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Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography
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Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (30 November 2018) | Viewed by 102170

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Michele Crosetto

E-Mail Website
Guest Editor
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Remote Sensing Department, Division of Geomatics, Av. Gauss, 7 E-08860 Castelldefels (Barcelona), Spain
Interests: radar interferometry; persistent scatterer interferometry; ground-based SAR; deformation monitoring; real-aperture-radar; vibration monitoring
Dr. Oriol Monserrat

E-Mail Website
Guest Editor
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Remote Sensing Department, Division of Geomatics, Av. Gauss, 7 E-08860 Castelldefels, Barcelona, Spain
Interests: remote sensing data processing; SAR data; SAR interferometry; geohazards monitoring; landslide mapping; building monitoring; land subsidence
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandra Budillon

E-Mail Website
Guest Editor
Engineering Department, Universita' degli studi di Napoli Parthenope, Centro Direzionale, Isola C4, 80143 Napoli, Italy
Interests: statistical signal processing; SAR processing; SAR interferometry; SAR tomography; urban monitoring

Special Issue Information

Dear Colleagues,

Our capability to monitor deformation using satellite-based SAR sensors has increased substantially in the last years, thanks to the availability of multiple SAR sensors and the development of several data processing and analysis procedures. This Special Issue is focused on the deformation monitoring in urban areas based on two techniques: Persistent Scatterer Interferometry (PSI) and SAR tomography (TomoSAR). The Special Issue targets collecting the latest innovative research results related to at least one of the abovementioned techniques. These can include new data processing algorithms and procedures, results based on new types of SAR data, and the development of innovative urban deformation monitoring applications. The topics of interest include, but are not limited to:

·         New PSI algorithms for urban deformation monitoring,
·         PSI results based on new types of data, included polarimetric SAR data,
·         Persistent Scatterers and Distributed Scatterers in urban deformation monitoring,
·         Integration and fusion with data from multiple sources,
·         Development of innovative urban deformation monitoring PSI applications,
·         New TomoSAR algorithms for urban deformation monitoring,
·         TomoSAR results based on new types of data, including polarimetric SAR data,
·         Development of innovative urban deformation monitoring TomoSAR applications,
·         PSI and TomoSAR cross-comparison,
·         PSI and TomoSAR validation,
·         Assessment of PSI and TomoSAR performances for urban deformation monitoring, and
·         Review papers on PSI and TomoSAR for urban deformation monitoring.

Dr. Michele Crosetto
Dr. Oriol Monserrat
Dr. Alessandra Budillon
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Satellite-based Synthetic Aperture Radar,
  • Differential Interferometric SAR,
  • Persistent Scatterer Interferometry,
  • SAR tomography,
  • Deformation monitoring,
  • Urban deformation monitoring,
  • Monitoring applications,
  • Cross-comparison,
  • Validation.

Published Papers (16 papers)

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Editorial

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3 pages, 190 KiB  
Editorial
Editorial for the Special Issue “Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR Tomography”
by Alessandra Budillon, Michele Crosetto and Oriol Monserrat
Remote Sens. 2019, 11(11), 1306; https://doi.org/10.3390/rs11111306 - 31 May 2019
Cited by 4 | Viewed by 2513
Abstract
This Special Issue hosts papers related to deformation monitoring in urban areas based on two main techniques: Persistent Scatterer Interferometry (PSI) and Synthetic Aperture Radar (SAR) Tomography (TomoSAR). Several contributions highlight the capabilities of Interferometric SAR (InSAR) and PSI techniques for urban deformation [...] Read more.
This Special Issue hosts papers related to deformation monitoring in urban areas based on two main techniques: Persistent Scatterer Interferometry (PSI) and Synthetic Aperture Radar (SAR) Tomography (TomoSAR). Several contributions highlight the capabilities of Interferometric SAR (InSAR) and PSI techniques for urban deformation monitoring. In this Special Issue, a wide range of InSAR and PSI applications are addressed. Some contributions show the advantages of TomoSAR in un-mixing multiple scatterers for urban mapping and monitoring. This issue includes a contribution that compares PSI and TomoSAR and another one that uses polarimetric data for TomoSAR. Full article
(This article belongs to the Special Issue Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography)

Research

Jump to: Editorial, Other

22 pages, 11568 KiB  
Article
How Groundwater Level Fluctuations and Geotechnical Properties Lead to Asymmetric Subsidence: A PSInSAR Analysis of Land Deformation over a Transit Corridor in the Los Angeles Metropolitan Area
by Mohammad Khorrami, Babak Alizadeh, Erfan Ghasemi Tousi, Mahyar Shakerian, Yasser Maghsoudi and Peyman Rahgozar
Remote Sens. 2019, 11(4), 377; https://doi.org/10.3390/rs11040377 - 13 Feb 2019
Cited by 46 | Viewed by 6425
Abstract
Los Angeles has experienced ground deformations during the past decades. These ground displacements can be destructive for infrastructure and can reduce the land capacity for groundwater storage. Therefore, this paper seeks to evaluate the existing ground displacement patterns along a new metro tunnel [...] Read more.
Los Angeles has experienced ground deformations during the past decades. These ground displacements can be destructive for infrastructure and can reduce the land capacity for groundwater storage. Therefore, this paper seeks to evaluate the existing ground displacement patterns along a new metro tunnel in Los Angeles, known as the Sepulveda Transit Corridor. The goal is to find the most crucial areas suffering from subsidence or uplift and to enhance the previous reports in this metropolitan area. For this purpose, we applied a Persistent Scatterer Interferometric Synthetic Aperture Radar using 29 Sentinel-1A acquisitions from June 2017 to May 2018 to estimate the deformation rate. The assessment procedure demonstrated a high rate of subsidence in the Inglewood field that is near the study area of the Sepulveda Transit Corridor with a maximum deformation rate of 30 mm/yr. Finally, data derived from in situ instruments as groundwater level variations, GPS observations, and soil properties were collected and analyzed to interpret the results. Investigation of geotechnical boreholes indicates layers of fine-grained soils in some parts of the area and this observation confirms the necessity of more detailed geotechnical investigations for future constructions in the region. Results of investigating line-of-sight displacement rates showed asymmetric subsidence along the corridor and hence we proposed a new framework to evaluate the asymmetric subsidence index that can help the designers and decision makers of the project to consider solutions to control the current subsidence. Full article
(This article belongs to the Special Issue Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The study area for PSInSAR analysis, including the Sepulveda Transit Corridor.</p> Full article ">Figure 2
<p>The spatiotemporal baseline configuration of interferometric pairs showing the SLC data in this study (29 images): Sentinel-1A, descending mode (track 71), and polarization VV.</p> Full article ">Figure 3
<p>The coherence map obtained in PSInSAR analysis in the study area.</p> Full article ">Figure 4
<p>Mean velocity map of land deformation (mm/yr) in the region covering a period between June 2017 and May 2018 overlapped onto Google Earth high-resolution imagery. The black line shows the boundary of Sepulveda Transit Corridor study area. The corridor categorized into three zones based on the trend of displacement rates: (a) from 0 to 12 km; (b) from 12 to 24 km; and (c) from 24 to 34 km.</p> Full article ">Figure 5
<p>Deformation map in the Inglewood area.</p> Full article ">Figure 6
<p>Average rate of displacement along the Sepulveda Transit Corridor from south to north.</p> Full article ">Figure 7
<p>The location of GPS stations (squares) and piezometers (circles) in the study area.</p> Full article ">Figure 8
<p>Comparison between PSInSAR-derived time series deformation (red triangles) and GPS observations (blue dots), Line-of-Sight direction, from June 2017 to May 2018.</p> Full article ">Figure 9
<p>Temporal evaluation of groundwater level variations for six piezometers (P1 to P6) located in the study area of Sepulveda Transit Corridor.</p> Full article ">Figure 10
<p>Surface soil map of the study area. The map is created based on raw soil data provided by the Los Angeles County Department of Public Works, Water Resources Division.</p> Full article ">Figure 11
<p>The location of Lithological logs.</p> Full article ">Figure 12
<p>Lithological logs in the interest area.</p> Full article ">Figure 13
<p>An example for ASI calculation for an infrastructure in the study area.</p> Full article ">
22 pages, 6373 KiB  
Article
Subsidence Zonation Through Satellite Interferometry in Coastal Plain Environments of NE Italy: A Possible Tool for Geological and Geomorphological Mapping in Urban Areas
by Mario Floris, Alessandro Fontana, Giulia Tessari and Mariachiara Mulè
Remote Sens. 2019, 11(2), 165; https://doi.org/10.3390/rs11020165 - 16 Jan 2019
Cited by 30 | Viewed by 4477
Abstract
The main aim of this paper is to test the use of multi-temporal differential interferometric synthetic aperture radar (DInSAR) techniques as a tool for geological and geomorphological surveys in urban areas, where anthropogenic features often completely obliterate landforms and surficial deposits. In the [...] Read more.
The main aim of this paper is to test the use of multi-temporal differential interferometric synthetic aperture radar (DInSAR) techniques as a tool for geological and geomorphological surveys in urban areas, where anthropogenic features often completely obliterate landforms and surficial deposits. In the last two decades, multi-temporal DInSAR techniques have been extensively applied to many topics of Geosciences, especially in geohazard analysis and risks assessment, but few attempts have been made in using differential subsidence for geological and geomorphological mapping. With this aim, interferometric data of an urbanized sector of the Venetian-Friulian Plain were considered. The data derive by permanent scatterers InSAR processing of synthetic aperture radar (SAR) images acquired by ERS 1/2, ENVISAT, COSMO SKY-Med and Sentinel-1 missions from 1992 to 2017. The obtained velocity maps identify, with high accuracy, the border of a fluvial incised valley formed after the last glacial maximum (LGM) and filled by unconsolidated Holocene deposits. These consist of lagoon and fluvial sediments that are affected by a much higher subsidence than the surrounding LGM deposits forming the external plain. Displacement time-series of localized sectors inside the post-LGM incision allowed the causes of vertical movements to be explored, which consist of the consolidation of recent deposits, due to the loading of new structures and infrastructures, and the exploitation of the shallow phreatic aquifer. Full article
(This article belongs to the Special Issue Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>(<b>a</b>) Simplified geomorphological sketch of north-eastern Italy, with an indication of the study area (red square). Legend: (1) rivers; (2) upstream limit of the spring line; (3) boundary of the Tagliamento alluvial megafan; (4) Alps; (5) morainic amphitheater; (6) gravelly plain; (7) fine-dominated distal plain; (8) reclaimed areas currently under sea level; 9) coastal sand ridges and beaches. (<b>b</b>) Digital elevation model of the study area (modified from [<a href="#B47-remotesensing-11-00165" class="html-bibr">47</a>]).</p> Full article ">Figure 2
<p>(<b>a</b>) Map of the geological units (after [<a href="#B43-remotesensing-11-00165" class="html-bibr">43</a>]). Legend: (1) lagoon deposits of late Holocene; (2) swamp organic deposits; (3) organic deposits at the bottom of the valley of Reghena River; (4) alluvial deposits of Early Middle Age; (5) alluvial deposits of Roman age; (6) alluvial deposits of early Holocene; (7) Last Glacial Maximum (LGM) alluvial deposits. (<b>b</b>) Map of the thickness of the post-LGM deposits (modified from [<a href="#B42-remotesensing-11-00165" class="html-bibr">42</a>]).</p> Full article ">Figure 3
<p>Reference cross section of the stratigraphic setting near Concordia Sagittaria (modified after [<a href="#B47-remotesensing-11-00165" class="html-bibr">47</a>]). The location of the section is reported in <a href="#remotesensing-11-00165-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 4
<p>Persistent Scatterers (PS) velocity maps derived from PSI processing of ERS (1992-2000) (<b>a</b>,<b>b</b>), ENVISAT (2003–2010) (<b>c</b>), CSK (2012–2016) (<b>d</b>), and Sentinel-1 (2014–2017) (<b>e</b>) synthetic aperture radar (SAR) data. Graduated blue lines (Isopach) show the thickness of post-LGM sediments.</p> Full article ">Figure 5
<p>East-West (<b>a</b>) and vertical (<b>b</b>) components of displacement rate estimated by combining ERS ascending and descending PSI results. The green color (positive values) indicates displacements toward East (<b>a</b>) and uplift (<b>b</b>), red color (negative values) indicates movements to West (<b>a</b>) and down-lift (<b>b</b>). Graduated blue lines (Isopach) show the thickness of post-LGM sediments (same classification of <a href="#remotesensing-11-00165-f004" class="html-fig">Figure 4</a>).</p> Full article ">Figure 6
<p>Velocity map (<b>a</b>) and cross sections (<b>b</b>–<b>g</b>) showing the variation in the displacement rate due to the presence of post-LGM sediments. Gray continuous lines indicate the variation in the ground surface elevation. Gray dashed lines indicate the boundary of LGM and post-LGM deposits.</p> Full article ">Figure 7
<p>Land cover change from 1989 to 2012 in the area indicated by the red rectangle in (<b>a</b>) and PS velocities calculated through PSI processing of ERS (<b>c</b>), ENVISAT (<b>d</b>), COSMO SkyMED (<b>e</b>) and Sentinel-1 (<b>f</b>) SAR data. Purple circles in (<b>b</b>) indicate the sectors where the time series of displacement have been plotted in <a href="#remotesensing-11-00165-f008" class="html-fig">Figure 8</a>. The blue line (<b>b</b>–<b>f</b>) indicates the border of the post-LGM incision.</p> Full article ">Figure 8
<p>Time series of vertical displacements (Vv) calculated through Equation 3 applied to PSI results in the sectors 1 (<b>a</b>), 2 (<b>b</b>) and 3 (<b>c</b>) of the area showed in <a href="#remotesensing-11-00165-f007" class="html-fig">Figure 7</a>b. To better follow the temporal evolution, the displacements derived by ENVISAT and COSMO –SkyMED (CSK) datasets are plotted starting from the linearly interpolated value (continuous lines) of the previous dataset. Note the similar results from PSI processing of CSK and Sentinel SAR data during the overlapping period of acquisition, which shows the high precision of the calculations.</p> Full article ">Figure 9
<p>Land cover change before (<b>b</b>) and after (<b>c</b>) 2010 in the area indicated by the red square in (<b>a</b>) showing the different rate of subsidence outside and inside the post-LGM incision calculated through PSI processing of Sentinel-1 SAR data (<b>c</b>).</p> Full article ">Figure 10
<p>PS velocity maps of the area indicated by the red square in (<b>a</b>) ERS (<b>b</b>), ENVISAT (<b>c</b>), COSMO SKY-Med, (<b>d</b>) and Sentinel (<b>e</b>) interferometric data. The comparison between (<b>f</b>) and (<b>g</b>) shows that no land cover changes occurred during the observation period.</p> Full article ">
17 pages, 14122 KiB  
Article
Measuring Urban Subsidence in the Rome Metropolitan Area (Italy) with Sentinel-1 SNAP-StaMPS Persistent Scatterer Interferometry
by José Manuel Delgado Blasco, Michael Foumelis, Chris Stewart and Andrew Hooper
Remote Sens. 2019, 11(2), 129; https://doi.org/10.3390/rs11020129 - 11 Jan 2019
Cited by 133 | Viewed by 18897
Abstract
Land subsidence in urban environments is an increasingly prominent aspect in the monitoring and maintenance of urban infrastructures. In this study we update the subsidence information over Rome and its surroundings (already the subject of past research with other sensors) for the first [...] Read more.
Land subsidence in urban environments is an increasingly prominent aspect in the monitoring and maintenance of urban infrastructures. In this study we update the subsidence information over Rome and its surroundings (already the subject of past research with other sensors) for the first time using Copernicus Sentinel-1 data and open source tools. With this aim, we have developed a fully automatic processing chain for land deformation monitoring using the European Space Agency (ESA) SentiNel Application Platform (SNAP) and Stanford Method for Persistent Scatterers (StaMPS). We have applied this automatic processing chain to more than 160 Sentinel-1A images over ascending and descending orbits to depict primarily the Line-Of-Sight ground deformation rates. Results of both geometries were then combined to compute the actual vertical motion component, which resulted in more than 2 million point targets, over their common area. Deformation measurements are in agreement with past studies over the city of Rome, identifying main subsidence areas in: (i) Fiumicino; (ii) along the Tiber River; (iii) Ostia and coastal area; (iv) Ostiense quarter; and (v) Tivoli area. Finally, post-processing of Persistent Scatterer Inteferometry (PSI) results, in a Geographical Information System (GIS) environment, for the extraction of ground displacements on urban infrastructures (including road networks, buildings and bridges) is considered. Full article
(This article belongs to the Special Issue Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography)
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Figure 1

