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Land Subsidence Monitoring in Greater VancouverThrough Synergy of InSAR and PolarimetricAnalysis

Zhaohua Chen, Jinfei Wang & Xiaodong Huang

To cite this article: Zhaohua Chen, Jinfei Wang & Xiaodong Huang (2018) Land SubsidenceMonitoring in Greater Vancouver Through Synergy of InSAR and Polarimetric Analysis, CanadianJournal of Remote Sensing, 44:3, 202-214, DOI: 10.1080/07038992.2018.1481736

To link to this article: https://doi.org/10.1080/07038992.2018.1481736

© Copyright of the Crown in Canada 2018Environment and Climate Change Canada

Published online: 29 Dec 2018.

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Land Subsidence Monitoring in Greater Vancouver Through Synergy ofInSAR and Polarimetric Analysis

Zhaohua Chena,b, Jinfei Wanga, and Xiaodong Huanga

aDepartment of Geography, University of Western Ontario, 1151 Richmond St, London, ON N6A 3K7; bLandscape Science andTechnology, Environment and Climate Change Canada, National Wildlife Research Centre, 1125 Colonel By Drive, Ottawa, ON, K1A0H3 (613) 990-9941, Canada

ABSTRACTUp-to-date spatial information on ground movements and land use is useful for emergency manage-ment of coastal regions. Time series InSAR techniques have proven to be effective tools for providingthe former; however, InSAR results alone cannot be used to characterize the relationship betweenmovements and land use. The focus of this study is to evaluate the potential to use high resolutionradar satellite imagery for monitoring urban land subsidence associated with the construction of newbuilding in Canadian coastal cities. To do this, we propose to integrate InSAR and polarimetric SARinformation for deformation analysis. The methodology included multidimensional small baseline sub-set (MSBAS) InSAR analysis, polarimetric SAR change detection, and integration of the coherence,deformation, and polarimetric information for identifying the urban surface movements related tonew buildings. The study was conducted in the Vancouver region, BC using RADARSAT-2 satellitedata of ultra-fine mode and fine-quad mode acquired during 2010-2016. Results demonstrated thatthe integration of polarimetric and InSAR data permitted identification of ground movement, and theassociation of these movements to the new constructions in the urban environment. Several loca-tions have been experiencing subsidence at a rate of up to 10 cm/year, and horizontal motion of5 cm/year.

R�ESUM�EL'information g�eospatiale �a jour des mouvements du sol et de son utilisation est utile pour la gestiondes urgences en r�egions coti�eres. Les techniques InRSO sur des s�eries temporelles ont d�emontr�eesleur utilit�e comme outils efficace pour extraire les mouvements des sols. Par contre, les r�esultats del'InRSO seul ne peuvent etre utilis�es pour caract�eriser la relation entre les mouvements et l'utilisationdes sols. L'objectif de cette �etude est d'�evaluer le potentiel de l'imagerie satellitaire radar �a hauter�esolution pour le suivi de l'affaissement des sols urbains associ�es �a la construction de nouveauxbatiments dans les villes coti�eres canadiennes. Pour ce faire, nous proposons d'int�egrer l'InRSO etl'information polarim�etrique RSO afin d'analyser la d�eformation. La m�ethodologie inclue l'analyseInRSO de petits sous-ensembles de bases multi-dimensionnels, la d�etection de changement polarim�e-trique RSO et l'int�egration de la coh�erence, la d�eformation et l'information polarim�etrique RSO afind'identifier les mouvements de surfaces urbains reli�es aux nouveaux batiments. Cette �etude a �et�emen�ee dans la r�egion de Vancouver, en Colombie-Britannique �a l'aide de donn�ees satellitairesRADARSAT-2 acquise entre 2010 et 2016 dans les modes ultra-fin et quad-fin. Les r�esultatsd�emontrent que l'int�egration des donn�ees polarim�etriques et InRSO permet d'identifier les mouve-ments des sols et de les associer aux nouvelles constructions dans un environnement urbain.Plusieurs endroits d�emontrent de l'affaissement �a un rythme allant jusqu'�a 10 cm/an et des mouve-ments horizontaux de 5 cm/an.

ARTICLE HISTORYReceived 29 September 2017Accepted 31 March 2018

Introduction

Coastal cities have been undergoing land subsidenceas a result of sediment compaction due to urbandevelopment and rising global sea level (Syvitski et al.2009). Space-based remote sensing provides

observations needed to understand environmentalchanges, and for vulnerability analyses of urban areasin coastal regions. Independent of weather conditions,Synthetic Aperture RADAR (SAR) data, containingamplitude and phase information of the illuminated

CONTACT Zhaohua Chen zhaohua.chen@canada.ca� Copyright of the Crown in Canada 2018 Environment and Climate Change CanadaThis is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed,or built upon in any way.

