农作物重金属污染胁迫遥感弱信息增强与计算
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摘要
农作物重金属污染是当今世界面临的重大生态环境问题之一,直接影响农业生产、粮食安全,危及人类生存环境。如何运用遥感技术动态、准确、大面积地监测农作物重金属污染状况已经成为迫切需要解决的现实问题。然而,自然环境下农田土壤重金属含量较低,农作物受重金属污染胁迫的光谱响应信号微弱且不稳定,同时还受其他环境因素如水肥耦合、光照、大气等的影响,很容易淹没于其他干扰信号之中,难以甄别。增强并计算这种大面积范围内微小变化量的光谱弱信息,是遥感技术应用向定量化、精细化方向发展必须解决的科学问题。
本文以自然农田生态系统中具有复杂隐蔽性的农作物重金属污染胁迫遥感动态识别和准确度量为研究目标,在长春、吉林等地选取若干重金属污染程度不同的玉米、水稻农田样地作为实验区,在作物关键生长期通过典型区域取样、实验区连续观测和室内分析测试,系统获取实验区农作物及其环境的特征参数、重金属污染状况和对应的高光谱数据;采用实验验证、物理机制分析、数学建模等方法探索农作物重金属污染胁迫下叶绿素含量变化、氮素含量变化和水分含量变化监测的敏感光谱指数;运用小波分析和多级动态模糊神经网络模型等方法获得针对性更强的遥感指数,在此基础上建立农作物重金属污染胁迫遥感综合评价模型,实现大范围内微小变化量的遥感识别与计算。论文主要工作内容与结论如下:
1、重金属污染胁迫下土壤-作物系统理化性质、生物参数及其与高光谱遥感数据的响应关系。
(1)基于实验数据分析土壤和农作物重金属元素含量的分布状况,研究重金属污染胁迫对农作物叶绿素、氮素和水分含量变化的影响机制。研究发现,利用生态危害综合指数对农作物污染胁迫等级进行评价相对于综合污染指数准确性更高。
(2)综合分析农作物反射光谱特征,研究并验证光谱指数与重金属污染胁迫下农作物生化参数变化的响应关系,建立作物叶绿素、氮素和水分含量微小变化的高光谱反演模型。结果表明,诸多用于农作物生理指标反演的常规遥感指数(如NDVI、MCARI、OSAVI等)在重金属污染胁迫下反演精度严重下降。通过分析各遥感指数与作物生化指标、重金属污染胁迫水平的关系,利用特征光谱空间构建出新光谱参数SIr,经验证,新参数对重金属胁迫具有良好的探测能力。
2、农作物重金属污染胁迫光谱诊断与快速发现。
(1)分析污染胁迫下农作物叶绿素、氮素、水分含量微小变化的光谱响应机制及其表征,提出农作物重金属污染胁迫综合光谱表征参数,如水稻(SDg/SDr、FD933和WI3)、玉米(X23、NI15*NI17和D1025),建立作物重金属污染胁迫多判据诊断模型。由于采用的三个参数分别对叶绿素、氮素、水分含量非常敏感,所构建的多判据诊断模型可以很好地识别作物的重金属污染胁迫程度。
(2)利用光谱二值编码技术研究反射光谱的全局特征,发现在680-720nm波长范围内的光谱二值编码能够快速辨识出农作物(尤其是水稻)重金属污染胁迫特征。
(3)根据小波分析的信号奇异性理论进行高光谱信号异常值的检测,对比不同小波母函数对光谱特征的分解效果,选择利用db3小波基对反射光谱曲线进行5层小波分解,发现在700-750nm波段小波系数变化剧烈,并且出现异常极值。对异常极值、异常幅值等与农作物重金属污染胁迫水平的相关分析发现,无污染地区奇异极大值均小于0.01;同时,异常极值、异常幅值等奇异指标随污染胁迫水平加剧而不断增大,证明利用小波分解可以实现对重金属污染胁迫的有效识别。
3.农作物重金属污染胁迫等级评价。
将模糊理论与人工神经网络系统相结合,集成模糊推理对模糊信息的表达能力,人工神经网络的自学习与非线性映射能力,建立由输入层、模糊化层、模糊规则推理层和输出层构成的动态模糊神经网络模型。模型以农作物重金属污染胁迫综合光谱敏感因子为输入,农作物重金属污染胁迫等级为输出。经多组数据分析、检验,证明该模型高效而稳定,能够实现对农作物重金属污染胁迫等级的快速、准确评价。
论文的创新之处在于综合分析农作物重金属污染胁迫下叶绿素、氮素和水分含量的微小变化及其光谱响应特征,获取和度量响应各因子微弱变化的敏感光谱参数,克服传统研究中仅利用单一指标表达和分析重金属污染胁迫信息的片面性;通过小波分析方法计算农作物重金属污染胁迫的光谱奇异性特征,充分利用了高光谱数据的全局和局部细节信息,有效地增强了农作物重金属污染胁迫遥感微弱信号。基于多重判据理论,利用动态模糊神经网络模型和多维特征光谱空间构造农作物重金属污染胁迫综合诊断指标,建立定量的重金属污染胁迫等级评价模型,克服了传统方法缺乏普适性和不稳定的弱点。
Being one of the main eco-environmental issues, heavy metal contamination stress on crops seriously threats not only agricultural production and food security, but also human survival and the global environmental quality, which has attracted increasing attention recently. To monitor pollution in crops and farmland at large scale by remote sensing technology is regarded as one of the most urgent and practical problems to be solved. However, the heavy metal content of agricultural soil is generally tiny in natural environment, thereby, the spectral response of crops is weak and unstable. Moreover, the source of spectral variation hardly distinguishes from other environmental factors, such as coupling water and fertilizer, solar illumination, atmosphere etc. On the other hand, the response is so weak that there is no obvious representation in the hyper spectrum, and may easily be ignored. Therefore, to explore effective theories and methods for enhancing such tiny spectral information on heavy metal pollution stresses in large area farmland meets increasing demands on quantitative and fine remote sensing applications currently and, as a scientific problems, is necessary to be solved.
