利用高光谱图像分类与解混监测荒原生境状况

S. Delalieux, B. Somers, B. Haest, L. Kooistra, C. A. Mücher, J. V. Borre
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引用次数: 9

摘要

Natura 2000是一个欧盟范围内的自然保护区网络,其主要目标是实现或维持受欧盟栖息地指令保护的栖息地的良好保护状态。在此框架内,本研究考察了一种策略,利用比利时法兰德斯Kalmthoutse Heide的机载高光谱AHS数据来表征荒原植被的状态。建立了一个层次分类方案,其中最详细的内容集中在确定栖息地保护状况的植被结构元素上。尽管传统的分类算法在区分广泛的土地覆盖类别和栖息地类型(1 ~ 3级)方面表现良好(准确率> 90%),但在区分石南石楠生境结构质量的重要指标石南石楠年龄类别(4级)方面表现不佳。基于决策树分类的亚像素解混方法,实现了对荒原形态变化的映射,从而提高了遥感数据信息的生态价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Monitoring heathland habitat status using hyperspectral image classification and unmixing
Natura 2000, an EU-wide network of nature protection areas, has as main objective the achievement or maintenance of a favorable conservation status of habitats protected by the EU Habitats directives. Within this framework, this study examines a strategy to characterize the status of heathland vegetation from airborne hyperspectral AHS data in the Kalmthoutse Heide, Flanders, Belgium. A hierarchical classification scheme was set-up with the highest detail focusing on vegetation structural elements that determine the conservation status of the habitat. Although conventional classification algorithms performed very well (accuracies > 90%) in discriminating broad land cover classes and habitat types (level 1 to 3), they failed in accurately distinguishing different heather age classes which are an important indicator for the structural quality of the heathland habitat (level 4). Since all heather life stages have their specific structural characteristics, a subpixel unmixing approach succeeded by a decision tree classification was implemented to map variations in heathland morphology and as such enhance the ecological value of information derived from remote sensing data.
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