Skip to main content
SpringerLink
Log in

FIM-based DSInSAR method for mapping and monitoring of reservoir bank landslides: an application along the Lancang River in China

  • Technical Note
  • Published:
Landslides Aims and scope Submit manuscript

Abstract

The conventional multi-temporal InSAR (MT-InSAR) technology suffers from the problems of low spatial density of monitoring points (MPs) and poor interferogram quality in monitoring reservoir bank landslides in mountainous areas. To address it, this study builds upon the Eigendecomposition-based Maximum-likelihood estimator of Interferometric phase (EMI) method, introduces Fisher information to adjust the weight of each interferometric pair, and further develops a new distributed scatterer (DS) phase optimization method, which is referred to as FEMI-DSInSAR in the text. We applied the FEMI-DSInSAR to retrieve the deformation history of landslides along a ~100 km section of the Lancang River using 33 C-band Sentinel-1 images (January 2019–January 2022). The accuracy and reliability were validated by comparing the results with those obtained with EMI-DSInSAR and Stanford Method for Persistent Scatterers-Small Baseline Subset (StaMPS-SBAS) methods in terms of both interferogram quality and large-area deformation results. The differential interferograms obtained by the FEMI-DSInSAR method not only show better quality fringes, but also reduce the sum of phase difference (SPD) and the standard deviation of the phase (PSD) values by 8.9% and 25.2%, respectively. Moreover, the FEMI-DSInSAR method yields the most significant number of MPs under various geomorphic regions and achieves reliable surface displacements, compared to 10 in situ GPS measurements. Finally, we projected the FEMI-DSInSAR-derived line of sight (LOS) deformation results onto the slope direction and successfully identified 4 previously mapped and 46 newly detected unstable slopes. The characteristics of whole-area landslide deformation characteristics and the influencing factors were analyzed.

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Data availability

The data that support the findings of this study are openly available in the Supplementary information.

References

  • Ansari H, De Zan F, Bamler R (2017) Sequential estimator: toward efficient InSAR time series analysis. IEEE Trans Geosci Remote Sens 55:5637–5652

    Article  Google Scholar 

  • Ansari H, De Zan F, Bamler R (2018) Efficient phase estimation for interferogram stacks. IEEE Trans Geosci Remote Sens 56:4109–4125

    Article  Google Scholar 

  • Berardino P, Fornaro G, Lanari R, Sansosti E (2002) A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Trans Geosci Remote Sens 40:2375–2383

    Article  Google Scholar 

  • Calò F, Ardizzone F, Castaldo R et al (2014) Enhanced landslide investigations through advanced DInSAR techniques: the Ivancich case study, Assisi, Italy. Remote Sens Environ 142:69–82

    Article  Google Scholar 

  • Cao N, Lee H, Jung HC (2015) A phase-decomposition-based PSInSAR processing method. IEEE Trans Geosci Remote Sens 54:1074–1090

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Ferretti A, Prati C, Rocca F (2001) Permanent scatterers in SAR interferometry. IEEE Trans Geosci Remote Sens 39:8–20

    Article  Google Scholar 

  • Ferretti A, Fumagalli A, Novali F, Prati C, Rocca F, Rucci A (2011) A new algorithm for processing interferometric data-stacks: SqueeSAR. IEEE Trans Geosci Remote Sens 49:3460–3470

    Article  Google Scholar 

  • Fialko Y, Simons M, Agnew D (2001) The complete (3-D) surface displacement field in the epicentral area of the 1999 MW7.1 Hector Mine Earthquake, California, from space geodetic observations Geophys Res Lett 28(16):3063–3066

  • Fornaro G, Verde S, Reale D, Pauciullo A (2014) CAESAR: an approach based on covariance matrix decomposition to improve multibaseline–multitemporal interferometric SAR processing. IEEE Trans Geosci Remote Sens 53:2050–2065

