Advertisement

Natural Hazards

, Volume 98, Issue 2, pp 485–505 | Cite as

Monitoring the regional deformation of loess landslides on the Heifangtai terrace using the Sentinel-1 time series interferometry technique

  • Qingkai MengEmail author
  • Qiang Xu
  • Baocun Wang
  • Weile Li
  • Ying Peng
  • Dalei Peng
  • Xing Qi
  • Dongdong Zhou
Original Paper
  • 130 Downloads

Abstract

The Heifangtai terrace of Gansu Province is a hotspot for loess landslide research, as massive and continuous landslides occur here every year. Detecting the spatial and temporal deformations of landslides and acquiring precursor information are very important for hazard prediction and risk management. In this paper, 51 newly launched Sentinel-1a scenes using the novel terrain observation with progressive scans (TOPS) mode from March 2015 to November 2017 are gathered, and a preprocessed chain of TOPS with the small baseline subset interferometric synthetic aperture radar technology is generated to obtain the deformation time-series. Our results show that (1) 44 active landslides with mean deformation velocities ranging from − 12.3 to − 58.57 mm yr−1 along the steepest slope, were detected and consisted of 18 loess-bedrock landslides, 12 loess flows, 7 loess flow-slides, and 7 loess slides; (2) four typical active regions and two potential risk places were recognized on the basis of high coherent point distribution, the average measured velocities along the steep slope and high-resolution orthographic images; (3) geological structures and special geomorphologies (e.g., cracks, sinkholes and concave gullies) can be mainly attributed to induce reactivity via long term irrigation. Finally, our research also demonstrates the potential ability of Sentinel-1 TOPS images to be applied to the monitoring of loess landslides, which is essential for risk mitigation and emergence management.

Keywords

Loess landslides Time-series analysis Sentinel-1 TOPS Heifangtai 

Notes

Acknowledgements

This research was supported by National Natural Science Foundation of China (41807290), Key R&D and transformation plan of Qinghai Province (2019-SF-130), the Open foundation of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology (SKLGP2017K019), the Open foundation of State Key Laboratory of Hydroscience and Engineering, Tsinghua university (SKLHSE-2017-A-03), The Natural Science Foundation of Qinghai Province (2017-ZJ-926Q), the Open foundation of State Key Lab of Plateau Ecology and Agriculture (2017-ZZ-01). We also thank for ESA support the Sentinel data and anonymous reviewers for their careful work in improving the quality of the paper.

