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The role of forests in carbon storage in a climate change context is relevance. In this paper, we will assess the carbon stock in the Libreville area using remote sensing data over a 2001-2018 period. This assessment of carbon stocks in forests is mainly based on Landsat optical imagery. A first analysis aimed at producing a land use mapping between 2001 and 2018 was carried out. These preliminary results were then used as a proxy for the assessment of carbon stocks using terrestrial and mangrove forests. The average value of carbon stock contained in each class comes from the bibliographical work carried out mainly in the same study area. The total value of the carbon stock was estimated at 70 913 018 ± 896 and 68 340 383 ± 927 tons in 2001 and 2018 respectively. [ABSTRACT FROM AUTHOR]

Remote sensing is a particularly suitable tool for monitoring land use but also particularly suited for déforestation monito-ring. By launching the Setinel-1 and Sentinel-2 satellites in the Copernicus program, the community now has free data with important time-revisits allowing the greatest number of people to effectively monitor land cover of a study area. This paper présents a land use monitoring method based on the combination of two approaches computed using Open Source tools (QGIS, Orfeo ToolBox, python). First, we focus on land use monitoring at an annual time scale using Sentinel-1 and Sentinel-2 data, and then, by the use f entienl-1 data, we detect changes in the forest cover due to déforestation at bi-monthly time scale. The results obtained show a very good synergy of thèse approaches allowing the complementary of optical and radar data. In order to make accessible the proposed method, ail the used open source tools are available on this link http ://remotesensing4all.net/index.php/2018/09/11/kit-use-des-donnees-radar-sentinel-1-in-de-latelier-radar-du-FOSS4G-en-2018-2/. [ABSTRACT FROM AUTHOR]

Monitoring forest state is required to estimate the levels of greenhouse gases emissions from deforestation. The objective of this study was to estimate deforestation trends for 2005-2010 and 2010-2013 periods in the eastern rainforests ecoregion of Madagascar. A supervised classification approach of multi-date Landsat images using the Random Forest algorithm was used. A complete 30 m map of the forest cover change was produced on 20.5 million hectares. The result was validated by photo interpretation of nearly 11 000 points from a regular sampling grid on the basis of SPOTand Landsat satellite images acquired in 2010-2013. The overall mapping accuracy was estimated to be about 90%. The area of Madagascar rainforests has been estimated at 4.5 million hectares in 2005, 4.4 million hectares in 2010 and 4.3 million hectares in 2013. The annual rate of deforestation has increased from 0.5%. to 0.9 % between the two periods. This study could be considered as a reliable cost-effective and reproductible approach for monitoring forests in Madagascar. [ABSTRACT FROM AUTHOR]