    Article  Google Scholar 

  • Goldstein RM, Zebker HA, Werner CL (1988) Satellite radar interferometry: two-dimensional phase unwrapping. Radio Sci 23:713–720

    Article  Google Scholar 

  • Guarnieri AM, Tebaldini S (2008) On the exploitation of target statistics for SAR interferometry applications. IEEE Trans Geosci Remote Sens 46:3436–3443

    Article  Google Scholar 

  • Herrera G, Gutiérrez F, García-Davalillo JC, Guerrero J, Notti D, Galve JP, Cooksley G (2013) Multi-sensor advanced DInSAR monitoring of very slow landslides: the Tena Valley case study (Central Spanish Pyrenees). Remote Sens Environ 128:31–43

    Article  Google Scholar 

  • Hilley GE, Burgmann R, Ferretti A, Novali F, Rocca F (2004) Dynamics of slow-moving landslides from permanent scatterer analysis. Science 304:1952–1955

    Article  Google Scholar 

  • Hooper A, Segall P, Zebker H (2007) Persistent scatterer interferometric synthetic aperture radar for crustal deformation analysis, with application to Volcán Alcedo, Galápagos. J Geophys Res Solid Earth 112(B7)

  • Hu X, Bürgmann R, Schulz WH et al (2020) Four-dimensional surface motions of the Slumgullion landslide and quantification of hydrometeorological forcing. Nat Commun 11:2792

  • Hu J, Motagh M, Guo J, Haghighi MH, Li T, Qin F, Wu W (2022) Inferring subsidence characteristics in Wuhan (China) through multitemporal InSAR and hydrogeological analysis. Eng Geol 297:106530

  • Huffman GJ, Stocker EF, Bolvin DT, Nelkin EJ, Tan J (2019) GPM IMERG final precipitation L3 1 day 0.1 degree x 0.1 degree V06, Edited by Andrey Savtchenko, Greenbelt, MD. Goddard Earth Sciences Data and Information Services Center (GES DISC). https://10.5067/GPM/IMERGDF/DAY/06. Accessed [Data Access Date]

  • Kang Y, Lu Z, Zhao C, Xu Y, Kim JW, Gallegos AJ (2021) InSAR monitoring of creeping landslides in mountainous regions: a case study in Eldorado National Forest California. Remote Sens Environ 258

  • Li Z, Zou W, Ding X, Chen Y, Liu G (2004) A quantitative measure for the quality of InSAR interferograms based on phase differences. Photogramm Eng Remote Sens 70:1131–1137

    Article  Google Scholar 

  • Liang H, Zhang L, Ding X, Lu Z, Li X, Hu J, Wu S (2021) Suppression of coherence matrix bias for phase linking and ambiguity detection in MTInSAR. IEEE Trans Geosci Remote Sens 59:1263–1274

    Article  Google Scholar 

  • Ly A, Marsman M, Verhagen J, Grasman RP, Wagenmakers EJ (2017) A tutorial on Fisher information. J Math Psychol 80:40–55

    Article  Google Scholar 

  • Manzoni M, Monti-Guarnieri AV, Realini E, Venuti G (2020) Joint exploitation of SAR and GNSS for atmospheric phase screens retrieval aimed at numerical weather prediction model ingestion. Remote Sens 12:654

  • Nishiguchi T, Tsuchiya S, Imaizumi F (2017) Detection and accuracy of landslide movement by InSAR analysis using PALSAR-2 data. Landslides 14:1483–1490

    Article  Google Scholar 

  • Ren J, Xu X, Lv Y, Wang Q, Li A, Li K, Liu S (2022) Late Quaternary slip rate of the northern Lancangjiang fault zone in eastern Tibet: seismic hazards for the Sichuan-Tibet Railway and regional tectonic implications. Eng Geol 106748

