Evaluation of linear spectral unmixing and ΔNBR for predicting post‐fire recovery in a North American ponderosa pine forest

Abstract

The Differenced Normalized Burn Ratio (ΔNBR) is widely used to map post‐fire effects in North America from multispectral satellite imagery, but has not been rigorously validated across the great diversity in vegetation types. The importance of these maps to fire rehabilitation crews highlights the need for continued assessment of alternative remote sensing approaches. To meet this need, this study presents a first preliminary comparison of immediate post‐fire char (black ash) fraction, as measured by linear spectral unmixing, and ΔNBR, with two quantitative one‐year post‐fire field measures indicative of canopy and sub‐canopy conditions: % live tree and dry organic litter weight (gm⁻²). Image analysis was applied to Landsat 7 Enhanced Thematic Mapper (ETM+) imagery acquired both before and immediately following the 2000 Jasper Fire, South Dakota. Post‐fire field analysis was conducted one‐year post‐fire. Although the immediate post‐fire char fraction (r ² = 0.56, SE = 28.03) and ΔNBR (r ² = 0.55, SE = 29.69) measures produced similarly good predictions of the % live tree, the standard error in the prediction of litter weight with the char fraction method (r ² = 0.55, SE = 4.78) was considerably lower than with ΔNBR (r ² = 0.52, SE = 8.01). Although further research is clearly warranted to evaluate more field measures, in more fires, and across more fire regimes, the char fraction may be a viable approach to predict longer‐term indicators of ecosystem recovery and may potentially act as a surrogate retrospective measure of the fire intensity.

Acknowledgements

This fieldwork component of this study was funded by the Black Hills National Forest through In Service Agreement No. 0203‐01‐007, Monitoring Fire Effects and Vegetation Recovery on the Jasper Fire, Black Hills National Forest, South Dakota to Rocky Mountain Research Station and Colorado State University. The subsequent research was supported in part by funds provided by the USSA/USDI Joint Fire Sciences Program (Projects 03‐2‐1‐02 and 05‐4‐1‐07). The authors would like to thank Mike Bobbitt for his assistance with the data preparation. We thank the anonymous reviewers and the editor for their comments, which improved the content of this letter.