Bottom Returns Research Articles

Polyculture of Genetically Improved Farmed Tilapia (GIF tilapia) and Penaeus vannamei Using Biofloc Technology – A Review

According to Food and Agriculture Organization of the United Nations (FAO), aquaculture has grown faster and its expansion aimed at meeting the increase of world fish demand, and preserving natural fish stocks. Currently, to produce fish in quantity and quality requires reduction of the environmental impact from aquaculture, through the improvement of culture systems. Disease is the major factor affecting the development and expansion in aquaculture. Losses due to disease in shrimp farming are high. Various approaches to minimize the impact of disease on production are possible. Another approach to keep the pathogen pressure low is polyculture of shrimp and finfish. This practice makes shrimp farming more sustainable by reducing the environmental impact and the incidence of shrimp disease. Antimicrobial peptides in the fish skin kill shrimp pathogens, keeping pathogen pressure of bacteria and viruses low. In polyculture, shrimps can eat tilapia faeces and unused fish feed, while tilapia filter phytoplankton, reducing the risk of low dissolved oxygen levels at night. In addition, shrimp bioturbation at the pond bottom returns nutrients to the water column, enhancing phytoplankton production and consequently the natural feed available for the tilapia. Biofloc technology (BFT) is one of the most applicable and promising systems for sustainable aquaculture development. This technology is essentially based on the recycling of nutrients via microorganisms, primarily (i) heterotrophic bacteria, which convert nitrogen compounds into microbial biomass, in addition to serves as a source of food for aquatic organisms, and (ii) chemoautotrophic bacteria, which convert ammonia to nitrite and nitrate.

Deep learning assisted exponential waveform decomposition for bathymetric LiDAR

Abstract. The processing of bathymetric LiDAR waveforms is an important task, as it provides range and radiometric information to determine the precise location of water surface and bottom, and other characteristics like amplitude. The exponential waveform decomposition proved to be an effective algorithm for bathymetric LiDAR waveforms processing, however, it heavily relies on the high-quality initial estimates of the model parameters. This paper proposes to make use of deep learning to obtain the initial values directly from the input received waveforms without any hand-crafted features and prior-knowledges. Additionally, to provide training samples, we presents a method to create the synthetic bathymetric LiDAR waveforms by simulating of the backscatter cross function returned from water bodies. Two networks with different sensitivities of weak signals were trained by these synthetic waveforms, and used to estimate the initial values of the model parameters, a least square optimization follows up to obtain the final waveform decomposition result. This deep learning assisted exponential waveform decomposition method is applied to the real waveforms acquired by RIEGL VQ-840-G. The results show that estimations with the help of deep learning is less influenced by the intermediate peaks backscattered from objects and particles in water, producing a cleaner point cloud with less isolated points below water surface than the original exponential waveform decomposition. Moreover, the proposed sensitive DL-XDC is even able to detect some very weak bottom returns with low SNR.

Using ICESat-2 and Operation IceBridge altimetry for supraglacial lake depth retrievals

Abstract. Supraglacial lakes and melt ponds occur in the ablation zones of Antarctica and Greenland during the summer months. Detection of lake extent, depth, and temporal evolution is important for understanding glacier dynamics. Previous remote sensing observations of lake depth are limited to estimates from passive satellite imagery, which has inherent uncertainties, and there is little ground truth available. In this study, we use laser altimetry data from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) over the Antarctic and Greenland ablation zones and the Airborne Topographic Mapper (ATM) for Hiawatha Glacier (Greenland) to demonstrate retrievals of supraglacial lake depth. Using an algorithm to separate lake surfaces and beds, we present case studies for 12 supraglacial lakes with the ATM lidar and 12 lakes with ICESat-2. Both lidars reliably detect bottom returns for lake beds as deep as 7 m. Lake bed uncertainties for these retrievals are 0.05–0.20 m for ATM and 0.12–0.80 m for ICESat-2, with the highest uncertainties observed for lakes deeper than 4 m. The bimodal nature of lake returns means that high-confidence photons are often insufficient to fully profile lakes, so lower confidence and buffer photons are required to view the lake bed. Despite challenges in automation, the altimeter results are promising, and we expect them to serve as a benchmark for future studies of surface meltwater depths.

