Spectral testing and quantitative inversion for dust of iron tailings on leaf

doi:10.6046/gtzyyg.2017.01.25

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Abstract

In China, iron tailings dumps have been accumulated up to about 5 billion tons. The tailings have led to extremely serious dust pollution. Therefore, dust effects on leaf spectra were studied on the basis of the observation of real experiments with Anshan mine tailings dust and by means of artificial simulated dust and spectral measurements. The dust samples of iron tailings were taken from the Anshan mining area. The quantitative inversion of foliar dustfall was realized by using the band of the best correlation between the dustfall and the vegetation leaf spectrum and the characteristics of absorption spectra of iron respectively. The results show that, when the dustfall of iron tailings on leaf increased, the differences of spectral curve between leaf and dust decreased. In the two inversion methods, dustfall and vegetation leaf spectral variables were significantly related to each other. Furthermore, the precision of the inversion modeling according to spectral characteristics of iron is higher than that of the one according to best correlation band. The results could provide basic model and technical basis for quantifying the amount of mining dust monitoring with hyperspectral remote sensing.

Keywords lake salinity levels information extraction support vector machine(SVM) spectral analysis Badain Jaran

TP79

Issue Date: 23 January 2017

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	DIAO Shujuan
	LIU Chunling
	ZHANG Tao
	HE Peng
	GUO Zhaocheng
	TU Jienan

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DIAO Shujuan,LIU Chunling,ZHANG Tao, et al. Spectral testing and quantitative inversion for dust of iron tailings on leaf[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 164-169.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2017.01.25 OR https://www.gtzyyg.com/EN/Y2017/V29/I1/164

