Sea Surface Temperature and Sea Surface Chlorophyll in Relation to Bigeye Tuna Fishery in the Southern Waters off Java and Bali

12 th Biennial Conference of Pan Ocean Remote Sensing Conference (PORSEC 214) Sea Surface Temperature and Sea Surface Chlorophyll in Relation to Bigeye Tuna Fishery in the Southern Waters off Java and Bali 12* Martiwi Diah Setiawati, 1 Fusanori Miura 1 Graduate School of Science and Engineering, Department of Environmental Science and Engineering, Yamaguchi University, 2-16-1 Tokiwadai, Ube, 755-8611, Japan 2 Center of Remote sensing and Ocean Science (CReSOS), Udayana University, Sudirman campus, Postgraduate building (3rdFl), Jl. P. B. Sudirman, Denpasar, Bali, 8113 Indonesia *Corresponding author: s53wf@yamaguchi-u.ac.jp ABSTRACT Southeast Indian Ocean, particularly in the Southern waters off Java-Bali was identified as potential fishing ground of bigeye tuna (Thunnus obesus). We used satellite remote sensing data of Sea Surface Temperature (SST) and Sea Surface Chlorophyll (SSC) and daily fish catch data from PT Perikanan Nusantara, Bali during 26-21. To understand the preferred habitat of bigeye tuna in the current study area, the relationship between SST, SSC and the number of bigeye tuna are important to investigate. To understand the relationship between SST, SSC and bigeye tuna, fisheries data classification and polynomial regression were used. This study considers to evaluate which parameter will influence the abundance of bigeye tuna, and to determine the range value of each parameter that is optimum for bigeye tuna. The results clearly show that both of SST and SSC, which derived from satellite observation confirmed a strong relationship with the abundance of bigeye tuna. Statistical analysis showed the optimum of SST for bigeye tuna is less than 29.1 C and more than 27.4 C. In addition, the optimum value of SSC was.55 to.175 mg m -3. Moreover, meteorological data and ocean dynamic analysis seems to be other parameters that affect the abundance of bigeye tuna. Keywords: Bigeye tuna, SST, SSC, remote sensing, Southern waters off Java-Bali, Indian Ocean 1. INTRODUCTION Indonesia's marine fisheries potential, including pelagic fishery and commercial fisheries were found almost in all parts of Indonesia's marine waters, which may consist of the territorial sea, the archipelago waters, and Exclusive Economic Zone (EEZ) waters (Bailey et al., 1987). In addition, Indonesia s tuna and billfish fisheries are of significance, but largely unquantified, economic value, both in terms of domestic markets and foreign currency earnings, through the export of products such as yellowfin, bigeye and southern bluefin tuna (Proctor et al., 23). Tuna fisheries play an important role in the economic sector related to the fisheries resources utilization in Indonesia and its exports reached U.S. $ 4 million in 211 and continues to increase every year (MMAF, 211). 1

12 th Biennial Conference of Pan Ocean Remote Sensing Conference (PORSEC 214) Southern waters off Java and Bali as a part of Indian Ocean were identified as a potential fishing ground of big pelagic fishes (Bailey et al., 1987; Osawa and Julimantoro, 21). Pelagic fish, especially for tuna, has an important role for Indonesian angler, which is more than 75% of total fish stock in Indonesia is a pelagic fish (Hendiarti et al., 25). Bigeye tuna is one of the big pelagic species and occupies the highest catches and commercial value in the Indian Ocean (Proctor et al., 23). For that reason, the availability of biological and environmental data around the Indian Ocean is needed to increase the efficiency in fishing operations and simultaneously it would achieve better management of these resources. The complexity of oceanic processes and distribution of ocean environmental parameter on a range of temporal and spatial scales as well as the expense and limited range of research vessels provide difficulties in fisheries research. To identify relationships among ocean processes, environmental parameter distribution, biological responses and corresponding species distributions, scientific information and statistical analysis, the environmental parameters must accommodate the life cycle characteristics of the targeted species (Valavanis et al., 28). If tuna has their preferred biophysical living environment, fish catch statistics should have some correlation with the ocean environment parameters, like sea surface temperature (SST: Hanamoto, 1987; Andrade