Monitoring Typical Construction Sites of Sichuan-Tibet Traffic Corridor by InSAR and Intensive Distortion Analysis
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摘要: 为了探明川藏交通廊道典型工点高陡岸坡的稳定性，以及明确SAR(synthetic aperture radar)几何畸变对InSAR(interferometric synthetic aperture radar)形变结果的影响，利用时序InSAR技术开展川藏交通廊道典型工点高陡岸坡形变监测，并提出了一种可识别所有几何畸变类型的SAR几何畸变精细判识方法.成功识别出9处不稳定的高陡岸坡，获取了各轨道SAR几何畸变精细识别结果.在精细划分几何畸变的前提下，通过进一步地将几何畸变与形变结果联合分析，首次揭示了各类几何畸变(包括透视收缩、主被动叠掩、主被动阴影)对InSAR形变结果的影响效果.研究明确了InSAR技术在川藏交通廊道高陡山区的应用能力、适用范围以及几何畸变区结果可靠性的判识，可为后续高陡岸坡形变监测、精确解译等研究提供重要参考.Abstract: To determine the stability of high steep bank slope and clarify the influence of SAR (synthetic aperture radar) geometric distortion on the InSAR (interferometric synthetic aperture radar) displacement results in typical construction sites of the Sichuan-Tibet traffic corridor, in this paper it uses time series InSAR technique for monitoring the deformation of steep slopes in typical construction sites of the Sichuan-Tibet railway, and proposes an intensive SAR geometry distortion analysis method which can identify all types of geometric distortion. 9 unstable steep slopes were successfully identified, and the intensive SAR geometric distortion results of different orbits were acquired. In the prerequisite condition of intensive distortion classification, the influences of different geometric distortion (including foreshortening, active layover passive layover, shadow, and active passive shadow) on InSAR displacement results for the first time revealed by combining geometric distortion and deformation results. This research reveals the application capability and scope of InSAR technique, and the reliability of the InSAR measurement in geometric, providing important reference for subsequent on research of steep slope deformation monitoring and accurate interpretation.
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图 1 研究区概况
a.川藏交通廊道昌都至林芝段线路方案示意图(修改自郭长宝等，2017)；b.研究区高程图；c.研究区SAR影像覆盖情况图
Fig. 1. Overview of the study area
图 2 升轨(a)和降轨(b)Sentinel-1影像时空基线图
Fig. 2. The spatial and temporal baselines of Sentinel-1 images, ascending orbit (a) and descending orbit (b)
图 4 升轨(a)和降轨(b)InSAR平均形变速率结果
Fig. 4. InSAR derived mean velocity map, ascending orbit (a) and descending orbit (b)
图 5 升轨(a)和降轨(b)时间序列形变
Fig. 5. Time series deformation, ascending orbit (a) and descending orbit (b)
图 6 NJ典型工点InSAR形变速率与光学遥感影像
a1.升轨整体InSAR形变速率；a2.升轨显著形变区InSAR形变速率；b1.降轨整体InSAR形变速率；b2.降轨显著形变区InSAR形变速率
Fig. 6. InSAR derived deformation velocity and optical remote sensing images of the NJ typical construction site
图 7 XLG典型工点InSAR形变速率与光学遥感影像
a1.升轨整体InSAR形变速率；a2，a3.升轨显著形变区InSAR形变速率；b1.降轨整体InSAR形变速率；b2，b3.降轨显著形变区InSAR形变速率
Fig. 7. InSAR derived deformation velocity and optical remote sensing images of the XLG typical construction site
图 8 升轨(a)和降轨(b)的几何畸变分布以及联立升降轨的监测效果(c)
Fig. 8. Distortion distribution, ascending orbit (a), descending orbit (b) and monitoring effects by combining ascending and descending (c)
图 9 各类几何畸变对于InSAR形变结果影响对比
a1，b1，c1.升轨几何畸变分布；a2，b2，c2.降轨几何畸变分布；a3，b3，c3.升轨形变速率速率结果；a4，b4，c4.降轨形变速率速率结果