Figure 1
<p>Area of interest and footprint of the selected Sentinel-1 master scenes for both ascending (A117) and descending (D022) tracks. ALOS World 3D DSM used as background.</p> Full article ">Figure 2
<p>Schematic diagram presenting the different chains of the SentiNel Application Platform (SNAP) workflow to prepare the interferometric inputs for Stanford Method for Persistent Scatterers (StaMPS) Persistent Scatterer Interferometry (PSI) processing [<a href="#B9-remotesensing-11-00129" class="html-bibr">9</a>]. Part (<b>A</b>–<b>C</b>) illustrate the workflow employed for slave splitting, coregistration, interferometric computation and StaMPS export, respectively.</p> Full article ">Figure 3
<p>Ascending and descending decomposition in vertical and horizontal components (<b>A</b>) and the Azimuth Look Direction (ALD) for the descending orbit pass (<b>B</b>).</p> Full article ">Figure 4
<p>Sentinel-1 average Line-Of-Sight (LOS) deformation rate maps over the period March 2015–April 2018 for descending (<b>left</b>) and ascending (<b>right</b>) acquisition geometries. Positive values indicate motion towards the sensor or uplift, whereas negative values motion away from the sensor or subsidence. Selected reference point (M0SE00ITA EUREF station) is shown as square. ALOS World 3D Digital Surface Model (DSM) as background.</p> Full article ">Figure 5
<p><b>Figure</b><b>5.</b> Sentinel-1 average vertical motion rates for the period March 2015–April 2018. For the decomposition, PS points at full resolution were spatially down sampled by a window of 40 m radius (see <a href="#sec2dot2dot2-remotesensing-11-00129" class="html-sec">Section 2.2.2</a>). Selected reference point (M0SE00ITA EUREF station) is marked by a black square. ALOS World 3D DSM as background. The locations of other figures are also shown.</p> Full article ">Figure 6
<p>Sentinel-1 PS density over a 200 m resolution grid (<b>left</b>) and PS locations for both ascending and descending geometries at full resolution. Black square indicates the location of the zoom image shown on the right. ALOS World 3D DSM (<b>left</b>) and CartoSat-1 DSM (<b>right</b>) as backgrounds.</p> Full article ">Figure 7
<p>Average vertical motion rates along motorways as well as primary and secondary road networks (<b>left</b>) and a detail over Rome city, including tertiary, residential and pedestrian roads (<b>right</b>). ALOS World DSM (AW3D30) (<b>left</b>) and CartoSat-1 DSM (<b>right</b>) as backgrounds.</p> Full article ">Figure 8
<p>Average vertical deformation rates along the Tiber River and its tributaries (<b>left</b>) and detailed views presenting the estimated maximum deformation rates per building block within the center of Rome (<b>right</b>). The selected reference point (M0SE00ITA EUREF station) is shown in the black square. CartoSat-1 DSM as background.</p> Full article ">Figure 9
<p>Spatial distribution of average vertical deformation rates at Fiumicino (FCO) airport area overlain on CartoSat-1 DSM (<b>A</b>). PSI LOS displacement time series for selected point target (<b>B</b>) and cumulative motion plot for the Roma-Fiumicino highway (<b>C</b>) are also shown.</p> Full article ">Figure 10
<p>Deformation patterns at the “A1 Diramazione Roma Nord” north of Rome, showing subsidence of alluvial deposits and relative stability of constructed bridges. Geolocation accuracy of the obtained PS results can be visually assessed based on the overlap on the CartoSat-1 DSM in the background.</p> Full article ">Figure 11
<p>Maximum subsidence rate per soil lithological type in the broader metropolitan area of Rome [<a href="#B31-remotesensing-11-00129" class="html-bibr">31</a>]. Sorted from lower to higher maximum subsidence value.</p> Full article ">
23 pages, 29099 KiB  
Article
Using TSX/TDX Pursuit Monostatic SAR Stacks for PS-InSAR Analysis in Urban Areas
by Ziyun Wang, Timo Balz, Lu Zhang, Daniele Perissin and Mingsheng Liao
Remote Sens. 2019, 11(1), 26; https://doi.org/10.3390/rs11010026 - 24 Dec 2018
Cited by 11 | Viewed by 4427
Abstract
Persistent Scatterer Interferometry (PS-InSAR) has become an indispensable tool for monitoring surface motion in urban environments. The interferometric configuration of PS-InSAR tends to mix topographic and deformation components in differential interferometric observations. When the upcoming constellation missions such as, e.g., TanDEM-L or TWIN-L [...] Read more.
Persistent Scatterer Interferometry (PS-InSAR) has become an indispensable tool for monitoring surface motion in urban environments. The interferometric configuration of PS-InSAR tends to mix topographic and deformation components in differential interferometric observations. When the upcoming constellation missions such as, e.g., TanDEM-L or TWIN-L provide new standard operating modes, bi-static stacks for deformation monitoring will be more commonly available in the near future. In this paper, we present an analysis of the applicability of such data sets for urban monitoring, using a stack of pursuit monostatic data obtained during the scientific testing phase of the TanDEM-X (TDX) mission. These stacks are characterized by extremely short temporal baselines between the TerraSAR-X (TSX) and TanDEM-X acquisitions at the same interval. We evaluate the advantages of this acquisition mode for urban deformation monitoring with several of the available acquisition pairs. Our proposed method exploits the special properties of this data using a modified processing chain based on the standard PS-InSAR deformation monitoring procedure. We test our approach with a TSX/TDX mono-static pursuit stack over Guangzhou, using both the proposed method and the standard deformation monitoring procedure, and compare the two results. The performance of topographic and deformation estimation is improved by using the proposed processing method, especially regarding high-rise buildings, given the quantitative statistic on temporal coherence, detectable numbers, as well as the PS point density of persistent scatters points, among which the persistent scatter numbers increased by 107.2% and the detectable height span increased by 78% over the standard processing results. Full article
(This article belongs to the Special Issue Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Interferometric formation of the proposed method for height estimation from TerraSAR-X (TSX) and TanDEM-X (TDX) image pairs.</p> Full article ">Figure 2
<p>Interferometric formation of the proposed method for deformation estimation.</p> Full article ">Figure 3
<p>Flow charts of the standard processing chain (<b>left</b>) and the proposed processing method (<b>right</b>) for PS-InSAR.</p> Full article ">Figure 4
<p>Study area of the staring spotlight monostatic pursuit stack over Guangzhou.</p> Full article ">Figure 5
<p>Temporal coherence map of the selected persistent scatterer candidates in the standard processing result: (<b>A</b>) The whole study area; (<b>B</b>) Subset of the map in test region A; (<b>C</b>) Subset of the map in test region B.</p> Full article ">Figure 6
<p>Residual height map for pursuit monostatic (PM) data in Guangzhou using the standard PS-InSAR method.</p> Full article ">Figure 7
<p>3D demonstration of the highly temporal coherent persistent scatterers in test region A using standard PS-InSAR processing.</p> Full article ">Figure 8
<p>Linear deformation map generated from standard processing results using PS points with a temporal coherence bigger than 0.85.</p> Full article ">Figure 9
<p>3D-view of the estimated linear deformation trends visualized with Google Earth for the standard processing method.</p> Full article ">Figure 10
<p>Temporal coherence map of the selected PSC in the standard processing result: (<b>A</b>) The whole study area (<b>B</b>) Subset of the map in test region A (<b>C</b>) Subset of the map in test region B.</p> Full article ">Figure 11
<p>Residual height map for pursuit mono-static data in Guangzhou, using a proposed processing chain.</p> Full article ">Figure 12
<p>3D-visualization of PS-InSAR results, using the proposed processing method.</p> Full article ">Figure 13
<p>Linear deformation map generated from the proposed processing results, using PS points with a temporal coherence of larger than 0.85.</p> Full article ">Figure 14
<p>3D view of the estimated linear deformation trends visualized with Google Earth for the proposed method.</p> Full article ">Figure 15
<p>Histogram of PS points for temporal coherence in both standard and proposed processing results.</p> Full article ">Figure 16
<p>Histogram of PS point heights for both the standard and proposed processing results.</p> Full article ">Figure 17
<p>Statistics result of the two methods in sub-region 1: (<b>A</b>) variance of the standard processing method (<b>B</b>) mean height of the standard processing method (<b>C</b>) variance of the proposed processing method (<b>D</b>) mean height of the proposed processing method (<b>E</b>) Histogram of the variance difference to the standard processing method (<b>F</b>) Histogram of the mean height difference to the standard processing method.</p> Full article ">Figure 18
<p>Statistical result of the two methods in sub-region 2: (<b>A</b>) variance of the standard processing method (<b>B</b>) mean height of the standard processing method (<b>C</b>) variance of the proposed processing method (<b>D</b>) mean height of the proposed processing method (<b>E</b>) Histogram of the variance difference to the standard processing method (<b>F</b>) Histogram of the mean height difference to the standard processing method.</p> Full article ">Figure 19
<p>Linear regression results between thermal deformation and PS height: (<b>A</b>) the proposed processing result in sub-region 1, (<b>B</b>) the standard processing result in sub-region 1, (<b>C</b>) the proposed processing result in sub-region 2, (<b>D</b>) the standard processing result in sub-region 2.</p> Full article ">
21 pages, 9451 KiB  
Article
Super-Resolution Multi-Look Detection in SAR Tomography
by Cosmin Dănișor, Gianfranco Fornaro, Antonio Pauciullo, Diego Reale and Mihai Datcu
Remote Sens. 2018, 10(12), 1894; https://doi.org/10.3390/rs10121894 - 27 Nov 2018
Cited by 17 | Viewed by 4886
Abstract
Synthetic Aperture Radar (SAR) Tomography (TomoSAR) allows extending the 2-D focusing capabilities of SAR to the elevation direction, orthogonal to the azimuth and range. The multi-dimensional extension (along the time) also enables the monitoring of possible scatterer displacements. A key aspect of TomoSAR [...] Read more.
Synthetic Aperture Radar (SAR) Tomography (TomoSAR) allows extending the 2-D focusing capabilities of SAR to the elevation direction, orthogonal to the azimuth and range. The multi-dimensional extension (along the time) also enables the monitoring of possible scatterer displacements. A key aspect of TomoSAR is the identification, in the presence of noise, of multiple persistent scatterers interfering within the same 2-D (azimuth range plane) pixel. To this aim, the use of multi-look has been shown to provide tangible improvements in the detection of single and double interfering persistent scatterers at the expense of a minor spatial resolution loss. Depending on the system acquisition characteristics, this operation may require also the detection of multiple scatterers interfering at distances lower than the Rayleigh resolution (super-resolution). In this work we further investigated the use of multi-look in TomoSAR for the detection of multiple scatterers located also below the Rayleigh resolution. A solution relying on the Capon filtering was first analyzed, due to its improved capabilities in the separation of the responses of multiple scatterers and sidelobe suppression. Moreover, in the framework of the Generalized Likelihood Ratio Test (GLRT), the single-look support based detection strategy recently proposed in the literature was extended to the multi-look case. Experimental results of tests carried out on two datasets acquired by TerraSAR-X and COSMO-SkyMED sensors are provided to show the performances of the proposed solution as well as the effects of the baseline span of the dataset for the detection capabilities of interfering scatterers. Full article
(This article belongs to the Special Issue Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography)
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<p>Amplitude of the test area from the TerraSAR-X dataset (National Arena in Bucharest, Romania), averaged across dataset’s acquisitions.</p> Full article ">Figure 2
<p>Distribution of the acquisitions, depicted as circles, in the temporal/spatial baseline domain. Red circle corresponds to the master acquisition.</p> Full article ">Figure 3
<p>Maps of the estimated dominant scatterers elevation, corresponding to the peaks of the BF (<b>a</b>) and Capon (<b>b</b>) reconstructions. Colormap is in meters and set according to the estimated elevation.</p> Full article ">Figure 3 Cont.
