基于分形—小波模型的水稻铅污染胁迫遥感弱信息提取方法研究
|
摘要
在农田生态系统中,铅是一种具有潜在危害的重要污染物,它不但会影响水稻的产量和品质,而且还可通过食物链富集等方式危害人类的健康。因此,研究水稻铅污染胁迫状况,分析铅污染水稻光谱曲线的遥感弱信息变化,对于防止粮食污染、保障人体健康有着重要的意义。
本文重点研究基于分形-小波模型的水稻铅污染胁迫遥感弱信息增强与提取方法。在对分形理论、小波理论的分析、比较和总结的基础上,利用小波变换法计算分形维数实现对水稻铅污染胁迫遥感弱信息的提取。首先对健康水稻与铅污染胁迫水稻进行了奇异性分析,然后分别对健康水稻和铅污染水稻在分蘖期、抽穗期的原始高光谱曲线(450nm～700nm)及其二阶导曲线计算分形维数。结果表明,铅污染水稻的奇异性较健康水稻有所降低,所以铅污染水稻的原始高光谱曲线分形维数有所降低,同时铅污染水稻二阶导曲线分形维数较健康水稻也随之降低,且铅污染水稻二阶导曲线在分蘖期与抽穗期分形维数的差值比健康水稻的差值大,认为利用二阶导曲线的分形维数来对水稻是否受铅污染进行检测的效果比用原始光谱曲线的分形维数效果要好。同时还得出分蘖期是监测受铅污染水稻的最佳时期的结论。最后建立了基于分形维数的叶绿素浓度预测模型,并得到了不错的拟合效果。
全文共分为五章。第一章阐明了利用遥感技术监测水稻铅污染胁迫水平的意义,在综述国内外研究现状的基础上,提出了本文研究的重点问题和解决方法。第二章总结了水稻铅污染的生化响应及遥感信息机理,遥感植被指数及其在农作物污染胁迫中的遥感应用,并指出这些遥感植被指数的不足。第三章介绍了实验样地、数据采集、研究波段的选择依据以及如何利用分形和小波理论来增强和提取受铅污染水稻的遥感弱信息。第四章对农田铅污染水稻光谱数据的弱信息进行增强和提取,分析增强的效果,并建立基于分形维数的叶绿素浓度预测模型,并根据实验结果分析该模型的性能优劣。第五章是对本文研究的总结,并在此基础上提出了今后进一步研究的设想。
Pb is a potential hazardous pollution in farmland soil. Pb pollution has significant impacts on the growth of rice, and it may cause reduction of rice yield. The concentrations of pollutants will increasing as toxins are passed up the food chain and ultimately threatening the health of human beings. So studying of rice under Pb pollution stress and analysis of hyperspectral change of rice under Pb pollution stress are important for avoiding rice polluted and protecting agricultural environment and human being heath.
The goal of this research is to derive a new model named fractal- wavelet model for extraction from remotely sensed information of rice under Pb pollution stress. According to analysis and compare and summarize of wavelet and fractal theory, this paper proposes the method of extracting hyperspectral weak change of rice under Pb pollution stress by using fractal dimension, which was performed by wavelet transform. First, singularity analysis were carried out between healthy rice and rice under Pb pollution respectively, and then for healthy rice and rice under Pb pollution respectively, the fractal dimensions of hyperspectral curves (450nm-700nm) and second derivative curves were calculated at the tillering stage and the heading stage. Results showed that the fractal dimensions of hyperspectral curves of the rice under Pb pollution decrease, compared with these of the healthy rice. The fractal dimensions of second derivative curves also decreased, and the difference in the fractal dimension between tillering stage and heading stage of the rice under Pb pollution is larger than that of healthy rice, according to which we regard that the fractal dimensions of second derivative curves in judging whether the rice was polluted is better. Meanwhile, it can be concluded that it is the best time to monitor the rice under Pb pollution at the tillering stage. Finally, the paper establishes a predict model for chlorophyll concentration based on fractal dimension, which we achieve a nice accuracy.
