A simple and integrated approach for fire severity assessment using bi-temporal airborne LiDAR data

Abstract This study proposes a new method (profile area change, PAC) to quantify fire-induced forest structural changes at the individual tree and clump of trees scales using pre- and post-fire LiDAR data. The PAC measures the difference in profile area summarized from pre- and post-fire LiDAR points. We applied the PAC method to assess the effects of the 2013 American Fire in the Sierra Nevada, California, USA. Our LiDAR PAC metrics were compared to changes in commonly used LiDAR-derived canopy cover and tree height metrics at tree level, and to Landsat-8 imagery-derived relative differenced normalized burn ratio (RdNBR) at the 30m pixel level. A quantitative validation using field measured changes in basal area and leaf area index (LAI) confirmed that correlations between PAC metrics and field measurements (R2 ≥ 0.67) were significantly higher than those from canopy cover or tree height metrics (R2 ≤ 0.43), and much stronger than that from RdNBR (R2 ≤ 0.26). The PAC metrics can also be used to infer the extent of tree canopy disturbance caused by fire, based on whether the majority of biomass loss occurred above or below the tree crown base height. Mapping of canopy disturbance indicated that over half (57.0%) of the American Fire region had tree canopy loss from fire, 22.5% of trees had sub-canopy loss, while the remaining area had no detectable tree canopy change. Overall, the LiDAR PAC metric, as a simple and integrated method, demonstrated promising potential in characterizing fine-grained changes in forest structure. The method can be beneficial for forest managers in evaluating fire-induced environmental and economic losses, and provide useful information for forest restoration design.

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