Sains Malaysiana 44(2)(2015): 225–232
Determination of Potential
Fishing Grounds of Rastrelliger kanagurta Using Satellite Remote Sensing and GIS
Technique
(Penentuan Kawasan Penangkapan Potensi Rastrelliger
kanagurta Menggunakan Satelit Penderiaan Jauh dan Teknik
GIS)
SUHARTONO NURDIN1,3, MUZZNEENA AHMAD MUSTAPHA1,2*, TUKIMAT LIHAN1 & MAZLAN ABD GHAFFAR1
1School of
Environmental and Natural Resource Sciences, Faculty of Science and Technology
Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor Darul Ehsan, Malaysia
2Research
Centre for Tropical Climate Change System (IKLIM), Faculty of Science and
Technology
Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor Darul Ehsan, Malaysia
3Fisheries
and Marine Services, Government of South Sulawesi Province
90126
Makassar, Indonesia
Diserahkan: 24 April 2014/Diterima: 31 Julai 2014
ABSTRACT
Analysis of
relationship between sea surface temperature (SST)
and Chlorophyll-a (chl-a)
improves our understanding on the variability and productivity of the marine
environment, which is important for exploring fishery resources. Monthly level
3 and daily level 1 images of Moderate Resolution Imaging Spectroradiometer Satellite (MODIS) derived SST and chl-a from July 2002 to June 2011 around the
archipelagic waters of Spermonde Indonesia were used
to investigate the relationship between SST and chl-a and to forecast the potential fishing
ground of Rastrelliger kanagurta. The results indicated that there was positive correlation between SST and chl-a (R=0.3, p<0.05). Positive
correlation was also found between SST and chl-a with the
catch of R. kanagurta (R=0.7, p<0.05).
The potential fishing grounds of R. kanagurta were
found located along the coast (at accuracy of 76.9%). This study indicated
that, with the integration of remote sensing technology, statistical modeling
and geographic information systems (GIS)
technique were able to determine the relationship between SST and chl-a and also able to
forecast aggregation of R. kanagurta. This may contribute in decision making
and reducing search hunting time and cost in fishing activities.
Keywords: chl-a; fish forecasting;
satellite imageries; Spermonde Indonesia; SST
ABSTRAK
Analisis hubungan antara
suhu permukaan
laut (SST)
dan klorofil-a meningkatkan pemahaman kita berkaitan kepelbagaian serta produktiviti persekitaran marin
kerana ia
penting untuk meneroka
sumber perikanan.
Imej SST dan klorofil-a bulanan (peringkat
3) dan harian (peringkat
1) daripada Satelit
Pengimejan Spektroradiometer
Resolusi Sederhana (MODIS) dari
Julai 2002 hingga
Jun 2011 di sekitar perairan
kepulauan Spermonde Indonesia
telah digunakan untuk mengkaji hubungan antara SST
dan klorofil-a
serta meramal
kawasan potensi penangkapan bagi spesies Rastrelliger kanagurta. Keputusan menunjukkan bahawa terdapat korelasi positif antara SST
dengan klorofil-a
(R=0.3, p<0.05). Hubungan
positif juga diperoleh
antara SST dan klorofil-a dengan tangkapanR.
kanagurta (R=0.7, p<0.05).
Kawasan potensi penangkapan bagi spesies R. Kanagurtaadalah di sepanjang perairan pantai (pada ketepatan 76.9%).
Kajian ini menunjukkan bahawa, dengan integrasi teknologi penderiaan jauh, pemodelan statistik dan teknik sistem
maklumat geografi
(GIS) dapat
menentukan hubungan
antara SST dan chl-a dan juga
dapat meramalkan kawasan pengumpulan R. kanagurta.
Keputusan kajian
ini mampu menyumbang
dalam membuat
keputusan dan menjimatkan
masa dan kos
dalam aktiviti
penangkapan ikan.
Kata kunci: Klorofil-a; imej satelit; ramalan kawasan perikanan; Spermonde Indonesia; SST
RUJUKAN
Barnard, A.H., Stegmann, P.M. &
Yoder, J.A. 1997. Seasonal surface ocean
variability in the South Atlantic Bight derived from CZCS and AVHRR imagery. Continental
Shelf Research 17: 1181-1206.
Bellido, J.M., Brown, A.M., Valavanis, V.D., Giráldez, A., Pierce, G.J., Iglesias, M. & Palialexis, A. 2008. Identifying essential fish habitat for small pelagic species in
Spanish Mediterranean waters. Hydrobiologia 612: 171-184.