There are five chapters in this paper. Chapter one is an overview of previous research work on the usage of remote sensing technology in vegetation health monitoring area. Chapter two introduces the biochemical reaction mechanism of rice under Pb pollution stress, and the vegetation indexes which are used in this field. Then it points the disadvantages of these indexes. Chapter three introduces the sample plot and data collection and the choice of range of band, and then how to enhance and extract spectral weak information with fractal and wavelet. Chapter four specifies the result of enhancing and extracting spectral weak information with fractal and wavelet, and then analysis the effect of the model. This paper establish a predict model for chlorophyll concentration based on fractal dimension at last. Chapter five is the conclusion of this research work and the plan of next work.
本文重点研究基于分形-小波模型的水稻铅污染胁迫遥感弱信息增强与提取方法。在对分形理论、小波理论的分析、比较和总结的基础上,利用小波变换法计算分形维数实现对水稻铅污染胁迫遥感弱信息的提取。首先对健康水稻与铅污染胁迫水稻进行了奇异性分析,然后分别对健康水稻和铅污染水稻在分蘖期、抽穗期的原始高光谱曲线(450nm～700nm)及其二阶导曲线计算分形维数。结果表明,铅污染水稻的奇异性较健康水稻有所降低,所以铅污染水稻的原始高光谱曲线分形维数有所降低,同时铅污染水稻二阶导曲线分形维数较健康水稻也随之降低,且铅污染水稻二阶导曲线在分蘖期与抽穗期分形维数的差值比健康水稻的差值大,认为利用二阶导曲线的分形维数来对水稻是否受铅污染进行检测的效果比用原始光谱曲线的分形维数效果要好。同时还得出分蘖期是监测受铅污染水稻的最佳时期的结论。最后建立了基于分形维数的叶绿素浓度预测模型,并得到了不错的拟合效果。
全文共分为五章。第一章阐明了利用遥感技术监测水稻铅污染胁迫水平的意义,在综述国内外研究现状的基础上,提出了本文研究的重点问题和解决方法。第二章总结了水稻铅污染的生化响应及遥感信息机理,遥感植被指数及其在农作物污染胁迫中的遥感应用,并指出这些遥感植被指数的不足。第三章介绍了实验样地、数据采集、研究波段的选择依据以及如何利用分形和小波理论来增强和提取受铅污染水稻的遥感弱信息。第四章对农田铅污染水稻光谱数据的弱信息进行增强和提取,分析增强的效果,并建立基于分形维数的叶绿素浓度预测模型,并根据实验结果分析该模型的性能优劣。第五章是对本文研究的总结,并在此基础上提出了今后进一步研究的设想。
Pb is a potential hazardous pollution in farmland soil. Pb pollution has significant impacts on the growth of rice, and it may cause reduction of rice yield. The concentrations of pollutants will increasing as toxins are passed up the food chain and ultimately threatening the health of human beings. So studying of rice under Pb pollution stress and analysis of hyperspectral change of rice under Pb pollution stress are important for avoiding rice polluted and protecting agricultural environment and human being heath.
The goal of this research is to derive a new model named fractal- wavelet model for extraction from remotely sensed information of rice under Pb pollution stress. According to analysis and compare and summarize of wavelet and fractal theory, this paper proposes the method of extracting hyperspectral weak change of rice under Pb pollution stress by using fractal dimension, which was performed by wavelet transform. First, singularity analysis were carried out between healthy rice and rice under Pb pollution respectively, and then for healthy rice and rice under Pb pollution respectively, the fractal dimensions of hyperspectral curves (450nm-700nm) and second derivative curves were calculated at the tillering stage and the heading stage. Results showed that the fractal dimensions of hyperspectral curves of the rice under Pb pollution decrease, compared with these of the healthy rice. The fractal dimensions of second derivative curves also decreased, and the difference in the fractal dimension between tillering stage and heading stage of the rice under Pb pollution is larger than that of healthy rice, according to which we regard that the fractal dimensions of second derivative curves in judging whether the rice was polluted is better. Meanwhile, it can be concluded that it is the best time to monitor the rice under Pb pollution at the tillering stage. Finally, the paper establishes a predict model for chlorophyll concentration based on fractal dimension, which we achieve a nice accuracy.