and Garcia, 1999; Lu et al., 21) and sea surface chlorophyll (SSC: Song et al., 29; Song and Zhou, 21). On the other hand, SST and SSC is a parameter that is now easily available by remote sensing (Kostianoy, 21). Recent decades, the use of satellite remote sensing to provide synoptic measurements of the ocean is becoming increasingly in fisheries applications in any part of the world (e.g. Laurs et al., 1984; Laurs, 1986; Lehodey et al., 1997; Santos, 2; Zagaglia et al., 24; Zainuddin et al., 26; Druon, 21; Yen et al., 212; ). However, studies of marine environment related to the fisheries ecology using remote sensing data relative poor in the southern waters off Java and Bali (Osawa and Julimantoro, 21; Syamsuddin et al., 213). Satellite remotely sensed oceanographic data provide reliable global ocean coverage of sea surface temperature and ocean color, with relatively high spatial and temporal resolution as well as in near real-time (Polovina and Howell, 25). Oceanographic phenomena are often used in order to predict the potential fishing ground of the fish (Mohri, 1999; Mohri and Nishida, 1999; Lennert-Cody et al., 28; Song et al., 29; Osawa and Julimantoro, 21). Information on the changing ocean from satellite data, average from monthly ocean conditions, is necessary to understand and eventually to predict the effects of the marine environment on fish populations (Laurs, 1986). Based on the previous research, the availability of albacore tuna, yellow fin tuna and blue fin tuna highly related to the dynamics of the environmental conditions (Schick et al., 24; Zagaglia et al., 24; Zainuddin et al., 28). Temperature and chlorophyll-a concentration are some environmental factors that affect the distribution of bigeye tuna (Hanamoto, 1987; Lennert-Cody et al., 28). In the Indian Ocean, the range value of SST depends on the seasonal monsoon (Soman and Slingo, 1997). The surface layer of the tropical ocean is warm and the annual variation of temperature is normally small (Wyrtki, 1961). In addition, range of SSC inversely proportional with SST. Every year, higher concentrations of SSC occur in June to September and lower concentrations in December, January and February (Hendiarti et al., 25; Swardika et al., 212). However, ocean environmental parameter in the southern waters off Java and Bali has high vulnerability to the phenomena of oceanatmosphere interaction, such as the El Niño- Southern Oscillation (ENSO) and the Indian 2

Ocean Dipole Mode (IODM; Terray et al., 27; Ningsih et al., 213). These conditions directly affect the amount of fishing catches (Syamsuddin et al., 213). 12 th Biennial Conference of Pan Ocean Remote Sensing Conference (PORSEC 214) SST and SSC were used in this study as the oceanographic factors that assumed to have a direct effect to the abundance of bigeye tuna. SST is one of the indicators for bigeye tuna to choose the suitable habitat, not only for reproduction, but also for their migration (Howell et al., 21; Hyder et al., 29). In addition, SSC has effects on forage distribution, directly or indirectly (Wilson et al., 28). In this study, SST and SSC collected from Moderate Resolution Imaging Spectroradiometer (MODIS) Level 3 Standard Mapped Images (SMI) data. MODIS, with its 233 km viewing swath width flying on-board Terra and Aqua, provides almost complete global coverage in one day (Savtchenko et al., 24). The MODIS on-board Aqua spacecraft is used in this study. This study, therefore, focuses to evaluate how SST and SSC parameters will affect to the number of bigeye tuna i.e., to evaluate the optimum range value of each parameter by using the fisheries data classification method (Andrade and Garcia, 1999; Zainuddin et al., 28) and polynomial regression. The fisheries data classification is very important to eliminate the fisheries data bias, especially when no catch occurred. Polynomial regression was used to measure the determination value and the relationship between environmental factors from satellite remotely oceanographic data and fish catch from vessels. 