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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]

This work deals with the evaluation of the potential of multi-temporal and multi-sensor (optical and radar) images of the satellites Sentinel-1 and Sentinel-2 satellites for land cover mapping and coastline evolution monitoring in a tropical ecosystem over the period 2015 to 2020. The study area chosen is the Cameroon Estuary. This area represents a major ecological transition in the sub-region with the presence of protected natural reserves (mangroves, dense forest, wetlands) but also a strong anthropic activity (construction, agriculture, degraded forest). The methodological approach consisted of a chain of pre-processing and visual image analysis, followed by a combination of the bands from each sensor, a supervised Random Forest classification to draft a land use map and finally a digitization of the coastline. Overall, the results show that the classification with the Sentinel- 2 images using 10 bands and adding 4 vegetation indices is slightly more accurate (95.75 %) than the classification with the 13 initial bands (91.78 %). The classification performed with the Sentinel-1A multi-temporal image series (dual polarization (VV, VH) and VV/VH ratio) leads to an accuracy of 78.44 %. The combination of Sentinel-2 A&B bands (10 bands and 4 indices) and Sentinel-1 improves the results and leads to an accuracy of 98.76 %. Our results also show that the use of multi-temporal image time series significantly improves the classification accuracy compared to the use of a single image (mono-date), and this for both sensors, i.e. an additional gain of 13 % and 10 % respectively for Sentinel-2 and Sentinel-1. However, this gain remains small for temporally stable classes. The results of the coastline evolution analysis show that the Cameroon estuary is disturbed by different levels of erosion (Cape Cameroon, North Manoka Island, Sanaga River mouth), and accretion (Limbe, and South Manoka Island) and also by weak internal variations. The fusion of optical and radar remote sensing data in the discrimination of land use classes, has shown that the construction zones are the most vulnerable to coastal erosion. On the other hand, the presence of vegetation (mangrove, forest) stabilizes and protects the coast from possible risks of this nature. [ABSTRACT FROM AUTHOR]

The mapping of the land use of Gabon in 2010 and 2015 and the changes between these two dates was carried out as part of the OSFACO program (Space observation of Central and Western African Forests) by the Gabonese Agency for Space Studies and Observations (AGEOS). This is the first reference land use map at the national level, including the legend resulting from a consultation of major players in the Gabonese forest sector. This map was obtained by refining the forest cover map of Gabon (forest/non-forest) obtained in the framework of OSFT (Spatial Observation of Tropical Forests) and GEOFORAFRI project (Capacity building and access to earth observation satellite data for monitoring forests in Central and Western Africa). The mapping was based on the use of SPOT 5/7, Sentinel 2 satellite images for the year 2015 and the SPOT 4, ASTER and ALOS 1 images for the year 2010. Semi-automated classification method and Manual enhancement were combined for better accuracy of land use classes. The results obtained highlight ten land use classes representing the country's large landscape groups dominated by forests which occupied 89% of the total area of the country in 2015. The most dynamic land use classes are forests, savannas, agricultural land and artificial surfaces. The main forest losses are linked to the conversion of forests to artificial surfaces, cultivated land and bare soils. The most significant gains in forest are observed at the level of the closure of forest roads. The validation of the product, carried out by an independent expertise from the map production team, was based on a reference data resulting from a sampling plan combining a systematic and random component according to the methodology of Sannier et al. (2016). The analysis of the correspondence between the land use map produced and the reference data gave an overall accuracy of 95%. [ABSTRACT FROM AUTHOR]