Fig. 9. Comparison of the influence of distortion on InSAR deformation results
表 1 研究区Sentinel-1影像主要参数
Table 1. Main parameters of Sentinel-1 images in the study area
参数 描述 影像格式 Sentinel-1A TOPS SLC 波段 C 波长(cm) 5.6 工作模式 IW 极化方式 VV、VH 轨道方向 升轨 降轨 轨道号(Path) 172 106 图幅号(Frame) 94、99 492 飞行方位角(°) 347 193 视线入射角(°) 34.48 44.42 像元方位向×距离向分辨率(m) 13.99×2.33 13.94×2.32 影像时间 2017-01至2019-01 2017-01至2019-01 时间间隔(d) ≥12 ≥12 影像数量(景) 92 48 
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表 1 不同几何畸变分析方法识别结果对比
Table 1. Comparison of identification results of different distortion analysis methods
方法 轨道 主动叠掩 被动叠掩 主动阴影 被动阴影 透视收缩 高适用性 R指数法 升轨 6.40% 无法识别 无法识别 无法识别 39.36% 54.24% 降轨 1.84% 无法识别 无法识别 无法识别 50.56% 47.60% LSM法 升轨 13.44% 8.39% 1.29% 0.14% 无法识别 无法识别 降轨 6.76% 5.05% 3.82% 0.39% 无法识别 无法识别 精细分析法 升轨 13.44% 8.39% 1.29% 0.14% 25.70% 51.04% 降轨 6.76% 5.05% 3.82% 0.39% 40.85% 43.13%
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表 2 升降轨各类几何畸变的InSAR有效监测点密度统计
Table 2. InSAR deformation rate density statistics for distortion of ascending and descending orbits
轨道 主动
叠掩被动
叠掩主动
阴影被动
阴影透视收缩 高适用 高 中 低 升轨 19.36% 23.72% 61.09% 44.39% 29.93% 49.24% 67.97% 79.97% 降轨 24.08% 24.79% 63.69% 34.89% 24.99% 36.94% 60.91% 77.65%
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表 3 各类几何畸变对于形变结果影响统计
Table 3. Statistical results of the influence of distortion on InSAR deformation results
区域 几何畸变类型 有效监测点密度 最大形变速率(mm/a) 几何畸变的影响 升轨 降轨 升轨 降轨 升轨 降轨 升轨 降轨 W01 无 无 84.09% 53.65% -25 -39 - - W02 无 无 83.08% 84.86% -36 -34 - - F01 透视收缩 无 20.99% 87.04% -15 -52 有效监测点减少 - F02 无 透视收缩 80.53% 43.20% -67 -34 - 有效监测点减少 L01 主被动叠掩 无 1.83% 92.18% - -53 无法探测 - L02 主被动叠掩 无 0.66% 97.68% - -40 无法探测 - S01 主被动叠掩 主被动阴影 21.10% 27.75% - - 无法探测 形变结果不可靠 S02 主被动叠掩 主被动阴影 1.34% 48.14% - - 无法探测 形变结果不可靠
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表 4 几何畸变对InSAR形变结果影响的结论
Table 4. Conclusions of the influence of distortion on InSAR deformation results
几何畸变类型 结论 透视收缩 程度较高 不可探测(有效监测点极少) 程度较低 可探测(有效监测点减少，可能信息遗漏) 主被动叠掩 不可探测(信号混杂，有效监测点极少) 主被动阴影 不可探测(无信号，可能有其他原因产生的不可靠监测点)
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[1] Berardino, P., Fornaro, G., Lanari, R., et al., 2002. A New Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms. IEEE Transactions on Geoscience and Remote Sensing, 40(11): 2375-2383. https://doi.org/10.1109/tgrs.2002.803792 [2] Colesanti, C., Wasowski, J., 2006. Investigating Landslides with Space-Borne Synthetic Aperture Radar (SAR) Interferometry. Engineering Geology, 88(3-4): 173-199. https://doi.org/10.1016/j.enggeo.2006.09.013 [3] Cigna, F., Bateson, L. B., Jordan, C. J., et al., 2014. Simulating SAR Geometric Distortions and Predicting Persistent Scatterer Densities for ERS-1/2 and ENVISAT C-B and SAR and InSAR Applications: Nationwide Feasibility Assessment to Monitor the Landmass of Great Britain with SAR Imagery. Remote Sensing of Environment, 152: 441-466. https://doi.org/10.1016/j.rse.2014.06.025 [4] Chen, T., 2014. Study on the Exploration and Genesis of Hot Spring in Chamdo Region Basu County Baima Town (Dissertation). Chengdu University of Technology, Chengdu(in Chinese with English