Skip to main content

Advertisement

SpringerLink
Log in

An integrated framework for wide-area active landslide detection with InSAR observations and SAR pixel offsets

  • Original Paper
  • Published:
Landslides Aims and scope Submit manuscript

This article has been updated

Abstract

The Jinsha River flows through one of the most geologically complex regions in western China with extremely high altitudes and capricious climates. Frequent landslides (e.g., the Baige landslides on 11 October and 3 November 2018) occurred along its stretch which posed severe damage to bridges, dams, and roads, and put the safety of local residents at risk. Systematic and comprehensive landslide detection has been rarely carried out in the Jinsha River corridor because it is a wide and hard-to-reach region. With 30 years of development, InSAR has become an effective way to map ground motion, which in turn makes it feasible to detect active landslides over wide regions. However, several challenges remain in applying InSAR in the Jinsha River corridor, such as low coherence due to dense vegetation and/or large gradient surface movements, and atmospheric effects due to the spatio-temporal variations of the atmosphere (especially the part due to tropospheric water vapor). In this paper, we propose a framework to overcome the limitations of conventional InSAR through the integration of GACOS-assisted InSAR, advanced SBAS InSAR, and SAR pixel offset tracking techniques. This integrated framework enables us to detect active landslides under complex topographic and climate conditions. Our results show that the detected active landslides are largely controlled by active faults, most of them are found at elevations of 2500–4000 m, their slopes fall between \(25\) and \(45^\circ\) facing southwest and northwest, and 83.3% of the active landslides have a NDVI value of less than 0.3. Furthermore, precipitation is one critical triggering factor of active landslides, evidenced by the high temporal correlation between average precipitation and surface displacement time series.

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

Similar content being viewed by others

Change history

  • 04 September 2022

    Article was modified to incorporate some corrections in the reference section.

References

  • Bayer B, Simoni A, Mulas M, Corsini A, Schmidt D (2018) Deformation responses of slow moving landslides to seasonal rainfall in the northern Apennines, measured by InSAR. Geomorphology 308(MAY1):293–306

    Article  Google Scholar 

  • Behling R, Kleinschmit B, Golovko D, Roessner S (2016) Derivation of long-term spatiotemporal landslide activity-a multi-sensor time series approach. Remote Sens Environ 186:88–104

    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(11):2375–2383

    Article  Google Scholar 

  • Canuti P, Casagli N, Ermini L, Fanti R, Farina P (2004) Landslide activity as a geoindicator in Italy: significance and new perspectives from remote sensing. Environ Geol 45(7):907–919

    Article  Google Scholar 

  • Casagli N, Catani F, Ventisette CD, Luzi G (2010) Monitoring, prediction, and early warning using ground-based radar interferometry. Landslides 7(3):291–301

    Article  Google Scholar 

  • Casu F, Manconi A, Pepe A, Lanari R (2011) Deformation time-series generation in areas characterized by large displacement dynamics: the SAR amplitude pixel-offset SBAS technique. IEEE Trans Geosci Remote Sens 49(7):2752–2763

    Article  Google Scholar 

  • Colesanti C, Wasowski J (2006) Investigating landslides with space-borne synthetic aperture radar (SAR) interferometry. Eng Geol 88(3–4):173–199

    Article  Google Scholar 

  • Costantini M (1997) A novel phase unwrapping method based on network programming. IEEE Trans Geosci Remote Sens 36(3):813–821

    Article  Google Scholar 

  • Crippa C, Valbuzzi E, Frattini P, Crosta GB, Spreafico MC, Agliardi F (2021) Semi-automated regional classification of the style of activity of slow rock-slope deformations using PS InSAR and SqueeSAR velocity data. Landslides 18(7):2445–2463

    Article  Google Scholar 

  • Cruden DM, Varnes DJ (1996) Landslide types and processes. Special Report - National Research Council, Transportation Research Board 247:36–75

    Google Scholar 

  • Dai K, Li Z*, Xu Q, Burgmann R, Milledge D, Tomas R, Fan X, Zhao C, Liu X, Peng J, Zhang Q, Wang Z, Qu T, He C, Li D, Liu J (2020) Entering the era of Earth observation-based landslide warning systems. IEEE Geoscience and Remote Sensing Magazine 8(1):136 – 153

  • Dai K, Zhang L, Song C, Li Z, Zhuo G, Xu Q (2021) Quantitative analysis of Sentinel⁃1 imagery geometric distortion and their suitability along Sichuan⁃Tibet railway. Geomatics and Information Science of Wuhan University 46(10):1450–1460 (In Chinese)

