铜矿区植物光谱特征与信息提取
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摘要
本论文针对德兴铜矿区实际,采用了遥感生物地球化学方法,对矿区部分优势植物包括对盐肤木、狼尾蒿、臭蒿、香薷、芒萁等植物进行了深入探讨,研究了矿区酸性水污染区和尾矿库土壤—植物系统中重金属分布规律。利用小波分析方法进行了噪声方差估计,并通过仿真试验和理论分析确定了波谱中存在的噪声的类型,针对噪声类型提出了最佳空域相关去噪方法。结合光谱反射率、反射峰最大反射率及其位置、吸收谷位置、面积、深度、宽度、红边参数和植被指数等共计44个光谱特征参数,对研究区采集的五种植物的光谱特征进行了分析,并针对叶部重金属含量及其富集特征的逐步多元回归模型开展了研究。
     研究结果表明:德兴矿区酸性水污染区和尾矿库土壤中元素总含量呈现尾矿库坝中>坝项,河床>污染区>废石场汇水水沟边的趋势。与土壤地球化学背景值相比,各采样点土壤中Cu含量远高于中国A层土壤及A层红壤背景值。Zn、Mn、Cd、Ni、Pb等的含量高于背景值,而Cr含量与背景值相近。对比中国土壤环境质量标准值,大多数土壤不满足国家标准二级标准要求,但几乎所有的土壤均满足三级标准要求。这些土壤基本能够满足农林业生产和植物正常生长的需要,但不能保障农业生产,维护人体健康。
     在研究区采集的几种植物在重金属含量较高的土壤环境中生存,明显受到了土壤重金属含量影响。重金属大多富集于根部,部分植物在叶部富集。几种植物均表现出了对铜元素的不同程度的富集特性。香薷器官中重金属含量很高,有明显的富集铜的特征。
     野外光谱噪声估计与降噪方法研究认为,利用第一尺度分解的小波高频系数估计噪声方差具有较高精度和稳定性,基于空域相关算法可以实现该噪声方差的估计。结果表明,野外波谱数据中存在的噪声主要集中在第一、二尺度,而噪声方差也随波长变化而变化。1400nm和1900nm附近水吸收波段噪声显著,野外波谱噪声属于除性复合噪声,采用对数变换结合小波变换可以令其白化,有利于噪声的去除。三类小波降噪方法中,空域相关去噪最适合于野外波谱数据处理,其次是模极大去噪,阈值去噪方法不适于野外波谱数据降噪。改进的自适应空域相关去噪法适用于波谱数据处理。该方法可以有效地去350nm~2500nm波段的噪声,其中包含1400nm附近水吸收波段的噪声。但1900nm附近噪声不能有效去除,原因可能在于光谱仪器系统的精度低。
     光谱特征研究结果指出,盐肤木、狼尾蒿、臭蒿、香薷、芒萁等五种植物绝大部分光谱特征随环境变化显著,各光谱特征值与叶部重金属含量的线性相关性各不相同,存在的一个共同的规律是:重金属的效应表现为它改变了植物的水分含量、色素含量、叶部组织结构等,促进或阻碍水的吸收;部分重金属阻碍色素合成,而Zn等则能促进色素合成;部分重金属可以破坏叶部组织结构,使红外反射率升高。一种重金属效应在多处光谱特征有所反映。不同重金属如Pb、Zn等元素,在同一光谱特征上(比如在水吸收波段)都有不同程度的反映。重金属在植物体内往往与蛋白质结合,作为生物催化剂——酶的组成,参与了不同的生化过程,可能导致了以上规律的存在。但由于生物对必需重金属元素如Zn的需求也是有一定限度的。当生长基质中这种重金属含量增达到一定程度时,就会产生毒化效应。而这种毒化效应体现在某些生物机能上而非全面影响。对于生物非必需元素如Pb在产生毒化效应时,则将影响多种生物机能如水吸收、色素合成等正常进行,从而导致了多处光谱特征响应。但不同植物之间也存在一些差异,这可能与不同物种对重金属元素的吸收、富集、耐受能力不同有关。
     叶部重金属含量及其富集特征的逐步多元回归模型研究结果认为,利用逐步回归法可以建立盐肤木、狼尾蒿、臭蒿、香薷、芒萁等五种植物的光谱特征与叶部化学物质之间的关系模型,从模型计算值与实测值比较结果来看,模型比较稳定,相对偏差大多在±10%以下。进入各元素逐步回归模型的光谱特征各不相同,进入同种元素各种植物逐步回归模型的光谱特征差异很大。研究中采用了一阶、二阶微分光谱对狼尾蒿、臭蒿、香薷三种一年生草本植物叶部重金属含量进行逐步回归建模。研究结果表明,除Pb、Mn较差外,微分光谱模型的估测效果均较好,相对偏差大多低于15%。一阶微分和二阶微分模型的估测精度有差异。采用一阶微分光谱模型计算叶部Cu、Ni的估测效果较好,而对于Cr、Mn、Pb、Cd、Zn,采用二阶微分光谱模型计算效果较好。研究中还采用了微分光谱对狼尾蒿、臭蒿、香薷三种一年生草本植物叶部重金属富集系数进行逐步回归建模,结果表明,微分光谱模型对Mn的富集系数估测结果较好;Cu、Cr、Ni、Pb、Cd、Zn其他相对偏差太大,估测价值不高,有待于进一步研究和探讨。
According to distribution of barren and waste land in Dexing copper mine, remote sensing biochemistry methods were applied in the research on some of predominant plants, including Rhus Chinensis Mill, Sweet Wormwood Herb, Comnyza Canadensis (L.) Cronq. , Mosla Chinensis Maxim and Dicranopteris pedata. Heavy metals' distributing pattern in soil-plant system at AMD contaminated land and tailings dam was analysed. Wavelet transformation was taken to estamate noise variance and denosing. Type of noise was made sure via theoretical analysis and emulational experiments. And spacially correlated filtration was chosen for field spectrum denoising. 44 indexes were employed to analyze the features of spectrum of these five plants. And heavy metals contained in leaves and its enrichment index were estimated by stepwise multianalytical regression model.
