scholarly journals Displacement Monitoring in Airport Runways by Persistent Scatterers SAR Interferometry

2020 ◽  
Vol 12 (21) ◽  
pp. 3564 ◽  
Author(s):  
Luca Bianchini Ciampoli ◽  
Valerio Gagliardi ◽  
Chiara Ferrante ◽  
Alessandro Calvi ◽  
Fabrizio D’Amico ◽  
...  
Keyword(s):  
Time Series ◽  
Leonardo Da Vinci ◽  
Sar Interferometry ◽  
Subgrade Soils ◽  
Ground Deformations ◽  
Persistent Scatterers ◽  
Deformation Velocity ◽  
Open Issue ◽  
Remote Sensing Techniques ◽  
Surrounding Areas

Deformations monitoring in airport runways and the surrounding areas is crucial, especially in cases of low-bearing capacity subgrades, such as the clayey subgrade soils. An effective monitoring of the infrastructure asset allows to secure the highest necessary standards in terms of the operational and safety requirements. Amongst the emerging remote sensing techniques for transport infrastructures monitoring, the Persistent Scatterers Interferometry (PSI) technique has proven effective for the evaluation of the ground deformations. However, its use for certain demanding applications, such as the assessment of millimetric differential deformations in airport runways, is still considered as an open issue for future developments. In this study, a time-series analysis of COSMO–SkyMed satellite images acquired from January 2015 to April 2019 is carried out by employing the PSI technique. The aim is to retrieve the mean deformation velocity and time series of the surface deformations occurring in airport runways. The technique is applied to Runway 3 at the “Leonardo da Vinci” International Airport in Rome, Italy. The proposed PSI technique is then validated by way of comparison with the deformation outcomes obtained on the runway by traditional topographic levelling over the same time span. The results of this study clearly demonstrate the efficiency and the accuracy of the applied PSI technique for the assessment of deformations in airport runways.

scholarly journals Imaging Multi-Age Construction Settlement Behaviour by Advanced SAR Interferometry

2018 ◽  
Vol 10 (7) ◽  
pp. 1137 ◽  
Author(s):  
Francesca Bozzano ◽  
Carlo Esposito ◽  
Paolo Mazzanti ◽  
Mauro Patti ◽  
Stefano Scancella
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Boundary Conditions ◽  
Methodological Approach ◽  
Sar Interferometry ◽  
Evolutionary Stage ◽  
Remotely Sensed Data ◽  
Consolidation Process ◽  
Persistent Scatterers ◽  
External Loads

This paper focuses on the application of Advanced Satellite Synthetic Aperture Radar Interferometry (A-DInSAR) to subsidence-related issues, with particular reference to ground settlements due to external loads. Beyond the stratigraphic setting and the geotechnical properties of the subsoil, other relevant boundary conditions strongly influence the reliability of remotely sensed data for quantitative analyses and risk mitigation purposes. Because most of the Persistent Scatterer Interferometry (PSI) measurement points (Persistent Scatterers, PSs) lie on structures and infrastructures, the foundation type and the age of a construction are key factors for a proper interpretation of the time series of ground displacements. To exemplify a methodological approach to evaluate these issues, this paper refers to an analysis carried out in the coastal/deltaic plain west of Rome (Rome and Fiumicino municipalities) affected by subsidence and related damages to structures. This region is characterized by a complex geological setting (alternation of recent deposits with low and high compressibilities) and has been subjected to different urbanisation phases starting in the late 1800s, with a strong acceleration in the last few decades. The results of A-DInSAR analyses conducted from 1992 to 2015 have been interpreted in light of high-resolution geological/geotechnical models, the age of the construction, and the types of foundations of the buildings on which the PSs are located. Collection, interpretation, and processing of geo-thematic data were fundamental to obtain high-resolution models; change detection analyses of the land cover allowed us to classify structures/infrastructures in terms of the construction period. Additional information was collected to define the types of foundations, i.e., shallow versus deep foundations. As a result, we found that only by filtering and partitioning the A-DInSAR datasets on the basis of the above-mentioned boundary conditions can the related time series be considered a proxy of the consolidation process governing the subsidence related to external loads as confirmed by a comparison with results from a physically based back analysis based on Terzaghi’s theory. Therefore, if properly managed, the A-DInSAR data represents a powerful tool for capturing the evolutionary stage of the process for a single building and has potential for forecasting the behaviour of the terrain–foundation–structure combination.

