杨可明,张文文,程龙,王晓峰,赵骏武.玉米叶片重金属铜污染的ED-T-DSGA光谱分析模型[J].农业机械学报,2017,48(4):154-159.
YANG Keming,ZHANG Wenwen,CHENG Long,WANG Xiaofeng,ZHAO Junwu.ED-T-DSGA Spectral Analysis Model on Monitoring Heavy Metal Copper Pollution of Corn Leaves[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(4):154-159.
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玉米叶片重金属铜污染的ED-T-DSGA光谱分析模型   [下载全文]
ED-T-DSGA Spectral Analysis Model on Monitoring Heavy Metal Copper Pollution of Corn Leaves   [Download Pdf][in English]
投稿时间:2017-01-03  
DOI:10.6041/j.issn.1000-1298.2017.04.020
中文关键词:  盆栽玉米  铜胁迫  重金属污染  叶片光谱  光谱分析模型
基金项目:国家自然科学基金项目(41271436)和中央高校基本科研业务费专项资金项目(2009QD02)
作者单位
杨可明 中国矿业大学(北京) 
张文文 中国矿业大学(北京) 
程龙 中国矿业大学(北京) 
王晓峰 中国矿业大学(北京) 
赵骏武 中国矿业大学(北京) 
中文摘要:监测农作物的重金属污染和污染程度是高光谱遥感研究的一个热点。通过盆栽玉米的不同Cu2+胁迫梯度实验,在测定玉米叶片光谱和Cu2+含量的基础上,针对不同Cu2+胁迫梯度下玉米叶片光谱仍具有极高相似度以及传统光谱测度方法难以区分污染程度的问题,进行相似光谱的差异性有效区分方法研究。结合欧氏距离(ED)与光谱微分梯度角(DSAG)的正切处理,提出了一种基于光谱相似性测度的ED-T-DSGA光谱分析模型,并通过传统光谱测度方法应用比较、谐波分析(HA)技术和5种HA分解次数下的光谱重构结果分析,验证了ED-T-DSGA分析模型在区分极度相似光谱的微小差异上具有可行性与有效性。同时,ED-T-DSGA分析模型可用于测度不同Cu2+胁迫梯度下玉米叶片光谱间差异与污染程度。实验结果表明,ED-T-DSGA分析模型值越大,Cu2+胁迫梯度越大,玉米的重金属铜污染越严重;并且基于ED-T-DSGA分析模型进一步提取到“黄边”、“红谷”、“红边”和“近峰B”为Cu2+胁迫光谱响应的有效波段,这些敏感位置可为监测Cu2+污染程度提供有利依据。
YANG Keming, ZHANG Wenwen, CHENG Long, WANG Xiaofeng and ZHAO Junwu
China University of Mining & Technology (Beijing)
,China University of Mining & Technology (Beijing)
,China University of Mining & Technology (Beijing)
,China University of Mining & Technology (Beijing)
and China University of Mining & Technology (Beijing)
Key Words:potted corn  copper stress  heavy metal pollution  leaf spectrum  spectral analysis model
Abstract:Monitoring on heavy metal pollution and pollution degree of crops is a hot spot in hyperspectral remote sensing research. By conducting the potted-corn experiment stressed by copper, on the basis of the measured spectra and Cu2+ contents of corn leaves under Cu2+ stress with different concentrations, the research was carried out on the effective differentiating method of similar spectra according to the thinking that the corn leaves’spectra with different Cu2+ stress concentrations still have very high similarity and it is difficult to distinguish the different pollution degrees by using the traditional spectral measurement methods. The ED-T-DSGA spectral analysis model was proposed for spectral similarity measurement with the Euclidean distance (ED) and the tangent of spectral differential gradient angle (DSAG), which was proved to be feasible and effective in distinguishing the very small difference of extreme similarity spectra by comparing the traditional spectral measurement method, harmonic analysis (HA) technique and the results of spectral reconstruction of five HA decomposition times. Meanwhile, the ED-T-DSGA spectral analysis model can be used to measure spectral difference and monitor pollution degree of corn leaves stressed by different Cu2+concentrations. The experiment results showed that the greater the value of ED-T-DSGA spectral analysis model was, the greater the Cu2+ stress concentration was, which meant that corn was more seriously polluted by heavy metal copper. And some effective sub-band intervals such as the “yellow edge”, “red valley”, “red edge” and “near-peak B” were extracted by further study based on the ED-T-DSGA analysis model, these interval positions were the spectral responses of Cu2+ stress, the sensitive positions could be used as some favorable basis to monitor Cu2+ pollution degrees.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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