CANADIAN JOURNAL OF REMOTE SENSING2018, VOL. 44, NO. 3, 202–214https://doi.org/10.1080/07038992.2018.1481736

area, have been used for various environmental moni-toring applications. Interferometric SAR (InSAR)measures surface movements through evaluation ofthe phase information from two RADAR acquisitionswith the same polarization. Differential interferometricSAR (DInSAR) is an established methodology thatmeasures the difference of ground movementsbetween two SAR acquisitions. Use of DInSAR can belimited, however, since these data can show sensitivityto atmospheric effects (Massonnet and Feigl 1998;Hooper et al. 2012), and it can be difficult to usethese to characterize three-dimensional movements(Hooper et al. 2012; Samsonov et al. 2017a).

Time-series of line-of-sight (LOS) deformation from asingle dataset (same mode) can be processed using thesmall baseline subset (SBAS) technique (Berardino et al.2002; Usai 2003). Relying on distributed scatterers, SBASuses singular value decomposition (SVD) to connectindependent interferograms in time, corrects atmosphericand topographic effects, and generates a chronology ofdeformations for the study period (Berardino et al. 2002;Usai 2003; Samsonov et al. 2011 Hooper et al. 2012;Samsonov et al. 2014b). Using multi-temporal SARimages of the same polarization, InSAR has been suc-cessfully used to measure ground surface movementswith sub-centimeter accuracy in urban environments(Mazzotti et al. 2009; Samsonov et al. 2014b, Samsonovet al. 2014c; Samsonov and d’Oreye 2017), in permafrost(Chen et al. 2012a; Short et al. 2014; Liu et al. 2015;Chen et al. 2016b; Samsonov et al. 2016), in seismic andvolcanic activities (Lu et al. 2005; Lu et al. 2010;Samsonov and d’Oreye 2012; Samsonov et al. 2014c,Samsonov et al. 2017a; Samsonov et al. 2017b), in car-bon sequestration and mining activities (Samsonov et al.2014a; Samsonov et al. 2015).

Most of the SBAS methods deal only with SARacquisitions from the same orbital direction (eitherascending or descending) and produce only the LOSdeformation (Samsonov and d’Oreye 2012). By com-bining ascending and descending InSAR data, east-westand vertical movements may be detected (Sircar et al.2002, Sircar et al. 2004; Wright et al. 2004; Ozawa andUeda 2011). Recently, a Multidimensional SmallBaseline Subset (MSBAS) method was proposed toreconstruct two or three-dimensional deformation timeseries from ascending and descending DInSAR data(Samsonov and d’Oreye 2012; Samsonov and d’Oreye2017). Various studies have demonstrated the success-ful application of MSBAS algorithms (Samsonov andd’Oreye 2012; Samsonov et al. 2014b; Samsonov et al.2015; Samsonov et al. 2016; Samsonov and d’Oreye2017; Samsonov et al. 2017b).

Usually, urban areas, including: roads, barren areas,and building rooftops will appear as coherent targetsin SAR images, and InSAR deformation measure-ments from these coherent areas are reliable. It is wellunderstood that InSAR is capable of detecting themovement of a target without rapid physical changesthrough time because same surface ensures highcoherence between acquisitions (Massonnet and Feigl1998; Hanssen 2001). Time series InSAR analysis canprovide accurate movement information including thelocation and history of movement during the studyperiod but does not reveal the relationship betweenmovements and land use types. No detailed informa-tion of deformation associated with specific land use/land cover (LU/LC) types or change is provided fromInSAR products. In order to relate the deformation toa specific LU/LC, ground surveys and GIS analysisusing auxiliary LU/LC maps is required.

Alternatively, SAR amplitude or intensity can beused to provide LU/LC information. Although SARintensity has been widely explored for urban footprintand general land use mapping (Esch et al. 2010, Chenet al. 2012b), the information content of an imageacquired in a single polarization, tends to provide lim-ited information. Instead, complete backscatteringinformation about the target structure can be obtainedfrom polarimetric RADAR, thus providing moreinformation compared to single or dual polarizationmodes (Lee et al. 2001; Cloude 2009).

Various target decomposition techniques have beendeveloped to fully exploit polarimetric informationand understand the scattering mechanism of targets(Freeman and Durden 1998; Yamaguchi et al. 2005;Cloude 2009). Model-based decomposition algorithms,directly relate physical scattering mechanisms toobserved responses, thus have recently gained moreattention than eigenvalue-eigenvector-based methods(Lee and Ainsworth 2011; Yamaguchi et al. 2011;Chen et al. 2014a, Chen et al. 2014b, Chen et al.2014c). Readers are referred to Chen et al. (2014a) fora review of recent advances in polarimetric analysis.Using a model-based decomposition method, quadpolarimetric SAR data of distributed targets, charac-terized by either a covariance or coherency matrix,can be decomposed into different components repre-senting canonical scattering mechanisms, such as dou-ble bounce, rough surface and volume. A number ofstudies have demonstrated the benefits of polarimetricSAR data for use in a variety of urban applications(Ainsworth et al. 2009; Yamaguchi 2012; Chen andSato 2013, Chen et al. 2016a).

