Estimation of fire severity using pre- and post-fire LiDAR data in sagebrush steppe rangelands

Abstract

Reflectance-based indices derived from remote-sensing data have been widely used for detecting fire severity in forested areas. Rangeland ecosystems, such as sparsely vegetated shrub-steppe, have unique spectral reflectance differences before and after fire events that may not make reflectance-based indices appropriate for fire severity estimation. As an alternative, average vegetation height change ( dh ) derived from pre- and post-fire Light Detection and Ranging (LiDAR) data were used in this study for fire severity estimation. Theoretical deductions were conducted to demonstrate that LiDAR-derived dh is related to biomass combustion and thus can be used for fire severity estimation in rangeland areas. The Jeffreys–Matusita (JM) distance was calculated to evaluate the separability for each pair of fire severity classes, with an average JM distance of 1.14. Thresholds for classifying the level of fire severity were determined according to the mean and standard deviation of each class. A fire-severity classification map with 84% overall accuracy was obtained from the LiDAR dh method. Importantly, this method was sensitive to the difference between the moderate and high fire-severity classes.