Abstract
Monitoring deformation in high undulating mountainous environments is critical for surface process research and disaster prevention studies. Although observations based on interferometric Synthetic Aperture Radar (InSAR) are an excellent tool for monitoring deformation, the shadow phenomena can limit its application. Based on a series of geomorphic parameters and limited InSAR observation data, surface deformations were reconstructed in areas with missing observations by constructing a random forest model to compensate for the shadow phenomenon at the grid-scale. The findings suggest that this method can be used to rebuild landscape variation characteristics in places where observation data is lacking. The dominant slope direction in the observation area corresponded to a more significant correlation between the reconstructed topography deformation characteristics and the observation. In addition, when building this model, consideration was given to the geomorphic parameter selection, elevation variation, hypsometric integral value, slope form, lithology, slope variation, and aspect variation; these parameters can significantly affect the surface deformation, which is closely related to their spatial autocorrelation. These findings are significant for eliminating the shadow phenomenon, which often occurs in InSAR observations taken over alpine canyon regions. The terrain and lithology of the underlying surface should be considered when reconstructing the surface deformation characteristics of the shadow region by using satellite observation data.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Binnie SA, Phillips WM, Summerfield MA, et al. (2010). Tectonic and climatic controls of denudation rates in active orogens: The San Bernardino Mountains, California. Geomorphology 118: 249–261. https://doi.org/10.1016/j.geomorph.2010.01.005
Blahut J, Westen CJv, Sterlacchini S (2010). Analysis of landslide inventories for accurate prediction of debris-flow source areas. Geomorphology 119: 36–51. https://doi.org/10.1016/j.geomorph.2010.02.017
Cao P, Cheng S, Lin H, et al. (2021). DEM in quantitative analysis of structural geomorphology: application and prospect. Journal of Geomechanics 27(6): 949–962 (In Chinese with English abstract). https://doi.org/10.12090/j.issn.1006-6616.2021.27.06.077
Chaussard E, Wdowinski S, Cabral-Cano E, et al. (2014). Land subsidence in central Mexico detected by ALOS InSAR time-series. Remote Sens Environ 140: 94–106. https://doi.org/10.1016/j.rse.2013.08.038
Chen W, Li X, Wang Y, et al. (2013). Landslide susceptibility mapping using LiDAR and DMC data: a case study in the Three Gorges area, China. Environ Earth Sci 70: 673–685. https://doi.org/10.1007/s12665-012-2151-8
Chong Y, Chen G, Meng X, et al. (2021). Quantitative analysis of artificial dam failure effects on debris flows-A case study of the Zhouqu ‘8.8’ debris flow in northwestern China. Sci Total Environ 792: 148439. https://doi.org/10.1016/j.scitotenv.2021.148439
Du X, Yang Q, Cai B, et al. (2017). A New Method on Shadow and Layover Detection of InSAR, 6th IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)/Asia-Pacific Electromagnetic Week (APEMW). IEEE, New York, USA.