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In urban areas, observed SAR intensity can berelated to the size, density and orientation of build-ings, streets/roads, vegetation and soil (Chen et al.2012b). When roads and buildings are aligned to theazimuthal direction or perpendicular to the RADARLOS, a dihedral corner reflector is formed, resultingin strong double-bounce scattering (when incidentwaves strike the wall of a building, then the groundsurface and go back to the antenna), which can beused to classify urban structures. However, a similarresponse can be observed as a result of ground-trunkinteractions from trees. It is also notable that model-based decompositions tend to overestimate volumescattering contributions, thus resulting in ambiguitybetween forest and some buildings in urban areas(Ainsworth et al. 2008; Lee and Ainsworth 2011;Yamaguchi et al. 2011; Chen et al. 2013, Chen et al.2014a, Chen et al. 2014b, Chen et al. 2016a).

Recently, advanced polarimetric techniques havebeen proposed to improve the characterization ofbuildings in urban areas (Ainsworth et al. 2008, Leeand Ainsworth 2011; Yamaguchi et al. 2011; Chenet al. 2014a, Chen et al. 2014b; Xiao et al. 2014).Deorientation, a process used to compensate theorientation effect of buildings, may be applied toimprove the performance of decompositions andenhance the separability between buildings that arenot aligned perpendicular to the RADAR LOS andforests (Ainsworth et al. 2008; Chen et al. 2013, Chenet al. 2014b, Chen et al. 2016a). Thus, exploiting thecomplete polarimetric information with advancedtechniques, polarimetric SAR data can be used to gen-erate more accurate urban land use classes and changedetection maps than single and dual polarized data(Lee et al. 2001; Ainsworth et al. 2009). Althoughpolarimetric analysis with advanced algorithms canimprove classification of urban areas, ambiguity stillexists for some buildings, and they remain undetectedbecause the deorientation process cannot fully com-pensate for the effects of orientation (Chen et al.2013; Chen et al. 2014b).

It is of interest to explore the potential of synergy ofInSAR and Polarimetric SAR for measuring and moni-toring urban land subsidence. Combined, informationon deformation and coherence from InSAR anddouble-bounce scattering from Polarimetric SAR canovercome the limitations of using InSAR or polarimet-ric SAR alone. High coherence can be expected forbuildings from two SAR acquisitions over a long-timeperiod, but not from forests or naturally vegetatedareas. Therefore, time series InSAR coherence can beused to separate buildings from the forests or other

natural features. By combining deformation measure-ments with building information, deformation in build-ing areas can be isolated from other areas. ThoughInSAR has been applied for monitoring land subsidencein urban areas, and polarimetric SAR has been widelyused for urban land use mapping, very little researchexists on the synergy of InSAR and polarimetric SARfor exploring the relationship between ground move-ments and land use patterns. This work is important tounderstand deformation mechanisms and could be usedfor vulnerability analyses and or disaster mitigation.

From this perspective, the goal of this study is todevelop a synergistic approach of InSAR and polari-metric SAR for urban land subsidence related to theconstruction of new buildings. The specific objectivesare to

1. Detect the long-term ground movement usingtime series SAR data; and

2. Combine InSAR results and polarimetric informa-tion to identify the deformation related to newbuilding construction.

The proposed method was tested in the GreaterVancouver area, Canada. With over two million peo-ple living in the municipality of Vancouver, it is oneof the most populated regions in Canada. Previousstudies have detected rapid subsidence in several loca-tions here since 1992 (Mazzotti et al. 2009; Samsonovet al. 2014b). Continued monitoring of ground move-ments will therefore provide useful information foremergency management purposes. In this paper, wepresent a new approach to link InSAR derived dis-placements to land use change, and in particular, theconstruction of new buildings. In order to ensure thephase quality in the interferograms generated, a two-step method including closed-loop and segmentationwas applied to correct the phase unwrapping errors.MSBAS method was used to remove the noise andanalyze the long-term movement trend in both a ver-tical and east-west direction. A polarimetric SARchange detection method was applied to extract build-ings based on the double bounce scattering compo-nent from the Freeman-Durden decomposition.Compared to more advanced polarimetric algorithms,Freeman-Durden is a simple and computationally effi-cient method for time series analysis, so was appliedin this study. Finally, a combination of coherence,deformation and double bounce information was usedto identify new buildings, and associated groundmovements. To validate the InSAR measurements, aKalman filter was used to interpolate and predict

204 Z. CHEN ET AL.

missing GPS measurements corresponding to theInSAR dates.

Study area and materials

The study site is located in the Greater Vancouverarea, Canada. Situated along Fraser River delta, theregion consists of the cities of Vancouver, Richmond,New Westminster and Burnaby. The area is suscep-tible to various natural hazards, including earthquakes,flooding, landslides and wildfire due to its particularlocation and geological conditions. A study fromMazzotti et al (2009) using InSAR and ground surveytechniques indicated an average subsidence of -1 to-2mm/year in this region during 1992-1999. Usingmulti-sensor data (ERS, ENVISAT and RADARSAT-2) collected during 1995-2012, Samsonov et al.(2014b) confirmed widespread ground subsidencearound 2 cm/year in delta region. This subsidence wasmainly caused by consolidation of sediments(Mazzotti et al. 2009), however, fast subsidence in sev-eral areas was also associated with the construction oflarge man-made structures, including: residential,industrial, highways, Vancouver International Airport,and the Skytrain station (Mazzotti et al. 2009;Samsonov et al. 2014b).

