Quantifying surface severity of the 2014 and 2015 fires in the Great Slave Lake area of Canada

Abstract

The focus of this paper was the development of surface organic layer severity maps for the 2014 and 2015 fires in the Great Slave Lake area of the Northwest Territories and Alberta, Canada, using multiple linear regression models generated from pairing field data with Landsat 8 data. Field severity data were collected at 90 sites across the region, together with other site metrics, in order to develop a mapping approach for surface severity, an important metric for assessing carbon loss from fire. The approach utilised a combination of remote sensing indices to build a predictive model of severity that was applied within burn perimeters. Separate models were created for burns in the Shield and Plain ecoregions using spectral data from Landsat 8. The final Shield and Plain models resulted in estimates of surface severity with 0.74 variance explained (R2) for the Plain ecoregions and 0.67 for the Shield. The 2014 fires in the Plain ecoregion were more severe than the 2015 fires and fires in both years in the Shield ecoregion. In further analysis of the field data, an assessment of relationships between surface severity and other site-level severity metrics found mixed results.