Estimation of number and position of fish aggregating devices in Indonesia Archipelagic Waters

. The research conducted in 2021 aimed to address the limitation on the use of Fish Aggregating Devices (FADs) as one of the management measures in the tuna harvest strategy in Indonesia Archipelagic Waters (IAW), specifically in Indonesia FMA 713, 714, and 715. The lack of data on the number and position of FADs posed challenges for effective management. The research utilized Sentinel-1 satellite image data from July and August 2020 to overcome this issue. These images were analyzed using the Constant False Alarm Rate algorithm and a geospatial approach. The research focused on water areas greater than 12 nautical miles from the coastline of IAW. The results estimated the number of FADs in Indonesia FMA 713, 714, and 715 to be 661, 608, and 291 units. The distance between FADs was generally less than ten nautical miles. These findings provide valuable information for managing and implementing FADs in the designated areas. However, it is important to note that the estimates are based on data from 2020, and the actual number and position of FADs may have changed. The research highlights the need for continued monitoring and registration of FADs to ensure accurate and up-to-date information for effective management measures.


Introduction
The empirical observations of successful fishers targeting flotsam as a fishing strategy form the primary basis for developing and implementing Fish Aggregating Devices (FADs) in fishing operations.While the precise mechanisms underlying the attraction of fishes, particularly tunas, to flotsam are not fully understood, scientific studies have indicated that FADs serve as focal points for fish aggregation and feeding [1][2][3].
Originally designed to enhance fishing efficiency in shallow waters, FADs have progressively been deployed in deeper waters as integral components of tuna fisheries since Corresponding author: anungwd@yahoo.co.id the 1990s [4].The utilization of FADs in fishing operations is regarded favorably due to their potential to augment both the effectiveness and efficiency of catch rates.It was reported that FADs contribute to approximately 35% of global tuna catches.In the specific case of the Indonesia Fisheries Management Area (IFMA) 713-717, FADs account for a substantial proportion, approximately 72%, of the total catch, corresponding to a staggering 514,519 metric tons (mT) of tuna.
Various fishing techniques associated with FADs have been employed in the IFMA, including hand line (HL), pole and lines (PL), trolling line (TR) and purse seine (PS) methods.HL targeted yellowfin tuna (Thunnus albacares) and bigeye tuna (T.obesus), PL and PS targeted skipjack tuna (Katsuwonus pelamis).The visual representation depicted in Figure 1 showcases the HL boat, which employs additional trolling line (TR) and surface kite line (sKL) fishing techniques, as well as the PL and PS boats operating within the Indonesia Archipelagic Water (IAW), specifically IFMA 713, 714, and 715.However, it is crucial to acknowledge the adverse ecological consequences associated with FADs usage.These include the significant capture of juvenile tuna [6] and the disruption of normal tuna migration patterns caused by anchored FADs and drifting FADs [7].These ecological impacts serve as valid concerns and must be carefully considered when evaluating the overall implications of FAD implementation in fisheries management.
Since 1997, Indonesia has implemented FAD regulations in its fisheries law, with several subsequent amendments.The most recent regulation is MMAF Decree No.7/2022.However, adequate implementation and enforcement have not been achieved despite these regulations.One major challenge is the reluctance of fishers to register the FADs they deploy at sea, making it difficult to obtain crucial data such as the number and positions of FADs.Research activities aiming to gather such data are also rare.One of the few studies conducted focusing on a specific part of the Indonesia Archipelagic Water (IAW), namely the Maluku Sea (IFMA 715) and Sulawesi Sea (IFMA 716), which are not representative of the entire IAW.The research estimated the presence of approximately 914 FADs in IFMA 715 and 328 FADs in IFMA 716.
To ensure the sustainability of tuna resources, including skipjack tuna (Katsuwonus pelamis), yellowfin tuna (Thunnus albacares), and bigeye tuna (T.obesus) in the IAW, a tuna harvest strategy (HS) has been developed.Limiting the use of FADs is one of the management measures resulting from the HS work [8].Collaborative research was conducted in 2021 between the Center for Fisheries Research (CFR)-MMAF and the MDPI Foundation to support the implementation of this management measure.The primary objective of this research was to estimate the number and positions of FADs in the IAW.The current paper presents and discusses the findings of this research endeavor.