There are five chapters in this paper. Chapter one is an overview of previous research work on the usage of remote sensing technology in vegetation health monitoring area. Chapter two introduces the biochemical reaction mechanism of rice under Pb pollution stress, and the vegetation indexes which are used in this field. Then it points the disadvantages of these indexes. Chapter three introduces the sample plot and data collection and the choice of range of band, and then how to enhance and extract spectral weak information with fractal and wavelet. Chapter four specifies the result of enhancing and extracting spectral weak information with fractal and wavelet, and then analysis the effect of the model. This paper establish a predict model for chlorophyll concentration based on fractal dimension at last. Chapter five is the conclusion of this research work and the plan of next work.
引文
[1]滕应,黄昌勇.重金属污染土壤的微生物生态效应及其修复研究进展.土壤与环境,2002,11(1):85～89
[2]宋书巧,吴浩东,蓝唯源.刁江沿岸土壤重金属污染状况及土地的合理利用模式.环境与健康杂志,2008,25(4):317～319
[3]林健等.公路交通污染土壤和稻谷中镉铅分布特征.环境与健康杂志, 2002,19(2):119～121
[4]张义贤.汞、镉、铅胁迫对油菜的毒害效应.山西大学学报(自然科学版), 2004,27(4):410～413
[5]谭周镃.稻米重金属污染的调查研究及其对策思考.湖南农业科学, 1999,5(13):26～28
[6]章力建等.我国水稻生产中的立体污染及防治.中国稻米, 2005,5:1～4
[7]杨科璧.中国农田土壤重金属污染与其植物修复研究.世界农业,2007(8):58～61
[8]蒋先军,骆永明,赵其国.土壤重金属污染的植物提取修复技术及其应用前景.农业环境保护,2000,19(3):179～183
[9]崔德杰,张玉龙。土壤重金属污染现状与修复技术研究进展.土壤通报,2004,35(3):366～370
[10]李民赞,韩东海.光谱分析技术及其应用.北京:科学出版社,2006:176～179
[11]任红艳,庄大方,潘剑君,邱冬生等.铅污染水稻的冠层高光谱特征研究.地球信息科学,2008,10(3):314～319
[12]刘帅,高永光等.铜胁迫下玉米叶绿素质量比与光谱反射率关系.辽宁工程技术大学学报,2008,27(1):125～128
[13]田国良,包佩利,李建军,等.土壤中镉、铜伤害对水稻光谱特性的影响.环境遥感,1990,5(2):140～149
[14] Pablo H. Rosso a, James C. Pushnik b, Mui Lay c, Susan L. Ustin. Reflectance properties and physiological responses of Salicornia virginica to heavy metal and petroleum contamination. Environmental Pollution, 2005, 137:241-252
[15] Sarah C. Dunagan, Martha S. Gilmore, Johan C. Varekamp. Effects of mercury on visible/near-infrared reflectance spectra of mustard spinach plants (Brassica rapa P.). Environmental Pollution, 2007, 148: 301-311
[16] Andrew C Schuerger, Gene A Capelle, John A Di Benedetto. Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments for the detection of zinc stress and chlorophyll concentration in bahia grass. Remote Sensing of Environment, 2003, 84:572-588
[17]马跃良等.广东省河台金矿生物地球化学特征及遥感找矿意义.矿物学报,2000,20(1):81～85
[18]甘甫平,刘圣伟,周强等.德兴铜矿矿山污染高光谱遥感直接识别研究.地球科学,2004,29(1):119～126
[19] Mars, J. C. , Crowley, J. K. Mapping mine wastes and analyzing areas affected by selenium-rich water runoff southeast Idaho using AVIRIS imagery anddigital elevation data. Remote Sensing of Environment, 2003, 84:422～436
[20] Swayze, G. A., Smith , K. S. , Clark , R. N. , et al. Using imaging spectroscopy to map acidic mine waste. Environmental Science and Technology, 2000, 34: 47-54
[21] MINEO. Monitoring and assessing the environment impact of mining in Europe using advance earth observation techniques. http : // www. brgm.fr/ mineo. 2002
[22] Reinhaeckel, G., B.Zhukov, D.Oertel, A.Mueller. Unmixing of Simulated ASTER Data with Applications for the Assessment of Mining Impacts in Central GERMANY, Imaging Spectrometry IV Proceedings of SPIE, 1998, 3438:345-354
