Identify Landslide Precursors from Time Series InSAR Results

Abstract

Landslides cause huge human and economic losses globally. Detecting landslide precursors is crucial for disaster prevention. The small baseline subset interferometric synthetic-aperture radar (SBAS-InSAR) has been a popular method for detecting landslide precursors. However, non-monotonic displacements in SBAS-InSAR results are pervasive, making it challenging to single out true landslide signals. By exploiting time series displacements derived by SBAS-InSAR, we proposed a method to identify moving landslides. The method calculates two indices (global/local change index) to rank monotonicity of the time series from the derived displacements. Using two thresholds of the proposed indices, more than 96% of background noises in displacement results can be removed. We also found that landslides on the east and west slopes are easier to detect than other slope aspects for the Sentinel-1 images. By repressing background noises, this method can serve as a convenient tool to detect landslide precursors in mountainous areas.

Keywords

Monotonously changing displacements / Moving landslides / SBAS-InSAR / Time series of deformation

References

[1] Bai SB, Wang J, Zhang ZG, Cheng C. Combined landslide susceptibility mapping after Wenchuan Earthquake at the Zhouqu segment in the Bailongjiang Basin, China. Catena, 2012, 99: 18-25

[2] Barra, A., L. Solari, M. Béjar-Pizarro, O. Monserrat, S. Bianchini, G. Herrera, M. Crosetto, R. Sarro, et al. 2017. A methodology to detect and update active deformation areas based on sentinel-1 SAR images. Remote Sensing 9(10): Article 1002.

[3] Bayer B, Schmidt D, Simoni A. The influence of external digital elevation models on PS-InSAR and SBAS results: Implications for the analysis of deformation signals caused by slow moving landslides in the northern Apennines (Italy). IEEE Transactions on Geoscience and Remote Sensing, 2017, 55: 2618-2631

[4] Berardino P, Fornaro G, Lanari R, Sansosti E. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40: 2375-2383

[5] Bian SQ, Chen G, Zeng RQ, Meng XM, Jin JC, Lin LX, Zhang Y, Shi W. Post-failure evolution analysis of an irrigation-induced loess landslide using multiple remote sensing approaches integrated with time-lapse ERT imaging: Lessons from Heifangtai, China. Landslides, 2022, 19: 1179-1197

[6] Bianchini S, Cigna F, Righini G, Proietti C, Casagli N. Landslide hotspot mapping by means of persistent scatterer interferometry. Environmental Earth Sciences, 2012, 67: 1155-1172

[7] Casu F, Manconi A. Four-dimensional surface evolution of active rifting from spaceborne SAR data. Geosphere, 2016, 12(3): 697-705

[8] Chen, Y., S.W. Yu, Q.X. Tao, G.L. Liu, L.Y. Wang, and F.Y. Wang. 2021. Accuracy verification and correction of D-InSAR and SBAS-InSAR in monitoring mining surface subsidence. Remote Sensing 13(21): Article 4365.

[9] Dai, C., W.L. Li, H.Y. Lu, and S. Zhang. 2023. Landslide hazard assessment method considering the deformation factor: A case study of Zhouqu, Gansu Province, Northwest China. Remote Sensing 15(3): Article 596.

[10] Dai K, Li ZH, Xu Q, Bürgmann R, Milledge DG, Tomas R, Fan XM, Zhao CY. Entering the era of earth observation-based landslide warning systems: A novel and exciting framework. IEEE Geoscience and Remote Sensing Magazine, 2020, 8(1): 136-153

[11] Fan XM, Xu Q, Alonso-Rodriguez A, Subramanian SS, Li WL, Zheng G, Dong XJ, Huang RQ. Successive landsliding and damming of the Jinsha River in eastern Tibet, China: Prime investigation, early warning, and emergency response. Landslides, 2019, 16: 1003-1020

[12] Froude MJ, Petley DN. Global fatal landslide occurrence from 2004 to 2016. Natural Hazards and Earth System Sciences, 2018, 18(8): 2161-2181

[13] Guéguen Y, Deffontaines B, Fruneau B, Al Heib M, de Michele M, Raucoules D, Guise Y, Planchenault J. Monitoring residual mining subsidence of Nord/Pas-de-Calais coal basin from differential and persistent scatterer interferometry (Northern France). Journal of Applied Geophysics, 2009, 69: 24-34

[14] Intrieri E, Raspini F, Fumagalli A, Lu P, Del Conte S, Farina P, Allievi J, Ferretti A, Casagli N. The Maoxian landslide as seen from space: Detecting precursors of failure with Sentinel-1 data. Landslides, 2018, 15: 123-133

[15] Lacroix P, Bièvre G, Pathier E, Kniess U, Jongmans D. Use of Sentinel-2 images for the detection of precursory motions before landslide failures. Remote Sensing of Environment, 2018, 215: 507-516

[16] Lanari R, Casu F, Manzo M, Zeni G, Berardino P, Manunta M, Pepe A. An overview of the small baseline subset algorithm: A DInSAR technique for surface deformation analysis. Deformation and Gravity Change: Indicators of Isostasy, Tectonics, Volcanism, and Climate Change, 2007, 164: 637-661.

