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EO big data analytics for the discovery of new trends of marine species habitats in a changing global climate

EO big data analytics for the discovery of new trends of marine species habitats in a changing global climate
EO big data analytics for the discovery of new trends of marine species habitats in a changing global climate
Climate change has been observed using multiple methods of Earth Observation (EO) including in situ, air-borne and space-borne sensing methods. These use multi-modal observation platforms, with various geospatial coverages, spatio-temporal resolutions and accuracies. The resulting EO Big Data from heterogeneous sources constitute valuable sources for scientists to investigate on the manifested responses of natural species behaviour to climate change. In the EO4wildlife1 research project, we have access to Copernicus and Argos EO Big Data for conducting studies on the changes of habitats for a variety of marine species. The challenge is to discover causality of Metocean environmental observations and their relationship with the changing habitats of species. Nevertheless, there is a need to deploy Big Data technologies for connecting, ingesting, processing of EO data, as well as implementing specialised open data analytics services in this study. The particular services shall be made accessible to the scientific community for setting up modelling scenarios concerning the potential discovery of new trends of marine species habitats due to climate change. Three marine species are being studied in the EO4wildlife project. They include the Bluefin Tuna in the Atlantic-Mediterranean migratory regions, the black-footed albatross seabirds across the sub-tropical Atlantic Ocean and Loggerhead sea turtles along the North West coast of the African continent and Cape Verde. Large data representing geospatial migratory tracks and settlements of these respective marine species have been acquired in the project over period of times together with Metocean EO data from Copernicus and Argos satellites. These are currently analysed and modelled with a set of features obtained by searching in a large space of possible measured and derived Metocean parameters. A two-step search was used involving significance measurement and an iterative breadth first search based wrapper type feature selection algorithm. Furthermore, the analysis is useful for improving the performance of our habitat prediction models across the three marine species in the study. The discovery of new habitats geospatial and temporal trends which may be associated to the changing climate under these analyses will be achieved through the deployment of web-enabled data mining and analytics open services. A dedicated Big Data platform supported by generic data management services in the cloud is therefore deployed for assuring the scalability of the data processing and analytics services.
1831-9424
OP KJ-NA-28783-EN-N
445-448
European Union
Sabeur, Zoheir
74b55ff0-94cc-4624-84d5-bb816a7c9be6
Correndo, Gianluca
fea0843a-6d4a-4136-8784-0d023fcde3e2
Veres, Galina
3c2a37d2-3904-43ce-b0cf-006f62b87337
Arbab-Zavar, Banafshe
40e175ea-6557-47c6-b759-318d7e24984b
Neumann, Geoffrey
9dfe6611-52bb-4ba6-ad83-b92c7acb4bb3
Ivall, Thomas D
c999bc30-dc39-4aed-89e0-c2db6dbea4fd
Castel, F
cdd27192-6603-44f4-a6d2-c30cc06340ec
Zigna, J M
9bc63400-b5e1-4a19-9b40-daaca0ba5583
Lorenzo, J.
5f3929b3-c3ce-47af-977b-17e8db42baed
Soille, Pierre
Marchetti, Pier Giorgio
Sabeur, Zoheir
74b55ff0-94cc-4624-84d5-bb816a7c9be6
Correndo, Gianluca
fea0843a-6d4a-4136-8784-0d023fcde3e2
Veres, Galina
3c2a37d2-3904-43ce-b0cf-006f62b87337
Arbab-Zavar, Banafshe
40e175ea-6557-47c6-b759-318d7e24984b
Neumann, Geoffrey
9dfe6611-52bb-4ba6-ad83-b92c7acb4bb3
Ivall, Thomas D
c999bc30-dc39-4aed-89e0-c2db6dbea4fd
Castel, F
cdd27192-6603-44f4-a6d2-c30cc06340ec
Zigna, J M
9bc63400-b5e1-4a19-9b40-daaca0ba5583
Lorenzo, J.
5f3929b3-c3ce-47af-977b-17e8db42baed
Soille, Pierre
Marchetti, Pier Giorgio

Sabeur, Zoheir, Correndo, Gianluca, Veres, Galina, Arbab-Zavar, Banafshe, Neumann, Geoffrey, Ivall, Thomas D, Castel, F, Zigna, J M and Lorenzo, J. (2017) EO big data analytics for the discovery of new trends of marine species habitats in a changing global climate. Soille, Pierre and Marchetti, Pier Giorgio (eds.) In Publications Office of the European Union,, 2017. European Union. pp. 445-448 . (doi:10.2760/383579).

Record type: Conference or Workshop Item (Paper)

Abstract

Climate change has been observed using multiple methods of Earth Observation (EO) including in situ, air-borne and space-borne sensing methods. These use multi-modal observation platforms, with various geospatial coverages, spatio-temporal resolutions and accuracies. The resulting EO Big Data from heterogeneous sources constitute valuable sources for scientists to investigate on the manifested responses of natural species behaviour to climate change. In the EO4wildlife1 research project, we have access to Copernicus and Argos EO Big Data for conducting studies on the changes of habitats for a variety of marine species. The challenge is to discover causality of Metocean environmental observations and their relationship with the changing habitats of species. Nevertheless, there is a need to deploy Big Data technologies for connecting, ingesting, processing of EO data, as well as implementing specialised open data analytics services in this study. The particular services shall be made accessible to the scientific community for setting up modelling scenarios concerning the potential discovery of new trends of marine species habitats due to climate change. Three marine species are being studied in the EO4wildlife project. They include the Bluefin Tuna in the Atlantic-Mediterranean migratory regions, the black-footed albatross seabirds across the sub-tropical Atlantic Ocean and Loggerhead sea turtles along the North West coast of the African continent and Cape Verde. Large data representing geospatial migratory tracks and settlements of these respective marine species have been acquired in the project over period of times together with Metocean EO data from Copernicus and Argos satellites. These are currently analysed and modelled with a set of features obtained by searching in a large space of possible measured and derived Metocean parameters. A two-step search was used involving significance measurement and an iterative breadth first search based wrapper type feature selection algorithm. Furthermore, the analysis is useful for improving the performance of our habitat prediction models across the three marine species in the study. The discovery of new habitats geospatial and temporal trends which may be associated to the changing climate under these analyses will be achieved through the deployment of web-enabled data mining and analytics open services. A dedicated Big Data platform supported by generic data management services in the cloud is therefore deployed for assuring the scalability of the data processing and analytics services.

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Accepted/In Press date: 20 September 2017
Published date: November 2017
Venue - Dates: Big Data from Space. BIDS' 2017, France, 2017-11-28 - 2017-11-30

Identifiers

Local EPrints ID: 416802
URI: http://eprints.soton.ac.uk/id/eprint/416802
ISSN: 1831-9424
PURE UUID: fc812726-f6e5-4795-b280-1dbaf938b109
ORCID for Zoheir Sabeur: ORCID iD orcid.org/0000-0003-4325-4871
ORCID for Gianluca Correndo: ORCID iD orcid.org/0000-0003-3335-5759

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Date deposited: 10 Jan 2018 17:30
Last modified: 07 Jan 2020 17:32

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Contributors

Author: Zoheir Sabeur ORCID iD
Author: Gianluca Correndo ORCID iD
Author: Galina Veres
Author: Banafshe Arbab-Zavar
Author: Geoffrey Neumann
Author: Thomas D Ivall
Author: F Castel
Author: J M Zigna
Author: J. Lorenzo
Editor: Pierre Soille
Editor: Pier Giorgio Marchetti

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