Human activities in tropical rainforests cause disturbances and degradation by opening up the forest canopy. Capacities for measuring the extent of the damage are essential to the calculation of carbon emissions under REDD+ programmes (Reducing Emissions from Deforestation and Forest Degradation). Remote sensing is a powerful tool to provide this type of information (e.g. on logging, mining or infrastructure projects). Various techniques have been used to identify and quantify canopy opening. A further step is to cross-reference past and present canopy opening in order to document the recovery of ecosystems after logging. The method proposed in this article involves a semi-automatic processing chain adapted for LANDSAT imagery. During post-processing, canopy opening information is extracted by means of specific algorithms. A spatial index, calibrated to a low-resolution radiometer, indicates present and past degrees of opening. This process produces estimations of forest degradation that can describe lowresolution remotely-sensed data (such as MODIS) used in continental mapping. These estimations are then cross-referenced with continental land cover maps to gain insights into present-day forest categories. This tool was developed under the CoForChange project, whose overall objective is to predict changes in forests and in tree species distribution in the Congo Basin as a result of global change, and to develop decision-making tools. The article focuses on a full-scale example in real time, in the rainforest located at the borders of the Central African Republic, the Republic of the Congo and Cameroon, which was analysed on a year-by-year basis with thirty years of LANDSAT image archives.