abstract). [5] Chen, X. H., Sun, Q., Hu, J., 2018. Generation of Complete SAR Geometric Distortion Maps Based on DEM and Neighbor Gradient Algorithm. Applied Sciences, 8(11): 2206. https://doi.org/10.3390/app8112206 [6] Dai, K. R., Li, Z. H., Tomás, R., et al., 2016. Monitoring Activity at the Daguangbao Mega-Landslide (China) Using Sentinel-1 Tops Time Series Interferometry. Remote Sensing of Environment, 186: 501-513. https://doi.org/10.1016/j.rse.2016.09.009 [7] Dai, K. R., Li, Z. H., Xu, Q., et al., 2020. Entering the Era of Earth Observation-Based Landslide Warning Systems: A Novel and Exciting Framework. IEEE Geoscience and Remote Sensing Magazine, 8(1): 136-153. https://doi.org/10.1109/mgrs.2019.2954395 [8] Dai, K. R., Tie, Y. B., Xu, Q., et al., 2020. Early Identification of Potential Landslide Geohazards in Alpine-Canyon Terrain Based on SAR Interferometry: A Case Study of the Middle Section of Yalong River. Journal of Radars, 9(3): 554-568(in Chinese with English abstract). [9] Dai, K. R., Zhang, L. L., Song, C., et al., 2021. Quantitative Analysis of Sentinel-1 Imagery Geometric Distortion and Their Suitability along Sichuan-Tibet Railway. Geomatics and Information Science of Wuhan University, 46(10): 1450-1460(in Chinese with English abstract) [10] Dong, J., Zhang, L., Li, M. H., et al., 2018. Measuring Precursory Movements of the Recent Xinmo Landslide in Mao County, China with Sentinel-1 and ALOS-2 PALSAR-2 Datasets. Landslides, 15(1): 135-144. https://doi.org/10.1007/s10346-017-0914-8 [11] Ge, D. Q., Dai, K. R., Guo, Z. C., et al., 2019. Early Identification of Serious Geological Hazards with Integrated Remote Sensing Technologies: Thoughts and Recommendations. Geomatics and Information Science of Wuhan University, 44(7): 949-956(in Chinese with English abstract). [12] Gelautz, M., Frick, H., Raggam, J., et al., 1998. SAR Image Simulation and Analysis of Alpine Terrain. ISPRS Journal of Photogrammetry and Remote Sensing, 53(1): 17-38. https://doi.org/10.1016/s0924-2716(97)00028-2 [13] Guo, C. B., Zhang, Y. S., Jiang, L. W., et al., 2017. Discussion on the Environmental and Engineering Geological Problems along the Sichuan-Tibet Railway and Its Adjacent Area. Geoscience, 31(5): 877-889(in Chinese with English abstract). [14] Kropatsch, W. G., Strobl, D., 1990. The Generation of SAR Layover and Shadow Maps from Digital Elevation Models. IEEE Transactions on Geoscience and Remote Sensing, 28(1): 98-107. https://doi.org/10.1109/36.45752 [15] Li, T. D., Xiao, Q. H., Pan, G. T., et al., 2019. A Consideration about the Development of Ocean Plate Geology. Earth Science, 44(5): 1441-1451(in Chinese with English abstract). [16] Li, Z. H., Song, C., Yu, C., et al., 2019. Application of Satellite Radar Remote Sensing to Landslide Detection and Monitoring: Challenges and Solutions. Geomatics and Information Science of Wuhan University, 44(7): 967-979(in Chinese with English abstract). [17] Liu, X. J., Zhao, C. Y., Zhang, Q., et al., 2018. Multi-Temporal Loess Landslide Inventory Mapping with C-, X- and L-B and SAR Datasets: A Case Study of Heifangtai Loess