龙里某塌陷时序InSAR变形监测的PS修正

doi:10.6046/zrzyyg.2021334

摘要
图/表
参考文献
相关文章
Metrics

全文: PDF(3674 KB)

HTML
输出: BibTeX | EndNote (RIS)

摘要

基于永久散射体的信号在很长时间范围内都能保持较高的干涉相干性的优点,为了解决小基线集合成孔径雷达干涉测量技术(small baseline subset interferometric synthetic aperture Radar,SBAS-InSAR)轨道精炼步骤时人工选择地面控制点可能会影响到监测结果这一问题,首先,该文将永久散射体与SBAS-InSAR结合,通过设置相干系数的阈值、振幅离差指数的阈值以及地表形变速率的阈值选出稳定的永久散射体,并将这些点作为SBAS-InSAR轨道精炼中的地面控制点,从而修正监测结果的准确度; 然后,选用2019年9月1日—2021年4月11日20景覆盖贵州省龙里县洗马镇的Sentinel-1A双极化影像为主要数据源,进行地表形变监测; 最后,将该方法所得结果、人工选择地面控制点的方法所得结果与北斗位移监测数据进行对比分析,可知该文方法比人工选择地面控制点的方法更精准。

	服务

	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	罗雪玮
	向喜琼
	吕亚东

关键词 ：永久散射体, 多重阈值, SBAS-InSAR, 地面控制点

Abstract：

The signals of permanent scatterers can maintain high interferometric coherence for a long time. To solve the problem that manually selecting ground control points may affect the monitoring results during the orbit refinement of the SBAS-InSAR, this study combined permanent scatterers with SBAS-InSAR. Firstly, by setting the thresholds of the coherence coefficient, the amplitude dispersion index, and the surface deformation rate, this study selected robust permanent scatterers as the ground control points in the orbit refinement of the SBAS-InSAR in order to correct the accuracy of the monitoring results. Then, this study selected 20 scenes of Sentinel-1A dual-polarization images that covered Xima Town, Longli County, Guizhou Province from September 1, 2019 to April 11, 2021 as the main data source for surface deformation monitoring. Finally, this study compared the results obtained using the proposed method and those obtained through manually selecting ground control points with the displacement monitoring data of the Beidou satellite, concluding that the data obtained using the method proposed in this study were more accurate.

Key words： permanent scatterer multi-threshold SBAS-InSAR ground control points

收稿日期: 2021-10-12 出版日期: 2022-09-21

ZTFLH:

P23

基金资助:中央引导地方科技发展资金项目(黔科中引地[2021]4001)

通讯作者: 向喜琼

作者简介: 罗雪玮(1997-),女,硕士研究生,主要从事地球探测与信息技术研究。Email: lxwei0608@163.com。

引用本文:

罗雪玮, 向喜琼, 吕亚东. 龙里某塌陷时序InSAR变形监测的PS修正[J]. 自然资源遥感, 2022, 34(3): 82-87.
LUO Xuewei, XIANG Xiqiong, LYU Yadong. PS correction of InSAR time series deformation monitoring for a certain collapse in Longli County. Remote Sensing for Natural Resources, 2022, 34(3): 82-87.

链接本文:

https://www.gtzyyg.com/CN/10.6046/zrzyyg.2021334 或 https://www.gtzyyg.com/CN/Y2022/V34/I3/82