[23]迟光宇,刘新会.环境污染监测中的植物光谱效应研究.环境科学与技术,2005,28(6):16～19
[24]李艳菊.元宝枫叶片色素吸收光谱研究.西北林学院学报,2004,19(2):28～30
[25] Hoque , E. , Huntzler , J . S. Spectral blue shift of red edge monitors damage class of beech trees. Remote sensing of environment, 1992, 39:81～84
[26] Rock,B.N, Hoshizake,T, miller, J.R, Comparison of in situ and airborne spectral measurements of the blue shift associated with forest decline. Remote Sensing of Environment, 1988, 24:109-127
[27] Jordan C F. Derivation of leaf area index from quality of light on the foresr floor. Ecology, 1969, 50:663-666
[28] Kauth R J, Thomas G S. The tasseled cap-a graphic description of the spectral-temporal development of agriculture crops as seen by Landsat. Pros Symposium on Machine Processing of Remotely Sensed Data. Purdure University, West Lafayette, Indiana, 1976:41-51
[29] Wheeler S G, Misra P N. Linear dimensionality of landsat agricultural data with implications for classifications. Pros Symposium on Machine Processing of Remotely Sensed Data. West Lafayette, Indiana, 1976. Laboratory for the Applications of Remote Sensing
[30] Kaufman Y J, Tanre D. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans on Geosci and Remote Sensing, 1992, 30(2): 261-270
[31] Rouse J W, Haas R H, Schell J A, Deering D W. Monitoring vegetation systems in the Great Plains with ERTS[A]. Proceedings of Third Earth Resources Technology Satellite-1 Symposium[C], Greenbelt: NASA SP-351,1974:310-317
[32] Huete A R. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 1988, 25: 295-309
[33] Qi J A. Modified soil adjusted vegetation index. Remote Sensing of Environment, 1994, 48: 119-126
[34] Baret F, Guyot G, Major D J. TSAVI: A vegetation index which minimize soil brightness effects on LAI and APAR estimation[A]. Proceedings of the 12th Canadian Symposium on Remote sensing and IGARSS'89[C], Vancouver, Canada, 1989, 3:1355-1358
[35] Major D J, Baret F, Guyot G. A ratio vegetation index adjusted for soil brightness. Int. J. Remote Sensing, 1990, 11: 727-740
[36] Deering D W, Rouse J W, Haas R H, Schell J A. Measuring forage production of grazing units from Landsat MSS data[A]. Proceedings of Tenth International Symposium on Remote Sensing of Environment[C], Ann Arbor, ERIM, 1975, 2: 1169-1178
[37] Roujean J L, Breon F M. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 1995, 51: 375-384
[38] Gitelson A, Kaufman Y J, Merzlyak M N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 1996, 58(3): 289-298
[39] Liu H Q, Huete A R. A feedback based modification of the NDVI to minimize canopy background and atmosphere noise. IEEE Trans Geosci Remote Sensing, 1995, 33:457-465
[40]唐世浩,朱启疆,王锦地.三波段梯度差植被指数的理论基础及其应用.中国科学(D辑),2003,33(11):1094～1102
[41] George Alan Blackburn. Auantifying Chlorophylls and Caroteniods at Leaf and Capopy Scales: An Evaluation of Some Hyperspevtral Approaches. Remote Sensing of Environment, 1998, 66: 273-285
[42]杨吉龙,李家存,杨德明.高光谱分辨率遥感在植被监测中的应用综述.世界地质,2001,20(3):307～312
