Fusing Landsat and MODIS data to retrieve multispectral information from fire-affected areas over tropical savannah environments in the Brazilian Amazon

Abstract

ABSTRACTIn this study, the combination of surface reflectance products from Terra- Moderate Resolution Imaging Spectroradiometer and Landsat-Enhanced Thematic Mapper Plus sensors are explored through the Flexible Spatiotemporal DAta Fusion (FSDAF) algorithm within the framework of forest fire studies over tropical savannah environments. Thus, 60 fusion-derived images were generated from four spectral bands [red, near-infrared, shortwave infrared (SWIR1 and SWIR2)] and six spectral indices [normalized difference vegetation index, normalized difference moisture index, global environment monitoring index, soil-adjusted vegetation index, normalized burn ratio (NBR), and differenced normalized burn ratio (dNBR)] over two selected study sites. For all fusion processes performed, the actual Landsat images for the corresponding dates are available, which supports validation of the blended images. Additionally, integration of blended spectral indices in the immediate post-fire evaluation and the generation of fire severity were analysed. The blended bands presented correlation and Structure Similarity Index Measure (SSIM) values that were consistently higher than 0.819 and root mean square error values of less than 0.027, which confirms good accuracy levels obtained from the model. Similar correlation and SSIM accuracy levels were observed in the blended indices assessment for both study sites, which enables its values to be well-integrated for an analysis of the immediately post-fire date. However, the fire severity mapping from fused images needs to be carefully implemented since the dNBR index is generally less accurate than other blended indices. FSDAF fusion proved to be a useful alternative to retrieving multispectral information from savannah environments affected by fires.