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Automatic Classification of Biological Targets in a Tidal Channel Using a Multibeam SonarAbstract
Multibeam sonars are widely used for environmental monitoring of fauna at marine renewable energy sites. However, they can rapidly accrue vast volumes of data, which poses a challenge for data processing. Here, using data from a deployment in a tidal channel with peak currents of 1–2 m s−1, we demonstrate the data-reduction benefits of real-time automatic classification of targets detected and tracked in multibeam sonar data. First, we evaluate classification capabilities for three machine learning algorithms: random forests, support vector machines, and k-nearest neighbors. For each algorithm, a hill-climbing search optimizes a set of hand-engineered attributes that describe tracked targets. The random forest algorithm is found to be most effective—in postprocessing, discriminating between biological and nonbiological targets with a recall rate of 0.97 and a precision of 0.60. In addition, 89% of biological targets are correctly classified as either seals, diving birds, fish schools, or small targets. Model dependence on the volume of training data is evaluated. Second, a real-time implementation of the model is shown to distinguish between biological targets and nonbiological targets with nearly the same performance as in postprocessing. From this, we make general recommendations for implementing real-time classification of biological targets in multibeam sonar data and the transferability of trained models.
Abstract
Multibeam sonars are widely used for environmental monitoring of fauna at marine renewable energy sites. However, they can rapidly accrue vast volumes of data, which poses a challenge for data processing. Here, using data from a deployment in a tidal channel with peak currents of 1–2 m s−1, we demonstrate the data-reduction benefits of real-time automatic classification of targets detected and tracked in multibeam sonar data. First, we evaluate classification capabilities for three machine learning algorithms: random forests, support vector machines, and k-nearest neighbors. For each algorithm, a hill-climbing search optimizes a set of hand-engineered attributes that describe tracked targets. The random forest algorithm is found to be most effective—in postprocessing, discriminating between biological and nonbiological targets with a recall rate of 0.97 and a precision of 0.60. In addition, 89% of biological targets are correctly classified as either seals, diving birds, fish schools, or small targets. Model dependence on the volume of training data is evaluated. Second, a real-time implementation of the model is shown to distinguish between biological targets and nonbiological targets with nearly the same performance as in postprocessing. From this, we make general recommendations for implementing real-time classification of biological targets in multibeam sonar data and the transferability of trained models.
Comparative Evaluation of Volumetric Current Measurements in a Tidally Dominated Coastal Setting: A Virtual Field ExperimentAbstract
High-resolution, four-dimensional mapping of currents in tidally dominated coastal settings can be conducted with a range of instrumentation. Here, we assess four approaches to data collection: an X-band radar, a stationary (bottom mounted) acoustic Doppler current profiler (ADCP), a mobile (vessel based) ADCP, and a swarm of Lagrangian floats. Using the output from a hydrodynamic simulation, a virtual field campaign was performed at 24 locations in Admiralty Inlet, Puget Sound, Washington, during spring and neap tidal exchanges. A reconstruction of the volumetric currents was generated for each platform every 15 min and evaluated against the true currents to assess accuracy over a horizontal extent of 400 m × 500 m at 5 m resolution and vertically through the entire water column (20–80 m) at 2 m resolution. Results demonstrate that, for this survey extent and resolution, a vessel-based ADCP survey is most accurate, followed closely by the float swarm. The overall performance hierarchy persists over most locations and times. Thus, if mapping currents at high resolution (<10 m) and short time scales (<1 day) is the primary scientific objective, vessel-based ADCP surveys are likely the best option. For longer-duration surveys, a combined deployment with a stationary ADCP and X-band radar system is the best choice. Last, if in situ measurements of scalar properties (e.g., salinity, temperature, dissolved oxygen) are also desired, float swarms can simultaneously sample these while surveying currents with accuracy comparable to mobile ADCPs.