Landslides, China. Remote Sensing, 10(11): 1756. https://doi.org/10.3390/rs10111756 [18] Meng, Q. K., Xu, Q., Wang, B. C., et al., 2019. Monitoring the Regional Deformation of Loess Landslides on the Heifangtai Terrace Using the Sentinel-1 Time Series Interferometry Technique. Natural Hazards, 98(2): 485-505. https://doi.org/10.1007/s11069-019-03703-3 [19] Mu, J. L., 2018. Research on Surface Deformation Laws Caused by Deep Anhydrite Mining with SBAS-InSAR (Dissertation). China University of Geosciences, Beijing(in Chinese with English abstract). [20] Notti, D., Davalillo, J. C., Herrera, G., et al., 2010. Assessment of the Performance of X-B and Satellite Radar Data for Landslide Mapping and Monitoring: Upper Tena Valley Case Study. Natural Hazards and Earth System Science, 10(119): 1865-1875. https://doi.org/10.5194/nhess-10-1865-2010. [21] Notti, D., Herrera, G., Bianchini, S., et al., 2014. A Methodology for Improving Landslide PSI Data Analysis. International Journal of Remote Sensing, 35(6): 2186-2214. https://doi.org/10.1080/01431161.2014.889864. [22] Peng, J. B., Cui, P., Zhuang, J. Q., 2020. Challenges to Engineering Geology of Sichuan-Tibet Railway. Chinese Journal of Rock Mechanics and Engineering, 39(12): 2377-2389(in Chinese with English abstract). [23] Pan, G. T., Ren, F., Yin, F. G., et al., 2020. Key Zones of Oceanic Plate Geology and Sichuan-Tibet Railway Project. Earth Science, 45(7): 2293-2304(in Chinese with English abstract). [24] Sun, Q., Hu, J., Zhang, L., et al., 2016. Towards Slow-Moving Landslide Monitoring by Integrating Multi-Sensor InSAR Time Series Datasets: The Zhouqu Case Study, China. Remote Sensing, 8(11): 908. https://doi.org/10.3390/rs8110908. [25] Shi, G. L., Chen, Q., Liu, X. W., et al., 2020. Deformation Velocity Field in the Aspect Direction of an Ancient Landslide in Taoping Village Derived from Ascending and Descending Sentinel-1a Data. Journal of Engineering Geology(in press)(in Chinese with English abstract). [26] Wang, R. Y., 2015. The Study of Land Subsidence Monitoring Technology Based on SBAS-InSAR with High Resolution (Dissertion). China University of Geosciences, Beijing(in Chinese with English abstract). [27] Wang, Z. L., Liao, M. S., Zhang, L., et al., 2019. Detecting and Characterizing Deformations of the Left Bank Slope near the Jinping Hydropower Station with Time Series Sentinel-1 Data. Remote Sensing for Land & Resources, 31(2): 204-209(in Chinese with English abstract). [28] Xu, Y. D., Yao, L. K., 2017. Some Cognitions and Thinkings about the Specific Geo-Environmental Problems along the Sichuan-Tibet Railway. Journal of Railway Engineering Society, 34(1): 1-5, 59(in Chinese with English abstract) [29] Xiang, Q. W., Pan, J. P., Zhang, G. Z., et al., 2020. Monitoring and Analysis of Surface Deformation in the Zheduoshan Area if Sichuan-Tibet Railway Based in SBAS Technology. Engineering of Surveying and Mapping, 29(4): 48-54, 59(in Chinese with English abstract). [30] Zhang, H. P., Oskin, M. E., Jing, L. Z., et al., 2016. Pulsed Exhumation of Interior Eastern Tibet: Implications for Relief Generation