Bali Strait‘s Potential Fishing Zone of Sardinella lemuru

https://doi.org/10.22146/ijg.66380

Dinarika Jatisworo(1*), Bambang Sukresno(2), Denny Wijaya Kusuma(3), Eko Susilo(4)

(1) Institute for Marine Research and Observation (IMRO)
(2) Institute for Marine Research and Observation (IMRO), Indonesia
(3) Institute for Marine Research and Observation (IMRO), Indonesia
(4) Institute for Marine Research and Observation (IMRO), Indonesia
(*) Corresponding Author

Abstract


Catch fluctuation of Sardinella lemuru in the Bali Strait in the period 2007 - 2019 shows a significant decrease. The fishermen of this area demanded information on the Potential Fishing Zone (PFZ) specifically targeted for Sardinella lemuru beyond their traditional. PFZ will be very helpful, especially during the famine years. Identification of a Potential Fishing Zone (PFZ) is highly important for increased fishing yields and also reduced fishing time for fishermen. Bali strait is dominated by Sardinella lemuru and contributes 16,2% of the total small pelagic fishery production in Fisheries Management Area (FMA) 573. Bali Strait also supports the fishing industry in Muncar (Banyuwangi-East Java) and Pengambengan (Jembrana-Bali). This study will produce a special PFZ for Sardinella lemuru that is not yet available in Indonesia by using remotely sensed and observer data. Here, we apply the Empirical Cumulative Distribution Function (ECDF) algorithm approach for Sardinella lemuru detection. ECDF was developed using Sea Surface Temperature (SST) and Chlorophyll-a (Chl-a) data from Aqua MODIS and extracted according to observer data during 2011-2014. PFZ for Sardinella lemuru in Bali strait was affected by 72,8 % Chl-a conditions and 27,2% by SST conditions. The maximum suitable preference for Sardinella lemuru in Bali Strait is Chl-a condition at 0,2 mg/m3 and SST condition at 28,38°C in northwest monsoon, while in southeast monsoon are 0,97 mg/m3 for Chl-a and 25,61°C for SST. ECDF model result has 69,33% accuracy, which shows the result of Sardinella lemuru PFZ has good accuracy.


Keywords


Bali Strait;Sardinella lemuru;Potential Fishing Zone;Empirical Cumulative Distribution Function

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References

Buchary, E. (2010). In search of viable policy options for responsible use of sardine resources in the Bali Strait, Indonesia. Retrieved from https://circle.ubc.ca/handle/2429/21645

Carpenter, K. E., & Niem, V. H. (1999). The Living Marine Resources of the Western Central Pacific. Volume 3. Batoid Fishes, Chimaeras and Bony Fishes Part 1 (Elopidae to Linophrynidae). In FAO Species Identification Guide for Fishery Purposes.

Fachruddin Syah, A., Lumban Gaol, J., Zainuddin, M., Apriliya, N. R., Berlianty, D., & Mahabror, D. (2019). Habitat Model Development of Bigeye tuna (Thunnus obesus) during Southeast Monsoon in the Eastern Indian Ocean using Satellite Remotely Sensed Data. IOP Conference Series: Earth and Environmental Science, 276(1). https://doi.org/10.1088/1755-1315/276/1/012011

Fitrianah, D. (2015). Feature Exploration for Prediction of Potential Tuna Fishing Zones. International Journal of Information and Electronics Engineering, 5(4), 270–274. https://doi.org/10.7763/IJIEE.2015.V5.543

Fitrianah, D., Fahmi, H., Hidayanto, A. N., & Arymurthy, A. M. (2016). A Data Mining Based Approach for Determining the Potential Fishing Zones. International Journal of Information and Education Technology, 6(3), 187–191. https://doi.org/10.7763/ijiet.2016.v6.682