Abstract
High-resolution, four-dimensional mapping of currents in tidally dominated coastal settings can be conducted with a range of instrumentation. Here, we assess four approaches to data collection: an X-band radar, a stationary (bottom mounted) acoustic Doppler current profiler (ADCP), a mobile (vessel based) ADCP, and a swarm of Lagrangian floats. Using the output from a hydrodynamic simulation, a virtual field campaign was performed at 24 locations in Admiralty Inlet, Puget Sound, Washington, during spring and neap tidal exchanges. A reconstruction of the volumetric currents was generated for each platform every 15 min and evaluated against the true currents to assess accuracy over a horizontal extent of 400 m × 500 m at 5 m resolution and vertically through the entire water column (20–80 m) at 2 m resolution. Results demonstrate that, for this survey extent and resolution, a vessel-based ADCP survey is most accurate, followed closely by the float swarm. The overall performance hierarchy persists over most locations and times. Thus, if mapping currents at high resolution (<10 m) and short time scales (<1 day) is the primary scientific objective, vessel-based ADCP surveys are likely the best option. For longer-duration surveys, a combined deployment with a stationary ADCP and X-band radar system is the best choice. Last, if in situ measurements of scalar properties (e.g., salinity, temperature, dissolved oxygen) are also desired, float swarms can simultaneously sample these while surveying currents with accuracy comparable to mobile ADCPs.
Experimental Validation of Float Array Tidal Current Measurements in Agate Pass, WashingtonAbstract
Tidal currents, particularly in narrow channels, can be challenging to characterize due to high current speeds (>1 m s−1), strong spatial gradients, and relatively short synoptic windows. To assess tidal currents in Agate Pass, Washington, we cross evaluated data products from an array of acoustically tracked underwater floats and from acoustic Doppler current profilers (ADCPs) in both station-keeping and drifting modes. While increasingly used in basin-scale science, underwater floats have seen limited use in coastal environments. This study presents the first application of a float array toward small-scale (<1 km), high-resolution (<5 m) measurements of mean currents in energetic tidal channel and utilizes a new prototype float, the μFloat, designed specifically for sampling in dynamic coastal waters. We show that a float array (20 floats) can provide data with similar quality to ADCPs, with measurements of horizontal velocity differing by less than 10% of nominal velocity, except during periods of low flow (0.1 m s−1). Additionally, floats provided measurements of the three-dimensional temperature field, demonstrating their unique ability to simultaneously resolve in situ properties that cannot be remotely observed.
Significance Statement
The purpose of this research was to validate measurements of tidal currents in a fast-flowing tidal channel using a prototype technology composed of 20 drifting underwater sensors called μFloats (“microFloats”) and five surface buoys against standard devices (acoustic Doppler current profilers). Float measurements matched those from the standard devices within 10% of the mean water speed and simultaneously provided three-dimensional mapping of temperature in the test region. Results demonstrate how moderate numbers of simultaneously deployed μFloats can provide high-resolution sensing capacity that will improve our understanding of physical, chemical, and biological processes in coastal waters.
Abstract
Tidal currents, particularly in narrow channels, can be challenging to characterize due to high current speeds (>1 m s−1), strong spatial gradients, and relatively short synoptic windows. To assess tidal currents in Agate Pass, Washington, we cross evaluated data products from an array of acoustically tracked underwater floats and from acoustic Doppler current profilers (ADCPs) in both station-keeping and drifting modes. While increasingly used in basin-scale science, underwater floats have seen limited use in coastal environments. This study presents the first application of a float array toward small-scale (<1 km), high-resolution (<5 m) measurements of mean currents in energetic tidal channel and utilizes a new prototype float, the μFloat, designed specifically for sampling in dynamic coastal waters. We show that a float array (20 floats) can provide data with similar quality to ADCPs, with measurements of horizontal velocity differing by less than 10% of nominal velocity, except during periods of low flow (0.1 m s−1). Additionally, floats provided measurements of the three-dimensional temperature field, demonstrating their unique ability to simultaneously resolve in situ properties that cannot be remotely observed.
Significance Statement
The purpose of this research was to validate measurements of tidal currents in a fast-flowing tidal channel using a prototype technology composed of 20 drifting underwater sensors called μFloats (“microFloats”) and five surface buoys against standard devices (acoustic Doppler current profilers). Float measurements matched those from the standard devices within 10% of the mean water speed and simultaneously provided three-dimensional mapping of temperature in the test region. Results demonstrate how moderate numbers of simultaneously deployed μFloats can provide high-resolution sensing capacity that will improve our understanding of physical, chemical, and biological processes in coastal waters.