scholarly journals Testing Sentinel-1 SAR Interferometry Data for Airport Runway Monitoring: A Geostatistical Analysis

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5769 ◽  
Author(s):  
Valerio Gagliardi ◽  
Luca Bianchini Ciampoli ◽  
Sebastiano Trevisani ◽  
Fabrizio D’Amico ◽  
Amir M. Alani ◽  
...  
Keyword(s):  
Spatial Data ◽  
Leonardo Da Vinci ◽  
Geostatistical Analysis ◽  
Sar Interferometry ◽  
Spatial Data Analysis ◽  
Maintenance Strategies ◽  
Spatial Continuity ◽  
Persistent Scatterers ◽  
Sar Data

Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques are gaining momentum in the assessment and health monitoring of infrastructure assets. Amongst others, the Persistent Scatterers Interferometry (PSI) technique has proven to be viable for the long-term evaluation of ground scatterers. However, its effectiveness as a routine tool for certain critical application areas, such as the assessment of millimetre-scale differential displacements in airport runways, is still debated. This research aims to demonstrate the viability of using medium-resolution Copernicus ESA Sentinel-1A (C-Band) SAR products and their contribution to improve current maintenance strategies in case of localised foundation settlements in airport runways. To this purpose, “Runway n.3” of the “Leonardo Da Vinci International Airport” in Fiumicino, Rome, Italy was investigated as an explanatory case study, in view of historical geotechnical settlements affecting the runway area. In this context, a geostatistical study is developed for the exploratory spatial data analysis and the interpolation of the Sentinel-1A SAR data. The geostatistical analysis provided ample information on the spatial continuity of the Sentinel 1 data in comparison with the high-resolution COSMO-SkyMed data and the ground-based topographic levelling data. Furthermore, a comparison between the PSI outcomes from the Sentinel-1A SAR data—interpolated through Ordinary Kriging—and the ground-truth topographic levelling data demonstrated the high accuracy of the Sentinel 1 data. This is proven by the high values of the correlation coefficient (r = 0.94), the multiple R-squared coefficient (R2 = 0.88) and the Slope value (0.96). The results of this study clearly support the effectiveness of using Sentinel-1A SAR data as a continuous and long-term routine monitoring tool for millimetre-scale displacements in airport runways, paving the way for the development of more efficient and sustainable maintenance strategies for inclusion in next generation Airport Pavement Management Systems (APMSs).

scholarly journals Study of Systematic Bias in Measuring Surface Deformation with SAR Interferometry

2020 ◽  
Author(s):  
Homa Ansari ◽  
Francesco De Zan ◽  
Alessandro Parizzi
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Sar Interferometry ◽  
Systematic Bias ◽  
Earth Surface ◽  
Phase Component ◽  
Measuring Surface ◽  
Deformation Velocity ◽  
Temporal Decorrelation ◽  
Efficient Processing

<div>This paper investigates the presence of a new interferometric signal in multilooked Synthetic Aperture Radar (SAR) interferograms which cannot be attributed to atmospheric or earth surface topography changes. The observed signal is short-lived and decays with temporal baseline; however, it is distinct from the stochastic noise usually attributed to temporal decorrelation. The presence of such fading signal introduces a systematic phase component, particularly in short temporal baseline interferograms. If unattended, it biases the estimation of Earth surface deformation from SAR time series. <br></div><div>The contribution of the mentioned phase component is quantitatively assessed. For short temporal baseline interferograms, we quantify the phase contribution to be in the regime of 5 rad at C-band. The biasing impact on deformation signal retrieval is further evaluated. As an example, exploiting a subset of short temporal baseline interferograms which connects each acquisition with the successive 5 in the time series, a significant bias of -6.5 mm/yr is observed in the estimation of deformation velocity from a four-year Sentinel-1 data stack. A practical solution for mitigation of this physical fading signal is further discussed; special attention is paid to the efficient processing of Big Data from modern SAR missions such as Sentinel-1 and NISAR. Adopting the proposed solution, the deformation bias is shown to decrease to -0.24 mm/yr for the Sentinel-1 time series.</div>Based on these analyses, we put forward our recommendations for efficient and accurate deformation signal retrieval from large stacks of multilooked interferograms.