    Google Scholar 

  • Dou F, Lv X, Chai H (2021) Mitigating atmospheric effects in InSAR stacking based on ensemble forecasting with a numerical weather prediction model. Remote Sensing 13(22):4670. https://doi.org/10.3390/rs13224670

    Article  Google Scholar 

  • Fan X, Xu Q, Alonso-Rodriguez A, Subramanian S, 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(5):1003–1020

    Article  Google Scholar 

  • Feng W, Li Z, Elliott JR, Fukushima Y, Hoey T, Singleton A, Cook R, Xu Z (2013) The 2011 Mw 6.8 Burma earthquake: fault constraints provided by multiple SAR techniques. Geophys J Int 195(1):650–660

  • Ferretti A, Prati C, Rocca F (2001) Permanent scatterers in SAR interferometry. IEEE Trans Geosci Remote Sens 39(1):8–20

    Article  Google Scholar 

  • Goldstein RM, Werner CL (1998) Radar interferogram filtering for geophysical applications. Geophys Res Lett 25:4035–4038

    Article  Google Scholar 

  • Gong W, Meyer FJ, Liu S, Hanssen RF (2015) Temporal filtering of InSAR data using statistical parameters from NWP models. IEEE Trans Geosci Remote Sens 53(7):4033–4044

    Article  Google Scholar 

  • Guo C, Yan Y, Zhang Y, Zhang X, Zheng Y, Li X, Yang Z, Wu R (2021) Study on the creep-sliding mechanism of the giant Xiongba ancient landslide based on the SBAS-InSAR method, Tibetan Plateau. China Remote Sensing 13(17):3365. https://doi.org/10.3390/rs13173365

    Article  Google Scholar 

  • Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang KT (2012) Landslide inventory maps: new tools for an old problem. Earth Sci Rev 112(1–2):42–66

    Article  Google Scholar 

  • Highland LM, Bobrowsky P (2008) The landslide handbook-a guide to understanding landslides. Reston, Virginia, U.S. Geological Survey Circular 1325. p 129

  • Huang L, Li Z (2011) Comparison of SAR and optical data in deriving glacier velocity with feature tracking. Int J Remote Sens 32(9–10):2681–2698

    Article  Google Scholar 

  • Jain S, Khosa R, Gosain AK (2022) Impact of landslide size and settings on landslide scaling relationship: a study from the Himalayan regions of India. Landslides 19(2):373–385

    Article  Google Scholar 

  • Li Z, Elliott JR, Feng W, Jackson JA, Parsons BE, Walters RJ (2011) The 2010 Mw 6.8 Yushu (Qinghai, China) earthquake: constraints provided by InSAR and body wave seismology. J Geophys Res Solid Earth 116(B10):B10302

  • Li Z, Fielding EJ, Cross P (2009) Integration of InSAR time-series analysis and water-vapor correction for mapping postseismic motion after the 2003 Bam (Iran) earthquake. IEEE Trans Geosci Remote Sens 47(9):3220–3230

    Article  Google Scholar 

  • Li Z, Fielding EJ, Cross P, Muller JP (2006) Interferometric synthetic aperture radar atmospheric correction: GPS topography-dependent turbulence model. J Geophys Res Solid Earth 111:B02404

    Article  Google Scholar 

  • Li Z, Song C, Yu C, Xiao R, Chen L, Luo H, Dai K, Ge D, Ding Y, Zhang Y, Zhang Q (2019) Application of satellite radar remote sensing to landslide detection and monitoring: challenges and solutions. Geomatics and Information Science of Wuhan University 44(7):967–979 (In Chinese)

    Google Scholar 

  • Li Z, Zhang C, Chen B, Zhan J, Ding M, Lv Y, Li X, Peng J (2022) A technical framework of landslide prevention based on multi-source remote sensing and its engineering application. Earth Sci 47(6):1901–1916 (In Chinese)

    Google Scholar 

  • Liao M, Tang J, Wang T, Balz T, Zhang L (2012) Landslide monitoring with high-resolution SAR data in the Three Gorges region. China Earth SCI 42(2):217–229

    Google Scholar 

  • Liu X, Zhao C, Zhang Q, Lu Z, Li Z, Yang C, Zhu W, Liu-Zeng J, Chen L, Liu C (2021) Integration of Sentinel-1 and ALOS/PALSAR-2 SAR datasets for mapping active landslides along the Jinsha River corridor. China Engineering Geology 284:106033