     The findings indicated that, total heavy metal concentration in acid mining drainage contaminated soil and in soil of the tailings storehouse was higher in the middle of storehouse than the top of dam, and showed the tendency as the river bed > the pollutes area > nearby the aqueduct of waste rock dam. Comparing with the soil geochemistry background value, the Zn, Mn, Cd, Ni, Pb, Cu content were higher than the their background value of the Chinese A-layer soil and A-layer laterite, especially Cu was far higher than its background value, while Cr is nearly equal to its background value. Comparing with the Chinese soil environmental quality standard value, the majority soils didn't satisfy the standards requests of the second levels of the national standard, but nearly all soils satisfy the standards requests of the third levels. These soils can just satisfy production of the farming and forestry industry and just can meet the need for plant growth, but cannot safeguard the agricultural production, and the human body health.
     The five gathered plants survived in the study area where the heavy metal content is very high in soils, and was obviously influenced by the soil heavy metal. The heavy metals mostly concentrates in the root, while concentrate in the leaf of partial plants. The plants displayed varous enrichment characteristic to the copper element. In the organs of Mosla Chinensis Maxim the heavy metal content is very high, and has obviously enrichment characteristic of copper. Research result pointed that, the first scale decomposed details could be well employed to estimate the noise variance and this method has high accuracy and stability. This estimation could be realized based on the spacially correlation algorithm. The noise mainly distributed in the first and the second wavelet decomposed scale, and the noise variance also changes along with the wavelength change.
     Noise is remarkable nearby 1400nm and 1900nm where the water absorption exists. The emulational experiments and the theoretical analysis indicated that, the noise in the field vegetation spectrum belongs to be dividing noise, and the logarithm transformation and wavelet transformation can make it white. In three kind of wavelets noise reduction method, the spacially correlation filtration most suits to field vegetation spectral data processing, the next is the module maximum filtration. The threshold shrinking method is not suitable to spectral data noise reduction. The improved self-adapted spacially correlation filtration is suitable for spectral data processing. This method may effectively reduce the noise in the bands from 350nm to 2500nm, including bands nearby 1400nm where water absorption exists.But the noise nearby 1900nm cannot be effectively removed. The reason possibly lies in the precision insufficiency of the spectrum instrument system.
     The spectrum feature findings pointed out that, major spectrum indexes of the five kind of plants is remarkable different along with environmental variation. Linear relevance of spectrum indexes and heavy metal content in the leaf is various. An common rule is: The heavy metal effect often displayed as it changed plant's moisture content, the pigment content, the leaf structure, and so on, promoting or counteract the water absorption. Some heavy metals hinder the pigment synthesis, but Zn can promote it. Some of the heavy metals may destroy the leaf structure and causes the higher valuesof the infrared indexes of reflection spectrum. One kind of heavy metal effect often has many response features in reflection spectrum. Different heavy metals like Pb, Zn, has the varying degree in the identical feature of the reflection spectrum, for instance in water absorption band. The reason lies in that the heavy metal in plant often combined with the protein, and acted as the biocatalyst, the enzyme, and participated in the different biochemistry process. But the biology also has certain limit of the demand of the essential heavy metals i.e. Zn. When essential heavy metal content achieved certain degree in the growth matrix, the poisoned effect presented. But this kind of poisoned effect influence on certain body functions but not on all of them. Regarding biological non-essential element like Pb, when the poisoned effect presented, many biological functions like water absorption, the pigment synthesis, and so on, were affected and could not be normmally carried on, thus it caused many spectral response. But between the different plant there were also some differences, it maybe owned to the different species ability of the heavy metal absorpting, concentrating and tolerating.
     The research on leaf heavy metal content and its enrichment indexes proved that, Stepwise multianalytical Regression model can be found. The model predicted value and the actual value comparison showed that the model is stable, and the relative deviation mostly below±10%. The first order and the second differential spectrum was employed to estamate the heavy metals contained in the leaves and their enrichment indexes for Sweet Wormwood Herb, Comnyza Canadensis (L.) Cronq. and Mosla Chinensis Maxim. The findings indicated that, except Pb and Mn, the differential spectrum model has good estimating effect, the relative deviation mostly is lower than 15%. The firstorder differential model and the second differential model has the difference on estamation of various heavy metal. The first order differential spectrum model can well estamate Cu and Ni in the leaves, while the second differential spectrum model can well estamate the Cr, Mn, Pb, Cd and Zn. The differential spectrum model can only well estimate the enrichment coefficient of Mn. As relative deviations of estimation on enrichment indexes of Cu, Cr, Ni, Pb, Cd and Zn are too high, the model can not be use to estimate the enrichment coefficients of these heavy metals. It looks forward for further studies and discussion.