[43] Demetriades-Shah T H, Steven M D, Clark J A. High resolution derivative spectra in remote sensing. Remote Sensing of Environment, 1990, 33:55-64
[44]王秀珍,王人潮,黄敬峰.微分光谱遥感及其在水稻农学参数测定上的应用研究.农业工程学报,2002,18(1):9～13
[45]吴长山,项月琴,郑兰芬等.利用高光谱数据对作物群体叶绿素密度的研究.遥感学报,2000,4(3):228～232
[46] Sarah C. Dunagan, Martha S. Gilmore, Johan C. Varekamp.. Effects of mercury on visible/near-infrared reflectance spectra of mustard spinach plants (Brassica rapa P.). Environmental Pollution, 2007, 148: 301-311
[47] Andrew C Schuerger, Gene A Capelle, John A Di Benedetto. Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments for the detection of zinc stress and chlorophyll concentration in bahia grass. Remote Sensing of Environment, 2003, 84:572-588
[48]王芳,黎夏,卓莉等.基于Hyperion高光谱数据的城市植被胁迫评价.应用生态学报,2007,18(6):1286～1292
[49]杜华强,金伟,葛宏立等.用高光谱曲线分形维数分析植被健康状况.光谱学与光谱分析, 2009,29(8):2136～2140
[50] Lindsay W L, Norvell W A. Development of a DTPA soil test for zinc, iron, manganese, and copper. Soil Science Society of America Journal, 1978, 42:421-428
[51]倪健.环境与生态论丛.厦门:厦门大学出版社,1993:46～51
[52] Mandelbrot B B. How long is the coast of Britain? Statistical self-similarityand fractional dimension. Science, 1967, 155: 636-638
[53]周子勇,李朝阳.高光谱遥感数据光谱曲线分形特征研究.中北大学学报:自然科学版, 2005,26(6):451～454
[54]江东,王建华.遥感信息科学中的分形思维.甘肃科学学报,2000,12(1):53～57
[55]葛世荣,索双富.表面轮廓分形维数计算方法的研究.摩擦学报,1997,17(4):354～362
[56]王安良,杨春信.评价机械加工表面形貌的小波变换方法.机械工程学报, 2001,37(8):65～70
[57]成礼智,王红霞,罗永.小波的理论与应用.北京:科学出版社,2004
[58]张德丰。MATLAB小波分析,北京,机械工业出版社,2009
[59]马波,裘正定.小波变换的分形特性.铁道学报,1998,20(6):74～80
[60]葛世荣,朱华.摩擦学的分形.北京:机械工业出版社, 2005
[61] Daubechies I. The wavelet transform, time-frequency localization and signal analysis. IEEE Transactions on Information Theory, 1990, 36(5): 961-1005
[62]刘美玲、刘湘南,水稻锌污染胁迫的光谱奇异性分析,农业工程学报,2010,26(3):191～197
[63] Lam N S N, De Cola L. Introduction to fractals in geography, Fractal Measurement//Dit, Dominick Mosco & Ann Sullivan. Fractal in Geography, Englewood Cliffs, New Jersey, 1993:(3-9; 23-55)
[64]杨丹慧.重金属对高等植物质膜结构和功能的影响.植物学通报,1991,8 (3):26～29
[65]张金恒,王珂,王人潮.高光谱评价植被叶绿素含量的研究进展.上海交通大学学报(农业科学版),2003,21(3):76～83
[2]宋书巧,吴浩东,蓝唯源.刁江沿岸土壤重金属污染状况及土地的合理利用模式.环境与健康杂志,2008,25(4):317～319
[3]林健等.公路交通污染土壤和稻谷中镉铅分布特征.环境与健康杂志, 2002,19(2):119～121
[4]张义贤.汞、镉、铅胁迫对油菜的毒害效应.山西大学学报(自然科学版), 2004,27(4):410～413
[5]谭周镃.稻米重金属污染的调查研究及其对策思考.湖南农业科学, 1999,5(13):26～28
[6]章力建等.我国水稻生产中的立体污染及防治.中国稻米, 2005,5:1～4
[7]杨科璧.中国农田土壤重金属污染与其植物修复研究.世界农业,2007(8):58～61
[8]蒋先军,骆永明,赵其国.土壤重金属污染的植物提取修复技术及其应用前景.农业环境保护,2000,19(3):179～183
[9]崔德杰,张玉龙。土壤重金属污染现状与修复技术研究进展.土壤通报,2004,35(3):366～370
[10]李民赞,韩东海.光谱分析技术及其应用.北京:科学出版社,2006:176～179
[11]任红艳,庄大方,潘剑君,邱冬生等.铅污染水稻的冠层高光谱特征研究.地球信息科学,2008,10(3):314～319
[12]刘帅,高永光等.铜胁迫下玉米叶绿素质量比与光谱反射率关系.辽宁工程技术大学学报,2008,27(1):125～128
[13]田国良,包佩利,李建军,等.土壤中镉、铜伤害对水稻光谱特性的影响.环境遥感,1990,5(2):140～149
[14] Pablo H. Rosso a, James C. Pushnik b, Mui Lay c, Susan L. Ustin. Reflectance properties and physiological responses of Salicornia virginica to heavy metal and petroleum contamination. Environmental Pollution, 2005, 137:241-252
[15] Sarah C. Dunagan, Martha S. Gilmore, Johan C. Varekamp. Effects of mercury on visible/near-infrared reflectance spectra of mustard spinach plants (Brassica rapa P.). Environmental Pollution, 2007, 148: 301-311
