| 光谱学与光谱分析 |
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| Study on Canopy Spectral Characteristics of Paddy Polluted by Heavy Metals |
| REN Hong-yan1, ZHUANG Da-fang2, 3*, PAN Jian-jun3, SHI Xue-zheng1, SHI Run-he4, WANG Hong-jie1 |
1. State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
2. Resource and Environmental Science Data Center, Chinese Academy of Sciences, Beijing 100101, China
3. College of Resource and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China
4. Key Laboratory of Geographic Information Science for Ministry of Education, East China Normal University, Shanghai 200062, China |
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Abstract Because of frequent mining, heavy metals are brought into environment like soils, water and atmosphere, resulting heavy metal contamination in the agricultural region beside mines. Heavy metals contamination causes vegetation stress like destruction of chloroplast structure, chlorophyll content decrease, blunt photosynthesis, etc. Spectral responses to changes in chlorophyll content and photosynthesis make it possible that remote sensing is applied in monitoring heavy metals stress on paddy plants. Field spectroradiometer was used to acquire canopy reflectance spectra of paddy plants contaminated by heavy metals released from local mining. The present study was conducted to (1) investigate discrimination of canopy reflectance spectra of heavy metal polluted and normal paddy plants; (2) extract spectral characteristics of contaminated paddy plants and compare them. By means of correlation analysis, sensitive bands (SB) were firstly picked out from canopy spectra. Secondly, on the basis of these sensitive bands, normalized difference vegetation indices (NDVI) were established, and then red edge position (REP) was extracted from canopy spectra via curve fitting of inverted Gaussian model. As a result of correlation analysis, 460, 560, 660 and 1 100 nm were considered respectively as sensitive band for Pb, Zn, Cu and As concentration in paddy leaves. Furthermore, heavy metal concentrations (Pb, Zn, Cu and As) were significantly correlated with NDVIs (Pb, NDVI(510, 810);Zn, NDVI(510, 870);Cu, NDVI(660, 870); As, NDVI(510, 810)). Heavy metals were also significantly correlated with REP, however, the inflexion termed as spectral critical value (SCV) between low and high heavy metals concentrations should be considered during applying REP in remote sensing monitoring. Moreover, NDVI and REP are much better than SB in terms of capability of expressing spectral information. Therefore, heavy metals contamination in paddy plants can be remotely monitored via ground spectroradiometer when NDVI and REP are selected as spectral characteristics.
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Received: 2009-08-12
Accepted: 2009-11-16
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Corresponding Authors:
ZHUANG Da-fang
E-mail: zhuangdf@lreis.ac.cn
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[1] Vanek A, Ettler V, Grygar T, et al. Pedosphere, 2008, 18(4): 464.
[2] WEI Hai-ying, FANG Yan-ming(魏海英, 方炎明). Journal of Nanjing Forestry University (Natural Science Edition)(南京林业大学学报·自然科学版), 2004, 28(5): 77.
[3] LIU Yu-rong, DANG Zhi, SHANG Ai-an(刘玉荣, 党 志, 尚爱安). Environmental Pollution and Control(环境污染与防治), 2003, 25(4): 215.
[4] Patsikka E, Aro E M. Plant Physiology, 1998, 117(2): 619.
[5] Rousos P A, Horrison H C, Steffen K L. Journal of America Society of Horticultural Science, 1989, 114: 149.
[6] Vinit-Dunand F, Epron D, Alaoui-Sosse B, et al. Plant Science, 2002, 163: 53.
[7] XU Qin-song, SHI Guo-xin, WANG Xue, et al (徐勤松, 施国新, 王 学, 等). Acta Hydrobiologica Sinica(水生生物学报), 2006, 30(1): 107.
[8] ZHAO Ying-shi(赵英时). Principle and Methodology of Remote Sensing Application and Analysis(遥感应用分析原理与方法). Beijing: Science Press(北京: 科学出版社), 2003. 25.
[9] REN Hong-yan, ZHUANG Da-fang, PAN Jian-jun, et al(任红艳, 庄大方, 潘剑君, 等). Geo-Information Science(地球信息科学), 2008, 10(3): 314.
[10] JIANG Huan-yu, YING Yi-bin(蒋焕煜, 应义斌). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(3): 499.
[11] GUAN Li, LIU Xiang-nan, CHENG Cheng-qi(关 丽，刘湘南，程承旗). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2009, 29(10): 2713.
[12] Dawson T P, Curran P J. International Journal of Remote Sensing, 1998, 19(11)：2133.
[13] Moses A C, Andrew K S. Remote Sensing of Environment, 2006, 101: 181.
[14] Tang Y L, Wang R C, Huang J F. Pedosphere, 2004, 14(4): 467.
[15] TIAN Guo-liang, LIU Hou-tian(田国良, 刘厚田). Journal of Remote Sensing (遥感学报), 1990, 5(2)：140.
[16] Ren H Y, Zhuang D F, Pan J J, et al. Journal of Soils and Sediments, 2008, 8: 323.
[17] Bonham-Carter G F. Computers and Geosciences, 1988, 14(3): 339.
[18] Kokaly R F, Clark R N. Remote Sensing of Environment, 1999, 67: 267.
[19] Sumfleth K, Duttmann R. Ecological Indicators, 2008, 8(5): 485.
[20] XIN Jing-feng, YU Zhen-rong(辛景峰, 宇振荣). Driceasen P M. Journal of Remote Sensing(遥感学报), 2001, 5(6): 442.
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