Applicability of remote sensing oceanographic data in the detection of potential fishing grounds of Rastrelliger kanagurta in the archipelagic waters of Spermonde, Indonesia

Nurdin, Suhartono; Mustapha, Muzzneena Ahmad; Lihan, Tukimat; Zainuddin, Mukti

doi:10.1016/j.fishres.2017.07.029

Cited by 41 publications

(33 citation statements)

References 33 publications

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“…The distribution of R. kanagurta from the suitability index was observed in the range of chl-a, SST and SSH were between 0.24 -0.42 mg/m 3 , 29 -31°C and 1.12-1.28 m, respectively. This was similar to the results of the study from Nurdin et al (2017b) in the archipelagic waters of Spermonde, Indonesia where it was found that the highest fishing frequency of R. kanagurta was at chl-a range of 0.24 -0.30 mg/m 3 and SST range of 29.6 -30.2°C. Based on Figure 2, cluster 1 showed that R. kanagurta catches were fairly high throughout the year (>127 kg/m 3 ) but highest catch was recorded in cluster 2 which were in April, May, September and October.…”

Section: Oceanographic Parameters and Seasonal Variabilitysupporting

confidence: 91%

The Indian Mackerel Aggregation Areas in Relation to Their Oceanographic Conditions

K.¹,

M.A.²,

M.A.³

2019

JSM

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In order to determine the favourable oceanographic conditions which influence fish aggregation areas, the integration of remote sensing and GIS technique was applied. This paper aims to classify the spatial distribution and abundance of R. kanagurta in the South China Seas (SCS) using principal component analysis (PCA) and cluster analysis (CA). Remotely-sensed satellite oceanographic data of chlorophyll-a concentration (chl-a), sea surface temperature (SST) and sea surface height (SSH) together with high catch fish data were used to characterize seasonal abundance of the R. kanagurta. PCA identified two principal components that had eigenvalues >1 (PC1 and PC2) which accounted for 59.3% of the cumulative variance. Factor loading in the PCA proved that all environmental variables used in this study; chl-a (PC1), SSH and SST (PC2) had influenced the CPUE of R. kanagurta. Using CA, two clusters of CPUE abundance were identified. In cluster 1, an average CPUE of 350.7 kg/m³ with highest catch were recorded in January, April, May, July and October. Meanwhile, in cluster 2, an average CPUE of 1033.9 kg/m³ with highest catch were recorded in April, May, September and October. Preferred range for fish aggregations showed SST, SSH and chl-a were observed in between 29-31°C, 1.12-1.28 m and 0.24-0.42 mg/m 3 , respectively. Binary habitat suitability index was used to model the potential aggregation areas. The highest potential fish aggregations areas of R. kanagurta were found located along the coast of Peninsular Malaysia in early and late Southwest monsoon (at accuracy of 83.68% with kappa of 0.7). ABSTRAKIntegrasi antara data penderiaan jauh dan teknik GIS diaplikasi bagi menentukan keadaan oseanografi yang mempengaruhi kawasan pengumpulan ikan. Objektif dalam kajian ini adalah untuk mengelaskan taburan reruang dan kelimpahan R. kanagurta di Laut China Selatan menggunakan analisis komponen prinsipal (PCA) dan analisis kelompok (CA) serta mengenal pasti perhubungan antara taburan ikan dengan keadaan persekitaran. Hubungan antara data taburan klorofil-a (chl-a), suhu permukaan laut (SST) dan ketinggian permukaan laut (SSH) daripada satelit penderiaan jauh serta taburan tangkapan R. kanagurta digunakan untuk mengenal pasti hubungan taburan musiman ikan pelagik. PCA mengenal pasti dua komponen prinsipal yang mempunyai nilai eigen >1 (PC1 dan PC2) dengan nilai peratus kumulatif varians adalah 59.3%. Faktor penentuan dalam komponen prinsipal menunjukkan bahawa parameter persekitaran mempengaruhi data tangkapan ikan. CA menunjukkan dua kelompok tangkapan ikan dengan kelompok 1, nilai purata tangkapan ikan sebanyak 350.7 kg/m³ dengan catatan tangkapan ikan tertinggi pada bulan Januari, April, Mei, Julai, September dan Oktober. Manakala, di dalam kelompok 2, nilai purata tangkapan ikan sebanyak 1033.9 kg/m³ dengan catatan tangkapan ikan tertinggi pada bulan April, Mei, September dan Oktober. Julat kesesusaian cerapan pengumpulan ikan bagi SST, SSH dan chl-a didapati pada suhu 29-31°C, 1.12-1.28 m dan 0.24-0.42 mg/m³. Kawasan be...

