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How can sustainable agricultural development be reconciled with the fight against climate change and the preservation of biodiversity? To reach such a challenge, political, technical, and socio-economic decision-makers now have access to accurate and valuable environmental information. These include satellite images and environmental data produced from these images. In Guinea, in order to carry out public policies for the sustainable management of territories and natural resources, the government must be in a position to have reliable, consistent, precise, and as recent as possible information on the state and dynamics of its territory (soil artificialisation, agricultural extension, deforestation, mining...) and on the evolution of certain features of the biosphere (forests, savannas, wetlands, mangroves...). To this end, the Guinean Ministry of Agriculture has relied on the French technical expertise to draw up an agroecological zoning for the country, aimed at identifying and quantifying current agricultural areas, assessing agricultural potential, and mapping land cover and land cover change over a 10-year period (2005-2015). This project was implemented over two years in a partnership framework between a team of French thematic experts and a team of Guinean technicians and experts involving a specific organisation and procedure, to ensure a complete transfer of skills on all project activities, from the realisation of a land cover layer to relevant applications in the field of agriculture and the environment, with the aim of providing decision support tools to multiple Guinean actors. [ABSTRACT FROM AUTHOR]

The organic matter (OM) is an important element of the fertility of cultivated soils. Reflectance measurements have been successfully used to predict soil organic carbon (OC) content of agricultural soils, measured in the laboratory on samples of dried soil or directly in the field using field spectro-radiometer or satellite imagery. This study proposes a method for spatial prediction of soil OC, from UAV imagery. An agricultural plot of 13 ha "Grande Borne" located in the plain of Versailles (78) was analysed in April 2013, shortly before sowing while it was still bare soil. The AIRINOV® UAV used was equipped with a four channels multispectral camera MultiSPEC 4C® (550nm, 660nm, 735 nm and 790 nm) and flying at an altitude of 150m. Twenty three ground control points, distributed in the plot were delineated by target located with a centimetric DGPS and different measures have been made synchronously with the drone flight: Spectral Measurements (spectroradiometer ASD FieldSpec 3®), roughness measurements by photogrammetric method, samples of soil samples for analysis of chemical composition and water content. The images obtained with a resolution of 15cm have been georeferenced with an infra-pixel accuracy. The field spectra were aggregated on the spectral bands of the Multispec 4C for validation by linear regression. The obtained R2, between 0.8 and 0.9 by band, with an error between 1 and 3%, have shown good reliability of the Multispec 4C® sensor. The OC content was then estimated by partial least squares regression (PLSR) from spectral measurements of the 23 plots. The mean squared error of cross validation (RMSECV) by LOO (Leave One Out) method was 1.97 g of OC per kg of soil. A second correction of the model incorporating the effects of moisture and roughness on reflectance, has improved the quality of the prediction by 18% and a RMSECV of 1.61 g / kg. The model was finally spatialized in GIS and compared to simple spatial interpolations of soil analyses. [ABSTRACT FROM AUTHOR]