<p>Maps of the estimated dominant scatterers elevation, corresponding to the peaks of the BF (<b>a</b>) and Capon (<b>b</b>) reconstructions. Colormap is in meters and set according to the estimated elevation.</p> Full article ">Figure 4
<p>Distribution of the detected single scatterers (<b>a</b>,<b>b</b>) and higher double (<b>c</b>,<b>d</b>) scatterers, for single-look (<b>a</b>,<b>c</b>) and multi-look (<b>b</b>,<b>d</b>) analysis. Colormap is in meters and set according to the estimated elevation.</p> Full article ">Figure 5
<p>Amplitude of the test area from the COSMO-SkyMED dataset relevant to the Basilica S. Paolo area in Rome, Italy, averaged across dataset’s acquisitions.</p> Full article ">Figure 6
<p>Distribution of the acquisitions, depicted as circles, in the temporal/spatial baseline domain. Red circle corresponds to the master acquisition.</p> Full article ">Figure 7
<p>Distribution of the detected single scatterers (<b>a</b>,<b>b</b>) and higher double (<b>c</b>,<b>d</b>) scatterers, for single-look (<b>a</b>,<b>c</b>) and multi-look (<b>b</b>,<b>d</b>) analysis Colormap is in meters and set according to the estimated elevation.</p> Full article ">Figure 8
<p>Histograms of the differences on a common grid of pixels between the residual topography (height) associated with the single scatterers single-look and the closest (<b>a</b>) and farthest (<b>b</b>) double scatterer resulting from the multi-look processing.</p> Full article ">Figure 9
<p>Geocoded scatterers detected on Rome dataset for the single-look (<b>a</b>,<b>b</b>) and multi-look (<b>c</b>,<b>d</b>) cases for the single scatterers (<b>a</b>,<b>c</b>) and both single and double scatterers (<b>b</b>,<b>d</b>).</p> Full article ">
13 pages, 3131 KiB  
Article
A Persistent Scatterer Interferometry Procedure Based on Stable Areas to Filter the Atmospheric Component
by Michele Crosetto, Núria Devanthéry, Oriol Monserrat, Anna Barra, María Cuevas-González, Marek Mróz, Joan Botey-Bassols, Enric Vázquez-Suñé and Bruno Crippa
Remote Sens. 2018, 10(11), 1780; https://doi.org/10.3390/rs10111780 - 10 Nov 2018
Cited by 7 | Viewed by 3023
Abstract
This paper describes a Persistent Scatterer Interferometry (PSI) procedure to monitor the land deformation in an urban area induced by aquifer dewatering and the consequent drawdown of the water table. The procedure, based on Sentinel-1 data, is illustrated considering the construction works of [...] Read more.
This paper describes a Persistent Scatterer Interferometry (PSI) procedure to monitor the land deformation in an urban area induced by aquifer dewatering and the consequent drawdown of the water table. The procedure, based on Sentinel-1 data, is illustrated considering the construction works of Glories Square, Barcelona (Spain). The study covers a period from March 2015 to November 2017, which includes a dewatering event in spring 2017. This paper describes the proposed procedure, whose most original part includes the estimation of the atmospheric phase component using stable areas located in the vicinity of the monitoring area. The performances of the procedure are analysed, characterising the original atmospheric phase component and the residual one that remains after modelling the atmospheric contribution. This procedure can work with any type of deformation phenomena, provided that its spatial extension is sufficiently small. The quality of the obtained time series is illustrated discussing different deformation results, including a validation result using piezometric data and a thermal expansion case. Full article
(This article belongs to the Special Issue Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography)
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<p>Study area (included in the red perimeter) and main area of interest (included in the yellow circle), which is the maximum area potentially affected by the water pumping activities. The area outside the yellow circle is considered stable. The figure inset shows the city of Barcelona.</p> Full article ">Figure 2
<p>Example of correlation drop. Normalised EAFs (i.e., EAF divided by σ<sup>2</sup><sub>corr</sub>) of the phase image #14: before the atmospheric correction (<b>red</b>) and residual phase image after the correction (<b>green</b>).</p> Full article ">Figure 3
<p>Atmospheric component estimation using stable areas. Original phases that cover an area of approximately 16 km<sup>2</sup> (<b>left</b>). The black circles show the 1-km radius area of interest. Estimated linear atmospheric components (<b>middle</b>). Residual phase after removing the linear atmospheric component (<b>right</b>). The black colour means no-used data. The first image is set to zero (green colour) because it is used as reference image.</p> Full article ">Figure 4
<p>Example of time series validation: the deformation time series (green) is strongly correlated with the piezometric data of the same area. The black deformation time series represents a solution based on spatio-temporal filters, see [<a href="#B16-remotesensing-10-01780" class="html-bibr">16</a>]: a 96-day moving average was used. The location of the point and the piezometer is shown in <a href="#remotesensing-10-01780-f005" class="html-fig">Figure 5</a>. The deformation values refer to the radar line-of-sight (LOS).</p> Full article ">Figure 5
<p>Examples of LOS accumulated deformation maps corresponding to the maximum of the displacement (12 April 2017, above) and to the recovery of the displacements (3 September 2017, below). In the above image, the three rectangles (grey-zone 1, orange-zone 2 and pink-zone 3) show the location of the three zones shown in <a href="#remotesensing-10-01780-f006" class="html-fig">Figure 6</a>. The green circle A shows the location of the point considered in <a href="#remotesensing-10-01780-f004" class="html-fig">Figure 4</a>. The white circle shows the location of the piezometer.</p> Full article ">Figure 6
<p>Examples of LOS deformation time series of three zones located in the deformation area shown in <a href="#remotesensing-10-01780-f005" class="html-fig">Figure 5</a>. The time series display the median values of the points contained in each zone. The blue time series concerns the piezometric data.</p> Full article ">Figure 7
<p>Example of LOS deformation time series related to three zones located in the stable area shown in <a href="#remotesensing-10-01780-f001" class="html-fig">Figure 1</a>. The blue line shows the piezometric data. The plotted values represent the median deformation measured within the 3 green circles from <a href="#remotesensing-10-01780-f001" class="html-fig">Figure 1</a>.</p> Full article ">Figure 8
<p>LOS displacement time series (<b>orange line</b>) of one single PS and plot of the corresponding temperatures (<b>grey line</b>). This is a clear example of thermal expansion displacements.</p> Full article ">
18 pages, 8584 KiB  
Article
Displacement Monitoring and Health Evaluation of Two Bridges Using Sentinel-1 SAR Images
by Qihuan Huang, Oriol Monserrat, Michele Crosetto, Bruno Crippa, Yian Wang, Jianfeng Jiang and Youliang Ding
Remote Sens. 2018, 10(11), 1714; https://doi.org/10.3390/rs10111714 - 30 Oct 2018
Cited by 29 | Viewed by 4722
Abstract
Displacement monitoring of large bridges is an important source of information concerning their health state. In this paper, a procedure based on satellite Persistent Scatterer Interferometry (PSI) data is presented to assess bridge health. The proposed approach periodically assesses the displacements of a [...] Read more.
Displacement monitoring of large bridges is an important source of information concerning their health state. In this paper, a procedure based on satellite Persistent Scatterer Interferometry (PSI) data is presented to assess bridge health. The proposed approach periodically assesses the displacements of a bridge in order to detect abnormal displacements at any position of the bridge. To demonstrate its performances, the displacement characteristics of two bridges, the Nanjing-Dashengguan High-speed Railway Bridge (NDHRB, 1272 m long) and the Nanjing-Yangtze River Bridge (NYRB, 1576-m long), are studied. For this purpose, two independent Sentinel-1 SAR datasets were used, covering a two-year period with 75 and 66 images, respectively, providing very similar results. During the observed period, the two bridges underwent no actual displacements: thermal dilation displacements were dominant. For NDHRB, the total thermal dilation parameter from the PSI analysis was computed using the two different datasets; the difference of the two computations was 0.09 mm/°C, which, assuming a temperature variation of 30 °C, corresponds to a discrepancy of 2.7 mm over the total bridge length. From the total thermal dilation parameters, the coefficients of thermal expansion (CTE) were calculated, which were 11.26 × 10−6/°C and 11.19 × 10−6/°C, respectively. These values match the bridge metal properties. For NYRB, the estimated CTE was 10.46 × 10−6/°C, which also matches the bridge metal properties (11.26 × 10−6/°C). Based on a statistical analysis of the PSI topographic errors of NDHRB, pixels on the bridge deck were selected, and displacement models covering the entire NDHRB were established using the two track datasets; the model was validated on the six piers with an absolute mean error of 0.25 mm/°C. Finally, the health state of NDHRB was evaluated with four more images using the estimated models, and no abnormal displacements were found. Full article
(This article belongs to the Special Issue Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography)
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<p>Flow chart of the bridge health evaluation procedure.</p> Full article ">Figure 2
<p>Scheme of the selected pixels connection.</p> Full article ">Figure 3
<p>Sentinel SAR image coverage over the two bridges. (<b>a</b>) Location of the two bridges and burst coverage (white and red rectangles) of the two ascending SAR datasets used in this study; (<b>b</b>) Footprint of the two tracks; (<b>c</b>) Photo of the Nanjing-Dashengguan High-speed Railway Bridge (NDHRB), the heights of the structure are taken from Reference [<a href="#B24-remotesensing-10-01714" class="html-bibr">24</a>]; (<b>d</b>) Cross-section of the NDHRB; (<b>e</b>) Photo of the Nanjing-Yangtze River Bridge (NYRB); (<b>f</b>) Layout of the NYRB.</p> Full article ">Figure 4
<p>Relation between the line of sight (LOS) displacements and those in the bridge longitudinal direction. Scheme in the vertical plane (<b>a</b>) and in the horizontal one (<b>b</b>). It is worth noting that in this analysis it has been assumed that the bridge slope is almost zero.</p> Full article ">Figure 5
<p>Relation of the sensitivity <math display="inline"><semantics> <mi mathvariant="normal">s</mi> </semantics></math> as a function of the radar incidence angle <math display="inline"><semantics> <mi mathvariant="sans-serif">θ</mi> </semantics></math> and the horizontal angle <math display="inline"><semantics> <mi mathvariant="sans-serif">α</mi> </semantics></math>.</p> Full article ">Figure 6
<p>Spatial and temporal baselines of the Sentinel-1 datasets: Track 01 (<b>a</b>) and Track 02 (<b>b</b>).</p> Full article ">Figure 7
<p>Estimated topographic errors and their statistics for the NDHRB. The figure marked with ‘(<b>a</b>)’ is related to Track 01, and that with ‘(<b>b</b>)’ refers to Track 02.</p> Full article ">Figure 8
<p>Estimated thermal dilation parameter in the LOS direction. The figure marked with ‘(<b>a</b>)’ is related to Track 01, and that marked with ‘(<b>b</b>)’ refers to Track 02.</p> Full article ">Figure 9
<p>Average cross thermal dilation parameters in the longitudinal direction. The figure marked with ‘(<b>a</b>)’ is related to Track 01, and that marked with ‘(<b>b</b>)’ refers to Track 02.</p> Full article ">Figure 10
<p>Average cross velocities in the longitudinal direction. The figure marked with ‘(<b>a</b>)’ is related to Track 01, and that marked with ‘(<b>b</b>)’ refers to Track 02.</p> Full article ">Figure 11
<p>Thermal dilations measured by Track 01, Track 02, and the in-situsensors.</p> Full article ">Figure 12
<p>LOS phase components of the NYRB, estimated with Track 01 (upper) and Track 02 (lower); the reference point is marked with the red triangle, (<b>a</b>) thermal dilation coefficient, (<b>b</b>) topography error, and (<b>c</b>) linear velocity.</p> Full article ">Figure 13
<p>Accuracy evaluation of the bridge displacement model over Track 01. Measured displacement time series (<b>left</b>), modeled displacement time series (<b>middle</b>), and their difference (<b>right</b>).</p> Full article ">Figure 14
<p>NDHRB health evaluation using the proposed method for the two tracks. From top to bottom of each interferometric pair: longitudinal displacement interferometric measurements, averaged cross values, modeled value, and difference between the measurement and the modeled one.</p> Full article ">Figure 15
