Abstract
Measuring fire effects using remote sensing is critical to facilitating an ecological understanding of wildfire at the landscape scale, but questions remain regarding the relationship between spectral changes caused by fire, forest biometrics, and pre-fire agents of change. This thesis addresses these questions by (1) comparing structural forest changes estimated from Light Detection and Ranging (LiDAR) with spectral changes captured by Landsat, and (2) exploring the effects of an antecedent mountain pine beetle (Dendroctonus ponderosae; MPB) outbreak and forest management on subsequent fire effects. We observed the strongest correlations between LiDAR-estimated change in canopy cover (dCC) and spectral indices incorporating the shortwave infrared band. Compared to areas experiencing no pre-fire agent of change, dCC was higher in pre-fire MPB and lower in pre-fire management. These results demonstrate the utility of multi-temporal LiDAR to measure fire effects and the importance of antecedent MPB and forest management on subsequent wildfire severity.