Download (PDF 5.02 MB): https://research.fs.usda.gov/download/treesearch/69689.pdf
Abstract
Biomass consumed in fires has direct ties to the carbon cycle and atmospheric emissions. Though pre- and postfire aboveground fuel biomass can be estimated using ground measurements, scaling of fuel load and consumption estimates requires remote sensing. Increased availability of pre- and post-fire airborne lidar scans is enabling the spatially explicit quantification of fuel load changes. The most commonly applied multitemporal lidar-based fuel consumption mapping method is modeled fuel load change (MFLC), an indirect approach that differences separately modeled pre- and post-fire fuel loads to estimate consumption. In this study, we compared MFLC to a new modeling approach that directly predicts consumption from a suite of bitemporal point cloud structural change metrics. Kernel density change (KDC) quantifies pre-fire to post-fire change in Gaussian kernel point density at a series of aboveground heights. The fundamental assumption of KDC is that relative point density at a particular height corresponds to the amount of fuel at that same height; thus, the fire-induced change in this density should correspond to fuel consumption. Indeed, we found KDC outperformed the MFLC approach in predicting consumption across a diverse array of fuelbeds, from ground to canopy fuels. We also examined the degree to which adding Landsat time series data to the suite of lidar-based predictor variables could improve consumption models, finding that KDC saw substantial benefits from Landsat’s inclusion whereas MFLC demonstrated little change. Our results suggest that KDC is a valuable method that could be employed where accurate maps of fuel consumption are needed.
Citation
Campbell, Michael J.; Hudak, Andrew T.; McCarley, T. Ryan; Bright, Benjamin C.; Dennison, Philip E. 2025. Kernel density change: A new bitemporal lidar metric for directly mapping wildland fire fuel consumption. Science of Remote Sensing. 12: 100268.
