Remote sensing of habitat characteristics using echo metrics and image-based seabed classes

Cutter, George R.; Stierhoff, Kevin L.; Demer, David A.

doi:10.1093/icesjms/fsw024

Advanced Search

Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

Advanced Search
Custom Query

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields

Language:

Dates

Publication Date Range:

to

Document Data

Title:

Document Type:

Library

Collection:

Series:

People

Author:

Clear All

Query Builder

Query box

Clear All

For additional assistance using the Custom Query please check out our Help Page

The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.

i

Remote sensing of habitat characteristics using echo metrics and image-based seabed classes

2016
By Cutter, George R. ; Stierhoff, Kevin L. ; Demer, David A.
Source: ICES Journal of Marine Science, 73(8), 1965-1974

[PDF-1.06 MB]

Details You May Also Like

Details:

Journal Title:

ICES Journal of Marine Science
Personal Author:

Cutter, George R. ; Stierhoff, Kevin L. ; Demer, David A.

Cutter, George R. ; Stierhoff, Kevin L. ; Demer, David A. Less -
NOAA Program & Office:

NMFS (National Marine Fisheries Service) ; SWFSC (Southwest Fisheries Science Center)

NMFS (National Marine Fisheries Service) ; SWFSC (Southwest Fisheries Science Center) Less -
Description:

The seabed can be classified using data from vertical, split-beam echosounders. This was demonstrated recently using a model parameterized with acoustic estimates of slope, roughness, normal-incidence backscattering strength, and variation of backscattering strength by frequency and incidence angle. These seabed classifications were interpreted and validated using published surficial geology maps, but the acoustic data indicated greater spatial variability. Here, classifications of sediment grain or feature size are ascribed to areas ∼10 m2. First, images of the seabed in the study area are ascribed, based on per cent coverage, to seven primary classes ranging from mud through high-relief rock, and 25 primary–secondary classes. Then, a refined seabed classifier, based on the acoustic model parameters is trained, using a nearest-neighbours algorithm, on a subset of the class data. The classifier accurately predicts 96% of the primary classes, and 93% of the primary–secondary classes from an independent data subset. These methods should be useful for characterizing, mapping, and quantifying potential seabed habitat domains of demersal fish and benthic invertebrates.
Keywords:

[+]

Aquatic Science Ecology Ecology, Evolution, Behavior And Systematics Oceanography
Source:

ICES Journal of Marine Science, 73(8), 1965-1974
DOI:

https://doi.org/10.1093/icesjms/fsw024
ISSN:

1095-9289;1054-3139;
Format:

PDF
Publisher:

Oxford University Press (OUP)
Document Type:

Journal Article
License:

https://creativecommons.org/publicdomain/zero/1.0/
Rights Information:

CC0 Public Domain
Compliance:

Library
Main Document Checksum:

[+]

urn:sha-512:63b7b02d9524497df56c955414e8f2143725659a21bb1dcf5483e75451632297fa8d8392ed6da1363a215554225fa2d729cfec7daa5f37ec34f187db022cd94a
Download URL:

https://repository.library.noaa.gov/view/noaa/62960/noaa_62960_DS1.pdf
File Type:

No Additional Files

More +

Remote sensing of habitat characteristics using echo metrics and image-based seabed classes

Details:

Supporting Files

You May Also Like