Fitrianah, D., Hidayanto, A. N., Gaol, J. L., Fahmi, H., & Arymurthy, A. M. (2016). A Spatio-Temporal Data-Mining Approach for Identification of Potential Fishing Zones Based on Oceanographic Characteristics in the Eastern Indian Ocean. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(8), 3720–3728. https://doi.org/10.1109/JSTARS.2015.2492982

Gao, F., Chen, X., Guan, W., & Li, G. (2016). A new model to forecast fishing ground of Scomber japonicus in the Yellow Sea and East China Sea. Acta Oceanologica Sinica, 35(4), 74–81. https://doi.org/10.1007/s13131-015-0767-8

Gaol, J. L., Arhatin, R. E., & Ling, M. M. (2014). Pemetaan Suhu Permukaan Laut Dari Satelit Di Perairan Indonesia Untuk Mendukung “ One Map Policy .” Proceeding Seminar Nasional Penginderaan Jauh 2014, (1), 433–442.

Gaol, J. L., Wudianto, -, B. P. Pasaribu, -, D. Manurung, -, & R., - Endriani. (2004). the Fluctuation of Chlorophyll-a Concentration Derived From Satellite Imagery and Catch of Oily Sardine (Sardinella Lemuru0)in Bali Strait. International Journal of Remote Sensing and Earth Sciences (IJReSES), 1(1). https://doi.org/10.30536/j.ijreses.2004.v1.a1325

Ghofar. (2005). Co-existence in Small-pelagic Fish Resources of The South Coast of East Java, Straits of Bali, Alas and Sape - Indonesia. Ilmu Kelautan - Indonesian Journal of Marine Sciences, 10(3), 149–157. https://doi.org/10.14710/ik.ijms.10.3.149-157

Hastie, T. J., & Tibshirani, R. J. (2017). Generalized additive models. In Generalized Additive Models. https://doi.org/10.1201/9780203753781

Huot, Y., Babin, M., Bruyant, F., Grob, C., Twardowski, M. S., & Claustre, H. (2007). Does chlorophyll textlessitextgreateratextless/itextgreater provide the best index of phytoplankton biomass for primary productivity studies? Biogeosciences Discussions, 4, 707–745.

Ilahude, A. G. (1978). On The Factors Affecting The productivity of The Southern Makassar Strait. Marine Research in Indonesia, 21, 81–107. https://doi.org/10.14203/mri.v21i0.391

Kohavi, R., & Provost, F. (1998). Glossary of Terms: Special Issue on Applictions of Macine Learning and the Knowledge Discovery Process. Machine Learning, 30(2).

Lanz, E., López-Martínez, J., Nevárez-Martínez, M., & Dworak, J. A. (2009). Small pelagic fish catches in the Gulf of California associated with sea surface temperature and chlorophyll. California Cooperative Oceanic Fisheries Investigations Reports, 50, 134–146.

Mugo, R. M., Saitoh, S. I., Takahashi, F., Nihira, A., & Kuroyama, T. (2014). Evaluating the role of fronts in habitat overlaps between cold and warm water species in the western North Pacific: A proof of concept. Deep-Sea Research Part II: Topical Studies in Oceanography, 107, 29–39. https://doi.org/10.1016/j.dsr2.2013.11.005

Ningsih, N. S., Rakhmaputeri, N., & Harto, A. B. (2013). Upwelling Variability Along The Southern Coast of Bali and in Nusa Tenggara Waters. Ocean Science Journal, 48(1), 49–57. https://doi.org/10.1007/s12601-013-0004-3

Phillips, S. J., & Dudík, M. (2008). Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography, 31(2), 161–175. https://doi.org/10.1111/j.0906-7590.2008.5203.x

Pranowo, W., & Realino, B. (2006). Sirkulasi Arus Vertikal Di Selat Bali Pada Monsun Tenggara. Prosiding Forum Perairan Umum Indonesia, (November).

R Core Team. (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria.

Republik Indonesia. KEPMEN-KP Nomor 77. , Pub. L. No. 77, 93 (2016).