scholarly journals Persistent Scatterer SAR Interferometry (PSI) for Airport Runways monitoring

2020 ◽  
Author(s):  
Luca Bianchini Ciampoli ◽  
Valerio Gagliardi ◽  
Fabio Tosti ◽  
Alessandro Calvi ◽  
Andrea Benedetto
Keyword(s):  
Time Series ◽  
Sar Interferometry ◽  
Transport Infrastructure ◽  
Image Stack ◽  
Subsidence Rate ◽  
Natural Neighbor ◽  
Persistent Scatterer ◽  
Deformation Velocity ◽  
Permanent Scatterers ◽  
The Mean

&lt;p&gt;In the last decades, monitoring the regional-scale deformation of international airports has become a priority, in order to ensure the highest operational security and safety standards. Within this context, among the most innovative and suitable techniques for transport infrastructures monitoring purpose, Persistent Scatterer SAR Interferometry (PSI) technology has proven to be an effective technique to investigate ground deformations [1-3].&lt;/p&gt;&lt;p&gt;However, the application of PSI to effectively and continuously monitor settlement in airports is an open challenge. In this study, a long time-series analysis of a high-resolution COSMO-Skymed satellite image-stack, acquired from September 2011 to October 2019, was collected and processed by PSI technique to retrieve the mean deformation velocity and time series of surface deformation of the runways of Leonardo Da Vinci-International Airport.&lt;/p&gt;&lt;p&gt;The mean PS velocity information is compared to the ground-based levelling-data, collected on the runway using a total station, in order to validate and increase the feasibility of the monitoring processing.&lt;/p&gt;&lt;p&gt;Finally, various Deformation maps using the Natural Neighbor Geostatistical interpolation algorithm [4], were created and demonstrated a maximum subsidence rate is up to 15.3 mm/yr during the investigated period. The results confirmed the well-known major down-lifting phenomenon over an area, which has undergone routine maintenance.&lt;/p&gt;&lt;p&gt;Results have demonstrated the viability of integrating InSAR and topographical in-situ survey methods, paving the way to future implementations in prioritizing maintenance activities and helping for decision-making to have a comprehensive and inclusive information data system for the investigation of survey sites.&lt;/p&gt;&lt;p&gt;The research is supported by the Italian Ministry of Education, University and Research under the National Project &amp;#8220;Extended resilience analysis of transport networks (EXTRA TN): Towards a simultaneously space, aerial and ground sensed infrastructure for risks prevention&amp;#8221;, PRIN 2017, Prot. 20179BP4SM&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;[1] Bianchini Ciampoli, L., Gagliardi, V., Clementini, C. et al. Transport Infrastructure Monitoring by InSAR and GPR Data Fusion. Surv Geophys (2019). https://doi.org/10.1007/s10712-019-09563-7&lt;/p&gt;&lt;p&gt;[2] Ferretti, A., Prati, C., Rocca, F., 2000. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE Trans. Geosci. 38 (5), 2202&amp;#8211;2212. https://doi.org/10.1109/36.868878.&lt;/p&gt;&lt;p&gt;[3] Ferretti, A., Prati, C., Rocca, F.,2001. Permanent scatterers in SAR interferometry. IEEE Trans. Geosci. Remote Sens. 2001, 39, 8&amp;#8211;20.&lt;/p&gt;&lt;p&gt;[4] Sibson, R. (1981). &quot;A brief description of natural neighbor interpolation (Chapter 2)&quot;. In V. Barnett (ed.). Interpolating Multivariate Data. Chichester: John Wiley. pp. 21&amp;#8211;36.&lt;/p&gt;

TIME-SERIES SAR INTERFEROMETRY ANALYSIS OF SURFACE DEFORMATION AT MT. BROMO INDONESIA

2019 ◽  
Vol 3 ◽  
pp. 771
Author(s):  
Arliandy Pratama Arbad ◽  
Wataru Takeuchi ◽  
Yosuke Yosuke ◽  
Mutiara Jamilah ◽  
Achmad Ardy
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Sar Interferometry ◽  
Time Interval ◽  
Infrastructure Management ◽  
Alos Palsar ◽  
Volcanic System ◽  
Deformation Velocity ◽  
Small Baseline Subset ◽  
Small Baseline