To investigate ground movements in Vancouver,we collected RADARSAT-2 Ultra-Fine 18 (U18) ofHH polarization data in ascending mode (incidenceangle of 43 degrees; 1.3 m � 2 m pixel spacing), andRADARSAT-2 Fine-Quad 28 (FQ28) data with four

polarizations - HH, HV, VH and VV in descendingmode (incidence angle of 46 degrees; 4.7 m x 4.7 mpixel spacing). U18 and FQ28 were selected for thestudy because of availability of archival data andbecause of the similar incidence angles of the twomodes. With 30 scenes of U18 acquired during 2013-2016 and 21 scenes of FQ28 acquired during 2010-2015, RADARSAT-2 provided good spatial and tem-poral coverage for InSAR analysis in this study(Figure 1). GPS measurements from BRN3 station ofWashington State Reference Network located withinstudy site were used for the validation. BRN3 GPSdata were processed by the Pacific NorthwestGeodetic Array (PANGA). A 1-arc-second ShuttleRadar Topography Mission (SRTM) DEM of 30 mresolution was used in the InSAR analysis and to geo-code the SAR products.

Methodology

The proposed method for integration of InSAR andpolarimetric analysis for urban land movementincluded DInSAR analysis, phase unwrapping correc-tion, MSBAS, polarimetric SAR change detection, andcombination of InSAR results with polarimetricchange detection results.

InSAR analysis

Traditional DInSAR processing was applied to eachtrack individually to generate the differential

Figure 1. Study area in the Greater Vancouver region. Black SAR frames used in the study are RADARSAT-2 data of ascending U18and descending FQ28 modes.

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interferogram, coherence and unwrapped phase. Allthe InSAR interferograms from one track were co-reg-istered and resampled to the same reference image.The interferograms were improved using adaptive fil-tering (Goldstein and Werner 1998) and unwrappedusing the Minimum Cost Flow algorithm (Costantini1998) with a well-known reference point. To make theunwrapped phase consistent, the same reference loca-tion was identified in each interferogram and used forboth ascending and descending mode data. InSARprocessing was performed using GAMMA software.InSAR products from both U18 and FQ28 were geore-ferenced to the same resolution – 6m.

In this study, phase unwrapping errors were cor-rected in two steps. First, we corrected for the phaseshift of whole interferogram. These phase unwrappingerrors are due to the decorrelation noise between thereference point and the rest of the image which cancause a phase shift of the whole deformation pattern.To detect these errors, we used the closed loopmethod (Usai 2003), using three interferograms takenon successive dates. Second, we corrected errorsassociated with phase jumps, which occur on a morelocal scale. Usually, the problematic region can beidentified visually, as a result of the high contrastbetween it and surrounding areas. Segmentation wasapplied to the problematic interferogram to groupthe homogeneous regions into segments. Then, theclosed loop method was applied to the problematicregion or segment, and the correction was donelocally. In this study, a super-pixel segmentationmethod was applied to generate the segments(Achanta et al. 2012).

Once the interferograms were correctlyunwrapped, only interferograms with high coherencemaintained in large urban areas were kept. TheMSBAS algorithm was then applied to calculate thelong-term movement trend. By exploiting all theinterferograms with good quality, the SBAS tech-nique is useful for increasing the temporal samplingrate, filtering out interferogram-dependent errors,filling gaps due to temporal decorrelation, and miti-gating the influence of topographic artifacts andatmospheric effects.

All movements detected along LOS (or slant-rangedirection) using SAR observations can be expressed bythree components, namely east, north and up direc-tions respectively. According to Hanssen (2001), theInSAR deformation along LOS Vlos can be expressedas the projection of a 3D displacement vector withcomponents Vn, Ve, and Vu, in North, East and Updirection, respectively. Given a satellite orbit with

heading (azimuth) of ah, incidence angle of hinc,deformation rate Vlos is written as:

Vlos ¼ Vu cos hincð Þ � sin hincð Þ Ve cos ahð Þ�Vn sin ahð Þ� �

(1)

However, for most current SAR sensors with right-looking geometry the sensitivity for detecting move-ment in the north-south direction is low (Sircar et al.2004; Ozawa and Ueda 2011). Therefore, in multidi-mensional SBAS (MSBAS), the north-south compo-nent Vn is excluded. As such, subsidence (vertical) andhorizontal (east-west) deformation monitoring werethe focus in this study. Using the coincident coverageof all interferograms in the time series, MSBAS wasapplied to process both ascending and descendingDInSAR data simultaneously and compute the two-dimensional vertical and horizontal (east-west)deformation rates by numerical integration (Samsonovand d’Oreye 2012; Samsonov and d’Oreye 2017).

