Can turbid water and refuges with aquatic macrophytes increase the survival and growth of Brycon amazonicus larvae?

The productivity of aquatic organisms and higher concentration of suspended solids that occur in areas flooded by Whitewater Rivers and the presence of aquatic macrophytes are conducive to the initial development of matrinxã (Brycon amazonicus). The objective of this study was to evaluate whether turbid waters and refuges with aquatic macrophytes improve the survival and growth of the larvae of Brycon amazonicus, a species of great interest in Amazonian fish farming. For this, two experiments were conducted using a completely randomized design and with four replicas per treatment up to 120 hours after hatching (HAH), In experiment I: Brycon amazonicus larvae at 24 HAH were subjected to three treatments with different types of water: clear water (CW); clayey water (AW) and green water (GW). Experiment II: larvae with 24 HAH were maintained in clear water (CW); clear water with refuge (CWR); green water (GW); green water with refuge (GWR); clayey water (AW) and clayey water with refuge (AWR). Experiment I showed that clayey water, followed by green water, increased the survival of larvae (73.92 and 54.32%). Growth was best in larvae maintained in green water. In experiment II, the use of aquatic macrophytes did not influence the survival and growth of the larvae. Thus, we suggest the use of turbid (clayey and green) waters without refuge to increase the survival and growth of Brycon amazonicus larvae.

Effect of Turbidity, Temperature and Salinity of Waters on Depth Data from Airborne LiDAR Bathymetry

The influence of seawater parameters cannot be ignored when conducting bathymetric LiDAR (Laser Induced Detection and Ranging or Light Detection and Ranging) surveys such as turbidity, temperature, and salinity. Turbidity affects the attenuation diffusion coefficient of the green laser is penetrating the air column. The comparison of LiDAR bathymetric depth with Secchi disk depth is used as a reference in determining the effect of turbidity. The results are in locations with primarily clear water the ability of LiDAR can penetrate up to 7m, while in turbid waters up to 3m. On average, the ability of the green laser LiDAR bathymetry can penetrate the waters of 1.5-2 times the depth at the location of this study around the bay of Lampung Indonesia. Other water parameters are temperature and salinity. These parameters are used to calculate the refractive index value of water. The Different temperature and salinity values in a water column can result in differences in the accuracy of the bathymetry LiDAR depth of 4-6mm. The influence of water column parameters can be a concern in planning and processing airborne LiDAR altimetry (ALB) surveys.

Condition of Hard Corals and Quality of The Turbid Waters in Spermonde Islands (Case Studies in Kayangan Island, Samalona Island and Kodingareng Keke Island)

Coral reefs in Spermonde waters have experienced degradation as a result of the increasing turbidity in these waters. This research was conducted on Kayangan Island, Samalona Island and Kodingareng Keke Island, Spermonde Islands, Makassar City. The purpose of this study was to determine the percentage of coral cover, types and the how much the turbidity of the waters influence the coral. The researcher measured the coral cover using Underwater Photo Transect and processed the data using CPCe software. The levels of turbidity were taken at three locations and examined in the laboratory and the other parameters were measured on site. The correlation between turbidity and coral cover was analysed by regression analysis method. Based on the regression results, the turbidity level has a negative effect on coral cover. The level of turbidity is higher on Kayangan Island with 0.89 NTU with a cover percentage of 4.95 %, Temperature of 30°C, Salinity of 30 ppm, DO of 5.44 mg/l, pH 7.8 and the species found were Porites and Faviidae. Samalona Island is 0.43 NTU with a percentage of coral cover of 14.08 %, Temperature of 30°C, Salinity of 30 ppm, DO of 5.44 mg/l, pH of 7.7 and the most common types are Acropora and Fungiidae. Kodingareng Keke Island has a turbidity value of 0.32 NTU, Temperature of 29°C, Salinity of 30 ppm, DO of 6.08 mg/l, pH 7.3 and the most common coral species, Fungiidae. Higher levels of turbidity in Kayangan Island will result in the death of certain coral species (unable to adapt) and will also have an impact on the percentage of hard coral cover.

