A boreal wildfire and harvesting database with ensemble confidence attributes for Ontario (1972–2021+)

Abstract

We present a repeatable workflow that produced a comprehensive wildfire and timber harvesting database for Ontario (1972–2021) that accommodates annual updates after each new fire season. Training sites for classification are identified at the individual scene-level to avoid spectral variations introduced by time and distance. ISODATA classification on the training data produces clusters that are modelled by a smooth polynomial function to identify a local minimum point along the classification clusters that distinguishes disturbances from non-disturbances. This threshold is then applied to map disturbances on independent scenes of Landsat MSS, TM, ETM+, or OLI imagery. Results are aggregated to 1.44 ha cells and converted to points for dissemination; we do not map explicit boundaries to avoid issues of context- and scale-dependence, or the realities of transitional boundaries. Disturbance points are cross-referenced through time, ensuring that the earliest date for each disturbance is recorded. Disturbance points are intersected with other harvesting and fire databases to assess their ensemble confidence which is attached to each mapped location. We present summaries of Ontario’s boreal disturbance mapping with respect to levels of assessed confidence.

Keywords

• Wildfire
• Harvesting
• Landsat
• Classification
• Confidence
• Points