One of the most active volcanoes in Indonesia is Mt. Bromo, volcanic activities at Mt. Bromo has been recorded in 1775. We observe the surface deformation of the Mt. Bromo which located at eastern Java Indonesia area that includes neighborhood volcanic system on TNBTS (Taman Nasional Bukit Tengger Semeru). Recently, remote sensing has played as an important role to observe volcano behavior. We apply the SAR Interferometry (InSAR) algorithm referred to as Small Baseline Subset (SBAS) approach that allows us to generate mean deformation velocity maps and displacement time series for the studied area. The common SBAS technique, the set of interferometric phase observations writes as a linear combination of individual SAR scene phase values for each pixel independently. Particularly, the proposed analysis is based on 22 SAR data acquired by the ALOS/PALSAR sensors during the 2007–2017 time interval. A fewer studies have been able to show capability of InSAR analysis for investigating cycle of volcano especially of Mt. Bromo which characterized eruption stratovolcano in ranging one to five years. The results expected in this work represent an advancement of previous InSAR studies of the area that are mostly focused on the deformation affecting the caldera. According to the result, we expected this study could implement on risk management or infrastructure management.

scholarly journals Study of Systematic Bias in Measuring Surface Deformation with SAR Interferometry

2020 ◽  
Author(s):  
Homa Ansari ◽  
Francesco De Zan ◽  
Alessandro Parizzi
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Sar Interferometry ◽  
Systematic Bias ◽  
Earth Surface ◽  
Phase Component ◽  
Measuring Surface ◽  
Deformation Velocity ◽  
Temporal Decorrelation ◽  
Efficient Processing

<div>This paper investigates the presence of a new interferometric signal in multilooked Synthetic Aperture Radar (SAR) interferograms which cannot be attributed to atmospheric or earth surface topography changes. The observed signal is short-lived and decays with temporal baseline; however, it is distinct from the stochastic noise usually attributed to temporal decorrelation. The presence of such fading signal introduces a systematic phase component, particularly in short temporal baseline interferograms. If unattended, it biases the estimation of Earth surface deformation from SAR time series. <br></div><div>The contribution of the mentioned phase component is quantitatively assessed. For short temporal baseline interferograms, we quantify the phase contribution to be in the regime of 5 rad at C-band. The biasing impact on deformation signal retrieval is further evaluated. As an example, exploiting a subset of short temporal baseline interferograms which connects each acquisition with the successive 5 in the time series, a significant bias of -6.5 mm/yr is observed in the estimation of deformation velocity from a four-year Sentinel-1 data stack. A practical solution for mitigation of this physical fading signal is further discussed; special attention is paid to the efficient processing of Big Data from modern SAR missions such as Sentinel-1 and NISAR. Adopting the proposed solution, the deformation bias is shown to decrease to -0.24 mm/yr for the Sentinel-1 time series.</div>Based on these analyses, we put forward our recommendations for efficient and accurate deformation signal retrieval from large stacks of multilooked interferograms.

scholarly journals Analysis of Groundwater Depletion/Inflation and Freeze–Thaw Cycles in the Northern Urumqi Region with the SBAS Technique and an Adjusted Network of Interferograms

2021 ◽  
Vol 13 (11) ◽  
pp. 2144
Author(s):  
Baohang Wang ◽  
Qin Zhang ◽  
Antonio Pepe ◽  
Pietro Mastro ◽  
Chaoying Zhao ◽  
...  
Keyword(s):  
Time Series ◽  
Ground Deformation ◽  
Groundwater Depletion ◽  
Selection Algorithm ◽  
Ground Displacement ◽  
Groundwater Exploitation ◽  
Freeze Thaw ◽  
Ground Deformations ◽  
Deformation Velocity ◽  
Small Baseline