<p>Vertical displacement time series induce by a high-speed train (<b>a</b>) and a metro (<b>b</b>) in the middle of the 192-m-span monitored using IBIS-S.</p> Full article ">
25 pages, 8428 KiB  
Article
Multi-Sensor InSAR Analysis of Progressive Land Subsidence over the Coastal City of Urayasu, Japan
by Yusupujiang Aimaiti, Fumio Yamazaki and Wen Liu
Remote Sens. 2018, 10(8), 1304; https://doi.org/10.3390/rs10081304 - 18 Aug 2018
Cited by 36 | Viewed by 7228
Abstract
In earthquake-prone areas, identifying patterns of ground deformation is important before they become latent risk factors. As one of the severely damaged areas due to the 2011 Tohoku earthquake in Japan, Urayasu City in Chiba Prefecture has been suffering from land subsidence as [...] Read more.
In earthquake-prone areas, identifying patterns of ground deformation is important before they become latent risk factors. As one of the severely damaged areas due to the 2011 Tohoku earthquake in Japan, Urayasu City in Chiba Prefecture has been suffering from land subsidence as a part of its land was built by a massive land-fill project. To investigate the long-term land deformation patterns in Urayasu City, three sets of synthetic aperture radar (SAR) data acquired during 1993–2006 from European Remote Sensing satellites (ERS-1/-2 (C-band)), during 2006–2010 from the Phased Array L-band Synthetic Aperture Radar onboard the Advanced Land Observation Satellite (ALOS PALSAR (L-band)) and from 2014–2017 from the ALOS-2 PALSAR-2 (L-band) were processed by using multitemporal interferometric SAR (InSAR) techniques. Leveling survey data were also used to verify the accuracy of the InSAR-derived results. The results from the ERS-1/-2, ALOS PALSAR and ALOS-2 PALSAR-2 data processing showed continuing subsidence in several reclaimed areas of Urayasu City due to the integrated effects of numerous natural and anthropogenic processes. The maximum subsidence rate of the period from 1993 to 2006 was approximately 27 mm/year, while the periods from 2006 to 2010 and from 2014 to 2017 were approximately 30 and 18 mm/year, respectively. The quantitative validation results of the InSAR-derived deformation trend during the three observation periods are consistent with the leveling survey data measured from 1993 to 2017. Our results further demonstrate the advantages of InSAR measurements as an alternative to ground-based measurements for land subsidence monitoring in coastal reclaimed areas. Full article
(This article belongs to the Special Issue Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography)
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<p>The map of the study area, Urayasu City, Japan. (<b>a</b>) The geographic location of Urayasu City; (<b>b</b>) the distribution and development history of the reclaimed areas, namely Moto-Machi (old town) outlined in green, Naka-Machi (central town) outlined in yellow and Shin-Machi (new town) outlined in red. A to G represent the reclaimed areas at different times. The background image is a Phased Array L-band Synthetic Aperture Radar onboard the Advanced Land Observation Satellite (ALOS-2 PALSAR-2) intensity image acquired on 4 December 2014; and (<b>c</b>) the topography of the study area [<a href="#B36-remotesensing-10-01304" class="html-bibr">36</a>].</p> Full article ">Figure 2
<p>The temporal and spatial baseline distributions of the SAR interferograms from the ERS-1/-2, ALOS PALSAR and ALOS-2 PALSAR-2 data sets (a–e), where each acquisition is represented by a diamond associated to an ID number; the green diamonds represent the valid acquisitions and the yellow diamonds represent the selected master image of persistent scatterers interferometry (PSI) and super master image of the small baseline subset (SBAS). (<b>a</b>) Time–position plot of PSI interferograms generated by the ERS-1/-2 data, with 24 January 2000 as the master image; (<b>b</b>) time–baseline plot of SBAS interferograms generated by the ERS-1/-2 data, with 2 August 1999 as the super master image; (<b>c</b>) time–position plot of PSI interferograms generated by the ALOS PALSAR data, with 5 August 2009 as the master image; (<b>d</b>) time–position Delaunay 3D plot of SBAS interferograms generated by the ALOS PALSAR data, with March 20, 2009 as the super master image; (<b>e</b>) time–position Delaunay 3D plot of SBAS interferograms generated by the ALOS-2 PALSAR-2 data, with 4 December 2014 as the super master image; and (<b>f</b>) the histogram of the average coherence for the three satellite datasets. These connections in (<b>d</b>,(<b>e</b>) are a subset of the whole main network and represent such interferograms that will be unwrapped in a 3D way.</p> Full article ">Figure 3
<p>Line of sight (LOS) displacement velocity in Urayasu City from 1993 to 2006 for the ERS-1/-2 data: (<b>a</b>) Estimated mean displacement velocity using the PSI method; (<b>b</b>) estimated mean displacement velocity using the SBAS method. The background image is an ERS-2 intensity image acquired on 24 May 1999. The red points P1 to P8 are the selected points to show the time-series LOS displacements estimated by the PSI and SBAS measurements in (<b>a</b>,<b>b</b>), respectively.</p> Full article ">Figure 4
<p>Histogram distribution for the ERS-1/-2-derived displacement rates from May 1993 to February 2006: (<b>a</b>) the corresponding histogram of the PSI measurements from the ERS-1/-2 data; and (<b>b</b>) the corresponding histogram of the SBAS measurements from the ERS-1/-2 data.</p> Full article ">Figure 5
<p>Time-series LOS displacement plots of the PSI and SBAS measurements from the ERS-1/-2 data (<b>a</b>–<b>h</b>) for the selected points P1 to P8, which are indicated by red points in <a href="#remotesensing-10-01304-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 6
<p>Mean LOS displacement velocity in Urayasu City from 2006 to 2010 for the PALSAR data: (<b>a</b>) estimated mean displacement velocity using the PSI method; (<b>b</b>) estimated mean displacement velocity using the SBAS method. The background image is a PALSAR-2 intensity image acquired on 04 December 2014. The red points P1 to P8 are the selected points to show the time-series LOS displacements estimated by the PSI and SBAS measurements in (<b>a</b>,<b>b</b>), respectively. A-G represent the reclaimed areas and districts which described in <a href="#remotesensing-10-01304-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 7
<p>Histogram distribution for the PALSAR-derived results from June 2006 to December 2010. (<b>a</b>) The corresponding histogram of the PSI measurements from the PALSAR data; and (<b>b</b>) the corresponding histogram of the SBAS measurements from the PALSAR data.</p> Full article ">Figure 8
<p>Time-series LOS displacement plots of the PSI and SBAS measurements (<b>a</b>–<b>h</b>) for points P1 to P8, which are indicated as red points in <a href="#remotesensing-10-01304-f006" class="html-fig">Figure 6</a>a,b, respectively.</p> Full article ">Figure 9
<p>Mean LOS displacement velocity in Urayasu City from 2014 to 2017 for the PALSAR-2 data. The background image is a PALSAR-2 intensity image acquired on 4 December 2014. P1–P1’ to P6–P6’ are the selected profiles to show the displacement velocities at different sites.</p> Full article ">Figure 10
<p>The corresponding histogram of the SBAS measurements from the PALSAR-2 data.</p> Full article ">Figure 11
<p>Mean LOS displacement velocities for the three observation periods (<b>a</b>–<b>f</b>) along the six profiles whose positions are indicated as purple lines in <a href="#remotesensing-10-01304-f009" class="html-fig">Figure 9</a>.</p> Full article ">Figure 12
<p>Comparison between InSAR-derived linear subsidence velocity and leveling measured linear subsidence velocity during the three InSAR observation periods: (<b>a</b>,<b>b</b>) ERS-1/-2 derived linear subsidence rate (May 1993 to February 2006) and leveling-derived linear subsidence rate (January 1993 to January 2006); (<b>c</b>,<b>d</b>) PALSAR-derived linear subsidence rate (June 2006 to December 2010) and leveling-derived linear subsidence rate (January 2006 to January 2011); (<b>e</b>) PALSAR-2-derived linear subsidence rate (December 2014 to December 2016) and leveling-derived linear subsidence rate (January 2015 to January 2017); and (<b>f</b>) spatial distribution of leveling points in Urayasu City.</p> Full article ">Figure 13
<p>The spatial distribution map of difference of land subsidence rates during the three observation periods: (<b>a</b>) ERS-1/-2 derived subsidence rate using the SBAS method; (<b>b</b>) difference between ERS-1/-2 and PALSAR derived subsidence rates (subtracting ERS-1/-2 from PALSAR); (<b>c</b>) difference between PALSAR and PALSAR-2 derived subsidence rates (subtracting PALSAR from PALSAR-2); (<b>d</b>) difference between ERS-1/-2 and PALSAR-2 derived subsidence rates (subtracting ERS-1/-2 from PALSAR-2).</p> Full article ">Figure 14
<p>Depth of the upper surface of the solid geological stratum (<b>a</b>) in Urayasu City (adapted from the public report by the technical committee of Urayasu City [<a href="#B54-remotesensing-10-01304" class="html-bibr">54</a>]). The points refer to the locations of borehole sites; (<b>b</b>) soil cross sections along the A–A’ line. FS + AS refer to filled sandy soil and alluvial sand layers, and AC and DS refer to the alluvial clay layer and diluvial dense sandy layer, respectively. The borehole investigation data were obtained from the Chiba Prefecture [<a href="#B55-remotesensing-10-01304" class="html-bibr">55</a>].</p> Full article ">Figure 15
<p>Subsidence rate map (2006–2010) generated with ALOS PALSAR data overlaid on a Google Earth image. The green polygons indicate the park area, red polygons indicate the location of high-rise buildings, the yellow polygon shows the highly populated residential area. The blue polygon indicates the border of Urayasu City and corresponds to the location of <a href="#remotesensing-10-01304-f014" class="html-fig">Figure 14</a>a, and the A–A’ line corresponds to the soil cross section in <a href="#remotesensing-10-01304-f014" class="html-fig">Figure 14</a>a,b.</p> Full article ">
26 pages, 16213 KiB  
Article
SAR Tomography as an Add-On to PSI: Detection of Coherent Scatterers in the Presence of Phase Instabilities
by Muhammad Adnan Siddique, Urs Wegmüller, Irena Hajnsek and Othmar Frey
Remote Sens. 2018, 10(7), 1014; https://doi.org/10.3390/rs10071014 - 25 Jun 2018
Cited by 5 | Viewed by 4549
Abstract
The estimation of deformation parameters using persistent scatterer interferometry (PSI) is limited to single dominant coherent scatterers. As such, it rejects layovers wherein multiple scatterers are interfering in the same range-azimuth resolution cell. Differential synthetic aperture radar (SAR) tomography can improve deformation sampling [...] Read more.
The estimation of deformation parameters using persistent scatterer interferometry (PSI) is limited to single dominant coherent scatterers. As such, it rejects layovers wherein multiple scatterers are interfering in the same range-azimuth resolution cell. Differential synthetic aperture radar (SAR) tomography can improve deformation sampling as it has the ability to resolve layovers by separating the interfering scatterers. In this way, both PSI and tomography inevitably require a means to detect coherent scatterers, i.e., to perform hypothesis testing to decide whether a given candidate scatterer is coherent. This paper reports the application of a detection strategy in the context of “tomography as an add-on to PSI”. As the performance of a detector is typically linked to the statistical description of the underlying mathematical model, we investigate how the statistics of the phase instabilities in the PSI analysis are carried forward to the subsequent tomographic analysis. While phase instabilities in PSI are generally modeled as an additive noise term in the interferometric phase model, their impact in SAR tomography manifests as a multiplicative disturbance. The detection strategy proposed in this paper allows extending the same quality considerations as used in the prior PSI processing (in terms of the dispersion of the residual phase) to the subsequent tomographic analysis. In particular, the hypothesis testing for the detection of coherent scatterers is implemented such that the expected probability of false alarm is consistent between PSI and tomography. The investigation is supported with empirical analyses on an interferometric data stack comprising 50 TerraSAR-X acquisitions in stripmap mode, over the city of Barcelona, Spain, from 2007–2012. Full article
(This article belongs to the Special Issue Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography)
