基于高光谱激光雷达的滞尘叶片光谱特征分析

城市绿植为城市生态系统提供自净功能, 起到净化空气以及滞尘降尘等多种环境保护作用, 而滞尘等因素也 会对绿植产生影响。为了研究滞尘对城市绿植叶片光谱特征的影响, 采集了四种常绿绿植 (八角金盘、石楠、香樟和 玉兰) 叶片样本, 使用高光谱激光雷达系统获取高光谱点云数据, 分析了滞尘对叶片光谱特征的影响。分析结果表明: 对于不同种类叶片, 滞尘对可见光波段反射率均有较大影响; 对于同种类叶片, 滞尘对近红外波段的反射率差异影响 较大, 可见光波段的反射率差异为 1.21%∼3.41%, 近红外为 1.76%∼8.49%; 线性四点内插法计算和光谱导数分析表明 滞尘对四种叶片的红边位置无显著影响; 四种叶片的叶面水含量指数 (LWI) 对滞尘的响应程度最小 (均小于 3.7%), 而 比值植被指数 (RVI) 对滞尘的响应程度最大 (除香樟外, 均大于 20.0%), 红边指数 (SDr)、简单比值指数 (SR) 和叶面叶 绿素指数 (LCI) 的响应程度稳定性较差。进一步建立了滞尘植被指数和响应程度的线性相关性拟合模型并进行了检 验, 其中以 LCI 为自变量建立的模型为最稳定拟合模型, 可表示为 y = −1.527x + 0.6597, 决定系数约为 0.88。

Spectral characteristics analysis of dust retention leaves based on hyperspectral Lidar

Urban green plants provide purification functions for the urban ecosystem, which play a variety of environmental protection roles such as air purification, dust retention and dust fall. On the other hand, dust retention can also affect green plants in turn. To study how dust retention affects the spectral characteristics of urban plant leaves, the point cloud data of four kinds of evergreen leaf samples (Fatsia japonica, Photinia stenophylla, Deyeuxia langsdorffii and Magnolia grandiflora) are collected with hyperspectral lidar, and then the effect of dust retention on leaf spectral characteristics is analyzed. The results show that dust retention has an influence on the reflectance in visible light band despite of plant species, and has a great impact on the reflectance difference in the near-infrared band for the same kind of leaves. The reflectance difference in visible light band is 1.21%∼3.41%, and that in near infrared is 1.76%∼8.49%. To all types of leaves, dust retention has no significant effect on the red edge position based on the linear four-point interpolation technique and spectral derivative analysis. The responsiveness of the leaf water content index (LWI) of the four kinds of leaves to dust retention is the smallest (less than 3.7%), that of the ratio vegetation index (RVI) is the largest (more than 20.0%, except Deyeuxia langsdorffii), and that of the red edge index (SDr), simple ratio index (SR) and leaf chlorophyll index (LCI) is low and unstable for all leaves. Furthermore, the fitting models based on the correlation between the dust retention vegetation index and responsiveness are established and tested, it is shown that the best one is based on LCI, which can be expressed as b = −1.527a + 0.659, and the determination coefficient R2 is about 0.88.