Area and time of research
The research was conducted in the Indonesia Archipelagic Water (IAW), specifically in the Indonesia Fisheries Management Area (IFMA) 713, 714, and 715, as illustrated in Figure 2.However, the research excluded territorial waters within less than 12 nautical miles (nm).This exclusion was made due to the presence of numerous moored or anchored boats in this area, which could introduce biases when analyzing satellite image data of both boats and FADs.The research activities covered several months between April and December 2022.

Anchored FADs
The definition of FADs used in this paper is based on the document "Indonesia FADs Management Plan in WCPO" [9].According to this definition, FADs refer to manmade or partly man-made attractors designed to lure fish and facilitate their aggregation around the object, thereby increasing fishing operation efficiency and effectiveness.FADs are categorized into three types: surface, mid-water, and bottom FADs.In tuna fisheries, the most used type is surface FADs, which can further be classified into drifting FADs (dFADs) and anchored FADs (aFADs) [10][11].
The primary components of dFADs typically consist of a buoy with sub-water appendages or attractants.On the other hand, aFADs are primarily composed of a buoy or float, an attractant or attractor, mooring lines, and an anchor [12].Fishers often keep the specific design features of their FADs secret to maintain a competitive advantage [13].In the Indonesia Archipelagic Water (IAW), tuna fishers exclusively use aFADs, and the general construction of these FADs [14][15].
Three buoy types of aFADs are commonly used in the IAW: steel pontoon, polystyrene block/cylindrical, and bamboo raft [5,[16][17], as depicted in Figure 3.The dimensions of these buoy types vary.Steel pontoon dimensions range from 3 to 5 meters in length and 0.8 to 0.9 meters in diameter.Polystyrene block dimensions range from 3 to 5 meters in length, 1.0 to 1.2 meters in width, and 0.8 to 1.0 meters in height.Bamboo raft dimensions vary from 6.0 to 8.0 meters in length, 2.5 to 3.0 meters in width, and 0.4 to 0.5 meters in height.In order to mitigate the risk of damage to the mooring lines of anchored FADs (aFADs) resulting from changes in oceanographic conditions such as strong winds, currents, and tides, fishers employ a strategy of installing mooring lines with a length between 1.2 to 1.5 times the sea depth at the location where the aFADs is deployed.This approach allows flexibility and prevents excessive tension on the mooring lines.
As a consequence of this practice, the buoy of the aFADs can experience significant movement, up to 250 meters from its normal position, when there are variations in oceanographic conditions.This movement occurs as a result of the swinging motion of the mooring line caused by changes in wind, current, and tidal forces.Figure 4 provides a visual representation of this phenomenon.Fig. 4. Sifting the position of the FADs buoy from its normal, i.e., B when the sea water is calm so that the mooring line (rope) of FADs is vertical and moves to position B1 when the water situation is terrible due to wind and currents and low-high tide so that the FADs mooring line swings (L 1 ).

Data
Sentinel-1 satellite image data from July and August 2020 were utilized for the current research.The data was downloaded in August 2021.The Sentinel-1 satellite, operated by the European Space Agency (ESA), carries a Copernicus synthetic aperture radar (SAR-C).This radar system can capture and generate images of objects with a length of 5 meters or greater on both land and water surfaces.
In the context of this research, the SAR-C radar recorded and detected the buoys of anchored FADs (aFADs) in the Indonesia Archipelagic Water (IAW).However, due to the radar's capabilities, only aFAD buoys with a length equal to or greater than 5 meters could be effectively recorded.The image data used in the research was acquired from 7 orbits and 44 sweep areas, specifically from sequences 01 to 44, as depicted in Figure 5.The data type of the images is categorized as level 1, specifically in the Ground Range Detected (GRD) format, requiring approximately 78.25 GB of memory storage.

Data analysis
In the current research, floating objects were detected in Sentinel-1 satellite image data using a combination of the Constant False Alarm Rate (CFAR) algorithm and a geospatial approach.The CFAR algorithm was implemented using the SNAP toolbox application, while the geospatial approach was executed using Quantum GIS (QGIS).The CFAR algorithm, when applied to the satellite image data, allows for the identification of potential floating objects based on a constant false alarm rate threshold.It helps distinguish objects of interest from the surrounding background noise.
In conjunction with the CFAR algorithm, the geospatial approach, implemented in QGIS, enables the analysis and interpretation of the detected floating objects in their geographic context.This approach utilizes various geospatial techniques and tools to extract meaningful information from the satellite images, such as spatial analysis and visualization.By combining the CFAR algorithm and the geospatial approach, the research aimed to identify and analyze the presence of floating objects accurately, specifically focusing on the buoys of anchored FADs, using the Sentinel-1 satellite image data.

