MANGROVE MAPPING STRATEGIES USING GOOGLE EARTH ENGINE AND LANDSAT-8 AND SENTINEL-2 IMAGERY DATA

Vegetation indices based on remote sensing data have been widely used for mangrove monitoring. Current availability of high-performance computing allows the processing of large datasets of orbital imagery with moderate spatial and spectral resolutions and the computation of numerous vegetation spectral indices to map coastal vegetated wetlands. This study presents the performance of the Mangrove Vegetation Index (MVI) algorithm, embedded in the Google Earth Engine, applied to Landsat-8 and Sentinel-2 data, to map tracts of mangroves in Aracaju (Sergipe). Results reveal that the Cobweb clustering algorithm applied to MVI-derived from Landsat-8 data favors reliable and practical mangrove mapping, considering the broad diversity of vegetation species observed in this habitat.