2. STUDY AREA The southern waters off Java-Bali as a part of the eastern Indian Ocean was selected as a study area which located between latitude 1 S to 18 S and longitude 11 E to 118 E as shown on Fig 1. Figure 1: Research location The study area has a tropical monsoon type of climate, resulting from the Asia- Australian monsoon wind systems, which change direction according to the seasons. In July September, the prevailing southeast monsoon favours upwelling along the coast off Java-Bali and Sumatra (Du et al., 28). These conditions are reversed during the northwest monsoon (November to April; Susanto et al., 26). The Southern waters off Java-Bali is not only forced by intense annually reversing monsoonal winds, but also influenced by the throughflow variability (Feng and Wijffels, 22). Physically, the study area and the surroundings have some complex dynamic currents and wave systems (Feng and Wijffels, 22). 3. DATA AND METHODS In this study, we used fisheries data and satellite remotely sensed data. We analyzed bigeye tuna catch data and remotely sensed environmental data for the period of 26 21. Monthly SST ( C) and SSC (mg m -3 ) Level 3 Standard Mapped Images (SMI) in 4 km spatial resolution from 26 to 21 was measured and collected by Aqua MODIS satellite data. It was used to evaluate the environmental parameters impact to the abundance of bigeye tuna. MODIS has 36 spectral bands with wavelengths from.41 to 14.4μm and spatial resolutions of 25m (bands 1-2), 5m (bands 3-7), and 1km (bands 8-36) at nadir (Xiong et al., 26). The Aqua MODIS SST was collected from 3

Number of bigeye tuna Number of bigeye tuna 12 th Biennial Conference of Pan Ocean Remote Sensing Conference (PORSEC 214) http://oceandata.sci.gsfc.nasa.gov/modisa/ L3SMI/. Meanwhile, the Aqua MODIS SSC was collected from the website http://oceandata.sci.gsfc.nasa.gov/cgi/. A set of bigeye tuna data from January 26 to December 21 were obtained from nineteen longline fishing logbooks, provided by PT Perikanan Nusantara, an incorporated company of the Indonesian government, at Benoa, Bali. The data sets consist of the geographic position of fishing activity (latitude and longitude), operational days, vessel number, and the number of fish caught per day during 26 21. The unit of daily catch data is the number of bigeye tuna. The majority of fishing operations was conducted by medium-size vessels (1 gross tonnage). The number of vessels in operation was 19 2 per month, and vessels used the same fishing gear (longline sets) and similar fishing techniques (Syamsuddin et al., 213). Only one day activity on the vessel was reported with almost absent reports concerning sailing and search time, bad weather conditions, and catch of bait in the logbooks. Two analyses were conducted in this study. First was a direct comparison between ocean environmental factors and bigeye tuna catch data. A second analysis was a statistical relationship between bigeye tuna catch data and both ocean environmental factors. Fisheries data classification as a part of the direct comparison analysis, was conducted to to infer the type of fish catch data and to find out the optimum range of oceanographic parameter. The classification of fisheries data was divided into three groups based on Quartile method: (a) null catches-cases with the number of bigeye tuna equal to zero; (b) positive catches-cases with the number of bigeye tuna 1~3; and (c) high catches-cases with the number of bigeye tuna equal or greater than 4. The value of 4 is equal to the lower limit of the upper quartile (Q3) of the number of bigeye tuna. This classification was modified based on Andrade and Garcia (1999). Polynomial regression was used to analyze the relationship, both ocean environmental factors and the number of bigeye tuna. Using polynomial regression is one challenge because the relationship between both ocean environmental factors and number of bigeye tuna is non linier. 4. RESULTS 4.1. Direct comparison and null catches analysis Figure 2 shows the raw data of bigeye tuna catches per trip related to the SST and SSC. Number of bigeye tuna was used as a response variable. 35 3 25 2 15 1 5 24.5 25.5 26.5 27.5 28.5 29.5 3.5 SST( o C) Figure 2. Scatter plots of number of bigeye tuna related to the SST (left) and SSC (right) from 26-21 Scatter plots are used to investigate the possible relationship between the number of bigeye tuna catches per fishing trip with SST and SSC that it's commonly used by other researchers. On the condition that the points follow a certain pattern, then it can be said that there was a high correlation between both entries of paired data. However, any trend cannot be seen in Fig. 2. Then a new data processing through descriptive statistic, fisheries data classification and regression analysis are needed to make an understandable result. We conducted a simple descriptive analysis to understand the direct comparison between ocean environmental factors and bigeye tuna and fisheries data classification analysis to understand the null catches trend. The relation between bigeye tuna fishing days and the SST in the southern part off 35 3 25 2 15 1 5.1.2.3.4.5.6.7 SSC (mg m -3 ) 4

