Application of Generalized Additive Model for Identification of Potential Fishing Zones Using Aqua and Terra MODIS Imagery Data

Abstract

Remote sensing applications can provide information on oceanographic conditions for identification of potential fishing zones by combining statistical approaches. Determination of fish catch zones needs to be studied on the relationship between oceanographic parameters and fish catches to improve the efficiency and effectiveness of fishing operations by fishermen. Based on this, identification of potential fishing zones needs to examine the relationship between fish catches and oceanographic parameters using the Generalized Additive Model (GAM) in the Java Sea. GAM analysis was carried out using fish catch data as response variables and oceanographic parameters such as sea surface temperature (SST) and chlorophyll-a image processing results from MODIS, SSS from CMES, and Depth data as predictor variables. The selection of the best model is determined by the highest percentage of CDE and the lowest AIC. GAM modeling results show that 60.3% of fish catches in the Java Sea are influenced by oceanographic factors and 39.7% by other factors. The oceanographic parameter that has the most influence on fish catches is the concentration of chlorophyll-a. GAM modeling results show that fish in the Java Sea tend to be found in sea that have chlorophyll-a concentrations of 0.2 mg/m3 – 0.5 mg/m3, SST 280C – 310C, salinity 31.8 PSU – 33 PSU, and a depth of 20 m. – 50 meters. Potential fishing zones were identified based on the results of the GAM modeling analysis. Potential fishing zones in the Java Sea from March 2021 to June 2021 have varying spatial distributions. The results of the most fishing potential zones were found on June 3, 2021, which were distributed the most in the sea around Pulau Laut, in the southern part of the island of Borneo, and the north on the island of Madura.

Keywords

• generalized additive model (gam), java sea, modis, potential fishing zone