Integrating image segmentation in the delineation of burned areas on Sentinel-2 and Landsat 8 data

Precise and regularly updated maps of surface burned area extent are essential for fire management. It is challenging to obtain this information through ground surveys due to high cost. Satellite remote sensing has become a fundamental and cost-effective tool for acquiring such information in a temporal context. This paper features the integration of image segmentation in a methodological framework to delineate the extent of burned areas on Sentinel-2 and Landsat 8 optical data. A rule-based and a supervised classification approach are employed to support this aim. In order to evaluate the performance of the proposed approaches, the results were compared to the relevant Copernicus Emergency Management Service (EMS) maps. The results obtained using the rule-based approach depicted adequate agreement with the EMS product (86.9% using Landsat 8 data and 85.4% using Sentinel-2 data), whereas those obtained using a supervised classification approach depicted noticeable agreement with the EMS product (88.6% using Landsat 8 data and 90.7% using Sentinel-2 data). Finally, the widely used error matrix was calculated for each classification approach. We obtained 96% overall accuracy value using Landsat 8 data and 98% overall accuracy value using Sentinel-2 data. The results depict that the proposed methodology can be very useful for the delineation of burned areas.