Detecting and characterizing deformations of the left bank slope near the Jinping hydropower station with time series Sentinel-1 data

doi:10.6046/gtzyyg.2019.02.28

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Abstract

tability monitoring of bank slopes along the reservoirs of hydropower projects is a fundamental task for the safety of dam operation. And deformation detection is a major approach for stability monitoring. Spaceborne InSAR technique has been recognized as an effective tool for deformation detection with its high observation accuracy and capability to work independent of weather and solar illumination. The deformation information of left bank slope of Jinping hydropower station in the Yalong River Basin was obtained by processing 56 images of C-band Sentinel-1 data with small baselines time series InSAR technique. The result indicated that there was a large landslide on the left bank slope about 1.5 km away from the dam upstream of the Jinping I hydropower station, with a surface area of more than 750,000 square meters. The maximum deformation rate in the line of sight exceeded 200 mm/a from 2015 to 2018. The deformation area was mainly concentrated in the middle and upper part of the bank slope. And the maximum cumulative deformation of the line of sight in the observation period was more than 500 mm. The time series of deformation was basically a linear sliding trend without obvious periodicity. The same method was used to process 22 archived images of L-band ALOS-PALSAR data from 2006 to 2011. The results show that the left bank slope was stable before the reservoir impoundment. It is therefore inferred that the sharp rise of water level of the reservoir might be a main trigger factor for this landslide activation.

Keywords Jinping hydropower station deformation InSAR SBAS landslide Sentinel-1

TP79

Issue Date: 23 May 2019

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	Zhenlin WANG
	Mingsheng LIAO
	Lu ZHANG
	Heng LUO
	Jie DONG

Cite this article:

Zhenlin WANG,Mingsheng LIAO,Lu ZHANG, et al. Detecting and characterizing deformations of the left bank slope near the Jinping hydropower station with time series Sentinel-1 data[J]. Remote Sensing for Land & Resources, 2019, 31(2): 204-209.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.02.28 OR https://www.gtzyyg.com/EN/Y2019/V31/I2/204

Tab.1 Parameters of satellite SAR datasets

Fig.1 Coverage of satellite SAR datasets

Fig.2 LOS deformation rates derived from two datasets

Fig.3 Accumulated deformation time series derived from two datasets

Fig.4 Accumulated LOS deformation derived from Sentinel-1 data

Fig.5 DEM and accumulated deformation of two profiles

Fig.6 Atmospheric delay phase and precipitation

Fig.7 Comparison of accumulated deformation before and after atmospheric correction

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