重金属铜胁迫下玉米的光谱特征及监测研究

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李燕, 杨可明, 荣坤鹏, 张超, 高鹏, 程凤. 重金属铜胁迫下玉米的光谱特征及监测研究[J]. 光谱学与光谱分析, 2019,39(9): 2823-2828.
LI Yan, YANG Ke-ming, RONG Kun-peng, ZHANG Chao, GAO Peng, CHENG Feng. Spectral Characteristics and Identification Research of Corn under Copper Stress[J]. Spectroscopy and Spectral Analysis, 2019,39(9): 2823-2828.
Doi:10.3964/j.issn.1000-0593(2019)09-2823-06 复制到剪切板

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重金属铜胁迫下玉米的光谱特征及监测研究

李燕, 杨可明^*, 荣坤鹏, 张超, 高鹏, 程凤

中国矿业大学(北京)煤炭资源与安全开采国家重点实验室, 北京 100083

*通讯联系人 e-mail: ykm69@163.com

作者简介: 李燕, 女, 1992年生, 中国矿业大学(北京)地球科学与测绘工程学院硕士研究生 e-mail: 1811149569@qq.com

收稿日期: 2018-10-11 接受日期: 2018-12-09

基金: 煤炭资源与安全开采国家重点实验室2017年开放基金课题基金项目(SKLCRSM17KFA09), 国家自然科学基金项目(41271436), 中央高校基本科研业务费专项资金项目(2009QD02)资助

摘要

农作物重金属污染监测是当今高光谱遥感研究的重要内容之一, 旨在设计一种新的窄带植被指数, 以实现不同培育期的两种玉米品种的重金属铜胁迫监测。研究设计了不同浓度的铜污染实验, 采用SVCHR-1024I型高性能地物光谱仪测量不同浓度铜离子(Cu²⁺)胁迫下玉米叶片的光谱反射率, 并同步获取了玉米叶片中Cu²⁺含量数据。首先, 对玉米叶片原始光谱数据进行一阶差分处理, 并计算一阶差分反射率与叶片中Cu²⁺含量的相关系数( r), 筛选对铜胁迫敏感的波段。计算结果显示, 489~497, 632和677 nm波长附近的一阶差分反射率与叶片中Cu²⁺含量显著相关, 可将其视为敏感波段。其次, 根据以上3个敏感波段, 建立基于一阶差分反射率的铜胁迫植被指数(dVI)。对所有可能的dVIs和Cu²⁺含量进行一元回归分析, 并采用决定系数( R²)和均方根误差(RMSE)对回归结果进行评估, 以筛选最佳指数。最后, 采用不同生长年份的玉米实验数据对敏感波段的稳定性及dVI的适用性进行了验证评估; 同时, 通过与归一化植被指数(NDVI)、红边叶绿素指数(CI_red-edge)、红边位置(REP)、光化学反射指数(PRI)等常规重金属胁迫植被指数进行应用比较, 证明dVI更具有优越性。结果表明: 一阶差分处理后, 在450~500, 630~680和677 nm波长处的叶片反射率与Cu²⁺含量的相关系数明显增大。基于一阶差分反射率的特征波段具有稳定性, 对于不同生长年份的玉米叶片数据, 特征波段的波长位置不变。一元回归分析结果表明, 结合497, 632和677 nm波长的一阶差分反射率的指数与Cu²⁺含量具有显著的相关性, 对于不同生长年份的2种玉米品种数据集, R²都高达0.75以上。另外, 与常规植被指数比较结果表明, 该研究所提出的dVI具有更好的鲁棒性及有效性, 可为冠层尺度的重金属胁迫监测提供理论基础。

关键词: 玉米; 铜胁迫; 植被指数; 特征波段; 高光谱

中图分类号:X87 文献标志码:A

Spectral Characteristics and Identification Research of Corn under Copper Stress

LI Yan, YANG Ke-ming^*, RONG Kun-peng, ZHANG Chao, GAO Peng, CHENG Feng

State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology (Beijing), Beijing 100083, China