No of fishing days Null catches freq No of Fishing days 12 th Biennial Conference of Pan Ocean Remote Sensing Conference (PORSEC 214) Java and Bali during 26-21 was shown in Fig. 3. In this case, the number of fishing days was used as a response variable and we used simple descriptive analysis to obtain this graphic. Figure 3: Number of bigeye tuna fishing days in relation to the SST from 26-21 This figure indicates that the fishing activity conducted in the area when SST ranged from 24.5 C to 3.8 C. Most of fishing activity was carried out in the range of SST between 26.8 C and 29.4 C (28.1±.1.3 C) with the highest frequency in 28.6 C and decline dramatically in the range of temperature around 3 C. The amount of fishing days distribution showed a negative skew. It indicates few low value of SST around research location. Figure 4 showed the relation between the number fishing days of bigeye tuna and SSC. In this case, the number of fishing days was also used as a response variable. Fishing activity was carried out in the area that has the SSC range from.2 to.34 mg m -3. The most of fishing activity occurred when the SSC ranged from.11 to.13 mg m -3 (.12±..1 mg m -3 ). 14 12 1 8 6 4 2 6 5 4 3 2 1 24.5 25.5 26.5 27.5 28.5 29.5 3.5 SST ( C)..5.1.15.2.25.3.35.4 Null catches analysis was conducted by fisheries data classification method. We used frequency as a unit to analyzed null catches. Figure 5 showed null catches frequency related to the SST in 21. Based on the fisheries data during 26 to 21, the average of null catches is about 18%. We only considered in 21 because in this year was the highest of null catches frequency (3%). 4 3 2 1 26.5 27. 27.5 28. 28.5 29. 29.5 3. 3.5 31. SST ( C) Figure 5: The 21 of null catches frequency related to the SST Figure 5 describes when SST in the southern part off Java and Bali increased, the null catches number also increased. The magnitude of SST affect to the null catches amount is 66.26%. It means that SST influence the frequency of null catches as 66.26%. A zero value of null catches occurs when the SST lower than 27 C. During the periods of 26 until 21, the null catches tend to high on the highest SST and low on the lowest SST. Figure 6 showed the null catches frequency related to the SSC. Even though, as shown in Fig. 4, the mode of the number of fishing days occurred when SSC is less than.1 mg m -3 but the null catches frequency also tends to high when the SSC concentration is less than.1 mg m -3. The optimum concentration of SSC is discussed later. SSC (mg m -3 ) Figure 4: Number of bigeye tuna fishing days in relation to the SSC from 26-21 5

Average number of big eye tuna (year -1 ) Average number of big eye tuna (year -1 ) Average number of bigeye tuna (year -1 ) Average number of bigeye tuna (year -1 ) Null catches frequency 12 th Biennial Conference of Pan Ocean Remote Sensing Conference (PORSEC 214) 35 3 25 2 15 1 5..5.1.15.2.25.3.35 SSC (mg m -3 ) Figure 6: Null catches frequency in relation to SSC from 26-21 4.2. Relationship between ocean environmental factors and Bigeye tuna Figure 7 showed the relationship between SST and the average number of bigeye tuna in the high catches groups. In this analysis, we only used the high catches because the high catches is the most significant to the fisherman angler. bigeye tuna. Based on Fig. 7, the optimum SST to catch bigeye tuna is less than 29.1 C and more than 27.4 C. In this study, polynomial regression was also used to analyze the relationship between the number of bigeye tuna and SSC. It was described in the Figure 8. Based on the analysis, there is a relationship between SSC and the number of bigeye tuna (F calculated >F critical ) with the confidence level of 95%. The relationship between SSC and the number of bigeye tuna can be expressed easily by calculating the determination coefficient. Critical point of SSC is.1 mg m -3. It means that when the SSC is lower or higher than.1 mg m -3 the number of bigeye tuna will decrease. 5 4 3 2 R 2 =.91 5 4 3 2 R 2 =.91 1 1 25 R 2 =.82 25 R 2 =.97..2.4.6.8.1.1.15.2.25.3.35.4.45 2 2 SSC (mg m -3 ) SSC (mg m -3 ) 15 15 1 5 24.5 25. 25.5 26. 26.5 27. 27.5 28. 