboveground biomass stock and resilience in a tropical landscape of Thailand, Biogeosciences, vol.2020, pp.121-134
URL : https://hal.archives-ouvertes.fr/hal-02404726

M. Réjou-méchain, B. Tymen, L. Blanc, S. Fauset, T. Feldpausch et al., Using repeated small-footprint LiDAR acquisitions to infer spatial and temporal variations of a high-biomass Neotropical forest, Remote. Sens. Environ, vol.169, pp.93-101, 2015.

H. Adhikari, J. Heiskanen, M. Siljander, E. Maeda, V. Heikinheimo et al., Determinants of Aboveground Biomass across an Afromontane Landscape Mosaic in Kenya, Remote. Sens, vol.9, 2017.

E. M. Ordway and G. P. Asner, Carbon declines along tropical forest edges correspond to heterogeneous effects on canopy structure and function, Proc. Natl. Acad. Sci. USA 2020, vol.117, pp.7863-7870

D. Wang, B. Wan, J. Liu, Y. Su, Q. Guo et al., Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots, Uav-Lidar data and Sentinel-2 imagery, Int. J. Appl. Earth Obs. Geoinform, vol.85, 2020.

G. P. Asner, J. Mascaro, C. B. Anderson, D. Knapp, R. Martin et al., High-fidelity national carbon mapping for resource management and REDD, Carbon Balance Manag, vol.8, issue.7, 2013.

Y. Hirata, N. Furuya, H. Saito, C. Pak, C. Leng et al., Object-Based Mapping of Aboveground Biomass in Tropical Forests Using LiDAR and Very-High-Spatial-Resolution Satellite Data, Remote. Sens, vol.10, 2018.

A. Baccini, S. J. Goetz, W. S. Walker, N. T. Laporte, M. Sun et al., Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps, Nat. Clim. Chang, vol.2, pp.182-185, 2012.

O. Csillik, P. Kumar, J. Mascaro, T. O'shea, and G. P. Asner, Monitoring tropical forest carbon stocks and emissions using Planet satellite data, Sci. Rep, vol.9, pp.17831-17912, 2019.

G. P. Asner, P. G. Brodrick, C. D. Philipson, N. R. Vaughn, R. E. Martin et al., Mapped aboveground carbon stocks to advance forest conservation and recovery in Malaysian Borneo, Boil. Conserv, vol.217, pp.289-310, 2018.

, Remote Sens, vol.12, 2020.

E. Buendia, K. Tanabe, A. Kranjc, J. Baasansuren, M. Fukuda et al., Ipcc Guidelines for National Greenhouse Gas Inventories, vol.IPCC, 2006.

R. E. Mcroberts, E. Naesset, G. C. Liknes, Q. Chen, B. Walters et al., Using a Finer Resolution Biomass Map to Assess the Accuracy of a Regional, Map-Based Estimate of Forest Biomass, Surv. Geophys, vol.40, pp.1001-1015, 2019.

R. Diziain and . Word, Atlas Of Agriculture; International Association of Agricultural Economists/Committee for the World Atlas of Agriculture, 1976.

M. V. Djoufack, B. Fonteine, M. É. Tsalefac, and . Multi, Echelles des Précipitations et du Couvert Végétal au Cameroun: Analyses Spatiales, Tendances Temporelles, Facteurs Climatiques et Anthropiques de Variabilité du NDVI

, Food andAgriculture Organization (FAO), Unesco Soil Map of the World, vol.1, 1977.

P. Jagoret, I. Michel-dounias, D. Snoeck, H. T. Ngnogué, and E. Malézieux, Afforestation of savannah with cocoa agroforestry systems: A small-farmer innovation in central Cameroon, Agrofor. Syst, vol.86, pp.493-504, 2012.
URL : https://hal.archives-ouvertes.fr/hal-02650125

P. Daget and J. Poissonet, Prairies et Pâturages Méthodes d'Etude de Terrain et Interprétations

/. Cnrs and . Cir, Umr Selment (Systèmes d, Elevage Méditerranéens et Tropicaux), p.955, 2010.

M. Réjou-méchain, A. Tanguy, C. Piponiot, J. Chave, and B. Hérault, Biomass: An package for estimating above-ground biomass and its uncertainty in tropical forests, Methods Ecol. Evol, vol.8, pp.1163-1167, 2017.

R. Jean-romain, A. David, and . Lidr, Airborne LiDAR Data Manipulation and Visualization for Forestry Applications

A. Defence and . Geoland2-biopar, Methods Compendium of MERIS FR Biophysical Products. GISci. Remote Sens, p.52, 2014.

F. X. Kneizys, E. P. Shettle, W. O. Gallery, and J. H. Chetwynd, Atmospheric Transmittance/Radiance: Computer Code Lowtran 5, Atmos. Trans. Rad. Com. Code Low, 1980.

