Vegetation mapping provides important information for understanding ecological condition through calculation of vegetation density. It based on vegetation indices developed through algorithms of a mathematical model within the visible and near-infrared reflectance bands. The index is an estimate of either leaf density per species or vegetation types, respectively. This study aimed to evaluate those indices and find the best algorithm using Sentinel-2 satellite image. Twenty four algorithms of vegetation indices were analyzed for mangrove density mapping, i.e., BR, GNDVI BR, GR, SAVI, MSAVI, NDRE, NDVI, NDVI2, NDWI, NNIP, PSRI, RR, RVI, VIRE, SVI, VIRRE, MTV1, MTVI2, RDVI, VARI, VI green, MSR, and TVI. During pre-processing stage, a digital number of a Sentinel-2 image was converted into radiance and reflectance value. The analysis resulted in three algorithms that provide the highest accuracy, i.e., NDVI (normalized difference vegetation indices) with exponential regression approach, RVI (Ratio Vegetation indices) with the exponential approach and NDVI (normalized difference vegetation indices) with a polynomial approach. The mangrove biomass spatial modeling NDVI with exponential regression approach (RMSE = 89 kg) showed the average of each pixel (10x10m) was 0.97 ton / 100 m2. Total mangrove biomass for above ground and underground vegetation in the study area was 22,365.6 tons. Sentinel-2 satellite image may best use one of those three algorithms, especially applied for mangrove vegetation.
