模拟实验研究利用植被冠层光谱探测CO<SUB>2</SUB>轻微泄漏点

doi:10.3964/j.issn.1000-0593(2015)10-2781-06

摘要
参考文献
相关文章 (15)

全文: PDF (3434 KB)
输出: BibTeX | EndNote (RIS)

摘要：温室气体(CO₂)过量排放可以导致全球气候变暖，而碳捕捉与储存(carbon capture and storage, CCS)技术是一种减少CO₂气体排放的有效措施。但存储在地下的CO₂有泄漏的风险，如何快速监测CO₂轻微泄漏点是一个值得研究的问题。该文通过野外模拟实验，研究草地和大豆在CO₂轻微泄漏胁迫下的冠层光谱特征，构建CO₂轻微泄漏点高光谱遥感探测模型。在2008年5月—9月于英国诺丁汉大学Sutton Bonington校区(52.8N, 1.2W)进行了野外模拟实验。实验共设置16个小区，8个草地及8个大豆地，其中各有4个小区进行CO₂泄漏胁迫。冠层光谱采用美国ASD光谱仪进行测量，草地测量了6次数据，大豆地测量了3次数据。实验结果表明，草地与大豆地的冠层光谱反射率在580～680 nm波段范围内随CO₂泄漏胁迫程度的增大而增大，且在整个试验期内都保持同样的规律，因此构建面积指数AREA(580～680 nm)(光谱曲线在580～680 nm波段范围内包围的面积)识别遭受CO₂泄漏胁迫下的植被。通过J-M距离检验，发现该指数能够较好地识别出CO₂轻微泄漏胁迫下center区与core区的草地，但对edge区草地的识别能力不足(J-M距离小于1.8);该指数可以可靠且稳健地识别出遭受CO₂轻微泄漏胁迫的大豆。该研究结果可为未来应用高光谱遥感探测CO₂轻微泄漏点提供理论依据与方法支持。

关键词：冠层光谱;CO₂泄漏胁迫;地表植被;识别模型

Abstract：With the global warming, people now pay more attention to the problem of the emission of greenhouse gas (CO₂). Carbon capture and storage (CCS) technology is an effective measures to reduce CO₂ emission. But there is a possible risk that the CO₂ might leak from underground. However, there need to research and develop a technique to quickly monitor CO₂ leaking spots above sequestration fields. The field experiment was performed in the Sutton Bonington campus of University of Nottingham(52.8N, 1.2W) from May to September in 2008. The experiment totally laid out 16 plots, grass(cv Long Ley) and beans(Vicia faba cv Clipper) were planted in eight plots, respectively. However, only four grass and bean plots were stressed by the CO₂ leakage, and CO₂ was always injected into the soil at a rate of 1 L·min^-1. The canopy spectra were measured using ASD instrument, and the grass was totally collected 6 times data and bean was totally collected 3 times data. This paper study the canopy spectral characteristics of grass and beans under the stress of CO₂ microseepages through the field simulated experiment, and build the model to detect CO₂ microseepage spots by using hyperspectral remote sensing. The results showed that the canopy spectral reflectance of grass and beans under the CO₂ leakage stress increased in 580～680 nm with the stressed severity elevating, moreover, the spectral features of grass and beans had same rule during the whole experimental period. According to the canopy spectral features of two plants, a new index AREA_{(580～680 nm)} was designed to detect the stressed vegetations. The index was tested through J-M distance, and the result suggested that the index was able to identify the center area and the core area grass under CO₂ leakage stress, however, the index had a poor capability to discriminate the edge area grass from control. Then, the index had reliable and steady ability to identify beans under CO₂ leakage stress. This result could provide theoretical basis and methods for detecting CO₂ leakage spots using hyperspectral remote sensing in the future.

