There is a pressing need to map changes in forest structure from the earliest time period possible given forest management policies and accelerated disturbances from climate change. The availability of Landsat data from over four decades helps researchers study an ecologically meaningful length of time. Forest structure is most often mapped utilizing lidar data, however these data are prohibitively expensive and cover a narrow temporal window relative to the Landsat archive. Here we describe a technique to use the entire length of the Landsat archive from Multispectral Scanner to Operational Land Imager (M2O) to produce three novel outcomes: (1) we used the M2O dataset and standard change vector analysis methods to classify annual forest structure in northwestern Montana from 1972 to 2015, (2) we improved the accuracy of each yearly forest structure classification by applying temporal continuity rules to the whole time series, with final accuracies ranging from 97% to 68% respectively for two and six-category classifications, and (3) we demonstrated the importance of pre-1984 Landsat data for long-term change studies. As the Landsat program continues to acquire Earth imagery into the foreseeable future, time series analyses that aid in classifying forest structure accurately will be key to the success of any land management changes in the future.