[16] Andrew C Schuerger, Gene A Capelle, John A Di Benedetto. Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments for the detection of zinc stress and chlorophyll concentration in bahia grass. Remote Sensing of Environment, 2003, 84:572-588
[17]马跃良等.广东省河台金矿生物地球化学特征及遥感找矿意义.矿物学报,2000,20(1):81～85
[18]甘甫平,刘圣伟,周强等.德兴铜矿矿山污染高光谱遥感直接识别研究.地球科学,2004,29(1):119～126
[19] Mars, J. C. , Crowley, J. K. Mapping mine wastes and analyzing areas affected by selenium-rich water runoff southeast Idaho using AVIRIS imagery anddigital elevation data. Remote Sensing of Environment, 2003, 84:422～436
[20] Swayze, G. A., Smith , K. S. , Clark , R. N. , et al. Using imaging spectroscopy to map acidic mine waste. Environmental Science and Technology, 2000, 34: 47-54
[21] MINEO. Monitoring and assessing the environment impact of mining in Europe using advance earth observation techniques. http : // www. brgm.fr/ mineo. 2002
[22] Reinhaeckel, G., B.Zhukov, D.Oertel, A.Mueller. Unmixing of Simulated ASTER Data with Applications for the Assessment of Mining Impacts in Central GERMANY, Imaging Spectrometry IV Proceedings of SPIE, 1998, 3438:345-354
[23]迟光宇,刘新会.环境污染监测中的植物光谱效应研究.环境科学与技术,2005,28(6):16～19
[24]李艳菊.元宝枫叶片色素吸收光谱研究.西北林学院学报,2004,19(2):28～30
[25] Hoque , E. , Huntzler , J . S. Spectral blue shift of red edge monitors damage class of beech trees. Remote sensing of environment, 1992, 39:81～84
[26] Rock,B.N, Hoshizake,T, miller, J.R, Comparison of in situ and airborne spectral measurements of the blue shift associated with forest decline. Remote Sensing of Environment, 1988, 24:109-127
[27] Jordan C F. Derivation of leaf area index from quality of light on the foresr floor. Ecology, 1969, 50:663-666
[28] Kauth R J, Thomas G S. The tasseled cap-a graphic description of the spectral-temporal development of agriculture crops as seen by Landsat. Pros Symposium on Machine Processing of Remotely Sensed Data. Purdure University, West Lafayette, Indiana, 1976:41-51
[29] Wheeler S G, Misra P N. Linear dimensionality of landsat agricultural data with implications for classifications. Pros Symposium on Machine Processing of Remotely Sensed Data. West Lafayette, Indiana, 1976. Laboratory for the Applications of Remote Sensing
[30] Kaufman Y J, Tanre D. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans on Geosci and Remote Sensing, 1992, 30(2): 261-270
[31] Rouse J W, Haas R H, Schell J A, Deering D W. Monitoring vegetation systems in the Great Plains with ERTS[A]. Proceedings of Third Earth Resources Technology Satellite-1 Symposium[C], Greenbelt: NASA SP-351,1974:310-317
[32] Huete A R. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 1988, 25: 295-309
[33] Qi J A. Modified soil adjusted vegetation index. Remote Sensing of Environment, 1994, 48: 119-126
[34] Baret F, Guyot G, Major D J. TSAVI: A vegetation index which minimize soil brightness effects on LAI and APAR estimation[A]. Proceedings of the 12th Canadian Symposium on Remote sensing and IGARSS'89[C], Vancouver, Canada, 1989, 3:1355-1358