References

  1. Achache J, Fruneau B, Delacourt C (1996) Applicability of sar interferometry for monitoring of landslides. ERS Appl 383(383):165Google Scholar
  2. Arnaud A, Adam N, Hanssen R et al (2003) ASAR ERS interferometric phase continuity. In: Proceedings international geoscience and remote sensing symposium, Toulouse, 21–25 July, pp 1133–1135.  https://doi.org/10.1109/IGARSS.2003.1294035
  3. Batuhan O, Filiz S, Shimon W, Enrique CC (2016) Time series analysis of insar data: methods and trends. ISPRS J Photogramm 115:90–102.  https://doi.org/10.1016/j.isprsjprs.2015.10.003 CrossRefGoogle Scholar
  4. Bianchini S, Cigna F, Righini G, Proietti C, Casagli N (2012) Landslide hotspot mapping by means of persistent scatterer interferometry. Environ Earth Sci 67(4):1155–1172.  https://doi.org/10.1007/s12665-012-1559-5 CrossRefGoogle Scholar
  5. Blanco-Sánchez P, Mallorquí JJ, Duque S et al (2008) The coherent pixels technique (CPT): an dced DInSAR technique for nonlinear deformation monitoring. Pure Appl Geophys 165(6):1167–1193.  https://doi.org/10.1007/s00024-008-0352-6 CrossRefGoogle Scholar
  6. Bouali EH, Oommen T, Escobar-Wolf R (2017) Mapping of slow landslides on the Palos Verdes Peninsula using the California landslide inventory and persistent scatterer interferometry. Landslides 9:1–14Google Scholar
  7. Cascini L, Fornaro G, Peduto D (2010) Advanced low- and full- resolution DInSAR map generation for slow-moving landslide analysis at different scales. Eng Geol 112:29–42.  https://doi.org/10.1016/j.enggeo.2010.01.003 CrossRefGoogle Scholar
  8. Cigna F, Bianchini S, Casagli N (2013) How to assess landslide activity and intensity with Persistent Scatterer Interferometry (PSI): the PSI-based matrix approach. Landslides 10:267–283.  https://doi.org/10.1007/s10346-012-0335-7 CrossRefGoogle Scholar
  9. Colesanti C, Wasowski J (2006) Investigating landslides with space-borne synthetic aperture radar (sar) interferometry. Eng Geol 88(3–4):173–199.  https://doi.org/10.1016/j.enggeo.2006.09.013 CrossRefGoogle Scholar
  10. Costantini M, Falco S, Malvarosa F, Minati F (2009). A new method for identification and analysis of persistent scatterers in series of SAR images. In: IEEE international geoscience and remote sensing symposium, 2008. IGARSS 2008, vol 2, pp II-449–II-452.  https://doi.org/10.1109/igarss.2008.4779025
  11. Derbyshire E (2011) Geological hazards in loess terrain, with particular reference to the loess regions of China. Earth-Sci Rev 54(1):231–260.  https://doi.org/10.1016/s0012-8252(01)00050-2 CrossRefGoogle Scholar
  12. Dijkstra TA, Rogers CDF, Smalley IJ, Derbyshire E, Li Y, Meng XM (1994) The loess of north-central china: geotechnical properties and their relation to slope stability. Eng Geol 36(3–4):153–171.  https://doi.org/10.1016/0013-7952(94)90001-9 CrossRefGoogle Scholar
  13. Feda J (1988) Collapse of loess upon wetting. Eng Geol 25(2–4):263–269.  https://doi.org/10.1016/0013-7952(88)90031-2 CrossRefGoogle Scholar
  14. Ferretti A, Prati C, Rocca F (2000) Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE Trans Geosci Remote 38(5):2202–2212.  https://doi.org/10.1109/36.868878 CrossRefGoogle Scholar
  15. Ferretti A, Prati C, Rocca F (2001) Permanent scatterers in SAR interferometry. IEEE Trans Geosci Remote 39(1):8–20.  https://doi.org/10.1109/36.898661 CrossRefGoogle Scholar
  16. Ferretti A, Novali F, Fumagalli A, Prati C, Rocca F, Rucci A (2009) Beyond PSInSAR: the SqueeSAR approach. In AGU fall meeting abstractsGoogle Scholar
  17. Fornaro G, Pauciullo A, Serafino F (2007) Multipass SAR processing for urbanized areas imaging and deformation monitoring at small and large scales. In: Proceedings of the urban remote sensing joint event. Paris 11–13 April, pp 1–7. http://dx.doi.org/10.1109/URS.2007.371879
  18. Frankowski Z (1994) Physico-mechanical properties of loess in poland (studied in situ). Quat Int 24:17–23.  https://doi.org/10.1016/1040-6182(94)90033-7 CrossRefGoogle Scholar
