Abstract
The Lancang River flows through the alpine canyon region of southwest China, an area that has experienced frequent geological disasters over the years. Early monitoring of geological hazards is essential for disaster prevention and mitigation. However, traditional ground monitoring techniques are limited by the complex terrain conditions in high-altitude valley regions. In contrast, interferometric synthetic aperture radar (InSAR) technology can provide a high-precision, wide-range monitoring of slow rock-slope deformation, making it an effective tool for studying geological hazards. Within the study area, multiple synthetic aperture radar (SAR) images from the Sentinel-1A satellite were collected, and surface deformation was obtained using the small baseline subset InSAR (SBAS-InSAR). The results demonstrate that combining ascending and descending orbit images can be successfully applied to landslide monitoring in complex mountainous areas. Over 30 potential landslides were identified by combining InSAR results with optical images. The Line-Of-Sight (LOS) direction deformation features and their relationship with precipitation were analyzed based on two typical landslides, and two-dimensional/three-dimensional (2D/3D) deformation decomposition was carried out to reveal its motion characteristics. It was found that the cumulative deformation fluctuation amplitude was higher during the rainy season, and the main movement direction of the landslide was east–west. In addition, based on the spatial distribution and statistical analysis of deformation points along with meteorological data, geological elements, human activities, and topographic conditions, it is inferred that factors such as low vegetation coverage, tectonic movements, human activities, and high-altitude glacier thawing may contribute to the occurrence of disasters. And it was found that areas with high vegetation cover, high rainfall, and snow cover exhibit lower coherence coefficients. This study offers valuable insights for investigating large-scale geological in alpine canyon regions.
This is a preview of subscription content, log in via an institution to check access.
We’re sorry, something doesn't seem to be working properly.
Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data Availability
The Sentinel-1A SAR images and Precision Orbit Data (POD) in this research were obtained from the ASF Data website.
References
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. https://doi.org/10.1109/TGRS.2002.803792
Cao C, Zhu K, Song T, Bai J, Zhang W, Chen J, Song S (2022) Comparative study on potential landslide identification with ALOS-2 and Sentinel-1A data in heavy forest reach, upstream of the Jinsha River. Remote Sens 14:1962. https://doi.org/10.3390/rs14091962
Cigna F, Bateson L, Jordan C, Dashwood C (2014) Simulating SAR geometric distortions and predicting persistent scatterer densities for ERS-1/2 and ENVISAT C-band SAR and InSAR applications: Nationwide feasibility assessment to monitor the landmass of Great Britain with SAR imagery. Remote Sens Environ 152:441–466. https://doi.org/10.1016/j.rse.2014.06.025
Colesanti C, Wasowski J (2006) Investigating landslides with space-borne synthetic aperture radar (SAR) interferometry. Eng Geol 88:173–199. https://doi.org/10.1016/j.enggeo.2006.09.013
Dong J, Zhang L, Tang M, Liao M, Xu Q, Gong J, Ao M (2018) Mapping landslide surface displacements with time series SAR interferometry by combining persistent and distributed scatterers: a case study of Jiaju landslide in Danba, China. Remote Sens Environ 205:180–198. https://doi.org/10.1016/j.rse.2017.11.022
Du Q, Li G, Chen D, Yu Z, Qi S, Wu G, Chai M, Tang L, Jia H, Peng W (2021) SBAS-InSAR-based analysis of surface deformation in the eastern Tianshan Mountains, China. Front Earth Sci 9:729454. https://doi.org/10.3389/feart.2021.729454
