This project represents a multi-institutional activity aimed at producing a coherent quantification and synthesis of forest cover change over the past 35 years for a large and important region: the Far East of Northern Eurasia. The research team will integrate findings on the natural and anthropogenic drivers contributing to changes in regional forest extent and composition.  The study region includes the more southerly reaches of East Siberia, the Russian Far East (RFE) and northeastern China (NE China), together comprising a relatively ecologically similar but highly politically and economically divided region. The abrupt changes in sociopolitical paradigms and economic growth trajectories in Russia and China over the past 35 years coupled with the region’s importance in terms of forest resources (logging), energy resources and biological diversity, make this area of particular interest as a hotspot of forest- and land-cover change, and increasingly a nexus of conflicting economic interests surrounding the forest resource.  The main research questions driving this project are “How have human-driven disturbances related to use of forest resources, combined with natural and other disturbances (fire, insects, agriculture), created the landscapes of the region over the past 35 years? How might they change in the future?” In answering, we will integrate human and natural drivers and their consequences. The project involves a multi-institutional team of researchers who have each developed essential components including Drs. K. Bergen, D. Brown, S. Hitztaler and J. Newell ( University of Michigan), Drs. T. Loboda and G. Sun ( University of Maryland), and Dr. O. Krankina (Oregon State University).  Within this project, the University of Maryland team will develop disturbance history at selected sites from Landsat data and a wall-to-wall disturbance history from a Moderate Resolution Imaging Spectroradiometer (MODIS)-based algorithm.  Together with other project participant, UMD team will compile and add value to available research and data for the region and make these available to the research community.  The project findings will advance our understanding of the processes, drivers and impacts of land cover and land use change and integrate that knowledge through both data analysis and model integration.