Completing fishing monitoring with spaceborne Vessel Detection System (VDS) and Automatic Identification System (AIS) to assess illegal fishing in Indonesia

2018 ◽  
Vol 131 ◽  
pp. 33-39 ◽  
Author(s):  
Nicolas Longépé ◽  
Guillaume Hajduch ◽  
Romy Ardianto ◽  
Romain de Joux ◽  
Béatrice Nhunfat ◽  
...  
Keyword(s):  
Detection System ◽  
Automatic Identification ◽  
Identification System ◽  
Automatic Identification System ◽  
Illegal Fishing ◽  
Vessel Detection

scholarly journals ALGORITMA PERINGATAN DINI PENCURIAN IKAN PADA DATA AUTOMATIC IDENTIFICATION SYSTEM (AIS) BERBASIS TERESTRIAL DAN SATELIT (ILLEGAL FISHING EARLY WARNING ALGORITHM FOR TERESTRIAL AND SATELLITE-BASED AUTOMATIC IDENTIFICATION SYSTEM (AIS) DATA)

2017 ◽  
Vol 14 (2) ◽  
pp. 81 ◽  
Author(s):  
Emir Mauludi Husni ◽  
Muhammad Riksa Andanawari R. S ◽  
Robertus Heru Triharjanto
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Automatic Identification ◽  
Identification System ◽  
Ray Casting ◽  
International Regulation ◽  
Automatic Identification System ◽  
Illegal Fishing ◽  
Satellite Sensors

Illegal fishing has created heavy financial losses for Indonesia, meanwhile, the large Indonesian water territory made it very difficult to detect such activities. The international regulation that obligates all ships above 300 GT to transmit data using AIS provide opportunity to detect ships conducting illegal fishing. The capability of Indonesia to detect AIS signals from LAPAN-A2/Orari satellite enhances such opportunity. The objective of the research is to develop part of the illegal fishing early warning system, based on AIS data received by terrestrial and satellite sensors. The detection is done by analyzing the course of the ships. Types of illegal fishing activities to be detected are trans-shipment, trawl usage, fishing zone violation, reporting avoidance, and AIS is switching off. The algorithm used is Ray Casting method to determine whether a ship is in its designated zone. The improvement of performance of the algorithm is done by multithreading on the used Phyton code. The algorithm is tested using AIS data from LAPAN-A2 and simulated AIS data.  The results show that the algorithm designed for the analysis of illegal fishing early warning system using AIS data is successfully in detecting six types of offenses in accordance with the Ministry of Marine Affairs and Fisheries Republic of Indonesia mentioned above by using simulation data. Abstrak Pencurian ikan merupakan kegiatan yang menyebabkan kerugian sangat besar untuk Indonesia, sementara wilayah perairan Indonesia yang luas membuat kegiatan pengawasan pencurian ikan tersebut menjadi sulit dilakukan. Peraturan internasional yang mewajibkan setiap kapal di atas 300 GT untuk mengirimkan data menggunakan AIS menjadi kesempatan untuk mendeteksi kapal-kapal yang melakukan pencurian ikan. Kemampuan Indonesia untuk mendeteksi sinyal AIS dari satelit LAPAN-A2/Orari memperbesar kesempatan tersebut. Penelitian ini bertujuan membangun bagian dari sistem peringatan dini aktivitas pencurian ikan, berdasarkan data AIS yang diterima oleh sensor di garis pantai dan di satelit. Proses pendeteksian dilakukan dengan menganalisa data perjalanan dari sistem AIS. Jenis-jenis pencurian ikan yang dapat dideteksi oleh algoritma ini adalah trans-shipment, penggunaan pukat harimau, pelanggaran zona teritorial, pelanggaran tidak melapor, pelanggaran wilayah penangkapan, dan pelanggaran tidak mengaktifkan pemancar sinyal AIS. Algoritma yang digunakan adalah metode Ray Casting, untuk menentukan suatu kapal berada dalam satu wilayah atau tidak. Perbaikan performa algoritma ini dilakukan dengan melakukan proses multithreading menggunakan kode Python. Algoritma diuji dengan data AIS dari LAPAN-A2/Orari dan data simulasi. Hasil menunjukkan bahwa algoritma yang dirancang untuk sistem analisis peringatan dini pencurian ikan (illegal fishing) dengan data AIS berhasil mendeteksi 6 jenis pelanggaran sesuai ketentuan Kementerian Kelautan dan Perikanan (KKP) Republik Indonesia yang telah disebutkan di atas dengan menggunakan data simulasi.

scholarly journals Analisa Pergerakan Kapal dengan Memanfaatkan Data AIS (Automatic Identification System) Guna Mendukung Operasi Keamanan dan Keselamatan di Perairan Yuridiksi dan Perairan NKRI

2016 ◽  
Vol 2 (2) ◽  
pp. 129-138
Author(s):  
Zulqarnain Zulqarnain ◽  
Eddy Prahasta ◽  
Arief Meidyando ◽  
Dwi Jantarto
Keyword(s):  
Automatic Identification ◽  
Identification System ◽  
Automatic Identification System ◽  
Illegal Fishing

