In November 2009, AWS was commissioned to remotely assess logging rates of an area under a selective logging regime. By extrapolating the rate of logging to an intact adjacent rainforest estimations of potential carbon savings which would accrue from altered forest management that prevents logging may be possible.
Using Geographic Information Systems (GIS) to analyse remotely sensed data (Landsat ETM), logging disturbance was detected. Data from six Landsat ETM images (1989-2009) and Forest Inventory Mapping (FIM) were used to calculate the logging rate of the merchantable forest. Applying the Reference Area rate of logging to the Project Area, we estimated the potential forest disturbance due to logging in the Project Area. GIS analysis of remotely sensed data was found to be an appropriate method for detecting forest canopy change and disturbance. Landsat ETM images with low cloud cover were adequate to discern change through time.
Nevertheless there were significant methodological constraints and assumptions that introduce errors in the assessment, but they were made conservatively and therefore are likely to lead to underestimates rather than overestimates.