  19. Fruneau B, Achache J, Delacourt C (1996) Observation and modelling of the Saint-Etienne-de-Tinée landslide using SAR interferometry. Tectonophysics 265(3–4):181–190.  https://doi.org/10.1016/s0040-1951(96)00047-9 CrossRefGoogle Scholar
  20. Gao GR (1980) The loess collapsibility and microstructure classification. Sci China Math 23(12):1203–1208 (in Chinese) Google Scholar
  21. Geological Environmental Monitoring Institute of Gansu (2011) A feasibility study report of landslide management projects in Heifangtai, Yongjing. (in Chinese) Google Scholar
  22. Herrera G, Gutiérrez F, García-Davalillo JC et al (2013) Multi-sensor advanced DInSAR monitoring of very slow landslides: the Tena Valley case study (Central Spanish Pyrenees). Remote Sens Environ 128:31–43.  https://doi.org/10.1016/j.rse.2012.09.020 CrossRefGoogle Scholar
  23. Holzner J, Bamler R (2002) Burst-mode and ScanSAR interferometry. IEEE Trans Geosci Remote 40(9):1917–1934.  https://doi.org/10.1109/tgrs.2002.803848 CrossRefGoogle Scholar
  24. Hooper A, Zebker H, Segall P et al (2004) A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers. Geophys Res Lett 31(23):1–5.  https://doi.org/10.1029/2004GL021737 CrossRefGoogle Scholar
  25. Huang RQ (2007) Large-scale landslide and their sliding mechanisms in China since the 20th century. Chin J Rock Mech Eng 26(3):433–454.  https://doi.org/10.3321/j.issn:1000-6915.2007.03.001 (in Chinese) CrossRefGoogle Scholar
  26. Jin YL, Dai FC (2007) The mechanism of irrigation-induced landslides of loess. J Geotechn Eng 29(10):1493–1499 (in Chinese) Google Scholar
  27. Kampes BM, Adam N (2005) The STUN algorithm for persistent scatterer interferometry. Fringe Workshop 610(610):16Google Scholar
  28. Kampes BM, Hanssen RF (2004) Ambiguity resolution for permanent scatterer interferometry. IEEE Trans Geosci Remote 42(11):2446–2453.  https://doi.org/10.1109/tgrs.2004.835222 CrossRefGoogle Scholar
  29. Li PC, Liu JM, Wei XM et al (1999) Study on the mechanism and regulation of three types of water transformation. Shaanxi Science and Technology Press, Xi’an (in Chinese) Google Scholar
  30. Liu P, Li ZW, Hoey T, Kincal C, Zhang JF, Zeng QM, Muller JP (2013) Using advanced InSAR time series techniques to monitor landslide;movements in Badong of the Three Gorges region, China. Int J Appl Earth Obs 21(1):253–264.  https://doi.org/10.1016/j.jag.2011.10.010 CrossRefGoogle Scholar
  31. Meisina C, Zucca F, Notti D et al (2008) Geological interpretation of PSInSAR data at regional scale. Sensors 8(11):7469–7492.  https://doi.org/10.3390/s8117469 CrossRefGoogle Scholar
  32. Mora O, Lanari R, Mallorqui JJ, Berardino P, Sansosti E (2002) A new algorithm for monitoring localized deformation phenomena based on small baseline differential SAR interferograms. In: 2002 IEEE international geoscience and remote sensing symposium, 2002. IGARSS’02, vol 2, pp 1237–1239.  https://doi.org/10.1109/igarss.2002.1025900
  33. Mora O, Mallorqui JJ, Broquetas A et al (2003) Linear and nonlinear terrain deformation maps from a reduced set of interferometric SAR images. IEEE Trans Geosci Remote Sens 41:2243–2253.  https://doi.org/10.1109/TGRS.2003.814657 CrossRefGoogle Scholar
  34. Notti D, Davalillo JC, Herrera G, Mora O (2010) Assessment of the performance of X-band satellite radar data for landslide mapping and monitoring: upper Tena Valley case study. Nat Hazard Earth Syst 10(9):1865–1875.  https://doi.org/10.5194/nhess-10-1865-2010 CrossRefGoogle Scholar
  35. Notti D, Herrera G, Bianchini S et al (2014) A methodology for improving landslide PSI data analysis. Int J Remote Sens 35(6):2186–2214.  https://doi.org/10.1080/01431161.2014.889864 CrossRefGoogle Scholar
  36. Peng DL, Xu Q, Liu F et al (2017) Distribution and failure modes of the landslides in Heitai terrace, China. Eng Geol 236:97–110.  https://doi.org/10.1016/j.enggeo.2017.09.016 CrossRefGoogle Scholar