*Corresponding author

Abstract

The monitoring of heavy metal pollution in crops is one important application of hyperspectral remote sensing study. The objective of this work was to develop a new narrow-band vegetation index to characterize the Cu (copper) stress degree in two corn species at two growing years. The experiment on the copper pollution was designed based on its different concentrations, meanwhile, the hyperspectral reflectance of corn leaves stressed by different Cu²⁺ concentrations were measured using hand-held spectrometer(SVC, USA) and leaf Cu²⁺ contents were also measured. The first difference reflectance and biochemical data of corn were analyzed using Pearson correlation coefficient ( r) to select wavelengths sensitive to Cu stress. The calculated Pearson correlation coefficients suggested that the first difference reflectance near 489~497, 632 and 677 nm wavelengths was significantly correlated with Cu²⁺ contents in leaves. The selected wavelengths of 489~497, 632 and 677 nm were used to establish the Cu stress vegetation index based on the first difference reflectance (dVI). To select index with the highest possible correlation to Cu stress, all possible dVIs were related through simple regression models with Cu²⁺ contents andthe predictive abilities of those models were evaluated through the R² values and the root mean square error (RMSE). The stability of the sensitive bands and the applicability of dVI were assessed using corn data from different growth years. Meanwhile, the performance of dVI was compared with that of existing popular vegetation index (VIs) related to heavy metal stress, such asnormalized difference vegetation index (NDVI), red-edge chlorophyll index (CI_red-edge), red-edge position (REP), photochemical reflectance index (PRI). The results suggest that the corn spectral characteristics in response to copper stress are enhanced with the first-order difference treatment. Compared with the original reflectance, the correlation coefficient between first difference reflectance at wavelengths of 450~500, 630~680 and 677 nm and Cu²⁺ content increases. The wavelength position of copper stress sensitive band based on the first-order differential reflectance is stable for the data sets of different growth years. The index that combined the first difference reflectance in 497, 632 and 677 nm wavelengths is found to be a potential useful index to predict leave Cu concentration for different data sets. And the correlation of dVI was much stronger than that of other VIs for all the tested data sets from two corn species at two growing years. The proposed dVI characterizes the Cu stress degree on vegetation with advantages of better effectiveness and robustness. This study focuses on the spectral reflectance at the leaf scale, so it is expected that future work will extend it to canopy scale.

Keyword: Corn; Copper stress; Vegetation index; Characteristic wavelength; Hyperspectral

文章图片

引言

矿物加工、废水灌溉及矿物肥料使用等工农业活动造成的土壤重金属污染问题日益严重, 给人类的生产生活带来严重影响^[1]。土壤中的重金属具有不可降解、不易迁移等特点, 当其累积量超过背景值时, 会对作物生长发育产生严重影响^{[2, 3]}, 并通过食物链威胁动物和人类生命健康。植被重金属胁迫程度是土壤污染的重要指标, 实现植被重金属污染快速有效的监测对生态系统的安全与稳定具有重要意义。植被受重金属胁迫后, 正常生长状态受到抑制, 其生理生化参数会发生变化, 从而影响其光谱特征^[4]。

近年来, 国内外很多学者对利用遥感监测植被重金属污染进行了研究, 而基于植被指数(vegetation indices, VIs)的重金属胁迫监测技术是最常用方法之一。其中, 基于可见光和近红外波段的植被指数被广泛应用于植被胁迫监测之中^[5]。李庆亭等^[6]揭示了红边特征、归一化植被指数(normalized difference vegetation index, NDVI)与盐木叶片中重金属铜含量关系显著, 可以作为植被铜污染遥感图像特征提取的参考。 Liu等^[7]利用红边叶绿素指数(red-edge chlorophyll index, CI_red-edge)、红边位置(red-edge position, REP)和归一化红边指数(normalized differences red edge, NDRE), 并结合Sentinel-2影像数据对水稻镉胁迫进行了监测, 结果表明基于红边植被指数的时空异常探测法可有效区分水稻镉胁迫和与病害胁迫。 Asmaryan^[8]利用World View-2影像的红边波段成功地提取了植被胁迫异常信息; Deventer等^[9]利用NDVI和REP研究了酸性矿井水对植被的影响, 结果显示NDVI和REP能有效地提供植被胁迫状况的指示信息; Martinez等^[10]利用REP和NDVI等植被指数并采用多元线性回归法成功地量化了铯(Cs)污染与反射率之间的关系。除红边植被指数外, Rathod等^[11]指出叶绿素指数、水胁迫指数及波段深度与植被叶片中重金属含量具有显著相关性, 也可用于区分植被重金属胁迫程度。尽管现有的这些VIs与植被重金属含量均有较好的相关性, 但由于它们与植被生理生化参数有关, 如NDVI对叶绿素浓度及细胞结构较为敏感, 因而稳定性易受到这些因素影响。近年来, 一些新的重金属胁迫指数陆续被提出, Wang和刘美玲等^{[12, 13]}提出基于小波面积和小波系数的重金属胁迫指数, 然而由于计算复杂, 小波变换难以应用于生产实践。因此, 发展计算简单、稳定性好且能有效区分作物重金属胁迫的新指数势在必行, 以期为不同问题的解决提供更多选择。

