Applications of generalized additive model (GAM) to satellite-derived variables and fishery data for prediction of fishery resources distributions in the Arabian Sea

This study aims to illustrate how remotely sensed oceanic variables and fishing operations data can be used to predict suitable habitat of fishery resources in Geographic Information System. We used sea surface height anomaly (SSHa), sea surface temperature (SST), chlorophyll concentration (CC), photosynthetically active radiation (PAR) and fishing depth as predictor variables. Fishery data of Indian squid (Loligo spp.) and catfish (Tachysurus spp.) for study period (1998–2004) were segregated randomly to create training and validation. Catch was normalized into Catch per unit Effort (kg h^?1). Generalized additive modelling was performed on training data and then tested on validation data. Suitable ranges of SST, CC, SSHa and PAR for different species distributions were derived and integrated to predict their spatial distributions. Results indicated good match between predicted and actual catch. Monthly probability maps of predicted habitat areas coincide with high catch of the particular month for the study period.     

Integrative Analysis of AltiKa-SSHa,MODIS-SST,and OCM-Chlorophyll Signatures for Fisheries Applications

The study focused on integrative signature analysis of synchronous chlorophyll concentrations (CC), Sea Surface Temperature (SST), and Sea Surface Height anomaly (SSHa) for fisheries applications. CC and SST were derived from IRS-OCM and MODIS, respectively, and SSHa was derived from SARAL-AltiKa, AVISO Ssalto/Duacs, and TOPEX/Poseidon. Spatial profiles were generated to visualize patterns of variability in signatures, their distribution, persistence, and interrelationship. The patterns of SST and SSHa signatures are co-varying in many cases indicating linearly related and the correlation was r = 0.79, whereas chlorophyll and SST/SSHa profiles are inversely related and the correlation was r = ?0.82 and ?0.73, respectively. Time series analysis of these variables shows areas of negative SSHa consist of high CC and relatively low SST. This suggests that negative SSHa areas consist of dense nutrient rich water and can be used as an indicator of enhanced biological production sites. Fishing operations data were procured from Fishery Survey of India (FSI). Fish catch in terms of catch per unit efforts (CPUE) were related with signatures of variables. High CPUE locations/contours were found in the vicinity of low SSHa/SST and high CC consisting waters.