Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 1079–1083, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-1079-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 1079–1083, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-1079-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Oct 2019

19 Oct 2019

INTERFEROMETRIC POINT TARGET ANALYSIS (IPTA) FOR LANDSLIDE MONITORING

M. Yarmohammad Touski, M. Veiskarami, and M. Dehghani M. Yarmohammad Touski et al.
  • Dept. of Civil and Environmental Engineering, School of Engineering, Shiraz University, Shiraz, Iran

Keywords: Landslide, SAR Interferometry, IPTA, Persistent Scatterer

Abstract. By the advent of Persistent Scatterer Interferometry (PSI) time-series analysis, this technique has demonstrated high performance in producing accurate measurements of ground displacements. However, due to several limitations such as high deformation rate, lack of man-made features and rough topographic characteristic, the efficiency of the PSI significantly decreases. The main goal of this paper is to illustrate the potential of one of the PSI methods namely the Interferometric Point Target Analysis (IPTA) to measure the deformation caused by landslide. The landslide occurs in an area lacking man-made features with rough topography. To this end, 28 Sentinel-1A SLC images spanning from October 14 2014 to October 27 2016, were used to generate single-master interferograms. The PS pixels were identified using amplitude dispersion and spectral diversity criteria. The PS pixels were unwrapped considering a linear model for the deformation behavior in an iterative manner. The residual topography and atmospheric contributions were estimated in each iteration and subtracted from the PS pixels phases. The results were finally compared to those extracted from conventional Small Baseline Subset (SBAS) time series analysis applied on the same datasets. A good agreement existed between two methods in some locations whereas unwrapping errors probably due to improper deformation model were observed in a couple of points.