引文
[1] Naicker K., Cukrowska E., et al. Acid mine drainage arising from gold mining activity in Johannesburg,South Africa and environs, Environmental Pollution, 122 (2003):29-40
    [2] Winterbourn M.J., McDiffett W.F., et al.Aluminium and iron burdens of aquatic biota in New Zealand streams contaminated by acid mine drainage:effects of trophic level, The Science of the Total Environment, 254(2000): 45-54
    [3] Joukainen S., Markku Y.H., Environmental impacts and acid loads from deep sulfidic layers of two well-drained acid sulfate soils in western Finland, Agriculture, Ecosystems and Environment, 95 (2003):297-309
    [4] Bradshaw A., The use of natural processes in reclamation:advantages and difficulties. Landscape and Urban Planning, 51(2000):89-100
    [5] 李洪远等,生态恢复的原理与实践[M]北京:化学工业出版社,2005。
    [6] Williamson T., Johnson M S. Reclamation of metallicferrous mine wastes. In: Lepp N W. ed. Effect of Heavy Metal Pollution on Plants. Vol. 2. Metals in the Environment. London and New Jersey: Applied Science Publishers,1981.185-212
    [7] 格默尔R.P.著(倪彭年等译),工业废弃地上的植物定居[M].北京:科学出版社,1987。
    [8] Hanson A.T. Transport and Remediation of Subsurface Contaminants [M].Washington D. C.: American Chemical Society, 1992.
    [9] 宋静.土壤重金属污染修复技术[J].农业环境保护,1998,6:271-273
    [10] Rose M. V. et al. Mercury clean up: the Commercial Application of a New Mercury Removal[J], Remediation Summer, 1995, 3:89-101
    [11] Jordao C.P., Fialho L.L., Neves J.C.L., et al., Reduction of heavy metal contents in liquid effluents by vermicomposts and the use of the metal-enriched vermicomposts in lettuce cultivation, Bioresource Technology, Volume 98, Issue 15, November 2007, 2800-2813
    [12] Suresh B., Ravishankar G.A.,Phytoremediation - A novel and promising approach for environmental clean-up, Critical Reviews in Biotechnology, 24(2004): 97-124
    [13] Pulford I.D. , Watson, C. , Phytoremediation of heavy metal-contaminated land by trees - A review, Environment International, 29(2003): 529-540
    [14] 陈玉成.污染环境生物修复工程[M].北京:化学工业出版社,2003:35~47
    [15] Zu Y.Q., Li Y.,et al. Accumulation of Pb, Cd, Cu and Zn in plants and hyperaccumulator choice in Lanping lead-zinc mine area, China. Environment International 30 (2004) 567-576.
    [16] 王长耀,牛铮,唐华俊,等.对地观测技术与精细农业[M].北京:科学出版社,2001.
    [17] 赵碧云,贺彬,朱云燕.滇池水体中叶绿素A含量的遥感定量模型[J].云南环境科学,2001.20(3):1~3.
    [18] 浦瑞良,宫鹏.高光谱遥感及其应用[M].北京:高等教育出版社,2000。
    [19] 徐瑞松,马跃良,何在成.遥感生物地球化学[M].广州:广东科技出版社2003,第1版
    [20] 崔锦泰著,程正兴译.小波分析导论[M]西安:西安交通大学出版社,1995
    [21] 李建平,小波分析与信号处理[M]重庆:重庆出版社,1997
    [22] 徐佩霞等.小波分析与应用实例[M]合肥:中国科学技术大学出版社,1996
    [23] 赵凯等,小波变换及其在分析化学中的应用[M]北京:地质出版社,2000
    [24] Andy M.,John M.,废弃土地的林业复垦技术[M.]郑州:黄河水利出版社,2001年09月第1版。
    [25] LR霍斯纳[美].中国土地学会土地复垦分会露天开采复垦专业委员会译.露天矿土地复垦.1988。
    [26] 黄铭洪,环境污染与生态恢复[M]北京:科学出版社,2003。
    [27] 孙铁珩,周启星,李培军,污染生态学,[M]北京:科学出版社,2001.11
    [28] Mukhopadhyay K.. Environmental ramifications in surface mining with respect to land degradation under Indian context [J]. Journal of mines, metals &fuels, Aug-Sep, 1994:200-205
    [29] 杨福海,李富平等著,矿山生态复垦与露天地下联合开采[M.]北京:冶金工业出版社,2002年1月.
    [30] 杨修,高林,吴刚.矿山废弃地复垦的理论与技术.社会-经济-自然复合生态系统可持续发展研究.北京:中国环境科学出版社,1999:124~136.
    [31] Lloyd R. Reclamation of surface mined lands. CRC Press Inc, 1988.
    [32] Cairns J. Rehabilitating damaged ecosystems. 2nd ed. Boca Raton: Lewis Publishers, 1995.
    [33] Chaturvedi A N. Wasteland afforestation. Indian-For. Dehra Dun: N.M. Misra. 1985, 111(11): 919~926.
    [34] Kumar P.B.,Dushenkov V, Motto H.,et al.Phytoextraction of copper, lead,cadmium and zinc in soils,Environmental Science and Technology.1995,29:1232-1238.
