锰胁迫青葙的高光谱变异特征与叶冠金属元素含量的关系模型
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摘要
针对喜锰植物青葙,在模拟15个浓度级别的锰含量胁迫生长环境下,观测其生长的过程中不同时段的高光谱变异特征,最终测定锰等33种主微量元素的含量,提取11种可见光波段(400～1 200 nm)的波谱特征参量,与选取的13种重金属元素,以及其经过因子分析组成的地球化学因子之间进行偏回归分析,从而建立胁迫植物青葙的光谱特征与叶冠中重金属含量间的反演模型。研究表明:随着锰胁迫浓度的增加,光谱在红边、蓝红谷位移以及吸收深度产生规律变化。同时,绿峰面积等波谱特征参量随着锰胁迫浓度以及生长周期发生了先增后减的律动。对多组波谱特征参量和重金属元素进行多元回归分析,叶冠中Mn、Cr、Co、Cu、Sr、Mo、Sn、Cs、Zr、Hf、Ti、Th、U和地球化学指标因子,与波谱特征参量组的复相关系数大于0. 8,相关性较强。在此基础上,F_1/F_4的地球化学指标组合,可以作为中观层面指示沉积型锰矿的富集指标,此间接模型也可以为高植被覆盖区开展高光谱遥感寻找矿产预测,以及为通过遥感进行生态修复评估提供依据。
Hyperspectral variation characteristics of high-manganese plant Celosia argentea L.in different periods of growth were observed by simulating the stress-growth environment of manganese at 15 levels of concentration.The concentrations of 33 main and trace elements such as manganese were finally determined.The spectral characteristics of 11 visible light bands( 400-1 200 nm) were extracted,to make the partial regression analysis between the 13 selected heavy metal elements and their geochemical factors.Finally,the inversion model between characteristics and heavy metal content is established in leaf crown.The results show that the plant spectra shift in the red edge,blue and red valley,and the absorption depth produces regular changes along with the increase of manganese stress concentration.The spectral characteristic parameters,such as the green peak area,increase first and then decrease along with manganese stress concentration and growth cycle.A multivariable regression equation was built between the spectral characteristics parameters and the contents of metal elements.The complex correlation coefficients among metal elements in leaf crown are Mn,Cr,Co,Cu,Sr,Mo,Sn,Cs,Zr,Hf,Ti,Th,U and geochemical indicators.The spectral characteristics parameters are greater than0.8,which shows a strong correlation.On this basis,The geochemical indices combination of F1/F4 is established and can be used as an indicator for the enrichment of sedimentary manganese ores from the meso-level.The indirect model can provide convenience for making mineral predictions in hyperspectral remote sensing in high-vegetation-covered areas and performing ecological restoration assessments through remote sensing.
Hyperspectral variation characteristics of high-manganese plant Celosia argentea L.in different periods of growth were observed by simulating the stress-growth environment of manganese at 15 levels of concentration.The concentrations of 33 main and trace elements such as manganese were finally determined.The spectral characteristics of 11 visible light bands( 400-1 200 nm) were extracted,to make the partial regression analysis between the 13 selected heavy metal elements and their geochemical factors.Finally,the inversion model between characteristics and heavy metal content is established in leaf crown.The results show that the plant spectra shift in the red edge,blue and red valley,and the absorption depth produces regular changes along with the increase of manganese stress concentration.The spectral characteristic parameters,such as the green peak area,increase first and then decrease along with manganese stress concentration and growth cycle.A multivariable regression equation was built between the spectral characteristics parameters and the contents of metal elements.The complex correlation coefficients among metal elements in leaf crown are Mn,Cr,Co,Cu,Sr,Mo,Sn,Cs,Zr,Hf,Ti,Th,U and geochemical indicators.The spectral characteristics parameters are greater than0.8,which shows a strong correlation.On this basis,The geochemical indices combination of F1/F4 is established and can be used as an indicator for the enrichment of sedimentary manganese ores from the meso-level.The indirect model can provide convenience for making mineral predictions in hyperspectral remote sensing in high-vegetation-covered areas and performing ecological restoration assessments through remote sensing.