S. Jacquemoud and F. Baret, Prpspect: A model of leaf optical properties spectra, Remote. Sens. Environ, vol.34, pp.75-91, 1990.

W. Verhoef, Light scattering by leaf layers with application to canopy reflectance modeling: The Sail model, Remote. Sens. Environ, vol.16, pp.125-141, 1984.

J. Chave, D. Coomes, S. Jansen, S. L. Lewis, N. G. Swenson et al., Towards a worldwide wood economics spectrum, Ecol. Lett, vol.12, pp.351-366, 2009.

A. E. Zanne, G. Lopez-gonzalez, D. A. Coomes, J. Ilic, S. Jansen et al., Global Wood Density Database, p.16, 2009.

J. Chave, M. Réjou-méchain, A. Burquez, E. Chidumayo, M. S. Colgan et al., Improved allometric models to estimate the aboveground biomass of tropical trees, Glob. Chang. Boil, vol.20, pp.3177-3190, 2014.
URL : https://hal.archives-ouvertes.fr/hal-02063299

M. S. Colgan, G. P. Asner, T. Swemmer, and A. Swemmer, Harvesting tree biomass at the stand level to assess the accuracy of field and airborne biomass estimation in savannas, Ecol. Appl, vol.23, pp.1170-1184, 2013.

D. Sims and J. A. Gamon, Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages, Remote. Sens. Environ, vol.81, pp.337-354, 2002.

C. J. Tucker, Red and photographic infrared linear combinations for monitoring vegetation, Remote Sens. Environ, vol.8, pp.127-150, 1979.

A. Huete, K. Didan, T. Miura, E. Rodriguez, X. Gao et al., Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote. Sens. Environ, vol.83, pp.195-213, 2002.

C. F. Jordan, Derivation of Leaf-Area Index from Quality of Light on the Forest Floor, Ecology, vol.50, pp.663-666, 1969.

, Remote Sens, vol.12, 2020.

W. J. Frampton, J. Dash, G. Watmough, and E. J. Milton, Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation, ISPRS J. Photogramm. Remote. Sens, vol.82, pp.83-92, 2013.

A. Kross, J. W. Seaquist, N. T. Roulet, R. Fernandes, and O. Sonnentag, Estimating carbon dioxide exchange rates at contrasting northern peatlands using MODIS satellite data, Remote. Sens. Environ, vol.137, pp.234-243, 2013.

R. Sharma, A. Chakraborty, and P. K. Joshi, Geospatial quantification and analysis of environmental changes in urbanizing city of Kolkata, Environ. Monit. Assess, vol.187, 2014.

C. Särndal, I. Thomsen, J. M. Hoem, D. V. Lindley, O. Barndorff-nielsen et al., Design-Based and Model-Based Inference in Survey Sampling

.. J. Scand and . Stat, , vol.5, pp.27-52, 1978.

L. Breiman, Random forests, Mach. Learn, vol.45, pp.5-32, 2001.

L. Andy and W. Matthew, Classification and Regression by randomForest, R News, vol.2, pp.18-22, 2002.

B. Mariana and D. Lucian, Random forest in remote sensing: A review of applications and future directions, ISPRS J. Photogramm. Remote Sens, vol.114, pp.24-31, 2016.

H. Meyer, C. Reudenbach, S. Wöllauer, and T. Nauss, Importance of spatial predictor variable selection in machine learning applications-Moving from data reproduction to spatial prediction, Ecol. Modell, p.411, 2019.

A. Bouvet, S. Mermoz, T. Le-toan, L. Villard, R. Mathieu et al., An above-ground biomass map of African savannahs and woodlands at 25 m resolution derived from Alos Palsar, Remote. Sens. Environ, vol.206, pp.156-173, 2018.

D. Timothy, M. Onisimo, S. Cletah, S. Adelabu, and B. Tsitsi, Remote sensing of aboveground forest biomass: A review, Trop. Ecol, vol.57, pp.125-132, 2016.

L. Zhang, M. Xu, S. Qiu, R. Li, H. Zhao et al., Improving the estimate of forest biomass carbon storage by combining two forest inventory systems, Scand. J. For. Res, vol.32, pp.1-9, 2016.

S. Quegan, T. Le-toan, J. Chave, J. Dall, J. Exbrayat et al., The European Space Agency Biomass mission: Measuring forest above-ground biomass from space, Remote. Sens. Environ, vol.227, pp.44-60, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02609425