Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR)

Multi-temporal change detection is commonly used in the detection of changes to ecosystems. Differencing single band indices derived from multispectral pre- and post-fire images is one of the most frequently used change detection algorithms. In this paper we examine a commonly used index used in mapping fire effects due to wildland fire. Subtracting a post-fire from a pre-fire image derived index produces a measure of absolute change which then can be used to estimate total carbon release, biomass loss, smoke production, etc. Measuring absolute change however, may be inappropriate when assessing ecological impacts. In a pixel with a sparse tree canopy for example, differencing a vegetation index will measure a small change due stand-replacing fire. Similarly, differencing will produce a large change value in a pixel experiencing stand-replacing fire that had a dense pre-fire tree canopy. If all stand-replacing fire is defined as severe fire, then thresholding an absolute change image derived through image differencing to produce a categorical classification of burn severity can result in misclassification of low vegetated pixels. Misclassification of low vegetated pixels also happens when classifying severity in different vegetation types within the same fire perimeter with one set of thresholds. Comparisons of classifications derived from thresholds of dNBR and relative dNBR data for individual fires may result in similar classification accuracies. However, classifications of relative dNBR data can produce higher accuracies on average for the high burn severity category than dNBR classifications derived from a universal set of thresholds applied across multiple fires. This is important when mapping historic fires where precise field based severity data may not be available to aid in classification. Implementation of a relative index will also allow a more direct comparison of severity between fires across space and time which is important for landscape level analysis. In this paper we present a relative version of dNBR based upon field data from 14 fires in the Sierra Nevada mountain range of California, USA. The methods presented may have application to other types of disturbance events.