Monitoring the regional deformation of loess landslides on the Heifangtai terrace using the Sentinel-1 time series interferometry technique

Qingkai Meng (), Qiang Xu, Baocun Wang, Weile Li, Ying Peng, Dalei Peng, Xing Qi and Dongdong Zhou
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Qingkai Meng: Qinghai University
Qiang Xu: Chengdu University of Technology
Baocun Wang: Institute of Surveying, Mapping and Geoinformation of Henan Provincial Bureau of Geo-exploration and Mineral Development
Weile Li: Chengdu University of Technology
Ying Peng: Chengdu Univeristy of Technology
Dalei Peng: Chengdu University of Technology
Xing Qi: Chengdu University of Technology
Dongdong Zhou: Qinghai University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2019, vol. 98, issue 2, No 7, 485-505

Abstract: Abstract The Heifangtai terrace of Gansu Province is a hotspot for loess landslide research, as massive and continuous landslides occur here every year. Detecting the spatial and temporal deformations of landslides and acquiring precursor information are very important for hazard prediction and risk management. In this paper, 51 newly launched Sentinel-1a scenes using the novel terrain observation with progressive scans (TOPS) mode from March 2015 to November 2017 are gathered, and a preprocessed chain of TOPS with the small baseline subset interferometric synthetic aperture radar technology is generated to obtain the deformation time-series. Our results show that (1) 44 active landslides with mean deformation velocities ranging from − 12.3 to − 58.57 mm yr−1 along the steepest slope, were detected and consisted of 18 loess-bedrock landslides, 12 loess flows, 7 loess flow-slides, and 7 loess slides; (2) four typical active regions and two potential risk places were recognized on the basis of high coherent point distribution, the average measured velocities along the steep slope and high-resolution orthographic images; (3) geological structures and special geomorphologies (e.g., cracks, sinkholes and concave gullies) can be mainly attributed to induce reactivity via long term irrigation. Finally, our research also demonstrates the potential ability of Sentinel-1 TOPS images to be applied to the monitoring of loess landslides, which is essential for risk mitigation and emergence management.

Keywords: Loess landslides; Time-series analysis; Sentinel-1 TOPS; Heifangtai (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11069-019-03703-3

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