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<p>Estimates of the coherence magnitude obtained with <math display="inline"><semantics> <msup> <mn>10</mn> <mn>5</mn> </msup> </semantics></math> Monte Carlo iterations assuming the residual phases have a von Mises distribution with concentration parameter, <math display="inline"><semantics> <mi>κ</mi> </semantics></math>. Each solid line indicates the estimates for a specific number of acquisitions, <span class="html-italic">M</span> in the data stack. The vertical bars represent ± 1-<math display="inline"><semantics> <mi>σ</mi> </semantics></math> from the mean. The dashed line shows the coherence magnitude under the assumption that the residual phases follow a linear normal distribution, cf. Equation (<a href="#FD24-remotesensing-10-01014" class="html-disp-formula">24</a>) (assuming <math display="inline"><semantics> <mrow> <msubsup> <mi>σ</mi> <mrow> <mi>w</mi> </mrow> <mn>2</mn> </msubsup> <mo>=</mo> <msup> <mi>κ</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>).</p> Full article ">Figure 2
<p>Empirically estimated inverse coefficient of variation (iCV) of the test statistic <math display="inline"><semantics> <mover accent="true"> <munder> <mi>α</mi> <mo>̲</mo> </munder> <mo>^</mo> </mover> </semantics></math> against concentration parameter for von Mises distributed phase residuals, for different number of scatterers, <span class="html-italic">Q</span> in the same resolution cell. The dashed lines enclosing the gray region indicate the theoretical bounds on the iCV (cf. Equation (<a href="#FD51-remotesensing-10-01014" class="html-disp-formula">51</a>)) , where <math display="inline"><semantics> <mrow> <msub> <mi>ν</mi> <mn>1</mn> </msub> <mo>=</mo> <mfrac> <mrow> <msub> <mi>I</mi> <mn>1</mn> </msub> <mfenced open="(" close=")"> <mi>κ</mi> </mfenced> </mrow> <mrow> <msub> <mi>I</mi> <mn>0</mn> </msub> <mfenced open="(" close=")"> <mi>κ</mi> </mfenced> </mrow> </mfrac> </mrow> </semantics></math> (Equation (<a href="#FD22-remotesensing-10-01014" class="html-disp-formula">22</a>)).</p> Full article ">Figure 3
<p>Numerically estimated probability of detection against inverse coefficient of variation (iCV) of the test statistic <math display="inline"><semantics> <mover accent="true"> <munder> <mi>α</mi> <mo>̲</mo> </munder> <mo>^</mo> </mover> </semantics></math>, using <math display="inline"><semantics> <msup> <mn>10</mn> <mn>5</mn> </msup> </semantics></math> Monte Carlo realizations of the phase residuals, (<b>a</b>) for <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math> acquisitions and fixed levels of false alarm, <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>F</mi> </msub> <mo>∈</mo> <mfenced separators="" open="{" close="}"> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> <mo>,</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> <mo>,</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </mfenced> </mrow> </semantics></math>, and number of scatterers, <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>∈</mo> <mfenced separators="" open="{" close="}"> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> </mfenced> </mrow> </semantics></math>; (<b>b</b>) and for different number of different number of acquisitions, <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>∈</mo> <mfenced separators="" open="{" close="}"> <mn>25</mn> <mo>,</mo> <mn>35</mn> <mo>,</mo> <mn>50</mn> <mo>,</mo> <mn>75</mn> </mfenced> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>F</mi> </msub> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>.</p> Full article ">Figure 4
<p>Numerical analysis of the inverse coefficient of variation (iCV) of the test statistic <math display="inline"><semantics> <mover accent="true"> <munder> <mi>α</mi> <mo>̲</mo> </munder> <mo>^</mo> </mover> </semantics></math> when point scatterers are embedded in different clutter levels, for <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math> acquisitions. (<b>a</b>) iCV against concentration of the phase residuals for different levels of signal-to-clutter ratio (SCR) and number of scatterers, <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>∈</mo> <mfenced separators="" open="{" close="}"> <mn>1</mn> <mo>,</mo> <mn>2</mn> </mfenced> </mrow> </semantics></math>; (<b>b</b>) probability of detection against iCV for fixed levels of false alarm, <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>F</mi> </msub> <mo>∈</mo> <mfenced separators="" open="{" close="}"> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> <mo>,</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> <mo>,</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </mfenced> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>∈</mo> <mfenced separators="" open="{" close="}"> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> </mfenced> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>Data characteristics. (<b>a</b>) distribution of spatial (orthogonal component) and temporal baselines; (<b>b</b>) 2-D point spread function (PSF); (<b>c</b>) footprint of the reference acquisition over Barcelona, Spain.</p> Full article ">Figure 6
<p>PSI solution obtained with iterative least-squares regression-based processing using the interferometric point target analysis (IPTA) toolbox. The colored dots are the PSs identified in the PSI processing. (<b>a</b>) estimated height, relative to the WGS-84 reference ellipsoid; (<b>b</b>) deformation velocity in the line-of-sight; (<b>c</b>) phase-to-temperature sensitivity; (<b>d</b>) sample coherence, and histogram of the estimated concentration parameter (shown as inset).</p> Full article ">Figure 7
<p>Point cloud of single scatterers obtained with differential SAR tomography. The detection threshold is set corresponding to <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>.</mo> <mn>1</mn> </mrow> </semantics></math> rad under the proposed detection scheme (see Equations (<a href="#FD52-remotesensing-10-01014" class="html-disp-formula">52</a>) and (<a href="#FD53-remotesensing-10-01014" class="html-disp-formula">53</a>)). The color coding represents the estimated height. Some false alarms can be seen over the water surface , as highlighted in the inset.</p> Full article ">Figure 8
<p>False alarm rate observed over the sea patch in the viewed scene at different detection thresholds. The colored solid lines represent the case of 3/2/1-D tomographic inversion. The detection is performed on the retrieved reflectivity, <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <mover accent="true"> <mi>α</mi> <mo>^</mo> </mover> <mrow> <mo>|</mo> </mrow> </mrow> </semantics></math> according to Equation (<a href="#FD53-remotesensing-10-01014" class="html-disp-formula">53</a>). The dot-dashed lines shows the case of PSI whereby the detection is performed on the sample coherence, <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <mover accent="true"> <mi>γ</mi> <mo>^</mo> </mover> <mrow> <mo>|</mo> </mrow> </mrow> </semantics></math> without fitting any phase model to the observed interferometric phases.</p> Full article ">Figure 9
<p>Point cloud of single scatterers obtained with differential SAR tomography. The detection threshold is set corresponding to <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>.</mo> <mn>0</mn> </mrow> </semantics></math> rad under the proposed detection scheme, see Equations (<a href="#FD52-remotesensing-10-01014" class="html-disp-formula">52</a>) and (<a href="#FD53-remotesensing-10-01014" class="html-disp-formula">53</a>). (<b>Top</b>) Estimated height, relative to the WGS-84 reference ellipsoid. (<b>Middle</b>): Deformation velocity in the line-of-sight. (<b>Bottom</b>) Phase-to-temperature sensitivity. In comparison with <a href="#remotesensing-10-01014-f007" class="html-fig">Figure 7</a> where a more relaxed detection threshold (corresponding to <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>.</mo> <mn>1</mn> </mrow> </semantics></math> rad) is used, fewer false alarms are observed here, as highlighted in the inset.</p> Full article ">Figure 10
<p>Point cloud of double scatterers obtained with differential SAR tomography. The detection threshold is set corresponding to <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>.</mo> <mn>0</mn> </mrow> </semantics></math> rad under the proposed detection scheme. (<b>Top</b>) Estimated height, relative to the WGS-84 reference ellipsoid. (<b>Middle</b>) Deformation velocity in the line-of-sight. (<b>Bottom</b>) Phase-to-temperature sensitivity. The left column shows the lower layer and the right column shows the upper layer of the double scatterers, respectively. The inset focuses on a commercial complex (Diagonal Mar). The red polygon encloses a single building, part of which is in layover with a nearby building of shorter height.</p> Full article ">
23 pages, 65641 KiB  
Article
A Methodology to Detect and Characterize Uplift Phenomena in Urban Areas Using Sentinel-1 Data
by Roberta Bonì, Alberto Bosino, Claudia Meisina, Alessandro Novellino, Luke Bateson and Harry McCormack
Remote Sens. 2018, 10(4), 607; https://doi.org/10.3390/rs10040607 - 14 Apr 2018
Cited by 31 | Viewed by 7114
Abstract
This paper presents a methodology to exploit the Persistent Scatterer Interferometry (PSI) time series acquired by Sentinel-1 sensors for the detection and characterization of uplift phenomena in urban areas. The methodology has been applied to the Tower Hamlets Council area of London (United [...] Read more.
This paper presents a methodology to exploit the Persistent Scatterer Interferometry (PSI) time series acquired by Sentinel-1 sensors for the detection and characterization of uplift phenomena in urban areas. The methodology has been applied to the Tower Hamlets Council area of London (United Kingdom) using Sentinel-1 data covering the period 2015–2017. The test area is a representative high-urbanized site affected by geohazards due to natural processes such as compaction of recent deposits, and also anthropogenic causes due to groundwater management and engineering works. The methodology has allowed the detection and characterization of a 5 km2 area recording average uplift rates of 7 mm/year and a maximum rate of 18 mm/year in the period May 2015–March 2017. Furthermore, the analysis of the Sentinel-1 time series highlights that starting from August 2016 uplift rates began to decrease. A comparison between the uplift rates and urban developments as well as geological, geotechnical, and hydrogeological factors suggests that the ground displacements occur in a particular geological context and are mainly attributed to the swelling of clayey soils. The detected uplift could be attributed to a transient effect of the groundwater rebound after completion of dewatering works for the recent underground constructions. Full article
(This article belongs to the Special Issue Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography)
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Figure 1
<p>Location (sources: Esri, DeLorme, USGS, NPS) and geological map of London based upon the 1:50,000 bedrock geology, with the permission of the British Geological Survey. All rights reserved. British National Grid. Projection: Transverse Mercator. Datum: OSGB 1936. The geological cross section A-A’ is also reported (modified from [<a href="#B28-remotesensing-10-00607" class="html-bibr">28</a>]).</p> Full article ">Figure 2
<p>Average line of sight (LOS) velocity measured by the use of Sentinel-1 data during the period 2015 to 2017 across the study area. The location of the GNSS station is also reported.</p> Full article ">Figure 3
<p>Flowchart of the methodological approach to detect and characterize uplift phenomena in urban areas.</p> Full article ">Figure 4
<p>Average displacement time series (TS) of the targets characterized by coherence higher than 0.9 and LOS velocity in the range ±0.5 mm/year. The black dotted line represents the regression line of the average TS whereas the red ones are the upper and lower threshold line.</p> Full article ">Figure 5
<p>Comparison between the PSI and GNSS vertical displacement time series. See <a href="#remotesensing-10-00607-f002" class="html-fig">Figure 2</a> for the GNSS stations location. The antenna installation images are also reported (available data at <a href="http://www.bigf.ac.uk/files/network_maps/script_all_pcsn_30s.html" target="_blank">http://www.bigf.ac.uk/files/network_maps/script_all_pcsn_30s.html</a>).</p> Full article ">Figure 6
<p>Principal component score maps of the first (<b>a</b>) and second (<b>b</b>) component of motion. Eigenvector value (<b>c</b>) of the principal component (PC) and the percentage of explained variance (<b>d</b>) are also reported.</p> Full article ">Figure 7
<p>TS trends in the uplift zone (<b>a</b>). Average TS of the non-linear targets located in the uplift zone. The detected breaks (red dotted lines) are also reported (<b>b</b>). See the location of the uplift zone in Figure 10.</p> Full article ">Figure 8
