Determination of Potential Fishing Grounds of Rastrelliger kanagurta Using Satellite Remote Sensing and GIS Technique

Nurdin, Suhartono; Mustapha, Muzzneena Ahmad; Lihan, Tukimat; Ghaffar, Mazlan Abd.

doi:10.17576/jsm-2015-4402-09

Cited by 33 publications

(22 citation statements)

References 33 publications

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“…From the fishing management perspective, works like [16] propose a seasonal forecast system that combines environmental and fish habitat data (collected by fish tagging) to predict tuna distribution. In [15], the authors, integrate satellite data and statistical models output to investigate the relationship between sea surface temperature and chlorophyll-a concentration, and also define simple methods to forecast potential fishing grounds. The work of [9] tries to forecast 1-month catches considering only the anchovy catches in previous months as inputs.…”

Section: Fishing Activities Forecastmentioning

confidence: 99%

Predicting Fishing Effort and Catch Using Semantic Trajectories and Machine Learning

Adibi

Pranovi

Raffaetà

et al. 2020

Lecture Notes in Computer Science

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In this paper we explore a unique, high-value spatio-temporal dataset that results from the fusion of three data sources: trajectories from fishing vessels (obtained from terrestrial Automatic Identification System, or AIS, data feed), the corresponding fish catch reports (i.e., the quantity and type of fish caught), and relevant environmental data. The result of that fusion is a set of semantic trajectories describing the fishing activities in Northern Adriatic Sea over two years. We present early results from an exploratory analysis of these semantic trajectories, as well as from initial predictive modeling using Machine Learning. Our goal is to predict the Catch Per Unit Effort (CPUE), an indicator of the fishing resources exploitation useful for fisheries management. Our predictive results are preliminary in both the temporal data horizon that we are able to explore and in the limited set of learning techniques that are employed on this task. We discuss several approaches that we plan to apply in the near future to learn from such data, evidence, and knowledge that will be useful for fisheries management. It is likely that other centers of intense fishing activities are in possession of similar data and could use the methods similar to the ones proposed here in their local context.

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Section: Fishing Activities Forecastmentioning

confidence: 99%

Predicting Fishing Effort and Catch Using Semantic Trajectories and Machine Learning

Adibi

Pranovi

Raffaetà

et al. 2020

Lecture Notes in Computer Science

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“…Hal tersebut disebabkan oleh faktor penyinaran matahari yang lebih optimum di sekitar ekuator, sehingga timbul pergerakan udara (angin). Faktor intensitas cahaya matahari mempengaruhi proses produksi nutrien oleh fitoplanton yang memanfaatkan fotosintesis (Nurdin et al, 2015). Kecepatan angin yang berakibat pada arus laut dapat membawa fitoplanton juga nutrien pada kawasan perairan ikan pelagis (Ho et al, 2013).…”

Section: Hasil Dan Pembahasanunclassified

Estimating Potential Zones of Pelagic Fish in WPPNRI 711 (Study Case of Natuna Sea)

Hidayat

2019

jpsl

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The sea resources is the future for the people of Indonesia, especially the fish as a main commodity. One of the potential fish are pelagic fish are spread throughout the waters of the sea of Indonesia. Pelagic fish is one of his life-cycle patterns of fish that move or migrate to foraging and also spawning. Based on habitat and the distance of the migrated fish, pelagic fish are differentiated into small and large pelagic fish. Therefore, the research on the distribution of pelagic fish is very important for Indonesia's economy and the realization of a sustainable fish resources. WPPNRI 711 has choosen to be research location since known as shallow water which has nurient wealth of sea water. The research conducted to analyze estimation of potential zones of pelagic fish in WPPNRI 711 sea of Natuna and its relation to pelagic fishing ground. The data taken during research is fish density. Analysis of spatial data is used to display the information of the regional fisheries. The results show that fish density is highest in WPPNRI 711 Natuna Sea waters was in the waters around island of the Tambelan Islands, Anambas and around.

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“…It is difficult to obtain accurate information such as spatial pattern distribution and overall marine aquaculture areas. With the development of remote sensing technology, multi-temporal, short-period, and wide-coverage remote sensing data provide new technical support for the monitoring of aquaculture areas and dynamic changes [15][16][17][18][19]. At present, from the perspective of spatial distribution, aquaculture mainly consists of two parts: one is land pond aquaculture, the other is marine aquaculture.…”

Section: Introductionmentioning

confidence: 99%

Sea Surface-Visible Aquaculture Spatial-Temporal Distribution Remote Sensing: A Case Study in Liaoning Province, China from 2000 to 2018

et al. 2019

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Aquaculture plays an important role in providing food and reducing poverty but it affects environmental change and coastal ecosystems. Remote sensing is a technology that is helpful in the spatial-temporal dynamic monitoring of aquaculture, coastal management, and environmental monitoring. Most research focuses on inland and coastal areas, and little attention is paid to the extensive distribution of marine aquaculture. As an example, we use the freely available Landsat data of the developed marine aquaculture Liaoning Province of China and use the object-oriented automatic extraction method to analyze the spatial and temporal distribution information of marine aquaculture from 2000 to 2018. The accuracy evaluation from the randomly distributed sample points in high-resolution remote sensing images shows that the extraction accuracy for all of the five individual years of aquaculture area was higher than 82%. The results showed that (1) in the past 19 years, the area of marine aquaculture in Liaoning Province showed an increasing trend, which increased from 35.41 km2 in 2000 to 201.83 km2 in 2018, approximately 5.7 times increase in total area, but the growth rate decreased slightly due to government policy and the environmental quality of the sea area. (2) The centroid of offshore aquaculture in Liaoning Province shows a migration pattern to the northeast, in general, extending from the Dalian Bay sea area to the eastern sea area of the Dalian Chengshantou National Nature Reserve of Coastal Landform in the northeastern direction, and the migration distance reached 48.78 km. Moreover, the migration distance between 2005 and 2010 was the largest of all of the periods, reaching 35.43 km. The new marine aquaculture areas are mainly concentrated in the eastern direction of Xiaoyao Bay, the Changshan Islands, and Guanglu Island in Changhai County. (3) The landscape pattern of marine aquaculture in Liaoning Province is split, large-scale aquaculture and small-scale aquaculture are symbiotic, and landscape ecological activities are active. For local managers, this study can provide valuable supporting data for the assessment of marine aquaculture yield in this region, comprehensive control and management of the marine environment, and stability of the marine ecosystem. For other countries or regions, this work provides a great reference value for monitoring the dynamic spatial distribution of marine aquaculture.

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Determination of Potential Fishing Grounds of Rastrelliger kanagurta Using Satellite Remote Sensing and GIS Technique

Abstract: Analysis of relationship between sea surface temperature (SST) and Chlorophyll-a (chl-a)

Cited by 33 publications

References 33 publications

Predicting Fishing Effort and Catch Using Semantic Trajectories and Machine Learning

Predicting Fishing Effort and Catch Using Semantic Trajectories and Machine Learning

Estimating Potential Zones of Pelagic Fish in WPPNRI 711 (Study Case of Natuna Sea)

Sea Surface-Visible Aquaculture Spatial-Temporal Distribution Remote Sensing: A Case Study in Liaoning Province, China from 2000 to 2018

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