基于以上分析, 本研究基于不同培育期的两种玉米品种的铜胁迫试验, 旨在设计一种更加简单、有效、稳定的重金属胁迫指数, 以实现玉米重金属胁迫状况的监测。该研究结果为进一步在冠层尺度及像素尺度获取玉米重金属胁迫信息提供了有效的方法, 同时对其他作物重金属胁迫的遥感监测也具有重要的借鉴和参考价值。

1 实验部分

本研究采用的试验数据来源于2014年、 2016年“ 中糯1号” 和2017年“ 密糯8号” 的玉米种子盆栽试验。试验采用CuSO₄· 5H₂O溶液胁迫玉米生长, 玉米叶片光谱采用SVCHR-1024I型高性能地物光谱仪进行测定, 其光谱测量范围为350~2 500 nm, 其中350~1 000 nm光谱采样间隔为1.5 nm, 1 000~1 900 nm光谱采样间隔为3.6 nm, 900~2 500 nm光谱采样间隔为2.5 nm。玉米叶片中Cu²⁺含量采用WFX-120型原子吸收分光光度计及电感耦合等离子发射光谱仪(ICP-OES)进行测定。

14年中糯1号: 选用有底漏的盆钵进行“ 中糯1号” 玉米种子培育。设置胁迫梯度为0和250 μ g· g^-1的CuSO₄· 5H₂O溶液, 将其翻土加入到玉米实验盆钵中, 每梯度设置3组平行试验, 共6盆。 2014年5月6日对玉米种子进行催芽处理, 出苗后根据玉米生长情况向各盆栽中添加适量NH₄NO₃, KH₂PO₄和KNO₃营养液。玉米培育期间, 各盆栽统一水肥管理, 定期通风与浇水并保持适宜的培育温度与湿度。 2014年7月18日对玉米叶片进行反射光谱测定。光谱测定在50 W卤素灯光源照射下进行, 视场角设置为4° , 并使用标准白板对光谱数据进行校正。光谱测试每盆玉米植株的老、中、新三种代表性叶片, 取3组数据均值作为该盆玉米反射率值, 最终共获取6组光谱数据。对采集过光谱数据的叶片进行冲洗、烘干、粉碎等样品预处理, 再经高纯硝酸和高氯酸消化处理后用WFX-120型原子吸收分光光度计对各盆玉米叶片中Cu²⁺含量进行测定。

16年中糯1号: 玉米培育方式、光谱数据采集及Cu²⁺含量测定方式均与14年相同。胁迫浓度设置为0(ck), 100, 200, 300, 400, 500, 600和700 μ g· g^-1 8个梯度, 每梯度设置3组平行试验。采用3组平行试验光谱数据的均值作为各胁迫梯度光谱值, 最终获取8组光谱数据及各梯度玉米叶片中的Cu²⁺含量数据。

17年密糯8号: 玉米培育方式、光谱数据采集均与14年相同, Cu²⁺含量采用电感耦合等离子发射光谱仪(ICP-OES)进行测定。胁迫浓度设置0, 50, 100, 150, 200, 300, 400, 600, 800, 1 000和1 200 μ g· g^-1 11个梯度, 每个梯度设置3组平行实验, 共33盆盆栽。采集各盆光谱数据, 最终获取33组光谱值及对应的Cu²⁺含量数据。

本文将数据集分为两部分, 75%用于实验分析, 包括16年“ 中糯1号” 数据集及17年“ 密糯8号” 数据集中的27组数据; 25%用于指数验证, 包括2014年“ 中糯1号” 数据集及17年“ 密糯8号” 数据集中的6组数据进行模型验证。

1.1 光谱数据预处理

文献[14]表示植被光谱的可见光及近红外波段对胁迫较为敏感, 因此本文选取400~1 000 nm波段区间的光谱数据进行分析。为消除背景噪声及其他影响, 本文对原始光谱数据进行一阶差分处理, 一阶差分计算方法^[15]如下

$ρ' (λ_{i}) = [ρ (λ_{i + 4}) - ρ (λ_{i - 4})] / 8$ (1)

式中, λ _i为波长, ρ '(λ _i)为波长λ _i处的一阶差分反射率, ρ (λ _i₊₄), ρ (λ _i_-4)为波长λ _i₊₄, λ _i_-4对应的光谱反射率, i=1, 2, …, n(n为总的波段数)。

1.2 敏感波段选择

敏感波段的选择依据光谱反射率与叶片中Cu²⁺含量相关系数的大小, 基于玉米叶片原始反射率和一阶差分反射率的相关系数随波长变化的曲线如图1所示。不同生长期的两种玉米植株的相关系数曲线变化趋势相似。由于“ 中糯1号” 和“ 密糯8号” 玉米胁迫实验控制条件相同, 因此两者相关系数曲线的这种相似性变化是由重金属铜胁迫所致。通过图1(a)和(b)对比发现, 玉米叶片光谱反射率经一阶差分处理后, 在波长450~500, 630~680和677 nm处相关系数值明显增大, 这表明一阶差分处理能够增强玉米铜胁迫的光谱响应, 因此可以使用一阶差分反射率对铜胁迫下的玉米光谱特征做进一步的分析。

