Results 201 to 210 of about 23,689 (244)

Tere Tohorā, Karanga Tāngata: Weaving Māori Knowledge With Conventional Science to Characterise a Biodiversity Hotspot for Marine Megafauna in an Area Facing Multiple Anthropogenic Impacts. [PDF]

Ecol Evol
Brough T, Kereopa H, Shirkey T, Zaeschmar J, Leunissen E, Milner D, Chetham J.   +6 more
europepmc   +1 more source

Sanctuary for vulnerable Arctic species at the Borealis Mud Volcano. [PDF]

Nat Commun
Panieri G, Argentino C, Savini A, Ferré B, Hemmateenejad F, Eilertsen MH, Mattingsdal R, Ramalho SP, Eidvin T, Youngs S, Colson BC, Michel APM, Kapit JA, Swanborn D, Rogers AD, Angeles IB, Polteau S, Kalenitchenko D, Buenz S, Mazzini A.   +19 more
europepmc   +1 more source

Habitat selection and influence on hunting success in female Australian fur seals. [PDF]

Sci Rep
Bartes SN, Monk J, Jenkins C, Hindell MA, Costa DP, Arnould JPY.   +5 more
europepmc   +1 more source

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Fuzzy clustering for seafloor classification

Marine Geology, 2009
In order to develop quantitative seafloor sediment classification techniques it is important to acknowledge that by nature the boundaries between soft sediments are characterized by transition zones and therefore are indeterminate and gradual. A fuzzy clustering method, fuzzy c-means (FCM), was used to identify these transition zones within a subset of
V. Lucieer, A. Lucieer
openaire   +1 more source

Seafloor Classification With Neural Networks

Conference Proceedings on Engineering in the Ocean Environment, 2005
A seafloor classification methodology, based on a parametrization of the reverberation probability den­ sity function in conjunction with neural net classifiers, is evaluated through computer simulations. Different seafloor "provinces" are represented by a number of scatterer distributions exhibiting various degrees of de­ parture from the nominal ...
D. Alexandrou, D. Pantzartzis
openaire   +1 more source

Using multibeam echoes in seafloor classification

OCEANS 2009-EUROPE, 2009
The method of seabed identification and classification from multibeam sonar echoes is presented. The proposed approach is based on calculation of a set of parameters of an echo envelope, similarly as in seafloor classification using single beam echosounder.
Z. Lubniewski, A. Chybicki
openaire   +1 more source

A methodology for acoustic seafloor classification

IEEE Journal of Oceanic Engineering, 1993
A seafloor classification methodology, based on a parameterization of the reverberation probability density function in conjunction with neural network classifiers, is evaluated through computer simulations. Different seafloor provides are represented by a number of scatterer distributions exhibiting various degrees of departure from the nominal ...
D. Alexandrou, D. Pantzartzis
openaire   +1 more source

Broadband Seismic Data used for Seafloor Sediment Classification

OCEANS '78, 1978
The echo returns from a broadband deep towed seismic system are examined. It is shown to be possible to separate four different sediment types based on two metrics. These are i) the maximum value of the normalized cross-correlation function and ii) the normalized water-sediment interface energy.
A. Dunsiger, R. MacIsaac
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Automated and Integrated Seafloor Classification Workflow (AI-SCW)

2023
The Automated and Integrated Seafloor Classification Workflow (AI-SCW) is a semi-automated underwater image processing pipeline that has been customized for use in classifying the seafloor into semantic habitat categories. The current implementation has been tested against a sequence of underwater images collected by the Ocean Floor Observation System (
Benson Mbani, Timm Schoening, Jens Greinert   +2 more
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Lassoo!: an interactive graphical tool for seafloor classification

OCEANS 96 MTS/IEEE Conference Proceedings. The Coastal Ocean - Prospects for the 21st Century, 2002
Lassoo! is an interactive graphical tool that facilitates the empirical classification of seafloor materials. Lassoo! can: (1) input a multivariate geo-referenced data set (e.g. geophysical properties, acoustic data or acoustic derivative data sets); (2) display data in geo-referenced map space and/or in multivariate data space (e.g.
S.J. Dijkstra, L.A. Mayer
openaire   +1 more source