Forest disturbance, recovery, and conversion are driving forces for long-term carbon dynamics, and affect ecosystem functioning and health. Here we propose develop an algorithm suite to perform wall-to-wall mapping of forest disturbance using the EOS ASTER archive. Over the last three years, the LEDAPS project at GSFC has focused on creating a Landsat-based record of stand-clearing disturbance from North American forests for the period 1975-2000. This proposal builds on that effort by integrating ASTER imagery into a multi-sensor ESDR for monitoring forest dynamics. Specifically, we propose to: (i) use MODIS aerosol data to atmospherically correct ASTER imagery to surface reflectance; (ii) develop new approaches to use observed MODIS phenology to automate change detection in deciduous forests and cross-calibrate reflectance between ASTER, MODIS, and Landsat; and (iii) assess recent trends in North American forest disturbance from the fused data set. This work also paves the way for the integrated use of multiple, international sources of data (e.g. IRS, CBERS) for characterizing land cover change in the future.