  37. Pepe A, Lanari R (2006) On the extension of the minimum cost flow algorithm for phase unwrapping of multitemporal differential SAR interferograms. IEEE Trans Geosci Remote 44(9):2374–2383.  https://doi.org/10.1109/tgrs.2006.873207 CrossRefGoogle Scholar
  38. Perissin D, Wang T (2012) Repeat-pass SAR interferometry with partially coherent targets. IEEE Trans Geosci Remote Sens 50(1):271–280CrossRefGoogle Scholar
  39. Plank S, Singer J, Minet C et al (2012) Pre-survey suitability evaluation of the differential synthetic aperture radar interferometry method for landslide monitoring. Int J Remote Sens 33(20):6623–6637CrossRefGoogle Scholar
  40. Prats-Iraola P, Nannini M, Scheiber R et al (2015) Sentinel-1 assessment of the interferometric wide-swath mode. In: Geoscience and remote sensing symposium. IEEE, pp 5247–5251.  https://doi.org/10.1109/IGARSS.2015.7327018
  41. Qi X, Xu Q, Liu FZ (2018) Analysis of retrogressive loess flowslides in Heifangtai, China. Eng Geol.  https://doi.org/10.1016/j.enggeo.2017.08.028 CrossRefGoogle Scholar
  42. Raspini F, Bianchini S, Ciampalini A et al (2018) Continuous, semi-automatic monitoring of ground deformation using Sentinel-1 satellites. Sci Rep 8:7253.  https://doi.org/10.1038/s41598-018-25369-w CrossRefGoogle Scholar
  43. Rott H, Siegel A (1999) Analysis of mass movements in alpine terrain by means of SAR interferometry. In IEEE 1999 international geoscience and remote sensing symposium, 1999. IGARSS’99 Proceedings, vol 4, pp 1933–1936.  https://doi.org/10.1109/igarss.1999.774991
  44. Scheiber R, Moreira A (2000) Coregistration of interferometric SAR images using spectral diversity. IEEE Trans Geosci Remote 38(5):2179–2191.  https://doi.org/10.1109/36.868876 CrossRefGoogle Scholar
  45. Scheiber R, Moreira A (2002) Improving co-registration accuracy of interferometric SAR images using spectral diversity. In IEEE 1999 international geoscience and remote sensing symposium, 1999. IGARSS’99 Proceedings, vol 3, pp 1709–1711.  https://doi.org/10.1109/igarss.1999.772069
  46. Sousa JJ, Hooper AJ, Hanssen RF, Bastos LC, Ruiz AM (2011) Persistent scatterer InSAR: a comparison of methodologies based on a model of temporal deformation vs. spatial correlation selection criteria. Remote Sens Environ 115(10):2652–2663.  https://doi.org/10.1016/j.rse.2011.05.021 CrossRefGoogle Scholar
  47. Sowter A, Amat MBC, Cigna F, Marsh S, Athab A, Alshammari L (2016) Mexico City land subsidence in 2014–2015 with Sentinel-1 IW TOPS: results using the Intermittent SBAS (ISBAS) technique. Int J Appl Earth Obs 52:230–242.  https://doi.org/10.1016/j.jag.2016.06.015 CrossRefGoogle Scholar
  48. Strozzi T, Delaloye R, Kääb A et al (2010) Combined observations of rock mass movements using satellite SAR interferometry, differential GPS, airborne digital photogrammetry, and airborne photography interpretation. J Geophys Res: Earth Surf 115(F1):F01014.  https://doi.org/10.1029/2009jf001311 CrossRefGoogle Scholar
  49. Tantianuparp P, Shi XG, Zhang L, Balz T, Liao MS (2013) Characterization of landslide deformations in Three Gorges area using multiple InSAR data stacks. Remote Sens 5(6):2704–2719.  https://doi.org/10.3390/rs5062704 CrossRefGoogle Scholar
  50. Tomás R, Li Z, Liu P et al (2014) Spatiotemporal characteristics of the Huangtupo landslide in the Three Gorges region (China) constrained by radar interferometry. Geophys J Int 197(1):213–232.  https://doi.org/10.1093/gji/ggu017 CrossRefGoogle Scholar
  51. Torres R, Snoeij P, Geudtner D et al (2012) GMES Sentinel-1 mission. Remote Sens Environ 120:9–24.  https://doi.org/10.1016/j.rse.2011.05.028 CrossRefGoogle Scholar
  52. Wang ZQ, Wu WJ, Zhou ZQ (2004) Landslide induced by over-irrigation in loess platform areas in Gansu Province. Chin J Geol Hazard Control 15(3):43–47 (in Chinese) Google Scholar
