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.
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References
BPS Jateng, 2020. Produksi Perikanan Laut yang Dijual di Tempat Pelelangan Ikan Provinsi Jawa Tengah 2029, Berita Resmi Statistik. Badan Pusat Statistik Provinsi Jawa Tengah, Semarang.
Mugo, R., Saitoh, S.I., Nihira, A., Kuroyama, T., 2010. Habitat Characteristics of Skipjack Tuna (Katsuwonus pelamis) in the Western North Pacific: A Remote Sensing Perspective. Fish. Oceanogr. 19, 382–396. https://doi.org/10.1111/j.1365-2419.2010.00552.x
Nurdin, S., Mustapha, M.A., Lihan, T., Zainuddin, M., 2017. Applicability of Remote Sensing Oceanographic Data in the Detection of Potential Fishing Grounds of Rastrelliger kanagurta in the Archipelagic Waters of Spermonde, Indonesia. Fish. Res. 196, 1–12. https://doi.org/10.1016/j.fishres.2017.07.029
Rivai, A.A., Siregar, V.P., Agus, S.B., Yasuma, H., 2017. Pemetaan Daerah Potensial Penangkapan Ikan Menggunakan Pendekatan Model GIS Hotspot dan Analisis Time Series: Studi Kasus Pada Perikanan Bagan Perahu Di Kepulauan Seribu. J. Ilmu dan Teknol. Kelaut. Trop. 9, 337–356.
Safruddin, Rezkyanti, N.I., Hajar, M.A.I., Farhum, S.A., Zainuddin, M., 2014. Aplikasi Generalized Additive Model untuk Mengungkap Keterkaitan Faktor Oseanografi dan Distribusi Ikan Cakalang ( Katsuwonus Pelamis ): Studi Kasus di Perairan Teluk Bone September 2013 – Februari 2014. Simp. Nas. 1 Kelaut. dan Perikan. Univ. Hasanuddin 1–6.
Shaari, N.R., Mustapha, M.A., 2018. Predicting potential rastrelliger kanagurta fish habitat using MODIS satellite data and GIS modeling: A case study of exclusive economic zone, Malaysia. Sains Malaysiana 47, 1369–1378. https://doi.org/10.17576/jsm-2018-4707-03
Situmorang, S.H., 2010. Analisis Data: Untuk Riset Manajemen dan Bisnis. USU Press, Medan
Solanki, H.U., Bhatpuria, D., Chauhan, P., 2017. Applications of generalized additive model (GAM) to satellite-derived variables and fishery data for prediction of fishery resources distributions in the Arabian Sea. Geocarto Int. 32, 30–43. https://doi.org/10.1080/10106049.2015.1120357
Suhartono, Haruna, Paillin, J, B., 2013. Identifikasi Dan Prediksi daerah Penangkapan Ikan Kembung (Rastrelliger sp.) Di Perairan Kabupaten Pangkep. J. “Amanisal” PSP FPIK Unpatti-Ambon 2, 55–65
Sukresno B., and Kusuma D. W., 2021. Pengembangan Peta Prakiraan Daerah Tangkapan Ikan. Journal of Fisheries and Marine Research. Vol. 5. No. 2. 387-395.
Susilo, E., Islamy, F., Saputra, A.., Hidayat, J.., Zaky, A.., Suniada, K.., 2015. Dinamika Oseanografi Terhadap Hasil Tangkapan Ikan Pelagis PPN Pengambengan dari Data Satelit MODIS. Semin. Nas. Penginderaanjauh 20015 567–574
Triarso, I., 2012. Potensi dan Peluang Pengembangan Usaha Perikanan Tangkap di Pantura Jawa Tengah. SAINTEK Perikan. Indones. J. Fish. Sci. Technol. 8, 65–73. https://doi.org/10.14710/ijfst.8.1.65-73
Valavanis, V.D., Pierce, G.J., Zuur, A.F., Palialexis, A., Saveliev, A., Katara, I., Wang, J., 2008. Modelling of essential fish habitat based on remote sensing, spatial analysis and GIS. Hydrobiologia 612, 5–20. https://doi.org/10.1007/s10750-008-9493-y
Wibawa, E.S., Arief, T., 2017. Pemanfaatan Data Satelit Oseanografi untuk Memprediksi Daerah Penangkapan Ikan Lemuru Berbasis Rantai Makanan dan Pendekatan Statistik GAM. Penelitian, Balai Kkp, Laut 11, 77–87.
Wood, S.N., 2006. Generalized Additive Models (An Introduction with R), Statistical Models in S. CRC Press, UK. https://doi.org/10.1201/9780203738535
Y.Siregar, E.S., Siregar, V.P., B.Agus, S., 2018. Analisis Daerah Penangkapan Ikan Tuna Sirip Kuning Thunnus albacares di Perairan Sumatera Barat berdasarkaan Model GAM. J. Ilmu dan Teknol. Kelaut. Trop. 10, 501–516
Zuur, A.F., Ieno, E.N., Smith, G.M., 2009. Analysing Ecological Data. Springer Science + Business Media, LLC, USA. https://doi.org/10.1016/s0076-6879(09)68026-5
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