Tropical forest type mapping by remote sensing using C-Sar and optical data from sentinel-1 and -2

The Congo Basin forest is the second largest tropical ecosystem in the world. As global changes and anthropic degradation continually increase pressure on this valuable lung for the planet, monitoring programmes and protected areas are established in order to protect wildlife and ensure the sustainable development of forest-related activities (logging, slash and burn farming, inhabited area extension…). Monitoring large territories require up to date documentation. In wide inaccessible areas, remote sensing appear as a major asset to produce accurate and objective land cover mapping without travelling through entire regions. The recent European Earth Observation Copernicus programme now provides free satellite imagery from – among others – Sentinel-1 and Sentinel-2 platforms. Very high resolution C-SAR and optical data are accessible and represent valuable data in Earth Observation domains. Those remotely-sensed data also constitute excellent raw material for current mapping programmes. Since 2017, the RIOFAC project has been reinforcing and institutionalizing the OFAC (Observatory of the Central African Forest) by increasing its monitoring capacity of the forest, climate and biodiversity. For the mapping expertise, the UCLouvain has been consulted to produce a wide land cover of the entire Congo Basin, following many references in the remote sensing field. From a partnership with a local WWF organization located in Bayanga (Central African Republic), the APDS region has been chosen as study area, to describe the actual forest stratification, develop a forest typology and produce a forest-type mapping by remote sensing. The output product of this master thesis is a full land cover of the study area produced out of very high resolution imagery from Sentinel-1 and -2. As this work represent a full methodology to develop and map forest stratification in a remote tropical region, every step from field sample collection, satellite data acquisition, classification method and validation assessment can improved by field and methodological experience.