    Article  Google Scholar 

  • Lu H, Li W, Xu Q, Dong X, Dai C, Wang D (2019) Early detection of landslides in the upstream and downstream areas of the Baige landslide, the Jinsha River based on optical remote sensing and InSAR technologies. Geomatics and Information Science of Wuhan University 44(9):1342–1354 (In Chinese)

    Google Scholar 

  • Lu L, Wang Q, Wang GQ, Liu YL, Liu CS (2016) Trend of climate change over the recent 60 years and its hydrological responses for Jinsha river basin. Journal of North China University of Water Resources and Electric Power (natural Science Edition) 37(05):16–21 (In Chinese)

    Google Scholar 

  • Martha TR, Kerle N, Jetten V, Westen C, Kumar KV (2010) Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods. Geomorphology 116(1–2):24–36

    Article  Google Scholar 

  • Massonnet D, Feigl KL (1998) Radar interferometry and its application to changes in the Earth’s surface. Rev Geophys 36(4):441–500

    Article  Google Scholar 

  • Metternicht G, Hurni L, Gogu R (2005) Remote sensing of landslides: an analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments. Remote Sens Environ 98(2–3):284–303

    Article  Google Scholar 

  • Ouyang CJ, Zhao W, Xu Q, Peng DL, Li WL, Wang DP, Zhou S, Hou SW (2018) Failure mechanisms and characteristics of the 2016 catastrophic rockslide at Su village, Lishui. China Landslides 15(7):1391–1400

    Article  Google Scholar 

  • Pathier E, Fielding EJ, Wright TJ, Walker R, Parson BE, Hensley S (2006) Displacement field and slip distribution of the 2005 Kashmir earthquake from SAR imagery. Geophys Res Lett 33:L20310

    Article  Google Scholar 

  • Paulin GL, Mickelson KA, Contreras TA, Gallin W, Jacobacci KE, Bursik M (2022) Assessing landslide volume using two generic models: application to landslides in Whatcom County, Washington, USA. Landslides 19(4):901–912

    Article  Google Scholar 

  • 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 Sens 44:2374–2383. https://doi.org/10.1109/TGRS.2006.873207

    Article  Google Scholar 

  • Ran YH, Li X, Cheng GD, Zhang TJ, Wu QB, Jin HJ, Jin R (2012) Distribution of permafrost in China: an overview of existing permafrost maps. Permafrost Periglac Process 23(4):322–333

    Article  Google Scholar 

  • Riddick SN, Schmidt DA, Deligne NI (2012) An analysis of terrain properties and the location of surface scatterers from persistent scatterer interferometry. ISPRS J Photogramm Remote Sens 73(SEP.):50–57

  • Rosi A, Tofani V, Tanteri L, Tacconi Stefanelli C, Agostini A, Catani F, Casagli N (2018) The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: geomorphological features and landslide distribution. Landslides 15(1):5–19

    Article  Google Scholar 

  • Sassa K (2004) The international consortium on landslides. Landslides 1(1):91–94

    Article  Google Scholar 

  • Shi X, Zhang L, Balz T, Liao M (2015) Landslide deformation monitoring using point-like target offset tracking with multi-mode high-resolution TerraSAR-X data. ISPRS J Photogramm Remote Sens 105(Jul.):128–140

  • Singleton A, Li Z, Hoey T, Muller JP (2014) Evaluating sub-pixel offset techniques as an alternative to D-InSAR for monitoring episodic landslide movements in vegetated terrain. Remote Sens Environ 147:133–144

    Article  Google Scholar 

  • Sun Q, Zhang L, Ding X, Hu J, Li Z, Zhu J (2015) Slope deformation prior to Zhouqu, china landslide from InSAR time series analysis. Remote Sens Environ 156:45–57

    Article  Google Scholar 

  • Squarzoni G, Bayer B, Franceschini S, Simoni A (2020) Pre and post failure dynamics of landslides in the northern Apennines revealed by space-borne synthetic aperture radar interferometry (InSAR). Geomorphology 369:107353

    Article  Google Scholar 

  • Tofani V, Segoni S, Agostini A, Catani F, Casagli N (2013) Technical note: use of remote sensing for landslide studies in Europe. Natural Hazards and Earth System Science 13(2):299–309

    Article  Google Scholar 

  • Westen CJV, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102(3–4):112–131