Bottom Detection Method of Side-Scan Sonar Image for AUV Missions

In order to obtain the measurement parameters of the sea bottom geomorphology or underwater objects, the first step in side-scan sonar (SSS) image processing is bottom detection. Due to the complexity of the marine environment, the acoustic signals received by SSS are usually polluted by noises, which affect its image quality and make the extraction of image features difficult. To address this problem, this study proposes an automatic detection method for the sea bottom line based on the actual experimental acquisition of SSS images, which is supposed to support the autonomous underwater vehicle (AUV) for intelligent target detection and classification. The proposed method comprises four main steps. First, the raw SSS data is analyzed to obtain a grayscale image, and the blind zone boundary of the image is obtained using the threshold method. Then, the noise characteristics of the image are analyzed and the denoising algorithm is optimized to effectively remove high-frequency noise. Next, spatial-temporal matching calculations are performed on each ping port and starboard data, and the accurate coordinates of first bottom returns are obtained through extreme value detection. Finally, automatic and accurate detection of the bottom line is realized according to the smooth processing of the coordinate sequence of first bottom returns. The experiments have demonstrated the effectiveness of the proposed method. As the method does not require human intervention in adjusting parameters during operation, the proposed method with a certain time window imposed during image acquisition will be suitable for AUV missions when the SSS is determined.

Remote Sensing of River Bathymetry: Evaluating a Range of Sensors, Platforms, and Algorithms on the Upper Sacramento River, California, USA

AbstractRemote sensing has become an increasingly viable tool for characterizing fluvial systems. In this study, we used field measurements with a 1.6‐km reach of the upper Sacramento River, CA, to evaluate the potential of mapping water depths with a range of platforms, sensors, and depth retrieval methods. Field measurements of water column optical properties also were compared to similar data sets from other rivers to provide context for our results. We considered field spectra, a multispectral satellite image, hyperspectral data collected from conventional and unmanned aircraft, and a bathymetric LiDAR and applied a generalized version of Optimal Band Ratio Analysis and the K nearest neighbors regression machine learning algorithm. Linear, quadratic, exponential, power, and lowess Optimal Band Ratio Analysis models enabled flexible curve‐fitting in calibrating spectrally based quantities to depth; an exponential formulation avoided artifacts associated with other model types. K nearest neighbors regression increased observed versus predicted (OP) R2 values, particularly for the satellite image; we also found that preprocessing of satellite images was unnecessary and that a basic data product could be used for depth retrieval. Bathymetric LiDAR was highly accurate and precise in shallow water, but a lack of bottom returns from areas greater than 2 m deep resulted in large gaps in coverage. The maximum detectable depth imposes an important constraint on fluvial remote sensing and a hybrid approach combined with field surveys of deep areas might be a more realistic operational strategy for bathymetric mapping. Future work will focus on scaling up from short reaches to long river segments.

Acoustic Pressure and Particle Velocity for Spatial Filtering of Bottom Arrivals

This paper discusses the advantages of using a combination of acoustic pressure and particle velocity motion for filtering bottom arrivals. A possible area of application is reflection seismology where, traditionally, the seismic image is extracted from the bottom-reflected broadband acoustic signals received on hydrophones. Since hydrophones are omnidirectional in nature, the received bottom returns are often contaminated by waterborne signals, sea surface reflections, and noise. A substantial part of the processing of the data is dedicated to filtering out these unwanted signals. Today, vector sensors allow us to measure both acoustic pressure and particle velocity motion in a single and compact sensor. The combination of pressure and particle velocity measured at a single location or particle velocity and particle velocity gradient at closely spaced locations allows for spatial beam steering to predetermined directions and filter out unwanted replicas from other directions. Moreover, this can be done at the sensor level, dramatically decreasing the offline processing. The spatial filtering capabilities of various pressure-pressure, particle velocity-particle velocity, and pressure-particle velocity combinations are analyzed in view of filtering the bottom arrivals. It is shown that the combination of pressure and vertical particle velocity and, particularly, the combination of vertical particle velocity and particle velocity gradient enhance bottom arrivals. Moreover, a simple steering procedure combining pressure and particle velocity components of a triaxial sensor allows us to determine the tridimensional structure of the acoustic field and the separation of the bottom reflections. The spatial selectivity of the various sensor combinations is shown with simulations and verified with experimental data acquired with 10 cm separated vector sensors in the 800-1250-Hz band, during the Makai 2005 sea trial, off Kauai Island, HI, USA.

Multifeature Extraction and Seafloor Classification Combining LiDAR and MBES Data around Yuanzhi Island in the South China Sea.

