Skip to main content
SpringerLink
Log in

Application of Radar Polarimetry to Monitor Changes in Backscattering Mechanisms in Landslide Zones Using the Example of the Collapse of the Bureya River Bank

  • PHYSICAL BASES AND METHODS OF STUDYING THE EARTH FROM SPACE
  • Published:
Izvestiya, Atmospheric and Oceanic Physics Aims and scope Submit manuscript

Abstract

Possibilities of using radar polarimetry methods for identifying landslide zones are analyzed. The transformation of the dominant mechanism of signal scattering by the reflecting surface was used as a key feature of landslide zones. The polarimetric data from the PALSAR-2 radar of the ALOS-2 satellite are processed using the Freeman–Durden and Cloude–Pottier decompositions at four test sites selected in the region of the landslide caused by the collapse of the bank of the Bureya River. It is found that the results of decompositions are consistent with each other; however, in some areas there are significant differences due to the specific features of the basic model assumptions. It is shown that, before the descent of the landslide masses, three main mechanisms of radar signal scattering existed in the analyzed region: single surface, volumetric, and double scattering. After the collapse, this area was dominated by single scattering characteristic of the reflective surface with large-scale irregularities free of vegetation, due to which the landslide descent zone can be confidently recognized. The significant potential of using radar polarimetry for remote diagnostics of the consequences of landslide phenomena has been demonstrated.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

Similar content being viewed by others

REFERENCES

  1. Akopian, S.Ts., Bondur, V.G., and Rogozhin, E.A., Technology for monitoring and forecasting strong earthquakes in Russia with the use of the seismic entropy method, Izv., Phys. Solid Earth, 2017, vol. 53, no. 1, pp. 32–51. https://doi.org/10.1134/S1069351317010025

    Article  Google Scholar 

  2. Bondur, V.G., Zakharova, L.N., Zakharov, A.I., et al., Long-term monitoring of the landslide process on Bureya riverbank based on interferometric L-band radar data, Sovr. Probl. Dist. Zond. Zemli Kosm. 2019, vol. 16, no. 5, pp. 113–119. https://doi.org/10.21046/2070-7401-2019-16-5-113-119

  3. Bondur, V.G., Chimitdorzhiev, T.N., Dmitriev, A.V., and Dagurov, P.N., Spatial anisotropy assessment of the forest vegetation heterogeneity at different azimuth angles of radar polarimetric sensing, Izv., Atmos. Oceanic Phys., 2019, vol. 55, no. 9, pp. 926–934.https://doi.org/10.1134/S0001433819090093

  4. Bondur, V.G. and Starchenkov, S.A., Methods and programs for aerospace imagery processing and classification, Izv. Vyssh. Uchebn. Zaved., Geod. Aerofotos’emka, 2001, no. 3, pp. 118–143.

  5. Bondur, V.G. and Smirnov, V.M., Method for monitoring seismically hazardous territories by ionospheric variations recorded by satellite navigation systems, Dokl., Earth Sci., 2005, vol. 403, no. 5, pp. 736–740.

    Google Scholar 

  6. Bondur, V.G. and Zverev, A.T., A method of earthquake forecast based on the lineament analysis of satellite images, Dokl., Earth Sci., 2005, vol. 402, no. 4, pp. 561–567.

    Google Scholar 

  7. Bondur, V.G. and Zverev, A.T., Mechanisms underlying the formation of lineament systems registered in space images during the monitoring of seismic danger areas, Issled. Zemli Kosmosa, 2007, no. 1, pp. 47–56.

  8. Bondur, V.G., Garagash, I.A., Gokhberg, M.B., Lapshin, V.M., Nechaev, Yu.V., Steblov, G.M., and Shalimov, S.L., Geomechanical models and ionospheric variations related to strongest earthquakes and weak influence of atmospheric pressure gradients, Dokl., Earth Sci., 2007, vol. 414, no. 4, pp. 666–669.

    Article  Google Scholar 

  9. Bondur, V.G., Pulinets, S.A., and Kim, G.A., Role of variations in galactic cosmic rays in tropical cyclogenesis: evidence of Hurricane Katrina, Dokl., Earth Sci., 2008a, vol. 422, no. 7, pp. 1124–1128. https://doi.org/10.1134/S1028334X08070283

    Article  Google Scholar 

  10. Bondur, V.G. and Chimitdorzhiev, T.N., Texture analysis of radar images of vegetation, Izv. Vyssh. Uchebn. Zaved., Geod. Aerofotos’emka, 2008a, no. 5, pp. 9–14.

