Multiple-level defoliation assessment with hyperspectral data: integration of continuum-removed absorptions and red edges
Multiple-level defoliation assessment with hyperspectral data: integration of continuum-removed absorptions and red edges
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DOI：
10.1080/01431161.2010.510492
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发表时间：
2011-10
影响因子：
3.4
通讯作者：
S. Ge;R. Carruthers;M. Kramer;J. Everitt;G. Anderson
中科院分区：
文献类型：
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作者：
S. Ge;R. Carruthers;M. Kramer;J. Everitt;G. Anderson
高光谱数据收集从40冠盐杉（多枝红柳）：10个健康的冠层和30冠落叶引进的生物控制剂，盐杉叶甲虫（Diorhabda carinata）。这些数据评估了生物防治过程中的多级落叶。高光谱数据的两个重要特征-红边和连续去除的反射-被用来区分四个落叶类盐雪松（健康的植物，新落叶植物，完全落叶植物和refoliating植物）在冠层水平。对于上述四个落叶阶段，红边位置分别位于711-716 nm、706-712 nm、694-698 nm和715-719 nm的范围内。仅凭这些红边位置不能清楚地判断与甲虫取食相关的四种落叶类型。只有完全落叶的树冠有明显的红色边缘位置，可以区分其他三种类型的树冠。当使用分类树将这五个连续体去除的吸收的红边位置及其导数与中心带深度进行整合时，发现仅选择了两个连续体去除的吸收的带深度，这两个带深度是在近红外区域（NIR）的570和716 nm之间的红色吸收和936和990 nm之间的水吸收。这意味着连续体去除的修剪优于用于识别落叶类别的红色边缘。总体准确率为87.5%。生产者的准确性分别为100%，70%，100%和80%的健康植物，新落叶，完全落叶植物和落叶冠层。相应的用户准确率分别为90.91%、77.78%、100%和80%。因此，我们的结论是，单一的光谱数据为基础的变量未能分开的四个阶段，但两个连续去除的反射位于蓝色吸收和第一水吸收在近红外光谱的组合，提高了识别落叶冠层与动态落叶过程的生物控制剂。本研究将常用的二值（即落叶和非落叶）落叶检测技术发展到多个植被落叶水平。我们预计将这些评估技术应用于覆盖上述两个光谱区域的大面积高光谱数据收集，以进一步评估这些生物防治甲虫的有效性及其对美国西部盐柏管理的影响。
Hyperspectral data were collected from 40 canopies of saltcedar (Tamarix ramosissima): 10 healthy canopies and 30 canopies defoliated by an introduced biological control agent, the saltcedar leaf beetle (Diorhabda carinata). These data assessed multiple-level defoliations in response to the process of biological control. Two important characteristics of the hyperspectral data – red edges and continuum-removed absorptions – were used to discriminate four defoliation categories of saltcedar (healthy plants, newly defoliated plants, completely defoliated plants and refoliating plants) at the canopy level. The red edge positions were located at ranges of 711–716 nm, 706–712 nm, 694–698 nm and 715–719 nm for the four defoliation stages described above, respectively. These red edge positions alone could not clearly judge the four defoliation categories associated with feeding by the beetles. Only the completely defoliated canopies had distinct red edge positions that could be differentiated from the other three types of canopies. While using a classification tree to integrate the red edge positions and their derivatives with the central band depths of these five continuum-removed absorptions, it was found that only two band depths of the continuum-removed absorptions were selected, which were the red absorption between 570 and 716 nm and the water absorption between 936 and 990 nm in the near-infrared region (NIR). This implied that the continuum-removed absorptions outperformed the red edges for identifying the defoliation categories. The resulting overall accuracy was 87.5%. The producer accuracy was 100%, 70%, 100% and 80% for the healthy plants, newly defoliated, completely defoliated plants and refoliating canopies, respectively. The corresponding user accuracy was 90.91%, 77.78%, 100% and 80%. Therefore, we concluded that single spectral data based variable failed to separate the four stages but a combination of the two continuum-removed absorptions located in the blue absorption and the first water absorption in the NIR improved the identification of defoliated canopies associated with the dynamic defoliation process of the biological control agent. This study developed the defoliation detection techniques of commonly used binary levels (i.e. defoliation and non-defoliation) to multiple vegetation defoliation levels. We anticipate applying these assessment techniques to wide-area collections of hyperspectral data covering the two spectral regions as described above to further evaluate the effectiveness of these biological control beetles and their impact on saltcedar management in the Western United States.