Abstract
Satellite-derived spectral indices such as the relativized burn ratio (RBR) allow fire severity maps to be produced in a relatively straightforward manner across multiple fires and broad spatial extents. These indices often have strong relationships with field-based measurements of fire severity, thereby justifying their widespread use in management and science. However, satellite-derived spectral indices have been criticized because their non-standardized units render them difficult to interpret relative to on-the-ground fire effects. In this study, we built a Random Forest model describing a field-based measure of fire severity, the composite burn index (CBI), as a function of multiple spectral indices, a variable representing spatial variability in climate, and latitude. CBI data primarily representing forested vegetation from 263 fires (8075 plots) across the United States and Canada were used to build the model. Overall, the model performed well, with a cross-validated R2 of 0.72, though there was spatial variability in model performance. The model we produced allows for the direct mapping of CBI, which is more interpretable compared to spectral indices. Moreover, because the model and all spectral explanatory variables were produced in Google Earth Engine, predicting and mapping of CBI can realistically be undertaken on hundreds to thousands of fires. We provide all necessary code to execute the model and produce maps of CBI in Earth Engine. This study and its products will be extremely useful to managers and scientists in North America who wish to map fire effects over large landscapes or regions.
Highlights
Fire severity, defined here as fire-induced change to physical ecosystem components such as vegetation and soil [1,2], is a critical fire regime component that influences landscape heterogeneity, soil erosion, nutrient cycling, wildlife habitat, post-fire successional trajectories, and other ecological factors [3,4,5,6,7]
The cross-validated stepwise procedure we used for variable selection resulted in a parsimonious Random Forest model describing composite burn index (CBI) as a function of relativized burn ratio (RBR), dMIRBI, dNDVI, post.MIRBI, climatic water deficit (CWD), and latitude
The relativized burn ratio (RBR) was clearly the most important variable that we evaluated in our models (Table S4)
Summary
Fire severity, defined here as fire-induced change to physical ecosystem components such as vegetation and soil [1,2], is a critical fire regime component that influences landscape heterogeneity, soil erosion, nutrient cycling, wildlife habitat, post-fire successional trajectories, and other ecological factors [3,4,5,6,7]. Over the last few decades, several satellite-derived spectral indices have been developed to measure fire severity, including the delta normalized burn ratio (dNBR) [1], the relativized delta normalized burn ratio (RdNBR) [22], and the relativized burn ratio (RBR) [23] These spectral indices are based on Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) imagery, several studies have evaluated spectral metrics produced with other sensors such as SPOT, AVIRIS, and Sentinel [24,25,26]. These indices quantify spectral differences between pre- and post-fire imagery and often have strong relationships with field-based measures of fire severity [27,28]. Criticisms consistently directed towards spectral fire severity indices are that their non-standardized units make them difficult to interpret in terms of on-the-ground fire effects [31,32,33] and the nonlinear relationship between field and spectral measures of fire severity complicates interpretation [34]
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