A New Metric for Quantifying Burn Severity: The Relativized Burn Ratio
By S. Parks, Gregory K. Dillon, Carol Miller. Remote Sensing, 2014
395 citations recorded by the source corpus.
Satellite-inferred burn severity data have become increasingly popular over the last decade for management and research purposes. These data typically quantify spectral change between pre-and post-fire satellite images (usually Landsat). There is an active debate regarding which of the two main equations, the delta normalized burn ratio (dNBR) and its relativized form (RdNBR), is most suitable for quantifying burn severity; each has its critics. In this study, we propose and evaluate a new Landsat-based burn severity metric, the relativized burn ratio (RBR), that provides an alternative to dNBR and RdNBR. For 18 fires in the western US, we compared the performance of RBR to both dNBR and RdNBR by evaluating the agreement of these metrics with field-based burn severity measurements. Specifically, we evaluated (1) the correspondence between each metric and a continuous measure of burn severity (the composite burn index) and (2) the overall accuracy of each metric when classifying into discrete burn severity classes (i.e., unchanged, low, moderate, and high). Results indicate that RBR corresponds better to field-based measurements (average R2 among 18 fires = 0.786) than both dNBR (R2 = 0.761) and RdNBR (R2 = 0.766). Furthermore, the overall classification accuracy achieved with RBR (average among 18 fires = 70.5%) was higher than both dNBR (68.4%) and RdNBR (69.2%). Consequently, we recommend RBR as a robust alternative to both dNBR and RdNBR for measuring and classifying burn severity.
Subfields: Global and Planetary Change, Safety, Risk, Reliability and Quality
View Paper: https://www.semanticscholar.org/paper/f021992beaba20c470820d9d73cd46aaea89c8d6
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In Global and Planetary Change
Citations in the corpus
- Soil Bacterial and Fungal Response to Wildfires in the Canadian Boreal Forest Across a Burn Severity Gradient (2019, methodology): “, 2008) The remotely-sensed relativised burn ratio (RBR (Parks et al., 2014)) combines satellite imagery from before and after burns, capturing changes in reflectance due to vegetation combustion and mortality, and com-”
- High-severity fire: evaluating its key drivers and mapping its probability across western US forests (2018, methodology): “We classified the RBR into binary categories representing high-severity (RBR≥ 298) and other severity (RBR 298) (Parks et al 2014a).”