引文
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[2]林刚.锰矿胁迫植物青葙的高光谱特征及土壤浓度效应的反演模型[D].桂林:桂林理工大学,2016.
[3]Newete S W,Erasmus B F N,Weiersbye I M,et al.Hyperspectral reflectance features of water hyacinth growing under feeding stresses of Neochetina spp.and different heavy metal pollutants[J].International Journal of Remote Sensing,2014,35(3):799-817.
[4]Li X Q,Liu X N,Liu M L,et al.A hyperspectral index sensitive to subtle changes in the canopy chlorophyll content under arsenic stress[J].International Journal of Applied Earth Observation&Geoinformation,2015,36:41-53.
[5]陈圣波,周超,王晋年.黑龙江多金属矿区植物胁迫光谱及其与金属元素含量关系研究[J].光谱学与光谱分析,2012,32(5):1310-1315.
[6]Rosso P H,Pushnik J C,Lay M,et al.Reflectance properties and physiological responses of Salicornia virginica to heavy metal and petroleum contamination.[J].Environmental Pollution,2005,137(2):241-252
[7]徐明星,吴绍华,周生路,等.重金属含量的高光谱建模反演:考古土壤中的应用[J].红外与毫米波学报,2011,30(2):109-114.
[8]龚绍琦,王鑫,沈润平,等.滨海盐土重金属含量高光谱遥感研究[J].遥感技术与应用,2010,25(2):169-177.
[9]肖捷颖,王燕,张倩,等.土壤重金属含量的高光谱遥感反演方法综述[J].湖北农业科学,2013,52(6):1248-1253.
[10]李娜.重金属胁迫下矿区植物波谱异常与图像特征研究[D].济南:山东科技大学,2007.
[11]滕靖,何政伟,倪忠云,等.西范坪矿区土壤铜元素的高光谱响应与反演模型研究[J].光谱学与光谱分析,2016,36(11):3637-3642.
[12]朴东范.水稻不同浓度铅污染胁迫下光谱特性与水稻生长的响应研究[D].延吉:延边大学,2013.
[13]孙晓琳.锰胁迫作用下青葙的地物波谱异常在时间序列效应上的提取及研究[D].桂林:桂林理工大学,2016.
[14]郭云开,曹小燕,石自桂.水稻冠层光谱变化特征的土壤重金属全量反演研究[J].遥感信息,2015,30(3):116-123.
[15]任红艳,庄大方,潘剑君,等.重金属污染水稻的冠层反射光谱特征研究[J].光谱学与光谱分析,2010,30(2):430-434.
[16]朱叶青,屈永华,刘素红,等.重金属铜污染植被光谱响应特征研究[J].遥感学报,2014,18(2):335-352.
[17]王璐,蔺启,忠贾东,等.基于反射光谱预测土壤重金属元素含量的研究[J].遥感学报,2007,11(6):906-913.
[18]陈三明.锡矿山锑矿田多元地学综合信息成矿预测研究[D].北京:中国地质大学(北京),2012.
[19]李娜,吕建升,Altermann W.光谱分析在植被重金属污染监测中的应用[J].光谱学与光谱分析,2010,30(9):2508-2511.
[20]Pu R L,Gong P,Biging G S,et al.Extraction of red edge optical parameters from Hyperion data for estimation of forest leaf area index[J].IEEE Transactions on Geoscience and Remote Sensing,2003,41(4):916-921.
[21]Cho M A,Skidmore A K.A new technique for extracting the red edge position from hyperspectral data:the linear extrapolation method[J].Remote Sensing of Environment,2006,101(2):181-193.
[22]邬登巍,吴昀昭,马宏瑞.植物污染胁迫遥感监测研究综述[J].遥感技术与应用,2009,24(2):238-245.
[23]江南.农作物重金属污染胁迫信息遥感提取方法研究[D].北京:中国地质大学(北京),2009.
[24]周超.植被重金属含量高光谱遥感反演方法研究[D].长春:吉林大学,2016.