This month in Remote Sensing of Environment, Potapov et al. report on their method of combining MODIS and Landsat imagery to estimate forest loss in the world’s boreal forests. Understanding forest loss in boreal forests is especially important given the large extent, resource importance (timber), carbon sequestration, and vulnerability (to climate change) of boreal forests. The article authors use MODIS to map annual boreal forest loss “hotspots” and then use a sampling of Landsat 7 data from 2000 and 2005 (SLC-off data) to more accurately estimate the area of change—change often occurs at spatial scales smaller then MODIS can discern, but MODIS has more frequent (and therefore cloud-free) image acquisitions. Using this data fusion technique, Potapov et al. are able to exploit the strengths of the two sensors to estimate that the area of boreal forest cover loss between 2000 and 2005 was 4.02%.  Of that loss, nearly 60% was due to wildfires and the rest was due to other factors such as logging, disease, or wind. The authors conclude, “The integrated use of MODIS and Landsat data is critical to improving forest cover change maps and estimates of the area of gross forest cover loss.”
Reference:
Potapov, P., M. Hansen, S. Stehman, T. Loveland, and K. Pittman (2008).Combining MODIS and Landsat imagery to estimate and map boreal forest cover loss. Remote Sensing of Environment, vol. 112, no. 9, pp. 3708–3719.