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Section: Oceanographic Parameters and Seasonal Variabilitysupporting

confidence: 91%

The Indian Mackerel Aggregation Areas in Relation to Their Oceanographic Conditions

K.¹,

M.A.²,

M.A.³

2019

JSM

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“…Research on Potential Fishing Zones (PFZ) is still growing, where many parameters have been used. As the well respond of fishes with changes in temperature and congregate around the upwelling makes potential fishing zones using thermal front have been developed in Indonesian water with 0.5 o C as a gradient threshold Harsanugraha et al, 2014;Jatisworo & Murdimanto, 2013;Jishad et al, 2019), water fertility can be seen from the distribution of chlorophyll-a concentration also used as parameters to predict potential fishing zones with 80% success effectiveness where the peak harvest of tuna usually appears when chlorophyll-a levels are high (Nurdin et al, 2017;Syah et al, 2020;and Zainuddin et al, 2006). The chlorophyll in high concentrations mostly associated with cool waters and its elevated structures are associated with ocean thermal front (Wijesekera et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, trophic status also calculated using more parameters such as Secchi depth, total Phosphorus, dissolved oxygen, and other nutrient parameters (El-Serehy et al, 2018;Wen et al, 2020). Mesotrophic are potential areas to be used as PFZ, in some locations (north-western North Pacific, archipelagic waters of Spermonde), where higher fish catches are found in the mesotrophic area with a concentration range of 0.2-0.5 mg/m 3 (Nurdin et al, 2017;Zainuddin et al, 2006). This region is also the potential area for floating net fishery (FNF) (Rustini et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Potential Fishing Zones Estimation Based on Approach of Area Matching Between Thermal Front and Mesotrophic Area

Suhadha

Asriningrum

2020

J. Ilmu dan Teknologi Kelautan Tropis

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Research in Potential Fishing Zone (PFZ) has undergone many developments, including parameter suitability selection. The thermal front has become the primary parameter input of ZPPI (LAPAN's PFZ). The accuracy of the thermal front parameter to predict PFZ cannot be known with certainty because of the radius between ZPPI with fishing areas, so it is necessary to develop parameters to support the thermal front. The thermal front described the meeting area of two water masses with different temperature characteristics associated with high nutrients (chlorophyll-a) and indicate an upwelling's appearance. This study aims to determine ZPPI by approaching the thermal front and mesotrophic area's matching area (chlorophyll-a concentration 0.2-0.5 mg/m3). Chlorophyll-a and sea surface temperature data for thermal fronts detection are derived from Aqua MODIS satellite on Google Earth Engine (GEE). The matching area's approach between the thermal front and mesotrophic area is used in the analysis of ZPPI. The results show thermal front and mesotrophic area on WPPNRI 715 have a variation seasonally where December appears like the peak event. The two parameters are distributed evenly from coastal areas to high seas. This method generates thermal fronts that have more than 60.3% matching with the mesotrophic area where the amount is acceptable due to has more than 50% amount of moderate ZPPI. The accuracy improvement in ZPPI both on the coast and open sea can be determined through this approach.

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“…Indonesia is an archipelago country, most of which is the ocean. The marine fisheries sector in Indonesia is one of the main activities that contribute to the national economy and also as part of national economic development, which has the aim of improving the living standards and welfare of fishermen (Apriliani et al 2018;Nurdin et al 2017). Indonesian Sea has various types of fish because Indonesia is located in the tropical region.…”

Section: Introductionmentioning

confidence: 99%

“…The biotic diversity and abundance can be influenced by the structural complexity of the habitat, however, the influence of the structural complexity of the habitat on marine ecosystems is rarely discussed. The understanding of the relationship between fish distribution and environmental factors is very important in order to explore fisheries resources (Nurdin et al 2017). Fish habitat is closely related to certain values of oceanographic properties, such as sea surface temperature and chlorophyll-a (Semedi & Hadiyanto 2013).…”

Section: Introductionmentioning

confidence: 99%

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

Aryaguna

2019

IJG

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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.

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Applicability of remote sensing oceanographic data in the detection of potential fishing grounds of Rastrelliger kanagurta in the archipelagic waters of Spermonde, Indonesia

Cited by 41 publications

References 33 publications

The Indian Mackerel Aggregation Areas in Relation to Their Oceanographic Conditions

The Indian Mackerel Aggregation Areas in Relation to Their Oceanographic Conditions

Potential Fishing Zones Estimation Based on Approach of Area Matching Between Thermal Front and Mesotrophic Area

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

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