Dun J, Feng W, Yi X, Zhang G, Wu M (2021) Detection and mapping of active landslides before impoundment in the Baihetan Reservoir Area (China) based on the time-series InSAR method. Remote Sens 13:3213. https://doi.org/10.3390/rs13163213
Guo R, Li S, Yn C, Li X, Yuan L (2021) Identification and monitoring landslides in Longitudinal Range-Gorge Region with InSAR fusion integrated visibility analysis. Landslides 18:551–568. https://doi.org/10.1007/s10346-020-01475-7
Kropatsch WG, Strobl D (1990) The generation of SAR layover and shadow maps from digital elevation models. IEEE Trans Geosci Remote Sens 28:98–107. https://doi.org/10.1109/36.45752
Kumar V, Venkataramana G, Høgda K (2011) Glacier surface velocity estimation using SAR interferometry technique applying ascending and descending passes in Himalayas. Int J Applied Earth Obs Geoinf 13:545–551. https://doi.org/10.1016/j.jag.2011.02.004
Li Y, Zuo X, Xiong P, Chen Z, Yang F, Li X (2022a) Monitoring land subsidence in North-central Henan Plain using the SBAS-InSAR method with Sentinel-1 imagery data. J Indian Soc Remote Sens 50:635–655. https://doi.org/10.1007/s12524-021-01484-6
Li Y, Zuo X, Zhu D, Wu X, Wu W, Bu J, Yang X, Huang C, Li F, Shi C, Liu X (2022b) Identification and analysis of landslides in the Ahai Reservoir Area of the Jinsha River Basin using a combination of DS-InSAR, optical images, and field surveys. Remote Sens 14:6274. https://doi.org/10.3390/rs14246274
Li B, Jiang W, Li Y, Luo Y, Jiao Q, Zhang Q (2023a) Monitoring and analysis of Woda landslide (China) using InSAR and Sentinel-1 data. Adv Space Res 72:1789–1802. https://doi.org/10.1016/j.asr.2023.04.055
Li M, Zhang L, Yang M, Liao M (2023b) Complex surface displacements of the Nanyu landslide in Zhouqu, China revealed by multi-platform InSAR observations. Eng Geol 317:107069. https://doi.org/10.1016/j.enggeo.2023.107069
Liang J, Dong J, Zhang S, Zhao C, Liu B, Yang L, Yan S, Ma X (2022) Discussion on InSAR identification effectivity of potential landslides and factors that influence the effectivity. Remote Sens 14:1952. https://doi.org/10.3390/rs14081952
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. Eng Geol 284:106033. https://doi.org/10.1016/j.enggeo.2021.106033
Liu Y, Yao X, Gu Z, Zhou Z, Liu X, Chen X, Wei S (2022) Study of the automatic recognition of landslides by Using InSAR images and the improved mask R-CNN model in the eastern Tibet Plateau. Remote Sens 14:3362. https://doi.org/10.3390/rs14143362
Ning Y, Tang H, Zhang G, Smith JV, Zhang B, Shen P, Chen H (2021) A complex rockslide developed from a deep-seated toppling failure in the upper Lancang River, Southwest China. Eng Geol 293:106329. https://doi.org/10.1016/j.enggeo.2021.106329
Notti D, Herrera G, Bianchini S, Meisina C, López-Davalillo JC, Zucca F (2014) A methodology for improving landslide PSI data analysis. Int J Remote Sens 35:2186–2214. https://doi.org/10.1080/01431161.2014.889864
Novellino A, Cigna F, Brahmi M, Sowter A, Bateson L, Marsh S (2017) Assessing the feasibility of a national InSAR ground deformation map of Great Britain with Sentinel-1. Geosciences 7. https://doi.org/10.3390/geosciences7020019
Ouimet W, Whipple K, Royden L, Sun ZM, Chen ZL (2007) The influence of large landslides on river incision in a transient landscape: eastern margin of the Tibetan Plateau (Sichuan, China). Bull Geol Soc Am 119:1462–1476. https://doi.org/10.1130/B26136.1
Perissin D, Wang T (2011) Time-series InSAR applications over urban areas in China. IEEE J Sel Top Appl Earth Obs Remote Sens 4:92–100. https://doi.org/10.1109/JSTARS.2010.2046883
Samsonov S, Dille A, Dewitte O, Kervyn F, d’Oreye N (2019) Satellite interferometry for mapping surface deformation time series in one, two and three dimensions: a new method illustrated on a slow-moving landslide. Eng Geol 266:105471. https://doi.org/10.1016/j.enggeo.2019.105471
