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Open AccessArticle

Classification of Atlantic Coastal Sand Dune Vegetation Using In Situ, UAV, and Airborne Hyperspectral Data

1
University of Bordeaux, CNRS, UMR 5805 EPOC, Allée Geoffroy Saint-Hilaire, 33615 Pessac CEDEX, France
2
University of Bordeaux, CNRS, UMR 5218 IMS, Cours de la libération, 33405 Talence CEDEX, France
3
University of Nantes, CNRS, UMR 6112 LPG, Rue de la Houssinière, 42322 Nantes, France
4
Office National des Forêts, 9 rue R Manaud, 33524 Bruges, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(14), 2222; https://doi.org/10.3390/rs12142222
Received: 10 June 2020 / Revised: 6 July 2020 / Accepted: 9 July 2020 / Published: 11 July 2020
(This article belongs to the Special Issue Hyperspectral Remote Sensing for Biodiversity Mapping)
Mapping coastal dune vegetation is critical to understand dune mobility and resilience in the context of climate change, sea level rise, and increased anthropogenic pressure. However, the identification of plant species from remotely sensed data is tedious and limited to broad vegetation communities, while such environments are dominated by fragmented and small-scale landscape patterns. In June 2019, a comprehensive multi-scale survey including unmanned aerial vehicle (UAV), hyperspectral ground, and airborne data was conducted along approximately 20 km of a coastal dune system in southwest France. The objective was to generate an accurate mapping of the main sediment and plant species ground cover types in order to characterize the spatial distribution of coastal dune stability patterns. Field and UAV data were used to assess the quality of airborne data and generate a robust end-member spectral library. Next, a two-step classification approach, based on the normalized difference vegetation index and Random Forest classifier, was developed. Results show high performances with an overall accuracy of 100% and 92.5% for sand and vegetation ground cover types, respectively. Finally, a coastal dune stability index was computed across the entire study site. Different stability patterns were clearly identified along the coast, highlighting for the first time the high potential of this methodology to support coastal dune management. View Full-Text
Keywords: airborne hyperspectral; coastal dune vegetation; end-member spectral library; pixel-based supervised classification; Random Forest; stability index airborne hyperspectral; coastal dune vegetation; end-member spectral library; pixel-based supervised classification; Random Forest; stability index
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MDPI and ACS Style

Laporte-Fauret, Q.; Lubac, B.; Castelle, B.; Michalet, R.; Marieu, V.; Bombrun, L.; Launeau, P.; Giraud, M.; Normandin, C.; Rosebery, D. Classification of Atlantic Coastal Sand Dune Vegetation Using In Situ, UAV, and Airborne Hyperspectral Data. Remote Sens. 2020, 12, 2222.

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