Articles | Volume 20, issue 22
https://doi.org/10.5194/bg-20-4551-2023
https://doi.org/10.5194/bg-20-4551-2023
Research article
 | 
20 Nov 2023
Research article |  | 20 Nov 2023

Toward coherent space–time mapping of seagrass cover from satellite data: an example of a Mediterranean lagoon

Guillaume Goodwin, Marco Marani, Sonia Silvestri, Luca Carniello, and Andrea D'Alpaos

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Cited articles

Amos, C., Bergamasco, A., Umgiesser, G., Cappucci, S., CLoutier, D., DeNat, L., Flindt, M., Bonardi, M., and Cristante, S.: The stability of tidal flats in Venice Lagoon – the results of in-situ measurements using two benthic, annular flumes, J. Mar. Syst., 51, 211–241, https://doi.org/10.1002/esp.4599, 2004. a
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Bakirman, T. and Gumusay, M. U.: Assessment of machine learning methods for seagrass classification in the mediterranean, Balt. J. Modern Comput., 8, 315–326, https://doi.org/10.22364/BJMC.2020.8.2.07, 2020. a, b
Belgiu, M. and Drăgu, L.: Random forest in remote sensing: A review of applications and future directions, ISPRS J. Photogram. Remote Sens., 114, 24–31, https://doi.org/10.1016/j.isprsjprs.2016.01.011, 2016. 011 a
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Short summary
Seagrass meadows are an emblematic coastal habitat. Their sensitivity to environmental change means that it is essential to monitor their evolution closely. However, high costs make this endeavor a technical challenge. Here, we used machine learning to map seagrass meadows in 148 satellite images in the Venice Lagoon, Italy. We found that adding information such as depth of the seabed and known seagrass location improved our capacity to map temporal change in seagrass habitat.
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