[17] Leprince S, Barbot S, Ayoub F, Avouac J-P. Automatic and precise orthorectification, coregistration, and subpixel correlation of satellite images, application to ground deformation measurements. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(6): 1529-1558

[18] Li, M.H., L. Zhang, C. Ding, W.L. Li, H. Luo, M.S. Liao, and Q. Xu. 2020. Retrieval of historical surface displacements of the Baige landslide from time-series SAR observations for retrospective analysis of the collapse event. Remote Sensing of Environment 240: Article 111695.

[19] Lu P, Bai SB, Tofani V, Casagli N. Landslides detection through optimized hot spot analysis on persistent scatterers and distributed scatterers. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 156: 147-159

[20] Ouyang CJ, Zhao W, An HC, Zhou S, Wang DP, Xu Q, Li WL, Peng DL. Early identification and dynamic processes of ridge-top rockslides: implications from the Su Village landslide in Suichang County, Zhejiang Province, China. Landslides, 2019, 16: 799-813

[21] Pepe A, Lanari R. On the extension of the minimum cost flow algorithm for phase unwrapping of multitemporal differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(9): 2374-2383

[22] Petley D. Global patterns of loss of life from landslides. Geology, 2012, 40(10): 927-930

[23] Provost, F., D. Michéa, J.-P. Malet, E. Boissier, E. Pointal, A. Stumpf, F. Pacini, M.-P. Doin, et al. 2022. Terrain deformation measurements from optical satellite imagery: The MPIC-OPT processing services for geohazards monitoring. Remote Sensing of Environment 274: Article 112949.

[24] Qi WW, Yang WT, He XL, Xu C. Detecting Chamoli landslide precursors in the southern Himalayas using remote sensing data. Landslides, 2021, 18: 3449-3456

[25] Roy, P., T.R. Martha, K. Khanna, N. Jain, and K.V. Kumar. 2022. Time and path prediction of landslides using InSAR and flow model. Remote Sensing of Environment 271: Article 112899.

[26] Shi XG, Zhang L, Zhou C, Li MH, Liao MS. Retrieval of time series three-dimensional landslide surface displacements from multi-angular SAR observations. Landslides, 2018, 15: 1015-1027

[27] Solari L, Del Soldato M, Montalti R, Bianchini S, Raspini F, Thuegaz P, Bertolo D, Tofani V A Sentinel-1 based hot-spot analysis: Landslide mapping in north-western Italy. International Journal of Remote Sensing, 2019, 40(20): 7898-7921

[28] Strom AL, Korup O. Extremely large rockslides and rock avalanches in the Tien Shan Mountains, Kyrgyzstan. Landslides, 2006, 3: 125-136

[29] Stumpf A, Malet J-P, Delacourt C. Correlation of satellite image time-series for the detection and monitoring of slow-moving landslides. Remote Sensing of Environment, 2017, 189: 40-55

[30] Urgilez Vinueza A, Handwerger AL, Bakker M, Bogaard T. A new method to detect changes in displacement rates of slow-moving landslides using InSAR time series. Landslides, 2022, 19: 2233-2247

[31] Ventura G, Vilardo G, Terranova C, Sessa EB. Tracking and evolution of complex active landslides by multi-temporal airborne LiDAR data: The Montaguto landslide (southern Italy). Remote Sensing of Environment, 2011, 115(12): 3237-3248

[32] Wang GJ, Xie MW, Chai XQ, Wang LW, Dong CX. D-InSAR-based landslide location and monitoring at Wudongde hydropower reservoir in China. Environmental Earth Sciences, 2013, 69: 2763-2777

[33] Werner, C., U. Wegmuller, T. Strozzi, and A. Wiesmann. 2003. Interferometric point target analysis for deformation mapping. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 21–25 July 2003, Toulouse, France, 4362–4364.

[34] Xu, Y., Z. Lu, W.H. Schulz, and J. Kim. 2020. Twelve‐year dynamics and rainfall thresholds for alternating creep and rapid movement of the Hooskanaden landslide from integrating InSAR, pixel offset tracking, and borehole and hydrological measurements. Journal of Geophysical Research: Earth Surface 125(10): Article e2020JF005640.

[35] Yu G, Zhang M, Cong K, Pei L. Critical rainfall thresholds for debris flows in Sanyanyu, Zhouqu County, Gansu Province, China. Quarterly Journal of Engineering Geology and Hydrogeology, 2015, 48(3–4): 224-233

[36] Zhang Y, Meng XM, Jordan C, Novellino A, Dijkstra T, Chen G. Investigating slow-moving landslides in the Zhouqu region of China using InSAR time series. Landslides, 2018, 15: 1299-1315

[37] Zhang, J.M., W. Zhu, Y.Q. Cheng, and Z.H. Li. 2021. Landslide detection in the Linzhi-Ya’an section along the Sichuan-Tibet Railway based on InSAR and hot spot analysis methods. Remote Sensing 13(18): Article 3566.

[38] Zhang Y, Meng XM, Allesandro N, Tom D, Chen G, Colm J, Li YX, Su XJ. Characterization of pre-failure deformation and evolution of a large earthflow using InSAR monitoring and optical image interpretation. Landslides, 2021, 19: 35-50.

[39] Zhang CL, Li ZH, Yu C, Chen B, Ding MT, Zhu W, Yang J, Liu ZJ, Peng JB. An integrated framework for wide-area active landslide detection with InSAR observations and SAR pixel offsets. Landslides, 2022, 19: 2905-2923