[35] Major D J, Baret F, Guyot G. A ratio vegetation index adjusted for soil brightness. Int. J. Remote Sensing, 1990, 11: 727-740
[36] Deering D W, Rouse J W, Haas R H, Schell J A. Measuring forage production of grazing units from Landsat MSS data[A]. Proceedings of Tenth International Symposium on Remote Sensing of Environment[C], Ann Arbor, ERIM, 1975, 2: 1169-1178
[37] Roujean J L, Breon F M. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 1995, 51: 375-384
[38] Gitelson A, Kaufman Y J, Merzlyak M N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 1996, 58(3): 289-298
[39] Liu H Q, Huete A R. A feedback based modification of the NDVI to minimize canopy background and atmosphere noise. IEEE Trans Geosci Remote Sensing, 1995, 33:457-465
[40]唐世浩,朱启疆,王锦地.三波段梯度差植被指数的理论基础及其应用.中国科学(D辑),2003,33(11):1094～1102
[41] George Alan Blackburn. Auantifying Chlorophylls and Caroteniods at Leaf and Capopy Scales: An Evaluation of Some Hyperspevtral Approaches. Remote Sensing of Environment, 1998, 66: 273-285
[42]杨吉龙,李家存,杨德明.高光谱分辨率遥感在植被监测中的应用综述.世界地质,2001,20(3):307～312
[43] Demetriades-Shah T H, Steven M D, Clark J A. High resolution derivative spectra in remote sensing. Remote Sensing of Environment, 1990, 33:55-64
[44]王秀珍,王人潮,黄敬峰.微分光谱遥感及其在水稻农学参数测定上的应用研究.农业工程学报,2002,18(1):9～13
[45]吴长山,项月琴,郑兰芬等.利用高光谱数据对作物群体叶绿素密度的研究.遥感学报,2000,4(3):228～232
[46] Sarah C. Dunagan, Martha S. Gilmore, Johan C. Varekamp.. Effects of mercury on visible/near-infrared reflectance spectra of mustard spinach plants (Brassica rapa P.). Environmental Pollution, 2007, 148: 301-311
[47] Andrew C Schuerger, Gene A Capelle, John A Di Benedetto. Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments for the detection of zinc stress and chlorophyll concentration in bahia grass. Remote Sensing of Environment, 2003, 84:572-588
[48]王芳,黎夏,卓莉等.基于Hyperion高光谱数据的城市植被胁迫评价.应用生态学报,2007,18(6):1286～1292
[49]杜华强,金伟,葛宏立等.用高光谱曲线分形维数分析植被健康状况.光谱学与光谱分析, 2009,29(8):2136～2140
[50] Lindsay W L, Norvell W A. Development of a DTPA soil test for zinc, iron, manganese, and copper. Soil Science Society of America Journal, 1978, 42:421-428
[51]倪健.环境与生态论丛.厦门:厦门大学出版社,1993:46～51
[52] Mandelbrot B B. How long is the coast of Britain? Statistical self-similarityand fractional dimension. Science, 1967, 155: 636-638
[53]周子勇,李朝阳.高光谱遥感数据光谱曲线分形特征研究.中北大学学报:自然科学版, 2005,26(6):451～454
[54]江东,王建华.遥感信息科学中的分形思维.甘肃科学学报,2000,12(1):53～57
[55]葛世荣,索双富.表面轮廓分形维数计算方法的研究.摩擦学报,1997,17(4):354～362
[56]王安良,杨春信.评价机械加工表面形貌的小波变换方法.机械工程学报, 2001,37(8):65～70
[57]成礼智,王红霞,罗永.小波的理论与应用.北京:科学出版社,2004
[58]张德丰。MATLAB小波分析,北京,机械工业出版社,2009
[59]马波,裘正定.小波变换的分形特性.铁道学报,1998,20(6):74～80
[60]葛世荣,朱华.摩擦学的分形.北京:机械工业出版社, 2005
[61] Daubechies I. The wavelet transform, time-frequency localization and signal analysis. IEEE Transactions on Information Theory, 1990, 36(5): 961-1005
[62]刘美玲、刘湘南,水稻锌污染胁迫的光谱奇异性分析,农业工程学报,2010,26(3):191～197
[63] Lam N S N, De Cola L. Introduction to fractals in geography, Fractal Measurement//Dit, Dominick Mosco & Ann Sullivan. Fractal in Geography, Englewood Cliffs, New Jersey, 1993:(3-9; 23-55)
[64]杨丹慧.重金属对高等植物质膜结构和功能的影响.植物学通报,1991,8 (3):26～29
[65]张金恒,王珂,王人潮.高光谱评价植被叶绿素含量的研究进展.上海交通大学学报(农业科学版),2003,21(3):76～83