    Article  Google Scholar 

  • Wegmüller U, Werner C, Strozzi T, Wiesmann A, Santoro M (2015) Sentinel-1 support in the GAMMA software. Fringe 100:1305–1312

    Google Scholar 

  • Xiao R, Yu C, Li Z, He X (2021) Statistical assessment metrics for InSAR atmospheric correction: applications to generic atmospheric correction online service for InSAR (GACOS) in Eastern China. Int J Appl Earth Obs Geoinf 96:102289

    Google Scholar 

  • Xiao R, Yu C, Li Z, Song C, He X (2020) General survey of large-scale land subsidence by GACOS-corrected InSAR stacking: case study in North China Plain. Proceedings of the International Association of Hydrological Sciences 382:213–218

    Article  Google Scholar 

  • Xu Q, Zheng G, Li W, He C, Dong X, Guo C, Feng W (2018) Study on successive landslide damming events of Jinsha River in Baige village of Octorber 11 and November 3, 2018. J Eng Geol 26(6):1534–1551 (In Chinese)

    Google Scholar 

  • Xu Q, Dong X, Li W (2019) Integrated space-air-ground early detection, monitoring and warning system for potential catastrophic landslides. Geomatics and Information Science of Wuhan University 44(7):957–966 (In Chinese)

    Google Scholar 

  • Xu Q, Peng D, Zhang S, Zhu X, He C, Qi X, Zhao K, Xiu D, Ju N (2020a) Successful implementations of a real-time and intelligent early warning system for loess landslides on the Heifangtai terrace, China – ScienceDirect. Eng Geol 278:105817

  • Xu Q (2020b) Understanding and consideration of related issues in early identification of potential geohazards. Geomatics and Information Science of Wuhan University 45(11):1651–1659 (In Chinese)

  • Xu W, Zhang Q, Zhang J, Wang R (2013) Deformation and control engineering related to huge landslide on left bank of Xiluodu reservoir, south-west China. Eur J Environ Civ Eng 17:249–268

    Article  Google Scholar 

  • Xu X, Han Z, Yang X, Zhang S, Yu G, Zhou B, Li F, Ma B, Chen G, Ran Y (2016) Seismotectonic map of China and adjacent areas. Earthquake Press. Data, Beijing. https://doi.org/10.12031/activefault.china.250.2016.db (In Chinese)

    Book  Google Scholar 

  • Yao X, Deng J, Liu X, Zhou Z, Yao J, Dai F, Ren K, Li L (2020) Primary recognition of active landslides and development rule analysis for Pan Three-river-parallel Territory of Tibet Plateau. Advanced Engineering Sciences 52(5):16–37 (In Chinese)

    Google Scholar 

  • Ye X, Kaufmann H, Guo X (2004) Landslide monitoring in the Three Gorges Area using D-INSAR and corner reflectors. Photogramm Eng Remote Sens 70(10):1167–1172

    Article  Google Scholar 

  • Yu C, Li Z, Penna NT (2018a) Interferometric synthetic aperture radar atmospheric correction using a GPS-based iterative tropospheric decomposition model. Remote Sens Environ 204:109–121

    Article  Google Scholar 

  • Yu C, Li Z, Penna NT, Paola C (2018b) Generic atmospheric correction model for Interferometric Synthetic Aperture Radar observations. Journal of Geophysical Research: Solid Earth 123(10):9202–9222

    Article  Google Scholar 

  • Yu C, Li Z, Penna NT (2020) Triggered after slip on the southern Hikurangi subduction interface following the 2016 Kaikōura earthquake from InSAR time series with atmospheric corrections. Remote Sens Environ 251(251):112097

    Article  Google Scholar 

  • Yu C, Penna NT, Li Z (2017) Generation of real-time mode high-resolution water vapor fields from GPS observations. Journal of Geophysical Research: Atmospheres 122(3):2008–2025

    Article  Google Scholar 

  • Zhang C, Li Z, Yu C, Song C, Xiao R, Peng J (2021) Landslide detection of the Jinsha River region using GACOS assisted InSAR stacking. Geomatics and Information Science of Wuhan University 46(11):1649–1657 (In Chinese)

    Google Scholar 

  • Zhang L, Liao M, Dong J, Xu Q, Gong J (2018) Early detection of landslide hazards in mountainous areas of west China using time series SAR interferometry-a case study of Danba, Sichuan. Geomatics and Information Science of Wuhan University 43(12):2039–2049 (In Chinese)

