In Canada, forest companies and government are faced with the important challenge of monitoring forest stand health. This task is especially difficult when the objective is to monitor the health of mature deciduous stands. Mapping methods for tree health have been proposed using multispectral and hyperspectral airborne sensors; however, acquiring airborne data over large areas remains costly. In addition, some studies have pointed out that forest dieback is characterized by multi-causality. Therefore, we propose a large-scale mapping method which includes a model to parameterize several factors influencing forest vigour. A high-spatial resolution satellite image was fused with a series of biophysical parameters using the Dempster–Shafer theory (DST). The study was performed over mature deciduous forests in the province of Québec, Canada. The fusion of a Satellite Pour l’Observation de la Terre (SPOT)-5 high resolution geometric (HRG) image with a surface deposit map and an ice storm damage-intensity map provided the best results, improving the overall accuracy by 15.1%, when compared with a K‐nearest-neighbour (KNN) algorithm using the SPOT-5 image alone. Moreover, the DST improved the accuracy of the vigour class identification, halving the standard deviation when compared with the KNN method. This study shows how the DST can be used to model the influence of biophysical parameters when combined with multispectral information to better assess the health of mature deciduous stands.