[1] 乔玉霜,王静,王建英.城市大气可吸入颗粒物的研究进展[J].中国环境监测,2011,27(2):22-26. Qiao Y S,Wang J,Wang J Y.Research progress of the inhalable particular in the urban air[J].Environmental Monitoring in China,2011,27(2):22-26.
[2] 彭杰,向红英,王家强,等.叶面降尘的高光谱定量遥感模型[J].红外与毫米波学报,2013,32(4):313-318,343. Peng J,Xiang H Y,Wang J Q,et al.Quantitative model of foliar dustfall content using hyperspectral remote sensing[J].Journal of Infrared and Millimeter Waves,2013,32(4):313-318,343.
[3] 陈虎,沈卫国,单来,等.国内外铁尾矿排放及综合利用状况探讨[J].混凝土,2012(2):88-92. Chen H,Shen W G,Shan L,et al.Situation of discharge and comprehensive utilization of iron tailings domestic and abroad[J].Concrete,2012(2):88-92.
[4] 肖慧玲.粉尘污染下园林植物的光谱特征及光合特性研究[D].武汉:华中农业大学,2013. Xiao H L.Research on the Spectral and Photosynthetic Characteristics of Landscape Plants Under Dust Pollution[D].Wuhan:Huazhong Agricultural University,2013.
[5] 尹航.环境监测方法汇编:大气环境[M].北京:中国标准出版社,2007. Yin H.Methods of Environment Monitoring:Air Environment Assembly[M].Beijing:Standards Press of China,2007.
[6] 孙家抦.遥感原理与应用[M].2版.武汉:武汉大学出版社,2009:25,33-57. Sun J B.Principles and Applications of Remote Sensing[M].2nd ed.Wuhan:Wuhan University Press,2009:25,33-57.
[7] Baddock M C,Bullard J E,Bryant R G.Dust source identification using MODIS:A comparison of techniques applied to the Lake Eyre Basin,Australia[J].Remote Sensing of Environment,2009,113(7):1511-1528.
[8] Gangale G,Prata A J,Clarisse L.The infrared spectral signature of volcanic ash determined from high-spectral resolution satellite measurements[J].Remote Sensing of Environment,2010,114(2):414-425.
[9] Markowicz K M,Zielinski T,Pietruczuk A,et al.Remote sensing measurements of the volcanic ash plume over Poland in April 2010[J].Atmospheric Environment,2012,48:66-75.
[10] 赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003:360. Zhao Y S.The Principle and Method of Analysis of Remote Sensing Application[M].Beijing:Science Press,2003:360.
[11] 谭德军,谢巨天,简季,等.万盛采矿区粉尘污染农作物光谱特性分析[J].国土资源遥感,2013,25(2):121-130.doi:10.6046/gtzyyg.2013.02.21. Tan D J,Xie J T,Jian J,et al.Analysis on spectral characteristics of dust polluted crops in Wansheng coal mineing district[J].Remote Sensing for Land and Resources,2013,25(2):121-130.doi:10.6046/gtzyyg.2013.02.21.
[12] 王涛,刘洋,吴海云,等.叶面尘对作物反射光谱及氮营养监测的影响[J].光谱学与光谱分析,2012,32(7):1895-1898. Wang T,Liu Y,Wu H Y,et al.Influence of foliar dust on crop reflectance spectrum and nitrogen monitoring[J].Spectroscopy and Spectral Analysis,2012,32(7):1895-1898.
[13] 万余庆,谭克龙,周日平.高光谱遥感应用研究[M].北京:科学出版社,2006:208-217. Wan Y Q,Tan K L,Zhou R P.Application of Hyperspectral Remote Sensing[M].Beijing:Science Press,2006:208-217.
[14] 韩跃新,孙永升,李艳军,等.我国铁矿选矿技术最新进展[J].金属矿山,2015,44(2):1-11. Han Y X,Sun Y S,Li Y J,et al.New development on mineral processing technology of iron ore resources in China[J].Metal Mine,2015,44(2):1-11.
[15] 刘永光,王晓雷.铁尾矿资源化综合利用的发展[J].现代矿业,2010,26(2):28-30. Liu Y G,Wang X L.Development of comprehensive utilization of iron tailings as resource[J].Modern Mining,2010,26(2):28-30.
[16] 黄勇刚.我国铁尾矿资源的利用现状及展望[J].资源与产业,2013,15(3):40-44. Huang Y G.Utilization status and outlook of China's iron ore tailings[J].Resources & Industries,2013,15(3):40-44.
[17] 王佳文,王磊,胡继元.东北老工业基地城市转型的规划思考——以《鞍山市城市总体规划(2013-2020)》为例[J].城市规划,2014,38(S2):105-113. Wang J W,Wang L,Hu J Y.Reflections on transition planning of old industrial base cities in northeast China:Taking the comprehensive plan of Anshan City as an example[J].City Planning Review,2014,38(S2):105-113.
[18] 王忠红,陈强,刘华艳.鞍山地区铁矿资源特点及开发利用建议[J].金属矿山,2008,38(7):64-66. Wang Z H,Chen Q,Liu H Y.Characteristics of iron ore resources in Anshan region and suggestions on their exploitation and utilization[J].Metal Mine,2008,38(7):64-66.
[19] 王晋年,郑兰芬,童庆禧.成象光谱图象光谱吸收鉴别模型与矿物填图研究[J].环境遥感,1996,11(1):20-31. Wang J N,Zheng L F,Tong Q X.The spectral absorption identification model and mineral mapping by imaging spectrometer data[J].Remote Sensing of Environment,1996,11(1):20-31.
[20] Yu H H,Xue X X,Huang D W.Synthesis of mesoporous silica materials(MCM-41) from iron ore tailings[J].Materials Research Bulletin,2009,44(11):2112-2115.
[21] 童庆禧,张兵,郑兰芬.高光谱遥感的多学科应用[M].北京:电子工业出版社,2006:153-155. Tong Q X,Zhang B,Zheng L F.Hyperspectral Remote Sensing and It's Multidisciplinary Applications[M].Beijing:Publishing House of Electronics Industry,2006:153-155.
[22] 刘良云.植被定量遥感原理与应用[M].北京:科学出版社,2014:42-44. Liu L Y.Principle and Application of Plant Quantitative Remote Sensing[M].Beijing:Science Press,2014:42-44.

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