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Classification of Atlantic Coastal Sand Dune Vegetation Using In Situ, UAV, and Airborne Hyperspectral Data
| dc.rights.license | open | en_US |
| hal.structure.identifier | Environnements et Paléoenvironnements OCéaniques [EPOC] | |
| dc.contributor.author | LAPORTE FAURET, Quentin | |
| hal.structure.identifier | Environnements et Paléoenvironnements OCéaniques [EPOC] | |
| dc.contributor.author | LUBAC, Bertrand | |
| hal.structure.identifier | Environnements et Paléoenvironnements OCéaniques [EPOC] | |
| dc.contributor.author | CASTELLE, Bruno
IDREF: 087596520 | |
| hal.structure.identifier | Environnements et Paléoenvironnements OCéaniques [EPOC] | |
| dc.contributor.author | MICHALET, Richard | |
| hal.structure.identifier | Environnements et Paléoenvironnements OCéaniques [EPOC] | |
| dc.contributor.author | MARIEU, Vincent | |
| hal.structure.identifier | Laboratoire de l'intégration, du matériau au système [IMS] | |
| dc.contributor.author | BOMBRUN, Lionel
ORCID: 0000-0001-9036-3988 IDREF: 137837461 | |
| hal.structure.identifier | Laboratoire de Planétologie et Géodynamique [UMR 6112] [LPG] | |
| dc.contributor.author | LAUNEAU, Patrick | |
| hal.structure.identifier | Laboratoire de Planétologie et Géodynamique [UMR 6112] [LPG] | |
| dc.contributor.author | GIRAUD, Manuel | |
| hal.structure.identifier | Environnements et Paléoenvironnements OCéaniques [EPOC] | |
| dc.contributor.author | NORMANDIN, Cassandra | |
| dc.contributor.author | ROSEBERY, David | |
| dc.date.accessioned | 2024-04-08T12:22:58Z | |
| dc.date.available | 2024-04-08T12:22:58Z | |
| dc.date.issued | 2020-07-11 | |
| dc.identifier.issn | 2072-4292 | en_US |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/196758 | |
| dc.description.abstractEn | 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. | |
| dc.description.sponsorship | Marier les objectifs de défense côtière avec ceux de la protection du milieu naturel grâce aux dunes sableuses - ANR-17-CE01-0014 | en_US |
| dc.language.iso | EN | en_US |
| dc.subject.en | airborne hyperspectral | |
| dc.subject.en | coastal dune vegetation | |
| dc.subject.en | end-member spectral library | |
| dc.subject.en | pixel-based supervised classification | |
| dc.subject.en | Random Forest | |
| dc.subject.en | stability index | |
| dc.title.en | Classification of Atlantic Coastal Sand Dune Vegetation Using In Situ, UAV, and Airborne Hyperspectral Data | |
| dc.type | Article de revue | en_US |
| dc.identifier.doi | 10.3390/rs12142222 | en_US |
| dc.subject.hal | Planète et Univers [physics]/Interfaces continentales, environnement | en_US |
| dc.subject.hal | Planète et Univers [physics]/Interfaces continentales, environnement | en_US |
| bordeaux.journal | Remote Sensing | en_US |
| bordeaux.volume | 12 | en_US |
| bordeaux.hal.laboratories | EPOC : Environnements et Paléoenvironnements Océaniques et Continentaux - UMR 5805 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | CNRS | en_US |
| bordeaux.team | METHYS | en_US |
| bordeaux.peerReviewed | oui | en_US |
| bordeaux.inpress | non | en_US |
| bordeaux.import.source | hal | |
| hal.identifier | hal-03044522 | |
| hal.version | 1 | |
| hal.popular | non | en_US |
| hal.audience | Internationale | en_US |
| hal.export | false | |
| workflow.import.source | hal | |
| dc.rights.cc | CC BY | en_US |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Remote%20Sensing&rft.date=2020-07-11&rft.volume=12&rft.eissn=2072-4292&rft.issn=2072-4292&rft.au=LAPORTE%20FAURET,%20Quentin&LUBAC,%20Bertrand&CASTELLE,%20Bruno&MICHALET,%20Richard&MARIEU,%20Vincent&rft.genre=article |
