Improving SAR-Based Burn Severity Assessment with Consideration of Non-Uniform Scattering Medium in Fire-Affected Areas

Traditional burn severity assessment methods had predominantly leveraged optical remote sensing data, yet such methods often overlook critical vegetation structural information inherent to post-fire ecosystems. Here, we demonstrated the untapped potential of Synthetic Aperture Radar (SAR), a technique sensitive to vegetation architecture, to quantify burn severity. Until now, the application of SAR decomposition technique was limited in assessing burn severity due to non-uniform scattering in fire-affected areas. In this study, quantitative polarimetric features were constructed from the metric that is representative of a non-uniform scattering condition, improving the precision of burn severity assessment. Initially, six decomposition features were extracted with the covariance matrix and then 14 feature groups were formed through metric and combination. Subsequently, sensitivity analyses were conducted for the first nine feature groups with the Composite Burn Index (CBI) values. Following this, the 14 feature groups were employed as inputs and the CBI values as outputs for random forest learning at a 7:3 training ratio to assess burn severity and generate burn severity maps. This study used the Jinyun Mountain fire in Chongqing as the primary case and eight fires in the United States as supplemental data to discuss the general applicability of the quantitative polarimetric features in assessing burn severity. Sensitivity analyses of the feature groups revealed that the quantitative polarimetric features exhibited strong correlations with the CBI values. Further the combination of quantitative polarimetric features demonstrated the potential to assess burn severity, with R of 0.77 and RMSE of 0.58. Notably, the developed methodology showcased superior results within all wildfires, offering a new outlook for future burn severity assessments utilizing vegetation structure information.