Froude MJ, Petley DN (2018). Global fatal landslide occurrence from 2004 to 2016. Nat Hazard Earth Sys 18: 2161–2181. https://doi.org/10.5194/nhess-18-2161-2018-supplement
García-Delgado H, Velandia F (2020). Tectonic geomorphology of the Serranía de San Lucas (Central Cordillera): Regional implications for active tectonics and drainage rearrangement in the Northern Andes. Geomorphology 349: 106914. https://doi.org/10.1016/j.geomorph.2019.106914
García-Delgado H, Villamizar-Escalante Na, Bermúdez MA, et al. (2021). Climate or tectonics? What controls the spatial-temporal variations in erosion rates across the Eastern Cordillera of Colombia? Global Planet Change 203: 103541. https://doi.org/10.1016/j.gloplacha.2021.103541
Glenn NF, Streutker DR, Chadwick DJ, et al. (2006). Analysis of LiDAR-derived topographic information for characterizing and differentiating landslide morphology and activity. Geomorphology 73: 131–148. https://doi.org/10.1016/j.geomorph.2005.07.006
Gourmelen N, Kim SW, Shepherd A, et al. (2011). Ice velocity determined using conventional and multiple-aperture InSAR. Earth Planet Sc Lett 307: 156–160. https://doi.org/10.1016/j.epsl.2011.04.026
Gu Z, Yao X, Yao C, et al. (2021). Mapping of geomorphic dynamic parameters for analysis of landslide hazards: A Case of Yangbi River Basin on the upper Lancang-Mekong of China. J Mt Sci-Engl 18(9): 2402–2411. https://doi.org/10.1007/s11629-021-6795-2
Guzzetti F, Carrara A, Cardinali M, et al. (1999). Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31: 181–216. https://doi.org/10.1016/S0169-555X(99)00078-1
Kang Y, Lu Z, Zhao C, et al. (2021). InSAR monitoring of creeping landslides in mountainous regions: A case study in Eldorado National Forest, California. Remote Sens Environ 258: 112400. https://doi.org/10.1016/j.rse.2021.112400
Kirkby M, Beven K (1979). A physically based, variable contributing area model of basin hydrology. Hydrolog Sci J 24(1): 43–69. https://doi.org/10.1080/02626667909491834
Li B, Zhang Q, Wang W, et al. (2020). Geohazard monitoring and risk management of high-steep slope in the Wudongde dam area. Int J Geomech 26(4): 556–564 (In Chinese with English abstract). https://doi.org/10.12090/j.issn.1006-6616.2020.26.04.048
Li C, Wang M, Liu K (2018). A decadal evolution of landslides and debris flows after the Wenchuan earthquake. Geomorphology 323: 1–2. https://doi.org/10.1016/j.geomorph.2018.09.010
Li F, Tang G, Yan S, et al. (2013). Digital elevation model experiment tutorial. Science Press, Beijing (In Chinese).
Liang L, Zhang Z, Dai F (2022). A Late Pleistocene landslide damming event and its implications for the evolution of river valley landforms in the upper Jinsha River, southeastern Tibetan Plateau. Quatern Int 622: 97–109. https://doi.org/10.1016/j.quaint.2022.01.006
Liu X, Zhao C, Zhang Q, et al. (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
Ma L (2002). Geological Atlas of China. Geological Publishing House, Beijing.
Min W, Shu-dao Z, Zhi-hua L, et al. (2012). The Characteristics of Flat-Topped and Pinnacle Building on SAR Image. Springer, Berlin, Heidelberg.
Mohseni N, Salar YS (2021). Terrain indices control the quality of soil total carbon stock within water erosion-prone environments. Ecohydrol Hydrobiol 21: 46–54. https://doi.org/10.1016/j.ecohyd.2020.08.006
Morelli S, Pazzi V, Frodella W, et al. (2018). Kinematic Reconstruction of a Deep-Seated Gravitational Slope Deformation by Geomorphic Analyses. Geosciences 8(26): 1–24. https://doi.org/10.3390/geosciences8010026
Murillo-GarcÍa FG, Steger S, AlcIrántara-Ayala I (2019). Landslide susceptibility: a statistically-based assessment on a depositional pyroclastic ramp. J Mt Sci-Engl 16(3): 561–580. https://doi.org/10.1007/s11629-018-5225-6
Pazzi V, Morelli S, Fanti R (2019). A Review of the Advantages and Limitations of Geophysical Investigations in Landslide Studies. International Journal of Geophysics 2019: 2983087. https://doi.org/10.1155/2019/2983087
