# Satellite Imaging Techniques for Ground Movement Monitoring of a Deep Pipeline Trench Backfilled with Recycled Materials

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Summary of Materials, Construction, and Instrumentation

#### 2.2. Satellite Data

#### 2.3. Satellite Imaging and Analysis

_{r}and A

_{s}are the amplitudes for the reference and secondary SAR images, φ

_{r}and φ

_{s}are the phase information for the reference and secondary SAR images, respectively, φ

_{topo}is the topographic phase, φ

_{defo}is the phase contribution due to surface deformation, φ

_{flat}is the flat-earth phase, φ

_{atm}is the atmospheric phase and φ

_{noise}is the noise-related phase. The value of φ

_{r}can be estimated as:

_{1}is the range distance between the location of the reference SAR image and the target surface, and φ

_{scatt−1}is the backscattering property at the first acquisition time. Similarly, the secondary SAR image φ

_{s}can be calculated as:

_{2}is the range distance between the location of the secondary SAR image and the target surface and φ

_{scatt−2}is the backscattering property at the second acquisition time. The value of R

_{2}can be substituted by R

_{1}+ ΔR, and the value of ΔR can be estimated as:

_{scatt−1}and φ

_{scatt−2}can also be assumed to have similar values. Hence, the interferometric phase can be described as [17]:

_{m}− φ

_{s}, can be used to estimate the height of the object on the ground. The temporal baseline (B

_{t}), which is the time delay between the reference and the secondary SAR images, is important to consider. This influences the probability of changes in backscattering properties, φ

_{scatt−1}and φ

_{scatt−2}, primarily due to changes in the land cover or vegetation and wind-induced tree movements [23].

## 3. Results

#### 3.1. Field Monitoring

#### 3.2. InSAR Estimates

## 4. Discussions

#### 4.1. Comparison between the Field Monitoring and InSAR Analysis

^{2}being 0.95. However, the calculated root mean square error (RMSE) between the DInSAR estimates and the collected SP data was exorbitant, equal to 24.47.

^{2}was 0.99 and this closer to the unity value may have been influenced by the limited available data points, similar to the previous DInSAR and SP comparison. However, the value of the calculated RMSE was half of the DInSAR and SP comparison, which was equal to 7.02. The second PSI and SP comparison considered all the time-series data points (Figure 11b). When all the time-series data points were considered, the regression coefficient remained acceptable equaling 0.96 with the PSI values overestimating the SP data. Conversely, the value of R

^{2}significantly decreased from 0.99 to 0.34, showing the overall dataset’s high dispersion. Similarly, the value of RMSE was negatively influenced by considering all the data points, increasing from 6.04 to 11.34. This value was comparable to the RMSE value calculated from the DInSAR and SP comparison.

^{2}was 1.00. However, the value of the calculated RMSE was almost twice that of the PSI and SP comparison, which was equal to 13.20 with consideration of RS-2. When all the time-series data points were considered, the regression coefficient remained acceptable equal to 0.74. Conversely, the value of R

^{2}decreased from 1.00 to 0.83, which is still an acceptable value with the SBAS values overestimating the SP data. The value of RMSE decreased from 13.20 to 9.26.