Wilayah perairan laut Indonesia yang sering dijadikan sebagai rute pelayaran yang efisien oleh kapal-kapal (lokal dan asing) untuk melintas dan merupakan daerah tangkapan ikan yang melimpah telah menimbulkan kerentanan sejumlah pelanggaran di laut. Sehubungan dengan hal ini, telah terbukti bahwa pada akhir-akhir ini pun tidak sedikit terjadi peristiwa tersebut di wilayah perairan laut Indonesia. Peristiwa-peristiwa ini kebanyakan berhubungan dengan katagori illegal fishing, perompakan/pembajakan, dan pelanggaran-pelanggaran lainnya di laut. Dengan memanfaatkan data AIS dapat dilakukan penelitian yang bersifat analisis-deskriptif, mencari atau menganalisa fakta-fakta dan kemudian mendapatkan gambaran atau deskripsi yang tepat; yaitu menganalisis data AIS yang dapat dispasialkan berupa History track dan informasi informasi lain tentang kapal yang kemudian disajikan dalam perangkat layar monitor atau dicetak sebagai peta tematik untuk memudahkan analisis dan pengambilan keputusan. Dari hasil analisa didapatkan Sebagian besar atau sebesar 61.36 % Kapal-kapal yang terdeteksi sebagai kapal terperiksa berjenis kapal kargo, dan pada rentang waktu 3(tiga) bulan (Januari-Maret 2016), telah terjadi peningkatan anomali kapal (kejadian)  sebesar 59.09 % terutama pada bulan maret 2016.

Automatic Identification System Receiver for Small Fishing Vessels

2020 ◽  
Author(s):  
Febus Reidj G. Cruz ◽  
Jeremiah A. Ordiales ◽  
Malvin Angelo C. Reyes ◽  
Pinky T. Salvanera
Keyword(s):  
Automatic Identification ◽  
Identification System ◽  
Automatic Identification System ◽  
Fishing Vessels

Automatic identification system data-driven model for analysis of ship domain near bridge-waters

2021 ◽  
pp. 1-22
Author(s):  
Lei Jinyu ◽  
Liu Lei ◽  
Chu Xiumin ◽  
He Wei ◽  
Liu Xinglong ◽  
...  
Keyword(s):  
Least Squares Method ◽  
Automatic Identification ◽  
Identification System ◽  
Collision Risk ◽  
Automatic Identification System ◽  
Domain Models ◽  
System Data ◽  
Quantification Model ◽  
Highly Correlated

Abstract The ship safety domain plays a significant role in collision risk assessment. However, few studies take the practical considerations of implementing this method in the vicinity of bridge-waters into account. Therefore, historical automatic identification system data is utilised to construct and analyse ship domains considering ship–ship and ship–bridge collisions. A method for determining the closest boundary is proposed, and the boundary of the ship domain is fitted by the least squares method. The ship domains near bridge-waters are constructed as ellipse models, the characteristics of which are discussed. Novel fuzzy quaternion ship domain models are established respectively for inland ships and bridge piers, which would assist in the construction of a risk quantification model and the calculation of a grid ship collision index. A case study is carried out on the multi-bridge waterway of the Yangtze River in Wuhan, China. The results show that the size of the ship domain is highly correlated with the ship's speed and length, and analysis of collision risk can reflect the real situation near bridge-waters, which is helpful to demonstrate the application of the ship domain in quantifying the collision risk and to characterise the collision risk distribution near bridge-waters.

Statistical Analysis of the Detection Probability of the TianTuo-3 Space-based AIS

2017 ◽  
Vol 71 (2) ◽  
pp. 467-481 ◽  
Author(s):  
Shiyou Li ◽  
Lihu Chen ◽  
Xiaoqian Chen ◽  
Yong Zhao ◽  
Lei Yang
Keyword(s):  
Statistical Analysis ◽  
Detection Probability ◽  
High Probability ◽  
Distribution Density ◽  
Automatic Identification ◽  
Identification System ◽  
Receiving Antenna ◽  
Defense Technology ◽  
Vessel Detection ◽  
National University

The Micro-Nano satellite TianTuo-3 (TT-3) developed by the National University of Defense Technology (NUDT) was successfully launched on 20 September 2015. The space-based Automatic Identification System (AIS) on board TT-3 works well and stably receives AIS signals from global vessels. In this work, we perform statistical analysis on the detection probability of the vessels in the concerned areas by using the TT-3 AIS data. The results suggest that the detection probability of vessels decreases as the distribution density increases, especially in the offshore areas of dense traffic and the TT3-AIS vessel detection probability in the oceans can be higher than 40%, indicating that the TT-3 AIS has achieved a high probability of coverage of vessels for a single receiving antenna. The analysis results will present helpful references both in evaluating the potential application of satellite-based AIS and for designing the next generation space-based AIS which might greatly improve the detection probability of ocean-going vessels.

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