Testing Sentinel-1 SAR Interferometry Data for Airport Runway Monitoring: A Geostatistical Analysis
Abstract
:1. Introduction
2. Displacement Monitoring Techniques and Data-Analysis Methods for Airport Infrastructure Management: Background and Open Issues
2.1. Topographic Levelling
2.2. Multi-Temporal SAR Interferometry (MT-InSAR)
MT-InSAR for Airport Infrastructure Monitoring: An Overview of Applications and Areas of Further Research
- The comparison between measurements from satellite databases (medium and high resolutions) and ground-truth measurements from topographic levelling directly collected on an airport runway;
- Analysing a medium resolution dataset (Sentinel 1, C-Band) for the monitoring of displacements in an airport runway affected by well-known deformations, including a long-term investigation for the suitability of the Sentinel-1 sensor to detect displacements in the area of subsidence;
- The dataset peculiarities: the runway is constructed on a flat area, hence, due to the high construction standard requirements [60], this can be assumed as a horizontal structure with limited and evenly-distributed settlements. In this paper, the effectiveness of the C-Band medium resolution is tested against a scenario where settlements have formed relatively rapidly in time and are localised in certain sections.
2.3. Data-Analysis Methods: An Overview on Geostatistical Approaches and Areas of Further Research
- The detection of local and global outliers; the detection of potential non-stationarity in spatial variability (e.g., the presence of a trend);
- An assessment of the necessity to transform the data due to highly skewed distributions (e.g., by means of log and box-cox transformations);
- The detection of spatial continuity anisotropy.
- To investigate into their spatial variability across the runway, which is affected by displacements of different scale (size and spatial distribution);
- To optimise the fitting model for interpolation purposes;
- To explore the variability characteristics of the satellite data (medium and high resolution).
3. Aim and Objectives
- To measure and evaluate the suitability of Sentinel 1 (C-Band) SAR data for monitoring airport runway pavements displacements on a multi-year temporal scale through the satellite-based PSI monitoring technique;
- To compare the results obtained by the PSI technique to the measurements collected via a dense topographic levelling campaign, through a geostatistical analysis;
- To evaluate the feasibility of the proposed geostatistical analysis as a reliable investigation approach for the comparison of satellite-based and ground-based displacement information, as well as a tool for the integration of satellite-based information within next generation APMS to improve upon current maintenance strategies.
4. Methodology
4.1. Implemented Displacement Monitoring Techniques and Data Exploration Approach
4.1.1. Topographic Levelling: Data Acquisition Methods
4.1.2. PSI Data Processing
- Creation of the “Connection Graphs” where a Master image is selected to allow the identification of the connections for the formation of the interferograms. Then, a statistical analysis of the amplitudes of the electromagnetic response is performed on a pixel-by-pixel basis to compute the Amplitude Dispersion Index. The reference master image was selected amongst those acquired in the middle of the temporal and perpendicular baseline domain, to minimise space and temporal decorrelations. Therefore, the corresponding interferograms for each pair of master-slave images are computed.
- The second step is based on the identification of the Persistent Scatterer Candidates (PSCs), selected by computing the amplitude dispersion index values relative to each pixel. The PSCs are pixels, associated to the resolution cell of the SAR sensor, with a value of stability index exceeding a fixed threshold (typically of 0.25). The interferometric phase Δϕi is computed for any PSC, at any ith interferogram.
- The atmospheric phase contributions (ϕatm) as well as, the orbital and noise-related effects (ϕnoise) are evaluated and removed from the interferometric phase (Δϕi), to identify the phase-shift uniquely related to the range variations. To elaborate, the topographic phase (ϕtopo) is removed using the Digital Elevation Model (DEM) acquired in the framework of the Shuttle Radar Topographic Mission (SRTM), with a pixel resolution of 3 arc seconds (90 × 90 m). This is made available by the National Aeronautics and Space Administration (NASA) in partnership with the United States Geological Survey (USGS). The DEM resolution is adopted considering that the area investigated for the airport runway is flat, and the phase values are slightly affected by this parameter.
4.2. Geostatistical Analysis
- ESDA: analysis of the spatial sampling geometry, statistical summaries, spatial explorative analysis and analysis of the spatial continuity by means of experimental variograms, with identification of the main directions of the anisotropy.
- Inference of a variogram model: fitting of the experimental variogram with a variogram model, considering the main directions of the anisotropy.
- Interpolation and accuracy assessment: data interpolation using the fitted variogram model and the appropriate kriging algorithm, considering the spatial statistical structure of the data.
5. Case Study
5.1. Area of Investigation
5.2. Displacement Monitoring Techniques: Equipment and Datasets
5.2.1. Satellite SAR Datasets
5.2.2. Topographic Levelling Equipment
6. Results and Discussion
6.1. Displacement Monitoring Techniques: Persistent Scatterers Interferometry (PSI) and Topographic Levelling Investigations
6.2. The Geostatistical Analysis
7. Conclusions and Future Developments
- The Sentinel1 (C-Band) SAR datasets, processed by means of the PSI technique, allow detecting airport runway displacements and quantify their velocity of motion (mm/yr) with high accuracy and correlation levels (e.g., correlation coefficient (r = 0.94)), compared to established on-site topographic levelling data.