The object of the study is to discriminate dynamically and measure accurately the weak and hidden information of heavy metal stress on crops in natural field eco-system. The maize and rice sample fields with different heavy metal pollution levels in Changchun and Jilin City were selected to carry out the hyper-spectral measurement for the key periods during growing season. The heavy metal content in soil and crops were analyzed in the laboratory. By the experiment validation, physical mechanism reasoning and mathematics modeling, the sensitive indices referring to the changes in chlorophyll, nutrient, water content of crops under heavy metal stress are studied. The more powerful indices are explored by wavelet analysis and dynamic fuzzy neural network, thereafter a comprehensive evaluation model for crops heavy metal stress is proposed in order to find out the tiny change using remote sensed data in large area. The main researches and conclusions of this dissertation are summarized as follows:
1. Relationships between soil-crops feature index and hyperspectral data
(1) Based on the experimental data, the distribution of heavy metal in soils and crops is analyzed. According to the change mechanism of chlorophyll, nitrogen and water content in crops under heavy metal stress, the ecological damage synthesized index can be used to evaluate the stress level more accurately than the complex pollution index.
(2) By investigating the response of spectrum to physiological reaction of crops under heavy metal contamination stress, the spectral retrieval models of chlorophyll, nitrogen and water content are constructed. It is found that many indices including NDVI, MCARI, OSAVI etc. lose efficacy when heavy metal pollution exists, and this can act as an indicator. On the basis of relationship among spectrum, crop physiological indices and heavy metal stress level, a new index called‘SIr’is developed which is then validated as a new efficient index for exploring the heavy metal stress.
2. Early diagnose of heavy metal stress on crops
(1) Combining the reaction of chlorophyll, nitrogen and water content under contamination stress and its spectral feature, the multi-dimension spectral feature space are constructed using the following indices , namely, SDg/SDr、FD933 and WI3 for rice and X23, NI15*NI17 and D1025 for maize to evaluate the stress level. Because the three indices are very sensitive to chlorophyll, nitrogen and water content variations respectively, such diagnose model has a better performance for indentifying the contamination stress.
(2) The whole spectrum characteristics are revealed by spectrum binary encoding. It is found that the sum of binary codes in 680-720nm region indicates well the existences of heavy metal stress on crops, especially for rice.
(3) The wavelet analysis method is applied to examine the details and abnormal of crops
spectrum. The decomposition using different mother functions are compared and db3 is selected to reconstruct the spectrum of crops. It is found that the wavelet coefficient in 700-750nm express an obvious different in crop spectral feature with different stress level. The correlation analysis of abnormal extreme values, amplitude and heavy metal stress level shows that the abnormal extreme values in non-polluted fields are less than 0.01, whereas the above abnormal parameters increase with increasing contamination stress. Wavelet analysis extracts the detail information of the spectrum, and easily find out the spectral feature abnormal.
3. Pollution stress level evaluation model
Integrating the fuzzy theory and artificial neutral net technology,a dynamic fuzzy neutral net (DFNN) model is construed to evaluate the stress level . The model makes use of the presentation ability for fuzzy information, and the self-study and non-linear computation ability of ANN. The model takes sensitive spectral index as the input layer; the output is heavy metal stress level. It is tested by much dataset that the DFNN model has strong classification accuracy, high efficiency and stability. Using this model, the heavy metal stress level can be evaluated rapidly and accurately.