Polarimetric SAR change detection

The polarimetric SAR change detection included bothpolarimetric analysis and building detection compo-nents. All polarimetric SAR analyses were conductedusing PolSARPro 5.0 software. First, speckle filteringusing Lee Sigma filter was used to reduce the effects ofspeckle using a window size of 7 � 7 pixels, a numberof look of 1 and a sigma value of 0.9. Consideringboth the edge preserving and speckle-smoothingeffects, a window size of 7 pixels was deemed suitablebased on visual comparison of speckle reduction fordifferent window sizes. Then, the Freeman-Durdendecomposition method was applied to divide the totalobserved power into contributions from three scatter-ing components: single bounce, double bounce, andvolume scattering. Building detection was conductedusing the following rules:

1. Candidate pixels for each acquisition were identi-fied by thresholding the double bounce backscat-tering coefficient. A threshold value was chosento include most building pixels. A binary map ofbuildings was generated for each acquisition.

2. A baseline map of existing buildings was deter-mined based on a frequency map of an earlyperiod. By adding all binary maps generatedbefore a reference date, a frequency map was cre-ated to represent building candidates detected(number of times the pixel mapped as a buildingin a sequence of SAR scenes) before the referencedate. Thresholding this frequency map using a

206 Z. CHEN ET AL.

value (usually a value higher than 2) resulted in abuilding map to represent existing buildings.Higher threshold value means higher confidence;however, this depends on the number of scenesused. The concept of thresholding based on fre-quency is that the more time series data collected,and the stronger signal of a building can bedetected, the more accurate building pixelsare mapped.

3. New building candidates built after the referencedate were determined based on a frequency mapof a later period and the baseline map. By addingall binary maps generated after the reference date,a frequency map representing building candidatesduring a later period was created. By thresholdingthis frequency map using a value, a map repre-senting buildings in the study area of a laterperiod was generated. Subtracting the existingbuilding area in the baseline map from this newmap for the later period resulted a mask repre-senting new buildings built during thestudy period.

Identification of deformation related tonew buildings

Identification of movements related to new buildingsinvolved the combination of deformation measure-ments from the InSAR analysis and building informa-tion from the polarimetric SAR analysis. First, pixelsextracted from the scene representing potential

building candidates were verified using coherencemaps generated from the InSAR analysis, and onlypixels with consistent coherence were kept. Thisremoved features like tree trunks that were incorrectlyincluded in the new building mask. Then, areas withexisting movements were separated from areas withnew movements by overlaying the map of new build-ings onto the deformation map. Thus, the final maponly included areas where deformation was identifiedfor new buildings.

Results

Results from deformation analysis using bothascending and descending data sets

With 30 scenes of ascending U18 SAR images, wegenerated 55 InSAR interferograms that maintainedgood coherence during 2013-2016. With 21 scenes ofdescending FQ28 SAR images, we generated 35 inter-ferograms for the period of 2010-2015. For eachmode, a single master scene was used to resample andco-register the remaining images. These interfero-grams have perpendicular baselines of less than 400 mand temporal separations of less than 72 days. For theMSBAS analysis, data from both ascending anddescending look directions must be acquired duringthe same time span, therefore only interferogramsgenerated from August, 2013 to January, 2015 wereused for the deformation analysis, and interferogramsfrom 2010 image pairs were not used. Images acquiredin 2010 were however used to generate the baseline

Figure 2. Vertical linear deformation rate measured withMSBAS technique using ascending RADARSAT-2 data acquiredduring August 2013-Janurary 2014. BRN3 is the GPS station,and R – reference region used for MSBAS processing. Positivevalues indicate upward movements and negative values indi-cate downward movement (subsidence).

Figure 3. Horizontal (east-west) linear deformation rate meas-ured with MSBAS technique using ascending RADARSAT-2 dataacquired during August 2013-Janurary 2014. BRN3 is the GPSstation, and R – reference region used for MSBAS processing.Positive values indicate eastward movement and negativevalues indicate westward movement.

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map of existing buildings for the polarimetric analysis.Data acquired after January, 2015 were not includedin MSBAS analysis.

Generally, consistent, good coherence (higher than0.5) was observed in urban areas (i.e. for buildings,roads, airport runways, and barren areas), while poorcoherence was observed mainly for forested areas in the90 InSAR pairs. Within a three-month time frame,good coherence was maintained in most urban areaswhich guaranteed reliable time series MSBAS for theInSAR analysis. In this study, only areas of coherencegreater than 40% were used for accurate InSAR deform-ation measurements. All time series ground movementswere calculated successfully using MSBAS technique.

To remove trend errors, we used a location near thecenter of the scene as reference point, since this locationdid not show significant movement in a previous study(Samsonov et al. 2014b). When selecting the location Ras our reference points for InSAR analysis, atmosphericand orbital signals proportional to the distance betweenthe reference and measurement locations were mini-mized due to the close distance between the referencepoint and the observation area (Samsonov et al. 2014b).We present results from both vertical movement andhorizontal movement in the following paragraphs.

