Understanding the evolution of natural systems spatio-temporal dynamics is paramount in modern ecology. We focused on highlighting and analysing temporal and spatial dynamics of remotely-sensed chlorophyll a concentration. This pigment is linked with phytoplankton production, which in turn play a pivotal role in marine environment. Satellite platforms offer a synoptic view of surface chlorophyll a concentration for the last two decades. Coupling this source of information with statistical and Machine Learning techniques could help highlighting eventual patterns. We merged the Mediterranean chlorophyll a satellite data for the last two decades into a single dataset. We tested several techniques for reconstructing missing data and performed a general analysis. Finally, we implemented a Dynamic Time Warping Self-Organizing Map algorithm to cluster our series showing that an elastic distance measure outperforms a non-elastic one. The proposed satellite data management and analysis provided insights on spatio-temporal chlorophyll a dynamics in the Mediterranean Basin.

Mattei, F., & Scardi, M. (2022). Mining satellite data for extracting chlorophyll a spatio-temporal patterns in the Mediterranean Sea, 150, 105353.

Mining satellite data for extracting chlorophyll a spatio-temporal patterns in the Mediterranean Sea

M. Scardi
2022

Abstract

Understanding the evolution of natural systems spatio-temporal dynamics is paramount in modern ecology. We focused on highlighting and analysing temporal and spatial dynamics of remotely-sensed chlorophyll a concentration. This pigment is linked with phytoplankton production, which in turn play a pivotal role in marine environment. Satellite platforms offer a synoptic view of surface chlorophyll a concentration for the last two decades. Coupling this source of information with statistical and Machine Learning techniques could help highlighting eventual patterns. We merged the Mediterranean chlorophyll a satellite data for the last two decades into a single dataset. We tested several techniques for reconstructing missing data and performed a general analysis. Finally, we implemented a Dynamic Time Warping Self-Organizing Map algorithm to cluster our series showing that an elastic distance measure outperforms a non-elastic one. The proposed satellite data management and analysis provided insights on spatio-temporal chlorophyll a dynamics in the Mediterranean Basin.
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore BIO/07
English
Mattei, F., & Scardi, M. (2022). Mining satellite data for extracting chlorophyll a spatio-temporal patterns in the Mediterranean Sea, 150, 105353.
Mattei, F; Scardi, M
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2108/303096
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