#### 4.2. Temporal Variation

#### 4.3. Spatiotemporal Variation

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Fityus, S.G.; Smith, D.W.; Allman, M.A. Expansive Soil Test Site near Newcastle. J. Geotech. Geoenviron. Eng.
**2004**, 130, 686–695. [Google Scholar] [CrossRef] - Li, J.; Cameron, D.A.; Ren, G. Case Study and Back Analysis of a Residential Building Damaged by Expansive Soils. Comput. Geotech.
**2014**, 56, 89–99. [Google Scholar] [CrossRef] [Green Version] - Rajeev, P.; Kodikara, J. Numerical Analysis of an Experimental Pipe Buried in Swelling Soil. Comput. Geotech.
**2011**, 38, 897–904. [Google Scholar] [CrossRef] - Water Services Association of Australia. WSA Conduit Inspection Reporting Code of Australia Version 2.2; Water Services Association of Australia: Docklands, VIC, Australia, 2008. [Google Scholar]
- Karunarathne, A.M.A.N.; Gad, E.F.; Rajeev, P. Effect of Insitu Moisture Content in Shrink-Swell Index. Geotech. Geol. Eng.
**2020**, 38, 6385–6392. [Google Scholar] [CrossRef] - Richards, R.A. Should Selection for Yield in Saline Regions Be Made on Saline or Non-Saline Soils? Euphytica
**1983**, 32, 431–438. [Google Scholar] [CrossRef] - Gedara, S.D.D.A.; Wasantha, P.L.P.; Teodosio, B.; Li, J. An Experimental Study of the Size Effect on Core Shrinkage Behaviour of Reactive Soils. Transp. Geotech.
**2022**, 33, 100709. [Google Scholar] [CrossRef] - Tran, K.M.; Bui, H.H.; Sánchez, M.; Kodikara, J. A DEM Approach to Study Desiccation Processes in Slurry Soils. Comput. Geotech.
**2020**, 120, 103448. [Google Scholar] [CrossRef] - Tran, K.M.; Bui, H.H.; Nguyen, G.D. A Hybrid Discrete-Continuum Approach to Model Hydro-Mechanical Behaviour of Soil during Desiccation. arXiv
**2021**, arXiv:2106.04676. [Google Scholar] - Gandini, A.; Quesada, L.; Prieto, I.; Garmendia, L. Climate Change Risk Assessment: A Holistic Multi-Stakeholder Methodology for the Sustainable Development of Cities. Sustain. Cities Soc.
**2021**, 65, 102641. [Google Scholar] [CrossRef] - Uchehara, I.; Moore, D.; Jafarifar, N.; Omotayo, T. Sustainability Rating System for Highway Design—A Key Focus for Developing Sustainable Cities and Societies in Nigeria. Sustain. Cities Soc.
**2022**, 78, 103620. [Google Scholar] [CrossRef] - Fauzi, A.; Djauhari, Z.; Juniansyah Fauzi, U. Soil Engineering Properties Improvement by Utilization of Cut Waste Plastic and Crushed Waste Glass as Additive. Int. J. Eng. Technol.
**2016**, 8, 15–18. [Google Scholar] [CrossRef] - Imteaz, M.A.; Arulrajah, A.; Horpibulsuk, S.; Ahsan, A. Environmental Suitability and Carbon Footprint Savings of Recycled Tyre Crumbs for Road Applications. Int. J. Environ. Res.
**2018**, 12, 693–702. [Google Scholar] [CrossRef] - Yaghoubi, E.; Yaghoubi, M.; Guerrieri, M.; Sudarsanan, N. Improving Expansive Clay Subgrades Using Recycled Glass: Resilient Modulus Characteristics and Pavement Performance. Constr. Build. Mater.
**2021**, 302, 124384. [Google Scholar] [CrossRef] - Yaghoubi, E.; Al-Taie, A.; Disfani, M.; Fragomeni, S. Recycled Aggregate Mixtures for Backfilling Sewer Trenches in Nontrafficable Areas. Int. J. Geomech.
**2022**, 22, 04021308. [Google Scholar] [CrossRef] - Al-Taie, A.; Yaghoubi, E.; Disfani, M.; Fragomeni, S.; Gmehling, E. Field Performance Evaluation of Recycled Aggregate Blends Used for Backfilling Deep Excavated Trenches. Int. J. Geomech.
**2022**. Revised in 2022. [Google Scholar] - Plank, S. Rapid Damage Assessment by Means of Multi-Temporal SAR—A Comprehensive Review and Outlook to Sentinel-1. Remote. Sens.
**2014**, 6, 4870–4906. [Google Scholar] [CrossRef] [Green Version] - European Space Agency. European Space Agency Sentinel-1 SAR User Guide; European Space Agency: Paris, France, 2020. [Google Scholar]
- Crosetto, M.; Monserrat, O.; Cuevas-González, M.; Devanthéry, N.; Crippa, B. Persistent Scatterer Interferometry: A Review. ISPRS J. Photogramm. Remote. Sens.
**2016**, 115, 78–89. [Google Scholar] [CrossRef] [Green Version] - Lu, P.; Bai, S.; Tofani, V.; Casagli, N. Landslides Detection through Optimized Hot Spot Analysis on Persistent Scatterers and Distributed Scatterers. ISPRS J. Photogramm. Remote. Sens.
**2019**, 156, 147–159. [Google Scholar] [CrossRef] - Schlögl, M.; Widhalm, B.; Avian, M. Comprehensive Time-Series Analysis of Bridge Deformation Using Differential Satellite Radar Interferometry Based on Sentinel-1. ISPRS J. Photogramm. Remote Sens.
**2021**, 172, 132–146. [Google Scholar] [CrossRef] - Johnston, P.J.; Filmer, M.S.; Fuhrmann, T. Evaluation of Methods for Connecting InSAR to a Terrestrial Reference Frame in the Latrobe Valley, Australia. J. Geod.