  11. Bondur, V.G. and Chimitdorzhiev, T.N., Remote sensing of vegetation by optical microwave methods, Izv. Vyssh. Uchebn. Zaved., Geod. Aerofotos’emka, 2008b, no. 6, pp. 64–73.

  12. Bondur, V.G., Pulinets, S.A., and Uzunov, D., Ionospheric effect of large-scale atmospheric vortex by the example of hurricane Katrina, Issled. Zemli. Kosmosa, 2008b, no. 6, pp. 3–11.

  13. Bondur, V.G., Krapivin, V.F., and Savinykh, V.P., Monitoring i prognozirovanie prirodnykh katastrof (Monitoring and Forecasting of Natural Disasters), Moscow: Nauchnyi mir, 2009.

  14. Bondur, V.G., Garagash, I.A., Gokhberg, M.B., Lapshin, V.M., and Nechaev, Yu.V., Connection between variations of the stress-strain state of the Earth’s crust and seismic activity: the example of Southern California, Dokl., Earth Sci., 2010, vol. 430, no. 1, pp. 147–150. https://doi.org/10.1134/S1028334X10010320

    Article  Google Scholar 

  15. Bondur, V.G., Aerospace methods and technologies for monitoring oil and gas areas and facilities, Izv., Atmos. Oceanic Phys., 2011, vol. 47, no. 9, pp. 1007–1018. https://doi.org/10.1134/S0001433811090039

    Article  Google Scholar 

  16. Bondur, V.G., Krapivin, V.F., Potapov, I.I., and Soldatov, V.Ju., Natural disasters and the environment, Probl. Okr. Sredy Prir. Resur., 2012, no. 1, pp. 3–160.

  17. Bondur, V.G., Garagash, I.A., and Gokhberg, M.B., Large scale interaction of seismically active tectonic provinces: the example of Southern California, Dokl., Earth Sci., 2016a, vol. 466, no. 2, pp. 183–186. https://doi.org/10.1134/S1028334X16020100

    Article  Google Scholar 

  18. Bondur, V.G., Garagash, I.A., Gokhberg, M.B., and Rodkin, M.V., The evolution of the stress state in Southern California based on the geomechanical model and current seismicity, Izv., Phys. Solid Earth, 2016b, vol. 52, no. 1, pp. 117–128. https://doi.org/10.1134/S1069351316010043

    Article  Google Scholar 

  19. Chimitdorzhiev, T.N., Dagurov, P.N., Bykov, M.E., Dmitriev, A.V., and Kirbizhekova, I.I., Comparison of ALOS PALSAR interferometry and field geodetic leveling for marshy soil thaw/freeze monitoring, case study from the Baikal lake region, Russia, J. Appl. Remote Sens., 2016, vol. 10, no. 1, pp. 016006–1–016006–12. https://doi.org/10.1117/1.JRS.10.016006

    Article  Google Scholar 

  20. Cloude, S.R. and Pottier, E., A review of target decomposition theorems in radar polarimetry, IEEE Trans. Geosci. Remote Sens., 1996, vol. 34, no. 2, pp. 498–518. https://doi.org/10.1109/36.485127

    Article  Google Scholar 

  21. Cloude, S.R. and Pottier, E., An entropy based classification scheme for land applications of polarimetric SAR, IEEE Trans. Geosci. Remote Sens., 1997, vol. 35, no. 1, pp. 68–78. https://doi.org/10.1109/36.551935

    Article  Google Scholar 

  22. Cloude, S.R., Polarisation: Applications in Remote Sensing, New York: Oxford University Press, 2010.

    Google Scholar 

  23. Czuchlewski, K.R., Weissel, J.K., and Kim, Y., Polarimetric synthetic aperture radar study of the Tsaoling landslide generated by the 1999 Chi-Chi earthquake, Taiwan, J. Geophys. Res., 2003, vol. 108, no. F1, p. 6006. https://doi.org/10.1029/2003JF000037

    Article  Google Scholar 

  24. Dmitriev, A.V., Chimitdorzhiev, T.N., Gusev, M.A., et al., Basic products of Earth imaging by space radars with synthesized aperture, Issled. Zemli. Kosmosa, 2014, no. 5, pp. 83–91.

  25. Dubina, B.A., Shamov, B.B., and Plotnikov, B.B., Catastrophic flood in Primorye in August 2018, Sovr. Probl. Dist. Zond. Zemli Kosmosa, 2018, vol. 15, no. 5, pp. 253–256.

