连续小波结合随机森林算法估算互花米草叶片叶绿素含量  

Estimation of Chlorophyll Content in Spartina Alterniflora Leaves Based on Continous Wavelet Transformation and Random Forest Algorithm

作　　者：管铖 刘明月 满卫东 张永彬 张清文 方铧 李想 高汇锋 GUAN Cheng;LIU Ming-yue;MAN Wei-dong;ZHANG Yong-bin;ZHANG Qing-wen;FANG Hua;LI Xiang;GAO Hui-feng(College of Mining Engineering,North China University of Science and Technology,Tangshan 063210,China;Hebei Industrial Technology Institute of Mine Ecological Remediation,Tangshan 063210,China;Collaborative Innovation Center of Green Development and Ecological Restoration of Mineral Resources,Tangshan 063210,China;Tangshan Key Laboratory of Resources and Environmental Remote Sensing,Tangshan 063210,China)

机构地区：[1]华北理工大学矿业工程学院,河北唐山063210 [2]河北省矿区生态修复产业技术研究院,河北唐山063210 [3]矿产资源绿色开发与生态修复协同创新中心,河北唐山063210 [4]唐山市资源与环境遥感重点实验室,河北唐山063210

出　　处：《光谱学与光谱分析》2024年第10期2993-3000,共8页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(41901375,42101393,52274166);河北省自然科学基金项目(D2019209322,D2022209005);河北省高等学校科学技术研究项目青年拔尖人才项目(BJ2020058);唐山市科技计划重点研发项目(22150221J)资助。

摘　　要：叶绿素含量是检测植物生理状态的关键指标,精准估算互花米草叶绿素含量对于表征其组分含量性状与量化其生理状态具有重要的意义。以独流减河湿地互花米草实测高光谱反射率和叶绿素含量为数据源,采用连续投影算法(sequential projection algorithm,SPA)对原始光谱及其数学变换和连续小波变换光谱进行特征提取,基于随机森林回归(random forest regression,RFR)算法建立互花米草叶片叶绿素含量的高光谱估算模型。结果表明:(1)连续小波分解低尺度下互花米草光谱时间分辨率更精确且频率更高,对应的小波函数较窄,可以更好区分光谱间差异,突出特征光谱信息。(2)除倒数(1/R)和对数的一阶微分[(logR)′]外,光谱数学变换与连续小波分解方法可有效反应光谱细节特征,且小波分解效果总体上优于数学变换,小波分解L10尺度与一阶微分(R′)分别与叶绿素含量的相关性达到0.78和0.77。(3)一阶微分(R′)、倒数的一阶微分[(1/R)′]、对数(logR)变换和连续小波分解可提升光谱对互花米草叶片叶绿素含量的估算能力,其中基于一阶微分R′(R^(2)=0.776,RMSE=0.510,RPD=1.893)和连续小波分解下L2、L3与L4多尺度相结合构建的模型(R^(2)=0.871,RMSE=0.305,RPD=3.846)分别为两种处理下的最优模型。研究表明高光谱技术可以作为互花米草叶片叶绿素含量的无损检测手段,连续小波分解后多尺度结合建立的高光谱估算模型可更加准确估算互花米草叶片叶绿素含量。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(R^(2)=0.776,RMSE=0.510,RPD=1.893)and CWT with the multiscale of L2,L3 and L4(R^(2)=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 Spar

关 键 词：互花米草 叶绿素含量 高光谱 连续小波分解 随机森林 

分 类 号：X87[环境科学与工程—环境工程]