In summery, the study makes full use the spectrum information completely ranging from 350 to 2500nm in this thesis. Integrating the three indices representing the chlorophyll, nitrogen and water content respectively, and combining the overall feature and detail information of the crops reflectance spectrum, the model for stress level evaluation is proposed based on the multi-criteria theory. The dynamic fuzzy neutral net works efficiently and stably and the stress level information can be extracted rapidly. The weak suitability and strong instability of previous methods are overcome by the DFNN.
本文以自然农田生态系统中具有复杂隐蔽性的农作物重金属污染胁迫遥感动态识别和准确度量为研究目标,在长春、吉林等地选取若干重金属污染程度不同的玉米、水稻农田样地作为实验区,在作物关键生长期通过典型区域取样、实验区连续观测和室内分析测试,系统获取实验区农作物及其环境的特征参数、重金属污染状况和对应的高光谱数据;采用实验验证、物理机制分析、数学建模等方法探索农作物重金属污染胁迫下叶绿素含量变化、氮素含量变化和水分含量变化监测的敏感光谱指数;运用小波分析和多级动态模糊神经网络模型等方法获得针对性更强的遥感指数,在此基础上建立农作物重金属污染胁迫遥感综合评价模型,实现大范围内微小变化量的遥感识别与计算。论文主要工作内容与结论如下:
1、重金属污染胁迫下土壤-作物系统理化性质、生物参数及其与高光谱遥感数据的响应关系。
(1)基于实验数据分析土壤和农作物重金属元素含量的分布状况,研究重金属污染胁迫对农作物叶绿素、氮素和水分含量变化的影响机制。研究发现,利用生态危害综合指数对农作物污染胁迫等级进行评价相对于综合污染指数准确性更高。
(2)综合分析农作物反射光谱特征,研究并验证光谱指数与重金属污染胁迫下农作物生化参数变化的响应关系,建立作物叶绿素、氮素和水分含量微小变化的高光谱反演模型。结果表明,诸多用于农作物生理指标反演的常规遥感指数(如NDVI、MCARI、OSAVI等)在重金属污染胁迫下反演精度严重下降。通过分析各遥感指数与作物生化指标、重金属污染胁迫水平的关系,利用特征光谱空间构建出新光谱参数SIr,经验证,新参数对重金属胁迫具有良好的探测能力。
2、农作物重金属污染胁迫光谱诊断与快速发现。
(1)分析污染胁迫下农作物叶绿素、氮素、水分含量微小变化的光谱响应机制及其表征,提出农作物重金属污染胁迫综合光谱表征参数,如水稻(SDg/SDr、FD933和WI3)、玉米(X23、NI15*NI17和D1025),建立作物重金属污染胁迫多判据诊断模型。由于采用的三个参数分别对叶绿素、氮素、水分含量非常敏感,所构建的多判据诊断模型可以很好地识别作物的重金属污染胁迫程度。
(2)利用光谱二值编码技术研究反射光谱的全局特征,发现在680-720nm波长范围内的光谱二值编码能够快速辨识出农作物(尤其是水稻)重金属污染胁迫特征。
(3)根据小波分析的信号奇异性理论进行高光谱信号异常值的检测,对比不同小波母函数对光谱特征的分解效果,选择利用db3小波基对反射光谱曲线进行5层小波分解,发现在700-750nm波段小波系数变化剧烈,并且出现异常极值。对异常极值、异常幅值等与农作物重金属污染胁迫水平的相关分析发现,无污染地区奇异极大值均小于0.01;同时,异常极值、异常幅值等奇异指标随污染胁迫水平加剧而不断增大,证明利用小波分解可以实现对重金属污染胁迫的有效识别。
3.农作物重金属污染胁迫等级评价。
将模糊理论与人工神经网络系统相结合,集成模糊推理对模糊信息的表达能力,人工神经网络的自学习与非线性映射能力,建立由输入层、模糊化层、模糊规则推理层和输出层构成的动态模糊神经网络模型。模型以农作物重金属污染胁迫综合光谱敏感因子为输入,农作物重金属污染胁迫等级为输出。经多组数据分析、检验,证明该模型高效而稳定,能够实现对农作物重金属污染胁迫等级的快速、准确评价。
论文的创新之处在于综合分析农作物重金属污染胁迫下叶绿素、氮素和水分含量的微小变化及其光谱响应特征,获取和度量响应各因子微弱变化的敏感光谱参数,克服传统研究中仅利用单一指标表达和分析重金属污染胁迫信息的片面性;通过小波分析方法计算农作物重金属污染胁迫的光谱奇异性特征,充分利用了高光谱数据的全局和局部细节信息,有效地增强了农作物重金属污染胁迫遥感微弱信号。基于多重判据理论,利用动态模糊神经网络模型和多维特征光谱空间构造农作物重金属污染胁迫综合诊断指标,建立定量的重金属污染胁迫等级评价模型,克服了传统方法缺乏普适性和不稳定的弱点。
Being one of the main eco-environmental issues, heavy metal contamination stress on crops seriously threats not only agricultural production and food security, but also human survival and the global environmental quality, which has attracted increasing attention recently. To monitor pollution in crops and farmland at large scale by remote sensing technology is regarded as one of the most urgent and practical problems to be solved. However, the heavy metal content of agricultural soil is generally tiny in natural environment, thereby, the spectral response of crops is weak and unstable. Moreover, the source of spectral variation hardly distinguishes from other environmental factors, such as coupling water and fertilizer, solar illumination, atmosphere etc. On the other hand, the response is so weak that there is no obvious representation in the hyper spectrum, and may easily be ignored. Therefore, to explore effective theories and methods for enhancing such tiny spectral information on heavy metal pollution stresses in large area farmland meets increasing demands on quantitative and fine remote sensing applications currently and, as a scientific problems, is necessary to be solved.