Deformation measurements in the vertical andhorizontal (east-west) direction were reconstructed for31 dates from August, 2013 to January, 2015 usingmeasurements from both ascending and descendingdata (Figure 2 and Figure 3). It was found that themaximum subsidence rate in the Vancouver regionwas -10 cm/year (Figure 2). The maximum eastwardmovement was 6 cm/year and maximum westwardmovement was 4 cm/year (Figure 3). In general,

subsidence was localized to several sites and occurredfaster than horizontal movements.

According to the MSBAS analysis, four areas withsevere subsidence during 2013-2015 were located atVancouver International Airport site (NP1), MitchellIsland (P5), New Westminster (near P10), andRichmond (near NP3) (Figure 2). Locations of subsid-ence were consistent with reported studies. The fastestsubsidence was detected at location NP5, near theintersection of North Fraser Way and Wiggins Street,New Westminster. Here, subsidence occurred at a rateof approximately -10 cm/year, or -16 cm in 1.5 years(Figure 4). At Vancouver airport, locations at NP1and NP2 experienced the fastest subsidence, and inparticular, about -15 cm subsidence for the latter over1.5 years. At the west end of Mitchell Island, wheremarine services are located, -5 cm subsidence wasobserved during the study period.

We selected 13 locations to plot the subsidence his-tory of 13 sites during the 1.5 years study period(Figure 4). Among these, the same eight locations (P1,P2, P4, P5, P6, P7, P9 and P10) used by Samsonovet al. (2014b) were also evaluated in this study tocompare results. Note that we could only comparecumulative displacements over 1.5 years from August2013 to January 2015 observed in our study, withobservations by Samsonov et al. (2014b) whichoccurred over 4 years, from February 2009 to October2012 (Table 1). It was observed that subsidence atlocations P1 and P2 slowed, but at locations P4, P5,P7, P9, and P10, subsidence was accelerated during2013-2015. Horizontal movements also differed inthese locations. We also plotted results for new loca-tions at NP1, NP2, NP3, NP4 and NP5 because these

Figure 4. Time series vertical ground movements at the select locations, detected from this study using MSBAS.

208 Z. CHEN ET AL.

locations were experiencing fast rates of subsidence(Table 1, Figures 4 and 5). It is clear that acceleratedsubsidence at locations NP2, NP3 and NP5 wasaccompanied by fast eastward movement as well.

Validation of InSAR results

InSAR results are usually verified using leveling orGPS measurements (Ferretti et al. 2007; Mazzottiet al. 2009; Samsonov et al. 2014b). In this study, GPSmeasurements from the BRN3 station since 2013, pro-vided by Pacific Network (Murray and Svarc 2017),were used for validation. Although the BRN3 GPS sta-tion has been working since 2013, the GPS andInSAR measurements considered here, covered differ-ent time periods. In order to compare measurements

from the GPS with that from the InSAR analysis,measurements from corresponding dates had to beestablished. Kalman filtering has been widely used tointegrate time series observations (Vijayakumar andPlale 2008). Therefore, a Kalman filter was used tobuild a model to interpolate the missing records andpredict the measurements corresponding to the InSARdates in this study. However, few GPS measurementswere recorded in 2014, and between August –October, 2015. Therefore, only the continuous meas-urements acquired during August, 2013 to Jan, 2014from both GPS and InSAR were compared in thisstudy. We found that GPS and InSAR measurementshad a good agreement in the vertical direction(R2¼ 0.7), but poor agreement in the east-west direc-tion (R2¼ 0.3) (Figure 6). In the vertical direction,

Table 1. Comparison of movements observed in the selected locations from this study over 1.5years from August 2013 to January 2015 and that from Samsonov et al (2014b) over 4 years fromFebruary 2009 to October 2012. Value sign for upward movement (þ), subsidence (�), eastwardmovement (þ), and westward movement (�).

Samsonov et al (2014b) (cm) This study (cm)

Location Vertical movement Horizontal movement Vertical movement Horizontal movement

P1 �4 0 0.5 0P2 �4 0 <¼�1 0P4 �4 0 �4 1.5P5 �5 0 �5 0P6 �6 �0.5 �1 �1P7 �1.5 �0.5 �3 0P9 �0.5 �0.5 �4 �1P10 �2 0 �7 1NP1 NA NA �10 0NP2 NA NA �15 9NP3 NA NA �6 5.5NP4 NA NA �5 0NP5 NA NA �16 6.5

Figure 5. Time series horizontal (east-west) ground movements at the select locations, detected from this study using MSBAS.

CANADIAN JOURNAL OF REMOTE SENSING 209

InSAR overestimated the movement by a scale factorof 1.7.