Airborne light detection and ranging (LiDAR) full waveforms and multibeam echo sounding (MBES) backscatter data contain rich information about seafloor features and are important data sources representing seafloor topography and geomorphology. Currently, to classify seafloor types using MBES, curve features are extracted from backscatter angle responses or grayscale, and texture features are extracted from backscatter images based on gray level co-occurrence matrix (GLCM). To classify seafloor types using LiDAR, waveform features are extracted from bottom returns. This paper comprehensively considers the features of both LiDAR waveforms and MBES backscatter images that include the eight feature factors of the LiDAR full waveforms (amplitude, peak location, full width half maximum (FWHM), skewness, kurtosis, area, distance, and cross-section) and the eight feature factors of MBES backscatter images (mean, standard deviation (STD), entropy, homogeneity, contrast, angular second moment (ASM), correlation, and dissimilarity). Based on a support vector machine (SVM) algorithm with different kernel functions and penalty factors, a new seafloor classification method that merges multiple features is proposed for a beneficial exploration of acousto-optic fusion. The experimental results of the seafloor classification around Yuanzhi Island in the South China Sea indicate that, when LiDAR waveform features are merged (using an Optech Aquarius system) with MBES backscatter image features (using a Sonic 2024) to classify three types of sands, reefs, and rocks, the overall accuracy is improved to 96.71%, and the kappa reaches 0.94. After merging multiple features, the classification accuracies of the SVM, genetic algorithm SVM (GA-SVM) and particle swarm optimization SVM (PSO-SVM) increase by an average of 9.06%, 3.60%, and 2.75%, respectively.

Remote Sensing of Suspended Sediment Concentrations Based on the Waveform Decomposition of Airborne LiDAR Bathymetry

Airborne LiDAR bathymetry (ALB) has been shown to have the ability to retrieve water turbidity using the waveform parameters (i.e., slopes and amplitudes) of volume backscatter returns. However, directly and accurately extracting the parameters of volume backscatter returns from raw green-pulse waveforms in shallow waters is difficult because of the short waveform. This study proposes a new accurate and efficient method for the remote sensing of suspended sediment concentrations (SSCs) in shallow waters based on the waveform decomposition of ALB. The proposed method approaches raw ALB green-pulse waveforms through a synthetic waveform model that comprises a Gaussian function (for fitting the air–water interface returns), triangle function (for fitting the volume backscatter returns), and Weibull function (for fitting the bottom returns). Moreover, the volume backscatter returns are separated from the raw green-pulse waveforms by the triangle function. The separated volume backscatter returns are used as bases to calculate the waveform parameters (i.e., slopes and amplitudes). These waveform parameters and the measured SSCs are used to build two power SSC models (i.e., SSC (C)-Slope (K) and SSC (C)-Amplitude (A) models) at the measured SSC stations. Thereafter, the combined model is formed by the two established C-K and C-A models to retrieve SSCs. SSCs in the modeling water area are retrieved using the combined model. A complete process for retrieving SSCs using the proposed method is provided. The proposed method was applied to retrieve SSCs from an actual ALB measurement performed using the Optech Coastal Zone Mapping and Imaging LiDAR in a shallow and turbid water area. A mean bias of 0.05 mg/L and standard deviation of 3.8 mg/L were obtained in the experimental area using the combined model.

Extreme Returns in the European financial crisis

AbstractWe examine the transmission of financial shocks among the euro‐periphery (Portugal, Ireland, Italy, Greece, Spain), the euro‐core (Germany, France, the Netherlands, Finland, Belgium), and the major European Union (but not euro) countries (Sweden, the United Kingdom, Poland, the Czech Republic, Denmark). Using extreme returns on daily stock market data from 2004 until 2013, we find transmission effects for the tails of the returns distributions for the pre‐crisis, US crisis and euro crisis periods from the euro‐periphery to the non‐euro and euro‐core groups. During the crises, the shocks transmitted were more substantial, indicating significantly higher losses on extreme return days.

