Development of Quantitative Single Beam Echosounder for Measuring Fish Backscattering

Target strength (TS) of marine fish is a key factor for target identification and stock quantification. Validation of measurement and model comparisons in fisheries acoustics is difficult, due to the uncertainty in ground truth obtained in the ocean. To overcome this problem is to utilize laboratory measurements, where fish parameter is more well controlled. In this research, the dorsal‐aspect TS of fish was measured as a function of the incidence angle in a water tank using a quantitative echo sounder. The measurement was compared with the theoretical prediction using the distorted‐wave born approximation (DWBA) model. TS of fish was proportional to body length and the directivity of TS was strongly dependent on its orientation. Computational DWBA modeling, experimental details, and data/model comparison were presented

Combining Two Classification Methods for Predicting Jakarta Bay Seabed Type Using Multibeam Echosounder Data

Classification of seabed types from multibeam echosounder data using machine learning techniques has been widely used in recent decades, such as Random Forest (RF), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Nearest Neighbor (NN). This study combines the two most frequently used machine learning techniques to classify and map the seabed sediment types from multibeam echosounder data. The classification model developed in this study is a combination of two machine learning classification techniques, namely Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN). This classification technique is called SV-KNN. Simply, SV-KNN adopts these two techniques to carry out the classification process. The SV-KNN technique begins with determining test data by specifying support vectors and hyperplanes, as was done on the SVM method, and executes the classification process using the K-NN. Clay, fine silt, medium silt, coarse silt, and fine sand are the five main classes produced by SVKNN. The SV-KNN method has an overall accuracy value of 87.38% and a Kappa coefficient of 0.3093

Autonomous Underwater Vehicle Untuk Survei Dan Pemantauan Laut

AUV is an unmanned submersible platform to accomplish a mission. Side-scan sonar, Conductivity Temperature Depth (CTD), and underwater video camera are usually attached on AUV. These sensors were used for identifying seawater and seabed condition. Data acquired from a survey with an AUV in Kepulauan Riau processed by Neptus software. Side-scan sonar (SSS) visualization is compared to the video image. SSS signal visualization has a unique pattern that can be identified within the video image. Different substrate structure caused different signal visualization. The relation between the video image and SSS visualization can be used for identifying habitat benthic profile

Discovery of a conical feature in Halmahera waters, Indonesia: traces of a late-stage hydrothermal activity

An expedition to confirm the presence of underwater hazards was carried out in Halmahera waters, Indonesia, to the west of Halmahera Island from August to September 2021. The expedition carried out a multibeam survey, surface-towed magnetic survey, and seafloor sampling. A ~ 615-m-tall conical feature with traces of hydrothermal activity was discovered. The feature is bounded on the southeastern (SE) side by a series of normal faults at the peak, with possible dextral strike-slip faults traced west of the feature. The feature displays the potential presence of volcanic rocks based on the observed contrasting magnetic anomaly signature of down to − 100 nT, which at the magnetic equator corresponds to the presence of highly magnetised material. Four 2.5-D magnetic models were built to test various scenarios on the subsurface structure of the feature, mainly focusing on the presence of volcanic rocks at different epochs and a possible presence of serpentinisation. X-ray diffraction (XRD) of the silt and clay sediments sampled confirms traces of late-stage hydrothermal activity, indicated by a high percentage of quartz (53.87%), followed by calcite (34.56%), kaolinite (6.54%), and illite minerals (5.04%). Non-carbonate materials are yet to be found in the sampled sand and gravel sediments, which mainly consist of shell and coral fragments. The discovery of the conical feature, now termed the Yudo Sagoro Hill, provides new information on the structure and activities on the seafloor of Halmahera waters

Potential Use of Deep-Sea Sediment Bacteria for Oil Spill Biodegradation: A Laboratory Simulation

