Abstract
Detecting slope deformations before major landslides is crucially important for mitigating landslide disasters. In this work, we explored the use of Sentinel-2 images and an image correlation method, Co-Registration of Optically Sensed Images and Correlation (COSI-Corr), to derive slope deformations before the 2018 Baige landslide along the Jinsha River. We found that larger sliding windows outperform smaller ones in extracting the moving slope. Different solar zenith angles in image pairs have an influence on the derived displacements. Although the spatial resolution of Sentinel-2 images is 10 m, uncertainties in estimating slope deformation are found within 2 m. Using the COSI-Corr technique, we monitored sub-pixel slope deformation in Sentinel-2 images from November 2015 to August 2018. We found that the slope moved faster in summer than in winter months, which was probably driven by the monsoon climate in this region. From 2015 to 2018, the slope movement became faster, and an acceleration stage was observed before the slope failed completely. To derive reliable slope displacements, this work suggests testing different sliding window size combinations in the use of image correlation methods, and optical images acquired with similar zenith angles are preferred. This work also demonstrated the capability of using optical remote sensing images to monitor large slope deformations before major landslides. As more optical images with high spatial resolution become available, monitoring minor slope deformations directly would be practicable for large remote mountain regions.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bridges NT, Ayoub F, Avouac JP, Leprince S, Lucas A, Mattson S (2012) Earth-like sand fluxes on Mars. Nature 485:339–342. https://doi.org/10.1038/nature11022
Fan X, Xu Q, Alonso-Rodriguez A, Subramanian SS, Li W, Zheng G, Dong X, Huang R (2019) Successive landsliding and damming of the Jinsha river in Eastern Tibet, China: prime investigation, early warning, and emergency response. Landslides 16:1003–1020. https://doi.org/10.1007/s10346-019-01159-x
Frattini P, Crosta GB, Rossini M, Allievi J (2018) Activity and kinematic behaviour of deep-seated landslides from PS-InSAR displacement rate measurements. Landslides 15:1053–1070. https://doi.org/10.1007/s10346-017-0940-6
Gascon F, Bouzinac C, Thépaut O, Jung M, Francesconi B, Louis J, Lonjou V, Lafrance B, Massera S, Gaudel-Vacaresse A, Languille F, Alhammoud B, Viallefont F, Pflug B, Bieniarz J, Clerc S, Pessiot L, Trémas T, Cadau E, De Bonis R, Isola C, Martimort P, Fernandez V (2017) Copernicus Sentinel-2A calibration and products validation status. Remote Sens 9:584. https://doi.org/10.3390/rs9060584
Hu S, Qiu H, Wang X, Gao Y, Wang N, Wu J, Yang D, Cao M (2018) Acquiring high-resolution topography and performing spatial analysis of loess landslides by using low-cost UAVs. Landslides 15:593–612. https://doi.org/10.1007/s10346-017-0922-8
Intrieri E, Raspini F, Fumagalli A, Lu P, Del Conte S, Farina P, Allievi J, Ferretti A, Casagli N (2018) The Maoxian landslide as seen from space: detecting precursors of failure with Sentinel-1 data. Landslides 15:123–133. https://doi.org/10.1007/s10346-017-0915-7
Lacroix P, Bièvre G, Pathier E, Kniess U, Jongmans D (2018) Use of Sentinel-2 images for the detection of precursory motions before landslide failures. Remote Sens Environ 215:507–516. https://doi.org/10.1016/j.rse.2018.03.042
Lacroix P, Araujo GE, Hollingsworth J, Taipe E (2019) Self-entrainment motion of a slow-moving landslide inferred from Landsat-8 time series. J Geophys Res Earth Surf 124:1201–1216. https://doi.org/10.1029/2018JF004920
Leprince S, Barbot S, Ayoub F, Avouac J (2007) Automatic and precise orthorectification, coregistration, and subpixel correlation of satellite images, application to ground deformation measurements. IEEE Trans Geosci Remote Sens 45:1529–1558. https://doi.org/10.1109/TGRS.2006.888937
Lucieer A, de Jong SM, Turner D (2014) Mapping landslide displacements using structure from motion (SFM) and image correlation of multi-temporal UAV photography. Prog Phys Geogr 38:97–116. https://doi.org/10.1177/0309133313515293
Necsoiu M, Onaca A, Wigginton S, Urdea P (2016) Rock glacier dynamics in Southern Carpathian Mountains from high-resolution optical and multi-temporal SAR satellite imagery. Remote Sens Environ 177:21–36. https://doi.org/10.1016/j.rse.2016.02.025
Ouyang C, An H, Zhou S, Wang Z, Su P, Wang D, Cheng D, She J (2019) Insights from the failure and dynamic characteristics of two sequential landslides at Baige village along the Jinsha river, China. Landslides 16:1397–1414. https://doi.org/10.1007/s10346-019-01177-9
Petley D (2013) Global losses from landslides associated with dams and reservoirs. Proceedings of the international conference on Vajont–1963-2013–thought and analyses after, pp 63–72
Rosu AM, Pierrot-Deseilligny M, Delorme A, Binet R, Klinger Y (2015) Measurement of ground displacement from optical satellite image correlation using the free open-source software MicMac. ISPRS J Photogramm Remote Sens 100:48–59. https://doi.org/10.1016/j.isprsjprs.2014.03.002
Solari L, Raspini F, Del Soldato M, Bianchini S, Ciampalini A, Ferrigno F, Tucci S, Casagli N (2018) Satellite radar data for back-analyzing a landslide event: the Ponzano (Central Italy) case study. Landslides 15:773–782. https://doi.org/10.1007/s10346-018-0952-x
Turner D, Lucieer A, de Jong SM (2015) Time series analysis of landslide dynamics using an unmanned aerial vehicle (UAV). Remote Sens 7:1736–1757. https://doi.org/10.3390/rs70201736
Wasowski J, Bovenga F (2014) Investigating landslides and unstable slopes with satellite multi temporal interferometry: current issues and future perspectives. Eng Geol 174:103–138. https://doi.org/10.1016/j.enggeo.2014.03.003
Wei S, Fielding E, Leprince S, Sladen A, Avouac J-P, Helmberger D, Hauksson E, Chu R, Simons M, Hudnut K, Herring T, Briggs R (2011) Superficial simplicity of the 2010 El Mayor–Cucapah earthquake of Baja California in Mexico. Nat Geosci 4:615. https://doi.org/10.1038/ngeo1213
Yang W, Wang Y, Sun S, Wang Y, Ma C (2019) Using Sentinel-2 time series to detect slope movement before the Jinsha River landslide. Landslides 16:1313–1324. https://doi.org/10.1007/s10346-019-01178-8
Zhang Y, Meng X, Jordan C, Novellino A, Dijkstra T, Chen G (2018) Investigating slow-moving landslides in the Zhouqu region of China using InSAR time series. Landslides 15:1299–1315. https://doi.org/10.1007/s10346-018-0954-8
Acknowledgements
Wentao Yang would like to show his gratitude to his parents for caring for his little baby while this work was completed.
Funding
This work was jointly supported by the “Fundamental Research Funds for the Central Universities” (No. 2019ZY33) and the National Science Foundation of China (No. 41807500/41602348). We gratefully acknowledge the Beijing Municipal Education Commission for their financial support through Innovative Transdisciplinary Program "Ecological Restroration Engineering".
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yang, W., Wang, Y., Wang, Y. et al. Retrospective deformation of the Baige landslide using optical remote sensing images. Landslides 17, 659–668 (2020). https://doi.org/10.1007/s10346-019-01311-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10346-019-01311-7