    [35] Dushenkkov V., Kumar P.B.,Motto H,et al. The use of plants to remove heavy metals from aqueous streams. Environmental Science and Technology.1995,29:1239-1245.
    [36] Watanabe M.E. Phytoremediation on the brink commercialization. Environmental Science and Pollution.2000,35:258-263.
    [37] Salt D R.Blaylock M,Kumar P.B.,et al.Phytoremediation:a novel strategy for the removal of toxic metals from the environment using plants. Bio/Technology. 1995,13:468-474. Technology/News. 1997,31:182A-186A.
    [38] Lazro J.D., .Kidd P.S., Martinez C.M.. A phytogeochemical study of the Tras-os-Montes region (NE Portugal): Possible species for plant-based soil remediation technologies [J]. Science of the Total Environment 354 (2006):265-277
    [39] Sims J.T., Kline J.S.. Chemical fractionation and plant uptake of heavy metals in soils amended with co-composed sewage sludge. [J]. Journal of Environmental Quality1991. Chemosphere 64 (2006):161-173
    [40] Archer, M.J.G.; Caldwell, R.A. Response of six Australian plant species to heavy metal contamination at an abandoned mine site. Water, Air, and Soil Pollution, v 157, n 1-4, September, 2004,:257-267
    [41] Batty, Lesley C.; Younger, Paul L.. Growth of Phragmit.es australis (Cav.) Trin ex. Steudel in mine water treatment wetlands: Effects of metal and nutrient uptake. Environmental Pollution, 2004 (1):85-93
    [42] Pratas J.; Prasad M.N.V., Freitas H.,et al. Plants growing in abandoned mines of Portugal are useful for biogeochemical exploration of arsenic, antimony, tungsten and mine reclamation. Journal of Geochemical Exploration, 2005 (3): 99-107
    [43] Baker A.J., McGrath S.P.,Sidoli C.M.,et al.The possibility of in situ heavy metal decontamination of polluted soils using crops of metal-accumulating plants. Resourse,Conservation and Recycling, 1994,11:41-49.
    [44] CEC Council directive of 12 Jun 1986 on the proptection of the environment,and in particular of the soil,when sewage sludge is used in agriculture.Official Journal of the European Communities. 1986,L181(86/2781 EEC),6-12.
    [45] Robinson R H.The potential of Thlaspi caerulescens for phytoremediation of contaminated soils.Plant and Soil,1998,203:47-56.
    [46] Reeves R.A., Baker A J M, Brooks R R.Abnormal accumulation of trace metals by plants.Mining Environmental Management,1995,9:4-8.
    [47] 束文圣,蓝崇钰,张志权.凡口铅锌影响植物定居的主要因素分析.应用生态学报.1997,8(3):314-318.
    [48] 陈怀满,郑春荣,涂从.铜矿尾矿库土壤植物体系中重金属和营养现状[J].国际土地复垦学术研讨会专辑.2000,30-32
    [49] 黄长干,张莉,余丽萍,陈金珠,李晓跃.德兴铜矿铜污染状况调查及植物修复研究[J].江西农业大 学学报.2004,26(4):630-632
    [50] 孙叶芳,矿区土壤重金属毒性评价及污染修复[D],浙江大学硕士学位论文,2005.
    [51] 涂从,郑春天,陈怀满.铜尾矿库土壤—植物体系的现状研究[J].土壤学报,2000,37(2):135-143.
    [52] 王庆仁,崔岩山,董艺婷,植物修复重金属污染土壤整治有效途径,生态学报,2001,21(2):326-331
    [53] 王祖伟,天津地区土壤环境中有效态重金属的分布特征与生态意义[J],土壤通报,2005:101-103.
    [54] Glass D.The 1998 United States Market for Phytoremediation.D Glass Associates,Inc,1998:1-20
    [55] 常青山等,重金属超富集植物筛选研究进展[J].农业环境科学学报,2005S:330~335
    [56] 张鑫,安徽铜陵矿区重金属元素释放迁移地球化学特征及其环境效应研究[D],合肥工业大学博士学位论文,2005.
    [57] 龙健,我国南方红壤矿区复垦土壤微生物生态特征及其恢复研究[D],浙江大学博士学位论文,2003.
    [58] 周以富,董亚英,几种重金属土壤污染及其防治的研究进展[J]环境科学动态,2003(1):15-17
    [59] 张曼夫,生物化学[M].北京:中国农业大学出版社,2002
    [60] 陈述彭,童庆禧,郭华东,高光谱分辨率遥感信息机理与地物识别,遥感信息机理研究[M]北京:科学出版社,1998.139-231
    [61] 陈永清,夏庆霖.金属矿产勘查技术发展现状与思考[J].地球物理学进展.2002,17(3):540-550
    [62] 宫鹏,史培军,浦瑞良地观测技术与地球系统科学[M]北京:科学出版社,1996:208
    [63] 郭华东.新疆北部地质矿产遥感[M]北京:科学出版社1995。
    [64] 马建伟,徐冠华.秦岭成矿带金矿遥感生物地球化学研究,《遥感在中国纪念中国国家遥感中心成立15周年论文集》[M]北京:测绘出版社,1996。
    [65] 马跃良.广东省河台金矿生物地球化学特征及遥感找矿意义[J].矿物学报,2000,20(1):81-85
    [66] 甘甫平,王润生,郭小方等.高光谱遥感信息提取与地质应用前景[J].国土资源遥感,2000,(3):38-41
    [67] William C.. airborne biogeophysical mapping of hidden mineral deposits,economic geology,1983,78(4):737-750
    [68] Rock B N, Hoshizaki T, Miller J R. Comparison of in situ and airborne spectral measurements of the blue shift associated with forest decline, remote sensing of environment,1986, 24:109-127
    [69] Rock B N. Remote detection of forest damage, bioscience,1986,36(7):439-445
    [70] Rock B.N. Monte Carlo clculation of canopy reflectance, remote sensing of environment 1986,24,213-225
    [71] 童庆禧,中国典型地物波普及其特征分析[M].北京:科学出版社,1990.
