Tree mortality is a complex process that exhibits great spatio-temporal variability. Long term mortality data is needed to understand this demographic parameter and how it is related to biotic and climatic disturbances. Here, we propose a Bayesian state-space model to estimate tree mortality time series in managed forests, where tree mortality is expressed as the annual proportion of the dead volume (subsequently, volume mortality rate). We argue that the volume mortality rate is an informative measure, and simulations and observed data suggests that the volume mortality rate is a good proxy of the annual mortality rate in its demographic sense (i.e. based on the number of trees), where the quality of the approximation depends on the particular management scheme. The proposed Bayesian state-space model combines two types of data: annual dead volumes from harvest reports and total growing stock volumes from periodic inventories. We illustrate the performance of the Bayesian state-space model using data simulated with an individual based and spatially explicit forest dynamic model. Then, we apply the Bayesian state-space model to data from four forests in the French Alps and recover, unprecedented, century long, time series of annual volume mortality rate. We advocate the use of forest management data for future research, since temperate forests are managed in many countries since decades, thus many other unexploited data sets must exist.