Articles | Volume 9, issue 12
Research article
10 Dec 2012
Research article |  | 10 Dec 2012

Mapping Congo Basin vegetation types from 300 m and 1 km multi-sensor time series for carbon stocks and forest areas estimation

A. Verhegghen, P. Mayaux, C. de Wasseige, and P. Defourny

Abstract. This study aims to contribute to the understanding of the Congo Basin forests by delivering a detailed map of vegetation types with an improved spatial discrimination and coherence for the whole Congo Basin region. A total of 20 land cover classes were described with the standardized Land Cover Classification System (LCCS) developed by the FAO. Based on a semi-automatic processing chain, the Congo Basin vegetation types map was produced by combining 19 months of observations from the Envisat MERIS full resolution products (300 m) and 8 yr of daily SPOT VEGETATION (VGT) reflectances (1 km). Four zones (north, south and two central) were delineated and processed separately according to their seasonal and cloud cover specificities. The discrimination between different vegetation types (e.g. forest and savannas) was significantly improved thanks to the MERIS sharp spatial resolution. A better discrimination was achieved in cloudy areas by taking advantage of the temporal consistency of the SPOT VGT observations. This resulted in a precise delineation of the spatial extent of the rural complex in the countries situated along the Atlantic coast. Based on this new map, more accurate estimates of the surface areas of forest types were produced for each country of the Congo Basin. Carbon stocks of the Basin were evaluated to a total of 49 360 million metric tons. The regional scale of the map was an opportunity to investigate what could be an appropriate tree cover threshold for a forest class definition in the Congo Basin countries. A 30% tree cover threshold was suggested. Furthermore, the phenology of the different vegetation types was illustrated systematically with EVI temporal profiles. This Congo Basin forest types map reached a satisfactory overall accuracy of 71.5% and even 78.9% when some classes are aggregated. The values of the Cohen's kappa coefficient, respectively 0.64 and 0.76 indicates a result significantly better than random.

Final-revised paper