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  • ISSN 1000-694X
  • 双月刊 创刊于1981年
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沙漠与沙漠化

基于高光谱影像数据的戈壁表面砾石粒径定量反演潜力评估

  • 曹晓阳 ,
  • 穆悦 ,
  • 曹晓明 ,
  • 冯益明
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  • 中国林业科学研究院 荒漠化研究所, 北京 100091
曹晓阳(1988-),男,山东潍坊人,博士研究生,主要从事遥感技术应用研究。Email: shadowcxy@163.com

收稿日期: 2015-01-27

  修回日期: 2015-03-11

  网络出版日期: 2015-07-20

基金资助

国家公益性行业(林业)科研专项项目(201404304);国家自然科学基金项目(31370708)

Identification of Gravel Size on the Gobi Surface using EO-1 Hyperspectral Data

  • Cao Xiaoyang ,
  • Mu Yue ,
  • Cao Xiaoming ,
  • Feng Yiming
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  • Institute of Desertification Studies, Chinese Academy of Forestry, Beijing 100091, China

Received date: 2015-01-27

  Revised date: 2015-03-11

  Online published: 2015-07-20

摘要

在新疆哈密境内的噶顺戈壁选取样地,采集不同粒径砾石的光谱数据,分析光谱吸收特征,并利用光谱混合分析技术,以地物光谱为端元从EO-1 Hyperion 高光谱影像中提取了不同粒径的丰度图,分析戈壁表面砾石与高光谱影像的相关关系。结果表明:获取的地物光谱特征显示出粒径的差异对光谱具有明显的影响。所有光谱都展现出在2 250 nm处具有Al–OH 的吸收特性,而且粒径d=41 cm的吸收特性比其他粒径都更显著。而与更小粒径相比,粒径d=53 cm和d=83 cm在480 nm和920 nm处具有较弱的Fe3+ 吸收特性。粒径d=0.8 cm(R2=0.637)、d=3.4 cm(R2=0.687)、d=16.3 cm(R2 =0.644)及d=41 cm(R2=0.622) 与相应的丰度影像具有显著相关性,而粒径d=53 cm(R2=0.181)和d=83 cm(R2=0.167)与相应的丰度影像相关性不显著。EO-1高光谱影像适用于对戈壁区砾石分布特征的确定,在进一步的研究中将高分辨率影像与高光谱影像相结合,可以提高判别精度。

本文引用格式

曹晓阳 , 穆悦 , 曹晓明 , 冯益明 . 基于高光谱影像数据的戈壁表面砾石粒径定量反演潜力评估[J]. 中国沙漠, 2015 , 35(4) : 850 -856 . DOI: 10.7522/j.issn.1000-694X.2015.00082

Abstract

The ground spectra of different size levels of gravel of Gaxun Gobi were obtained in Hami, Xinjiang, China. The spectral absorption features were analyzed, and the abundance images were discriminated from the Hyperion imagery using spectral mixture analysis to analyze the correlations between the gravel size levels and the hyperspectral images. The spectral features demonstrated that differences in gravel size levels can have a considerable influence on the obtained ground spectra. All of the spectra exhibited an Al-OH absorption feature at 2 250 nm and this feature was much more pronounced for gravel with diameter d=41 cm than for other gravel size levels. In contrast with the smaller gravels, gravels with d=53 cm and d=83 cm exhibited much weaker Fe3+ absorption at 480 nm and 920 nm. The gravels with d=0.8 cm (R2=0.637), d=3.4 cm (R2=0.687), d=16.3 cm (R2 =0.644), and d=41 cm (R2=0.622) exhibited significant relationships with the corresponding abundance images, whereas very poor correlations were found for gravels with d=53 cm (R2=0.181) and d=83 cm (R2=0.167).These spectral features and correlation results confirm that EO-1 Hyperion images are suitable for determining the distribution of gravels in the gobi region. The high resolution image should be combined with the hyperspectral images to improve the discriminating accuracy in further study.