An Approach to Determining Turbidity and Correcting for Signal Attenuation in Airborne Lidar Bathymetry

Airborne lidar bathymetry is an efficient technique for measuring the bottom of shallow water bodies. A characteristical feature of lidar bathymetry beam propagation is given by scattering and absorption effects in the water column, both leading to a loss of received signal intensity. This loss of signal intensity depends on the turbidity of the water body. Inversely, an analysis of the decay of the recorded waveform signal allows for deriving statements on the local degree of turbidity in the water. The paper shows a first approach on the determination of one turbidity measure per laser pulse by analysing the recorded waveform and fitting an exponential function, wherein the decay coefficient depicts an integral measure describing turbidity. The technique was applied to a shallow inland water, and the results were validated by conventional point-wise turbidity measurement techniques. An obvious consequence of attenuation and loss of signal intensity in lidar bathymetry is the fact that the bottom returns become rather weak. In many cases, conventional ground pulse echo detection techniques fail in detecting water bottom points, leading to a reduced number of water body bottom points and thus limiting the application range of the technique. To partly compensate for this effect, a differential backscatter cross section determination based signal attenuation correction method has been developed, which allows for a signal-derived re-amplification of the ground signal. Although the technique also amplifies noise, it could be shown that it is capable of delivering a higher number of additional ground points and thus extending the applicability of the technique.

Extreme Returns in the European Financial Crisis

We examine the transmission of financial shocks among three groups of countries: the Euro-periphery countries (Portugal, Ireland, Italy, Greece, Spain), the Euro-core countries (Germany, France, the Netherlands, Finland, Belgium), and the major European Union - but not Euro - countries (Sweden, UK, Poland, Czech Republic, Denmark). Using extreme returns on daily stock market data from January 2004 until March 2013, we find that transmission effects are present for the tails of the returns distributions for the Pre-crisis, the US-crisis and the Euro-crisis periods from the Euro-periphery group to the Non-euro and the Euro-core groups. Within group effects are stronger in the crisis periods. Even before the two crises there was a significant shock transmission channel from the Euro-periphery to the Euro-core and the Non-euro. During the crises the shocks transmitted were more substantial (in some cases, extreme bottom returns doubled). As extreme returns have become much more severe during the financial crisis periods, the expected losses on extreme return days have increased significantly. Given the fact that stock market capitalizations in these country groups are in trillions of Euros, a 1% or 2% increase in extreme bottom returns (in crisis periods) can lead to aggregate losses of tens of billions Euros in one single trading day.

Analysis of MABEL Bathymetry in Keweenaw Bay and Implications for ICESat-2 ATLAS

In 2018, the National Aeronautics and Space Administration (NASA) is scheduled to launch the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), with a new six-beam, green-wavelength, photon-counting lidar system, Advanced Topographic Laser Altimeter System (ATLAS). The primary objectives of the ICESat-2 mission are to measure ice-sheet elevations, sea-ice thickness, and global biomass. However, if bathymetry can be reliably retrieved from ATLAS data, this could assist in addressing a key data need in many coastal and inland water body areas, including areas that are poorly-mapped and/or difficult to access. Additionally, ATLAS-derived bathymetry could be used to constrain bathymetry derived from complementary data, such as passive, multispectral imagery and synthetic aperture radar (SAR). As an important first step in evaluating the ability to map bathymetry from ATLAS, this study involves a detailed assessment of bathymetry from the Multiple Altimeter Beam Experimental Lidar (MABEL), NASA’s airborne ICESat-2 simulator, flown on the Earth Resources 2 (ER-2) high-altitude aircraft. An interactive, web interface, MABEL Viewer, was developed and used to identify bottom returns in Keweenaw Bay, Lake Superior. After applying corrections for refraction and channel-specific elevation biases, MABEL bathymetry was compared against National Oceanic and Atmospheric Administration (NOAA) data acquired two years earlier. The results indicate that MABEL reliably detected bathymetry in depths of up to 8 m, with a root mean square (RMS) difference of 0.7 m, with respect to the reference data. Additionally, a version of the lidar equation was developed for predicting bottom-return signal levels in MABEL and tested using the Keweenaw Bay data. Future work will entail extending these results to ATLAS, as the technical specifications of the sensor become available.

