Siberia is an immense area stretching from the Ural Mountains on the west to the Russian Far East with total territory similar in size to the continental United States. The Siberian forests are an important source/sink of carbon with total carbon storage in Western Siberia estimated to be 4300 MT, and in Eastern Siberia about 12500 MT. Estimated annual carbon depositions are 20 and 60 MT/yr, respectively. Siberian forests are undergoing changes due to several natural and anthropogenic factors including insects, fires, logging, pollution and mineral exploration. This work is conducted by a team of scientists from NASA, University of Maryland and Sukuchev Institute of Forest and will develop and employ methods to map forest type and identify forest disturbances within the Siberian boreal forest using satellite data. This project benefits from a recent study funded by NASA’s Terrestrial Ecology Program by incorporating multitemporal RADARSAT-1 data sets into ongoing analyses of combined satellite data. The approach proposed herein is especially suited for areas with frequent cloud cover and long seasonal duration of low solar illumination such as Siberia. We are developing methods to improve mapping capabilities by using standard beam and ScanSAR RADARSAT-1 images to reveal disturbance and successional patterns within landscape classes. This project examining disturbance types with higher resolution (~30m) RADARSAT Standard Beam and Landsat 7 data and larger area mapping with lower resolution satellite data i.e., RADARSAT-1 ScanSAR, MODIS, and AVHRR. Detailed assessments also focus on two sites along the taiga-tundra ecotone to determine feasibility of using RADARSAT- 1 data for verifying the northward expansion of the taiga forest in response to climate change (Kharuk et al. 1999). Results are validated with ground truth available through the Sukachev Institute of Forest in Krasnoyarsk. During the first year's efforts we examined the utility of various radars for wildfire burn area mapping. In addition the first merger of MODIS and Radarsat data was accomplished. During the second year an in-depth analysis of disturbance mapping was performed using combinations of radar and Landsat data. The proposed third years effort will apply these findings to larger area analyses with MODIS and Radarsat data and use multiple angle Radarsat to detect the tundra taiga ecotone in central Siberia