Key words：Canopy spectra;CO₂ leakage stress;Surface vegetation;Identification model

收稿日期: 2014-04-09 修订日期: 2014-08-16

中图分类号:	O657.3
	S127

通讯作者: 何汝艳 E-mail: 420208358@qq.com

引用本文:

蒋金豹¹，何汝艳^1*，Michael D Steven²，胡卿杨¹ . 模拟实验研究利用植被冠层光谱探测CO₂轻微泄漏点 [J]. 光谱学与光谱分析, 2015, 35(10): 2781-2786.
JIANG Jin-bao¹, HE Ru-yan^1*, Michael D Steven², HU Qing-yang¹ . Simulated Experimental Research on Using Canopy Spectra of Surface Vegetation to Detect CO₂ Microseepage Spots . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(10): 2781-2786.

链接本文:

https://www.gpxygpfx.com/CN/10.3964/j.issn.1000-0593(2015)10-2781-06 或 https://www.gpxygpfx.com/CN/Y2015/V35/I10/2781

[1] Solomon S, Qin D, Man-ning M, et al. Contribution of Working Group 1 to the Fourth Assessment Report of the Inter-government Panel to Climate Change. UK: Cambridge University Press, Cambridge, 2007.
[2] Male E J, Pickles W L, Silver E A, et al. Environmental Earth Sciences, 2010, 60(2): 251.
[3] Steven, M D, Smith, K L. Sixth Framework Programme, European Commission, Brussels, Belgium. 2010. Initial Review Document. Document D3. 14.
[4] Al-Traboulsi M, Sjgersten S, Colls J, et al. Environmental and Experimental Botany, 2012, 80: 43.
[5] Pysek P, Pysek A. Weed Research, 1989, 29(3): 193-204.
[6] De Jong D J, Dijkema K S, Bossinade J H, et al. Rijkswaterstaat & IBN-DLO, Middelburg, NL, 1998.
[7] Smith K L, Steven M D, Colls J J. Remote Sensing of Environment, 2004, 92(2): 207.
[8] Smith K L, Steven M D, Colls J J. International Journal of Remote Sensing, 2005, 26(18): 4067.
[9] Noomen M F, Smith K L, Colls J J, et al. International Journal of Remote Sensing, 2008, 29(20): 5987.
[10] Noomen M F, Skidmore A K. International Journal of Remote Sensing, 2009, 30(2): 481.
[11] Bateson L, Vellico M, Beaubien S E, et al. International Journal of Greenhouse Gas Control, 2008, 2: 388.
[12] Keith C J, Repasky K S, Lawrence R L, et al. International Journal of Greenhouse Gas Control, 2009, 3(5): 626.
[13] Lakkaraju V R, Zhou X, Apple M E, et al. Ecological Informatics, 2010, 5(5): 379.
[14] Govindan R, Korre A, Durucan S, et al. International Journal of Greenhouse Gas Control, 2011, 5(3): 589.
[15] CHEN Yun-hao, JIANG Jin-bao, GONG A-du, et al(陈云浩, 蒋金豹, 宫阿都, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2012, 32(7): 18825.
[16] JIANG Jin-bao, Steven M D, HE Ru-yan, et al(蒋金豹, Steven M D, 何汝艳, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013, 29(12): 163.
[17] ZHANG Jin-heng(张金恒). Chinese Journal of Plant Ecology(植物生态学报), 2006, 30(1): 78.
[18] TONG Qing-xi, ZHANG Bing, ZHENG Lan-fen(童庆禧, 张兵, 郑兰芬). Hypersepctral Remote Sensing(高光谱遥感). Beijing: Higher Education Press(北京：高等教育出版社), 2006.
[19] ZHAO De-gang, ZHAN Yu-lin, LIU Xiang, et al(赵德刚, 占玉林, 刘翔, 等). Remote Sensing for Land & Resources(国土资源遥感), 2010, 22(3): 108.
[20] JIANG Jin-bao, LI Yi-fan, GUO Hai-qiang, et al(蒋金豹, 李一凡, 郭海强, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2012, 32(10): 2775.