Study of mangrove communities in Marine National Park and Sanctuary, Jamnagar, Gujarat, India, by fusing RISAT-1 SAR and Resourcesat-2 LISS-IV images. (2nd January 2017)

• Record Type: Journal Article
• Authors: Kumar, Mohit; Chauhan, H. B.; Rajawat, A. S.; Ajai,
• Is Part Of: International journal of image and data fusion. Volume 8:Number 1(2017)
• Page Start: 73
• Page End: 91
• Publication Date: 2017-01-02
• Subjects: Mangroves -- community zonation -- Marine National Park & Sanctuary -- image fusion -- class separability analysis; Image processing -- Periodicals; Multisensor data fusion -- Periodicals; Multisensor data fusion; Periodicals; 621.36705
• DOI: 10.1080/19479832.2016.1232755
• Languages: English
• ISSNs: 1947-9832

Abstract

ABSTRACT: In this study, RISAT-1 (Radar Imaging Satellite-1) HH image has been fused with Resourcesat 2 LISS-IV (Linear Imaging Self Scanning-IV) image to study the mangrove communities of Jindra-Chhad island complex, in Marine National Park and Sanctuary (MNP&S), Jamnagar, Gujarat, India. Three different methods were used to fuse RISAT-1 and LISS-IV images. In one case, the Synthetic Aperture Radar (SAR) data was simply integrated as an additional band to the three bands of LISS-IV data, whereas in the other, Intensity-Hue-Saturation method was used to merge the two data sets. In yet another exercise, the vegetative and sedimentary parts were separated from the optical data by computing normalised difference vegetation index (NDVI) and by averaging the Red and Green bands, respectively. These two layers, viz., NDVI and the average of Red and Green bands, were then integrated with the SAR data. All the merged products were put to supervised classification using maximum likelihood algorithm into following seven classes: mangrove communities ( Avicennia Dense, Avicennia Sparse, Rhizophora-Ceriops Dense), Intertidal Mudflat, Hightidal Mudflat, Sand and Sea. The class separability analysis indicated that the separability obtained among the classes in the case of fused products was higher than that obtained when both the data sets were classified individually.