dATBI: A New Remote Sensing Index for Burn Area Mapping Using Landsat Data and Google Earth Engine

This study presents a new differenced Automated Temporal Burn Index (dATBI) designed for mapping burned areas and assessing burn severity using Landsat data and Google Earth Engine (GEE). The dATBI can utilize atmospherically corrected surface reflectance pre- and post-fire images as well as single pre-fire and multi-temporal post-fire images to map burned areas accurately. The atmospheric correction was done using the Simplified Robust and Surface Reflectance Estimation Method (SREM). The effectiveness of dATBI was evaluated across various wildfire events, with its performance compared to the differenced Normalized Burn Ratio (dNBR), a key component in several initiatives such as the Burn Area Emergency Response (BAER), Monitoring Trends in Burn Severity (MTBS), and the Arctic-Boreal Vulnerability Experiment (ABoVE). The dNBR results were generated using Land Surface Reflectance Code (LaSRC) based surface reflectance images. The findings indicate that dATBI outperforms dNBR by accurately identifying fire-affected areas while excluding irrelevant pixels obscured by clouds, snow, water bodies, and other land features. In contrast, dNBR tended to misclassify these obscured features as burned areas, resulting in significant commission errors. The dATBI can generate seasonal or annual mean burned area maps using single pre-file and multi-temporal post-fire images. Overall, the results underscore the robustness of dATBI, demonstrating its applicability across diverse regions and its ability to manage large datasets effectively.