Alternate title

VG004003

Date (Creation)

2015-09-29

Citation identifier

e40207b8-8739-46b4-9177-03bc430dcd13

Point of contact

Organisation name	Individual name	Electronic mail address	Role
Centre for Environment Fisheries and Aquaculture Science (CEFAS)		datamanager@cefas.co.uk	Owner

GEMET - INSPIRE themes, version 1.0

• Oceanographic geographical features
• Habitats and biotopes

GEMET - Concepts, version 4.1.3

• habitat
• biotope

Access constraints

Other restrictions

Other constraints

no limitations to public access

Use constraints

Other restrictions

Other constraints

Open Government Licence (OGL) terms and conditions apply. These datasets are from work funded by DEFRA and the Darwin Initiative, therefore access is public and use will be under Crown Copyright and Open Government Licence. Data are freely available for research or commercial use providing that the originators and the Darwin Initiative are acknowledged in any publications produced. Data can be found at https://data.cefas.co.uk/view/18174.

Spatial representation type

Vector

Denominator

3700

Language

English

Topic category

• Biota
• Oceans

Begin date

2016-09-04

Reference system identifier

http://www.opengis.net/def/crs/EPSG/0/3857

Distribution format

Name	Version
ESRI Shapefile	1
GML	3.2.1

OnLine resource

Protocol	Linkage	Name
OGC:WMS	https://ows.emodnet-seabedhabitats.eu/geoserver/emodnet_view_maplibrary/wms	vg004003
OGC:WFS	https://ows.emodnet-seabedhabitats.eu/geoserver/emodnet_open_maplibrary/wfs	vg004003
WWW:LINK-1.0-http--link	https://files.emodnet-seabedhabitats.eu/data/EMODnetSBHsurvey_VG004003.zip	EMODnet Seabed Habitats Static Download

Hierarchy level

Dataset

Statement

A new habitat map for the site was produced by analysing and interpreting the available acoustic data and the ground truth data collected by the dedicated survey of North St George's Channel rMCZ. The process is a combination of two approaches, statistical modelling and image analysis.

To map substrata and assemblage types across the study site, object-based image analysis (OBIA; Blaschke, 2010) was utilised. The technique was implemented in the software package eCognition v8.8 combined with a predictive modelling approach using the Random Forest2 algorithm (Breiman, 2001) application within eCognition. This consists of a classification model aimed at predicting a target variable (in this case, sediment composition) based on exhaustively sampled auxiliary variables (in this case, the acoustic data). The technique has been used in previous studies to predict sediment type (Li et al., 2011a). Li et al. (2011b) showed that the Random Forest algorithm outperformed a range of other modelling techniques for predicting substrate type (Liaw and Wiener, 2002). More information about methods is available at https://data.cefas.co.uk/view/18174. For publication to EMODnet, SAERI set the shapefile's layer properties to the projection EPSG:3857 then reprojected the resulting layer to EPSG:4326 - WGS 84. SAERI then removed holes smaller than 25 meters squared and processed the attribute table to fit the specified data exchange format. All work was conducted in QGIS v3.28.4. SAERI used the original GUI (e40207b8-8739-46b4-9177-03bc430dcd13) asigned to the dataset by CEFAS as the UUID for the dataset's publication to EMODnet; SAERI created a new GUI (VG004003) for the dataset.

Metadata

File identifier

e40207b8-8739-46b4-9177-03bc430dcd13 XML

Metadata language

English

Character set

UTF8

Hierarchy level

Dataset

Date stamp

2023-10-30T14:38:54.203Z

Metadata author

Organisation name	Individual name	Electronic mail address	Role
South Atlantic Environmental and Research Institue (SAERI)		info@saeri.ac.fk	Point of contact