Fig.1 方法流程

Fig.2 Sentinel-1A影像位置

Tab.1 Sentinel-1A数据参数

Fig.3 基于3个阈值得到的PS点

Fig.4 SBAS-InSAR形变速率图

Fig.5 研究区地表形变时间序列

Tab.2 2种方法与北斗监测点比较情况

[1]	曹翠华. InSAR技术监测西南喀斯特地表形变应用研究[D]. 西安: 西安科技大学, 2020.
	Cao C H. Application of InSAR technology in monitoring southwest surface deformation[D]. Xi’an: Xi’an University of Science and Technology, 2020.
[2]	Colombo D, Farina P, Moretti S, et al. Land subsidence in the Firenze-Prato-Pistoia basin measured by means of spaceborne SAR interferometry[C]// IEEE International Symposium on Geoscience and Remote Sensing (IGARSS), 2003, 4(3):2927-2929.
[3]	Wu Q, Jia C T, Chen S B, et al. SBAS-InSAR based deformation detection of urban land,created from mega-scale mountain excavating and valley filling in the loess plateau:The case study of Yan’an City[J]. Remote Sensing, 2019, 11(14):1673. doi: 10.3390/rs11141673
[4]	刘一霖, 张勤, 黄海军, 等. 矿区地表大量级沉陷形变短基线集InSAR监测分析[J]. 国土资源遥感, 2017, 29(2):144-151.doi: 10.6046/gtzyyg.2017.02.21. doi: 10.6046/gtzyyg.2017.02.21
	Liu Y L, Zhang Q, Huang H J, et al. Monitoring and analyzing large scale land subsidence over the mining area using small baseline subset InSAR[J]. Remote Sensing for Land and Resources, 2017, 29 (2) :144-151.doi: 10.6046/gtzyyg.2017.02.21. doi: 10.6046/gtzyyg.2017.02.21
[5]	葛大庆, 殷跃平, 王艳, 等. 地面沉降-回弹及地下水位波动的InSAR长时序监测——以德州市为例[J]. 国土资源遥感, 2014, 26(1):103-109.doi: 10.6046/gtzyyg.2014.01.18. doi: 10.6046/gtzyyg.2014.01.18
	Ge D Q, Yin Y P, Wang Y, et al. Seasonal subsidence-rebound and ground water level changes monitoring by using coherent target InSAR technique:A case study of Dezhou,Shandong[J]. Remote Sensing for Land and Resources, 2014, 26(1):103-109.doi: 10.6046/gtzyyg.2014.01.18. doi: 10.6046/gtzyyg.2014.01.18
[6]	李金超, 高飞, 鲁加国, 等. 基于SBAS-InSAR和GM-SVR的居民区形变监测与预测[J]. 大地测量与地球动力学, 2019, 39(8):837-842.
	Li J C, Gao F, Lu J G, et al. Deformation monitoring and prediction of residential area based on SBAS-InSAR and GM-SVR[J]. Journal of Geodesy and Geodynamics, 2019, 39(8):837-842.
[7]	王道飘. 基于PS点大气延迟校正的时序InSAR在城市地面沉降监测中的研究及应用[D]. 武汉: 武汉理工大学, 2019.
	Wang D P. Research and application of time series InSAR based on atmospheric delay correction by PS points in urban land settlement monitoring[D]. Wuhan: Wuhan University of Technology, 2019.
[8]	Li N, Wu J. Research on methods of high coherent target extraction in urban area based on PSINSAR technology[J]. ISPRS - International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences, 2018, XLII-3 :901-908.
[9]	Ferretti A, Prati C, Rocca F. Analysis of permanent scatterers in SAR interferometry[C]// Proceedings of IEEE International Geoscience and Remote Sensing Symposium,Honolulu,HI, 2000.
[10]	李婉秋. 基于Sentinel-1数据的SBAS-InSAR技术在地表形变监测中的研究[D]. 成都: 成都理工大学, 2018.
	Li W Q. A research of SBAS-InSAR technology based on sentinel-1 data in surface deformation monitoring[D]. Chengdu: Chengdu University of Technology, 2018.
[11]	周吕, 郭际明, 李昕, 等. 基于SBAS-InSAR的北京地区地表沉降监测与分析[J]. 大地测量与地球动力学, 2016, 36(9):793-797.
	Zhou L, Guo J M, Li X, et al. Monitoring and analyzing on ground settlement in Beijing area based on SBAS-InSAR[J]. Journal of Geodesy and Geodynamics, 2016, 36(9):793-797.
[12]	Esri中国信息技术有限公司. SARscape培训教程[Z]. 北京: [s.n.], 2018.
	Esri China Information Technology Co.,LTD. SARscape training tutorial[Z]. Beijing: [s.n.], 2018.
[13]	何平. 时序InSAR的误差分析及应用研究[D]. 武汉: 武汉大学, 2014.
	He P. Error analysis and surface deformation application of time series InSAR[D]. Wuhan: Wuhan University, 2014.

[1]	潘建平, 付占宝, 邓福江, 蔡卓言, 赵瑞淇, 崔伟. 时序InSAR解析消落带区域岸坡地表形变及其水要素影响[J]. 自然资源遥感, 2023, 35(2): 212-219.
[2]	虎小强, 杨树文, 闫恒, 薛庆, 张乃心. 基于时序InSAR的新疆阿希矿区地表形变监测与分析[J]. 自然资源遥感, 2023, 35(1): 171-179.
[3]	张志华, 胡长涛, 张镇, 杨树文. 基于PS-InSAR上海地区地表沉降监测与分析[J]. 自然资源遥感, 2022, 34(3): 106-111.
[4]	徐子兴, 季民, 张过, 陈振炜. 基于SBAS-InSAR技术和Logistic模型的矿区沉降动态预测方法[J]. 自然资源遥感, 2022, 34(2): 20-29.
[5]	杨旺, 何毅, 张立峰, 王文辉, 陈有东, 陈毅. 甘肃金川矿区地表三维形变InSAR监测[J]. 自然资源遥感, 2022, 34(1): 177-188.
[6]	何海英, 陈彩芬, 陈富龙, 唐攀攀. 张家口明长城景观廊道Sentinel-1影像SBAS形变监测示范研究[J]. 国土资源遥感, 2021, 33(1): 205-213.
[7]	孙晓鹏, 鲁小丫, 文学虎, 甄艳, 王蕾. 基于SBAS-InSAR的成都平原地面沉降监测[J]. 国土资源遥感, 2016, 28(3): 123-129.

Viewed

Full text

Abstract

Cited

Shared

Discussed