**2021**, 95, 115. [Google Scholar] [CrossRef] - Teodosio, B.; Wasantha, P.L.P.; Yaghoubi, E.; Guerrieri, M.; Fragomeni, S.; van Staden, R.C. Monitoring of Geohazards Using Differential Interferometric Satellite Aperture Radar in Australia. Int. J. Remote Sens.
**2022**, 43, 3769–3802. [Google Scholar] [CrossRef] - Parker, A.L.; Castellazzi, P.; Fuhrmann, T.; Garthwaite, M.C.; Featherstone, W.E. Applications of Satellite Radar Imagery for Hazard Monitoring: Insights from Australia. Remote. Sens.
**2021**, 13, 1422. [Google Scholar] [CrossRef] - Du, Z.; Ge, L.; Li, X.; Ng, A. Subsidence Monitoring over the Southern Coalfield, Australia Using Both L-Band and C-Band SAR Time Series Analysis. Remote. Sens.
**2016**, 8, 543. [Google Scholar] [CrossRef] [Green Version] - De Novellis, V.; Atzori, S.; De Luca, C.; Manzo, M.; Valerio, E.; Bonano, M.; Cardaci, C.; Castaldo, R.; Di Bucci, D.; Manunta, M.; et al. DInSAR Analysis and Analytical Modeling of Mount Etna Displacements: The December 2018 Volcano-Tectonic Crisis. Geophys. Res. Lett.
**2019**, 46, 5817–5827. [Google Scholar] [CrossRef] [Green Version] - Look, B.G. Handbook of Geotechnical Investigation and Design Tables; Taylor & Francis: New York, NY, USA, 2014. [Google Scholar]
- UTS. UTS Design Guidelines P-PO.01.15; UTS: Ultimo, Australia, 2018. [Google Scholar]
- Kimmerling, R. Geotechnical Engineering Circular No. 6 Shallow Foundations; United States Federal Highway Administration, Office of Bridge Technology: New York, NY, USA, 2002.
- ASTM-D422; Standard Test Method for Particle-Size Analysis of Soils. ASTM International: West Conshohocken, PA, USA, 2007.
- ASTM-C127; Standard Test Method for Density, Relative Density (Specific Gravity), and Absorption of Fine Aggregate. ASTM International: West Conshohocken, PA, USA, 2012.
- ASTM-D2487; Standard Practice for Classification of Soils for Engineering Purposes (Unified Soil Classification System). ASTM International: West Conshohocken, PA, USA, 2011.
- ASTM-D698; Standard Test Methods for Laboratory Compaction Characteristics of Soil Using Standard Effort (12 400 Ft-Lbf/Ft3 (600 KN-m/M3)). ASTM International: West Conshohocken, PA, USA, 2012.
- ASTM-D854; Standard Test Methods for Specific Gravity of Soil Solids by Water Pycnometer. ASTM International: West Conshohocken, PA, USA, 2010.
- ASTM-D4318; Standard Test Methods for Liquid Limit, Plastic Limit, and Plasticity Index of Soils. ASTM International: West Conshohocken, PA, USA, 2017.
- Main Roads Western Australia. Melbourne Retail Water Agencies Backfill Specification; Main Roads Western Australia: Melbourne, Australia, 2013. [Google Scholar]
- Geudtner, D.; Torres, R.; Snoeij, P.; Davidson, M.; Rommen, B. Sentinel-1 System Capabilities and Applications. In Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada, 13–18 July 2014; IEEE: Piscataway, NJ, USA, 2014; pp. 1457–1460. [Google Scholar]
- De Zan, F.; Monti Guarnieri, A. TOPSAR: Terrain Observation by Progressive Scans. IEEE Trans. Geosci. Remote Sens.
**2006**, 44, 2352–2360. [Google Scholar] [CrossRef] - Meyer, F.; Bamler, R.; Jakowski, N.; Fritz, T. The Potential of Low-Frequency SAR Systems for Mapping Ionospheric TEC Distributions. IEEE Geosci. Remote. Sens. Lett.
**2006**, 3, 560–564. [Google Scholar] [CrossRef] - Goldstein, R.M.; Werner, C.L. Radar Interferogram Filtering for Geophysical Applications. Geophys. Res. Lett.
**1998**, 25, 4035–4038. [Google Scholar] [CrossRef] [Green Version] - Costantini, M. A Novel Phase Unwrapping Method Based on Network Programming. IEEE Trans. Geosci. Remote. Sens.
**1998**, 36, 813–821. [Google Scholar] [CrossRef] - Hooper, A.J.; Bekaert, D.P.S.; Hussain, E.; Spaans, K. StaMPS/MTI Manual Version 4.1b; School of Earth and Environment, University of Leeds: Leeds, UK, 2018. [Google Scholar]
- Pasquali, P.; Cantone, A.; Riccardi, P.; Defilippi, M.; Ogushi, F.; Gagliano, S.; Tamura, M. Mapping of Ground Deformations with Interferometric Stacking Techniques. Land Appl. Radar Remote Sens
**2014**, 1, 233–259. [Google Scholar] - Dobos, E.; Kovács, I.P.; Kovács, D.M.; Ronczyk, L.; Sz\Hucs, P.; Perger, L.; Mikita, V. Surface Deformation Monitoring and Risk Mapping in the Surroundings of the Solotvyno Salt Mine (Ukraine) between 1992 and 2021. Sustainability
**2022**, 14, 7531. [Google Scholar] [CrossRef] - Li, Y.; Zuo, X.; Xiong, P.; You, H.; Zhang, H.; Yang, F.; Zhao, Y.; Yang, Y.; Liu, Y. Deformation Monitoring and Analysis of Kunyang Phosphate Mine Fusion with InSAR and GPS Measurements. Adv. Space Res.
**2022**, 69, 2637–2658. [Google Scholar] [CrossRef]

