Sub-pixel correlation-based slope monitoring using multi-temporal optical remote sensing images: a case study of Xiluodu hydropower station

doi:10.13474/j.cnki.11-2246.2024.0107

Abstract

Abstract: The monitoring of high slope in hydropower plant is a key issue in disaster prevention and control of hydropower plant. The use of satellite remote sensing technology for reservoir landslide monitoring is an important way to solve the problem. The use of subpixel correlation algorithm to obtain the ground deformation displacement field can overcome the phase decorrelation factors in SAR images, and has great potential application in the monitoring of hydropower station slopes. In this paper, Google Earth images from 2015 to 2019 and Sentinel-2 images from 2019 to 2022 were used as data sources, and the slope deformation variables were calculated by the phase correlation algorithm. The trend error such as orbit error was removed by constructing a first-order polynomial surface fitting model, and the slope deformation values of the downstream slope of Xiluodu Station in 2016, 2017, and 2019 were obtained. The analysis showed that the slope deformation values obtained from two images had the same trend, and were in good agreement with the measured data of valley width. The results of this paper verified the feasibility of applying the subpixel correlation matching technique, based on multi-temporal remote sensing data, for monitoring the slope deformation of large hydropower plants.

Key words: slope monitoring, optical remote sensing images, subpixel correlation, trend errors

CLC Number:

P237

YE Jiang, LI Caiyi, GAO Hongqi, XU Weihong, XIANG Nansong. Sub-pixel correlation-based slope monitoring using multi-temporal optical remote sensing images: a case study of Xiluodu hydropower station[J]. Bulletin of Surveying and Mapping, 2024, 0(1): 38-43,108.

References

[1] BICKEL V,MANCONI A,AMANN F. Quantitative assessment of digital image correlation methods to detect and monitor surface displacements of large slope instabilities[J]. Remote Sensing,2018,10(6)13.
[2] 李晓恩,周亮,苏奋振,等. InSAR技术在滑坡灾害中的应用研究进展[J]. 遥感学报,2021,25(2): 614-629.
[3] LEPRINCE S,BARBOT S,AYOUB F,et al. Automatic and precise orthorectification,coregi stration,and subpizel correlation of satellite images,application to ground deformati on measurements[J]. IEEE Transactions on Geosaience and Remote Sensing,2007,45(6): 1529-1558.
[4] DAS S,SHARMA M C. Glacier surface velocities in the Jankar Chhu Watershed,western Himalaya,India: Study using Landsat time series data (1992—2020)[J]. Remote Sensing Applications: Society and Environment,2021,24(80):100615.
[5] ZHENG Z J,DU S,TAUBENBOCK H,et al. Remote sensing techniques in the investigation of aeolian sand dunes: a review of recent advances[J]. Remote Sensing of Environment,2022,271:112913.
[6] AHMAD N,SHAFIQUE M,HUSSAIN M L. Landslide assessment by using multi-temporal UAV datasets: a case study in northern Pakistan[J]. Arabian Journal of Geosciences,2021,14(18):1855.
[7] 黄俊. 改进量子粒子群算法优化的SVM边坡形变预测研究[D]. 赣州: 江西理工大学,2020.
[8] 柳志丹. 四川金沙江溪洛渡水电站边坡治理技术[J]. 黄河水利职业技术学院学报,2014,26(1):5-8.
[9] 方俊睿. 基于Google Earth的陆家嘴城市高度信息快速提取和历史变化分析[D]. 上海: 上海师范大学,2019.
[10] TÉLLEZ-VELÁZQUEZ A,CRUZ-BARBOSA R. On the feasibility of fast fourier transform separability property for distributed image processing[J]. Scientific Programming,2021:1-8.
[11] 宋豪峰. 基于光学和SAR影像偏移量估计的区域滑坡时序形变监测和应用[D].广州:广州大学,2021.
[12] 彭颖,李鹏. 融合光学与SAR遥感影像的同震地表三维形变监测适用性研究[J]. 测绘与空间地理信息,2019,42(10): 86-89.
[13] JIANG X,YU J,JIANG J. Research on feature point generation and matching method optimization in image matching algorithm[J/OL]. Wireless Networks.2021-07-19[2023-03-10].https://link.springer.com/article/10.1007/s11276-021-02688-x?utm_source=xmol&utm_content=meta.
[14] ANA M R,MARC P D,ARTHUR D,et al. Measurement of ground displacement from optical satellite image correlation using the free open-source software MicMac[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2015,100:48-59.
[15] 朱建军,李志伟,胡俊. InSAR形变监测方法与研究进展[J]. 测绘学报,2017,46(10): 1717-1733.
[16] CAPOROSSI D,MAZZANTI P,BOZZANO F. Digital image vorrelation (DIC) snalysis of the 3 December 2013 Montescaglioso landslide (Basilicata,southern Italy): results from a multi-dataset investigation[J]. ISPRS International Journal of Geo-Information,2018,7(9):372.