参考文献

[1] 冯益明,吴波,周娜,等.基于遥感影像识别的戈壁分类体系研究[J].中国沙漠,2013,33(3):635-641.
[2] 国家林业局.中国荒漠化和沙化状况公报[R].2011.
[3] Okin G S,Painter T H.Effect of grain size on remotely sensed spectral reflectance of sandy desert surfaces[J].Remote Sensing of Environment,2004,89:272-280.
[4] AlAbbas A H,Swain P H,Baumgardner M F.Relating organic matter and clay content to the multispectral radiance of soils[J].Soil Science,1972,114(6):477-485.
[5] Gerbermann A H.Reflectance of varying mixtures of clay soil and sand[J].Photogrammetric Engineering and Remote Sensing,1979,45:1145-1150.
[6] Hapke B.Bidirectional reflectance spectroscopy:1.theory[J].Journal of Geophysical Research,1981,86(B4):3039-3054.
[7] Mustard J F,Pieters C M.Photometric phase functions of common geologic minerals and applications to quantitative-analysis of mineral mixture reflectance spectra[J].Journal of Geophysical Research,1989,10:13619-13634.
[8] Pu R.Gong P,Michishita R,et al.Spectral mixture analysis for mapping abundance of urban surface components from the Terra/ASTER data[J].Remote Sensing of Environment,2008,112:939-954.
[9] Wu C.Quantifying high-resolution impervious surfaces using spectral mixture analysis[J].International Journal of Remote Sensing,2009,30:2915-2932.
[10] Smith M O,Ustin S L,Adams J B,et al.Vegetation in deserts:I.a regional measure of abundance form multispectral images[J].Remote Sensing of Environment,1990,31:1-26.
[11] Roberts D A,Gardner M,Church R,et al.Mapping Chaparral in the Santa Monica Mountains using Multiple End-member Spectral Mixture Models[J].Remote Sensing of Environment,1998,65:267-279.
[12] Leu D J.Visible and near-infrared reflectance of beach sands:a study on the spectral reflectance/grain size relationship[J].Remote Sensing of Environment,1977,(6):169-182.
[13] Xiao J Y,Shen Y J,Ryutaro T.Mapping soil degradation by topsoil grain size using MODIS data[EB/OL].http://www2.cr.chiba-u.jp/symp2005/documents/Postersession/p003_Jieyingxiao_paper.pdf.2005.
[14] 姚爱冬,曹晓阳,冯益明.基于主成分分析法的戈壁地表砾石粒径遥感估测模型研究[J].中国沙漠,2014,34(5):1215-1221.
[15] 罗乔顺.基于土地利用/覆盖变化的哈密地区遥生态经济可持续发展研究[D].乌鲁木齐:新疆农业大学,2008.
[16] 唐伯惠,姜小光,唐伶俐,等.星载高光谱Hyperion数据在海滩涂调查应用中的分析[J].地球信息科学,2004,6(2):81-85.
[17] 谭炳香,李增元,陈尔学,等.EO-1 Hyperion高光谱数据的预处理 [J].遥感信息,2005,(6):36-41.
[18] Richard Beck.EO-1 User Guide v.2.3,2003[EB/OL].http://eo1.usgs.gov& http://eo1.gsfs.nasa.gov.
[19] ENVI Research Systems Inc.ENVI User's Guide,the Environment for Visualizing Images,2000,Version 3.4[EB/OL]. Boulder,USA:Research Systems,Inc.
[20] Mason P.MMTG A-List Hyperspectral Data Processing Software[EB/OL].Sydney,Australia:920C,CSIRO,Division of Exploration and Mining,2002.
[21] Gao B,Goetz A F H.Column atmospheric water vapour and vegetation liquid water retrievals from airborne imaging spectrometer data[J].Journal of Geophysical Research,1990,95:3549-3564.
[22] Bateson A,Curtiss B.A method for manual end-member selection and spectral unmixing[J].Remote Sensing of Environment,1996,60: 229-243.
[23] Boardman J W,Kruse E A.Automated spectral analysis:a geological example using AVIRIS data[C]//ERM Tenth Thematic Conference on Geologic Remote Sensing.Michigan,USA:Environmental Research Institute of Michigan,1994:1407-1418.
[24] Hatchell D C.Technical Guide[R].Boulder,Colorado,USA:Analytical Spectral Devices,Inc.,1999.
[25] 梅安心,彭望禄,秦其明,等.遥感导论[M].北京:高等教育出版社,2001:46-47.
[26] 吴正.中国沙漠及其治理[M].北京:科学出版社,2009:126-409.
[27] Jafari R,Lewis M M.Arid land characterization with EO-1 Hyperion hyperspectral data[J].International Journal of Applied Earth Observation and Geoinformation,2012,19:298-307.
[28] Lewis M M.Discriminating Arid Vegetation Composition with Multispectral and High Spectral Resolution Imagery[D].Sydney,Australia:University of New South Wales,1999.
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