**Figure 1.**Images of (

**a**) the trial site and supplied recycled materials, and (

**b**) the geological map of the west of Melbourne and the location of the study site.

**Figure 4.**Standard PSI workflow using SNAP and StaMPS by [42].

**Figure 5.**Standard SBAS workflow using SNAP and StaMPS by [42].

**Figure 7.**Mean cumulative soil movement in (

**a**) Area 1 and (

**b**) Area 2. Error bars are showing the standard deviation of the measurements of each date.

**Figure 11.**Comparison between SP measurements and (

**a**) DInSAR estimates, (

**b**) PSI estimates, and (

**c**) SBAS estimates. The regression models with black lines only considered the final ground movement values, whilst the grey lines considered all the recorded and estimated values within the study period.

**Figure 12.**Temporal variation and coefficient of variation in (

**a**) SB-1, (

**b**) RS-1, (

**c**) SB-2, and (

**d**) RS-2.

Material | SB-1 | SB-2 | Specification | |
---|---|---|---|---|

RG:RP:RT content (% by mass) | 77:09:14 | 84:05:11 | -- | |

Particle composition (%) | >4.75 mm | 29 | 22 | ASTM-D422 (2007) [30] |

4.75–0.075 mm | 69 | 76 | ||

<0.075 mm | 2 | 2 | ||

Maximum particle size (D_{max}), mm | 19.0 | 19.0 | -- | |

Specific gravity (G_{s}) | 1.93 | 2.07 | ASTM-C127 (2012) [31] | |

Coefficient of uniformity (C_{u}) | 10.00 | 9.06 | ||

Coefficient of curvature (C_{c}) | 1.49 | 1.30 | ||

USCS classification | SW | SW | ASTM-D2487 (2011) [32] | |

Standard Proctor compaction | OMC (%) | 9.50 | 7.9 | ASTM-D698 (2012) [33] |

MDD (kN/m^{3}) | 1.36 | 1.37 |

Soil Property | Value | Specification |
---|---|---|

Specific gravity G_{s} | 2.71 | ASTM-D854 (2010) [34] |

Sand (4.75–0.075) mm | 3 | ASTM-D422 (2007) [30] |

Fine content (<0.075 mm) | 97 | |

Liquid limit (LL) (%) | 61 | ASTM-D4318 (2017) [35] |

Plastic limit (PL) (%) | 30 | |

Plasticity index (PI) (%) | 31 | |

USCS classification | CH | ASTM-D2487 (2011) [32] |

SP No. | Area 1 | Area 2 | ||||
---|---|---|---|---|---|---|

Depth (m) | Distance * (m) | Material | Depth (m) | Distance * (m) | Material | |

