作物重金属铜污染的HHT边际谱特征与污染预测模型

Abstract
Figure/Table
References
Related Citation (2)

Download: PDF (857 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract In order to understand the spectral responses and spectral characteristics of crop polluted by heavy metal, the cultivated experiments of corns were implemented based on the pot soil stressed by different CuSO₄·5H₂O concentrations, and the Hilbert marginal spectral characteristics and the predicting pollution degree method were researched by the Hilbert-Huang transform (HHT) according to the measured data of corn leaves' reflectance spectra and the Cu²⁺ contents in leaves under the different stress gradients. The characteristic changes of marginal spectra were analyzed on different Cu²⁺ pollution levels of corn leaves by constructing some marginal spectral characteristic parameters such as marginal spectrum entropy (MSE), marginal spectral amplitude (MSA), slope of marginal spectral slope (MSSS) and marginal spectral enclosing area (MSEA), at the same time, some exponential models on predicting the Cu²⁺ contents in corn leaves were put forward based on the correlation analysis and stepwise regressing statistics on the marginal spectral characteristic parameter values and the Cu²⁺ contents in leaves because of the heavy metal pollution. The results show that Hilbert marginal spectra of corn leaf under different Cu²⁺ stress gradients was distributed within 100Hz frequency continuously, the MSE values showed a variation trend of negative correlation with Cu²⁺ contents in leaves, but the MSA, MSSS and MSEA values showed a variation trend of positive correlation with the Cu²⁺ contents. And the MSEA can be used as the best index to measure or predict heavy metal pollution in crops due to its best correlation between MSEA values and Cu²⁺ contents in leaves. The application results of the exponential models, which were built by the MSE、MSA、MSSS and MSEA values for predicting the Cu²⁺ contents in corn leaves, were compared and proved that the MSEA exponential model had the best predicting ability.

Key words： potted corn copper pollution Hilbert-Huang transform marginal spectrum spectral characteristic parameter

Received: 18 June 2017

PACS:

X513

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	CHENG Long
	YANG Ke-ming
	WANG Xiao-feng
	ZHANG Wei
	SUN Tong-tong

Cite this article:

CHENG Long,YANG Ke-ming,WANG Xiao-feng等. Characteristic changes of HHT marginal spectra and pollution predicting models on crop polluted by the heavy metal copper[J]. CHINA ENVIRONMENTAL SCIENCECE, 2018, 38(1): 340-347.

URL:

http://manu36.magtech.com.cn/Jweb_zghjkx/EN/ OR http://manu36.magtech.com.cn/Jweb_zghjkx/EN/Y2018/V38/I1/340

[1]	王爽,李荣华,张增强,等.陕西潼关农田土壤及农作物重金属污染及潜在风险[J]. 中国环境科学, 2014,34(9):2313-2320.
[2]	施翔,陈益泰,吴天林,等.7个柳树无性系在Cu/Zn污染土壤中的生长及对Cu/Zn的吸收[J]. 中国环境科学, 2010,30(12):1683-1689.
[3]	赵兵.重金属铜污染对植物的影响[J]. 科教文汇旬刊, 2014,(28):102-103.
[4]	林义章,徐磊.铜污染对高等植物的生理毒害作用研究[J]. 中国生态农业学报, 2007,15(1):201-204.
[5]	韦美玉,刘丽萍.铜污染对芹菜生长及生理特性的影响[J]. 北方园艺, 2011,(9):33-36.
[6]	李春喜,张志娟,张黛静,等.Cu2+、Cd2+胁迫对小麦幼根SOD活性及其基因表达的影响[J]. 中国农业科技导报, 2011,13(4):92-98.
[7]	王琳,刘冉,李文慧,等.不同重金属离子胁迫对斜生栅藻生长及叶绿素荧光特性的影响[J]. 生态与农村环境学报, 2015, 31(5):743-747.
[8]	潘佩芬,杨武年,戴晓爱,等.不同森林植被的高光谱特征分析[J]. 遥感技术与应用, 2013,28(6):1000-1005.
[9]	关丽,刘湘南.镉污染胁迫下水稻生理生态表征高光谱识别模型[J]. 生态环境学报, 2009,18(2):488-493.
[10]	王京萌,赵文吉,郭逍宇,等.优势水生植物光谱特征识别研究[J]. 安徽农业科学, 2012,40(17):9424-9426.
[11]	Kloiber S M, Brezonik P L, Olmanson L G, et al. A procedure for regional lake water clarity assessment using Landsat multispectral data[J]. Remote Sensing of Environment, 2002,82(1):38-47.
[12]	Feng W, Yao X, Tian Y, et al. Monitoring leaf pigment status with hyperspectral remote sensing in wheat[J]. Australian Journal of Agricultural Research, 2008,59(8):748-760.
[13]	李蜜,刘湘南,刘美玲.基于模糊神经网络的水稻农田重金属污染水平高光谱预测模型[J]. 环境科学学报, 2010,30(10):2108-2115.
[14]	刘美玲,刘湘南,李婷,等.水稻锌污染胁迫的光谱奇异性分析[J]. 农业工程学报, 2010,26(3):191-197.
[15]	吕杰,刘湘南.利用支持向量机构建水稻镉含量高光谱预测模型[J]. 应用科学学报, 2012,30(1):105-110.
[16]	刘飞,杨可明,孙阳阳,等.谐波分析重金属铜铅胁迫玉米的污染效应[J]. 科学技术与工程, 2015,15(20):152-156.
[17]	Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis[J]. Proceedings of the Royal Society A Mathematical Physical & Engineering Sciences, 1998, 454(1971):903-995.
[18]	苗晟,王威廉,姚绍文.Hilbert-Huang变换发展历程及其应用[J]. 电子测量与仪器学报, 2014,28(8):812-818.
[19]	Zhang J, Tan X, Zheng P. Non-Destructive Detection of Wire Rope Discontinuities from Residual Magnetic Field Images Using the Hilbert-Huang Transform and Compressed Sensing[J]. Sensors, 2017,17(3):608.
[20]	Fu K, Qu J, Chai Y, et al. Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals[J]. Biomedical Signal Processing & Control, 2015,18:179-185.
[21]	Yang Z, Ling W K, Bingham C. Trend extraction based on separations of consecutive empirical mode decomposition components in Hilbert marginal spectrum[J]. Measurement, 2013, 46(8):2481-2491.
[22]	李庆亭,杨锋杰,张兵,等.重金属污染胁迫下盐肤木的生化效应及波谱特征[J]. 遥感学报, 2008,12(2):284-290.
[23]	刘锴,习岗,贺瑞瑞,等.渗透胁迫下玉米叶片电位波动边际谱的变化与意义[J]. 农业工程学报, 2017,33(1):199-205.
[24]	GB/T 14506.30-2010.硅酸岩石化学分析方法第30部分:44个元素量测定方法[S]. (国际、国家标准).