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Remote sensing for prediction of 1-year post-fire ecosystem condition
Lentile, Leigh B.; Smith, Alistair M. S.; Hudak, Andrew T.; Morgan, Penelope; Bobbitt, Michael J.; Lewis, Sarah A.; Robichaud, Peter R. 2009. Remote sensing for prediction of 1-year post-fire ecosystem condition. International Journal of Wildland Fire. 18: 594-608.
Appropriate use of satellite data in predicting >1 year post-fire effects requires remote measurement of surface properties that can be mechanistically related to ground measures of post-fire condition. The present study of burned ponderosa pine (Pinus ponderosa) forests in the Black Hills of South Dakota evaluates whether immediate fractional cover estimates of char, green vegetation and brown (non-photosynthetic) vegetation within a pixel are improved predictors of 1-year post-fire field measures,when compared with single-date and differenced Normalized Burn Ratio (NBR and dNBR) indices. The modeled estimate of immediate char fraction either equaled or outperformed all other immediate metrics in predicting 1-year post-fire effects. Brown cover fraction was a poor predictor of all effects (r2 <0.30), and each remote measure produced only poor predictions of crown scorch (r2 <0.20). Application of dNBR (1 year post) provided a considerable increase in regression performance for predicting tree survival. Immediate post-fire NBR or dNBR produced only marginal differences in predictions of all the 1-year post-fire effects, perhaps limiting the need for prefire imagery.Although further research is clearly warranted to evaluate fire effects data available 2-20 years after fire, char and green vegetation fractions may be viable alternatives to dNBR and similar indices to predict longer-term post-fire ecological effects.
Keywords: burn severity, char, Landsat ETM+, ponderosa pine, subpixel, unmixing
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Title: RMRS Other
Publications: Remote sensing for prediction of 1-year post-fire
Electronic Publish Date: November 25, 2009
Last Update: November 25, 2009
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