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

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