EO big data connectors and analytics for understanding the effects of climate change on migratory trends of marine wildlife

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Authors: Sabeur, Z.A., Correndo, G., Veres, G., Arbab-Zavar, B., Lorenzo, J., Habib, T., Haugommard, A., Martin, F., Zigna, J.M. and Weller, G.

Journal: IFIP Advances in Information and Communication Technology

Volume: 507

Pages: 85-94

ISBN: 9783319899343

ISSN: 1868-4238

DOI: 10.1007/978-3-319-89935-0_8

© IFIP International Federation for Information Processing 2017 Published by Springer International Publishing AG 2017. All rights reserved. This paper describes the current ongoing research activities concerning the intelligent management and processing of Earth Observation (EO) big data together with the implementation of data connectors, advanced data analytics and Knowledge Base services to a Big Data platform in the EO4Wildlife project (www.eo4wildlife.eu). These components support on the discovery of marine wildlife migratory behaviours, some of which may be a direct consequence of the changing Met-Ocean resources and the globe climatic changes. In EO4wildlife, we specifically focus on the implementation of web-enabled advanced analytics web services which comply with OGC standards and make them accessible to a wide research community for investigating on trends of animal behaviour around specific marine regions of interest. Big data connectors and a catalogue service are being installed to enable access to COPERNICUS sentinels and ARGOS satellite big data together with other in situ heterogeneous sources. Furthermore, data mining services are being developed for knowledge extraction on species habitats and temporal behaviour trends. Also, high level fusion and reasoning services which process big data observations are deployed to forecast marine wildlife behaviour with estimated uncertainties. These will be tested and demonstrated under targeted thematic scenarios in EO4wildlife using a Big Data platform a cloud resources.

This data was imported from Web of Science (Lite):

Authors: Sabeur, Z.A., Correndo, G., Veres, G., Arbab-Zavar, B., Lorenzo, J., Habib, T., Haugommard, A., Martin, F., Zigna, J.-M. and Weller, G.

Journal: ENVIRONMENTAL SOFTWARE SYSTEMS: COMPUTER SCIENCE FOR ENVIRONMENTAL PROTECTION

Volume: 507

Pages: 85-94

eISSN: 1868-422X

ISBN: 978-3-319-89934-3

ISSN: 1868-4238

DOI: 10.1007/978-3-319-89935-0_8

The data on this page was last updated at 05:16 on February 19, 2020.