Evaluating spectral indices and spectral mixture analysis for assessing fire severity, combustion completeness and carbon emissions.

We used a Landsat Thematic Mapper (TM) image from the 2011 Wallow fire in Arizona, USA, in combination with field data to assess different methods for determining fire severity. These include the normalised burn ratio (NBR), the differenced NBR (dNBR), the relative dNBR (RdNBR) and the burned fraction (BF) estimated by spectral mixture analysis (SMA). The Geo Composite Burn Index (GeoCBI) and vegetation mortality data were used as ground truth. Of all the remotely sensed measures evaluated the dNBR had the best performance (GeoCBI-dNBR R[sup 2] = 0.84), which supports the operational use of the dNBR for post-fire management. Of the other remotely sensed measures, the SMA-derived BF also had moderately high correlations with the GeoCBI (R[sup 2] = 0.66). Both approaches demonstrated their usefulness for refining modelled CC values, however, the SMA approach has the advantage of providing transferable quantitative estimates without the need for calibration with field data. The carbon emission estimates that included fire severity were more than 50% lower than the estimate derived from modelling alone. These results suggest that for certain fire types, especially mixed-severity fires, current emission estimates are significantly overestimated, which will affect global carbon emission estimates from wildfires. Traditional remote sensing approaches to assess fire severity provide information for post-fire management; however, they do not provide a useful approach to adjust combustion completeness (CC) and carbon emissions. We show a remotely sensed approach that provides a measure of fire severity that can be used to refine carbon emissions. [ABSTRACT FROM AUTHOR]