The political breakdown of the Soviet Union in 1991 provides a rare case of drastic changes in social and economic conditions, and as such a great opportunity to investigate the impacts of socioeconomic changes on the rates and patterns of forest harvest and regrowth. Our goal was to characterize forest-cover changes in the temperate zone of European Russia between 1985 and 2010 in 5-year increments using a stratified random sample of 12 Landsat footprints. We used Support Vector Machines and post-classification comparison to monitor forest area, disturbance and reforestation. Where image availability was sub-optimal, we tested whether winter images help to improve classification accuracy. Our approach yielded accurate mono-temporal maps (on average > 95% overall accuracy), and change maps (on average 93.5%). The additional use of winter imagery improved classification accuracy by about 2%. Our results suggest that Russia’s temperate forests underwent substantial changes during the observed period. Overall, forested areas increased by 4.5%, but the changes in forested area varied over time: a decline in forest area between 1990 and 1995 (− 1%) was followed by an increase in overall forest area in recent years (+1.4%, 2005–2010), possibly caused in part by forest regrowth on abandoned farmlands. Disturbances varied greatly among administrative regions, suggesting that differences in socioeconomic conditions strongly influence disturbance rates. While portions of Russia’s temperate forests experienced high disturbance rates, overall forest area is expanding. Our use of a stratified random sample of Landsat footprints, and of summer and winter images, allowed us to characterize forest dynamics across a large region over a long time period, emphasizing the value of winter imagery in the free Landsat archives, especially for study areas where data availability is limited.