Improved exploration of fishery resources through the integration of remotely sensed merged Sea level anomaly, Chlorophyll concentration, and Sea surface temperature

摘要

The capabilities of evolving satellite remote sensing technology, combined with conventional data collection techniques, provide a powerful tool for efficient and cost effective management of living marine resources. Fishes are the valuable living marine resources producing food, profit and pleasure to the human community. Variations in oceanic condition play a role in natural fluctuations of fish stocks. The Satellite Altimeter derived Merged Sea Level Anomaly(MSLA) results in the better understanding of ocean variability and mesosclae oceanography and provides good possibility to reveal the zones of high dynamic activity. This study comprised the synergistic analysis of signatures of SEAWIFS derived chlorophyll concentration, National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer(NOAA-AVHRR) derived Sea Surface Temperature and the monthly Merged Sea Level Anomaly data derived from Topex/Poseidon, Jason-1 and ERS-1 Altimeters for the past 7 years during the period from 1998 to 2004. The overlapping Chlorophyll, SST and MSLA were suggested for delineating Potential Fishing Zones (PFZs). The Chlorophyll and SST data set were found to have influenced by short term persistence from days to week while MSLA signatures of respective features persisted for longer duration. Hence, the study used Altimeter derived MSLA as an index for long term variability detection of fish catches along with Chlorophyll and SST images and the maps showing PFZs of the study area were generated. The real time Fishing statistics of the same duration were procured from FSI Mumbai. The catch contours were generated with respect to peak spectra of chlorophyll variation and trough spectra of MSLA and SST variation. The vice- a- versa patterns were observed in the poor catch contours. The Catch Per Unit Effort (CPUE) for each fishing trail was calculated to normalize the fish catch. Based on the statistical analysis the actual CPUEs were classified at each probable MSLA depth zones and plotted on the same images.