This work investigated the large-scale ground deformations threatening the Northern Urumqi district, China, which are connected to groundwater exploitation and the seasonal freeze–thaw cycles that characterize this frozen region. Ground deformations can be well captured by satellite data using a multi-temporal interferometric synthetic aperture radar (Mt-InSAR) approach. The accuracy of the achievable ground deformation products (e.g., mean displacement time series and related ground displacement time series) critically depends on the number and quality of the selected interferograms. This paper presents a straightforward interferogram selection algorithm that can be applied to identify an optimal network of small baseline (SB) interferograms. The selected SB interferograms are then used to produce ground deformation products using the well-known small baseline subset (SBAS) Mt-InSAR algorithm. The developed interferogram selection algorithm (ISA) permits the selection of the group of SB data pairs that minimize the relative error of the mean ground deformation velocity. Experiments were carried out using a group of 102 Sentinel-1B SAR data collected from 12 April 2017 to 29 October 2020. This research study shows that the investigated farmland region is characterized by a maximum ground deformation rate of about 120 mm/year. Periodic groundwater overexploitation, coupled with irrigation and freeze–thaw phases, is also responsible for seasonal (one-year) ground displacement signals, with oscillation amplitudes up to 120 mm in the zones of maximum displacement.

scholarly journals Meso- and microscale sea-ice motion in the East Siberian Sea as determined from ERS-1 SAR Data

1999 ◽  
Vol 45 (150) ◽  
pp. 370-383 ◽  
Author(s):  
Kim Morris ◽  
Shusun Li ◽  
Martin Jeffries
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Large Scale ◽  
Vector Fields ◽  
Lateral Displacement ◽  
Motion Vector ◽  
Sar Interferometry ◽  
East Siberian Sea ◽  
Pack Ice ◽  
Sar Data

Abstract Synthetic aperture radar- (SAR-)derived ice-motion vectors and SAR interferometry were used to study the sea-ice conditions in the region between the coast and 75° N (~ 560 km) in the East Siberian Sea in the vicinity of the Kolyma River. ERS-1 SAR data were acquired between 24 December 1993 and 30 March 1994 during the 3 day repeat Ice Phase of the satellite. The time series of the ice-motion vector fields revealed rapid (3 day) changes in the direction and displacement of the pack ice. Longer-term (≥ 1 month) trends also emerged which were related to changes in large-scale atmospheric circulation. On the basis of this time series, three sea-ice zones were identified: the near-shore, stationary-ice zone; a transitional-ice zone;and the pack-ice zone. Three 3 day interval and one 9 day interval interferometric sets (amplitude, correlation and phase diagrams) were generated for the end of December, the begining of February and mid-March. They revealed that the stationary-ice zone adjacent to the coast is in constant motion, primarily by lateral displacement, bending, tilting and rotation induced by atmospheric/oceanic forcing. The interferogram patterns change through time as the sea ice becomes thicker and a network of cracks becomes established in the ice cover. It was found that the major features in the interferograms were spatially correlated with sea-ice deformation features (cracks and ridges) and major discontinuities in ice thickness.

scholarly journals Sentinel-1 SAR Interferometry for Surface Deformation Monitoring in Low-Land Permafrost Areas

2018 ◽  
Vol 10 (9) ◽  
pp. 1360 ◽  
Author(s):  
Tazio Strozzi ◽  
Sofia Antonova ◽  
Frank Günther ◽  
Eva Mätzler ◽  
Gonçalo Vieira ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Displacement ◽  
Deformation Monitoring ◽  
In Situ Measurements ◽  
River Delta ◽  
Sar Interferometry ◽  
The Arctic ◽  
Lena River ◽  
Lena River Delta

Low-land permafrost areas are subject to intense freeze-thaw cycles and characterized by remarkable surface displacement. We used Sentinel-1 SAR interferometry (InSAR) in order to analyse the summer surface displacement over four spots in the Arctic and Antarctica since 2015. Choosing floodplain or outcrop areas as the reference for the InSAR relative deformation measurements, we found maximum subsidence of about 3 to 10 cm during the thawing season with generally high spatial variability. Sentinel-1 time-series of interferograms with 6–12 day time intervals highlight that subsidence is often occurring rather quickly within roughly one month in early summer. Intercomparison of summer subsidence from Sentinel-1 in 2017 with TerraSAR-X in 2013 over part of the Lena River Delta (Russia) shows a high spatial agreement between both SAR systems. A comparison with in-situ measurements for the summer of 2014 over the Lena River Delta indicates a pronounced downward movement of several centimetres in both cases but does not reveal a spatial correspondence between InSAR and local in-situ measurements. For the reconstruction of longer time-series of deformation, yearly Sentinel-1 interferograms from the end of the summer were considered. However, in order to infer an effective subsidence of the surface through melting of excess ice layers over multi-annual scales with Sentinel-1, a longer observation time period is necessary.

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