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

Authors

Leigh B. Lentile, University of IdahoFollow
Alistair M.S. Smith, University of IdahoFollow
Andrew T. Hudak, Rocky Mountain Research StationFollow
Penelope Morgan, University of IdahoFollow
Michael J. Bobbitt, University of IdahoFollow
Sarah A. Lewis, Rocky Mountain Research StationFollow
Pete Robichaud, USFS Rocky Mountain Research StationFollow

Document Type

Article

Date of this Version

2009

Citation

International Journal of Wildland Fire 2009, 18, 594–608

Abstract

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 (r²2