Research

Jump to: Other

1803 KiB

Open AccessArticle

Vocalization Source Level Distributions and Pulse Compression Gains of Diverse Baleen Whale Species in the Gulf of Maine

by Delin Wang, Wei Huang, Heriberto Garcia and Purnima Ratilal

Remote Sens. 2016, 8(11), 881; https://doi.org/10.3390/rs8110881 - 25 Oct 2016

Cited by 21 | Viewed by 5432

Abstract

The vocalization source level distributions and pulse compression gains are estimated for four distinct baleen whale species in the Gulf of Maine: fin, sei, minke and an unidentified baleen whale species. The vocalizations were received on a large-aperture densely-sampled coherent hydrophone array system [...] Read more.

The vocalization source level distributions and pulse compression gains are estimated for four distinct baleen whale species in the Gulf of Maine: fin, sei, minke and an unidentified baleen whale species. The vocalizations were received on a large-aperture densely-sampled coherent hydrophone array system useful for monitoring marine mammals over instantaneous wide areas via the passive ocean acoustic waveguide remote sensing technique. For each baleen whale species, between 125 and over 1400 measured vocalizations with significantly high Signal-to-Noise Ratios (SNR > 10 dB) after coherent beamforming and localized with high accuracies (<10% localization errors) over ranges spanning roughly 1 km–30 km are included in the analysis. The whale vocalization received pressure levels are corrected for broadband transmission losses modeled using a calibrated parabolic equation-based acoustic propagation model for a random range-dependent ocean waveguide. The whale vocalization source level distributions are characterized by the following means and standard deviations, in units of dB re 1

μ

Pa at 1 m: 181.9 ± 5.2 for fin whale 20-Hz pulses, 173.5 ± 3.2 for sei whale downsweep chirps, 177.7 ± 5.4 for minke whale pulse trains and 169.6 ± 3.5 for the unidentified baleen whale species downsweep calls. The broadband vocalization equivalent pulse-compression gains are found to be 2.5 ± 1.1 for fin whale 20-Hz pulses, 24 ± 10 for the unidentified baleen whale species downsweep calls and 69 ± 23 for sei whale downsweep chirps. These pulse compression gains are found to be roughly proportional to the inter-pulse intervals of the vocalizations, which are 11 ± 5 s for fin whale 20-Hz pulses, 29 ± 18 for the unidentified baleen whale species downsweep calls and 52 ± 33 for sei whale downsweep chirps. The source level distributions and pulse compression gains are essential for determining signal-to-noise ratios and hence detection regions for baleen whale vocalizations received passively on underwater acoustic sensing systems, as well as for assessing communication ranges in baleen whales. Full article

(This article belongs to the Special Issue Underwater Acoustic Remote Sensing)

► Show Figures

Graphical abstract

10938 KiB

Open AccessArticle

Fine-Scale Sea Ice Structure Characterized Using Underwater Acoustic Methods

by Vanessa Lucieer, Amy W. Nau, Alexander L. Forrest and Ian Hawes

Remote Sens. 2016, 8(10), 821; https://doi.org/10.3390/rs8100821 - 05 Oct 2016

Cited by 9 | Viewed by 7693

Abstract

Antarctic sea ice is known to provide unique ecosystem habitat at the ice–ocean interface. Mapping sea ice characteristics—such as thickness and roughness—at high resolution from beneath the ice is difficult due to access. A Geoswath Plus phase-measuring bathymetric sonar mounted on an autonomous [...] Read more.

Antarctic sea ice is known to provide unique ecosystem habitat at the ice–ocean interface. Mapping sea ice characteristics—such as thickness and roughness—at high resolution from beneath the ice is difficult due to access. A Geoswath Plus phase-measuring bathymetric sonar mounted on an autonomous underwater vehicle (AUV) was employed in this study to collect data underneath the sea ice at Cape Evans in Antarctica in November 2014. This study demonstrates how acoustic data can be collected and processed to resolutions of 1 m for acoustic bathymetry and 5 cm for acoustic backscatter in this challenging environment. Different ice textures such as platelet ice, smooth ice, and sea ice morphologies, ranging in size from 1 to 50 m were characterized. The acoustic techniques developed in this work could provide a key to understanding the distribution of sea ice communities, as they are nondisruptive to the fragile ice environments and provide geolocated data over large spatial extents. These results improve our understanding of sea ice properties and the complex, highly variable ecosystem that exists at this boundary. Full article

(This article belongs to the Special Issue Underwater Acoustic Remote Sensing)

► Show Figures

Graphical abstract

4701 KiB

Open AccessArticle

Diel and Spatial Dependence of Humpback Song and Non-Song Vocalizations in Fish Spawning Ground

by Wei Huang, Delin Wang and Purnima Ratilal

Remote Sens. 2016, 8(9), 712; https://doi.org/10.3390/rs8090712 - 30 Aug 2016

Cited by 16 | Viewed by 5916

Abstract

The vocalization behavior of humpback whales was monitored over vast areas of the Gulf of Maine using the passive ocean acoustic waveguide remote sensing technique (POAWRS) over multiple diel cycles in Fall 2006. The humpback vocalizations comprised of both song and non-song are [...] Read more.

