Exploring the utility of Sentinel-2 MSI derived spectral indices in mapping burned areas in different land-cover types

Abstract

The difference in structure and composition of landcover types requires accurate mapping of burned areas for post-fire ecological assessments. Spectral indices for burned area mapping are mostly hard-coded to particular environments. However, the best post-fire spectral index to use for burned area mapping in most unstudied landcover types is not known. In this study, out of nine burned mapping indices optimised using the red-edge band, we tested which index outperformed the others in different land cover types. We used the Random Forest (RF) classifier to detect burned areas from Sentinel 2A imagery in four study sites and assessed the classification accuracy. We found out that, the Burned Area Index (BAI) and Global Environmental Monitoring Index (GEMI) spectral indices outperformed other indices in open shrublands, evergreen forest and in needle-leaved and semi-deciduous forests. The lowest performing spectral indices in the four study sites were Optimised Soil Adjusted Vegetation Index (OSAVI), Normalise Burn Ratio (NBR), and Normalise Difference Vegetation Index (NDVI). We recommend that for future studies, researchers and ecologists should use BAI and GEMI in mapping fires in open shrublands, evergreen, needle-leaved and semi-deciduous forests. Our results provide necessary insight for burned mapping algorithms and the accurate estimation of post-fire carbon emission with uni-temporal spectral indices in open shrublands, evergreen, needle-leaved and semi-deciduous forests.