Filtered GPS measurements at the BRN3 station overfour years in 2013-2017 indicated that overall theannual north-south velocity was 3.3mm, the east-westvelocity was 4.7mm, and the annual vertical velocitywas -1.3mm (Murray and Svarc 2017). However, a ver-tical movement of 6mm and an east-west movement ofabout 2mm were recorded by the GPS measurementduring the period of August, 2013 - January, 2014. Aseast-west movements were relatively slow during thestudy period, InSAR methods may not have been ableto detect such subtle changes in the horizontal direc-tion. The poorer quality reconstruction of the horizon-tal east-west component of the displacement comparedto the vertical component may also account for the dis-crepancy in the observed north-south component whenapplying MSBAS (Samsonov et al. 2017b).

Results from polarimetric SAR change detection

Generally, it was found that buildings appeared brightin the SAR data. Stronger double bounce scattering wasfound from tall buildings than from low buildings, andfrom larger compared to smaller size buildings.Buildings with side walls facing perpendicular to theSAR look direction (range direction) appeared brighterthan buildings with walls facing away from the SARlook direction. Most buildings had double bounce val-ues greater than -10dB, therefore, a binary map ofbuildings was generated for each acquisition using athreshold value (>-10 dB) applied to double bouncevalues. A baseline map of existing buildings was gener-ated using all imagery acquired in 2010. If a pixel wasnot assigned as a building in 2010, but was detected asbuilding after 2010, it was considered as a new building

candidate. However, areas detected using the thresholdapplied to double bounce values possibly also includedtrees or other temporary targets. From this study, it wasobserved that coherence in forested areas was generallylow with an acquisition cycles of 24 days, and evenlower with 48 days for C-band RADARSAT-2; thus, nodeformation information was available. As such, coher-ence information was applied to mask out those areas.To separate the buildings from the forests or other nat-ural features, InSAR coherence values greater than 0.5,and consistent over 10 pairs of interferograms from theFQ28 pairs, was used. Therefore, only areas with con-sistent coherence were considered as buildings.

Results of deformation related to the constructionof new buildings

The final deformation related to the construction ofnew buildings since 2010 was generated by overlayingthe average deformation rate map with the buildingmask (Figure 7). Note that the accuracy of the build-ing mask was evaluated via visual comparison withhistorical high-resolution imagery available fromGoogle Earth, and it was confirmed that all the newbuildings were correctly classified.

We found that subsidence at locations P5, NP1, NP2,NP3, NP5 was associated with the construction of largebuildings. Extensive subsidence in the Richmond areawas associated with many small buildings, possibly resi-dential development. Deformation measurements ofthese new buildings can be found in Figure 7.

Discussion

The synergistic approach presented in this paper pro-vided useful information for monitoring urban

Figure 6. GPS measurement vs InSAR measurements at the BRN3 station. a. horizontal movement (east-west), and b. verti-cal movement.

210 Z. CHEN ET AL.

environments using SAR satellite images. It is evidentfrom the examples presented that this approach suc-cessfully distinguished the deformation caused by newconstruction from existing deformation caused byother factors. The utility of the synergy of InSAR andpolarimetric information presented in this paper isthat it provides an investigator with a quick idea ofthe movement associated with the construction of thenew buildings in the urban areas. Further, detailedInSAR analysis can be used to examine subtle differ-ences through time. Note that geological and engin-eering surveys may be required to investigate theexact causes of deformation, though it is notable thatno prior LU/LC knowledge of the area being studiedis necessary to successfully apply the method.

Double bounce scattering in urban areas has beenstudied for building extraction in a number of studies(Brunner et al. 2010; Chen et al. 2013, Chen et al.2014b, Chen et al. 2016a; Li et al. 2016). In general,double bounce scattering is strong from buildings fac-ing perpendicular to the RADAR LOS, and weaker forthose that are oriented at some different angles(Brunner et al. 2010), which in some cases can alsoresult in the categorization of features as predominantvolume scatterers (Chen et al. 2013, Chen et al. 2014b,Chen et al. 2016a; Li et al. 2016). Incidence anglesalso affect double bounce scattering processes. Assuch, not all buildings exhibit strong enough doublebounce scattering to be distinguished from their sur-roundings. As such, both buildings that do not facethe RADAR LOS and that are relatively small may nothave been detected in this study and improved build-ing detection via the advanced four component

decomposition (Yamaguchi) and deorientation proc-esses (Ainsworth et al. 2008; Chen et al. 2013, Chenet al. 2014b, Chen et al. 2016a) may be required.

A number of efficient urban change detection algo-rithms exist based either on advanced polarimetricanalysis or time series intensity images only. In thisstudy, we did not attempt to identify all the buildingsusing polarimetric information, but instead exploredthe possibility of applying polarimetric information toaid in the deformation analysis. Although those pixelsidentified as buildings may have included some othertargets such as trees, river banks, or boulders, thesewere eliminated using coherence and deformationinformation. It was observed that coherence fromtrees cannot last over more than three acquisitioncycles (a cycle of 24 days for RADARSAT-2).Alternatively, large boulders and or rock areas areusually stable, therefore, there were no deformationmeasurements from those areas, and consequentlythey were not shown in the map of deformationrelated to new buildings.