The object of the study is to discriminate dynamically and measure accurately the weak and hidden information of heavy metal stress on crops in natural field eco-system. The maize and rice sample fields with different heavy metal pollution levels in Changchun and Jilin City were selected to carry out the hyper-spectral measurement for the key periods during growing season. The heavy metal content in soil and crops were analyzed in the laboratory. By the experiment validation, physical mechanism reasoning and mathematics modeling, the sensitive indices referring to the changes in chlorophyll, nutrient, water content of crops under heavy metal stress are studied. The more powerful indices are explored by wavelet analysis and dynamic fuzzy neural network, thereafter a comprehensive evaluation model for crops heavy metal stress is proposed in order to find out the tiny change using remote sensed data in large area. The main researches and conclusions of this dissertation are summarized as follows:
1. Relationships between soil-crops feature index and hyperspectral data
(1) Based on the experimental data, the distribution of heavy metal in soils and crops is analyzed. According to the change mechanism of chlorophyll, nitrogen and water content in crops under heavy metal stress, the ecological damage synthesized index can be used to evaluate the stress level more accurately than the complex pollution index.
(2) By investigating the response of spectrum to physiological reaction of crops under heavy metal contamination stress, the spectral retrieval models of chlorophyll, nitrogen and water content are constructed. It is found that many indices including NDVI, MCARI, OSAVI etc. lose efficacy when heavy metal pollution exists, and this can act as an indicator. On the basis of relationship among spectrum, crop physiological indices and heavy metal stress level, a new index called‘SIr’is developed which is then validated as a new efficient index for exploring the heavy metal stress.
2. Early diagnose of heavy metal stress on crops
(1) Combining the reaction of chlorophyll, nitrogen and water content under contamination stress and its spectral feature, the multi-dimension spectral feature space are constructed using the following indices , namely, SDg/SDr、FD933 and WI3 for rice and X23, NI15*NI17 and D1025 for maize to evaluate the stress level. Because the three indices are very sensitive to chlorophyll, nitrogen and water content variations respectively, such diagnose model has a better performance for indentifying the contamination stress.
(2) The whole spectrum characteristics are revealed by spectrum binary encoding. It is found that the sum of binary codes in 680-720nm region indicates well the existences of heavy metal stress on crops, especially for rice.
(3) The wavelet analysis method is applied to examine the details and abnormal of crops
spectrum. The decomposition using different mother functions are compared and db3 is selected to reconstruct the spectrum of crops. It is found that the wavelet coefficient in 700-750nm express an obvious different in crop spectral feature with different stress level. The correlation analysis of abnormal extreme values, amplitude and heavy metal stress level shows that the abnormal extreme values in non-polluted fields are less than 0.01, whereas the above abnormal parameters increase with increasing contamination stress. Wavelet analysis extracts the detail information of the spectrum, and easily find out the spectral feature abnormal.
3. Pollution stress level evaluation model
Integrating the fuzzy theory and artificial neutral net technology,a dynamic fuzzy neutral net (DFNN) model is construed to evaluate the stress level . The model makes use of the presentation ability for fuzzy information, and the self-study and non-linear computation ability of ANN. The model takes sensitive spectral index as the input layer; the output is heavy metal stress level. It is tested by much dataset that the DFNN model has strong classification accuracy, high efficiency and stability. Using this model, the heavy metal stress level can be evaluated rapidly and accurately.
In summery, the study makes full use the spectrum information completely ranging from 350 to 2500nm in this thesis. Integrating the three indices representing the chlorophyll, nitrogen and water content respectively, and combining the overall feature and detail information of the crops reflectance spectrum, the model for stress level evaluation is proposed based on the multi-criteria theory. The dynamic fuzzy neutral net works efficiently and stably and the stress level information can be extracted rapidly. The weak suitability and strong instability of previous methods are overcome by the DFNN.
引文
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