Burned area detection and mapping using time series Sentinel-2 multispectral images

Abstract

Accurate mapping of burned areas (BAs) is essential for post-disaster reconstruction, air quality assessment, and estimation of fire emissions. Remote sensing has become the most practical means to map global/regional BAs. Space-borne coarse-resolution (250–1000 m) BA products play an essential role in fire-related science and applications, but small BAs are typically poorly represented due to resolution limitations. Moderate-resolution (10–30 m) imagery provides a more detailed spatial extent of BAs, but the various disturbances (e.g., plant phenology, cloud shading, flooding, harvesting, grazing, pests, and diseases) make application on a global scale challenging. Here, we propose an automatic BA detection algorithm based on time series images acquired by the Sentinel-2 MultiSpectral Instrument (MSI) and the Google Earth Engine. Specifically: (i) We determined an optimal set of spectral indices for MSI images based on global sampling to mitigate the background heterogeneity and BA spectral diversity. (ii) We jointly used three temporal scales, i.e., the single-temporal, dual-temporal, and short-time series MSI images, to identify potential BA regions (i.e., BA candidates). (iii) We integrated multi-source active fire (AF) products derived from the Moderate-resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Operational Land Imager (OLI), and MSI instead of single-source products to refine BA candidates. (iv) We constructed an interannual time series to improve the BA candidates not confirmed by AF products. The proposed BA detection method was applied to different land cover and regional scales, including forests, grasslands, savannas, shrublands, and croplands at the regional (scene) and national levels (Portugal). Validation and inter-comparison with similar products demonstrated that the proposed method is reliable: the Dice coefficient of our detection results was improved by ∼7% and ∼ 9%, respectively, compared to the moderate-resolution BA products (e.g., LBA_CU for the conterminous United States and FireCCISFD20 for sub-Saharan Africa in 2019). These results suggest that the proposed method can accurately and automatically map BAs, providing a foundation for fire monitoring programs and climate change research.