Implementation of Multi-Temporal InSAR to monitor pumping induced land subsidence in Pingtung Plain, Taiwan

Transcription

1 Implementation of Multi-Temporal InSAR to monitor pumping induced land subsidence in Pingtung Plain, Taiwan Presenter: Oswald Advisor: Chuen-Fa Ni Date: March 09, 2017

2 Literature Review Pingtung Plain has suffering land subsidence due to the overexploitations of groundwater. 12 hundred million m 3 /year [Hsu et al., 2007] Land subsidence rate measured between 1996 and 1999 in range to -2 mm/years by GPS stations [Hou et al., 2005] Resulting in flooding, infrastructure failure, and salt-water intrusion in coastal areas in Pingtung Plain. [Ting et al., 1998]

3 Literature Review Traditional measurements rely on GPS and leveling data. However, they are time consuming, costly and only supply data at few location [Chaussard et al., 2012] The Interferometric Synthetic Aperture Radar (InSAR) widely applied in the studies of earth-surface deformation because high spatial resolution, spatial coverage, and high precision [Hsieh et al., 2009; Hooper, 2008; Ng. A. H-M et al., 2012] Multi-temporal (Persistent scatterer-small Baseline) InSAR has successfully purpose and applied to improved the accuracy of land subsidence measurements [Hooper, 2008]

4 Purpose To examine and demonstrated the capability of the Multi-temporal InSAR by incorporating Persistent scatterer and small baseline methods to improved the accuracy of land subsidence measurements by minimizing the decorrelation. Introduction Purpose Study Area Methodologies Results Conclusion 01.

5 Study Area The Pingtung Plain with an area of km 2. Located in the southwest part of Taiwan. Consist of flat areas of the catchments of the Kaoping, Tungkang, and Linpien Rivers. [Hsu et al., 2007]

6 Study Area: Hydrogeological Background Pingtung Plain comprises by three aquifer and partially by three aquitard. Mainly composed of gravel and sand [Hsu et al., 2007]

7 Study Area:

8 Methodology Differential InSAR Persistent scatterers Small Baselines Multi-temporal InSAR By combining Persistent Scatterer and Small Baseline technique, optimized for the spatial sampling and detection of surface deformation [Hooper, 2008]

9 Methodology Persistent Scatterer Persistent scatterer (PS), identifying PS pixels based on phase characteristics (find pixels with low phase) [Hooper et al., 2007] [Hooper, 2011]

10 Methodology: Small Baseline Small baseline (small orbital separation), minimize deccorelation effect due to change in looking direction of radar platform Taking many interferogram (SAR imagery) to improved deformation detection [Hooper, 2008; Berardino, 2002; Hooper, 2011, 2007] ψ x.i = W φ D,x,i + φ A,x,i + φ S,x,i + φ θ,x,i + φ N,x,i φ D,x,i movement of the pixels in the satellite Line-of-Sight (LOS) φ A,x,i Phase atmospheric delay φ S,x,i Satelite orbit inaccuracies φ θ,x,i Look angel error φ N,x,i Noise term

11 Methodology: Multi-temporal InSAR Using StaMPS/MTI (Stanford Method for PS) for identifying Persistent Scatterers Small Baseline pixels and estimating the surface deformation

12 Methodology: Multi-temporal InSAR Using StaMPS/MTI (Stanford Method for PS) for identifying Persistent Scatterers Small Baseline pixels and estimating the surface deformation Displacement Estimation Phase Unwrapping Spatially Correlated Nuisance Terms Small Baseline Pair to images that has a small baseline Combined PS-SB PS interferogram and SB interferogram then combined

13 Methodology: Multi-temporal InSAR 12 SAR image pairs were selected for this study, acquired by ENVISAT ASAR satellites from Pair Date Baseline Pair Date Baseline as Master Image. Every image compare to the master image.

14 Methodology: Multi-temporal InSAR Pingtung Plain SAR Images. An image mode Single Look Complex (SLC). Track 232 Frame 3159 ASA_IMS_1PNESA _020203_ _00232_11809_0000

15 Results: wrapping images

16 Results: wrapping images

17 Results: Combining PSInSAR and Small Baselines PSInSAR Using Persistent Scatterer method, PS pixels were identified. Small Baseline Using small baseline, pixel were selected, the surface deformation measurement rate is mm/year

18 Results: Combining PSInSAR and Small Baselines Multi-temporal Using Multi-temporal InSAR, pixel were selected. the surface deformation measurement rate is mm/year

19 Conclusion The land subsidence measurements increased with LOS (Line-of-Sight) displacement rate in Linpien River area between is mm/year. Multi-temporal InSAR more reasonable to monitoring the land subsidence The longer baseline will affected to the Multi-temporal InSAR results

20 Future Work Pingtung Plain SAR Images. An image mode Single Look Complex (SLC). Track 461 Frame 3159 ASA_IMS_1PNESA _015857_ _004 61_21056_0000 Collect geodetic data measurements: GPS data, Leveling data, Groundwater level data and Rainfall data