28.5 SST ( C) Figure 7: Average number of bigeye tuna per year (high catches) in relation to the SST from 26-21. Left for SST less than 28.5 C and right for SST more than 28.5 C Polynomial regression was applied for estimating the relationship between SST and the number of bigeye tuna. Based on the analysis of variance with the confidence level of 95 %, there was a significant relationship between SST and the number of bigeye tuna (F calculated >F critical ) with the determination coefficient was 79.24 % when SST less than 28.5 C and 96.99% when SST more than 28.5 C. In addition, both of the correlation values (r) are very high, when SST is less than 28.5 C the correlation is high (.82). Furthermore, when SST is higher than 28.5 C, the correlation is very high (.97). It means that strong relationship occurred between SST and the number of 1 5 28.5 29. 29.5 3. 3.5 31. SST ( C) Figure 8: Average number of bigeye tuna per year in relation to SSC from 26-21. Left for SSC less than.1mg m -3, and right for SSC more than.1 mg m -3 Figure 8 explains that when SSC is lower than.1 mg m -3, it influences the number of bigeye tuna as 86.31%. When SSC is higher than.1 mg m -3, it influences the number of bigeye tuna as 9.87% and it indicates that the relationship between SSC and the number of bigeye tuna is very strong (r.9). In addition, polynomial analysis also shown the optimum value of SSC to catch big numbers of bigeye tuna, which have the range of.55 to.175 mg m -3. 5. DISCUSSION Satellite remote sensing has great capability in high temporal resolution to provide oceanographic parameters as an indicator for the pelagic species assessment including bigeye tuna. Combining satellite observations to ocean dynamic study and in situ observation related to the fisheries 6

abundant is necessary because of complimentary of these two types of data. In this study, we tried to combine remote sensing data and catch data of bigeye tuna in the southern waters off Java-Bali in order to examine the relevance between the number of bigeye tuna and oceanographic parameters. The availability of bigeye tuna catch data from one of the biggest tuna fishing industry in Indonesia helps much in providing the fish catch data in the southern waters off Java-Bali. Tuna longliner was operated in 3 to 6 months of fishing trips along the year, depend on vessel size and distributed in the 1 S - 18 S and 11 E - 118 E (Fig. 11). Figure 9: Fishing ground of bigeye tuna from 26-21 Based on the distribution of fishing activity, as shown on Fig. 9, it can be described that most of the fishing effort of the tuna longliner was concentrated in the 13 S 14 S. Associated with the migratory behavior of bigeye tuna, the highest number of catch found towards more west part of the study area. The fishing limits of the longliner, including fishing logistics, the fishermen`s skill, and weather condition may act as the other factors that influence the fishing ground distribution. Based on the SST and SSC concentration related to bigeye tuna, there is a possibility to catch bigeye tuna in the western area with the same range of latitude (19 E to 111 E and 12 S to 12 th Biennial Conference of Pan Ocean Remote Sensing Conference (PORSEC 214) 7 18 S). Moreover, we should analyze in each latitude and longitude that bigeye tuna catches mostly found in the southern waters off Java-Bali to estimate the exact place which suitable to bigeye tuna. This kind of research had been done (Howell et al., 21) in the North Pacific Ocean by using tagging method and the result showed bigeye tuna prefer to stay in the area that starting from 12 N to the north. Even though fishing data was not free from bias based on the fishermen`s choice of fishing locations, it was the low cost of bigeye tuna distribution data sets which available to fishery scientists (Mugo et al., 21). Fishing activity from PT. Perikanan Nusantara occurred at the nighttime in the depth around -1m. Based on the Howell et al., (21), in the case of shallow water bigeye tuna spend of all the days when the SST around 28 C and it seems the same result which the optimum value for SST is 28.5 o C (Fig. 7). Therefore, fishing data is preferable to use as a research material. Moreover, in this study, we found that most of the bigeye caught in the low SST. In addition, as an index of phytoplankton biomass that provides valuable information about trophic interaction in the marine ecosystem (Wilson et al., 28) chlorophylla used for predicting bigeye tuna abundant related to the water primary productivity. The range of SSC in Southern waters Java- Bali is to.45 mg m -3. This study showed that the highest frequency of bigeye tuna fishing were within SSC of.11 to.13 mg m -3, with the peak frequency is.1 mg m -3. Tuna is a predominantly visual predator, feeding opportunistically and unselective on micro nekton (Mugo et al., 21). Tuna schooling is often found close to the area where plankton and micro nekton are aggregated (Hendiarti et al., 25; Lehodey et al., 1998). For that reason, this species may also find in the highly primary productivity area, which is related to upwelling zones. Bigeye tuna prefers to stay in clear water where the nutrient

