Version 1
: Received: 3 November 2021 / Approved: 4 November 2021 / Online: 4 November 2021 (09:35:50 CET)
How to cite:
Collin, A.; James, D.; Mury, A.; Letard, M.; Houet, T.; Gloria, H.; Feunteun, E. Multiscale Mapping of the Salt Marshes Using Sentinel-2, Dove and UAV Imagery in the Bay of Mont-Saint-Michel. Preprints2021, 2021110098. https://doi.org/10.20944/preprints202111.0098.v1.
Collin, A.; James, D.; Mury, A.; Letard, M.; Houet, T.; Gloria, H.; Feunteun, E. Multiscale Mapping of the Salt Marshes Using Sentinel-2, Dove and UAV Imagery in the Bay of Mont-Saint-Michel. Preprints 2021, 2021110098. https://doi.org/10.20944/preprints202111.0098.v1.
Cite as:
Collin, A.; James, D.; Mury, A.; Letard, M.; Houet, T.; Gloria, H.; Feunteun, E. Multiscale Mapping of the Salt Marshes Using Sentinel-2, Dove and UAV Imagery in the Bay of Mont-Saint-Michel. Preprints2021, 2021110098. https://doi.org/10.20944/preprints202111.0098.v1.
Collin, A.; James, D.; Mury, A.; Letard, M.; Houet, T.; Gloria, H.; Feunteun, E. Multiscale Mapping of the Salt Marshes Using Sentinel-2, Dove and UAV Imagery in the Bay of Mont-Saint-Michel. Preprints 2021, 2021110098. https://doi.org/10.20944/preprints202111.0098.v1.
Abstract
The salt marshes, lying at the land-sea temperate interface, furnish a plethora of ecosystems services such as biodiversity niche support, ocean-climate change regulation, ornithology recreo-tourism or plant gathering by hand. They undergo significant worldwide losses due to their conversion into crop fields and to their spatial compression between the rising sea-level and the armoring shoreline. Their monitoring however requires to use a suite of remote sensing sensors to embrace the regional scale while capturing the plant details. This research innovatively adopts a multiscale approach using a cascading spaceborne and airborne process, from the 10-m Sentinel-2, through the 3-m Dove, to the 0.03-m unmanned airborne vehicle (UAV) imageries. The high to very high temporal resolution of the Sentinel-2 and Dove enabled to cover twenties and tens of km2 over five and four years, respectively, in the form of normalized difference vegetation index (NDVI) classes, associated with microphytobenthos, low, medium and high salt marsh vegetation, including the opportunistic Elyma genus. The NDVI was then modelled at the UAV scale (a few km2) using a three-layered NN prediction, providing the final near-infrared (NIR), and the intermediate red, green and blue reflectance imageries, calibrated/validated/tested with the Dove reflectance imageries (R2NIR=0.98, R2red=0.88, R2green=0.84, and R2blue=0.90). The 100fold increase in pixel size allowed to detect the decimeter-scale objects of the tidal flats and salt marshes, to enlarge the NDVI class ranges, and hold great promise to model other spectral bands at the UAV scale for further deeply enhancing the salt marsh mapping.
Keywords
spatiotemporal analysis; high to ultra high spatial resolution; high to very high temporal resolution; NDVI; NIR; neural network modelling, Bay of Mont-Saint-Michel
Subject
LIFE SCIENCES, Biophysics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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