    Article  Google Scholar 

  26. Ferro-Famil, L., Pottier, E., and Jong-Sen, L., Unsupervised classification of multifrequency and fully polarimetric SAR images based on the H/A/Alpha-Wishart classifier, IEEE Trans. Geosci. Remote Sens., 2001, vol. 39, no. 11, pp. 2332–2342. https://doi.org/10.1109/36.964969

    Article  Google Scholar 

  27. Freeman, A. and Durden, S.L., A three-component scattering model for polarimetric SAR data, IEEE Trans. Geosci. Remote Sens., 1998, vol. 36, no. 3, pp. 963–973. https://doi.org/10.1109/36.673687

    Article  Google Scholar 

  28. Kramareva, L.S., Lupjan, E.A., Amel’chenko, Ju.A., et al., Observation of the hill collapse zone near the Bureya River on December 11, 2018, Sovr. Probl. Dist. Zond. Zemli Kosmosa, 2018, vol. 15, no. 7, pp. 266–271. http://omdoki.nextgis.com/resource/103/display

  29. Lee, J.S., Grunes, M.R., and de Grandi, G., Polarimetric SAR speckle filtering and its implication for classification, IEEE Trans. Geosci. Remote Sens., 1999, vol. 37, no. 5, pp. 2363–2373. https://doi.org/10.1109/36.789635

    Article  Google Scholar 

  30. Lee, J.-S. and Pottier, E., Polarimetric Radar Imaging: from Basics to Applications, New York: CRC Press, 2009.

    Book  Google Scholar 

  31. Li, N., Wang, R., Deng, Y., Liu, Y., Li, B., Wang, C., and Balzc, T., Unsupervised polarimetric synthetic aperture radar classification of large-scale landslides caused by Wenchuan earthquake in hue-saturation-intensity color space, J. Appl. Remote Sens., 2014a, vol. 8, no. 1, pp. 083595-1–083595-8. https://doi.org/10.1117/1.JRS.8.083595

    Article  Google Scholar 

  32. Li, N., Wang, R., Deng, Y., Liu, Y., Wang, C., Balz, T., and Li, B., Polarimetric response of landslides at X-band following the Wenchuan earthquake, IEEE Geosci. Remote Sens. Lett., 2014b, vol. 11, no. 10, pp. 1722–1726. https://doi.org/10.1109/LGRS.2014.2306820

    Article  Google Scholar 

  33. Luo, S., Tong, L., Chen, Y., and Tan, L., Landslides identification based on polarimetric decomposition techniques using radarsat-2 polarimetric images, Int. J. Remote Sens., 2016, vol. 37, no. 12, pp. 2831–2843. https://doi.org/10.1080/01431161.2015.1041620

    Article  Google Scholar 

  34. Mihajlov, V.O., Kiseleva, E.A., Smol’janinova, E.I., et al., Some problems of landslide processes monitoring using satellite radar images with different wavelengths on the example of two landslides in the Great Sochi, Fiz. Zemli, 2014, no. 4, pp. 120–130.

  35. Plank, S., Twele, A., and Martinis, S., Landslide mapping in vegetated areas using change detection based on optical and polarimetric SAR data, Remote Sens., 2016, vol. 8, no. 4, p. 307. https://earth.esa.int/web/polsarprohttps://doi.org/10.3390/rs8040307PolSARpro

    Article  Google Scholar 

  36. Shibayama, T., Yamaguchi, Y., and Yamada, H., Polarimetric scattering properties of landslides in forested areas and the dependence on the local incidence angle, Remote Sens., 2015, vol. 7, no. 11, pp. 15424–15442. https://doi.org/10.3390/rs71115424

    Article  Google Scholar 

  37. Shimada, M., Watanabe, M., Kawano, N., Ohki, M., Motooka, T., and Wada, Y., Detecting mountainous landslides by sar polarimetry: a comparative study using Pi-SAR-L2 and X-band SARs, Trans. JSASS Aerospace Tech. Jpn., 2014, vol. 12, no. 29, pp. 9–15. https://doi.org/10.2322/tastj.12.Pn_9

    Article  Google Scholar 

  38. Shirzaei, M., Burgmann, R., and Fielding, E.J., Applicability of Sentinel-1 Terrain Observation by Progressive Scans multitemporal interferometry for monitoring slow ground motions in the San Francisco Bay Area, Geophys. Res. Lett., 2017, no. 44, pp. 2733–2742. https://doi.org/10.1002/2017GL072663

  39. Verba, V.S., Neronskii, L.B., Osipov, I.G., and Turuk, V.E., Radiolokatsionnye sistemy zemleobzora kosmicheskogo bazirovaniya (Space-Based Radar Systems for Earth Observation), Moscow: Radiotekhnika, 2010.