Evaluating the capabilities of the CASI hyperspectral imaging system and Aquarius bathymetric LiDAR for measuring channel morphology in two distinct river environments

AbstractRemote sensing is a powerful tool for examining river morphology. This study used detailed field surveys to assess the capability of the CASI hyperspectral imaging system and Aquarius bathymetric LiDAR to measure bed elevations in rivers with disparate optical characteristics. Field measurements of water column optical properties in the clear Snake River, the more complex Blue and Colorado, and highly turbid Muddy Creek were used to calculate depth retrieval precision and dynamic range. Differences in depth of a few centimeters were detectable via passive optical techniques in the clearest stream, but precision was greatly reduced under turbid conditions. The bathymetric LiDAR evaluated in this study could not detect shallow depths or differences in depth smaller than 11 cm owing to the difficulty of distinguishing water surface and bottom returns in laser waveforms. In clear water and with high radiometric resolution, hyperspectral systems such as CASI could detect depths approaching 10 m, but semi‐empirical analysis of the Aquarius LiDAR indicated that maximum detectable depths were of the order of 2–3 m in the clear‐flowing Snake River, and closer to 1 m in the more turbid streams. Turbidity also constrained spectrally based depth retrieval, and depth estimates from the Blue/Colorado were far less reliable than on the Snake. Both sensors yielded positively biased (0.03 m for CASI, 0.08 m for Aquarius) bed elevations on the Snake, with precisions of 0.16–0.17 m. For the Blue/Colorado, mean errors were of the order of 0.2 m, biased shallow for optical data and biased deep for LiDAR, although no Aquarius laser returns were recorded from the deepest parts of these channels; precisions were reduced to 0.29–0.32 m. Both approaches have advantages and limitations, and prospective users must understand the capabilities and constraints associated with various types of remote sensing to ensure efficient use of these evolving technologies. Copyright © 2015 John Wiley & Sons, Ltd.

News Flow, Web Attention and Extreme Returns in the European Financial Crisis

We attempt a connection between three entities: Extreme Stock Market Returns, the Web Attention factor and a set of News Flow factors, for three groups of countries during the European Financial Crisis: the Euro-periphery countries, the Euro-core countries, and the major European Union -but not euro- countries. Using daily stock market data from January 2004 till March 2013 and textual analysis on more than 24,000 news articles from seven leading international news providers, we find that the Euro-periphery Web Attention (SVI) and News Flow variables significantly affect the probabilities of extreme bottom returns for the Euro-periphery, the Non-euro and the Euro-core groups. More Web Attention and more bad news for the Euro-periphery in times of crisis are associated with higher probabilities of extreme bottom returns within and across groups. Granger-causality tests exhibit primarily a two-way causality between the information variables and stock market returns during the financial crisis.

Surface scattering rejection for clear sidescan images.

Surface scattering is a major source of interference for sidescan sonar systems operating in high traffic areas or in choppy water. This surface scattering from wakes, as a result of boat traffic or from the chop of the sea surface, obscures the bottom return. A sonar system with a multi-element array can separate surface signals from bottom signals using beamforming, thereby creating clear images of the seafloor. This multi-element array can have as few as six elements and still effectively remove surface scattering. In this paper, different beamforming techniques are applied and their impact on suppressing surface returns is shown. Comparisons between beamforming methods are made by comparing the relative path levels with and without beamforming applied. The ability of a multi-element array to successfully discriminate between bottom returns and surface returns using beamforming is then shown using both simulated and experimental data. It is concluded that sonar systems employing a multi-element array can produce clear images of the seafloor even in the presence of strong surface interference when beamforming is used to create a beam that has a broad main lobe pointed toward the bottom and low sidelobes pointed toward the surface.

A Normal Mode Solution for Low-Frequency Bottom-Refracted Wave Propagation

A normal mode solution for low-frequency bottom-sediment-refracted wave propagation is formulated for use with a normal mode method of solving the Helmholtz wave equation to describe the underwater sound field for a fixed point source in a plane multilayered medium. The approach utilizes a factorization of the Wronskian of Green's function which enables sediment reflecting and refracting modes to be summed independently. The sensitivity of propagation loss to the nature of mode interaction with the bottom is investigated for multiple frequencies in a sample environment, and results are compared to measurements of 100-Hz bottom returns in the Caribbean Basin.

Bistatic scattering in sediments: comparison of model and scaled tank experiments at 238 kHz

Multiple-aspect scattering is increasingly used to investigate seabeds and objects, buried or not. However, high-frequency scattering processes on/in sediments need to be better understood, particularly when the structure and/or composition of these sediments is not fully homogeneous. Scaled tank experiments were conducted using the University of Bath facilities. A narrow acoustic beam (10° beamwidth) was generated by a transducer resonant at 238 kHz, imaging a silt seabed at 45°. Scattering angles varied between ̃16° and ̃70° (50 distinct values); bistatic angles varied 40° either side of in-plane with a 2.5° step (33 distinct values). Bottom returns were picked through two methods (automatic and manual) and converted into scattering strengths, accounting for the size of the scattering patch. This large dataset has been compared with the model of Jackson and Williams for bistatic scattering, intended for 10-100 kHz but successfully tested at 240 kHz by other workers. Recursive fitting of model parameters to the experimental values shows the influence of sediment variations (backed by microscopic measurements of actual grain size distributions) and the importance of even small tilts in the surface of the sediments. These results can be used to test the validity of extending this scattering model to higher frequencies.