<p>(<b>a</b>) Location of the boreholes used to estimate the thickness of the clayey soils. The cross sections (red line) are also reported; (<b>b</b>) Example of the procedure applied to estimate the thickness of the clayey soils of the London Clay and by computing the sum of the thickness of the clayey soils of the London Clay and Lambeth Group; (<b>c</b>) Comparison of the LOS velocity estimates in the period 2015–2017 with the thickness of clayey soils of the London clay using the boreholes information; (<b>d</b>) Comparison of the LOS velocity estimates in the period 2015–2017 with total thickness of clayey lithologies of the boreholes considering the London Clay and the Lambeth Group. Based upon the 1:50,000 bedrock geology, with the permission of the British Geological Survey. All rights reserved.</p> Full article ">Figure 9
<p>(<b>a</b>) Location of the geotechnical boreholes; (<b>b</b>) Comparison of the LOS velocity estimates in the period 2015–2017 with the swelling pressure of the superficial (depth lower than 7 m) and deeper layers (higher than 7 m). Simplified Superficial Geology of Greater London modified from the DiGMapGB50, the Digital Geological Map of Great Britain at the 1:50,000 scale and bedrock geology at the 1:50,000 scale, with the permission of the British Geological Survey. All rights reserved.</p> Full article ">Figure 10
<p>Cross-comparison between the deformation rates detected using ERS-1/2, ENVISAT and Sentinel-1 data and groundwater level changes. The black and the blue lines represent the vertical displacement and the groundwater level data, respectively. In the map; the black lines represent the groundwater level change (m) between January 2015 and January 2016 from [<a href="#B41-remotesensing-10-00607" class="html-bibr">41</a>] and the red line represents the uplift area. Based upon the 1:50,000 bedrock geology, with the permission of the British Geological Survey. Contains Environment Agency information © Environment Agency and/or database right 2017.</p> Full article ">Figure 11
<p>Cross section and buildings age. Based upon Groundhog Desktop data; with the permission of the British Geological Survey. See the location of the cross section in <a href="#remotesensing-10-00607-f008" class="html-fig">Figure 8</a> and <a href="#remotesensing-10-00607-f009" class="html-fig">Figure 9</a>.</p> Full article ">Figure 12
<p>(<b>a</b>) Location of the Crossrail line 1 and the dewatering sites. (<b>b</b>) Cross-comparison of the average TS obtained using the Sentinel-1 data in a buffer zone of 50 m from Limmo shaft and the groundwater level changes measured at Limmo station. Dewatering periods are also reported.</p> Full article ">
18 pages, 13614 KiB  
Article
Analysis of Secular Ground Motions in Istanbul from a Long-Term InSAR Time-Series (1992–2017)
by Gokhan Aslan, Ziyadin Cakır, Semih Ergintav, Cécile Lasserre and François Renard
Remote Sens. 2018, 10(3), 408; https://doi.org/10.3390/rs10030408 - 6 Mar 2018
Cited by 42 | Viewed by 8201
Abstract
The identification and measurement of ground deformations in urban areas is of great importance for determining the vulnerable parts of the cities that are prone to geohazards, which is a crucial element of both sustainable urban planning and hazard mitigation. Interferometric synthetic aperture [...] Read more.
The identification and measurement of ground deformations in urban areas is of great importance for determining the vulnerable parts of the cities that are prone to geohazards, which is a crucial element of both sustainable urban planning and hazard mitigation. Interferometric synthetic aperture radar (InSAR) time series analysis is a very powerful tool for the operational mapping of ground deformation related to urban subsidence and landslide phenomena. With an analysis spanning almost 25 years of satellite radar observations, we compute an InSAR time series of data from multiple satellites (European Remote Sensing satellites ERS-1 and ERS-2, Envisat, Sentinel-1A, and its twin sensor Sentinel-1B) in order to investigate the spatial extent and rate of ground deformation in the megacity of Istanbul. By combining the various multi-track InSAR datasets (291 images in total) and analysing persistent scatterers (PS-InSAR), we present mean velocity maps of ground surface displacement in selected areas of Istanbul. We identify several sites along the terrestrial and coastal regions of Istanbul that underwent vertical ground subsidence at varying rates, from 5 ± 1.2 mm/yr to 15 ± 2.1 mm/yr. The results reveal that the most distinctive subsidence patterns are associated with both anthropogenic factors and relatively weak lithologies along the Haramirede valley in particular, where the observed subsidence is up to 10 ± 2 mm/yr. We show that subsidence has been occurring along the Ayamama river stream at a rate of up to 10 ± 1.8 mm/yr since 1992, and has also been slowing down over time following the restoration of the river and stream system. We also identify subsidence at a rate of 8 ± 1.2 mm/yr along the coastal region of Istanbul, which we associate with land reclamation, as well as a very localised subsidence at a rate of 15 ± 2.3 mm/yr starting in 2016 around one of the highest skyscrapers of Istanbul, which was built in 2010. Full article
(This article belongs to the Special Issue Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography)
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<p>Study area and satellite synthetic aperture radar data coverage used in the present study. The shaded topography is given by the Shuttle Radar Topography Mission (SRTM) along the North Anatolian Fault (NAF) in the Sea of Marmara, and major faults are drawn in red [<a href="#B35-remotesensing-10-00408" class="html-bibr">35</a>]. Rectangles labeled with sensor and track numbers indicate the coverage of the SAR images that were used in the present study. The red and black arrows indicate satellite’s line-of-sight look and flight directions, respectively. Circles with numbers show the study regions in the paper (details are given in the text).</p> Full article ">Figure 2
<p>Simplified geological and structural map of study areas 1 and 2 (circles 1 and 2 as in <a href="#remotesensing-10-00408-f001" class="html-fig">Figure 1</a>). Numerous active landslides (dark green patches) were mapped between the Küçükçekmece and Büyükçekmece lakes [simplified from Ergintav et al., Duman et al. and Ozgul et al. [<a href="#B37-remotesensing-10-00408" class="html-bibr">37</a>,<a href="#B38-remotesensing-10-00408" class="html-bibr">38</a>,<a href="#B39-remotesensing-10-00408" class="html-bibr">39</a>]. The inset map shows a figure area north of the Sea of Marmara, and the main segments of the NAF in red [<a href="#B35-remotesensing-10-00408" class="html-bibr">35</a>].</p> Full article ">Figure 3
<p>Baseline versus time plots for the seven tracks used in this study. The red dots indicate the master image used as a reference for each track. For the Sentinel data, the period when the two satellites 1A/1B were operational is indicated in orange (before this period, only satellite 1A was operational).</p> Full article ">Figure 4
<p>Averaged line-of-sight velocity maps of the Istanbul metropolitan area from an interferometric synthetic aperture radar (InSAR) time series analysis, with varying time spans depending on the sensor. Negative velocities (cold colors) represent the displacement of the ground toward the satellite, and positive velocities (warm colors) indicate the displacement away from the satellite. Red lines in the Sea of Marmara indicate the submarine branches of the NAF. Average line-of-sight velocity (<b>a</b>) for Sentinel ascending track 58. The solid black circles labeled from 1 to 6 indicate the locations of the subsidence anomalies that are discussed in the present study. 1: Haramidere River, 2: Ayamama Stream Valley, 3: Golden Horn, 4: Yenikapi reclamation area, 5: Skyscrapers in Levent neighbourhood, 6: Maltepe reclamation area; (<b>b</b>) for SENTINEL 1A/B descending track 36; (<b>c</b>) for Sentinel descending track 138; (<b>d</b>) for Envisat descending track 107; (<b>e</b>) for ERS descending track 336; (<b>f</b>) for ENVISAT descending track 336 and (<b>g</b>) for ERS descending track 107.</p> Full article ">Figure 5
<p>Quantitative comparison of the line-of-sight displacement rates between all of the tracks used in the present study. The upper-right triangle matrix shows pairwise correlations of different tracks with correlation values and color intensities (blue and red indicate low and high correlation, respectively). In the lower-left triangle, the black dots denote the points that can be extracted on both tracks. SEN and ENV in the panel represent SENTINEL and ENVISAT, respectively.</p> Full article ">Figure 6
<p>Zoom views of displacement rates in the Haramidere Valley and its surrounding area. The mean velocity value of the persistent scatterers (PS)-InSAR points within the solid black circle in the center of the maps has been used to illustrate the temporal evolution of the subsidence associated with weak lithology (<a href="#remotesensing-10-00408-f008" class="html-fig">Figure 8</a>). It is referenced to the mean value of the PS-InSAR points within the circle labeled R, which is considered a stable area. The DSC and ASC labels are for the descending and ascending orbits, respectively.</p> Full article ">Figure 7
<p>Decomposition of horizontal and vertical components of ground displacement using only S-1 datasets. (<b>a</b>) The shaded topography was given by the Shuttle Radar Topography Mission (SRTM) along the Avcilar region. Fault lines were simplified from Ergintav et al. [<a href="#B37-remotesensing-10-00408" class="html-bibr">37</a>]; (<b>b</b>) Vertical component. Patches with thick dark boundaries correspond to the landslides that were identified geological maps, as shown on <a href="#remotesensing-10-00408-f002" class="html-fig">Figure 2</a> [simplified from Duman et al. and Ozgul et al. [<a href="#B38-remotesensing-10-00408" class="html-bibr">38</a>,<a href="#B39-remotesensing-10-00408" class="html-bibr">39</a>]; (<b>c</b>) Horizontal component in the east–west direction. (<b>d</b>)Valley-perpendicular elevation profile extracted from (<b>a</b>); (<b>e</b>,<b>f</b>) are horizontal and vertical velocity profiles extracted from (<b>b</b>,<b>c</b>) respectively.</p> Full article ">Figure 8
<p>Time series of the vertical displacement at the selected PS points circled in center of <a href="#remotesensing-10-00408-f006" class="html-fig">Figure 6</a> (referenced to points in area labeled R in <a href="#remotesensing-10-00408-f006" class="html-fig">Figure 6</a>).</p> Full article ">Figure 9
<p>Spatiotemporal characteristics of subsidence along the Ayamama river valley study area. (<b>a</b>) Mean displacement rates in line-of-sight (LOS) obtained from a track 336 ERS dataset. The black dashed lines indicate two profiles, one in an area with an active subsidence (profile labeled 1–4), and the other one with the same length in an area considered as stable and used as a reference (profile labeled 1′–4′). The inset map indicates the temporal and spatial pattern of subsidence for the region, from 1992 to 2017; (<b>b</b>–<b>h</b>) Rates along the profiles 1–4 (black) and 1′–4′ (red) taken for each track; (<b>i</b>) Temporal evolution of the coastal subsidence of selected points around point 3 in <a href="#remotesensing-10-00408-f009" class="html-fig">Figure 9</a>a.</p> Full article ">Figure 10
<p>Vertical velocities obtained by the decomposition of mean velocity fields of Sentinel 1 data (T58 ascending track and T138 descending track) superimposed on a Google Earth image of Istanbul, and the relevant time series of the vertical displacement. Black, red, and blue triangles represent the ascending T58, descending T36, and descending T138 tracks, respectively. (<b>a</b>) Yenikapi coastal and land reclamation area (circle 4 in <a href="#remotesensing-10-00408-f004" class="html-fig">Figure 4</a>a). The color scale represents the vertical displacement of the surface; (<b>b</b>) Golden Horn area (circle 3 in <a href="#remotesensing-10-00408-f004" class="html-fig">Figure 4</a>a); (<b>c</b>) Highly urbanised area of Istanbul, with subsiding persistent scatterer points clustered around the highest skyscraper of Istanbul (circle 5 in <a href="#remotesensing-10-00408-f004" class="html-fig">Figure 4</a>a); (<b>d</b>) Maltepe reclamation zone (circle 6 in <a href="#remotesensing-10-00408-f004" class="html-fig">Figure 4</a>a).</p> Full article ">
18 pages, 8319 KiB  
Article
Spatio-Temporal Characterization of a Reclamation Settlement in the Shanghai Coastal Area with Time Series Analyses of X-, C-, and L-Band SAR Datasets
by Mengshi Yang, Tianliang Yang, Lu Zhang, Jinxin Lin, Xiaoqiong Qin and Mingsheng Liao
