Methods in Oceanography最新文献

We present a method to automatically measure fish from images taken using a stereo-camera system installed in a large trawl (CamTrawl). Different visibility and fish density conditions were evaluated to establish accuracy and precision of image-based length estimates when compared with physical length measurements. The automated image-based length estimates compared well with the trawl catch values and were comparable with manual image processing in good visibility conditions. Greatest agreement with trawl catch occurred when fish were within $20^{\circ }$ of fully lateral presentation to the cameras, and within 150 cm of the cameras. High turbidity caused substantial over- and underestimates of length composition, and a greater number of incompletely extracted fish outlines. Multiple estimates of individual fish lengths showed a mean coefficient of variation (CV) of 3% in good visibility conditions. The agreement between manual and automated fish measurement estimates was not correlated with fish length or range from the camera ($r^2=0\text{–}0.08$). Implementation of these methods can result in a large increase in survey efficiency, given the effort required to process the trawl catch.

Conventional ship-mounted vertically-oriented echosounders are poor at detecting organisms that are close to the sea surface. In contrast, omni-directional sonars can ensonify these near-surface waters unavailable to hull-mounted echosounders. If calibrated, sonars can provide quantitative biomass estimates of pelagic aggregations. However, for sonars that have not been designed as scientific and research instruments, the quantification and verification of the system performance is of heightened importance, and should include how parameters such as the shape and gain of the beams vary with system and operational configurations. We present a practical methodology for absolute calibration of omni-directional sonars when conventionally mounted on a vessel, illustrate the achievable calibration accuracies and precision, and document their variability over time and for a range of operating parameters. This work forms an essential prerequisite to the use of such sonars for quantitative measurement of backscatter, such as for echo-integration surveys and individual school density and biomass estimation.

This paper presents an underwater active stereo system that realizes 3D capture of dynamic objects in water such as swimming fish. The key idea on realizing a practical underwater 3D sensing is to model the refraction process by our pixel-wise varifocal camera model that provides efficient forward (3D to 2D) projections as well as an underwater projector–camera calibration. Evaluations demonstrate that our method achieves reasonable calibration accuracy using off-the-shelf cameras and projectors, and provides a 3D capture of real swimming fish in water.

Sonars and echosounders are widely used for remote sensing of life in the marine environment. There is an ongoing need to make the acoustic identification of marine species more correct and objective and thereby reduce the uncertainty of acoustic abundance estimates. In our work, data from multi-frequency echosounders working simultaneously with nearly identical and overlapping acoustic beams are processed stepwise in a modular sequence to improve data, detect schools and categorize acoustic targets by means of the Large Scale Survey System software (LSSS). Categorization is based on the use of an acoustic feature library whose main components are the relative frequency responses. The results of the categorization are translated into acoustic abundance of species. The method is tested on acoustic data from the Barents Sea, the Norwegian Sea and the North Sea, where the target species were capelin (Mallotus villosus L.), Atlantic mackerel (Scomber scombrus L.) and sandeel (Ammodytes marinus L.), respectively. Manual categorization showed a high conformity with automatic categorization for all surveys, especially for schools.

In an effort to more fully employ underutilized satellite observations in ocean modeling, this work demonstrates a method for quantifying the agreement between time-evolving spatial features evident in fields of differing, but functionally related, variables that are more commonly compared qualitatively via visual inspection. This is achieved through application of the Modified Hausdorff Distance metric to the evaluation of ocean model simulations of surface salinity near riverine sources using satellite ocean color data. The Modified Hausdorff Distance is a metric from the field of topology designed to compare shapes and the methodology provides quantitative assessment of similarity of spatial fields. The Modified Hausdorff Distance can be applied for comparison of many geophysical and ecological fields that vary spatially and temporally. Here, the utility of the metric is demonstrated by applying it to evaluate numerical simulations of the time-evolving spatial structure of the surface salinity fields from three ocean models in the vicinity of large riverine sources in the northeast Gulf of Mexico. Using the Modified Hausdorff Distance, quantitative comparison of modeled sea surface salinity contours to contours of a gridded satellite-derived ocean color product is made under the assumption that the modeled fields are related to optically significant quantities that indicate the spatial extent of riverine influenced water. Three different ocean models are evaluated and are compared individually to the satellite data. The sea surface salinity values and ocean color index values that most closely match (lowest Modified Hausdorff Distance score) are identified for each model. The Modified Hausdorff Distance scores for these best pairings are used to both determine the degree to which surface salinity fields from the models match the satellite observations and obtain an empirical relationship between the two variables for each model. Furthermore, the best pairings are compared between models allowing key differences in the simulated riverine water distributions to be distinguished. The Modified Hausdorff Distance proves a robust and useful diagnostic tool that has the potential to be utilized in many geophysical applications and facilitates the use of satellite ocean color data for quantitative evaluation of hydrodynamic ocean models.

