Using Landsat Imagery to Assess Burn Severity of National Forest Inventory Plots

As the frequency and size of wildfires increase, accurate assessment of burn severityis essential for understanding fire effects and evaluating post-fire vegetation impacts. Remotelysensedimagery allows for rapid assessment of burn severity, but it also needs to be field validated.Permanent forest inventory plots can provide burn severity information for the field validation ofremotely-sensed burn severity metrics, although there is often a mismatch between the size andshape of the inventory plot and the resolution of the rasterized images. For this study, we used twodistinct datasets: (1) ground-based inventory data from the United States national forest inventory tocalculate ground-based burn severity; and (2) remotely-sensed data from the Monitoring Trends inBurn Severity (MTBS) database to calculate different remotely-sensed burn severity metrics based onsix weighting scenarios. Our goals were to test which MTBS metric would best align with the burnseverity of national inventory plots observed on the ground, and to identify the superior weightingscenarios to extract pixel values from a raster image in order to match burn severity of the nationalinventory plots. We fitted logistic and ordinal regression models to predict the ground-based burnseverity from the remotely-sensed burn severity averaged from six weighting scenarios. Amongthe weighting scenarios, two scenarios assigned weights to pixels based on the area of a pixel thatintersected any parts of a national inventory plot. Based on our analysis, 9-pixel weighted averagesof the Relative differenced Normalized Burn Ratio (RdNBR) values best predicted the ground-basedburn severity of national inventory plots. Finally, the pixel specific weights that we present can beused to link other Landsat-derived remote sensing metrics with United States forest inventory plots.