The vocalization behavior of humpback whales was monitored over vast areas of the Gulf of Maine using the passive ocean acoustic waveguide remote sensing technique (POAWRS) over multiple diel cycles in Fall 2006. The humpback vocalizations comprised of both song and non-song are analyzed. The song vocalizations, composed of highly structured and repeatable set of phrases, are characterized by inter-pulse intervals of 3.5 ± 1.8 s. Songs were detected throughout the diel cycle, occuring roughly 40% during the day and 60% during the night. The humpback non-song vocalizations, dominated by shorter duration (≤3 s) downsweep and bow-shaped moans, as well as a small fraction of longer duration (∼5 s) cries, have significantly larger mean and more variable inter-pulse intervals of 14.2 ± 11 s. The non-song vocalizations were detected at night with negligible detections during the day, implying they probably function as nighttime communication signals. The humpback song and non-song vocalizations are separately localized using the moving array triangulation and array invariant techniques. The humpback song and non-song moan calls are both consistently localized to a dense area on northeastern Georges Bank and a less dense region extended from Franklin Basin to the Great South Channel. Humpback cries occur exclusively on northeastern Georges Bank and during nights with coincident dense Atlantic herring shoaling populations, implying the cries are feeding-related. Full article

(This article belongs to the Special Issue Underwater Acoustic Remote Sensing)

► Show Figures

Graphical abstract

3382 KiB

Open AccessArticle

Maximum Likelihood Deconvolution of Beamformed Images with Signal-Dependent Speckle Fluctuations from Gaussian Random Fields: With Application to Ocean Acoustic Waveguide Remote Sensing (OAWRS)

by Ankita D. Jain and Nicholas C. Makris

Remote Sens. 2016, 8(9), 694; https://doi.org/10.3390/rs8090694 - 23 Aug 2016

Cited by 7 | Viewed by 5461

Abstract

Wide area acoustic remote sensing often involves the use of coherent receiver arrays to determine the spatial distribution of sources and scatterers at any instant. The resulting acoustic intensity images are typically corrupted by signal-dependent noise from Gaussian random field fluctuations arising from [...] Read more.

Wide area acoustic remote sensing often involves the use of coherent receiver arrays to determine the spatial distribution of sources and scatterers at any instant. The resulting acoustic intensity images are typically corrupted by signal-dependent noise from Gaussian random field fluctuations arising from the central limit theorem and have a spatial resolution that depends on the incident direction, sensing array aperture and wavelength. Here, we use the maximum likelihood method to deconvolve the intensity distribution measured on a coherent line array assuming a discrete angular distribution of incident plane waves. Instantaneous wide area population density images of fish aggregations measured with Ocean Acoustic Waveguide Remote Sensing (OAWRS) are deconvolved to illustrate the effectiveness of this approach in improving angular resolution over conventional planewave beamforming. Full article

(This article belongs to the Special Issue Underwater Acoustic Remote Sensing)

► Show Figures

Graphical abstract

1647 KiB

Open AccessArticle

Feasibility of Acoustic Remote Sensing of Large Herring Shoals and Seafloor by Baleen Whales

by Dong Hoon Yi and Nicholas C. Makris

Remote Sens. 2016, 8(9), 693; https://doi.org/10.3390/rs8090693 - 23 Aug 2016

Cited by 10 | Viewed by 5338

Abstract

Recent research has found a high spatial and temporal correlation between certain baleen whale vocalizations and peak herring spawning processes in the Gulf of Maine. These vocalizations are apparently related to feeding activities with suggested functions that include communication, prey manipulation, and echolocation. [...] Read more.

Recent research has found a high spatial and temporal correlation between certain baleen whale vocalizations and peak herring spawning processes in the Gulf of Maine. These vocalizations are apparently related to feeding activities with suggested functions that include communication, prey manipulation, and echolocation. Here, the feasibility of the echolocation function is investigated. Physical limitations on the ability to detect large herring shoals and the seafloor by acoustic remote sensing are determined with ocean acoustic propagation, scattering, and statistical theories given baleen whale auditory parameters. Detection is found to be highly dependent on ambient noise conditions, herring shoal distributions, baleen whale time-frequency vocalization spectra, and geophysical parameters of the ocean waveguide. Detections of large herring shoals are found to be physically feasible in common Gulf of Maine herring spawning scenarios at up to 10 ± 6 km in range for humpback parameters and 1 ± 1 km for minke parameters but not for blue and fin parameters even at zero horizontal range. Detections of the seafloor are found to be feasible up to 2 ± 1 km for blue and humpback parameters and roughly 1 km for fin and minke parameters, suggesting that the whales share a common acoustic sensation of rudimentary features of the geophysical environment. Full article

(This article belongs to the Special Issue Underwater Acoustic Remote Sensing)

► Show Figures

Graphical abstract

12616 KiB

Open AccessArticle

Noise Localization Method for Model Tests in a Large Cavitation Tunnel Using a Hydrophone Array

by Cheolsoo Park, Gun-Do Kim, Young-Ha Park, Keunhwa Lee and Woojae Seong

Remote Sens. 2016, 8(3), 195; https://doi.org/10.3390/rs8030195 - 27 Feb 2016

Cited by 16 | Viewed by 8024

Abstract

Model tests are performed in order to predict the noise level of a full ship and to control its noise signature. Localizing noise sources in the model test is therefore an important research subject along with measuring noise levels. In this paper, a [...] Read more.