    [72] 田国良,包佩丽,土壤中镉、铜伤害对水稻光谱特性的影响[J].环境遥感,1990,5(2):140-149
    [73] Caetano M, Pereira J.M.C..1997.Analysis of the integrated hyper spectral response of pine stands [J].SPIE,3(222):26-37.
    [74] Cloutis E A.Hyperspectral geological remote sensing: evaluation of analytical techniques. Int J. Remote Sensing, 1996,17(12):2215-2242
    [75] Freek van der Meera.Analysis of spectral absorption features in hyperspectral imagery[J]. International Journal of Applied Earth Observation and Geoinformation,2004,5:55-68
    [76] Fuan T., William P.. Derivative Analysis of Hyperspectral Data [J].REMOTE SENS. ENVIRON.1998,66:41-51
    [77] Broge N.H., Leblanc E.. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density[J]. Remote Sensing of Environment 2000,76:156-172
    [78] P.M. Teillet, G. Fedosejevs, R.P. Gauthier, N.T. O'Neill. A generalized approach to the vicarious calibration of multiple Earth observation sensors using hyperspectral data[J]. Remote Sensing of Environment 2001,77:304-327
    [79] Thenkabail P.S., Smith R.B., Pauw E.D..Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics[J]. REMOTE SENS. ENVIRON. 2000,71:158-182
    [80] Aspinall R. J., Marcus W. A., et al. Considerations in collecting, processing, and analysing high spatial resolution hyperspectral data for environmental investigations[J]. Geographical Systems 2002,(4),15-29
    [81] Pu R., Gong P.. Wavelet transform applied to EO-1 hyperspectral data for forest LAI and crown closure mapping[J]. Remote Sensing of Environment 2004(91):212-224
    [82] Kutser T., Herlevi A., Kallio K., et al.. A hyperspectral model for interpretation of passive optical remote sensing data from turbid lakes[J]. The Science of the Total Environment 2001,268:47-58
    [83] Wessman C.A..1988. Remote sensing of canopy chemistry and nitrogen cycling in temperate forest ecosystem[J]Nature, 335: 154-156
    [84] Analytical spectral devices(ASD) Inc. technical Guide,3rd, USA, 1999
    [85] 吕国楷,烘启旺.遥感概论[M].北京:高等教育出版社,1995:22-25
    [86] 王桥,杨一鹏,环境遥感[M].北京:科学出版社,2005.
    [87] 赵英时,遥感应用分析原理与方法[M].北京:科学出版社2003:180-189.
    [88] 郑兰芬,王晋年.成像光谱遥感技术及其图像光谱信息提取分析研究[J]环境遥感,1992,7(1):49-58
    [89] CHAU F.T., LIANG Y.Z., GAO J.B., et al. Chemometrics From Basics to Wavelet Transform. New Jersey: Published by John Wiley & Sons, Inc., 2004.25-28
    [90] Mallat S., A wavelet tour of signal processing(2nd Edition)., Beijing: China Machine Press. 2003.1-637
    [91] 潘泉,张磊,孟晋丽等,小波滤波方法及应用,北京:清华大学出版社,2005年。
    [92] 孙延奎,小波分析及其应用,北京:机械工业出版社,2005年.
    [93] Daubechies, Ten Lectures on Wavelets. New York: SIAM 1992.
    [94] Donoho D., De-noising by soft-thresholding, IEEE Trans. on Inf. Theory, 1995.41, 3, pp. 613-627.
    [95] Sweldens W.. The lifting scheme: a custom-design construction of biorthogonal wavelets. J. of appl. And Comput. Harmonic analysis, 1996, 3(2):186-200.
    [96] Sweldens W.. The lifting scheme: A construction of second generation wavelets. SIAM J. of Math. Analysis, 1997. 29(2):511-546,1997
    [97] Donoho D., Progress in wavelet analysis and WVD: a ten minute tour, in Progress in wavelet analysis and applications, Frontieres Ed. Y. Meyer, S. Roques, 1993.:109-128.
    [98] Donoho D.; Johnstone I.M. Adapting to unknown smoothness via wavelet shrinkage via wavelet shrinkage, JASA, 1995. vol. 90, 432:1200-1224.
    [99] 李成,基于提升小波的数据处理及过程监测研究[D].浙江大学博士学位论文,2005年9月
    [100] Witkin A.. scale space filtering. Proc. 8th Int. Joint Conf. Artificial Intell. 1983.
    [101] Xu Y., Weaver J.B., Healy D.M., et al. wavelet transform filters: a spatially selective noise filtration technique. IEEE TRANSFORMS ON IMAGE PROCESSING, Vol.3, No.6,1994,747-758.
