Rapinel Sébastien, Clément Bernard, Magnanon Sylvie, Sellin Vanessa, Hubert-Moy Laurence
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.
ECOBIO UMR CNRS 6553, Université Rennes 1, Avenue Général Leclerc, 35042 Rennes cedex, France.
J Environ Manage. 2014 Nov 1;144:236-46. doi: 10.1016/j.jenvman.2014.05.027. Epub 2014 Jun 25.
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.
自然植被的识别与制图是生物多样性管理和保护的主要问题。目前,具有非常高空间分辨率的遥感数据被用于研究植被,但大多数卫星传感器仅限于四个光谱波段,这不足以识别某些自然植被类型。本研究的目的是利用Worldview - 2卫星图像区分自然植被并识别自然植被类型。使用基于像素和面向对象的混合方法对Worldview - 2图像和辅助专题数据进行分类。实施了一个使用三个层次的分级方案,从田间尺度的土地覆盖到植被类型。该方法应用于法国大西洋沿岸一个48平方公里的区域,该区域包括一个已分类的NATURA 2000沙丘和沼泽系统。分类精度非常高,在土地覆盖和植被类型层面,Kappa指数分别在0.90和0.74之间变化。这些结果表明,Worldview - 2图像适用于识别自然植被。从Worldview - 2图像得出的植被图比现有的植被图更详细。它们为脆弱地区的环境管理提供了一个有用的媒介。用于绘制自然植被图的方法对于环境管理者更广泛的应用来说是可重复的。