Calibration and Validation of Immediate Post-Fire Satellite-Derived Data to Three Severity Metrics

摘要

Since 2007, the USDA Forest Service’s Remote Sensing Applications Center (RSAC) has been producing fire severity data within the first 30 to 45 days after wildfire containment (i.e., initial assessments [IA]), for wildfires that occur on USDA Forest Service managed lands, to support post-fire management actions. Satellite image-derived map products are produced using calibrations of the relativized differenced normalized burn ratio (RdNBR) to the Composite Burn Index (CBI), percent change in tree basal area (BA), and percent change in canopy cover (CC). Calibrations for extended assessments (EA) based upon one-year post-fire images have previously been published. Given that RdNBR is sensitive to ash cover, which declines with time since fire, RdNBR values that represent total mortality can be different immediately post fire compared with one year post fire. Therefore, new calibrations are required for IAs. In this manuscript, we describe how we modified the EA calibrations to be used for IAs using an adjustment factor to account for changes in ash cover computed through regression of IA and EA RdNBR values. We evaluate whether the accuracy of IA and EA maps are significantly different using ground measurements of live and dead trees, and CBI taken one year post fire in 11 fires in the Sierra Nevada and northwestern California. We compare differences between error matrices using Z-tests of Kappa statistics and differences between mean plot values in mapped categories using Generalized Linear Models (GLM). We also investigate whether map accuracy is dependent upon plot distance from boundaries delineating mapped categories. The IAs and EAs produced similarly accurate broad-scale estimates of tree mortality. Between IAs and EAs of each severity metric, the Kappa statistics of error matrices were not significantly different (P 0.674) nor were mean plot values within mapped categories (P 0.077). Plots 30 m (one Landsat pixel) distance from mapped polygon boundaries were less accurate than plots ≥30 m inside mapped polygons (P 0.001). As land managers concentrate most post-fire management actions where tree mortality is high, it is desirable for map accuracy of severely burned areas to be high. Plots that were ≥30 m inside polygons depicting ≥75 % or ≥90 % BA mortality were correctly classified (producer’s accuracy) 92.3 % of the time, regardless of IA or EA.