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	图1 玉米叶片光谱原始反射率(a)和一阶差分反射率(b)与Cu²⁺含量相关系数随波长的变化阴影部分对应\|r\|< 0.75Fig.1 Variations with wavelength of correlation coefficient of original spectral reflectance(a) and first difference reflectance (b) against Cu²⁺ contents in corn leaves The shaded portion corresponds to \|r\|< 0.75

综合分析图1(b)中两条相关系数曲线可得, 在波长489~497和677 nm处, 一阶差分反射率与叶片中Cu²⁺含量呈显著正相关, 在632 nm处呈显著负相关, 说明重金属铜对这三个波段的反射率影响较大, 可将其作为监测重金属铜胁迫的敏感波段。

1.3 指数构造

为了充分利用489~497, 632和677 nm三个波段的信息, 减少外界因素影响, 设计基于一阶差分反射率的铜胁迫植被指数dVI

$dVI = ρ' (λ_{2}) ρ' (λ_{3}) / ρ' (λ_{1})$ (2)

其中λ ₁, λ ₂, λ ₃为波长, λ ₁∈ [489~497 nm], λ ₂=632 nm, λ ₃=677 nm, ρ '(λ )为一阶差分反射率。由于λ ₁取值不同, 计算所得的dVI不同, 故将不同λ ₁所对应的dVI值记为dVI(λ ₁)。

2 结果与讨论

2.1 dVI监测能力对比分析

为评估dVI对重金属铜胁迫的监测能力, 本文以实验组光谱数据的dVI值为x值, 叶片中Cu²⁺含量为y值, 建立一元回归模型, 并采用决定系数(R²)、均方根误差(RMSE)及F检验中的P值(p< 0.05表明结果具有统计学意义)对回归结果进行评估。不同λ ₁对应不同dVI值, 对所有dVI与Cu²⁺含量的回归系数R²进行降序排列, 前30%的拟合结果如图2所示。

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图2 dVI(497), dVI(496)和dVI(495)与玉米叶片中Cu²⁺含量的相关关系
(a)— (c): 17年密糯8号; (d)— (f): 16年中糯1号圆圈所标注的数据由于其学生化残差较大, 不参与回归分析Fig.2 Relations of leaf Cu²⁺ concentration against dVI(497), dVI(496) and dVI(495) obtained with the calibration datasets
(a)— (c): Minuo No.8; (d)— (f) Zhongnuo No.1The data point circumscribed with a dotted line was eliminated from the analysis because it had a very high external studentized residual

总体上, 三个dVI都能较好地反应植被铜胁迫程度, R²都大于0.8。对于17年密糯8号数据集[图2(a)— (c)], dVI(497)与Cu²⁺含量拟合效果较好, R²为0.852, RMSE为1.456, p< 0.000 1。对于16年中糯1号数据集[图2(d)— (f)], dVI(496)与Cu²⁺含量拟合效果较好, R²达到0.940, RMSE为9.38, p=0.000 3; 而dVI(497)与Cu²⁺含量也具有很好的相关性, 其R²达到0.940, RMSE为9.407, p=0.000 3。基于以上分析表明, dVI(497)对不同生长期的两种玉米重金属铜胁迫程度均具有较好的监测能力。

2.2 结果验证

2.2.1 敏感波段稳定性分析

为了评估敏感波段的稳定性与可靠性, 图3展示了基于验证组数据的一阶差分反射率与铜含量的相关系数曲线。由图3可以看到, 两条相关系数曲线形状与图1(b)相似, 在489~497和677 nm波长处, 一阶差分反射率与铜含量呈现显著正相关, 在632 nm波长处呈显著负相关, 证明铜胁迫的敏感波段具有较好的稳定性。

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	图3 玉米叶片一阶差分反射率与叶片中Cu²⁺含量相关系数随波长变化Fig.3 Variations with wavelength of correlation coefficient of first difference reflectance against Cu²⁺ contents in corn leaves

2.2.2 dVI监测能力验证

为评估dVI重金属胁迫监测的可靠性和适用性, 图4展示了基于验证组数据的dVI和铜含量的拟合结果。总体上, 三个dVI与Cu²⁺含量均有较显著相关性, R²都大于0.7, 其中dVI(497)拟合结果最好, R²为0.764, RMSE为3.686, p=0.000 2。基于以上结果表明, 对不同数据集, dVI仍有一定的稳定性, 其中dVI(497)具有最佳的铜胁迫监测能力。