CFAR algorithm approach
In the current research, a radiometric validation process was conducted on each downloaded image data, as depicted in Figure 6.This process ensures the accuracy and reliability of the radiometric information obtained from the Sentinel-1 satellite images.Thermal Noise Removal is one step in the radiometric validation process.This step aims to eliminate the impact of noise resulting from the reflection of electromagnetic signals on objects with high temperatures.By removing this noise, the overall image quality is improved, allowing for more accurate analysis and interpretation of the data.
Another important process in radiometric validation is applying the orbit file and calibration.This step is necessary due to the slight shifts in the satellite's orientation as it orbits the Earth.Using the appropriate orbit file and calibration allows the data to be corrected for any distortions or inconsistencies caused by these orbital movements, resulting in more precise and reliable radiometric measurements.
Additionally, the process of removing GRD border noise is performed.This correction addresses the noise interference on the edges or outside of the swept area in the Sentinel-1 data products.The final image is refined by eliminating this border noise, reducing any potential artifacts or inaccuracies caused by the interference.These radiometric validation processes are crucial in ensuring the quality and accuracy of the Sentinel-1 satellite image data used in the research.The object detection using the CFAR algorithm, implemented with the SNAP toolbox application, comprises three key steps: 1. Land-Sea Masking: The first step involves applying a land-sea mask to the satellite imagery.This process aims to exclude the land areas from the subsequent object detection model.By masking the land areas, the detection model focuses solely on the oceanic regions, which are the areas of interest for this study.2. Pre-screening: Pre-screening is a critical step in object detection.In this step, the CFAR model, which utilizes an adaptive thresholding algorithm, is employed to identify potential objects in the SAR-C imagery.The CFAR algorithm compares the radar backscatter intensity of each pixel with the surrounding background noise level and adapts the threshold accordingly.This adaptive thresholding approach allows for identifying potential objects based on their contrast with the surrounding environment.3. Object Discrimination: The final step involves the object discrimination process.Once the potential objects are identified through pre-screening, further analysis and discrimination are carried out to distinguish the desired objects from any false detections or noise.This process involves evaluating various characteristics and properties of the detected objects, such as shape, size, and intensity, to differentiate them from other features present in the imagery and ensure accurate object identification.Following these three steps, the CFAR algorithm implemented with the SNAP toolbox application enables detecting and discriminating objects in SAR-C imagery, specifically targeting identifying objects of interest in the oceanic regions.

Geospatial approach
The geospatial analysis in this research aims to determine objects, specifically focusing on the buoys of anchored FADs (aFADs), by identifying static or immovable objects based on the shift of object points between two time periods: T-1 (image data in July) and T-2 (image data in August).The analysis examines the positional shift of object points between T-1 and T-2 to identify static objects.If an object shows a position shift of less than 250 meters (as illustrated in Figure 4) during this period, it is classified as static or immovable.Objects within a 0 to 250-meter radius are considered single objects, and their detection is counted accordingly.
Static objects that meet the criteria for immovability are further classified as buoys of aFADs if they are located more than 12 nautical miles (NM) away from the nearest land.This criterion ensures that only buoys in the open ocean areas are included in the analysis as part of the aFADs.Figure 7 provides an overview of the flow of the geospatial analysis process, depicting the steps involved in identifying and classifying static objects, specifically the buoys of aFADs, based on their positional shifts and proximity to land.After the CFAR detection process, the object data obtained from both T1 and T2 images are overlaid to compare and analyze their proximity.This analysis utilizes the matrix distance tool, which calculates the distance between objects in meters.By applying the matrix distance tool to the overlaid object data, information on the distance between each pair of objects is generated.This information provides insights into the spatial relationships and proximity among the detected objects.The distance values obtained from the matrix distance analysis are measured in meters, allowing for a quantitative assessment of the distances between objects.This proximity analysis helps understand the detected objects' distribution patterns, clustering, or dispersion.It provides valuable information on the spatial arrangement of the objects, which can be further analyzed and interpreted to gain insights into their potential relationships, concentrations, or dispersal patterns.
By utilizing the matrix distance tool and analyzing the proximity of the detected objects in meters, the research can obtain valuable information on the distances between objects, which contributes to a deeper understanding of the spatial characteristics and arrangements of the objects detected in the study area.

