Mangrove species discrimination and health assessment using AVIRIS-NG hyperspectral data

Mangroves play a major role in supporting biodiversity, providing economic and ecological security to the coastal communities, mitigating the effects of climate change and global warming. Species level classification of mangrove forest, understanding physical as well as chemical properties of mangrove vegetation, mangrove health, pigments, and levels of stress are some of the key issues for making scientific and management decisions. Hyperspectral remote sensing owing to its narrow bands, yield information on structural details and canopy parameters. Hyperspectral data over Sundarban and Bhitarkanika mangrove forests are analyzed for species discrimination and forest health assessment. In all, 15 mangrove species in Sundarban and 7 mangrove species in Bhitarkanika have been identified and classified using Spectral Angle Mapper technique. In-situ spectro-radiometer data has been used along with AVIRIS-NG hyperspectral data. Based on response of vegetation in blue, red and near-infrared regions, combination of vegetation indices are used to assess mangrove forest's health. Reduction in NIR reflectance with shift towards lower wavelength has been observed in less healthy groups.