Detecting Tamarisk species (Tamarix spp.) in riparian habitats of Southern California using high spatial resolution hyperspectral imagery
Detecting Tamarisk species (Tamarix spp.) in riparian habitats of Southern California using high spatial resolution hyperspectral imagery
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DOI：
10.1016/j.rse.2007.01.003
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发表时间：
2007-07-30
影响因子：
13.5
通讯作者：
Hendricks, Leif W.
中科院分区：
文献类型：
--
作者：
Hamada, Yuki;Stow, Douglas A.;Hendricks, Leif W.
外来植物入侵是一个主要的环境和生态问题，是地中海型生态系统的一个特殊问题。早期发现入侵植物对有效的杂草管理至关重要。一些研究已经探索了利用高光谱图像绘制入侵植物的方法，并取得了很好的结果。然而，关于入侵植物检测的图像处理技术的广泛或比较的研究报道很少，涉及高空间和光谱分辨率图像的研究就更少了。本研究的主要目的是研究高空间（0.5 m）和光谱（4 nm）分辨率图像和几种分类方法在检测柽柳（Tamarix spp.）侵染中的应用，柽柳是南加州河岸栖息地中最具问题的入侵植物物种。分层聚类是一种特别有效的统计方法，用于识别具有最大判别能力的波段和光谱变换。由机载高光谱图像数据分类产生的产品因场景、输入数据类型、分类器和最小补丁大小而异。产品的整体图像分类准确率与委托错误率共变，与参考数据一致性强的产品也存在较高的误检率。综合定性图分析、面比例统计和基于目标的准确率评估结果表明，通过分层聚类选择多个窄波段的平行六面体分类器可以获得最准确、最可靠的红柳分类产品。(c) 2007爱思唯尔公司版权所有。
Exotic plant invasion is a major environmental and ecological concern and is a particular issue for Mediterranean-type ecosystems. Early detection of invasive plants is crucial for effective weed management. Several studies have explored hyperspectral imagery for mapping invasive plants with promising results. However, only a few extensive or comparative 'studies about image processing techniques for invasive plant detection have been reported, and even fewer studies have involved very high spatial and spectral resolution imagery. The primary goal of this study was to investigate the utility of very high spatial (0.5 m) and spectral (4 nm) resolution imagery and several classification approaches for detecting tamarisk (Tamarix spp.) infestations, the most problematic invasive plant species in the riparian habitats of southern California.Hierarchical clustering was a particularly effective and efficient statistical method for identifying wavebands and spectral transforms having the greatest discriminatory power. Products resulting from the classification of airborne hyperspectral image data varied by scene, input data type, classifier, and minimum patch size. Overall accuracy of image classification accuracy of products co-varied with commission error rates, such that products having strong agreement with reference data also had a high number of false detections. Integrating the findings from qualitative map analysis, areal proportion statistics, and object-based accuracy assessment indicates that the parallelepiped classifier with several narrow wavebands selected through hierarchical clustering yielded the most accurate and reliable tamarisk classification products. (c) 2007 Elsevier Inc. All rights reserved.