Result and Discussion
The data analysis results reveal the estimated number of floating objects on the water surface, specifically indicated as buoys of anchored FADs (aFADs) in the Indonesia Archipelagic Waters (IAW), encompassing IFMA 713, 714, and 715.The estimated numbers for each area are as follows: -IFMA 713: 661 units of aFADs -IFMA 714: 608 units of aFADs -IFMA 715: 291 units of aFADs The positions of these aFADs in each area are presented in Figures 8, 9, and 10, respectively.In IFMA 713, the highest distribution density of aFADs is observed in the latitude range of 4°-6° S and longitude range of 120°-121° E. Within this region, an estimated number of 436 units of aFADs are concentrated.The remaining 225 units are scattered at lower densities throughout IFMA 713.For IFMA 714, the density distribution of aFADs occurs relatively evenly across almost all waters within the area.
In IFMA 715, a high-density distribution of aFADs is observed in several areas, forming clusters.These areas are considered conventional fishing grounds where many aFADs are deployed.These findings provide insights into the spatial distribution and density patterns of aFADs in the respective IFMA areas, highlighting areas of higher concentration and potential fishing hotspots within the Indonesia Archipelagic Waters.In IFMA 713, the distance between aFADs ranges from approximately 0.8 to 33.0 km or 0.4 to 17.8 nm, with an average distance of 4.9 km or 2.6 nm (Figure 11).The distance between aFADs is narrower in densely populated areas, ranging from 0.8 to 3.0 km or 0.4 to 1.6 nm.Out of the total 661 aFADs in IFMA 713, approximately 623 (about 94%) of them have distances between aFADs less than 18.5 km or about 10.0 nm.In contrast, only 38 (about 6%) aFADs have distances between aFADs greater than 18.52 km or approximately 10.0 nm.In IFMA 714, the distance between aFADs ranges from approximately 0.4 to 40.0 km or 0.2 to 21.6 nm, with an average distance of 10.7 km or about 5.8 nm (Figure 12).However, most aFADs (505 units or approximately 83% of the total 606 units) have distances between aFADs less than 10 nm.Similarly, in IFMA 715, the distance between aFADs ranges from approximately 0.4 to 33.0 km or 0.2 to 17.8 nm, with an average distance of about 9.4 km or 5.1 nm (Figure 13).Most aFADs in this area (237 units or approximately 81% of the total 291 units) have distances between aFADs less than 10 nm.These findings demonstrate that in all three IFMA areas, a significant proportion of aFADs are located relatively close to each other, with distances less than 10 nm.This clustering of aFADs suggests the presence of specific fishing grounds or areas where a high density of aFADs is deployed, potentially indicating productive fishing locations.The current research findings highlight the challenges in managing aFADs due to the unavailability of comprehensive data and information on their number and position.The estimated number of aFADs obtained in this research is rough, and the actual number of aFADs in IAW is likely greater than what was identified.Following up this research by registering aFADs through the DGCF-MMAF is important to obtain more accurate and upto-date information.Furthermore, the limitation of the SAR-C radar to detect objects with a length of at least 5 meters means that aFADs with smaller buoy dimensions were not counted in this research.Additionally, aFADs located in coastal waters within 12 miles were not included in the analysis.
It is important to note that the research data used in this study was from 2020, and there is a possibility that the number and position of aFADs have changed since then.Therefore, regular updates and monitoring of aFADs are necessary to ensure accurate management.The research findings also indicate that the distance between aFADs, in general, is less than 10 nm, which is in contrast to the regulation stated in Minister of MMAF Decree No. 26/Permen KP/2014 on FADs, which requires a minimum distance of 10 nm between each FAD.This suggests a potential discrepancy between the observed practices and the regulatory requirements, highlighting the need for effective enforcement and monitoring of FAD management regulations.

Conclusion
In conclusion, the research estimates approximately 661, 608, and 291 units of aFADs in IFMA 713, 714, and 715, respectively, within the Indonesian Archipelagic Waters (IAW).However, it should be noted that this estimation is rough and does not include aFADs deployed in waters within 12 nautical miles and aFADs with buoys shorter than 5 meters.The research utilized satellite imagery data from 2020, and the number and position of aFADs have likely changed since then, considering the current year is 2023.
To obtain more accurate and up-to-date information on the number and position of aFADs, fishers need to register their aFADs with the DGCF-MMAF.This registration process will help ensure the management of aFADs is by the actual situation in IAW.

Fig. 5 .
Fig. 5. Coverage area of the distribution GRD data

Fig. 6 .
Fig. 6.Flow chart of radiometric validation process on downloaded SAR-C image data.

Fig. 7 .
Fig. 7.The flow of the geospatial analysis process.