Shankar H, Singh D, Chauhan P (2022) Landslide deformation and temporal prediction of slope failure in Himalayan terrain using PSInSAR and Sentinel-1 data. Adv Space Res 70:3917–3931. https://doi.org/10.1016/j.asr.2022.04.062
Shi X, Wang J, Jiang M, Zhang S, Wu Y, Zhong Y (2022) Extreme rainfall-related accelerations in landslides in Danba County, Sichuan Province, as detected by InSAR. Int J Appl Earth Obs Geoinf 115:103109. https://doi.org/10.1016/j.jag.2022.103109
Sun Q, Zhang L, Ding XL, Hu J, Li ZW, Zhu JJ (2015) Slope deformation prior to Zhouqu, China landslide from InSAR time series analysis. Remote Sens Environ 156:45–57. https://doi.org/10.1016/j.rse.2014.09.029
Sun Q, Jun H, Zhang L, Ding X (2016) Towards slow-moving landslide monitoring by integrating multi-sensor InSAR time series datasets: the Zhouqu case study, China. Remote Sens 8:908. https://doi.org/10.3390/rs8110908
Tu G, Deng H (2020) Formation and evolution of a successive landslide dam by the erosion of river: a case study of the Gendakan landslide dam on the Lancang River, China. Bull Eng Geol Env 79:2747–2761. https://doi.org/10.1007/s10064-020-01743-9
Xu Q, Dong X, Li W (2019) Integrated space-air-ground early detection, monitoring and warning system for potential catastrophic geohazards. Geomat Inf Sci Wuhan Univ 44:957–966
Yang S, Li D, Liu Y, Xu Z, Sun Y, She X (2023) Landslide identification in human-modified alpine and canyon area of the Niulan River Basin based on SBAS-InSAR and optical images. Remote Sens 15:1998. https://doi.org/10.3390/rs15081998
Yao J, Yao X, Liu X (2022) Landslide detection and mapping based on SBAS-InSAR and PS-InSAR: a case study in Gongjue County, Tibet, China. Remote Sens 14:4728. https://doi.org/10.3390/rs14194728
Zhang Y, Meng X, Jordan C, Novellino A, Dijkstra T, Chen G (2018) Investigating slow-moving landslides in the Zhouqu region of China using InSAR time series. Landslides 15:1299–1315. https://doi.org/10.1007/s10346-018-0954-8
Zhang L, Dai K, Deng J, Ge D, Rubing L, Li W-l, Xu Q (2021) Identifying potential landslides by stacking-InSAR in southwestern China and its performance comparison with SBAS-InSAR. Remote Sens 13:3662. https://doi.org/10.3390/rs13183662
Funding
This study was funded by the National Natural Science Foundation of China (Grant No. 41161070), the Research On Identification, Monitoring, And Early Warning Of Major Geological Hazards In The Alpine Canyon Area With “Sky And Ground” Coordination (2019FY003017), and Yunnan University’s 2nd Professional Master’s Degree Graduate Practice Innovation Project (ZC-22222175).
Author information
Authors and Affiliations
Contributions
Conceptualization, Xianjie Feng and Yimin Li; methodology, Xianjie Feng; software, Xianjie Feng; validation, Yuanting Li, Wenxue Jiang and Wenxuan Yu; formal analysis, Xianjie Feng; investigation, Yimin Li; resources, Yuanting Li; data curation, Yuanting Li and Wenxue Jing; writing—original draft preparation, Xianjie Feng; writing—review and editing, Yuanting Li and Wenxuan Yu; visualization, Xianjie Feng and Wenxuan Yu; supervision, Yimin Li; project administration, Wenxuan Yu; funding acquisition, Yimin Li. All authors have read and agreed to the published version of the manuscript.
Corresponding author
Ethics declarations
Ethics approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Responsible Editor: Philippe Garrigues
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) 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.
About this article
Cite this article
Li, Y., Feng, X., Li, Y. et al. Detection and analysis of potential landslides based on SBAS-InSAR technology in alpine canyon region. Environ Sci Pollut Res 31, 6492–6510 (2024). https://doi.org/10.1007/s11356-023-31473-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-023-31473-w