12 th Biennial Conference of Pan Ocean Remote Sensing Conference (PORSEC 214) concentration is low (Brill et al., 25). Low nutrient will decrease the SSC concentration and this condition affects to the clarity of water. Prey abundance and water clarity affect the rate of food encounter (Kirby et al., 2). Moreover, sensory mechanisms, such as vision, olfaction, hearing, and magnetic field detection allow tunas to adjust their environment for the purposes of food searching and reproduction, and to stay within physiologically tolerable ambient temperature and oxygen conditions. For that reason bigeye tuna can be found in the offshore which has low concentration of SSC. SST and SSC have an inverse pattern. When SST low, the SSC tends to high. In the condition where the SST tends to low, the current from the bottom of the water column, move to the surface and bring rich nutrient into the surface layer. As the consequence, chlorophyll concentration will increase. This phenomenon was significant for small pelagic fish study, which is shown that along coastal waters of the southern part of Java, in the same longitude with this current study, the increasing of chlorophyll-a positively followed by the increasing of fish catch (Sambah et al., 212). That case is an almost similar pattern with bigeye tuna in the offshore area, but there are some differences. First, in the Offshore the concentration of chlorophyll-a tends too small compared with the coastal area. In the case of bigeye tuna, there is the maximum value of chlorophyll-a which bigeye tuna prefers to stay around that area. The selections of fishing ground not only depend on the oceanographic factors, but also depend on the meteorological condition and fishing unit capacity (fishing gear and fishing vessel). 6. CONCLUSION Statistical analysis showed that the optimum of SST to catch big numbers of bigeye tuna is less than 29.1 C and more than 27.4 C. In addition, the optimum value of SSC to catch big number of bigeye tuna are between.55 and.175 mg m -3. Determination coefficient between bigeye tuna and SST is 79.24% when value of SST is less than 28.5 C and 96.99% when SST more than 28.5 C. Determination coefficient between bigeye tuna and SSC is 86.31% for the SSC lower than.1 mg m -3 and 9.87% in the case of SSC higher than.1 mg m -3. Both of the parameter have a significant relationship to the number of bigeye tuna. The highest number of bigeye tuna indicated in the area of 13 S - 14 S and 112 E - 115 E. Even though SST and SSC statistically show a significant effect to the bigeye tuna, it seems not to be the main factors controlling the migration and the abundance of this species in the study area. For the future research, more predictor variable is important to improve our understanding about the relation between environmental variables and bigeye tuna fisheries and other statistical analysis should be used which appropriate with an ecological case such as GAM (Generalized Additive Model) or GLM (Generalized Linier Model). 7. ACKNOWLEDGMENTS We would like to thank PT. Perikanan Nusantara, Benoa, Bali, Indonesia for providing fisheries data. We would like to thank to DIKNAS (ministry of education of Indonesia), LPDP (Indonesia Endowmnet Fund for Education), Yamaguchi University and JAXA (Japan Aerospace Exploration Agency) for financial support. We also gratefully acknowledge to the ocean color NASA for the AQUA-MODIS SST and SSC data that downloaded from ocean color homepage. 8. REFERENCES Andrade, H.A., and Garcia, C.A., 1999. Skipjack tuna fishery in relation to sea surface temperature off the southern Brazilian coast. Fisheries oceanography, 8, 245 254. Bailey, C., Dwiponggo, A., and Marahudin, F., (1987), Indonesia Marine Capture 8

