High-Resolution Burned Area Mapping Using Sentinel-2 Imagery and Robust Satellite Techniques

Abstract

High accuracy in burned area (BA) mapping is desirable for many fire-related applications. Most operational products are based on coarse-resolution satellite data with a final spatial resolution ranging from 250 m to 1000 m. The use of higher-resolution satellite data, such as that from the Multispectral Imager (MSI) on board Sentinel-2, is usually reserved for generating BA products only for specific countries and time periods. Moreover, BA algorithms are typically driven by fire detection products to minimize false identifications. The resulting accuracy of such BA products depends strongly on land cover, fire regime, and the reliability of the considered fire detection products. In this paper, we exploit the multitemporal Robust Satellite Technique (RST) to identify burned areas and implement a workflow based on Sentinel-2 images within Google Earth Engine. Test results from two complete region-wide experiments conducted under contrasting seasonal, phenological, and agricultural conditions showed that 97.85% of the mapped area was confirmed as burned within the adopted multi-source validation framework, with an area-based commission error of 1.38% and omission error of 0.60%. Missed detections documented by the Regional Spatial Data Infrastructure (RSDI) amounted to 10.60 ha, corresponding to approximately 0.9% of the burned area reported by RSDI over the tested periods. Polygon-level intercomparison with RSDI and the European Forest Fire Information System (EFFIS) yielded Intersection over Union (IoU) values of 0.69 and 0.83, respectively. The proposed approach mapped larger burned areas than those reported by both RSDI (+48%) and EFFIS (+143%) as well.