    [102] Fabrizio A.; Gionatan T.; Luciano A..MMSE filtering of generalised signal-dependent noise in spatial and shift-invariant wavelet domains. Signal Processing, v 86, n 8, August, 2006, p 2056-2066
    [103] Arslan F.T.; Moreno J. M.; Grigoryan A. M..Paired directional transform based methods of image enhancement. Proceedings of SPIE- The International Society for Optical Engineering, v 6246, Visual Information Processing XV, 2006, p 62460K
    [104] Baraniuk R.G..Wigner-Ville spectrum estimation via wavelet soft-thresholding. Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis, 1994, p 452-455
    [105] Bruzzone L. Proceedings of SPIE: Image and Signal Processing for Remote Sensing Ⅸ. Proceedings of SPIE - The International Society for Optical Engineering, v 5238, Image and Signal Processing for Remote Sensing Ⅸ, 2004, 584p
    [106] Philippe C, Christine F.M. .Undecimated wavelet shrinkage estimate of the 1D and 2D spectra. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, v 4, 2000, p 2310-2313
    [107] Philippe C, Christine F.M.,. Research of stationary partitions in nonstationary processes by measurement of spectral distance with the help of nondyadic Malvar's decomposition. Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis, 1998, p 429-432
    [108] Diniz, F.C. Da C.B.; Netto, S.L. .A package tool for general-purpose signal denoising. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, V, 2005 IEEE ICASSP '05 - Proc. - Design and Implementation of Signal Proces. Syst.,Indust. Technol. Track,Machine Learning for Signal Proces. Signal Proces. Education, Spec. Sessions, 2005, 573-576
    [109] Ehrentreich, F.; Summchen, L. Spike removal and denoising of Raman spectra by wavelet transform methods. Analytical Chemistry, v 73, n 17, Sep 1, 2001, 4364-4373
    [110] Ehsani, M.R.; Upadhyaya S.K.; Fawcett, W. R.;et al. Feasibility of detecting soil nitrate content using a mid-infrared technique. Transactions of the American Society of Agricultural Engineers, v 44, n 6, 2001,1931-1940
    [111] Eiceman, G.A.; Wang, M.; Prasad, S.;et al. Pattern recognition analysis of differential mobility spectra with classification by chemical family. Analytica Chimica Acta, v 579, n 1, Oct 2, 2006,1-10
    [112] Galvao, R.K.H.; Filho H. A. D.; Martins M. N.;et al. Sub-optimal wavelet denoising of coaveraged spectra employing statistics from individual scans. Analytica Chimica Acta, v 581, n 1, Jan 2, 2007,159-167
    [113] Ganesan R. .Wavelet-based multiscale statistical process monitoring: A literature review. HE Transactions (Institute of Industrial Engineers), v 36, n 9, September, 2004, 787-806
    [114] Guleryuz O.G. Linear, worst-case estimators for denoising quantization noise in transform coded images. IEEE Transactions on Image Processing, v 15, n 10, October, 2006, 2967-2986
    [115] Hossain I.; Moussavi Z.. An overview of heart-noise reduction of lung sound using wavelet transform based filter. Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, v 1, 2003, 458-461
    [116] Kurth F; Clausen, M.. Filter bank tree and M-band wavelet packet algorithms in audio signal processing. IEEE Transactions on Signal Processing, v 47, n 2, Feb, 1999, 549-554
    
    [117] Lada, E.K.; Wilson, J.R.. A wavelet-based spectral procedure for steady-state simulation analysis. European Journal of Operational Research, v 174, n 3, Nov 1, 2006, 1769-1801
    
    [118] Lan, T.H.; Bo C; Liu Q.; Lin, J.. Nonlinear kinetics, fractals and chaos: Applications to potassium single channel. 2005 First International Conference on Neural Interface and Control, Proceedings, 2005, 151-155
    [119] Solanki, S.K.; Regulo, C; Fligge, M.;et al. Noise reduction in helioseismic power spectra by non-orthogonal wavelets. Astronomy and Astrophysics, v 379, n 3, December, 2001,1039-1044
    [120] Okimoto, GS.; Parker, M.F.; Mooradian, G.C,;et al. New features for detecting cervical pre-cancer using hyperspectral diagnostic imaging. Proceedings of SPIE - The International Society for Optical Engineering, v 4255, 2001, 67-79
    [121] Othman H.; Qian S.. Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage. IEEE Transactions on Geoscience and Remote Sensing, v 44, n 2, February, 2006, 397-408
    [122] Porwik, P.; Lisowska A.. The haar-wavelet transform in digital image processing: Its status and achievements. Machine Graphics and Vision, v 13, n 1-2, 2005, 79-98
    [123] Roget E.; Lozovatsky, I.; Sanchez, X.; et al. Microstructure measurements in natural waters: Methodology and applications. Progress in Oceanography, v 70, n 2-4, August/September, 2006, Csanady: Understanding the Physiscs of teh Ocean, 126-148
    [124] Wang, J.L.; Wang Y.; Li, Li D.;et al .CR image filter methods research based on wavelet-domain hidden markov models. Proceedings of SPIE - The International Society for Optical Engineering, v 6027 I, ICO20: Optical Information Processing, 2006, 60270V
    [125] Yahya B. Extraction of signals buried in noise Part II: Experimental results. Signal Processing, v 86, n 10, October, 2006, Fractional Calculus Applications in Signals and Systems, 2994-3011
    [126] Yang, H.T.; Liao, C.C.. A de-noising scheme for enhancing wavelet-based power quality monitoring system. IEEE Transactions on Power Delivery, v 16, n 3, July, 2001, 353-360
    
    [127] Yang Y; Li Y. The methods of γ spectra denoising. IEEE Nuclear Science Symposium Conference Record, v1, 2003 IEEE Nuclear Science Symposium Conference Record - Nuclear Science Symposium, Medical Imaging Conference, 2003, 682-686
    [128] Zheng Y; Tay D. B.H.; Li L.. Signal extraction and power spectrum estimation using wavelet transform scale space filtering and Bayes shrinkage. Signal Processing, v 80, n 8, Aug, 2000, 1535-1549
    [129] Zhi H., Brian J. T., Stephen J. D. et al. Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis.[J]. Remote Sensing of Environment, Volume 93, Issues 1-2, 30 October 2004, 18-29
    [130] Agustin I., Michael W. P.. Visual Method for Spectral Band Selection[J]. IEEE Geoscience And Remote Sensing Letters 2004, 1(2), 101-106
    [131] Anatoly A.G., Mark N.. 1996. Detection of red position and chlorophyll content by reflectance measurements near 700 nm [J] . J . Plant Physiol., 148:501 - 508 .