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	图4 dVI(497), dVI(496)和dVI(495)与玉米叶片中Cu²⁺含量相关关系图中虚线分别为“ 14年中糯” 和“ 17年密糯” 数据集拟合结果Fig.4 Relations of leaf Cu²⁺ concentration against dVI(497), dVI(496) and dVI(495) obtained with the validation datasets from Zhongnuo No.1 and Minuo No.3

为进一步评价dVI(497)对同种作物在不同生长年份条件下的铜胁迫监测能力, 图5展示了基于中糯1号数据的dVI(497)与铜含量拟合结果。 dVI(497)与Cu²⁺含量具有显著相关性, R²为0.785, p< 0.000 1。该结果证明dVI(497)在不同生长年份条件下具有较强的稳定性与有效性。

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	图5 dVI(497)与玉米叶片中Cu²⁺含量相关关系Fig.5 Relation between leaf Cu²⁺ concentration and dVI(497) for Zhongnuo No.1 dataset

2.2.3 dVI(497)与常规指数比较分析

为了验证本文所提出的dVI的优越性, 将其与重金属污染监测的6个常规植被指数的监测结果进行对比分析, 常规指数的计算方法见表1。图6展示了基于实验组和验证组数据的VIs与玉米叶片中Cu²⁺含量的决定系数。对比六个指数发现REP, SIPI具有较高的R², 对于三个数据集, REP与叶片中铜含量的回归决定系数分别为0.820, 0.588和0.682, SIPI与铜含量的决定系数分别为0.767, 0.383和0.822。但与dVI(497)(R²分别为0.852, 0.940, 0.764)相比, REP和SIPI的监测能力仍然较低, 且不适用于所有数据集, 在监测16年中糯数据集污染程度时表现较差。本文对比研究显示, dVI在铜胁迫监测上优于常规植被指数, 具有更强的稳定性与监测能力。

表1 重金属污染监测相关光谱指数 Table 1 Spectral indices developed as heavy metal stress indicators

指数缩写	指数名称	计算公式	参考文献
CI_red-edge	Red-edge chlorophyll index	R₇₈₃/R₇₀₅-1
REP	Red edge position	705+35× $\frac{(R_{665} + R_{783}) / 2 - R_{705}}{R_{740} - R_{705}}$	[12]
MTCI	MERIS terrestrial chlorophyll index	(R₇₄₀-R₇₀₅)/(R₇₀₅-R₆₆₅)
NDVI	Normalized difference nitrogen index	(R₈₀₀-R₆₈₀)/(R₈₀₀+R₆₈₀)
SIPI	Structure insensitive pigment index	(R₈₀₀-R₄₄₅)/(R₈₀₀-R₆₈₀)	[15]
PRI	Photochemical reflectance index	(R₅₇₀-R₅₃₁)/(R₅₇₀+R₅₃₁)

表1 重金属污染监测相关光谱指数 Table 1 Spectral indices developed as heavy metal stress indicators

	Figure Option View Download New Window
	图6 常规光谱指数与玉米叶片中Cu²⁺含量决定系数Fig.6 Coefficients of determination for the ralationships between the published indices and Cu²⁺ contents in corn leaves

3 结论

通过选择敏感波段构造一种新的窄带植被指数对玉米重金属铜胁迫进行监测, 得出以下结论:

(1) 一阶差分处理可增强玉米铜胁迫的光谱响应特征, 特别是在对叶绿素强吸收的蓝光、红光区和对叶绿素强反射的“ 红边” 区域, 一阶差分反射率与Cu²⁺含量相关系数值均显著增大。

(2) 基于一阶差分光谱的玉米叶片铜胁迫敏感波段具有稳定性, 对于不同数据集, 在波长489~497和677 nm处, 一阶差分反射率与叶片中Cu²⁺含量呈显著正相关, 在632 nm处呈显著负相关。

(3) 将dVI(497)用于不同数据集的重金属铜胁迫监测, 发现dVI(497)与叶片铜含量都存在较强的相关性, 回归决定系数R²都高达0.75以上; 同时与常规植被指数比较, 证明dVI(497)在监测玉米重金属铜胁迫具有更好的稳定性与监测能力。

