Using MODIS time series to characterize the annual dynamics of photosynthesis in tropical forests of Central Africa

ln the framework of the European project CoForChange (predicting the effects of global change on forest biodiversity in the Congo Basin region) we propose a new method of satellite imagery analysis to characterize the spatial patterns of tropical forest in Central Africa. We used 10-year time series remotely sensed data from TERRAMDDI5 sensor to identify the variations of forest photosynthetic activity intensity within a synthetic year. The processing was done using the 16-days enhanced vegetation index (EVI) data. We crossed the resulting information with a data base of field inventories provided by forest companies (Central African Republic, Republic of Congo, and Cameroon). We found that the vegetation types detected with satellite imagery match with a gradient of evergreen dominance. These results are of crucial importance to predict spatial variations of carbon stocks and fluxes, because evergreen/deciduous forests (i) have contrasted annual dynamics of photosynthetic activity and foliar water content; (ii) differ in their community dynamics; and (iii) differ in ecosystem processes. This detailed spatial and temporal information should provide a better basis to monitor carbon in this poorly known region. (Résumé d'auteur