Acoustic seafloor discrimination with echo shape parameters: A comparison with the ground truth

Features extracted from echosounder bottom returns are compared with the ground truth in a North Sea survey area. The ground truth consists of 50 grab samples for which the grain size distribution, and the gravel and shell contents were determined. Echo envelopes are analysed for two single-beam echosounders, operated at frequencies of 66 and 150 kHz. It is shown that a set of six energetic, statistical, spectral and fractal parameters carries useful information that can be exploited for seafloor characterization and classification purposes. A quantitative comparison of the individual features with the grab sample mean grain size reveals significant correlations. The echo features are also subjected to a principal component analysis in tandem with a cluster analysis. Four sediment classes with different geo-acoustic properties are examined and compared with the grab samples and existing geological information. A subtle difference between the two sounder frequencies is observed in the rendition of an isolated trench with a soft infill of clay and Holocene channel deposits. The acoustic transition from the valley to neighbouring sand and gravel fields occurs more rapidly at the lower of the two examined frequencies. A direct comparison of the acoustic sediment classes with the ground truth reveals that the main sediment types mud, sand, and gravel are more clearly separated at 150 kHz. The acoustic bottom classification scheme also appears particularly sensitive to the presence of gravel at this sounder frequency.

Remote sensing of sediment characteristics by optimized echo-envelope matching.

A sediment geoacoustic parameter estimation technique is described which compares bottom returns, measured by a calibrated monostatic sonar oriented within 15 degrees of vertical and having a 10 degree-21 degree beamwidth, with an echo envelope model based on high-frequency (10-100 kHz) incoherent backscatter theory and sediment properties such as: mean grain size, strength, and exponent of the power law characterizing the interface roughness energy density spectrum, and volume scattering coefficient. An average echo envelope matching procedure iterates on the reflection coefficient to match the peak echo amplitude and separate coarse from fine-grain sediments, followed by a global optimization using a combination of simulated annealing and downhill simplex searches over mean grain size, interface roughness spectral strength, and sediment volume scattering coefficient. Error analyses using Monte Carlo simulations validate this optimization procedure. Moderate frequencies (33 kHz) and orientations normal with the interface are best suited for this application. Distinction between sands and fine-grain sediments is demonstrated based on acoustic estimation of mean grain size alone. The creation of feature vectors from estimates of mean grain size and interface roughness spectral strength shows promise for intraclass separation of silt and clay. The correlation between estimated parameters is consistent with what is observed in situ.

Monitoring coastal northern cod: towards an optimal survey of Smith Sound, Newfoundland

Abstract The extant, coastal northern cod (Gadus morhua) have over-wintered and spawned in Smith Sound, Newfoundland, since 1995, and acoustic surveys have been conducted in several seasons since then. Cod move into the Sound in late fall, over-winter in a dense, size- and age-structured aggregation, spawn between late March and early June and then disperse into and beyond Trinity Bay during summer to feed. The optimal survey time for biomass estimation is January–February, when the waters are ice-free and the cod are in mono-specific, relatively stationary, and well-defined aggregations with the highest densities and are typically clear of “bottom returns”. Biomass surveys have been conducted in mid-January since 1999. An error analysis indicated the main sources of uncertainty to be density variability and target strength (TS). Repeated quasi-synoptic (10 h) surveys were the optimal means of producing an estimate of uncertainty about population size. Some vertical movement led to night-time surveys consistently having higher estimates than day-time surveys by approximately 15%. Detectability ranged from 73 to 86% and deadzone-corrected, acoustic measures did not differ from swept-area densities found by bottom trawling. Biomass scaling by TS used length-dependant dB/kg to reduce the size-sampling error. Overall, population biomass doubled in approximately 7 years, consistent with a rate of increase around 0.2, largely through recruitment. The surveys are internally consistent and indicate instantaneous rates of mortality among year classes of 0.3 to >2 (very high on older fish) and provide a method for monitoring the annual biomass (cv<40%).