从叶片尺度上对dVI的性能进行了检验, 证明该指数在监测重金属铜污染方面具有一定潜力, 可进一步从冠层尺度将其应用于更多植被类型的重金属污染监测。

参考文献

文献列表

[1]	LI Rong-hua, FENG Jing, LI Xiao-long, et al(李荣华, 冯静, 李晓龙, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2015, 46(10): 223. [本文引用:1]
[2]	PAN Pan, YANG Jun-cheng, DENG Shi-huai, et al(潘攀, 杨俊诚, 邓仕槐, 等). Journal of Agro-Environment Science(农业环境科学学报), 2011, 30(12): 2389. [本文引用:1]
[3]	Srivastava V, Sarkar A, Singh S, et al. Frontiers in Environmental Science, 2017, 5: 64. [本文引用:1]
[4]	ZHU Ye-qing, QU Yong-hua, LIU Su-hong, et al(朱叶青, 屈永华, 刘素红, 等). Journal of Remote Sensing(遥感学报), 2014, 18(2): 335. [本文引用:1]
[5]	Sridhar B B M, Han F X, Diehl S V, et al. International Journal of Remote Sensing, 2007, 28(5): 1041. [本文引用:1]
[6]	LI Qing-ting, YANG Feng-jie, ZHANG Bing, et al(李庆亭, 杨锋杰, 张兵, 等). Journal of Remote Sensing(遥感学报), 2008, (2): 284. [本文引用:1]
[7]	Liu M, Wang T, Skidmore A K, et al. Science of the Total Environment, 2018, 637-638: 18. [本文引用:1]
[8]	Asmaryan S, Warner T A, Muradyan V, et al. Remote Sensing Letters, 2013, 4(2): 200. [本文引用:1]
[9]	Deventer H V, Cho M A. South African Journal of Science, 2014, 110(7/8): 1. [本文引用:1]
[10]	Martinez N E, Sharp J L, Kuhne W W, et al. International Journal of Remote Sensing, 2015, 36(23): 5887. [本文引用:1]
[11]	Rathod P H, Brackhage C, Meer F D V D, et al. European Journal of Remote Sensing, 2015, 48(3): 283. [本文引用:1]
[12]	LIU Mei-ling, LIU Xiang-nan, CAO Shi, et al(刘美玲, 刘湘南, 曹仕, 等). Journal of Remote Sensing(遥感学报), 2011, 15(4): 811. [本文引用:1]
[13]	Wang J, Wang T, Shi T. et al. Remote Sensing, 2015, 7(11): 15340. [本文引用:1]
[14]	Carter G. International Journal of Remote Sensing, 1994, 15(3): 697. [本文引用:1]
[15]	Castro P A, Martín F. Garbulsky. International Journal of Remote Sensing, 2018, 39(10): 3138. [本文引用:1]

2015

0.0

... 引言矿物加工、废水灌溉及矿物肥料使用等工农业活动造成的土壤重金属污染问题日益严重, 给人类的生产生活带来严重影响^[1] ...

2011

0.0

... 土壤中的重金属具有不可降解、不易迁移等特点, 当其累积量超过背景值时, 会对作物生长发育产生严重影响^[2,3], 并通过食物链威胁动物和人类生命健康 ...

2017

0.0

2014

0.0

ZHU

Ye-qing

, QU

Yong-hua

, LIU

Su-hong

, et al(朱叶青, 屈永华, 刘素红, 等). Journal of Remote Sensing(遥感学报), 2014, 18(2): 335.

The leaf reflectance spectra of plants polluted with heavy metal copper exhibit abnormal behavior. This paper investigates the spectral changes in copper-polluted leaves using seven spectral signatures and the spectral angle method. Wheat and lettuce were selected as the experimental subjects and were cultured in copper-polluted soil. Leaf spectra reflectance and scanning electron micrograph of ten copper treatment groups were measured in four regular periods. The reflectance spectra of the leaves were obtained using an ASD Field Spectrometer. Results showed that the irregular spectral changes of copper-polluted leaves depend on crop growth stages and crop types. The spectral angle, a simple method of comparing the vector angle of the leaf spectra with the given threshold values, has been justified as an efficient method of describing the spectral differences between copper-polluted leaves and healthy leaves. The method is very sensitive to light and the severity of copper pollution. Results also show that the leaf structural parameter ( N ) of copper-polluted leaves is larger than that of healthy leaves. The linear relationship between N and the reflectance value of the red shoulder was fitted with a correlation coefficient of 0.978. As a result, the deduced N from the leaf reflectance spectra may be used as an indicator to reflect the leaf structure distortion under copper pollution conditions. The significance of this study lies in its provision of data asbasis and theoretical support for the construction of a copper-polluted leaf reflectance spectral model.

