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RESEARCH ARTICLE
Contents Vol 18(5)

Remote sensing for prediction of 1-year post-fire ecosystem condition

Leigh B. Lentile A D * , Alistair M. S. Smith B * , Andrew T. Hudak C , Penelope Morgan B , Michael J. Bobbitt B , Sarah A. Lewis C and Peter R. Robichaud C
+ Author Affiliations
- Author Affiliations

A Department of Forestry and Geology, University of the South, Sewanee, TN 37383, USA.

B Department of Forest Resources, University of Idaho, Moscow, ID 83844-1133, USA.

C Rocky Mountain Research Station, US Department of Agriculture Forest Service, Moscow, ID 83843, USA.

D Corresponding author. Email: lblentil@sewanee.edu

International Journal of Wildland Fire 18(5) 594-608 https://doi.org/10.1071/WF07091
Submitted: 12 July 2007  Accepted: 21 August 2008   Published: 10 August 2009

Abstract

Appropriate use of satellite data in predicting >1year 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.

Additional keywords: burn severity, char, Landsat ETM+, ponderosa pine, subpixel, unmixing.


Acknowledgements

The present fieldwork component of the current 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 Rocky Mountain Research Station, Forest Service, US Department of Agriculture (03-JV-11222065–279) and the USDA/USDI Joint Fire Science Program (Projects 03–2-1–02 and 05–4-1–07). Partial support for Smith was obtained from the NSF Idaho EPSCoR Program and by the National Science Foundation under award number EPS-0814387. We thank the Associate Editor, Mark Cochrane and the other anonymous reviewer whose comments greatly improved the present manuscript.


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* Leigh B. Lentile and Alistair M. S. Smith contributed equally to the present paper.