Model tests are performed in order to predict the noise level of a full ship and to control its noise signature. Localizing noise sources in the model test is therefore an important research subject along with measuring noise levels. In this paper, a noise localization method using a hydrophone array in a large cavitation tunnel is presented. The 45-channel hydrophone array was designed using a global optimization technique for noise measurement. A set of noise experiments was performed in the KRISO (Korea Research Institute of Ships & Ocean Engineering) large cavitation tunnel using scaled models, including a ship with a single propeller, a ship with twin propellers and an underwater vehicle. The incoherent broadband processors defined based on the Bartlett and the minimum variance (MV) processors were applied to the measured data. The results of data analysis and localization are presented in the paper. Finally, it is shown that the mechanical noise, as well as the propeller noise can be successfully localized using the proposed localization method. Full article

(This article belongs to the Special Issue Underwater Acoustic Remote Sensing)

► Show Figures

Graphical abstract

5123 KiB

Open AccessArticle

Real-Time Classification of Seagrass Meadows on Flat Bottom with Bathymetric Data Measured by a Narrow Multibeam Sonar System

by Masahiro Hamana and Teruhisa Komatsu

Remote Sens. 2016, 8(2), 96; https://doi.org/10.3390/rs8020096 - 27 Jan 2016

Cited by 15 | Viewed by 7592

Abstract

Seagrass meadows, one of the most important habitats for many marine species, provide essential ecological services. Thus, society must conserve seagrass beds as part of their sustainable development efforts. Conserving these ecosystems requires information on seagrass distribution and relative abundance, and an efficient, [...] Read more.

Seagrass meadows, one of the most important habitats for many marine species, provide essential ecological services. Thus, society must conserve seagrass beds as part of their sustainable development efforts. Conserving these ecosystems requires information on seagrass distribution and relative abundance, and an efficient, accurate monitoring system. Although narrow multibeam sonar systems (NMBSs) are highly effective in resolving seagrass beds, post-processing methods are required to extract key data. The purpose of this study was to develop a simple method capable of detecting seagrass meadows and estimating their relative abundance in real time using an NMBS. Because most seagrass meadows grow on sandy seafloors, we proposed a way of discriminating seagrass meadows from the sand bed. We classify meadows into three categories of relative seagrass abundance using the 95% confidence level of beam depths and the depth range of the beam depth. These are respectively two times the standard deviation of beam depths, and the difference between the shallowest and the deepest depths in a 0.5 × 0.5 m grid cell sampled with several narrow beams. We examined Zostera caulescens Miki, but this simple NMBS method of seagrass classification can potentially be used to map seagrass meadows with longer shoots of other species, such as Posidonia, as both have gas filled cavities. Full article

(This article belongs to the Special Issue Underwater Acoustic Remote Sensing)

► Show Figures

Graphical abstract

Other

Jump to: Research

1021 KiB

Open AccessTechnical Note

Seafloor Sediment Study from South China Sea: Acoustic & Physical Property Relationship

by Zhengyu Hou, Changsheng Guo, Jingqiang Wang, Wenjing Chen, Yongtao Fu and Tiegang Li

Remote Sens. 2015, 7(9), 11570-11585; https://doi.org/10.3390/rs70911570 - 10 Sep 2015

Cited by 18 | Viewed by 6049

Abstract

Seafloor sediments of different geographical areas in the southern South China Sea (continental shelf, continental slope, and Okinawa Trough) were gravity cored at 21 locations. Sound velocities (V) of the samples were measured at 15-cm increments immediately upon retrieval, and porosity, wet bulk [...] Read more.

Seafloor sediments of different geographical areas in the southern South China Sea (continental shelf, continental slope, and Okinawa Trough) were gravity cored at 21 locations. Sound velocities (V) of the samples were measured at 15-cm increments immediately upon retrieval, and porosity, wet bulk density, and mean grain size were measured later in the laboratory. Empirical equations from previous studies were applied to predict V of sediment samples from the measured physical properties and it was found that the sound velocities derived from the existing equations did not closely match the measured sound velocities. Therefore empirical equations were reconstructed based on the measured data that represent the relationships between physical and acoustic properties of the different geographical area in the study area. Possible explanations for the discrepancies between the measured data and those of previous studies were investigated and found that physical properties, sediment types, geographical area, etc. are important factors that influence sound velocity. The empirical equations of this report should be preferred for prediction of sediment sound velocity for high-frequency acoustic experiments. Full article

(This article belongs to the Special Issue Underwater Acoustic Remote Sensing)

► Show Figures

Graphical abstract

Journal Menu

Journal Browser

Underwater Acoustic Remote Sensing

Share This Special Issue

Special Issue Editor

Special Issue Information

Published Papers (8 papers)

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI