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Background There are many studies on mangrove mapping, zonal demarcation, landuse and land cover changes, and also topics like its loss and restoration using multispectral data. In the recent past, advanced remote-sensing data have been used for species discrimination, mapping, etc. Purpose The current research aims at identifying and mapping mangroves species along Pichavarm coast of Tamil Nadu, India, using hyperspectral remote-sensing data. The study attempts to map the species by generating the reference spectra from the existing reports and research papers, as surrogate to expensive field work in conjunction with Hyperion data of January 2013. Methods Image was pre-processed followed by Minimum Noise Fraction (MNF) and Pixel Purity Index (PPI). The output of MNF and PPI has been analysed by visualizing it in n-dimensions for end-member extraction. There were eleven spectra taken from the end-members, which were matched with reference spectra. Results The spectra-matched-, have been used as an input for classification of data with classifiers like Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF) and Spectral Information Diversion (SID) to identify and map mangroves species. Further to monitor the exact presence of the species at sub-pixel level, linear spectral un-mixing (LSU) was also performed. Conclusions The study found SAM with LSU as the best approach for mangrove species mapping in Pichavaram coast.

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