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| Estimation of Chlorophyll Content in Spartina Alterniflora Leaves Based on Continous Wavelet Transformation and Random Forest Algorithm |
| GUAN Cheng1, LIU Ming-yue1, 2, 3, 4*, MAN Wei-dong1, 2, 3, 4, ZHANG Yong-bin1, ZHANG Qing-wen1, FANG Hua1, LI Xiang1, GAO Hui-feng1 |
1. College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China
2. Hebei Industrial Technology Institute of Mine Ecological Remediation, Tangshan 063210, China
3. Collaborative Innovation Center of Green Development and Ecological Restoration of Mineral Resources, Tangshan 063210, China
4. Tangshan Key Laboratory of Resources and Environmental Remote Sensing, Tangshan 063210, China
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Abstract Chlorophyll content is a key indicator of the physiological status of plants, and accurate estimation of chlorophyll content is important for characterizing its component content traits and quantifying its physiological status. In this paper, the hyperspectral reflectance and chlorophyll content (SPAD) of Spartina alterniflora in the Duliu-river wetland were used as the data source, the original spectrum was mathematically transformed and processed with continuous wavelet transformation (CWT). The spectral features were extracted using Sequential Projection Algorithm (SPA). And the hyperspectral estimation model of leaf chlorophyll content of Spartina alterniflora was developed based on random forest regression (RFR) algorithm. The results showed that: (1) CWT had more accurate time resolution and higher frequency in the low scale spectra, corresponding to a narrow wavelet function, which could better distinguish the differences between the spectra and highlight the characteristic spectral information. (2) Except for reciprocal and logarithmic first derivative spectrals, the spectral mathematical transform and CWT methods could effectively respond to the spectral detail features. CWT was generally better than the spectral mathematical transform, and the correlation between L10 scale and first derivative spectral reached 0.78 and 0.77. (3) First derivative spectral, reciprocal first derivative spectral, logarithmic derivative spectral and CWT could enhance the ability of spectral estimation of Spartina alterniflora chlorophyll content. The RF models based on first derivative spectral (R2=0.776, RMSE=0.510, RPD=1.893) and CWT with the multiscale of L2, L3 and L4 (R2=0.871, RMSE=0.305, RPD=3.846) were the optimal models. This study shows that hyperspectral techniques could be used as a non-destructive means of detecting chlorophyll content in leaves of Spartina alterniflora, and that the hyperspectral estimation model built by combining multiple scales after continuous wavelet decomposition could more estimate chlorophyll content in leaves of Spartina alterniflora.
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Received: 2023-07-18
Accepted: 2023-12-22
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Corresponding Authors:
LIU Ming-yue
E-mail: liumy917@ncst.edu.cn
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