Bertrand, A., Josse, E., Bach, P., Gros, P. & Dagorn, L. 2002. Hydrological and trophic characteristics of tuna habitat:
Consequences on tuna distribution and long line catchability. Canadian
Journal of Fisheries and Aquatic Science 59(6): 1002-1013.
Castillo, J., Barbieri, M.A. &
Gonzalez, A. 1996. Relationship
between sea surface temperature, salinity, and pelagic fish distribution off
northern Chile. ICES Journal of Marine Science 53(2): 139-146.
Chandran, R.V., Jeyaram, A., Jayaraman,
V., Manoj, S., Rajitha, K.
& Mukherjee, C.K. 2009. Prioritization of
satellite-derived potential fishery grounds: An analytical hierarchical
approach-based model using spatial and non-spatial data. International
Journal of Remote Sensing 30(17): 4479-4491.
Choudhury, S.B., Jena, B., Rao, M.V., Rao, K.H., Somvanshi, V.S., Gulati, D.K. & Sahu,
S.K. 2007. Validation of integrated potential
fishing zone (IPFZ) forecast using satellite based chlorophyll and sea surface
temperature along the east coast of India. International Journal of Remote Sensing 28(12): 2683-2693.
Collette, B.B. & Nauen, C.E. 1983. FAO Species Catalogue: Scombrids of
the World. Vol. 2, Rome: FAO Fisheries Synopsis. pp. 48-49.
Georgakarakos, S. & Kitsiou, D. 2008. Mapping abundance distribution of small pelagic
species applying hydroacoustics and Co-Kriging
techniques. Hydrobiologia 612:
155-169.
Guisan, A., Edwards Jr., T.C. & Hastie, T. 2002. Generalized linear and generalized additive models in studies of
species distributions: setting the scene. Ecological Modeling 157:
89-100.
Hendiarti, N., Suwarso, Aldrian,
E., Amri, K., Andiastuti,
R., Sachoemar, S.I. & Wahyono,
I.B. 2005. Seasonal variation of
pelagic fish catch around Java. Oceanography 18(4): 112-123.
Katara, I., Illian, J., Pierce, J., Scott, B.
& Wang, J. 2008. Atmospheric
forcing on chlorophyll concentration in the Mediterranean. Hydrobiologia 612: 33-48.
Ko, C.Y., Lin, R.S., Ding, T.S., Hsieh, C.H. & Le, P.F. 2008. Identifying biodiversity hotspots by predictive models: A case
study using Taiwan’s endemic bird species. Zoological Studies 48(3):
418-431.
KKP. 2011.
Peta Keragaan Perikanan
Tangkap di Wilayah Pengelolaan
Perikanan Republik Indonesia
(WPP-RI) [Map of Variety of Fisheries Resources in Fisheries Management
Area of Republic of Indonesia]. Ministry
of Marine and Fisheries of Republic Indonesia. Directorate
General of Fisheries, Jakarta, Indonesia.
Mustapha, A.M., Chan,
Y.L. & Lihan, T. 2010. Mapping
of potential fishing grounds of Rastrelliger kanagurta(Cuvier, 1871) using satellite images. Proceeding of Map Asia & ISG, July 2010, Kuala Lumpur,
Malaysia.
Navarro, G. & Ruiz, J. 2006. Spatial
and temporal variability of phytoplankton in the Gulf of Cadiz through remote
sensing images. Deep-Sea Research II 53: 1241-1260.
Nurdin, S., Mustapha, M.A.
& Lihan, T. 2014. The
relationship between sea surface temperature and chlorophyll-a concentration
in fisheries aggregation area in the archipelagic waters of Spermonde using satellite images. UKM-FST Postgraduate
Colloquium 2013, American Institute of Physics Conference Proceedings, July
2013, Bangi, Malaysia. 1571: 466-472.
Nurdin, S., Mustapha, M.A.
& Lihan, T. 2013. Spatial
and temporal variability of sea surface temperature in Makassar Strait,
Indonesia. Proceedings of the 12th International
UMT Annual Symposium UMTAS, October 2013, Kuala Terengganu, Malaysia.
O'Reilly, J.E., Maritorena, S., Mitchell,
B.G., Siegel, D.A., Carder, K.L., Garver,
S.A., Kahru, M. & McClain, C. 1998. Ocean color
chlorophyll algorithms for SeaWiFS.
Journal of Geophysical Research 103(11): 24937-24954.
Pittman, S.J., Christensen, J.D., Caldow,
C., Menza, C. & Monaco, M.E. 2007. Predictive mapping of fish species richness across shallow-water
seascapes in the Caribbean. Ecological Modelling 204: 9-21.
Planque, B., Loots, C., Petitgas, P., LindstrØm, U. & Vaz, S. 2011.