重金属铜污染植被的反射光谱特性会发生明显改变。在本研究中，采用不同程度的铜污染土壤作为培养基质，选择春小麦、上海青两种农作物进行铜胁迫实验，获取了4个不同生育期、10个不同铜污染强度下的植被叶片的反射光谱，并采用铜污染叶片7个特征波段和光谱角的方法研究了铜污染叶片的光谱特征。结果表明，铜污染叶片光谱差异与作物时期和作物类型有关，可以采用叶片光谱角描述铜污染叶片与健康叶片的光谱差异。该方法只需与阈值做简单的比较，方法简便易行，而且对轻度及重度铜污染十分敏感。叶片光谱辐射传输模型反演结果表明铜污染叶片内部结构参数N明显变大，这也证明了铜污染使叶片内部结构更加散乱无序。在此基础上进一步建立了N与红肩处反射率值的线性关系，相关系数为0.978。本文为铜污染叶片光谱反射模型的建立提供了初步的数据基础与理论支持。

... 植被受重金属胁迫后, 正常生长状态受到抑制, 其生理生化参数会发生变化, 从而影响其光谱特征^[4] ...

2007

0.0

... 其中, 基于可见光和近红外波段的植被指数被广泛应用于植被胁迫监测之中^[5] ...

2008

0.0

Qing-ting

, YANG

Feng-jie

, ZHANG

Bing

, et al(李庆亭, 杨锋杰, 张兵, 等). Journal of Remote Sensing(遥感学报), 2008, (2): 284.

The activities ofmining produce great heavymetal pollution which threatens the surrounding ecosystem and human s health, the heavymetals can be transported to the surrounding soi,l river and underground water. The heavy metal s stress to the biology can cause the variation ofplants and community, which is called biogeochemistry effects. The excessive copper mi pedes the growth and developmentofvegetation, such as greenness decreasing, photosynthesi sweakening, and the absorption capacity ofother elements decreasing. The leaves of the vegetation lackwater and getwithered,so the spectral characteristics changewith the influence of the stress.Dexing coppermine is an oversizemulti-metalmine bed in our country. The development ofmining has caused the pollution ofmine environment, such as the ecologic problem inmine vegetation, land, water and the heavy pollution of solid waste and tailingwater, particularly, the pollution ofheavymetals such as copper. These problems have influenced\nthe growth ofvegetation greatly.This paper studies the heavymetal contamination and the biogeochemistry responses ofvegetationRhus chinensisMill inDexing coppermine area. The spectralcharacteristicsofthe responses are systematically extracted and analyzedwith the principles andmethods of remote sensing biogeochemistry.Acid, Cu, Cd are themain environmentalpollution factors, the copper content in the soil ismuchmore than the national environment standard and the pH value of thewater in the lower reaches of the river is about three over the year.The plantRhus chinensisMillhas enriched and translocated responses to copper, thisplant is suitable for the environ-mental recoverywhich is one of themain vegetation and growswell in the area. From spectral processing such as derivative technology, continuum-removed spectra, red edge, vegetation index, we can get that the spectral position effects are notable to the influence ofheavymetals. W ith the increasing of the influence ofheavymetal stress:(1) the spectral reflectance ofRhus chinensisMillincreases obviously, about5%―30%,(2) spectral shape blue-shiftoccurs, about5―15nm,(3) the red edge slope increases, from 4.5534 to 8.9475, the variation is up to five,(4) the chlorophyll absorption depth decreases, the absorption center is near680 nm and blue-shift,(5) thewater absorption depth becomes lower and red-shiftoccurs about10―20nm,(6) vegetation indices alterobviously, the ratioB4/B3is from 18.763 to13.1839, indicating the decrease ofchloro-phyll content in leaves, theWEI( water effect index) changes from 0.098014 to0.226503, indicating the increase of in-tensity ofwater stress effect.Then some predominantcharacteristics are selectedwhich are suitable for the evaluation ofheavymetalpollutionwith the comprehensive analysis of spectral characteristics and the correlation between them and the contents ofheavymetals.

利用遥感生物地球化学的原理和方法,分析了德兴铜矿的重金属污染状况和植物盐肤木的生物地球化学效应,对盐肤木生物地球化学效应的波谱特征进行了系统的提取和分析.研究发现酸、铜、镉等是德兴铜矿地区主要的环境污染因子,野外调查及样品的化验分析表明盐肤木对铜元素呈现一定的富集作用和很强的位移效应,是适合铜矿复垦的植被.利用导数光谱、包络线去除、红边效应、植被指数等光谱信息处理方法对盐肤木的野外波谱分析表明,随着叶片中Cu等金属元素含量的增大,其产生的毒化效应的波谱特征越明显,盐肤木叶片光谱反射率明显升高,波形蓝移,红边陡坡斜率增大,叶绿素吸收深度变浅,吸收中心稍有蓝移,水的吸收深度变浅,吸收中心位置红移,绿度指数变化明显.对波谱特征及其与重金属含量的相关关系综合分析后认为,红边特征、植被指数NDVI、叶绿素吸收深度与叶片铜含量关系显著,可以作为植被铜污染遥感图像特征提取的参考.

