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

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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.

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/01431160701395161

Affiliations: 1: Department of Forest Resources, University of Idaho, Moscow, ID, 83844 2: Dept. of Forestry and Geology, University of the South, Sewanee, TN 37383 3: Rocky Mountain Research Station, USDA Forest Service, Moscow, Idaho, 83843

Publication date: January 1, 2007

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