    [132] Asner G.P. Biophysical and biochemical sources of variability in canopy reflectance [J].Remote Sens. Environ.,1998.64:234-253.
    [133] Barry D. 1998. A New within-leaf radiative transfer model [J].Remote Sens. Environ., 63:182-193.
    [134] Blackburn G.A.. Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves [J].Int. J. Remote Sens., 1998.19:657-675.
    [135] Gaoa B.C., Marcos J. M., Curtiss D.. Refinement of wavelength calibrations of hyperspectral imaging data using a spectrum-matching technique[J]. Remote Sensing of Environment 2004,90,424-433
    [136] Card DH, Peterson DL, Matson PA. 1988 . Prediction of leaf chemistry by use of visible and near infrared reflectance spectroscopy [J] Remote Sens. Environ., 26:123-147
    [137] Dawson T.P. The potential for understanding the biochemical signal in the spectra of forest canopies using a coupled leaf and canopy model [JJ.Physical Measurements and Signatures in Remote Sens. 1997,45: 463-470.
    [138] Dawson T.P., Curran P.J., North P.R.J.,et al . The propagation of foliar biochemical absorption features in forest canopy reflectance: a theoretical analysis[J]remote Sens.Environ., 1999. 67:147-159
    [139] Demarez V, Gastellu E.. Modeling approach for studying forest chlorophyll content [J].Remote Sens. Environ. 2000,71:226-238.
    [140] Grant L. Diffuse, specular characteristics of leaf reflectance [J].Remote Sens. Environ, 1987.22:309-322.
    [141] Huguenin R.L, Jones J.L.. Intelligent Information extraction from reflectance spectra: absorption band position[J] J. Geophysical Res., 1986, 91:9585 - 9598
    
    [142] Jacque M. S,Baret F. PROSPECT: a model of leaf optical properties spectra[J].Remote Sens.Environ., 1990.34:75-91.
    [143] Jacque M.S,Ustin S.L..Estimating leaf biochemistry using the PROSPECT leaf optical properties model[J].RemoteSens.Environ., 1996,56:194-202.
    [144] Jerry S., Stanley R.R.,Charlene E.C.. Segmentation of multi-dimensional infrared imagery[J].Infrared Physics & Technology 2004,45:191-200
    [145] John C. M., James K. C.. Mapping mine wastes and analyzing areas affected by selenium-rich water runoff in southeast Idaho using AVIRIS imagery and digital elevation data[J]. Remote Sensing of Environment 2003,84:422-436
    [146] John C. P.. An Approach for Analysis of Reflectance Spectra[J].REMOTESENS. ENVIRON.1998,64:316-330
    [147] Kokaly R.F., Clark R.N. Spectroscopic determination of leaf biochemistry using band- depth analysis of absorption features and step wise multiple linear regression [J].RemoteSens.Environ.,1999.67:267-287.
    [148] Kokaly R.F. Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration [J].Remote Sens. Environ., 2001.75:153-163.
    [149] North PRJ. Tree dimensional forest light interaction model using a Monte Carlo method [J].IEEE Trans. Geosci. Remote Sens. 1996.34:946-956.
    [150] Zarco-Tejada P.J., Miller J.R., Morales A.,et al. Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops[J].Remote Sensing of Environment 2004,90:463-476
    [151] Pelkki. Woody plant diversity on a revegitated abandonded coal wash sediment pond[J]. IJSM, R&E, 1995(9):161-166.
    [152] Jacauemoud S., Ustin S.L.. Estimating leaf biochemistry using the prospect leaf optical properties model[J]. REMOTE SENS. ENVIRON. 1996,56:194-202
    [153] 甘甫平,刘圣伟,周强.德兴铜矿矿山污染高光谱遥感直接识别研究[J].地球科学,2004,29(1):119-126
    [154] 甘甫平,王润生,马蔼乃.基于特征谱带的高光谱遥感矿物谱系识别[J].地学前缘.2003,10(2):446-450
    [155] 李小文,王锦地.植被光学遥感模型与植被结构参数化[M].北京:科学出版社,1995.
