Alternate title

AI004002

Date (Creation)

2018-03-30

Citation identifier

79db82a6-f839-4309-8240-1231a5c8db47

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 is public data with no limitations to public access and no limitations to reuse. .

Spatial representation type

Vector

Denominator

1800

Language

English

Topic category

• Biota
• Oceans

Begin date

2016-09-04

End date

2018-03-31

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	ai004002
OGC:WFS	https://ows.emodnet-seabedhabitats.eu/geoserver/emodnet_open_maplibrary/wfs	ai004002
WWW:LINK-1.0-http--link	https://files.emodnet-seabedhabitats.eu/data/EMODnetSBHsurvey_ai004002.zip	EMODnet Seabed Habitats Static Download

Hierarchy level

Dataset

Statement

Seabed classification and habitat maps were created from the remote sensing data collected by the Pleiades 1A Sensor. The data was mapped using Object based image analysis, a two-step process which involves the segmentation and then classification of an image. Before segmentation and classification, the satellite data underwent several different corrections including, radiometric calibration, atmospheric correction, orthorectification, land and cloud masking, sun glint correction and depth correction. The satellite data was segmented using the multi-resolution segmentation algorithm within Trimble's eCognition software (v9.0.0). The objects produced during the segmentation were classified using a mixture of K-nearest neighbour, random forest and support vector machines classification algorithms. All three of these algorithms take the statistics from the training dataset, created from the ground truthing data, and classify the remaining objects based on statistically driven decisions. The simplified map was classified to the simpler habitat classification of Seagrass, Algae, Bare and Mosaic. Maps were validated against a subset of the groundtruthing stations which were left out of the classification. The simplified map achieved an overall accuracy of 72.3%.

For publication to EMODnet, SAERI reprojected the vector using the EPSG:4326 - WGS 84 coordinate system, cleaned the vector in mapshaper.org ( https://mapshaper.org/) to eliminate gaps and overlaps, and processed the attribute table to fit the specified data exchange format. All work was conducted in QGIS v3.28.4. unless otherwise specified. SAERI used the original GUI assigned by CEFAS as this map's UUID and created the GUI AI004002.

Data originally downloaded from https://data.cefas.co.uk/view/19317

Metadata

File identifier

79db82a6-f839-4309-8240-1231a5c8db47 XML

Metadata language

English

Character set

UTF8

Hierarchy level

Dataset

Date stamp

2023-10-30T12:15:30.527Z

Metadata author

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