12 th Biennial Conference of Pan Ocean Remote Sensing Conference (PORSEC 214) Fisheries. Manila: Internatinal Center for Living Aquatic Resources. Brill, R.W., Bigelow, K.A., Musyl, M.K., Fritsches, K.A., and Warrant, E.J., (25), Bigeye tuna (Thunnus obesus) behaviour and physiology and their relevance to stock assessments and fishery biology. Collective Volume of Scientific Papers-ICCAT, 57, 142 161. Druon, J.N., (21), Habitat mapping of the Atlantic bluefin tuna derived from satellite data: Its potential as a tool for the sustainable management of pelagic fisheries. Marine Policy, 34, 293 297. Du, Y., Qu, T., and Meyers, G., (28), Interannual Variability of Sea Surface Temperature off Java and Sumatra in a Global GCM. Journal of Climate, 21, 2451 2465. Feng, M., and Wijffels, S., (22), Intraseasonal variability in the South Equatorial Current of the east Indian Ocean. Journal of physical oceanography, 32, 265 277. Hanamoto, E., (1987), Effect of Oceanographic Environment on Bigeye Tuna Distribution. Bulletin of the Japanese Society of Fisheries Oceanography, 51, 23 216. Hendiarti, N., Suwarso, Aldrian, E., Amri, K., Andiastuti, R., Sachoemar, S.I., and Wahyono, I.B., (25), Seasonal variation of pelagic fish catch around Java. Oceanography, 18, 113 123. Howell, E.A., Hawn, D.R., and Polovina, J.J., (21), Spatiotemporal variability in Bigeye tuna (Thunnus obesus) dive behaviour in the Central North Pacific Ocean. Progress in Oceanography, 86, 81 93. Kirby, D.S., Fiksen, Ø., and Hart, P.J.B., (2), A dynamic optimization model for the behaviour of tunas at ocean front. Fisheries Oceanography, 9, 317 325. 9 Kostianoy, A., (21), Remote sensing of SST in the coastal ocean and inland seas. 38 th COSPAR Scientic Assembly. Bremen, Germany: COSPAR. Laurs, R.M., (1986), Applications of satellite remote sensing to fisheries. Society of Airborne and Satellite, Physical and Fishery Oceanography, 8, 139 15. Laurs, R.M., Fiedler, P.C., and Montgomery, D.R., (1984), Albacore tuna catch distributions relative to environmental features observed from satellites. Deep-Sea Research, 31, 185 199. Lehodey, P., Bertignac, M., Hampton, J., Lewis, A., and Picaut, J., (1997), El Nino Southern Oscillation and tuna in the western Pacific. Nature, 389, 715 718. Lehodey, P., Andre, J-M., Bertignac, M., Hampton, J., Stoens, A., Menkes, C., Memery, L., and Grima, N., (1998), Predicting skipjack tuna forage distributions in the equatorial Pacific using a coupled dynamical bio geochemical model. Fisheries Oceanography, 7, 317 325. Lennert-Cody, C.E., Roberts, J.J., and Stephenson, R.J., (28), Effects of gear characteristics on the presence of Bigeye tuna (Thunnus obesus) in the catches of the purse-seine fishery of the eastern Pacific Ocean. ICES Journal of Marine Science, 65, 97 978. Lu, H-J., Lee, K-T., Lin, H-L., and Liao, C- H., (21), Spatio temporal distribution of yellowfin tuna Thunnus albacares and bigeye tuna Thunnus obesus in the Tropical Pacific Ocean in relation to large scale temperature fluctuation during ENSO episodes. Fisheries Science, 67, 146-152. MMAF, (211), Capture Fisheries Statistics of Indonesia 211. Jakarta-Indonesia; Indonesian Ministry of Marine Affairs and Fisheries. Mohri, M., (1999), Seasonal change in Bigeye tuna fishing areas in relation to the

12 th Biennial Conference of Pan Ocean Remote Sensing Conference (PORSEC 214) oceanographic parameters in the Indian Ocean. Journal of National Fisheries University, 47, 43 54. Mohri, M., and Nishida, T., (1999), Distribution of Bigeye tuna and its relationship to the environmental conditions in the Indian Ocean based on the Japanese longline fisheries information. IOTC Proceedings, 2, 27 22. Mugo, R., Saitoh, S-I., Nihira, A., and Kuroyama, T., (21), Habitat characteristics of skipjack tuna (Katsuwonus pelamis) in the Western North Pasific. Fisheries Oceanography, 19, 382 396. Ningsih, N. S., Rakhmaputeri, N., and Harto, A.B., (213), Upwelling variability along the southern coast of Bali and in Nusa Tenggara waters. Ocean Science Journal, 48, 49 57. Osawa, T., and Julimantoro, S., (21), Study of Fishery Ground Around Indonesia Archipelago Using Remote Sensing Data. In A. Sumi, K. Fukushi, and A. Hiramatsu (Eds.), Adaptation and Mitigation Strategies for Climate Change (pp. 57 69). Tokyo: Springer. Polovina, J.J., and Howell, E.A., (25), Ecosystem indicators derived from satellite remotely sensed oceanographic data for the North Pacific. ICES Journal of Marine Science, 62, 319 327. Proctor, C.H., Merta, I.G.S., Sondita, M.F.A., Wahuju, R.I., Davis, T.L.O., Gunn, J.S., and