Remote Sens. 2018, 10(2), 329; https://doi.org/10.3390/rs10020329 - 22 Feb 2018
Cited by 62 | Viewed by 5645
Abstract
Large-scale reclamation projects during the past decades have been recognized as one of the driving factors behind land subsidence in coastal areas. However, the pattern of temporal evolution in reclamation settlements has rarely been analyzed. In this work, we study the spatio-temporal evolution [...] Read more.
Large-scale reclamation projects during the past decades have been recognized as one of the driving factors behind land subsidence in coastal areas. However, the pattern of temporal evolution in reclamation settlements has rarely been analyzed. In this work, we study the spatio-temporal evolution pattern of Linggang New City (LNC) in Shanghai, China, using space-borne synthetic aperture radar interferometry (InSAR) methods. Three data stacks including 11 X-band TerraSAR-X, 20 L-band ALOS PALSAR, and 35 C-band ENVISAT ASAR images were used to retrieve time series deformation from 2007 to 2010 in the LNC. An InSAR analysis from the three data stacks displays strong agreement in mean deformation rates, with coefficients of determination of about 0.9 and standard deviations for inter-stack differences of less than 4 mm/y. Meanwhile, validations with leveling data indicate that all the three data stacks achieved millimeter-level accuracies. The spatial distribution and temporal evolution of deformation in the LNC as indicated by these InSAR analysis results relates to historical reclamation activities, geological features, and soil mechanisms. This research shows that ground deformation in the LNC after reclamation projects experienced three distinct phases: primary consolidation, a slight rebound, and plateau periods. Full article
(This article belongs to the Special Issue Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography)
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<p>Geographic locations of LNC.</p> Full article ">Figure 2
<p>Landsat TM/ETM+ images over LNC in (<b>a</b>) 3 November 1999 and, (<b>b</b>) 16 December 2003, and (<b>c</b>) 6 May 2009.</p> Full article ">Figure 3
<p>(<b>a</b>) The coverage of TerraSAR-X, ALOS PALSAR, and ENVISAT ASAR data; (<b>b</b>) The detailed map of LNC and the locations of leveling benchmarks. Black triangles indicate the locations of benchmarks. The background figure is a Landsat TM/ETM+ image acquired on 1 November 2010.</p> Full article ">Figure 4
<p>Temporal distributions of interferograms generated from the three data stacks.</p> Full article ">Figure 5
<p>Motion rates of LNC derived by time series analyses using: (<b>a</b>) X-band TerraSAR-X; (<b>b</b>) L-band ALOS PALSAR; (<b>c</b>) C-band ENVISAT ASAR data stacks. Red Star indicates the reference point. Black triangles indicate the locations of leveling benchmarks. The background is a mean amplitude map of 11 TerraSAR-X images.</p> Full article ">Figure 6
<p>Partition diagram of LNC, zone1 formed before 1973, zone 2 formed between 1973 and 1994, and zone 3 formed after 2002.</p> Full article ">Figure 7
<p>Time series displacements at six typical CPs: (<b>a</b>) P1, (<b>b</b>) P2, (<b>c</b>) P3, (<b>d</b>) P4, (<b>e</b>) P5, and (<b>f</b>) P6 derived from ASAR, PALSAR, and TerraSAR-X data stacks.</p> Full article ">Figure 7 Cont.
<p>Time series displacements at six typical CPs: (<b>a</b>) P1, (<b>b</b>) P2, (<b>c</b>) P3, (<b>d</b>) P4, (<b>e</b>) P5, and (<b>f</b>) P6 derived from ASAR, PALSAR, and TerraSAR-X data stacks.</p> Full article ">Figure 8
<p>Comparison of mean deformation rates among the three data stacks: (<b>a</b>) TerraSAR-X vs. PALSAR, (<b>b</b>) TerraSAR-X vs. ASAR, (<b>c</b>) ASAR vs. PALSAR.</p> Full article ">Figure 9
<p>Validation of InSAR-derived mean deformation rates at leveling benchmarks.</p> Full article ">Figure 10
<p>Engineering geologic layers of profile line I-I’. The location of profile line I-I’ is indicated in <a href="#remotesensing-10-00329-f006" class="html-fig">Figure 6</a>.</p> Full article ">Figure 11
<p>The compressibility of Shanghai soft soil [<a href="#B21-remotesensing-10-00329" class="html-bibr">21</a>,<a href="#B22-remotesensing-10-00329" class="html-bibr">22</a>]: (<b>a</b>) relationship between compression index <span class="html-italic">C<sub>c</sub></span> and matric suction <span class="html-italic">S,</span> (<b>b</b>) effects of matric suction <span class="html-italic">S</span> on the resilience index <span class="html-italic">C<sub>e</sub></span>.</p> Full article ">
28660 KiB  
Article
Wuhan Surface Subsidence Analysis in 2015–2016 Based on Sentinel-1A Data by SBAS-InSAR
by Lv Zhou, Jiming Guo, Jiyuan Hu, Jiangwei Li, Yongfeng Xu, Yuanjin Pan and Miao Shi
Remote Sens. 2017, 9(10), 982; https://doi.org/10.3390/rs9100982 - 22 Sep 2017
Cited by 127 | Viewed by 9044
Abstract
The Terrain Observation with Progressive Scans (TOPS) acquisition mode of Sentinel-1A provides a wide coverage per acquisition and features a repeat cycle of 12 days, making this acquisition mode attractive for surface subsidence monitoring. A few studies have analyzed wide-coverage surface subsidence of [...] Read more.
The Terrain Observation with Progressive Scans (TOPS) acquisition mode of Sentinel-1A provides a wide coverage per acquisition and features a repeat cycle of 12 days, making this acquisition mode attractive for surface subsidence monitoring. A few studies have analyzed wide-coverage surface subsidence of Wuhan based on Sentinel-1A data. In this study, we investigated wide-area surface subsidence characteristics in Wuhan using 15 Sentinel-1A TOPS Synthetic Aperture Radar (SAR) images acquired from 11 April 2015 to 29 April 2016 with the Small Baseline Subset Interferometric SAR (SBAS InSAR) technique. The Sentinel-1A SBAS InSAR results were validated by 110 leveling points at an accuracy of 6 mm/year. Based on the verified SBAS InSAR results, prominent uneven subsidence patterns were identified in Wuhan. Specifically, annual average subsidence rates ranged from −82 mm/year to 18 mm/year in Wuhan, and maximum subsidence rate was detected in Houhu areas. Surface subsidence time series presented nonlinear subsidence with pronounced seasonal variations. Comparative analysis of surface subsidence and influencing factors (i.e., urban construction, precipitation, industrial development, carbonate karstification and water level changes in Yangtze River) indicated a relatively high spatial correlation between locations of subsidence bowl and those of engineering construction and industrial areas. Seasonal variations in subsidence were correlated with water level changes and precipitation. Surface subsidence in Wuhan was mainly attributed to anthropogenic activities, compressibility of soil layer, carbonate karstification, and groundwater overexploitation. Finally, the spatial-temporal characteristics of wide-area surface subsidence and the relationship between surface subsidence and influencing factors in Wuhan were determined. Full article
(This article belongs to the Special Issue Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Location of study area and Sentinel-1A TOPS SAR data coverage: (<b>a</b>) the study area is outlined by a red rectangle; and (<b>b</b>) a SAR mean intensity image of Sentinel-1A covering the study area.</p> Full article ">Figure 2
<p>(<b>a</b>) Time–position of Sentinel-1A image interferometric pairs; and (<b>b</b>) time–baseline of Sentinel-1A image interferometric pairs. The red diamond denotes the super master image. Blue lines represent interferometric pairs. Green diamonds denote slave images.</p> Full article ">Figure 3
<p>Vertical deformation rates derived by SBAS InSAR for the whole study area during the period from 11 April 2015 to 29 April 2016. The background is a Google Earth image acquired in 2016. Red triangle and blue square denote locations of reference point and Hankou hydrological station, respectively. Regions S1–S4 marked with black rectangles are major subsidence areas in Wuhan. These areas will be further analyzed in the discussion section. HK, HH, WC, QS, YL, HY, and QL are the abbreviations of Hankou, Houhu, Wuchang, Qingshan, Yangluo, Hanyang, and Qingling, respectively.</p> Full article ">Figure 4
<p>Cumulative subsidence (in the vertical direction) time series from 2015 to 2016. The image acquired on 11 April 2015 was not shown because it was selected as a reference image. The background is Sentinel-1A TOPS mean intensity image of Wuhan. (<b>a</b>–<b>n</b>) Cumulative subsidence in 14 stages in Wuhan. (<b>a</b>) 11 April 2015–5 May 2015; (<b>b</b>) 11 April 2015–29 May 2015; (<b>c</b>) 11 April 2015–16 July 2015; (<b>d</b>) 11 April 2015–9 August 2015; (<b>e</b>) 11 April 2015–2 September 2015; (<b>f</b>) 11 April 2015–26 September 2015; (<b>g</b>) 11 April 2015–20 October 2015; (<b>h</b>) 11 April 2015–13 November 2015; (<b>i</b>) 11 April 2015–7 December 2015; (<b>j</b>) 11 April 2015–31 December 2015; (<b>k</b>) 11 April 2015–24 January 2016; (<b>l</b>) 11 April 2015–17 February 2016; (<b>m</b>) 11 April 2015–5 April 2016; (<b>n</b>) 11 April 2015–29 April 2016. HH region in HK is marked by black rectangle in (<b>n</b>).</p> Full article ">Figure 5
<p>Distributions of standard deviations of subsidence rates.</p> Full article ">Figure 6
<p>Distribution of benchmarks for surface subsidence monitoring over Wuhan. The red circle and triangle denote leveling point and reference point, respectively.</p> Full article ">Figure 7
<p>(<b>a</b>) Regression analysis between surface vertical deformation rates derived by SBAS InSAR and leveling; and (<b>b</b>) differences between SBAS InSAR- and leveling-derived results.</p> Full article ">Figure 8
<p>Surface vertical deformation rate map derived by SBAS InSAR superimposed on Google Earth image covering (<b>a</b>) Region S1. Black lines denote Wuhan Metro Lines, including metro lines in operation and under construction. Red circles represent leveling points distributed along Metro Line 6, which is under construction. Black and red triangles denote Wuhan Center and Wuhan World Trade Center, respectively. (<b>b</b>) Surface subsidence (in the vertical direction) time series with respect to PS points (marked by black crosses in <a href="#remotesensing-09-00982-f008" class="html-fig">Figure 8</a>a) labeled as A, B, C, and D in <a href="#remotesensing-09-00982-f008" class="html-fig">Figure 8</a>a versus average monthly precipitation of Wuhan area. (<b>c</b>–<b>e</b>) Structural damage caused by surface subsidence.</p> Full article ">Figure 9
<p>Surface vertical deformation rate map derived by SBAS InSAR superimposed on Google Earth image covering Region S3. Black lines denote the Wuhan Metro Lines, including the metro lines in operation and under construction. Red ellipses represent the two subsidence areas, i.e., XD and HBU subsidence bowls.</p> Full article ">Figure 10
<p>Surface vertical deformation rate map superimposed on Google Earth image covering (<b>a</b>) Region S2. Red, white, blue and black polygons denote the industrial areas of WISC, SWC, YCP, and HYPP, respectively. Black lines represent subsidence profiles that will be further analyzed in the <a href="#sec5dot3-remotesensing-09-00982" class="html-sec">Section 5.3</a>. (<b>b</b>,<b>c</b>) Structural damage due to surface subsidence.</p> Full article ">Figure 11
<p>(<b>a</b>–<b>c</b>) Variation in accumulated subsidence (in the vertical direction) during the period from 11 April 2015 to 29 April 2016 along profiles A-A′, B-B′, and C-C′ (in <a href="#remotesensing-09-00982-f010" class="html-fig">Figure 10</a>). Black dotted lines mark locations of the largest accumulated subsidence of profiles.</p> Full article ">Figure 12
<p>Surface vertical deformation rates map derived by SBAS InSAR superimposed on Google Earth image covering Region S4. Black lines denote the Wuhan Metro Lines, including metro lines in operation and under construction. The area enclosed by red line represents the Baishazhou Carbonate Rock belt. The red square denotes karst surface collapse. Areas 1, 2, 3, 4, and 5 marked with black rectangles are significant subsidence areas in Region S4.</p> Full article ">Figure 13
<p>Surface vertical deformation rate map superimposed on Google Earth image covering both sides of the Yangtze River. The black square and five-pointed star denote the Hankou hydrological station and PS point, respectively. JH and XD are the abbreviations of Jianghan and Xudong, respectively.</p> Full article ">Figure 14
<p>(<b>a</b>) Surface vertical deformation time series with respect to PS points labeled as PS1–PS6 in <a href="#remotesensing-09-00982-f013" class="html-fig">Figure 13</a> versus water level (WL) changes (red-dotted line) in Yangtze River; (<b>b</b>) Detrended surface vertical deformation time series relevant to the above PS points versus detrended water level changes (red dotted line) in Yangtze River.</p> Full article ">