Pike RJ, Wilson SE (1971). Elevation-Relief Ratio, Hypsometric Integral, and Geomorphic Area-Altitude Analysis. Geol Soc Am Bull 82(4): 1079–1083. https://doi.org/10.1130/0016-7606(1971)82[1079:ERHIAG]2.0.CO;2
Qiu H, Hu S, Yang D, et al. (2021). Comparing landslide size probability distribution at the landscape scale (Loess Plateau and the Qinba Mountains, Central China) using double Pareto and inverse gamma. B Eng Geol Environ 80: 1035–1046. https://doi.org/10.1007/s10064-020-02037-w
Salvatici T, Tofani V, Rossi G, et al. (2018). Application of a physically based model to forecast shallow landslides at a regional scale. Nat Hazard Earth Sys 18: 1919–1935. https://doi.org/10.5194/nhess-18-1919-2018
Sciarra M, Coco L, Urbano T (2017). Assessment and validation of GIS-based landslide susceptibility maps: a case study from Feltrino stream basin (Central Italy). B Eng Geol Environ 76: 437–456. https://doi.org/10.1007/s10064-016-0954-7
Segura C, Noone D, Warren D, et al. (2019). Climate, landforms, and geology affect baseflow sources in a mountain catchment. Water Resour Res 55: 1–17. https://doi.org/10.1029/2018WR023551
Song D, Qu Y, Xie Y (2016). FMCW CSAR Doppler shifting correction and the layover phenomenon analysising at a new received signal model, Fourth International Conference on Wireless and Optical Communications. Proc. SPIE 9902, Beijing, China.
Sousa JJ, Ruiz AM, Hanssen RF, et al. (2010). PS-InSAR processing methodologies in the detection of field surface deformation-Study of the Granada basin (Central Betic Cordilleras, southern Spain). J Geodyn 49: 181–189. https://doi.org/10.1016/j.jog.2009.12.002
Temme AJAM, Armitage J, Attal M, et al. (2017). Developing, choosing and using landscape evolution models to inform field-based landscape reconstruction studies. Earth Surf Proc Land 42: 2167–2183. https://doi.org/10.1002/esp.4162
Wang Z (2007). Geological Environment and Disasters along railway line in the Qinghai-Tibet Plateau. Earth Science Frontiers 14(6): 31–37. https://doi.org/10.1016/S1872-5791(08)60003-2
Wen L, Wei P, Li X, et al. (2020). Study on the river network, geomorphological features and tectonic activity in the Danjiangkou reservoir and its surrounding areas. Journal of Geomechanics 26(2): 252–262 (In Chinese with English abstract). https://doi.org/10.12090/j.issn.1006-6616.2020.26.02.024
Ye T, Huang C, Deng Z (2017a). Spatial database of 1:2500000 digital geologic map of People’s Republic of China, Geobal Geology Data. https://doi.org/10.12029/gc2017Z103
Ye T, Huang C, Deng Z (2017b). Spatial database of 1:2500000 digital geologic map of People’s Republic of China. Geology in China 44(S1): 24–31 (In Chinese with English abstract). https://doi.org/10.12029/gc2017Z103
Youberg AM, Webb RH, Fenton CR, et al. (2014). Latest Pleistocene—Holocene debris flow activity, Santa Catalina Mountains, Arizona; Implications for modern debris-flow hazards under a changing climate. Geomorphology 219: 87–102. https://doi.org/10.1016/j.geomorph.2014.04.034
Zhang Y, Meng X, Jordan C, et al. (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
Zhu S, Ran D (2017). Multi-angle SAR Image Fusion Algorithm Based on Visibility Classification of Non-layover Region Targets, International Conference on Security, Pattern Analysis, and Cybernetics (ICSPAC). IEEE, Shenzhen, China.
Acknowledgements
This study was financially supported by the National Natural Science Foundation of China (42107218), China Geology Survey Project (DD20221738), China Three Gorges Corporation (YMJ(XLD) (19) 110), the National Key Research and Development Program of China (2018YFC1505002). We are very grateful to the reviewers for the detailed and valuable suggestions.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gu, Zk., Yao, X. Reconstruction of surface deformation characteristics in alpine canyons under shadow conditions. J. Mt. Sci. 19, 3105–3117 (2022). https://doi.org/10.1007/s11629-021-7294-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11629-021-7294-1