Habitat Suitability Mapping of Rastrelliger Brachysoma Using MODIS Image in WPP 711

https://doi.org/10.22146/ijg.39919

Prama Ardha Aryaguna(1*)

(1) Universitas Esa Unggul
(*) Corresponding Author

Abstract


Important factors that needs to be understood in the management of fishery resources is fish habitat. Fish habitat is an ideal water conditions of a fish species to spawn, breed, feed and grow into adults. Distribution of fish habitat can be approach using variety method, such Habitat Suitability/Species Distribution Modeling. Remote sensing analysis is effective method in providing daily oceanography information. Modis is Remote sensing imagery can be used for modeling Rastrelliger brachysoma fish habitat. Date acquired MODIS image at 28 March 2015, depend on existing field data. The results indicate that, the highest probability of Rastrelliger brachysoma fish habitat location in WPP 711 are in the middle waters of the WPP border between the deep sea of Indonesia and the Pacific Ocean. The lowest probability value for habitat of Rastrelliger brachysoma fish is in the southern shallow waters of Bangka Belitung island which is around 0.1-0.25.

Keywords


Habitat Suitability;Species Distribution Modeling;MODIS;Rastrelliger brachysoma

Full Text:

PDF


References

Apriliani, I. M., Nurrahman, Y. A., & Dewanti, L. P. (2018). Determination of potential fishing ground for hairtail ( Trichiurus sp .) fishing based on chlorophyll- a distribution and sea surface temperature in Pangandaran waters , West Java ,. AACL Bioflux, 11(4), 1047–1054.


Aryaguna, P., Kamal, M., & Hartono. (2017). Analisis Penginderaan Jauh untuk Data Oseanografi : Perbandingan Salinitas dan Suhu pada Permukaan dan Kolom Air di Wilayah Pengelolaan Perikanan 711 ( WPP 711 ). In 5th Geoinformation Science Symposium (Vol. 711, pp. 149–160). Yogyakarta. Retrieved from http://puspics.ugm.ac.id/snsg2017/

Budiono, A. (2004). No TitleStudi Sebaran Fishing Ground Tuna Mata Besar (Thunnus obessus) Berdasar Kondisi Oseanografi di Perairan Selatan Jawa Pada Musim Timur. Universitas Diponegoro.

Brown. O., Minnett, P. (1999). MODIS Infrared Sea Surface Temperature Algorithm : Algorithm Theoretical Basis Document Version 2.0. University of Miami.

Carroci, Fabio. (2009). Aquatic Species Distributio Map Viewer : Rastrelliger brachysoma. Retrieved from http://www.fao.org/figis/geoserver/factsheets/species.html?species=RAB-m&prj=4326.

Chen, J., & Quan, W. (2013). An improved algorithm for retrieving chlorophyll-a from the Yellow River Estuary using MODIS imagery. Environmental Monitoring and Assessment, 185(3), 2243–2255. https://doi.org/10.1007/s10661-012-2705-y

Eastman, J. R., Crema, S., Zhu, H., Toledano, J., & Jiang, H. (2005). In-Process Classification Assessment of remotely sensed imagery. Geocarto International, 20(4), 33–43. https://doi.org/10.1080/10106040508542362

Feng, L., & Hu, C. (2016). Comparison of Valid Ocean Observations Between MODIS Terra and Aqua Over the Global Oceans. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 54(3), 1575–1585.

Foody, G. M. (2002). Hard and soft classifications by a neural network with a non-exhaustively defined set of classes. International Journal of Remote Sensing, 23(18), 3853–3864. https://doi.org/10.1080/01431160110109570

Foody, G. M., Campbell, N. A., Trodd, N. M., & Wood, T. F. (1992). Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification. Photogrammetric Engineering and Remote Sensing, 58(9), 1335–1341.

Franz, B. (2006). Implementation of SST Processing within the OBPG. Retrieved December 7, 2018, from https://oceancolor.gsfc.nasa.gov/docs/modis_sst/

Franz, B. A., Kwiatkowska, E. J., Meister, G., & Mcclain, C. R. (2008). Moderate Resolution Imaging Spectroradiometer on Terra : limitations for ocean color applications, 2(June), 1–17. https://doi.org/10.1117/1.2957964

Hernandez, P. A., Franke, I., Herzog, S. K., Pacheco, V., Paniagua, L., Quintana, H. L., … Young, B. E. (2008). Predicting species distributions in poorly-studied landscapes. Biodiversity and Conservation, 17(6), 1353–1366. https://doi.org/10.1007/s10531-007-9314-z

Hu, C., Chen, Z., Clayton, T. D., Swarzenski, P., Brock, J. C., & Muller-Karger, F. E. (2004). Assessment of estuarine water-quality indicators using MODIS medium-resolution bands: Initial results from Tampa Bay, FL. Remote Sensing of Environment, 93(3), 423–441. https://doi.org/10.1016/j.rse.2004.08.007

Insanu, R., Handayani, H., & Sukojo, B. (2013). Analisis Pemetaan Zona Penangkapan Ikan ( Fishing Ground ) Dengan Menggunakan Citra Satelit Terra Modis dan Parameter Oseanografi. Prosiding Seminar Nasiional Manajemenn Teknologi XVIII, (Murrachman 2006), B-27-1-B-27-13.

