In Canadian boreal forests, wildfire is the predominant agent of natural disturbance often with millions of hectares burning annually. In addition to fire, nearly one quarter of Canada's boreal forest is also managed for industrial wood production. Post-fire logging (or salvage harvesting) is increasingly used to minimize economic losses from fire, notwithstanding that the ecological impacts of successive disturbance events remain poorly understood. Improved monitoring and management of post-fire environments will require new information regarding the location and timing of past operations. In this paper we present and evaluate a data integration approach for detecting spatial and temporal trends in post-fire logging. Here we utilize a series of maps relating timing and extent of burned area (from the Canadian Large Fire Database) and year of harvest (from Landsat change detection) to identify occurrences of post-fire logging between 1987 and 2008 for a portion of boreal forest located in central Saskatchewan, Canada. Using a design-based, stratified random sampling framework we found that 68% (95% confidence interval (CI) [53 to 84%]) of the detected post-fire logging was correctly classified, such that both fire and clearcutting disturbances were positively verified by the reference data. The majority of map error resulted from spectral confusion between harvested areas and rock outcroppings exposed by fire and from mislabeling harvested unburned islands as post-fire logging. To add further confidence to our classification accuracy, we also found that mapped post-fire logging displayed similar temporal trends over a ten year period as salvage volume reported for a forest management area partially contained within the study area. Based upon the significant relationship between these estimates (r=0.97, pb0.001) and the good degree of observed map accuracy, we believe that the presented approach offers a viable and flexible option for characterizing the spatial and temporal dynamics of postfire logging in boreal forests. Maps which reliably identify areas of post-fire logging stand to improve our capacity to manage and model the ecological impacts associated with multiple disturbance events.