An Efficient and Accurate Method for Mapping Forest Clearcuts in the Pacific Northwest Using Landsat Imagery

Two variations of image differencing were compared. The first was based on unsupervised classification, repeated five times, using five sequential date-pairs of difference images between 1972 and 1993. Referred to as merged image differencing, this method required merging the results from five separate time intervals into a single map of forest harvest activity. The other method involved a single unsupervised classification of the full sequential difference image data set, and was referred to as simultaneous image differencing. A thorough harvest map error assessment using an independent reference database was compared to two methods of assessment based on visual interpretation of the Landsat data used to develop the difference images. Results indicate that harvest activity was mapped using merged image differencing with greater than 90 percent accuracy, and that visual methods of error assessment using the Landsat images gave nearly identical results with those of the independent reference data. Simultaneous image differencing resulted in a map that was consistent with merged image differencing, and was considerably more cost-effective to implement.