King, M., Closs, J., Spangler, S., Greenstone, R., Wharton, S., Myers, M. (2004). EOS Data Products Handbook Volume 1. NASA Goddard Space Flight Center; Greenbelt, MD, USA.

Komalasari, D. (2016). Dinamika Populasi Ikan Kembung Perempuan ( Rastrelliger brachysoma Bleeker, 1851) Di Perairan Selat Sunda. Institut Pertanian Bogor.

Komick, N. M., Costa, M. P. F., & Gower, J. (2009). Bio-optical algorithm evaluation for MODIS for western Canada coastal waters: An exploratory approach using in situ reflectance. Remote Sensing of Environment, 113(4), 794–804. https://doi.org/10.1016/j.rse.2008.12.005

Li, Z., & Eastman, J. R. (2010). Commitment and typicality measures for the Self-Organizing Map. International Journal of Remote Sensing, 31(16), 4265–4280. https://doi.org/10.1080/01431160903246725

Li, Z., & Fox, J. M. (2011). Integrating Mahalanobis typicalities with a neural network for rubber distribution mapping. Remote Sensing Letters, 2(2), 157–166. https://doi.org/10.1080/01431161.2010.505589

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. Fisheries Research, 196(August 2016), 1–12. https://doi.org/10.1016/j.fishres.2017.07.029

Parkinson, C. Greenstone, R. (2000). EOS Data Products Handbook Volume 2. NASA Goddard SpaceFlight Center; Greenbelt, MD, USA

Partosuwiryo, S. (2009). Pranata Mangsa Sebagai Alterbatif Pedoman Untuk Penangkapan Ikan.

Pitchaikani, J. S., & Lipton, a P. (2012). Impact of environment variables on pelagic fish landings: Special emphasis on Indian oil sardine off Tiruchendur coast , Gulf of Mannar. Journal of Oceanography and Marine Science, 3(3), 56–67. https://doi.org/10.5897/JOMS12.006

Richaud, B., Young-Oh, Kwon., Terrence, M., Paula, S., Steven, J. (2016). Surface and bottom temperature and salinity climatology along the continental shelf off the Canadian and U . S . East Coasts. Continental Shelf Research, Volume 124, 165-181. https://doi.org/ 10.1016/j.csr.2016.06.005

Rijnsdorp, A., Peck, M., Engelhard, G., Mo¨llmann, C., & Pinnegar, J. (2009). Resolving the effect of climate change on fish populations. ICES Journal Text, 1570–1583. Retrieved from http://icesjms.oxfordjournals.org/content/early/2009/04/02/icesjms.fsp056.short

Semedi, B., & Hadiyanto, a L. (2013). Forecasting the Fishing Ground of Small Pelagic Fishes in Makassar Strait Using Moderate Resolution Image Spectroradiometer Satellite Images. Journal of Applied Environmental and Biological Sciences, 3(2), 29–34. https://doi.org/10.13140/RG.2.1.3920.2966

Shaari, N., & Mustapha, M. (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/http://dx.doi.org/10.17576/jsm-2018-4707-03 Predicting

Solanki, H. U., Dwivedi, R. M., Nayak, S. R., Naik, S. K., John, M. E., & Somvanshi, V. S. (2005). Application of remotely sensed closely coupled biological and physical processes for marine fishery resources exploration. International Journal of Remote Sensing, 26(10), 2029–2034. https://doi.org/10.1080/01431160310001595028

Vermote, E., Kotchenova, S. and Ray, J., (2011). MODIS Surface Reflectance User”. Landsat Surface Reflectance Science Computing Facility, Greenbelt, MD, USA

Wong, M., Lee, K., Kim, Y., Nichol, J., Li, Z., & Emerson, N. (2007). Modeling of suspended solids and sea surface salinity in Hong Kong using Aqua/MODIS satellite images. Korean Journal of Remote Sensing, 23(3), 161–169.




DOI: https://doi.org/10.22146/ijg.39919

Article Metrics

Abstract views : 2449 | views : 2229

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2019 Indonesian Journal of Geography

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Accredited Journal, Based on Decree of the Minister of Research, Technology and Higher Education, Republic of Indonesia Number 225/E/KPT/2022, Vol 54 No 1 the Year 2022 - Vol 58 No 2 the Year 2026 (accreditation certificate download)

ISSN 2354-9114 (online), ISSN 0024-9521 (print)

Web Analytics IJG STATISTIC