Generating higher resolution regional seafloor maps from crowd-sourced bathymetry.

Seafloor mapping can offer important insights for marine management, spatial planning, and research in marine geology, ecology, and oceanography. Here, we present a method for generating regional bathymetry and geomorphometry maps from crowd-sourced depth soundings (Olex AS) for a small fraction of...

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Main Authors: Emilie Novaczek, Rodolphe Devillers, Evan Edinger
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2019-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0216792
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spelling doaj-63579b24969147df8645723acaa83fd02021-03-03T20:38:15ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01146e021679210.1371/journal.pone.0216792Generating higher resolution regional seafloor maps from crowd-sourced bathymetry.Emilie NovaczekRodolphe DevillersEvan EdingerSeafloor mapping can offer important insights for marine management, spatial planning, and research in marine geology, ecology, and oceanography. Here, we present a method for generating regional bathymetry and geomorphometry maps from crowd-sourced depth soundings (Olex AS) for a small fraction of the cost of multibeam data collection over the same area. Empirical Bayesian Kriging was used to generate a continuous bathymetric surface from incomplete and, in some areas, sparse Olex coverage on the Newfoundland and Labrador shelves of eastern Canada. The result is a 75m bathymetric grid that provides over 100x finer spatial resolution than previously available for the majority of the 672,900 km2 study area. The interpolated bathymetry was tested for accuracy against independent depth data provided by Fisheries and Oceans Canada (Spearman correlation = 0.99, p<0.001). Quantitative terrain attributes were generated to better understand seascape characteristics at multiple spatial scales, including slope, rugosity, aspect, and bathymetric position index. Landform classification was carried out using the geomorphons algorithm and a novel method for the identification of previously unmapped tributary canyons at the continental shelf edge are also presented to illustrate some of many potential benefits of crowd-sourced regional seafloor mapping.https://doi.org/10.1371/journal.pone.0216792
collection DOAJ
language English
format Article
sources DOAJ
author Emilie Novaczek
Rodolphe Devillers
Evan Edinger
spellingShingle Emilie Novaczek
Rodolphe Devillers
Evan Edinger
Generating higher resolution regional seafloor maps from crowd-sourced bathymetry.
PLoS ONE
author_facet Emilie Novaczek
Rodolphe Devillers
Evan Edinger
author_sort Emilie Novaczek
title Generating higher resolution regional seafloor maps from crowd-sourced bathymetry.
title_short Generating higher resolution regional seafloor maps from crowd-sourced bathymetry.
title_full Generating higher resolution regional seafloor maps from crowd-sourced bathymetry.
title_fullStr Generating higher resolution regional seafloor maps from crowd-sourced bathymetry.
title_full_unstemmed Generating higher resolution regional seafloor maps from crowd-sourced bathymetry.
title_sort generating higher resolution regional seafloor maps from crowd-sourced bathymetry.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2019-01-01
description Seafloor mapping can offer important insights for marine management, spatial planning, and research in marine geology, ecology, and oceanography. Here, we present a method for generating regional bathymetry and geomorphometry maps from crowd-sourced depth soundings (Olex AS) for a small fraction of the cost of multibeam data collection over the same area. Empirical Bayesian Kriging was used to generate a continuous bathymetric surface from incomplete and, in some areas, sparse Olex coverage on the Newfoundland and Labrador shelves of eastern Canada. The result is a 75m bathymetric grid that provides over 100x finer spatial resolution than previously available for the majority of the 672,900 km2 study area. The interpolated bathymetry was tested for accuracy against independent depth data provided by Fisheries and Oceans Canada (Spearman correlation = 0.99, p<0.001). Quantitative terrain attributes were generated to better understand seascape characteristics at multiple spatial scales, including slope, rugosity, aspect, and bathymetric position index. Landform classification was carried out using the geomorphons algorithm and a novel method for the identification of previously unmapped tributary canyons at the continental shelf edge are also presented to illustrate some of many potential benefits of crowd-sourced regional seafloor mapping.
url https://doi.org/10.1371/journal.pone.0216792
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