Ridha, U., Hartoko, A., & Muskanonfola, M. R. (2013). Analisa Sebaran Tangkapan Ikan Lemuru (Sardinella lemuru) Berdasarkan Data Satelit Suhu Permukaan Laut Dan Klorofil-A Di Perairan Selat Bali. Management of Aquatic Resources Journal, 2(4), 53–60.

Ryandhini, N. A., Zainuri, M., & D. K., A. R. T. (2015). Karakteristik Mixed Layer Depth dan Pengaruhnya Terhadap Konsentrasi Klorofil-a. ILMU KELAUTAN: Indonesian Journal of Marine Sciences, 19(4), 219. https://doi.org/10.14710/ik.ijms.19.4.219-225

Sadly, M., Hendiarti, N., Sachoemar, S. I., & Faisal, Y. (2009). Fishing ground prediction using a knowledge-based expert system geographical information system model in the South and Central Sulawesi coastal waters of Indonesia. International Journal of Remote Sensing, 30(24), 6429–6440. https://doi.org/10.1080/01431160902865780

Saputra, C., Arthana, I. W., & Hendrawan, I. G. (2017). The Vulnerability Study Of Lemuru (Sardinella lemuru) Fish Resources Sustainability In Bali Strait In Corellation With ENSO And IOD. ECOTROPHIC : Jurnal Ilmu Lingkungan (Journal of Environmental Science), 11(2), 140. https://doi.org/10.24843/ejes.2017.v11.i02.p02

Sartimbul, A., Rohadi, E., Yona, D., Yuli H., E., Bakar Sambah, A., & Arleston, J. (2016). Change In Species Composition and Its Implication On Climate Variation In Bali Strait: Case Study In 2006 and 2010. The 3rd International Conference on Fisheries and Aquaculture, 1–7. https://doi.org/10.17501/icfa.2016.3101

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(7), 1369–1378. https://doi.org/10.17576/jsm-2018-4707-03

Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45(4), 427–437. https://doi.org/10.1016/j.ipm.2009.03.002

Strobl, C., Hothorn, T., & Zeileis, A. (2009). Party on! A new, conditional variable importance measure available in the party package. The R Journal, 12.

Sukresno, B., Hartoko, A., Sulistyo, B., & Subiyanto. (2015). Empirical Cumulative Distribution Function (ECDF) Analysis of Thunnus.sp Using ARGO Float Sub-surface Multilayer Temperature Data in Indian Ocean South of Java. Procedia Environmental Sciences, 23, 358–367. https://doi.org/10.1016/j.proenv.2015.01.052

Susilo, E. (2015). Variabilitas Faktor Lingkungan Pada Habitat Ikan Lemuru Di Selat Bali Menggunakan Data Satelit Oseanografi Dan Pengukuran Insitu. Omni-Akuatika, 14(20), 13–22. Retrieved from http://www.omniakuatika.net/10.20884/1.OA.2015.01002.pdf

Ting, K. M. (2017). Confusion Matrix. In Encyclopedia of Machine Learning and Data Mining (p. 260). https://doi.org/10.1007/978-1-4899-7687-1_50

Tummala, S. K., Masuluri, N. K., & Nayak, S. (2008). Benefits derived by the fisherman using Potential Fishing Zone (PFZ) advisories. Remote Sensing of Inland, Coastal, and Oceanic Waters, 7150. https://doi.org/10.1117/12.804766

Wujdi, A., Suwarso, & Wudianto. (2012). Beberapa Parameter Populasi Ikan Lemuru (Sardinella lemuru Bleeker, 1853) Di Perairan Selat Bali. Bawal, 4(3), 177–184.

Zhang, X., Saitoh, S. I., & Hirawake, T. (2017). Predicting potential fishing zones of Japanese common squid (Todarodes pacificus) using remotely sensed images in coastal waters of south-western Hokkaido, Japan. International Journal of Remote Sensing. https://doi.org/10.1080/01431161.2016.1266114



DOI: https://doi.org/10.22146/ijg.66380

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