Mechanisms and the Origin of High-Elevation Planation Surfaces. Earth and Planetary Science Letters, 449: 176-185. https://doi.org/10.1016/j.epsl.2016.05.048 [31] Zhang, L., Liao, M. S., Dong, J., et al., 2018. Early Detection of Landslide Hazards in Mountainous Areas of West China Using Time Series SAR Interferometry—A Case Study of Danba, Sichuan. Geomatics and Information Science of Wuhan University, 43(12): 2039-2049(in Chinese with English abstract). [32] Zhang, Y., 2018. Detecting Ground Deformation and Investigating Landslides Using InSAR Technique: Taking Middle Reach of Bailong River Basin as an Example (Dissertation). Lanzhou University, Lanzhou(in Chinese with English abstract). [33] Zhang, J. J., Gao, B., Liu, J. K., et al., 2021. Early Landslide Detection in the Lancangjiang Region along the Sichuan-Tibet Railway Based on SBAS-InSAR Technology. Geoscience, 35(1): 64-73(in Chinese with English abstract). [34] Zhu, J. J., Li, Z. W., Hu, J., 2017. Research Progress and Methods of InSAR for Deformation Monitoring. Acta Geodaetica et Cartographica Sinica, 46(10): 1717-1733(in Chinese with English abstract). [35] 陈婷, 2014. 昌都市八宿县白马地区温泉勘查及成因研究(硕士学位论文). 成都: 成都理工大学. [36] 戴可人, 铁永波, 许强, 等, 2020. 高山峡谷区滑坡灾害隐患InSAR早期识别——以雅砻江中段为例. 雷达学报, 9(3): 554-568. https://www.cnki.com.cn/Article/CJFDTOTAL-LDAX202003012.htm [37] 戴可人, 张乐乐, 宋闯, 等, 2021. 川藏交通廊道沿线Sentinel-1影像几何畸变与升降轨适宜性定量分析. 武汉大学学报(信息科学版), 46(10): 1450-1460. [38] 葛大庆, 戴可人, 郭兆成, 等, 2019. 重大地质灾害隐患早期识别中综合遥感应用的思考与建议. 武汉大学学报(信息科学版), 44(7): 949-956. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201907001.htm [39] 郭长宝, 张永双, 蒋良文, 等, 2017. 川藏交通廊道沿线及邻区环境工程地质问题概论. 现代地质, 31(5): 877-889. doi: 10.3969/j.issn.1000-8527.2017.05.001 [40] 李廷栋, 肖庆辉, 潘桂堂, 等, 2019. 关于发展洋板块地质学的思考. 地球科学, 44(5): 1441-1451. doi: 10.3799/dqkx.2019.970 [41] 李振洪, 宋闯, 余琛, 等, 2019. 卫星雷达遥感在滑坡灾害探测和监测中的应用: 挑战与对策. 武汉大学学报(信息科学版), 44(7): 967-979. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201907003.htm [42] 穆家雷, 2018. 基于SBAS-InSAR的深部硬石膏开采引起地表形变规律研究(硕士学位论文). 北京: 中国地质大学. [43] 彭建兵, 崔鹏, 庄建琦, 2020. 川藏交通廊道对工程地质提出的挑战岩石力学与工程学报, 39(12): 2377-2389. [44] 潘桂棠, 任飞, 尹福光, 等, 2020. 洋板块地质与川藏交通廊道工程地质关键区带. 地球科学, 45(7): 2293-2304. doi: 10.3799/dqkx.2020.070 [45] 石固林, 陈强, 刘先文, 等, 2020. 联合升降轨Sentinel-1A数据监测桃坪乡古滑坡沿坡向的形变速度场. 工程地质学报(待刊). [46] 王如意, 2015. 基于SBAS-InSAR的高分辨率地面沉降监测技术研究(硕士学位论文). 北京: 中国地质大学. [47] 王振林, 廖明生, 张路, 等, 2019. 基于时序Sentinel-1数据的锦屏水电站左岸边坡形变探测与特征分析. 国土资源遥感, 31(2): 204-209. https://www.cnki.com.cn/Article/CJFDTOTAL-GTYG201902029.htm [48] 许佑顶, 姚令侃, 2017. 川藏交通廊道沿线特殊环境地质问题的认识与思考. 铁道工程学报, 34(1): 1-5, 59. [49] 向淇文, 潘建平, 张广泽, 等, 2020. 基于SBAS技术的川藏交通廊道折多山地区地表形变监测与分析. 测绘工程, 29(4): 48-54, 59. [50] 朱建军, 李志伟, 胡俊, 2017. InSAR变形监测方法与研究进展. 测绘学报, 46(10): 1717-1733. doi: 10.11947/j.AGCS.2017.20170350 [51] 张路, 廖明生, 董杰, 等, 2018. 基于时间序列InSAR分析的西部山区滑坡灾害隐患早期识别——以四川丹巴为例. 武汉大学学报(信息科学版), 43(12): 2039-2049. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201812031.htm [52] 张毅, 2018. 基于InSAR技术的地表变形监测与滑坡早期识别研究: 以白龙江流域中游为例(博士学位论文). 兰州: 兰州大学. [53] 张佳佳, 高波, 刘建康, 等, 2021. 基于SBAS-InSAR技术的川藏交通廊道澜沧江段滑坡隐患早期识别. 现代地质, 35(1): 64-73. -
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