Stratigraphic division of loess along loess profile based on hyperspectral remote sensing

doi:10.6046/gtzyyg.2018.02.27

Download: PDF(4981 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Invisible fault identifying in loess area is a difficult problem in active fault study in northern China. Detailed stratigraphic division of loess area by the naked eye is very difficult due to the insignificant difference of the granularities and the colors, which would affect the identification of the obscured fault and paleo-seismic event. Spectral technique has been used for magnetic susceptibility estimation. Magnetic susceptibility (MS) has been considered to be a measure of the degree of pedogenic activity and excellent proxies for terrestrial climatic fluctuations. In this study, multiple linear regression was used to build MS estimation models based on the spectral features. A model was built and was applied to hyperspectral image. Test of datasets indicates that this model is very successful. The applying of this model to hyperspectral image shows that the intensity distribution of MS could be used for stratigraphic division.

Keywords hyperspectral remote sensing magnetic susceptibility stratigraphic division of loess

TP79

Corresponding Authors: Rui DING E-mail: jingcui_86@yahoo.com;reiding@hotmail.com

Issue Date: 30 May 2018

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	Jing CUI
	Xinfeng DONG
	Rui DING
	Shimin ZHANG
	Conghe WANG
	Hengxin LU
	Yanyun SUN

Cite this article:

Jing CUI,Xinfeng DONG,Rui DING, et al. Stratigraphic division of loess along loess profile based on hyperspectral remote sensing[J]. Remote Sensing for Land & Resources, 2018, 30(2): 202-207.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.02.27 OR https://www.gtzyyg.com/EN/Y2018/V30/I2/202

Fig.1 Photograph of the studied section with the profiles labeled

Fig.2 Sepctral features of samples and their magnetic susceptibility values

Fig.3 Linear regression between band ratios and magnetic susceptibility

Fig.4 Comparison of the instrumentally measured and spectrally estimated magnetic susceptibility of the test data

Fig.5 Reflectance spectra from the UHD185 image and filed reflectance spectra

Fig.6 Magnetic susceptibilty estimates with the regression models

Fig.7 Comparison of the UHD185 image estimated and measured magnetic susceptibilty

[1]	卫蕾华 . 基于高分辨率粒度、磁化率分析的黄土地层划分与古地震研究[D]. 北京:中国地震局地质研究所, 2015.
[1]	Wei L H . Loess Stratigraphy and Paleo-Earthquake Identification Based on High-Resolution Analysis of Granularity and Magnetic Susceptibility[D].Beijing:Institute of Geology, China Earthquake Administration, 2015.
[2]	Balsam W, Ji J F, Chen J . Climatic interpretation of the Luochuan and Lingtai loess sections,China,based on changing iron oxide mineralogy and magnetic susceptibility[J]. Earth and Planetary Science Letters, 2004,223(3/4):335-348. doi: 10.1016/j.epsl.2004.04.023 url: http://linkinghub.elsevier.com/retrieve/pii/S0012821X04002717
[3]	Chen J, Ji J F, Balsam W , et al. Characterization of the Chinese loess-paleosol stratigraphy by whiteness measurement[J]. Palaeogeography,Palaeoclimatology,Palaeoecology, 2002,183(3/4):287-297. doi: 10.1016/S0031-0182(02)00246-8 url: http://linkinghub.elsevier.com/retrieve/pii/S0031018202002468
[4]	Liu X M, Hesse P, Rolph T . Origin of maghaemite in Chinese loess deposits:Aeolian or pedogenic?[J]. Physics of the Earth and Planetary Interiors, 1999,112(3/4):191-201. doi: 10.1016/S0031-9201(99)00002-3 url: http://linkinghub.elsevier.com/retrieve/pii/S0031920199000023
[5]	Maher B A, Thompson R . Mineral magnetic record of the Chinese loess and paleosols[J]. Geology, 1991,19(1):3-6. doi: 10.1130/0091-7613(1991)019<0003:MMROTC>2.3.CO;2 url: https://pubs.geoscienceworld.org/geology/article/19/1/3-6/205157
[6]	Maher B A . Magnetic properties of modern soils and Quaternary
[6]	loessic paleosols:Paleoclimatic implications[J]. Palaeogeography,Palaeoclimatology,Palaeoecology, 1998,137(1/2):25-54.
[7]	Zhou L P, Oldfield F, Wintle A G , et al. Partly pedogenic origin of magnetic variations in Chinese loess[J]. Nature, 1990,346(6286):737-739. doi: 10.1038/346737a0 url: http://www.nature.com/doifinder/10.1038/346737a0
[8]	季峻峰, 陈骏, 刘连文 , 等. 洛川黄土中绿泥石的化学风化与磁化率增强[J]. 自然科学进展, 1999,9(7):619-623. doi: 10.1088/0256-307X/16/9/027 url: http://www.cqvip.com/qk/96531X/199907/3787509.html
[8]	Ji J F, Chen J, Liu L W , et al. Chemical weathering of chlorite and enhancement of magnetic susceptibility in Luochuan Loess[J]. Progress in Natural Science, 1999,9(7):619-623.
[9]	甘甫平, 王润生 . 遥感岩矿信息提取基础与技术方法研究[M]. 北京: 地质出版社, 2004: 43-47.
[9]	Gan F P, Wang R S. Basis Theory and Technical Methods Study of Remote Sensing Rock and Mineral Information Extraction[M]. Beijing: Geological Publishing House, 2004: 43-47.
[10]	Deaton B C, Balsam W L . Visible spectroscopy:A rapid method for determining hematite and goethite concentration in geological materials[J]. Journal of Sedimentary Petorology, 1991,61(4):628-632. doi: 10.1306/D4267794-2B26-11D7-8648000102C1865D url: https://pubs.geoscienceworld.org/jsedres/article/61/4/628-632/98348
[11]	Ji J F, Balsam W, Chen J , et al. Rapid and quantitative measurement of hematite and goethite in the Chinese loess-epaleosol sequence by diffuse reflectance spectroscopy[J]. Clays and Clay Minerals, 2002,50(2):208-216. doi: 10.1346/000986002760832801 url: http://www.ingentaselect.com/rpsv/cgi-bin/cgi?ini=xref&body=linker&reqdoi=10.1346/000986002760832801
[12]	Scheinost A C, Chavernas A, Barrón V , et al. Use and limitation of second-derivative diffuse reflectance spectroscopy in the visible to near-infrared range to identify and quantify Fe oxide minerals in soils[J]. Clays and Clay Minerals, 1998,46(5):528-536. doi: 10.1346/CCMN.1998.0460506 url: http://www.clays.org/journal/archive/volume%2046
[13]	Smith M J, Stevens T, MacArthur A ,et al.Characterising Chinese loess stratigraphy and past monsoon variation using field spectroscopy[J]. Quaternary International, 2011,234(1/2):146-158 doi: 10.1016/j.quaint.2010.04.011 url: http://linkinghub.elsevier.com/retrieve/pii/S1040618210001333
[14]	Hunt G R, Salisbury J W, Lenhoff C J . Visible and near-infrared spectra of minerals and rocks:III.Oxides and hydroxides[J]. Modern Geology, 1971,2:195-205. url: http://www.researchgate.net/publication/310383711_Visible_and_near_infrared_spectra_of_minerals_and_rocks_III_Oxides_and_hydroxides
[15]	Cui J, Yan B K, Wang R S , et al. Regional-scale mineral mapping using ASTER VNIR/SWIR data and validation of reflectance and mineral map products using airborne hyperspectral CASI/SASI data[J]. International Journal of Applied Earth Observation and Geoinformation, 2014,33:127-141. doi: 10.1016/j.jag.2014.04.014 url: http://linkinghub.elsevier.com/retrieve/pii/S0303243414000981
[16]	Kruse F A, Lefkoff A B, Boardman J W , et al. The spectral image processing system(SIPS)-interactive visualization and analysis of imaging spectrometer data[J]. Remote Sensing of Environment, 1993,44(2/3):145-163. doi: 10.1063/1.44433 url: http://linkinghub.elsevier.com/retrieve/pii/003442579390013N

