A Wavelet-Based Area Parameter for Indirectly Estimating Copper Concentration in Carex Leaves from Canopy Reflectance

Remote Sensing

Junjie Wang³  Tiejun Wang²  Tiezhu Shi³  Guofeng Wu³  Andrew K. Skidmore²  Yoshio Inoue¹ 

[1] Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and Geo-Information & Shenzhen Key Laboratory of Spatial-temporal Smart Sensing and Services & College of Life and Marine Sciences, Shenzhen University, Shenzhen 518060, China;;Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500-AE Enschede, The Netherlands; E-Mails:;Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and Geo-Information & Shenzhen Key Laboratory of Spatial-temporal Smart Sensing and Services & College of Life and Marine Sciences, Shenzhen University, Shenzhen 518060, China; E-Mails:

关键词: canopy reflectance;    foliar chlorophyll concentration;    foliar copper concentration;    hyperspectral remote sensing;    spectral parameter;    wavelet transform;   

DOI  :  10.3390/rs71115340

来源: mdpi

【 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 (S_{WT (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 R²_Val (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 S_{WT (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., S_{WT (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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.