title: Identification and mapping of natural vegetation on a coastal site using a Worldview-2 satellite image.
abstract: Identification and mapping of natural vegetation are major issues for biodiversity management and conservation. Remotely sensed data with very high spatial resolution are currently used to study vegetation, but most satellite sensors are limited to four spectral bands, which is insufficient to identify some natural vegetation formations. The study objectives are to discriminate natural vegetation and identify natural vegetation formations using a Worldview-2 satellite image. The classification of the Worldview-2 image and ancillary thematic data was performed using a hybrid pixel-based and object-oriented approach. A hierarchical scheme using three levels was implemented, from land cover at a field scale to vegetation formation. This method was applied on a 48 km² site located on the French Atlantic coast which includes a classified NATURA 2000 dune and marsh system. The classification accuracy was very high, the Kappa index varying between 0.90 and 0.74 at land cover and vegetation formation levels respectively. These results show that Wordlview-2 images are suitable to identify natural vegetation. Vegetation maps derived from Worldview-2 images are more detailed than existing ones. They provide a useful medium for environmental management of vulnerable areas. The approach used to map natural vegetation is reproducible for a wider application by environmental managers.
authors: Conservatoire Botanique National de Brest, 52 allée du Bot, 29200 Brest, France; LETG-RENNES COSTEL UMR CNRS 6554, Université Rennes 2, Place du recteur Henri Le Moal, 35043 Rennes cedex, France. Electronic address: sebastienrapinel@gmail.com.
authors: Sébastien
authors: S
authors: Rapinel
authors: ECOBIO UMR CNRS 6553, Université Rennes 1, Avenue Général Leclerc, 35042 Rennes cedex, France.
authors: Bernard
authors: B
authors: Clément
authors: Conservatoire Botanique National de Brest, 52 allée du Bot, 29200 Brest, France.
authors: Sylvie
authors: S
authors: Magnanon
authors: Conservatoire Botanique National de Brest, 52 allée du Bot, 29200 Brest, France.
authors: Vanessa
authors: V
authors: Sellin
authors: LETG-RENNES COSTEL UMR CNRS 6554, Université Rennes 2, Place du recteur Henri Le Moal, 35043 Rennes cedex, France.
authors: Laurence
authors: L
authors: Hubert-Moy
mesh_terms: D044822:Biodiversity; D003247:Conservation of Natural Resources / Q000379:methods*; D004784:Environmental Monitoring / Q000379:methods*; D005602:France; D058998:Remote Sensing Technology; D018531:Spacecraft; D053833:Wetlands
keywords: Object-oriented classification; Remote-sensing; Super spectral resolution; Vegetation formations; Very high spatial resolution