North Arabian Sea experiences massive proliferation of variable algal species. The study presents variability of Noctiluca and its association with hydrographic parameters such as sea surface temperature (SST) and water column stability using ten years of satellite data. The area was categorized into three regions, North (23 to 26°N and 56 to 70°E), West (18 to 23°N and 56 to 62°E) and East (18 to 23°N and 62 to 74°E). The Noctiluca dominated area was extracted following approach of Dwivedi et. al. (2015) based on slope of Remote Sensing Reflectance (Rrs) between 488 to 443nm and 488 to 531nm. The data used in the present study depicted two distinct clusters based on regression between difference of Rrs(488) and Rrs(443) with Rrs(488) and Rrs(531). The major clusters representing Noctiluca falls within the range of 0.0004 to 0.0015 (Rrs488-Rrs443) and -0.0012 to -0.0004 (Rrs488-Rrs531). The occurrence of Noctiluca showed bi-modal distribution at an annual scale with the dominance in the northern region during winter monsoon (February- March). In western and eastern region higher frequency of Nuctiluca was during post monsoon having lag of one month from western (September) to eastern (October) region. The periodicity of Noctiluca, carried out using Fourier analysis, showed predominance at annual scale in Northern and semi-annual scale in Western and Eastern region. This indicates that the Noctiluca bloom in the northern region is primarily triggered by winter mixing whereas in western and eastern part of northern Arabian Sea it has combined effect of summer upwelling as well as winter mixing.
Present study employs reef-up approach to map coral reef zones along the Sentinel Island of Andaman using high spectral resolution offered by hyper spectral imagery by Hyperion mission of NASA. This data consisting of 242 spectral bands, provide a unique ability to identify Coral substrate based on their spectral properties. We applied atmospheric correction with the help of Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) module of ENVI software. This atmospherically corrected was used to extract Coral Reef Zones (CRZ) based on specific threshold limits after subtracting data of 782.95nm band from 579.45nm band of Hyperion imagery. Both of these bands were chosen due to their property of exhibiting maximum spectral contrast that determines threshold limits to distinguish a coral area from its non-coral counterpart. These CRZs were compared with the coral reef zones base map developed using LISS-III data by INCOIS, Hyderabad and SAC, Ahmadabad under CZS project. We observed that extracted CRZ area was 85.25 m2 and 110.1 m2 using LISS-III and Hyperion Data respectively. Despite the overestimation of CRZ by Hyperion data as compared to LISS-III, the spatial distribution of CRZ showed reasonable similarity in both.