    [156] 刘圣伟,甘甫平,王润生.用卫星高光谱资料提取德兴铜矿区植被污染信息[J].国土资源遥感.2004,(59):7-10
    [157] 刘荫椿,杨卫明.遥感技术在黑龙江呼玛地区金矿找矿中的意义[J].黄金地质,2000,9(3):65-68
    [158] 潘小菲.公婆泉地区植物地球化学异常特征及其指矿意义[J].物探与化探,2003,27(5):371-373
    [159] 王晋年,郑兰芬,童庆禧.成像光谱图像吸收鉴别模型与矿物填图研究[J]环境遥感,1996,11(1):20-30
    [160] 王效科,白艳莹.陆地生物地球化学模型的应用和发展[J].应用生态学报,2002,13(12):1703-1706
    [161] 王秀珍,王人潮,李云梅,等.2001.不同氮素营养水平的水稻冠层光谱红边参数及其应用研究[J].浙江大学学报(农业和生命科学版),27(3):301~306.
    [162] 王云鹏,苏北油田区植物微量元素地球化学特征及其对遥感光谱特性的影响,科学通报,2000,45:2716~2724
    [163] Pablo H.R., James C. P., Mui Lay and Susan L. Ustin. Reflectance properties and physiological responses of Salicornia virginica to heavy metal and petroleum contamination.[J].Environmental Pollution, Volume 137, Issue 2, September 2005, 241-252
    [164] Kooistra L., Salas E. A. L., Clevers J. G. P. W.,et al. Exploring field vegetation reflectance as an indicator of soil contamination in river floodplains.[J]. Environmental Pollution, Volume 127, Issue 2, January 2004, 281-290
    [165] Andrew C. S., Gene A. C., John A. D. B., et al. Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments for the detection of zinc stress and chlorophyll concentration in bahia grass (Paspalum notatum Flugge.)[J].Remote Sensing of Environment, Volume 84, Issue 4, 10 April 2003, 572-588
    [166] Olga R., Alexander B., Olle H., et al. Monitoring of forest damage in the Kola Peninsula, Northern Russia due to smelting industry[J].The Science of The Total Environment, Volume 229, Issue 3, 19 May 1999, 147-163
    [167] Josee L., Douglas J. K.. Airborne Digital Camera Image Semivariance for Evaluation of Forest Structural Damage at an Acid Mine Site[J]. Remote Sensing of Environment, Volume 68, Issue 2, May 1999, 112-124
    [168] 荀毓龙,地物波谱测试规范/遥感基础实验与应用[M]北京:中国科学技术出版社,1991。
    [169] Manahan, S.E. Environmental Science, Technology and Chemistry/Environmental Chemistry[M.] Boca Raton: CRC Press LLC, 2000:196326
    [170] NY/T395-2000.农田土壤环境质量监测技术规范[S]
    [171] Roger R., Introduction to environmental analysis, New York, John Wily & Sons Ltd. 2002:135-175
    [172] Tessier A, Campbell P G C, Blsson M. Sequential extraction procedure for the speciation of particulate trace metals .Analytical Chemistry, 1979, 51(7): 844-851.
    [173] Lu X.H. Ali A,, studies on the speciation distribution of heavy metals in particulates released from coal combustion., environmental chemistry,1995,15(4):337-342
    [174] 戎秋涛,翁焕新,环境地球化学[M.]北京:地质出版社,1998
    [175] 陈怀满.土壤中化学物质的行为与环境质量[M]北京:科学出版社,2002:601-620
    [176] 阎吉昌,环境分析/现代分析测试应用丛书[M]北京:化学工业出版社.2002:371-431
    [177] 中国土壤学会农业化学专业委员会.土壤农业化学常规分析方法[M]北京:科学出版社.1983:244-300
    [178] Freedman B. Environmental ecology-the ecology effects of pollution, distribution and other stresses. 2nd edition. London,Academic Press, 1995.
    [179] Verkleij J A C; Schat H, Mechanisms of metal tolerance in higher plants. In Shaw J(ed.) Heavy metal tolerance in plants: evolutionary aspects. Boca Raton, Florida, USA, CRC Press.1990:179193
    [180] GB 15618-1995中国土壤环境质量标准[S]
    [181] 徐希孺,遥感物理[M.]北京:北京大学出版社,2005。
    [182] 郑治真,波普分析基础[M.]北京:地震出版社,1979。
    [183] 胡广书,现代信号处理[M.]北京:清华大学出版社,2004。
    [184] 杨建国,小波分析及其工程应用[M.]北京:机械工业出版社,2005。
    [185] 卢小泉,刘宏德,分析化学中的小波分析技术[M].北京:化学工业出版社。2006。
    [186] 童庆禧,张兵,郑兰芬,高光谱遥感原理技术与应用北京:高等教育出版社,2006。
    [187] 严衍禄,赵龙莲,韩东海等.近红外光谱分析基础与应用[M].北京:中国轻工业出版社,2005。
    [188] 王耀南.智能信息处理技术[M].北京:高等教育出版社,2003。
    [189] 许禄,邵学广.化学计量学方法[M].北京:科学出版社,2004。
    [190] 苏金明,阮沈勇,王永利,MATLAB工程数学[M].学北京:电子工业出版社,2005。
    [191] Clark R N, Roush T L. reflectance spcetroscopy:quantitative analysis techniques for remote sensing applications. Journal of Geophysical Research, 1984,89(B7):6329-6340.

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