From this, and other previous studies (Mazzottiet al. 2009; Samsonov et al. 2014b), it was found thatground movements usually occurred in areas either ofhigh building density or large size. Double bouncescattering from large size buildings is typically highenough to be identified using the Freeman-DurdenDecomposition only, and InSAR coherence in theseareas is also generally very high and consistentthrough time. With the 30 U18 images acquired dur-ing 2013-2016, good coherence was maintained withinshort time intervals; thus, a consistent time seriesinterferogram was generated. Among the 21 FQ28images, there were four images acquired in 2010, and17 images during 2013-2015. Only the FQ28 imagesacquired during 2013-2015 which had a short intervalwhen forming interferograms were used for theInSAR deformation analysis.

Although most subsidence areas were located alongthe waterfront, some areas away from water, inRichmond, were found to be subsiding during thestudy period. We also identified some other movinglocations, which were different from the previousreports. In addition to MSBAS, we also applied SBASanalysis on either ascending and descending data.Results showed that although most of the locationsexperiencing movements could also be identified byeach individual dataset, the magnitude of movementalong LOS was different due to the differing geometryof the SAR data. It is possible to measure the move-ments using pairs of SAR images from the ascendingand descending directions (Sircar et al. 2002, Sircar

Figure 7. Subsidence locations related to the construction ofnew buildings (black dots), with background of SAR inten-sity image.

CANADIAN JOURNAL OF REMOTE SENSING 211

et al. 2004), however, MSBAS has proven to be anefficient algorithm for restructuring two-dimensionalmovements for time series deformation analysis.

To investigate the movement trend in the studyregion after 2015, a separate SBAS analysis of U18data from 2015-2016 was also conducted. Weobserved that ground movements in the LOS directioncontinued for those areas identified as part of the pre-vious analysis. We recommend continued monitoringof this region, and additional research to understandits causes. This information about two-dimensionalmovement patterns identified in Vancouver should beconsidered for decision making related to urban plan-ning and for emergency prevention.

As no readily available leveling or GPS data cov-ered the whole study area for validation for ourInSAR analysis, we used the measurements from anindependent GPS station. Using this single location asour validation, other areas were not validated.However, continuous GPS measurements were avail-able for half a year during the study period. From thecomparison of GPS with InSAR measurements, astrong correlation was found in the vertical direction,but not in the east-west direction. Based on the com-parison of GPS measurements, we found that verticaldeformation from InSAR was overestimated by a fac-tor of 1.7. Since all the movements were referred to alocation in the center of the scene assuming it wasstable during the study period, InSAR measurementswere calibrated using that reference point. Therefore,the InSAR measurements were relative measurements,and any movements at the reference point wouldaffect the InSAR analysis. Comparing the results fromthis study with that from a previous study, it wasfound that our results were generally consistent withthe reported movements in this area in terms of loca-tions, although the magnitudes of the movement dif-fered slightly. In the future, it would be ideal to havemore locations with GPS measurements distributedthroughout the region for consistent validation of theInSAR results.

Conclusions

In this paper, we described and evaluated a synergisticapproach for combining InSAR and polarimetric SARfor identifying urban land subsidence related to theconstruction of new buildings. Both descending andascending SAR data were used to provide long termground movement measurements in Vancouver dur-ing August, 2013 – January, 2015. In the study,MSBAS techniques were applied for InSAR time series

analysis to identify the long-term ground movementtrend, and minimize the noise and influence fromtopography, orbit, temporary incoherence, and atmos-pheric effects. Double bounce scattering values wereused for identifying buildings, and maps generatedfrom the project demonstrated that the combinedinformation contained in coherence, deformationfrom InSAR analysis, and double bounce from polari-metric SAR decomposition was effective in measuringaccurate subsidence and linking movements to urbandevelopment over time. A maximum subsidence rateof -10 cm/year was found in the study region duringthe study period. The most severe subsidence wasidentified near the Vancouver airport, Mitchell island,and New Westminster. In general, a linear subsidencepattern dominated several locations in the Vancouverregion. Horizontal movement (east-west) was not assevere as vertical movement, although fast eastwardmovements was also observed to be accompanied byfast subsidence at a few locations. It was observed thatobserved ground movement trends continued untilthe end of the observation period in August, 2016.Among the locations with fast subsidence, some of thesubsidence was caused by the new construction.

MSBAS combined with polarimetric analysis can beused synergistically with InSAR for monitoring theurban ground movements. By analyzing the two-dimensional movement (east-west and vertical) anddouble bounce scattering mechanism, new construc-tion areas experiencing ground movement can beeffectively identified. This information can be used fora follow-up investigation to determine the cause andproper mitigation. This method is expected to benefitCanadians directly, through use in urban planningand disaster monitoring and analysis.

Acknowledgements

This research was funded by Canadian Space Agency underthe SOAR-E 5331 program. All RADARSAT-2 images wereprovided by Canadian Space Agency. The authors wouldlike to thank Sergey Samsonov for providing the MSBAScode, and three anonymous reviewers for making valuablecomments that helped improve this manuscript.

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