The capacity to observe biodiversity and the marine physical and biological environment is central to support public policies and ensure the ecological and socio-economic sustainability of associated services. In the case of tropical coral ecosystems, observation faces important scientific and technological challenges due to the diversity of these ecosystems, their vast geographical scale and the difficulty to access potentially remote sites. The objective of this PhD thesis is to quantify changes in these ecosystems (surface, bleaching events, species diversity) using a multi-scale automatic image recognition approach (deep learning). This multiscale approach consists in using data collected at fine scale by autonomous boards and sea users (Seatizen project and Ifremer IOT) to identify habitats and species of interest of some characteristic areas of coral ecosystems. The habitats and species identified at this scale will serve as training sets for algorithms using drone data over larger areas. Again, these drone data will serve as training sets for algorithms using satellite images and thus covering very large spatial areas. This approach will be developed at the scale of the Indian Ocean but will be generic to be able to extend the method to other oceans.