Classification of floristic composition of mangrove forests using hyperspectral data: case study of Bhitarkanika National Park, India

The aim of the present work is to unveil the potential of some of the unexplored remote sensing techniques for mangrove studies. The paper deals with the classification of an Earth Observing–1 Hyperion image of the mangrove area of Bhitarkanika National Park, Odisha, India into mangrove floristic composition classes. Out of 196 calibrated bands of the image, 56 were found to be highly uncorrelated and contained maximum information; therefore, these 56 bands were used for classification. Amongst the three full–pixel classifiers tested in the investigation, Support Vector Machine produced the best results in terms of training pixel accuracy with overall precision of 96.85 %, in comparison to about 70–72.0 % for the other two classifiers. A total of five mangrove classes were obtained – pure or dominant class of Heritiera fomes, mixed class of H. fomes, mixed Excoecaria agallocha with Avicennia officinalis, mixed class of fringing Sonneratia apetala and class comprising of mangrove associates with salt resistant grasses. Post–classification field data also established the same. Pure or dominant classes of H. fomes occupied more than 50 % of the total mangrove vegetation in the forest blocks of the National Park. Spectral profile matching of image pixels with that of in–situ collected canopy reflectance profile revealed good match for H. fomes (pure or dominant stands). Red–edge index, which was a preferred criterion for matching was notably correlated in case of H. fomes and E. agallocha. The outcomes indicated the efficacy of hyperspectral canopy reflectance library for such kind of work. It is hoped that the methodology presented in this paper will prove to be useful and may be followed for producing mangrove floristic maps at finer levels.