  40. Wang, C., Mao, X., and Wang, Q., Landslide displacement monitoring by a fully polarimetric SAR offset tracking method, Remote Sens., 2016, vol. 8, no. 8, p. 624. https://doi.org/10.3390/rs8080624

    Article  Google Scholar 

  41. Watanabe, M., Yonezawa, C., Iisaka, J., and Sato, M., ALOS/PALSAR full polarimetric observations of the Iwate–Miyagi Nairiku earthquake of 2008, Int. J. Remote Sens., 2012, vol. 33, no. 4, pp. 1234–1245. https://doi.org/10.1080/01431161.2011.554453

    Article  Google Scholar 

  42. Watanabe, M., Thapa, R.B., and Shimada, M., Pi-SAR-L2 observation of the landslide caused by typhoon Wipha on Izu Oshima Island, Remote Sens., 2016, vol. 8, no. 4, p. 282. https://doi.org/10.3390/rs8040282

    Article  Google Scholar 

  43. Yamaguchi, Y., et al., Four-component scattering model for polarimetric SAR image decomposition, IEEE Trans. Geosci. Remote Sens., 2005, vol. 43, no. 8, pp. 1699–1706. https://doi.org/10.1109/TGRS.2005.852084

    Article  Google Scholar 

  44. Yonezawa, C., Watanabe, M., and Saito, G., Polarimetric decomposition analysis of alos palsar observation data before and after a landslide event, Remote Sens., 2012, vol. 4, no. 8, pp. 2314–2328. https://doi.org/10.3390/rs4082314

    Article  Google Scholar 

  45. Zakharov, A.I., Yakovlev, O.I., and Smirnov, V.M., Sputnikovyi monitoring Zemli: Radiolokatsionnoe zondirovanie poverhnosti (Satellite Monitoring of the Earth: Radar Imaging of the Surface, Moscow: Librokom, 2012.

  46. Zakharov, A.I., Zakharova, L.N., and Krasnogorskij, M.G., Landslide activity monitoring by radar interferometry methods using trihedral corner reflectors, Issled. Zemli Kosmosa, 2018, no. 3, pp. 80–92.

  47. Zakharova, L.N., Zakharov, A.I., and Mitnik, L.M., First results of the assessment of the landslide consequences on the Bureya River bank using Sentinel-1 radar data, Sovr. Probl. Dist. Zond. Zemli Kosm., 2019, vol. 16, no. 2, pp. 69–74.

    Article  Google Scholar 

  48. Zakharova, L.N. and Zakharov, A.I., Interferometric observation of landslide area dynamics on the Bureya River by means of Sentinel-1 radar data in 2017–2018, Sovr. Probl. Dist. Zond. Zemli Kosm., 2019, vol. 16, no. 2, pp. 273–277.

    Article  Google Scholar 

Download references

ACKNOWLEDGMENTS

We are grateful to the Japan Aerospace Exploration Agency (JAXA) for the ALOS‑2 PALSAR‑2 radar data provided within the ALOS‑2 RA‑6 projects (PI 3402 and PI 3092).

Funding

This work was performed as part of the framework of state assignments of the Institute of Physical Materials Science, Siberian Branch, Russian Academy of Sciences; the Kotelnikov Institute of Radioengineering and Electronics, Russian Academy of Sciences; and AEROCOSMOS Research Institute for Aerospace Monitoring.

Author information

Authors and Affiliations

  1. AEROCOSMOS Research Institute for Aerospace Monitoring, Moscow, Russia

    V. G. Bondur

  2. Institute of Physical Materials Science, Siberian Branch, Russian Academy of Sciences, Ulan-Ude, Russia

    T. N. Chimitdorzhiev, A. V. Dmitriev & P. N. Dagurov

  3. Kotelnikov Institute of Radioengineering and Electronics, Russian Academy of Sciences, Fryazino Branch, Fryazino, Russia

    A. I. Zakharov & L. N. Zakharova

Authors
  1. V. G. Bondur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. T. N. Chimitdorzhiev
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. V. Dmitriev
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. P. N. Dagurov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. A. I. Zakharov
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. L. N. Zakharova
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to V. G. Bondur, T. N. Chimitdorzhiev or L. N. Zakharova.

Additional information

Translated by E. Morozov

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bondur, V.G., Chimitdorzhiev, T.N., Dmitriev, A.V. et al. Application of Radar Polarimetry to Monitor Changes in Backscattering Mechanisms in Landslide Zones Using the Example of the Collapse of the Bureya River Bank. Izv. Atmos. Ocean. Phys. 56, 916–926 (2020). https://doi.org/10.1134/S0001433820090054

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0001433820090054

Keywords:

Search

Navigation