COMPARATIVE STUDY OF THE PHYSICO-CHEMICAL QUALITY OF THE WATER OF FOUR LAKES OF THE FERKESSEDOUGOU DAM (NORTH OF COTE DIVOIRE)

Runoff from the catchment areas of the reservoirs is partly responsible for the chemical quality of the water. The main objective of this study was to compare the physico-chemical quality of the waters of four lakes in the sub-county of Ferkessedougou located in the north of Cote dIvoire. After application of the referenced analytical methods, the contents of chemical elements were subjected to statistical processing, mainly principal component analysis (PCA). The study revealed a categorization of the water in the reservoirs. On the whole, the water in the reservoirs is poorly mineralized, thus justifying a low electrical conductivity (66.67 to 138.04 µS/cm). The Bakaryvogo lake, richer in dissolved elements, is opposed to the Korobelekaha and Dekokaha lakes, which have the lowest electrical conductivity, unlike the others. The lake of Sepenediokaha is distinguished by turbid waters rich in organic matter (COD) and suspended solids (SS). This study provided an overview of the quality of the water, which remains a function of the activities in the catchment areas. Furthermore, the waters of these reservoirs are potentially threatened by the phenomenon of eutrophization given the levels of nutrients measured. Indeed, the concentrations of nitrate, nitrite and phosphorus, although lower than the thresholds allowed by the WHO, are significant. There are also significant levels of phosphorus. In addition, the average Fe2+ content measured (0.78 mg/L) is above WHO standards.

6D Pose Estimation for Subsea Intervention in Turbid Waters

Manipulation tasks on subsea instalments require extremely precise detection and localization of objects of interest. This problem is referred to as “pose estimation”. In this work, we present a framework for detecting and predicting 6DoF pose for relevant objects (fish-tail, gauges, and valves) on a subsea panel under varying water turbidity. A deep learning model that takes 3D vision data as an input is developed, providing a more robust 6D pose estimate. Compared to the 2D vision deep learning model, the proposed method reduces rotation and translation prediction error by (−Δ0.39∘) and translation (−Δ6.5 mm), respectively, in high turbid waters. The proposed approach is able to provide object detection as well as 6D pose estimation with an average precision of 91%. The 6D pose estimation results show 2.59∘ and 6.49 cm total average deviation in rotation and translation as compared to the ground truth data on varying unseen turbidity levels. Furthermore, our approach runs at over 16 frames per second and does not require pose refinement steps. Finally, to facilitate the training of such model we also collected and automatically annotated a new underwater 6D pose estimation dataset spanning seven levels of turbidity.

Emerging Sensor Platforms Allow for Seagrass Extent Mapping in a Turbid Estuary and from the Meadow to Ecosystem Scale

Seagrass meadows are globally important habitats, protecting shorelines, providing nursery areas for fish, and sequestering carbon. However, both anthropogenic and natural environmental stressors have led to a worldwide reduction seagrass habitats. For purposes of management and restoration, it is essential to produce accurate maps of seagrass meadows over a variety of spatial scales, resolutions, and at temporal frequencies ranging from months to years. Satellite remote sensing has been successfully employed to produce maps of seagrass in the past, but turbid waters and difficulty in obtaining low-tide scenes pose persistent challenges. This study builds on an increased availability of affordable high temporal frequency imaging platforms, using seasonal unmanned aerial vehicle (UAV) surveys of seagrass extent at the meadow scale, to inform machine learning classifications of satellite imagery of a 40 km2 bay. We find that object-based image analysis is suitable to detect seasonal trends in seagrass extent from UAV imagery and find that trends vary between individual meadows at our study site Bahía de San Quintín, Baja California, México, during our study period in 2019. We further suggest that compositing multiple satellite imagery classifications into a seagrass probability map allows for an estimation of seagrass extent in turbid waters and report that in 2019, seagrass covered 2324 ha of Bahía de San Quintín, indicating a recovery from losses reported for previous decades.

An Improved Cloud Masking Method for GOCI Data over Turbid Coastal Waters

Clouds severely hinder the radiative transmission of visible light; thus, correctly masking cloudy and non-cloudy pixels is a preliminary step in processing ocean color remote sensing data. However, cloud masking over turbid waters is prone to misjudgment, leading to loss of non-cloudy pixel data. This research proposes an improved cloud masking method over turbid water to classify cloudy and non-cloudy pixels based on spectral variability of Rayleigh-corrected reflectance acquired by the Geostationary Ocean Color Imager (GOCI). Compared with other existing cloud masking methods, we demonstrated that this improved method can identify the spatial positions and shapes of clouds more realistically, and more accurate pixels of turbid waters were retained. This improved method can be effectively applied in typical turbid coastal waters. It has potential to be used in cloud masking procedures of spaceborne ocean color sensors without short-wave infrared bands.