Overlapping Landsat Scene Classifications and Focal Context to Identify Boreal Disturbance Mapping Uncertainty

The BorealDB dataset provides annual fire and timber harvesting disturbance classifications for Ontario that are derived from a collection of independently classified Landsat scenes. This study assesses the confidence of BorealDB classifications within overlapping scene margins since multiple classifications for common locations are available. For each focal point in BorealDB, the disturbance state of its four nearest spatial orthogonal neighbors were extracted and used to produce classification tree (CT) and random forest (RF) predictions of the focal class. Uncertainty was assessed as being greatest when predictions by neighboring locations or overlapping disturbance classes disagree with the focal class. The assessment found that identified locations of uncertainty within BorealDB varied with disturbance class, with fire having lower uncertainty than timber harvesting. With the results of the analysis, we recommend the inclusion of the analysis outputs and comparisons to supplement existing ensemble confidence attribute in BorealDB.