Search

National stratification maps are essential to improve forest management systems. For the Democratic Republic of the Congo, the existing maps derived from remote sensing techniques do not allow an optimal representation of the diverse land cover classes constituting the national stratification scheme. This situation is inherent to the cloud persistence, the seasonality effects and the spatial resolution of the input satellite imagery used that is not always adequate for the discrimination of certain land cover classes. This paper explores a cloud-based median luminance best pixel approach to obtain a cloud-free mosaic of optimal quality. The mosaic produced has necessitated nearly 2,500 Landsat scenes and a following object-based classification enabled the generation of a stratification map for the year 2000 according to the national stratification theme. A stratified random sampling approach that required 1,141 reference samples allowed estimating the map accuracy at 79.32%. Land cover classes areas computed using standard good practices recommendations to estimate land areas indicated that the dense moist forest area was about 158,810,975 ± 7,460,671 ha representing 68.40% ± 3.21% of the country area. Thanks to the free, user-friendly and cloud-based platforms for satellite images processing, the methodology implemented is easily replicable for other tropical countries. &nbsp

Africa is forecasted to experience large and rapid climate change1 and population growth2 during the twenty-first century, which threatens the world’s second largest rainforest. Protecting and sustainably managing these African forests requires an increased understanding of their compositional heterogeneity, the environmental drivers of forest composition and their vulnerability to ongoing changes. Here, using a very large dataset of 6 million trees in more than 180,000 field plots, we jointly model the distribution in abundance of the most dominant tree taxa in central Africa, and produce continuous maps of the floristic and functional composition of central African forests. Our results show that the uncertainty in taxon-specific distributions averages out at the community level, and reveal highly deterministic assemblages. We uncover contrasting floristic and functional compositions across climates, soil types and anthropogenic gradients, with functional convergence among types of forest that are floristically dissimilar. Combining these spatial predictions with scenarios of climatic and anthropogenic global change suggests a high vulnerability of the northern and southern forest margins, the Atlantic forests and most forests in the Democratic Republic of the Congo, where both climate and anthropogenic threats are expected to increase sharply by 2085. These results constitute key quantitative benchmarks for scientists and policymakers to shape transnational conservation and management strategies that aim to provide a sustainable future for central African forests. A large dataset of 6 million trees from 193 taxa is used to map the floristic and functional composition of central African forests and predict their vulnerability to climate change.