Fast responding FP-07 thermistors have been incorporated on autonomous profiling EM-APEX floats to measure microscale ocean temperature fluctuations produced by turbulence. In this implementation, the FP-07 thermistor generates an electrical signal corresponding to ocean temperature fluctuations, which is conditioned by an analog circuit board, and digitized and recorded on a custom data acquisition and storage board. The raw and processed temperature observations are stored on a microSD card. Results from eight microstructure EM-APEX floats deployed in the Sargasso Sea are presented here. The slow profiling speed of EM-APEX floats enables them to resolve the higher wavenumber regime of the microscale temperature gradient spectrum, beyond the roll-off wavenumber. The temperature variance dissipation rate $\chi$ is computed directly by integrating the observed temperature gradient spectrum over the turbulence wavenumber region without the need to fit the observed temperature gradient spectrum to the empirical spectral form. The accuracy of $\chi$ estimated from microstructure EM-APEX floats is confirmed by the agreement, within a factor of 2, between the temperature diffusivity $K_T$ computed from our estimates of $\chi$ and estimates of diapcynal diffusivity computed from simultaneous tracer measurements. The observed temperature gradient spectra averaged over many realizations resemble the Batchelor spectral form, though individual spectra often do not fit the empirical prediction. Estimates of $\chi$ from different floats have similar temporal fluctuations and vertical profiles, further supporting measurement quality. Estimates of $\chi$ exhibit a lognormal distribution, as expected for statistically homogeneous isotropic turbulence. Turbulence measurements derived from FP-07 sensors on autonomous profiling floats are of comparable quality to those on conventional free-fall microstructure profilers.

Development of acoustic techniques for the remote sensing of ocean velocity has been ongoing at Scripps for the past 42 years. While many scientifically productive systems have been created, there have also been technical surprises and dead-ends. Here the development of both pulse-to-pulse coherent and incoherent backscatter systems at SIO is reviewed, with an emphasis on the real-world problems encountered.

Optical methods for surveying populations are becoming increasingly popular. These methods often produce hundreds of thousands to millions of images, making it impractical to analyze all the images manually by human annotators. Computer vision software can rapidly annotate these images, but their error rates are often substantial, vary spatially and are autocorrelated. Hence, population estimates based on the raw computer automated counts can be seriously biased. We evaluated four estimators that combine automated annotations of all the images with manual annotations from a random sample to obtain (approximately) unbiased population estimates, namely: ratio, offset, and linear regression estimators as well as the mean of the manual annotations only. Each of these estimators was applied either globally or locally (i.e., either all data were used or only those near the point in question, to take into account spatial variability and autocorrelation in error rates). We also investigated a simple stratification scheme that splits the images into two strata, based on whether the automated annotator detected no targets or at least one target. The 16 methods resulting from a combination of four estimators, global or local estimation, and one stratum or two strata, were evaluated using simulations and field data. Our results indicated that the probability of a false negative is the key factor determining the best method, regardless of the probability of false positives. Stratification was the most effective method in improving the accuracy and precision of the estimates, provided the false negative rate was not too high. If the probability of false negatives is low, stratified estimation with the local ratio estimator or local regression (essentially geographically weighted regression) is best. If the probability of false negatives is high, no stratification with a simple global linear regression or simply the manual sample mean alone is recommended.