  • Samiei-Esfahany S, Martins JE, Van Leijen F, Hanssen RF (2016) Phase estimation for distributed scatterers in InSAR stacks using integer least squares estimation. IEEE Trans Geosci Remote Sens 54:5671–5687

    Article  Google Scholar 

  • Tebaldini S, Monti A (2010) Methods and performances for multi-pass SAR interferometry. In: Imperatore P, Riccio D (eds) Geoscience and remote sensing new achievements. InTech: London, UK, ISBN978–953–7619–97–8

  • Wang Y, Zhu XX (2015) Robust estimators for multipass SAR interferometry. IEEE Trans Geosci Remote Sens 54:968–980

    Article  Google Scholar 

  • Yu C, Li Z, Penna NT, Crippa P (2018) Generic atmospheric correction model for interferometric synthetic aperture radar observations. J Geophys Res Solid Earth 123:9202–9222

    Article  Google Scholar 

  • Yun SH, Zebker H, Segall P et al (2007) Interferogram formation in the presence of complex and large deformation. Geophys Res Lett 34

  • Zhang Z, Zeng Q, Jiao J (2022) Deformations monitoring in complicated-surface areas by adaptive distributed Scatterer InSAR combined with land cover: taking the Jiaju landslide in Danba, China as an example. ISPRS J Photogramm Remote Sens 186:102–122

    Article  Google Scholar 

  • Zhao C, Lu Z, Zhang Q, de La Fuente J (2012) Large-area landslide detection and monitoring with ALOS/PALSAR imagery data over Northern California and Southern Oregon, USA. Remote Sens Environ 124:348–359

    Article  Google Scholar 

  • Zhu CG, Zhang JX, Deng KZ, Long SC, Zhang LY, Wu WH (2022) Monitoring and analysis of subsidence along Ri-Lan high-speed railway at Juye coalfield based on the improved MT-InSAR. J China Coal Soc 47:1031–1042

    Google Scholar 

Download references

Acknowledgements

We are especially grateful to Marie Veth Chua and the anonymous reviewers for their valuable comments, which greatly improved the quality of this manuscript.

Funding

This work was sponsored by the National Natural Science Foundation of China (grant numbers: 42004006 and U21A2014), Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions (Henan University), Ministry of Education open project (grant number: GTYR202203), and the Natural Science Foundation of Hunan Province (grant number: 2021JJ40198). The Copernicus Sentinel data were provided by ESA.

Author information

Authors and Affiliations

  1. College of Geography and Environmental Science, Henan University, 475004, Kaifeng, China

    Jiyuan Hu, Fen Qin & Jiayao Wang

  2. Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Ministry of Education, Kaifeng, 475004, China

    Jiyuan Hu & Fen Qin

  3. School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China

    Wenhao Wu

  4. Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, 14473, Potsdam, Germany

    Mahdi Motagh

  5. Institute of Photogrammetry and GeoInformation, Leibniz University Hannover, 30167, Hannover, Germany

    Mahdi Motagh

  6. Henan Industrial Technology Academy of Spatial-Temporal Big Data, Henan University, Zhengzhou, 450000, China

    Jiyuan Hu, Fen Qin & Jiayao Wang

  7. PowerChina Sinohydro Engineering Bureau 4 Co., Ltd, Xining, 810007, China

    Shangyi Pan

  8. School of Geodesy and Geomatics, Wuhan University, Wuhan, 430079, China

    Jiming Guo & Chunyu Zhang

Authors
  1. Jiyuan Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenhao Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mahdi Motagh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fen Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jiayao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shangyi Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jiming Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Chunyu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fen Qin.

Ethics declarations

Conflict of interest

The authors declare no conflict of interests.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 20358 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, J., Wu, W., Motagh, M. et al. FIM-based DSInSAR method for mapping and monitoring of reservoir bank landslides: an application along the Lancang River in China. Landslides 20, 2479–2495 (2023). https://doi.org/10.1007/s10346-023-02097-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10346-023-02097-5

Keywords

Search

Navigation