Andamari, R., (23), A review of Indonesia's Indian Ocean tuna fisheries. akarta: CSIRO Marine Research, Research Institute of Marine Fisheries, and Bogor Agricultural University. Sambah, A.B., Miura, F., Kadarisman, H.P., and Sartimbul, A., (212), Remote sensing application for Sardinella lemuru assessment; a case study of the south waters of Malang Regency East Java, Indonesia. Proceeding of the SPIE, 8525, Remote 1 Sensing of the Marine Environment II (pp. 8525M1 8525M14). Kyoto: SPIE. Santos, A.M.P., (2), Fisheries oceanography using satellite and airborne remote sensing methods: a review. Fisheries Research, 49, 1-2. Savtchenko, A., Ouzounov, D., Ahmad, S., Acker, J., Leptoukh, G., Koziana, J., and Nickless, D., (24), Terra and Aqua MODIS products available from NASA GES DAAC. Advances in Space Research, 34, 71 714. Schick, R.S., Goldstein, J., and Lutcvage, M.E., 24. Bluefin tuna (Thunnus thynnus) distribution in relation to sea surface temperature front in the Gulf of Maine. Fisheries Oceanography, 13, 225 238. Soman, M.K., and Slingo, J., (1997), Sensitivity of the asian summer monsoon to aspects of sea surface temperature anomalies in the tropical pacific ocean. Quarterly Journal of the Royal Meteorological Society, 123, 39-336. Song, L., and Zhou, Y., (21), Developing an integrated habitat index for bigeye tuna Thunnus obesus in the Indian Ocean based on longline fisheries data. Fisheries research, 15, 63-74. Song, L., Zhou, J., Zhou, Y., Nishida, T., Jiang, W., and Wang, J., (29) Environmental preferences of Bigeye tuna, Thunnus obesus, in the Indian Ocean: an application to a longline fishery. Environmental Biology of Fishes, 85, 153 171. Susanto, R.D., Moore II, T.S., and Marra, J., (26), Ocean color variability in the Indonesian Seas during the SeaWiFS era. Geochemistry Geophysics Geosystems, 7, Q521. Swardika, I.K., Tanaka, T., and Ishida, H., (212), Study on the characteristics of the Indonesian seas using satellite remotesensing data for 1998 27. International

12 th Biennial Conference of Pan Ocean Remote Sensing Conference (PORSEC 214) Journal of Remote Sensing, 33, 2378 2394. Syamsuddin, M.L., Saitoh, S-I., Hirawake, T., Bachri, S., and Harto, A.B., (213), Effects of El Niño Southern Oscillation events on catches of Bigeye Tuna (Thunnus obesus) in the eastern Indian Ocean of Java. Fishery Bulletin, 111, 175 188. Terray, P., Chauvin, F., and Douville, H., (27), Impact of southeast Indian Ocean sea surface temperature anomalies on monsoon-enso-dipole variability in a coupled ocean atmosphere model. Climate dynamics, 28, 553 58. Tomczak, M., and Godfrey, J.S., (1994), Regional Oceanography: An Introduction. Oxford; Pergamon Press. Valavanis, V.D., Pierce, G.J., Zuur, A.F., Palialexis, A., Saveliev, A., Katara, I., and Wang, J., (28), Modelling of essential fish habitat based on remote sensing, spatial analysis and GIS. Hydrobiologia, 612, 5 2. 33, 757-7522. Zagaglia, C.R., Lorenzzetti, J.A., and Stech, J.L., (24), Remote sensing data and longline catches of yellowfin tuna (Thunnus alalunga) in the Equatorial Atlantic. Remote Sensing of Environment, 93, 267 281. Zainuddin, M., Saitoh, K., and Saitoh, S-I., (28), Albacore (Thunnus alalunga) fishing ground in relation to oceanographic condition in the Western North Pasific Ocean using remotely sensed satellite data. Fisheries Oceanography, 17, 61 73. Zainuddin, M., Kiyofuji, H., Saitoh, K., and Saitoh, S-I., (26), Using multi-sensor satellite remote sensing and catch data to detect ocean hot spots for albacore (Thunnus alalunga) in the northwestern North Pacific. Deep-Sea Research II, 53, 419 431. Wilson, C., Morales, J., Nayak, S., Asanuma, I., and Feldman, G.,(28), Ocean colour radiometry and fisheries. In T. Platt, N. Hoepffner, V. Stuart, and C. Brown, Why ocean colour? The societal benefit of ocean colour technology (pp. 47 58). Darmouth: International ocean colour coordinating group No.7. Wyrtki, K., (1961), Physical oceanography of the Southeast Asia waters. California: NAGA REPORT 2. Xiong, X., Che, N., Barnes, W., Xie, Y., Wang, L., and Qu, J., (26), Status of Aqua MODIS spatial characterization and performance. Proceedings of the SPIE, 6361, Sensors, Systems, and Next- Generation Satellites X (pp. 6361T1 6361T9). Stockholm, Sweden; SPIE. Yen, K.W., Lu, H.J., Chang, Y., and Lee, M.A., (212), Using remote-sensing data to detect habitat suitability for yellowfin tuna in the Western and Central Pacific Ocean. International Journal of Remote Sensing, 11