Long-Term Spatiotemporal Variability of Whitings in Lake Geneva from Multispectral Remote Sensing and Machine Learning

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Ressource 1Télécharger: remotesensing-14-06175.pdf (4561.67 [Ko])
Etat: Public
Version: Final published version
Licence: CC BY 4.0
ID Serval
serval:BIB_37C33EE1A049
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Long-Term Spatiotemporal Variability of Whitings in Lake Geneva from Multispectral Remote Sensing and Machine Learning
Périodique
Remote Sensing
Auteur⸱e⸱s
Many Gaël, Escoffier Nicolas, Ferrari Michele, Jacquet Philippe, Odermatt Daniel, Mariethoz Gregoire, Perolo Pascal, Perga Marie-Elodie
ISSN
2072-4292
Statut éditorial
Publié
Date de publication
06/12/2022
Peer-reviewed
Oui
Volume
14
Numéro
23
Pages
6175
Langue
anglais
Résumé
Whiting events are massive calcite precipitation events turning hardwater lake waters to a milky turquoise color. Herein, we use a multispectral remote sensing approach to describe the spatial and temporal occurrences of whitings in Lake Geneva from 2013 to 2021. Landsat-8, Sentinel-2, and Sentinel-3 sensors are combined to derive the AreaBGR index and identify whitings using appropriate filters. 95% of the detected whitings are located in the northeastern part of the lake and occur in a highly reproducible environmental setting. An extended time series of whitings in the last 60 years is reconstructed from a random forest algorithm and analyzed through a Bayesian decomposition for annual and seasonal trends. The annual number of whiting days between 1958 and 2021 does not follow any particular monotonic trend. The inter-annual changes of whiting occurrences significantly correlate to the Western Mediterranean Oscillation Index. Spring whitings have increased since 2000 and significantly follow the Atlantic Multidecadal Oscillation index. Future climate change in the Mediterranean Sea and the Atlantic Ocean could induce more variable and earlier whiting events in Lake Geneva.
Mots-clé
whitings, remote sensing, machine learning, climate index, ground data
Open Access
Oui
Financement(s)
Fonds national suisse / 200021_175530
Création de la notice
07/12/2022 7:47
Dernière modification de la notice
09/12/2022 7:09
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