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14 pages, 10364 KiB  
Letter
Urban Tomographic Imaging Using Polarimetric SAR Data
by Alessandra Budillon, Angel Caroline Johnsy and Gilda Schirinzi
Remote Sens. 2019, 11(2), 132; https://doi.org/10.3390/rs11020132 - 11 Jan 2019
Cited by 21 | Viewed by 4229
Abstract
In this paper, we investigate the potential of polarimetric Synthetic Aperture Radar (SAR) tomography (Pol-TomoSAR) in urban applications. TomoSAR exploits the amplitude and phase of the received data and offers the possibility to resolve multiple scatters lying in the same range–azimuth resolution cell. [...] Read more.
In this paper, we investigate the potential of polarimetric Synthetic Aperture Radar (SAR) tomography (Pol-TomoSAR) in urban applications. TomoSAR exploits the amplitude and phase of the received data and offers the possibility to resolve multiple scatters lying in the same range–azimuth resolution cell. In urban environments, this issue is very important since layover causes multiple coherent scatterers to be mapped in the same range–azimuth image pixel. To achieve reliable and accurate results, TomoSAR requires a large number of multi-baseline acquisitions which, for satellite-borne SAR systems, are collected with long time intervals. Then, accurate tomographic reconstructions would require multiple scatterers to remain stable between all the acquisitions. In this paper, an extension of a generalized likelihood ratio test (GLRT)-based tomographic approach, denoted as Fast-Sup-GLRT, to the polarimetric data case is introduced, with the purpose of investigating if, in urban applications, the use of polarimetric channels allows for reduction of the number of baselines required to achieve a given scatterer’s detection performance. The results presented show that the use of dual polarization data allows the proposed detector to work in an equivalent or better way than use of a double number of independent single polarization channels. Full article
(This article belongs to the Special Issue Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Multi-pass SAR geometry in the range–elevation plane (case <span class="html-italic">M</span> = 5).</p> Full article ">Figure 2
<p>Distribution of the perpendicular baselines vs. the temporal baselines, in (<b>a</b>) the 39 baselines of the single polarization case, in (<b>b</b>) the 20 baselines of the dual polarization case.</p> Full article ">Figure 3
<p>Normalized histogram of the perpendicular baseline distribution (<b>a</b>) and of the temporal baseline distribution (<b>b</b>); in green, the single polarization case and, in red, the dual polarization case.</p> Full article ">Figure 3 Cont.
<p>Normalized histogram of the perpendicular baseline distribution (<b>a</b>) and of the temporal baseline distribution (<b>b</b>); in green, the single polarization case and, in red, the dual polarization case.</p> Full article ">Figure 4
<p>Intensity HH image of the test area near Toulouse, France (copyright DLR 2013–2015).</p> Full article ">Figure 5
<p>Targets detected by Fast-Sup-GLRT and reported on the optical image (single scatterers in red, double scatterers in blue), (<b>a</b>) single polarization, (<b>b</b>) dual polarization.</p> Full article ">Figure 6
<p>Average coherence (<b>a</b>) single polarization, (<b>b</b>) dual polarization.</p> Full article ">Figure 7
<p>(<b>a</b>) Intensity HH image of the test area near Toulouse, with three range lines highlighted in red (A, B, C), (<b>b</b>) range line C reported on the optical image, (<b>c</b>) schematic geometrical diagram of the scene.</p> Full article ">Figure 8
<p>The double scatterers detected by Fast-Sup-GLRT in the dual polarization case, and their estimated heights, along the three range lines A (<b>a</b>), B (<b>b</b>), C (<b>c</b>).</p> Full article ">Figure 9
<p>Reflectivity profile obtained with beamforming (blue), Capon (red), Fast-Sup-GLRT (green), in correspondence of the three double scatterers in line C, respectively in (<b>a</b>–<b>c</b>).</p> Full article ">Figure 10
<p>Tomographic slices obtained with polarimetric beamforming (<b>a</b>,<b>d</b>,<b>g</b>), Capon (<b>b</b>,<b>e</b>,<b>h</b>) and Fast-Sup-GLRT (<b>c</b>,<b>f</b>,<b>i</b>) respectively, from top to down, for line A, B, and C. Blue circles indicate the double scatterers for the Fast-Sup-GLRT, and red and blue points respectively indicate the first and second peaks of the beamforming and Capon spectra.</p> Full article ">Figure 10 Cont.
<p>Tomographic slices obtained with polarimetric beamforming (<b>a</b>,<b>d</b>,<b>g</b>), Capon (<b>b</b>,<b>e</b>,<b>h</b>) and Fast-Sup-GLRT (<b>c</b>,<b>f</b>,<b>i</b>) respectively, from top to down, for line A, B, and C. Blue circles indicate the double scatterers for the Fast-Sup-GLRT, and red and blue points respectively indicate the first and second peaks of the beamforming and Capon spectra.</p> Full article ">
14 pages, 8066 KiB  
Letter
Comparison of Persistent Scatterer Interferometry and SAR Tomography Using Sentinel-1 in Urban Environment
by Alessandra Budillon, Michele Crosetto, Angel Caroline Johnsy, Oriol Monserrat, Vrinda Krishnakumar and Gilda Schirinzi
Remote Sens. 2018, 10(12), 1986; https://doi.org/10.3390/rs10121986 - 8 Dec 2018
Cited by 21 | Viewed by 4615
Abstract
In this paper, persistent scatterer interferometry and Synthetic Aperture Radar (SAR) tomography have been applied to Sentinel-1 data for urban monitoring. The paper analyses the applicability of SAR tomography to Sentinel-1 data, which is not granted, due to the reduced range and azimuth [...] Read more.
In this paper, persistent scatterer interferometry and Synthetic Aperture Radar (SAR) tomography have been applied to Sentinel-1 data for urban monitoring. The paper analyses the applicability of SAR tomography to Sentinel-1 data, which is not granted, due to the reduced range and azimuth resolutions and the low resolution in elevation. In a first part of the paper, two implementations of the two techniques are described. In the experimental part, the two techniques are used in parallel to process the same Sentinel-1 data over two test areas. An intercomparison of the results from persistent scatterer interferometry and SAR tomography is carried out, comparing the main parameters estimated by the two techniques. Finally, the paper addresses the complementarity of the two techniques, and in particular it assesses the increase of measurement density that can be achieved by adding the double scatterers from SAR tomography to the persistent scatterer interferometry measurements. Full article
(This article belongs to the Special Issue Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography)
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Graphical abstract
Full article ">Figure 1
<p>Mean SAR amplitude of the first test area: 401 (range) by 351 (azimuth) pixels, which cover an extension of approximately 1.6 (range) by 4.9 (azimuth) km.</p> Full article ">Figure 2
<p>Deformation velocity obtained by PSI (<b>Left</b>) and by TomoSAR (<b>Right</b>).</p> Full article ">Figure 3
<p>Histogram of the persistent scatterer interferometry (PSI) and TomoSAR velocity differences.</p> Full article ">Figure 4
<p>Residual topographic error (RTE) maps by PSI (<b>Left</b>) and by TomoSAR (<b>Right</b>).</p> Full article ">Figure 5
<p>Histogram of the PSI and TomoSAR RTE differences.</p> Full article ">Figure 6
<p>Thermal expansion parameter (THER) maps by PSI (<b>Left</b>) and by TomoSAR (<b>Right</b>).</p> Full article ">Figure 7
<p>Histogram of the PSI and TomoSAR THER differences.</p> Full article ">Figure 8
<p>3D view of the second test area with superimposed the TomoSAR height map on the <b>left</b> image; single (red) and double (blue) scatterers on the <b>right</b> image.</p> Full article ">Figure 9
<p>3D view of the second test area with the SAR image superimposed.</p> Full article ">Figure 10
<p>Difference in height estimate between TomoSAR and PSI in a common point.</p> Full article ">
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