1 | 0.2 | 5.36 | RS-1 | 0.2 | 1.50 | SB-2 |

2 | 0.2 | 3.25 | RS-1 | 3.0 | 2.50 | SB-2 |

3 | 0.2 | 2.13 | RS-1 | 0.2 | 3.25 | SB-2 |

4 | 0.2 | 1.82 | SB-1 | 1.5 | 4.00 | SB-2 |

5 | 0.2 | 2.92 | SB-1 | 0.2 | 5.50 | SB-2 |

6 | 0.2 | 3.84 | SB-1 | 1.5 | 16.75 | RS-2 |

7 | 0.2 | 6.50 | RS-1 | 0.2 | 18.25 | RS-2 |

**Table 4.**Spatiotemporal comparison between SP measurements and InSAR estimates in the early (between October 2020 and December 2020), middle (between October 2020 and April 2021), and final stages (between October 2020 and February 2022).

Coefficient of Variation, CoV, in % | |||||||||

Method | Early CoV (%) | Middle CoV (%) | Final CoV (%) | ||||||

Whole Strip | Area 1 | Area 2 | Whole Strip | Area 1 | Area 2 | Whole Strip | Area 1 | Area 2 | |

SP | 162 | 37 | 16 | 132 | 97 | 109 | 114 | 90 | 113 |

DInSAR | 76 | 77 | 26 | 74 | 201 | 11 | 55 | 19 | 75 |

PSI | 151 | 50 | - | 79 | 73 | - | 82 | 76 | - |

SBAS | 109 | 14 | 34 | 61 | 11 | 73 | 103 | 71 | 90 |

Minimum Value in mm | |||||||||

Method | Early Min Value (mm) | Middle Min Value (mm) | Final Min Value (mm) | ||||||

Whole Strip | Area 1 | Area 2 | Whole Strip | Area 1 | Area 2 | Whole Strip | Area 1 | Area 2 | |

SP | −0.63 | 2.90 | −0.63 | −49.97 | −9.47 | −49.97 | −109.60 | −35.51 | −109.60 |

DInSAR | 1.94 | 1.94 | 13.38 | −0.36 | 0.36 | 5.28 | −62.70 | −30.70 | −62.70 |

PSI | −13.84 | −13.84 | - | −33.58 | −33.58 | - | −41.35 | −41.35 | - |

SBAS | −11.80 | −1.87 | −11.80 | −36.35 | −13.74 | −36.35 | −84.75 | −30.17 | −84.75 |

Maximum value in mm | |||||||||

Method | Early Max Value (mm) | Middle Max Value (mm) | Final Max Value (mm) | ||||||

Whole Strip | Area 1 | Area 2 | Whole Strip | Area 1 | Area 2 | Whole Strip | Area 1 | Area 2 | |

SP | 4.93 | 4.93 | −0.50 | −1.76 | −1.76 | −6.49 | −7.97 | −7.97 | −12.00 |

DInSAR | 27.01 | 6.62 | 19.42 | 6.19 | 5.99 | 6.19 | −10.56 | −21.01 | −10.56 |

PSI | 2.89 | −4.56 | - | −0.60 | −3.69 | - | −5.80 | −8.90 | - |

SBAS | 0.85 | −1.44 | −5.74 | −9.72 | −11.00 | −11.03 | −4.15 | −4.15 | −6.01 |

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**MDPI and ACS Style**

Teodosio, B.; Al-Taie, A.; Yaghoubi, E.; Wasantha, P.L.P.
Satellite Imaging Techniques for Ground Movement Monitoring of a Deep Pipeline Trench Backfilled with Recycled Materials. *Remote Sens.* **2023**, *15*, 204.
https://doi.org/10.3390/rs15010204

**AMA Style**

Teodosio B, Al-Taie A, Yaghoubi E, Wasantha PLP.
Satellite Imaging Techniques for Ground Movement Monitoring of a Deep Pipeline Trench Backfilled with Recycled Materials. *Remote Sensing*. 2023; 15(1):204.
https://doi.org/10.3390/rs15010204

**Chicago/Turabian Style**

Teodosio, B., A. Al-Taie, E. Yaghoubi, and P. L. P. Wasantha.
2023. "Satellite Imaging Techniques for Ground Movement Monitoring of a Deep Pipeline Trench Backfilled with Recycled Materials" *Remote Sensing* 15, no. 1: 204.
https://doi.org/10.3390/rs15010204