An analysis of height precision in applying single frequency static GPS to landslide monitoring

doi:10.6046/gtzyyg.2014.02.13

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Abstract

As we all know,GPS has brought great convenience to surveying and mapping work. Its horizontal accuracy has reached sub-millimeter. However, due to the influences of such factors as the effects of ionosphere,multipath effect,GDOP(geometric dilution of precision) and height of antenna,the vertical deformation measurements from GPS are seldomly used by precision survey workers. Based on the reasonable layout of deformation monitoring network of Shuping landslide in the Three Gorges Reservoir Area,the authors obtained a large number of leveling and GPS data from long-term monitoring,and used leveling data as the reference to analyze the height precision of single-frequency static GPS in landslide vertical deformation monitoring. The results show that the vertical deformation monitoring precision of single-frequency static GPS is ± 2cm, that the deformation monitoring accuracy should be greater than 1/5 of deformation in the measurement period according to the monitoring code of landslide, and that single frequency static GPS is suitable for the landslide whose vertical deformation is above 10 cm in its monitoring period. For the Shuping landslide,GPS measurement can provide good monitoring data in the second to third period whose vertical deformation is up to 30 cm,but it is unsuitable for other monitoring periods in slow state.

Keywords wetland landscape pattern ecological security Pressure-State-Response (PSR) model remote sensing

:	TP79
	P228.4

Issue Date: 28 March 2014

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	DU Peijun
	CHEN Yu
	TAN Kun

Cite this article:

DU Peijun,CHEN Yu,TAN Kun. An analysis of height precision in applying single frequency static GPS to landslide monitoring[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(2): 74-79.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2014.02.13 OR https://www.gtzyyg.com/EN/Y2014/V26/I2/74

[1] 廖明生,唐婧,王腾,等.高分辨率SAR数据在三峡库区滑坡监测中的应用[J].中国科学:地球科学,2012,42(2):217-229. Liao M S,Tang J,Wang T,et al.Landslide monitoring with high-resolution SAR data in the Three Gorges Region[J].Scientia Sinica Terrae,2012,42(2):217-229.

[2] 刘根友,薛怀平,郝晓光,等.三峡库区秭归GPS滑坡监测网数据分析[J].大地测量与地球动学,2009,29(3):70-73. Liu G Y,Xue H P,Hao X G,et al.Data analysis of GPS slide monitoring network in Zigui Zone of Three Gorges Reservoir Area[J].Journal of Geodesy and Geodynamics,2009,29(3):70-73.

[3] 薛志宏,卫建东,金新平.GPS在雅砻江卡拉电站滑坡监测中的应用[J].测绘工程,2007,16(2):65-68. Xue Z H,Wei J D,Jin X P.The landslide monitoring using GPS in Kala reservoir area[J].Engineering of Surveying and Mapping,2007,16(2):65-68.

[4] 熊先才,岳仁宾,彭军怀,等.GPS技术在万州明镜滩滑坡监测中的应用[J].测绘科学,2008,33(3):145,149-150. Xiong X C,Yue R B,Peng J H,et al.Application of GPS technique in Wanzhou Mingjingtan slope deformation monitoring[J].Science of Surveying and Mapping,2008,33(3):145,149-150.

[5] 陈红卫,杨红生.GPS技术在滑坡监测中的应用[J].测绘与空间地理信息,2012,35(1):145-147,153. Chen W H,Yang H S.The application of GPS in the landslide monitoring[J].Geomatics and Spatial Information Technology,2012,35(1):145-147,153.

[6] 贾中甫,杨郁,冯启俊.精密三角高程代替二等水准测量的研究与实践[J].测绘信息与工程,2012,37(1):15-17. Jia Z F,Yang Y,Feng Q J.Research and practice on precise trigonometric leveling to replace second-class leveling survey in Erdos City[J].Journal of Geomatics,2012,37(1):15-17.

[7] 孙玉玲,王辉,李德钢.利用全站仪双向观测法进行高程测量[J].电大理工,2006(1):12-13. Sun Y L,Wang H,Li D G.Using the total station bidirectional observation method for height measurement[J].Study of Science and Engineering at RTVU,2006(1):12-13.

[8] Liu G Y,Liu L T,Chai Y J.Reference frame for GPS deformation analysis[C]//GPS/GNSS2005,December 8-10.HongKong:A9,2005:1-12.