- The proposed geostatistical analysis based on the Ordinary Kriging (OK) approach can be successfully implemented to compare results achieved by the application of the PSI technique to medium-resolution Sentinel-1 data with the measurements collected using the ground-based topographic levelling method. This is proven by the high values of the multiple R-squared coefficient (R2 = 0.88) and a Slope of 0.96.
- The presented geostatistical analysis has proven effective in comparing satellite-based and ground-based displacement information for airport runway monitoring. The relatively dense information gathered through the InSAR technique as well as the controlled conditions and the strict compliance to high standards of pavement quality and the operations in airport traffic management lends itself to be incorporated in specialist geostatistical investigations of millimetre-scale structural displacements. The information can be crucial for inclusion in next generation Airport Pavement Management Systems (APMSs).
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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NDT Technology | References |
---|---|
Accelerometers | [4,5,6] |
Strain Gauges | [7] |
Wireless Network Systems | [8] |
Laser Scanner | [9,10] |
Global Position System (GPS) | [11] |
Ground Penetrating Radar (GPR) | [12,13,14,15,16,17] |
Levelling data | [18] |
Ground-based Interferometer | [19] |
Satellite Missions | Sentinel 1A | COSMO-SkyMed |
---|---|---|
Band | C-Band | X-Band |
Property | European Space Agency (ESA) | Italian Space Agency (ASI) |
Reference Time Period | April 2017–December 2019 | November 2016–December 2019 |
Acquisition Geometry | Descending/Ascending | Descending |
Frequency/Wavelenght | 5.4 GHz/λ = 5.5 cm | 9.6 GHz/λ = 3.1 cm |
Range Resolution | 5 m | 3 m |
Azimuth Resolution | 20 m | 3 m |
Acquisition mode | Interferometric Wide Swath (IW) | Stripmap HIMAGE |
Processing Level | L1—Single Look Complex | L1A- Single look Complex Slant |
Number of Images | 25 Desc./23 Asc. | 39 Desc. |
Sub-Swath | IW3 for Desc. Geometry/ IW2 for Asc. Geometry | - |
Mean Incidence Angle (rad/degrees) | Desc.: 0.75/42.97 Asc.: 0.67/38.39 | Desc: 0.46/26.5 - |
Topographic Levelling Equipment | Leica DNA 03 |
---|---|
Measuring Time | Operator dependent |
Measuring Range | Up to 110 m |
Levelling Accuracy | 0.3 mm/km |
Compensator | Pendulum with magnetic damping |
Statistical Parameters of the Power Model of the Ordinary Kriging Sentinel 1—Cross Validation (CV) | ||||||
RMSE (mm/yr): 3.04 | RMSSE: 0.99 | |||||
Min | 1st Quartile | Median | Mean | 3rd Quartile | Max | |
Error (mm/yr) | −19.94 | −1.63 | −0.07 | −0.01 | 1.57 | 26.81 |
Absolute Error (mm/yr) | 0 | 0.73 | 1.59 | 2.13 | 2.86 | 26.81 |
Statistical Parameters: Ordinary Kriging Sentinel Error on Topographic Levelling (mm/yr) | ||||||
RMSE (mm/yr): 2.67 | ||||||
Min | 1st Quartile | Median | Mean | 3rd Quartile | Max | |
Error (mm/yr) | −9.47 | −1.84 | −0.14 | −0.21 | 1.32 | 10.69 |
Absolute Error (mm/yr) | 0.01 | 0.73 | 1.58 | 2.03 | 2.82 | 10.69 |
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Gagliardi, V.; Bianchini Ciampoli, L.; Trevisani, S.; D’Amico, F.; Alani, A.M.; Benedetto, A.; Tosti, F. Testing Sentinel-1 SAR Interferometry Data for Airport Runway Monitoring: A Geostatistical Analysis. Sensors 2021, 21, 5769. https://doi.org/10.3390/s21175769
Gagliardi V, Bianchini Ciampoli L, Trevisani S, D’Amico F, Alani AM, Benedetto A, Tosti F. Testing Sentinel-1 SAR Interferometry Data for Airport Runway Monitoring: A Geostatistical Analysis. Sensors. 2021; 21(17):5769. https://doi.org/10.3390/s21175769
Chicago/Turabian StyleGagliardi, Valerio, Luca Bianchini Ciampoli, Sebastiano Trevisani, Fabrizio D’Amico, Amir M. Alani, Andrea Benedetto, and Fabio Tosti. 2021. "Testing Sentinel-1 SAR Interferometry Data for Airport Runway Monitoring: A Geostatistical Analysis" Sensors 21, no. 17: 5769. https://doi.org/10.3390/s21175769
APA StyleGagliardi, V., Bianchini Ciampoli, L., Trevisani, S., D’Amico, F., Alani, A. M., Benedetto, A., & Tosti, F. (2021). Testing Sentinel-1 SAR Interferometry Data for Airport Runway Monitoring: A Geostatistical Analysis. Sensors, 21(17), 5769. https://doi.org/10.3390/s21175769