Deep-sea sedimentary hydrocarbonoclastic bacteria are still not widely used in the bioremediation field, especially for crude oil spill biodegradation. This study utilized a mixed culture of Raoultella sp., Enterobacter sp., and Pseudomonas sp. isolated from deep-sea sediment to determine the abilities of bacteria to degrade petroleum hydrocarbons while incorporating environmental variations in a microcosm study. The oil biodegradation extent was determined by measuring the remaining oil and grease in the sample vials. The highest percentage of biodegradation was 88.6%, with a constant degradation rate of 0.399 day–1. GC-MS analysis showed that the most degradable compound in the oil samples was paraffin. This study also observed that microbial degradation was optimized within three days of exposure and that degradation ability decreased at 35 °C. The salinity variation effects were insignificant. Based on all analyses, deep-sea sediment bacteria have great potential in oil spill biodegradation in a microcosm scale

Oil Spill Biodegradation by Bacteria Isolated from Jakarta Bay Marine Sediments

A laboratory study was conducted with the aim to isolate and identify bacteria from sea sediment and test their biodegradation ability in two place where contaminated with oil spill. Five sediment samples were dissolved by using sterile sea water, and then bacteria isolated with total plate count (TPC) method. Isolates bacteria was cultivated, and adapted using the nutrient conditioned sea water medium. Biodegradation process was done by mixing the bacteria with crude oil and shaken for few days. The number of bacteria isolated varied from 2 x 102 CFU ml-1 to 6 x 106 CFU ml-1 and apparently increased after cultivation and adaptation with oily media. Bacteria identified during this study were Fundibacter sp., Alcanivorax sp., and Marinobacter sp.. The result of biodegradation process was statistically analyzed and obtained that the bacteria are effective in degrading oil in seven days with constant of biodegradation rate was 0.1766. GC-MS analysis was conducted to prove the decomposition of carbon chain by bacteria and revealed oil degradation in carbon number 11 to 27. Based on all analysis, marine sediment bacteria can degrade the oil spill. Keywords : Bacteria, Biodegradation, GC-MS, Marine Sediments, Oil spill

Variation of Zooplankton Mean Volume Backscattering Strength from Moored and Mobile ADCP Instruments for Diel Vertical Migration Observation

Zooplankton can be detected by using acoustic Doppler current profiler (ADCP) instruments through acquiring the mean volume backscattering strength (MVBS) data. However, the precision of the backscattered signal measured by single ADCP measurement has a limitation in the MVBS variation of zooplankton. The objectives of this study were to analyze the MVBS and vertical velocity from ADCPs at the same time and location for zooplankton’s daily vertical migration (DVM) observation. Measurements were conducted in Lembeh Strait, North Sulawesi, Indonesia. Instruments used included a moored ADCP 750 kHz and a mobile ADCP 307.2 kHz. High MVBS value was found at 11.5−16 m depths and was identified as the sound scattering layer (SSL). The DVM patterns in the SSL displayed significant differences over time and had good relationships with the diurnal cycle. Theoretical target strength (TS) from the scattering models based on a distorted-wave Born approximation (DWBA) was estimated for Oithona sp. and Paracalanus sp.; the two dominant species found in the observed area. However, ΔMVBS and ΔTS proved that the dominant zooplankton species were not the main scatterers. The strong signal in SSL was instead caused by the schools of various zooplankton species

Development of the silver eel (

The Lake Poso system is located in Central Sulawesi and is connected to the Tomini Bay by the Poso River. It is known that five out of nine Indonesian eel species were found at the Poso River. Anguilla marmorata is the most caught species. However, uncontrolled catching during downstream migration and the construction of a hydropower plant threaten the silver eel in the Lake Poso system. Research on silver eel (A. marmorata) in Lake Poso was conducted to determine and compare the condition of gonad development as part of the reproductive process. This represents essential information in eel fisheries management in Lake Poso. The eels were collected through bamboo traps (waya masapi). The present study calculates the GSI and HSI values, and histological analyses characterize the gonad. The GSI of eel ranges from 1.95 to 5.69%, and the HSI value ranges from 0.83 to 1.16%. Histological observation showed that eels from Tentena (Lake Poso outlet) and from the estuary of Poso River have ovaries in the early vitellogenic stage (III) and the vitellogenic stage (IV)