ARTICLES

Method Comparison of Extraction of Gangue Yard Based on Remote Sensing

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  • 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2. Satellite Environment Application Center, Ministry of Environment Protection, Beijing 100029, China;
    3. Solid Waste Management Center of Inner Mongolia Autonomous Region, Hohhot 010000, China;
    4. Resource & Environment College of Huazhong Agricultural University, Wuhan 430070, China

Received date: 2012-09-21

  Revised date: 2013-04-19

  Online published: 2013-09-29

Abstract

Gangue, as one of the industry-specific solid waste produced from coal mining and coal washing, effects on around environment significantly. Therefore, monitoring of coal yard is essential for the management and protection of ecological environment. Before gangue yard detailed investigation, obtaining preliminary data of the location and area of gangue yard by remote sensing image is needed and good for subsequent investigations. This article took the image of Landsat5 TM, received in September, 2011 as data source and did the radiometric and geometric correction to the images. According to the composition and formation characteristics of gangue, we extracted the gangue yard with following two steps: firstly, got land classification information that is confused with gangue through spectral analysis and unsupervised classification; secondly, combined spectral information and terrain, temperature and other ancillary information of the study region, and used four methods, i. e., unsupervised classification, supervised classification, spectrum-photometric method and hierarchical classification respectively to extract the gangue yard of the study area. By comparison of the above methods, we found that the unsupervised classification and supervised classification methods had a faster data extraction but with low extraction accuracy. The accuracy of spectrum-photometric method is a little higher than the former two methods. The hierarchical classification method has the highest accuracy in preliminary data extraction, and the identification accuracy of the gangue yard is up to 78% after post-processing. The result basically meets the requirement on dynamic supervision of gangue yard. Thus, these methods are also useful, as guidance, to continuing extract data of the area and location of the gangue yard under high resolution remote sensing images. Especially, the hierarchical classification method is more suitable for gangue yard information extraction.

Cite this article

WANG Feng, LIU Qiang-Sheng, LIU Gao-Huan, SHEN Wen-Meng, LI Yan, ZHANG Chao-Mang, DONG Jin-Fa . Method Comparison of Extraction of Gangue Yard Based on Remote Sensing[J]. Journal of Geo-information Science, 2013 , 15(5) : 768 -774 . DOI: 10.3724/SP.J.1047.2013.00768

References

[1] 刘迪.煤矸石的环境危害及综合利用研究[J].气象与环境学报,2006,22(3):60-62.

[2] 祁星鑫,王晓军,黎艳,等.新疆主要煤区煤矸石的特征研究及其利用建议[J].煤炭学报,2010,35(7):1197-1201.

[3] 刘富强,钱建生,王新红,等.基于图像处理与识别技术的煤矸石自动分选[J].煤炭学报,2002,25(5):534-537.

[4] 荆青青,张志,王旭.基于ASTER遥感影像的煤矸石分布信息提取方法[J].煤炭科学技术,2008,36(5):93-96.

[5] 冯稳,张志,乌云其其格,等.采用决策树分类方法进行煤矸石信息提取研究[J].黑龙江大学自然科学学报,2011,28 (2):277-280.

[6] 鄂尔多斯统计局.鄂尔多斯市2010年社会发展和国民经济统计公报[EB/OL].http://www.ordos.gov.cn/pub/ordostj/TJGB/201111/t20111116_525042.html.2011.2.

[7] 鄂尔多斯环保局.鄂尔多斯市2010年全市固体废物污染环境防治信息[EB/OL]. http://www.ordoshb.gov.cn/Infos_ Show.asp?ID=1626,2011.6.

[8] 历华,曾永年,贠培东,等.利用多源遥感数据反演城市地表温度[J].遥感学报,2007,11(6):891-898.

[9] 覃志豪, Zhang M, Karnieli A,等.用陆地卫星TM6数据演算地表温度的单窗算法[J].地理学报,2001,56(4):456-466.

[10] NASA.NASA's Earth observing System Data and Information System [EB/OL]. http://earthdata.nasa.gov/abouteosdis/system-description/eos-metadata-clearinghouse-echo.

[11] 朱绍攀,陈宇.大气辐射校正方法分析[J].地理空间信息, 2010,8(1):113-116.

[12] 郝建亭,杨武年,李玉霞,等.基于FLAASH的多光谱影像大气校正应用研究[J].遥感应用,2008(1):78-81.

[13] 梅安新,彭望琭,秦其明,等.遥感概论[M].北京:高等教育出版社,2001,196-201.

[14] 周成虎,骆剑承,杨晓梅,等.遥感影像地学理解与分析 [M].北京:科学出版社,1999,75-78.

[15] 赵英时,等.遥感应用分析原理与方法[M].北京:科学出 版社,2003,222-241.

[16] 田庆久,闵祥军.植被指数研究进展[J].地球科学进展, 1998,13(4):327-332.

[17] Baret F, Guyot G, Major D J. TSAVI: A vegetation index which minimizes soil brightness effects on LAI and APAR estimation[C]. Proceedings of the 12th Canadian Symposium on Remote Sensing. Vancouver, Canada, 1989,1355-1358.

[18] 丁凤.基于新型水体指数(NWI)进行水体信息提取的实验研究[J].测绘科学,2009,34(4):155-157.

[19] 徐涵秋.从增强型水体指数分析遥感水体指数的创建 [J].地球信息科学,2008,10(6):776-780.

[20] 徐涵秋.利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J].遥感学报,2005,9(5):589-595.

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