Spatial Dimensions of Existing Conservation Areas and Other Land Uses in the Congo Basin

Abstract Remote sensing (RS) techniques and Geographic Information Systems (GIS) for earth observation have significantly improved the ability to map and measure forest composition, estimate height and biomass, detect degradation and deforestation, implement land use planning, track land use changes as well as monitoring the implementation and respect of land development policies at a finer scale. Machine learning approaches further enhance these capabilities by integrating multiple data sources to produce improved maps of forest attributes and track changes over time. Recent advances in remote sensing techniques and artificial intelligence (AI) now enable detailed mapping and modelling of conservation areas especially the demarcation of High Valued Conservation Forest. National governments follow an administrative procedure for national map design, validation, and standardization. National governments together with the national mapping institutions select particular government officials, the private sector, civil society organizations, and foreign partners to produce national maps. On the other hand, scientists use a rigorous methodology that combines cutting-edge tools and peer review approaches. Even if some of the maps mentioned have not undergone the validation process by the various governments of the sub-region, they are widely used or referred to in international negotiations and discussions. Adopting innovative policies on mapping (Indonesia’s one map policy, Rwanda National land registry) and spatial data management (Geoportal for the Region, The INSPIRE Directive) will be very helpful in planning and conserving the Congo Basin. Non the less, Gabon, the DRC and Cameroon have made exceptions by producing and using renowned National Geoportal.