[9] Fan J,Zhao H,Tu P,et al.CRInSAR for landslide deformation monitoring:A case in Three Gorge Area[C]//Proceedings of the IEEE International Geoscience and Remote Sensing Symposium(IGARSS),Honoluln,HI:IEEE,2010:3956-3959.

[10] Fan J H,Tu P,Guo X F,et al.Monitoring the deformation of a landslide with differential SAR interferometry using corner reflectors[C]//The 6th International Symposium on Digital Earth. International Society for Optics and Photonics,2009:78411I-78411I-7.

[11] 范景辉,李梅,郭小方,等.基于PSInSAR方法和ASAR数据监测天津地面沉降的试验研究[J].国土资源遥感,2007,19(4):23-27. Fan J H,Li M,Guo X F,et al.A preliminary study of the subsidence in Tianjin area using ASAR images based on PSInSAR technique[J].Remote Sensing for Land & Resources,2007,19(4):23-27.

[12] 范景辉,郭华东,郭小方,等.基于相干目标的干涉图叠加方法监测天津地区地面沉降[J].遥感学报,2008,12(1):111-118. Fan J H,Guo H D,Guo X F,et al.Monitoring subsidence in Tianjin Area using interferogram stacking based on Coherent Targets[J].Journal of Remote Sensing,2008,12(1):111-118.

[13] 薛怀平,刘根友,郝晓光,等.三峡库区秭归GPS-CR滑坡监测网的建立[C]//第三届湖北科技论坛优秀论文集.武汉:湖北省科学技术协会,2005. Xue H P,Liu G Y,Hao X G,et al.The establishment of GPS-CR landslide monitoring network in Zigui Zone of Three Gorges Reservoir Area[C]//3rd Excellent Symposium on Hubei Science and Technology Forum.Wuhan:Hubei Association for Science and Technology,2005.

[14] 黄声享,尹晖,蒋征.变形监测数据处理[M].武汉:武汉大学出版社,2010:103-106. Huang S X,Yin H,Jiang Z.Deformation monitoring data processing[M].Wuhan:Wuhan University Press,2010:103-106.

[15] 李征航,黄劲松.GPS测量与数据处理[M].武汉:武汉大学出版社,2005:128-132. Li Z H,Huang J S.GPS measurement and data processing[M].Wuhan:Wuhan University Press,2005:128-132.

[16] 何秀凤,桑文刚,贾东振.基于GPS的高边坡形变监测方法[J].水利学报,2006,37(6):746-750. He X F,Sang W G,Jia D Z.Deformation monitoring of steep rock slopes by means of GPS[J].Journal of Hydraulic Engineering,2006,37(6):746-750.

[17] 陈义.区域形变监测中的位移估计的方法[J].同济大学学报,2003,31(1):64-68. Chen Y.Method of displacement estimation in regional deformation monitoring[J].Journal of Tongji University,2003,31(1):64-68.

[18] 中华人民共和国国家质量监督检验检疫局,中国国家标准化管理委员会.GB/T12897—2006国家一、二等水准测量规范[S].北京:中国标准出版社,2006. General Administration of Quality Supervision,Inspection and Quarantine of the People's Republic of China(AQSIQ),Standardization Administration of the People's Republic of China.GB/T12897—2006 Specifications for the first and second order leveling[S].Beijing:China Standards Press,2006.

[19] 中华人民共和国国家质量监督检验检疫局,中国国家标准化管理委员会.DZ/T 0221—2006崩塌、滑坡、泥石流监测规范[S].北京:中国标准出版社,2006. General Administration of Quality Supervision,Inspection and Quarantine of the People's Republic of China(AQSIQ),Standardization Administration of the People's Republic of China.DZ/T 0221—2006 Monitoring code of rockfall,landslide and debris flow[S].Beijing:China Standards Press,2006.

[20] 夏耶,范景辉,李曼,等.高分辨率雷达数据三峡库区滑坡监测技术[C]//第十八届中国遥感大会论文集,2012:161-169. Xa Y,Fan J H,Li M,et al.Monitoring technique of Shuping landslide with high-resolution Radar images in Three Gorges Reservoir area,in China[C]//18th China symposium on Remote Sensing,2012:161-169.

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