  53. Wegnüller U, Werner C, Strozzi T et al (2006) EAS-ASAR integration in the interferometric point target analysis. Fringe Workshop 610(10):19Google Scholar
  54. Wegnüller U, Werner C, Strozzi T et al (2016) Sentinel-1 support in the GAMMA software. Procedia Comput Sci 100:1305–1312.  https://doi.org/10.1016/j.procs.2016.09.246 CrossRefGoogle Scholar
  55. Wen Y, Xu C, Liu Y et al (2016) Deformation and source parameters of the 2015 Mw 6.5 earthquake in Pishan, Western China, from Sentinel-1A and ALOS-2 data. Remote Sens 8(2):134.  https://doi.org/10.3390/rs8020134 CrossRefGoogle Scholar
  56. Wu CX, Xu L, Dai CF et al (2011) Topagraphic features and initiation of earth flows on Heifangtai loess plateau. Rock Soil Mech 32(6):1767–1773 (in Chinese) Google Scholar
  57. Xu L, Dai FC, Tham LG et al (2011a) Landslides in the transitional slopes between a loess platform and river terrace, Northwest China. Environ Eng Geosci 17(3):267–279CrossRefGoogle Scholar
  58. Xu L, Dai FC, Tham LG et al (2011b) Field testing of irrigation effects on the stability of a cliff edge in loess, North-west China. Eng Geol 120(1):10–17CrossRefGoogle Scholar
  59. Xu L, Gong QM, Tham LG et al (2012) Irrigation-induced loess flow failure in Heifangtai Platform, North-West China. Environ Earth Sci 66(6):1707–1713.  https://doi.org/10.1007/s12665-011-0950-y CrossRefGoogle Scholar
  60. Xu Q, Peng DL, Qi X et al (2016) DangChuan2# landslide of April 29 2015 in HeiFangTai Area of GanSu Province: characteristices and failure mechanism. J Eng Geol 24(2):167–180 (in Chinese) Google Scholar
  61. Yague-Martinez N, Zan FD, Prats-Iraola P (2017) Coregistration of Interferometric Stacks of Sentinel-1 TOPS Data. IEEE Geosci Remote Sens 14(7):1002–1006.  https://doi.org/10.1109/lgrs.2017.2691398 CrossRefGoogle Scholar
  62. Yan Y, Doin MP, Lopez-Quiroz P, Tupin F, Fruneau B, Pinel V, Trouvé E (2012) Mexico city subsidence measured by InSAR time series: joint analysis using PS and SBAS approaches. IEEE J Sel Top Appl Earth Obs Remote Sens 5(4):1312–1326.  https://doi.org/10.1109/jstars.2012.2191146 CrossRefGoogle Scholar
  63. Zan FD, Prats-Iraola P, Scheiber R et al (2014) Interferometry with TOPS: coregistration and azimuth shifts. In: 10th European conference on synthetic aperture radar, pp 949–952Google Scholar
  64. Zeng RQ, Meng XM, Zhang FY, Wang SY, Cui ZJ, Zhang MS, Chen G (2016) Characterizing hydrological processes on loess slopes using electrical resistivity tomography—a case study of the Heifangtai Terrace, Northwest China. J Hydrol 541:742–753.  https://doi.org/10.1016/j.jhydrol.2016.07.033 CrossRefGoogle Scholar
  65. Zhang FY, Wang GH (2017) Effect of irrigation-induced densification on the post-failure behavior of loess flowslides occurring on the Heifangtai area, Gansu, China. Eng Geol 236:111–118.  https://doi.org/10.1016/j.enggeo.2017.07.010 CrossRefGoogle Scholar
  66. Zhang FY, Wang GH, Kamai T et al (2013) Undrained shear behavior of loess saturated at different concentrations of sodium chlorate solution. Eng Geol 155:69–79.  https://doi.org/10.1016/j.enggeo.2012.12.018 CrossRefGoogle Scholar
  67. Zhao C, Lu Z, Zhang Q et al (2012) Large-area landslide detection and monitoring with ALOS/PALSAR imagery data over Northern California and Southern Oregon, USA. Remote Sens Environ 124(9):348–359.  https://doi.org/10.1016/j.rse.2012.05.025 CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.State Key Lab of Plateau Ecology and AgricultureQinghai UniversityXiningChina
  2. 2.State Key Laboratory of Geohazard Prevention and Geoenvironment ProtectionChengdu University of TechnologyChengduChina
  3. 3.Institute of Surveying, Mapping and Geoinformation of Henan Provincial Bureau of Geo-exploration and Mineral DevelopmentZhengzhouChina
  4. 4.The College of Nuckear Technology and Automation EngineeringChengdu Univeristy of TechnologyChengduChina
  5. 5.Department of Geo-engineerQinghai UniversityXiningChina

Personalised recommendations