[1]	WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[2]	GAO Wenlong, ZHANG Shengwei, LIN Xi, LUO Meng, REN Zhaoyi. The remote sensing-based estimation and spatial-temporal dynamic analysis of SOM in coal mining[J]. Remote Sensing for Natural Resources, 2021, 33(4): 235-242.
[3]	JIANG Yanan, ZHANG Xin, ZHANG Chunlei, ZHONG Chengcheng, ZHAO Junfang. Classification of remote sensing images based on multi-scale feature fusion using local binary patterns[J]. Remote Sensing for Natural Resources, 2021, 33(3): 36-44.
[4]	ZANG Chuankai, SHEN Fang, YANG Zhengdong. Aquatic environmental monitoring of inland waters based on UAV hyperspectral remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(3): 45-53.
[5]	HU Xinyu, XU Zhanghua, CHEN Wenhui, CHEN Qiuxia, WANG Lin, LIU Hui, LIU Zhicai. Construction and application effect of normalized shadow vegetation index NSVI based on PROBA/CHRIS image[J]. Remote Sensing for Land & Resources, 2021, 33(2): 55-65.
[6]	WANG Ruijun, ZHANG Chunlei, SUN Yongbin, WANG Shen, DONG Shuangfa, WANG Yongjun, YAN Bokun. Application of hyperspectral spectroscopy to constructing polymetallic prospecting model in Hongshan, Gansu Province[J]. Remote Sensing for Land & Resources, 2020, 32(3): 222-231.
[7]	Donghui ZHANG, Yingjun ZHAO, Kai QIN. Design and construction of the typical ground target spectral information system[J]. Remote Sensing for Land & Resources, 2018, 30(4): 206-211.
[8]	REN Guangli, YANG Min, LI Jianqiang, GAO Ting, LIANG Nan, YI Huan, YANG Junlu. Application of hyperspectral alteration information to gold prospecting: A case study of Fangshankou area,Beishan[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 182-190.
[9]	SU Hongjun, LIU Hao. A novel dynamic classifier selection algorithm using spatial-spectral information for hyperspectral classification[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 15-21.
[10]	LIN Na, YANG Wunian, WANG Bin. Pixel un-mixing for hyperspectral remote sensing image based on kernel method[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 14-20.
[11]	CHAI Ying, RUAN Renzong, CHAI Guowu, FU Qiaoni. Species identification of wetland vegetation based on spectral characteristics[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 86-90.
[12]	DAI Xiaoai, JIA Hujun, ZHANG Xiaoxue, WU Fenfang, GUO Shouheng, YANG Wunian, YANG Ye. Identification of hyperspectral features for subalpine typical vegetation in the upper reaches of the Minjiang River[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 174-180.
[13]	HE Hao, SHEN Yonglin, LIU Xiuguo, MA Li. Spatial-spectral constrained graph-based semi-supervised classification for hyperspectral image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 31-36.
[14]	CHEN Shulin, BI Yinli. Application of remote sensing technology to microbial reclamation[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(3): 16-23.
[15]	CHANG Ruichun, WANG Lu, WANG Maozhi. Application of FastICA in mineral information extraction from hyperspectral remote sensing data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(4): 129-132.

Viewed

Full text

Abstract

Cited

Shared

Discussed