... 李庆亭等^[6]揭示了红边特征、归一化植被指数(normalized difference vegetation index, NDVI)与盐木叶片中重金属铜含量关系显著, 可以作为植被铜污染遥感图像特征提取的参考 ...

2018

0.0

... Liu等^[7]利用红边叶绿素指数(red-edge chlorophyll index, CI_red-edge)、红边位置(red-edge position, REP)和归一化红边指数(normalized differences red edge, NDRE), 并结合Sentinel-2影像数据对水稻镉胁迫进行了监测, 结果表明基于红边植被指数的时空异常探测法可有效区分水稻镉胁迫和与病害胁迫 ...

2013

0.0

... Asmaryan^[8]利用World View-2影像的红边波段成功地提取了植被胁迫异常信息 ...

2014

0.0

... Deventer等^[9]利用NDVI和REP研究了酸性矿井水对植被的影响, 结果显示NDVI和REP能有效地提供植被胁迫状况的指示信息 ...

2015

0.0

... Martinez等^[10]利用REP和NDVI等植被指数并采用多元线性回归法成功地量化了铯(Cs)污染与反射率之间的关系 ...

2015

0.0

... 除红边植被指数外, Rathod等^[11]指出叶绿素指数、水胁迫指数及波段深度与植被叶片中重金属含量具有显著相关性, 也可用于区分植被重金属胁迫程度 ...

2011

0.0

LIU

Mei-ling

, LIU

Xiang-nan

, CAO

Shi

, et al(刘美玲, 刘湘南, 曹仕, 等). Journal of Remote Sensing(遥感学报), 2011, 15(4): 811.

How to extract and calculate subtle spectral feature information of crop under various environmental-induced stresses from hyperspectral remote sensing is crucial for the application of remote sensing in monitoring agricultural pollution. The objective of this paper is to monitor the stress levels of rice under the Pb pollution. Hyperspectral data and heavy metal content were collected in the fi eld experiment. The fi fth level high―frequency component (d5) was obtained by performing wavelet transform to hyperspectral refl ectance (350-1300 nm) and the fractal dimension of d5 was also calculated. Then the relationship between fractal dimension of d5 and different stress levels of rice was established by the fuzzy logic model. The results showed that: (1) d5 can effectively distinguish the stress levels of rice Pb pollutions; (2) the annual relative variation ratio for fractal dimension of d5 was below 4%, and the classifi cation accuracy of fractal dimension of d5 was above 75%. Namely, fractal dimension values of d5 for rice under high, medium and low pollutions have percentage of 86.7% between 1.160 and 1.200, 75% between 1.220 and 1.275, 91.7% between 1.280 and 1.320, respectively. (3) the high (low) semi-trapezoidal functions were carried out to construct a model to detect stress levels of rice.

通过研究铅污染胁迫下水稻的光谱高频组份的分维数来诊断水稻铅污染胁迫水平。根据实验区水稻冠层实测ASD高光谱数据和同步获取的农田土壤重金属含量数据，利用Daubechies小波系中的“Db5”母小波对水稻的350―1300 nm波段范围进行小波分解得到第5层高频组份(d5)，并采用盒维法计算d5的分维数，最后采用模糊数学建立d5的分维数与污染胁迫水平的数学模型。结果表明：d5能有效地探测到铅污染胁迫的光谱弱信息，并实现不同污染水平水稻高光谱信号的分离；d5分维数在同一污染水平的年际相对变化率小于4%，不同污染水平的区分度大于75%，即高、中和低污染水平水稻高光谱d5分维数的86.7%、75%和91.7% 分别集中在1.160―1.200，1.220―1.275和1.280―1.320三个区间；采用升(降)半梯形分布的隶属度函数建立水稻高光谱d5分维数与其污染胁迫水平的数学模型，并进行了模型精度检验，其判别精度大于90%。小波变换、分形分析和模糊数学三者相结合有效地实现了光谱弱信息提取、度量及建模，达到水稻重金属污染胁迫状况监测的目的，也为作物其他环境胁迫弱信息的动态识别与精确度量提供借鉴意义。

... 近年来, 一些新的重金属胁迫指数陆续被提出, Wang和刘美玲等^[12,13]提出基于小波面积和小波系数的重金属胁迫指数, 然而由于计算复杂, 小波变换难以应用于生产实践 ...

2015

0.0

1994

0.0

... 1 光谱数据预处理文献[14]表示植被光谱的可见光及近红外波段对胁迫较为敏感, 因此本文选取400~1 000 nm波段区间的光谱数据进行分析 ...

2018

0.0

... 为消除背景噪声及其他影响, 本文对原始光谱数据进行一阶差分处理, 一阶差分计算方法^[15]如下 ...