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首页> 外文期刊>Remote Sensing >A Wavelet-Based Area Parameter for Indirectly Estimating Copper Concentration in Carex Leaves from Canopy Reflectance
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A Wavelet-Based Area Parameter for Indirectly Estimating Copper Concentration in Carex Leaves from Canopy Reflectance

机译：基于小波的面积参数，可通过冠层反射率间接估算苔草叶中的铜含量

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摘要

Due to the absence of evident absorption features and low concentrations, the copper (Cu) concentration in plant leaves has rarely been estimated from hyperspectral remote sensing data. The capability of remotely-sensed estimation of foliar Cu concentrations largely depends on its close relation to foliar chlorophyll concentration. To enhance the subtle spectral changes related to chlorophyll concentration under Cu stress, this study described a wavelet-based area parameter (SWT (605?720), the sum of reconstructed detail reflectance at fourth decomposition level over 605?720 nm using discrete wavelet transform) from the canopy hyperspectral reflectance (350?2500 nm, N = 71) of Carex (C. cinerascens). The results showed that Cu concentrations had negative and strong correlation with chlorophyll concentrations (r = －0.719, p 0.001). Based on 1000 random dataset partitioning experiments, the 1000 linear calibration models provided a mean R2Val (determination coefficient of validation) value of 0.706 and an RPD (residual prediction deviation) value of 1.75 for Cu estimation. The bootstrapping and ANOVA test results showed that SWT (605?720) significantly (p 0.05) outperformed published chlorophyll-related and wavelet-based spectral parameters. It was concluded here that the wavelet-based area parameter (i.e., SWT (605?720)) has potential ability to indirectly estimate Cu concentrations in Carex leaves through the strong correlation between Cu and chlorophyll. The method presented in this pilot study may be used to estimate the concentrations of other heavy metals. However, further research is needed to test its transferability and robustness for estimating Cu concentrations on other plant species in different biological and environmental conditions.
机译：由于缺乏明显的吸收特征和低浓度，很少根据高光谱遥感数据估算植物叶片中的铜（Cu）浓度。遥感估算叶面铜浓度的能力在很大程度上取决于其与叶面叶绿素浓度的密切关系。为了增强与铜胁迫有关的叶绿素浓度相关的细微光谱变化，本研究描述了一种基于小波的面积参数（S WT（605？720）），即第四分解水平上重建的细节反射率之和。使用Carex（C. cinerascens）的冠层高光谱反射率（350-2500 nm，N = 71）使用离散小波变换检测605-720 nm。结果表明，铜的含量与叶绿素的含量呈负相关且极强的相关性（r = －0.719，p <0.001）。基于1000个随机数据集分区实验，这1000个线性校准模型提供的平均R 2 Val （验证的确定系数）值为0.706，RPD（残差预测偏差） Cu估计值为1.75。自举和方差分析测试结果表明，S WT（605？720）显着（p <0.05）优于已发表的叶绿素相关和基于小波的光谱参数。在此得出结论，基于小波的面积参数（即S WT（605？720））具有潜在的能力，因为Cu和叶绿素之间的强相关性间接估计了Carex叶片中的Cu浓度。本试验研究中介绍的方法可用于估算其他重金属的浓度。但是，需要进行进一步的研究以测试其可迁移性和稳健性，以估计不同生物学和环境条件下其他植物物种上的Cu浓度。

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