Understanding what controls the spatial distribution of fish populations using
a multi-model approach. Fisheries Oceanography 20(1): 1-117.
Qu, T., Du, Y., Strachan, J., Meyers, G. & Slingo,
J. 2005. Sea surface temperature and its variability in
the Indonesian region. Oceanography 18(4): 50-61.
Radiarta, I.N. & Saitoh, S.I. 2008. Satellite-derived
measurements of spatial and temporal chlorophyll-a variability in Funka Bay, southwestern Hokkaido, Japan. Estuarine, Coastal and Shelf Science 79: 400-408.
Radiarta, I.N., Saitoh, S.I. & Miyazono, A.
2008. GIS-based multi-criteria evaluation models for identifying
suitable sites for Japanese scallop (Mizuhopecten yessoensis) aquaculture in Funka Bay, southwestern Hokkaido, Japan. Aquaculture 284:
127-135.
Reese, D.C., O'Malley, R.T., Brodeur,
R.D. & Churnside, J.H. 2011. Epipelagic fish distributions in relation to thermal fronts in a
coastal upwelling system using high-resolution remote-sensing technique.
ICES Journal of Marine Science 68(9): 1865-1874.
Solanki, H.U., Mankodi, P.C., Dwivedi, R.M. & Nayak, S.R.
2008. Satellite observations of main oceanographic
processes to identify ecological associations in the northern Arabian Sea for
fishery resources exploration. Hydrobiologia 612: 269-279.
Solanki,
H.U., Mankodi, P.C., Nayak,
S.R. & Somvanshi, V.S. 2005a. Evaluation of
remote-sensing-based potential fishing zones (PFZs) forecast methodology. Continental
Shelf Research 25: 2163-2173.
Solanki, H.U., Dwivedi, R.M., Nayak, S.R., Naik, S.K., John,
M.E. & Somvanshi, V.S. 2005b. Cover:
Application of remotely sensed closely coupled biological and physical process
for marine fishery resources exploration. International Journal of Remote
Sensing 26(10): 2029-2034.
Solanki, H.U., Dwivedi, R.M., Nayak, S.R., Jadeja, J.V., Thakar, D.B., Dave, H.B. & Patel, M.I. 2001. Application of ocean color monitor chlorophyll and AVHRR SST for fishery
forecast: Preliminary validation results off Gujarat coast, northwest coast of
India. Indian Journal of Marine Science 30: 132-138.
StatSoft. 2014. Multiple Regression: How to Find Relationship between Variables.
https://www.statsoft.com/. Accessed on 07 January 2014.
Susanto, R.D., Moore II, T.R. & Marra, J.
2006. Ocean color variability in the Indonesian Seas during
the SeaWiFS era. Geochemistry Geophysics Geosystems7(5): 1-16.
Tang, D.L., Kawamura, H., Lee, M.A. & Dien,
T.V. 2003. Seasonal and spatial distribution of
chlorophyll-a concentrations and water conditions in the Gulf of Tonkin,
South China Sea. Remote Sensing of Environment 85: 475-483.
Thomas, A.C., Townsend, D.W. & Weatherbee,
R. 2003. Satellite-measured phytoplankton variability in
the Gulf of Mine. Continental Shelf Research 23: 971-989.
Vasconcelos, R.P., Le
Pape, O., Costa, M.J. & Cabral, H.N. 2013. Predicting
estuarine use patterns of juvenile fish with generalized linear models. Estuarine, Coastal and Shelf Science 120: 64-74.
Wajsowicz, R.C., Gordol, A.L., Ffield, A. & Susanto, R.D. 2003. Estimating
transport in Makassar Strait. Deep-Sea Research II 50: 2163-2181.
Webster, P.J., Magana, V.O., Palmer, T.N., Shukla, J., Thomas,
R.A., Yanai, M. & Yasunari,
T. 1998. Monsoon: Processes, predictability, and the prospects for
prediction. Journal of Geophysical Research 103: 14451-14510.
Yoder, J.A., Schollaert, S. & O'Reilly,
J.E. 2002. Climatological phytoplankton chlorophyll and sea surface temperature
patterns in continental shelf and slope waters off the northeast
US Coast. Limnology and Oceanography 47: 672-682.
Zainuddin, M. 2007. Pemetaan daerah potensial
penangkapan ikan
kembung lelaki (Rastrelliger kanagurta)
di perairan kabupaten
Bantaeng, Sulawesi Selatan (Mapping of potential fishing
grounds of Rastrelliger kanagurta
in Bantaeng waters, South Sulawesi).
Jurnal Sains dan Teknologi 7(2): 57-64.
*Pengarang untuk surat-menyurat; email: muzz@ukm.edu.my
|