    Google Scholar 

  • Zhang L, Xiao T, He J, Chen C (2019) Erosion-based analysis of breaching of Baige landslide dams on the Jinsha River, China, in 2018. Landslides 16(10):1965–1979

    Article  Google Scholar 

  • Zhu J, Li Z, Hu J (2017) Reasearch progress and methods of InSAR for deformation monitoring. Acta Geodaetica ET Cartographica Sinica 46(10):1717–1733

    Google Scholar 

  • Zhu Y, Qiu H, Yang D, Liu Z, Ma S, Pei Y, He J, Du C, Sun H (2021) Pre- and post-failure spatiotemporal evolution of loess landslides: a case study of the Jiangou landslide in Ledu, China. Landslides (8)

  • Zhu Q, Zeng H, Ding Y, Xie X, Liu F, Zhang L, Li H, Hu H, Zhang J, Chen L, Chen L, Zhang P, He H (2019) A review of major potential landslide hazards analysis. Acta Geodaetica Et Cartographica Sinica 48(12):1551–1561

    Google Scholar 

  • Zhuang J, Jia K, Zhan J, Zhang C, Kong J, Du C, Wang S, Cao Y, Peng J (2022) Scenario simulation of the landslide dynamic process of large-scale landslides: a case study of the Xiaomojiu landslide along the Jinsha River. Nat Hazards. https://doi.org/10.1007/s11069-022-05229-7

    Article  Google Scholar 

  • Zhuang J, Peng J, Wang G, Javed J, Wang Y, Li W (2018) Distribution and characteristics of landslide in Loess Plateau: a case study in Shaanxi province. Eng Geol 236:89–96

    Article  Google Scholar 

Download references

Acknowledgements

Thanks to the European Space Agency (ESA) for the Sentinel-1 and Sentinel-2 images, Google Earth for the optical images, National Aeronautics and Space Administration (NASA) and National Mapping Authority (NIMA) for SRTM DEM, Deutsches Zentrum für Luft- und Raumfahrt (DLR) for TanDEM DEM, USGS for the earthquake catalog and China Geological Survey for fault data. The monthly precipitation data was from the Global Precipitation Measurement (GPM, https://gpm.nasa.gov).

Funding

This work was supported by the National Natural Science Foundation of China (41941019). Part of this work was also supported by the Shaanxi Province Science and Technology Innovation team (Ref. 2021TD-51), the Shaanxi Province Geoscience Big Data and Geohazard Prevention Innovation Team (2022), and the Fundamental Research Funds for the Central Universities, CHD (Ref. 300102260301, 300102262902, 300102261108, and 300203211261).

Author information

Authors and Affiliations

  1. College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China

    Chenglong Zhang, Zhenhong Li, Bo Chen, Mingtao Ding, Wu Zhu, Jing Yang, Zhenjiang Liu & Jianbing Peng

  2. Big Data Center for Geosciences and Satellites, Xi’an 710054, China

    Chenglong Zhang, Zhenhong Li, Chen Yu, Bo Chen, Mingtao Ding, Wu Zhu, Jing Yang & Zhenjiang Liu

  3. Key Laboratory of Western China’s Mineral Resources and Geological Engineering, Ministry of Education, Xi’an, 710054, China

    Zhenhong Li, Mingtao Ding, Wu Zhu & Jianbing Peng

  4. School of Engineering, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK

    Chen Yu

Authors
  1. Chenglong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhenhong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chen Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bo Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mingtao Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wu Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jing Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Zhenjiang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jianbing Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C. Zhang: InSAR data processing, methodology, result analysis, writing/editing original draft. Z. Li: Concept, supervision, resources, research structure design, result analysis, writing/editing original draft, project administration; C. Yu: Result analysis, reviewing/editing draft; B. Chen, Z. Liu and J. Yang: Field Investigation; M. Ding and J. Peng: Result analysis, reviewing draft.

Corresponding author

Correspondence to Zhenhong Li.

Ethics declarations

Competing interests

The authors declare no competing interests.

Appendix

Appendix

The legend of Fig. 1b (http://geocloud.cgs.gov.cn/#/portal/home).

figure a

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, C., Li, Z., Yu, C. et al. An integrated framework for wide-area active landslide detection with InSAR observations and SAR pixel offsets. Landslides 19, 2905–2923 (2022). https://doi.org/10.1007/s10346-022-01954-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10346-022-01954-z

Keywords

Search

Navigation