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Airborne and Spaceborne Remote SensingCharacterization for Aquistore Carbon Captureand Storage Site

Magdalena Czarnogorska, Sergey V. Samsonov & Donald J. White

To cite this article: Magdalena Czarnogorska, Sergey V. Samsonov & Donald J. White(2016) Airborne and Spaceborne Remote Sensing Characterization for Aquistore CarbonCapture and Storage Site, Canadian Journal of Remote Sensing, 42:3, 274-290, DOI:10.1080/07038992.2016.1171131

To link to this article: https://doi.org/10.1080/07038992.2016.1171131

Accepted author version posted online: 30Mar 2016.Published online: 30 Mar 2016.

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Canadian Journal of Remote Sensing, 42:274–291, 2016Copyright c© 2015 Crown copyrightISSN: 0703-8992 print / 1712-7971 onlineDOI: 10.1080/07038992.2016.1171131

Airborne and Spaceborne Remote SensingCharacterization for Aquistore Carbon Captureand Storage Site

Magdalena Czarnogorska1, Sergey V. Samsonov1*, and Donald J. White2

1Canada Centre for Mapping and Earth Observation, Natural Resources Canada, 560 Rochester Street,Ottawa, Ontario K1A 0E4, Canada2Geological Survey of Canada, Natural Resources Canada, 615 Booth Street, Ottawa, Ontario K1A 0E9,Canada

Abstract. Aquistore is a carbon storage research and monitoring project in Southern Saskatchewan, Canada, which intends todemonstrate that storing carbon dioxide (CO2) in the deep underground is a safe, workable solution to reduce greenhouse gases.Aquistore receives carbon dioxide transported by pipeline from the nearby Boundary Dam coal-fired power plant and injects itinto the deepest saline formation of the Williston Basin at a 3200-m depth. The Aquistore site is located in a region reclaimed afteropen-pit coal mining that was continued until the 1990s. To characterize ground conditions and anthropogenic features of theAquistore area, we used a RapidEye Constellation optical image. In this study we detect, evaluate, and interpret the backgroundsurface changes using RADARSAT-2 satellite Synthetic Aperture Radar (SAR) and airborne Light Detection and Ranging (LiDAR)data prior to carbon dioxide injection. We have applied Multidimensional Small Baseline Subset Differential Interferometric SAR(MSBAS-DInSAR) to measure ground motion due to postmining processes and geomorphological changes during the period ofJune 2012 to October 2014. Four RADARSAT-2 beam modes were used to compute MSBAS-DInSAR deformation maps andtime series of horizontal and vertical deformation rate components with high precision and spatial resolution. MSBAS-DInSARdeformation rates for selected sites were combined with LiDAR geomorphometric profiles to analyze dynamics of the motion. Inorder to detect seasonal surface changes, polarimetric analysis of RADARSAT-2 quad-pol images acquired during spring andsummer of 2014 was performed.

Resume. Aquistore est un projet de recherche et de surveillance du stockage du carbone dans le sud de la Saskatchewan, auCanada, qui vise a demontrer que le stockage du dioxyde de carbone (CO2) dans le sous-sol profond est une solution realisable entoute securite pour reduire les gaz a effet de serre. Aquistore recoit le dioxyde de carbone transporte par gazoduc a partir de lacentrale electrique au charbon situee a proximite a Boundary Dam, et l’injecte dans la plus profonde formation saline du bassinde Williston a 3200 m de profondeur. Le site Aquistore est situe dans une zone rehabilitee apres que l’extraction du charbon a cielouvert y a ete poursuivie jusque dans les annees 1990. Pour caracteriser les conditions du sol et les caracteristiques anthropiques dela zone Aquistore, nous avons utilise une image optique de la constellation de satellites RapidEye. Dans cet etude, nous detectons,evaluons, et interpretons les changements de la surface en utilisant les donnees du radar a synthese d’ouverture «syntheticaperture radar» (SAR) du satellite RADARSAT-2 et du lidar «Light Detection and Ranging (LiDAR)» aeroporte, obtenues avantl’injection de dioxyde de carbone. Nous avons applique «Multidimensional Small Baseline Subset Differential Interferometric SAR»(MSBAS-DInSAR) pour mesurer le mouvement du sol du aux processus de post-extraction et de changements geomorphologiquespendant la periode de juin 2012 a octobre 2014. Quatre modes de faisceaux RADARSAT-2 ont ete utilises pour calculer les cartesde deformation MSBAS-DInSAR et des series chronologiques de composants de taux de deformation horizontale et verticaleavec une grande precision et une haute resolution spatiale. Les taux de deformation MSBAS-DInSAR pour les sites selectionnesont ete combines avec des profils geomorphometriques LiDARs pour analyser la dynamique du mouvement. Afin de detecter leschangements de surface saisonniers, l’analyse polarimetrique des images RADARSAT-2 en quadruple polarisation, acquises aucours du printemps et de l’ete 2014, a ete realisee.

INTRODUCTIONCarbon Capture and Storage (CCS) includes a broad range

of technologies for capturing carbon dioxide (CO2) from fossil-fuelpowerplants, transporting it to the suitable storage sites and

Received 8 July 2015. Accepted 1 February 2016.∗Corresponding author e-mail: sergey.samsonov@canada.ca.

storing it for the long term (IPCC 2005; Gibbins and Chalmers2008; Rackley 2010; Lu, K. 2014). Potential reservoirs for CCSare geological formations located deeper than 800 m (Holloway1996), such as depleted hydrocarbon reservoir, saline aquifers,deep coal beds, and deep ocean or mineral carbonation (Mead-owcroft and Langhelle 2009). Carbon dioxide is also injectedinto geological formations supporting enhanced oil recovery(EOR).

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The first commercial CCS projects in the world were Sleip-ner in the North Sea (1996–present; Arts et al. 2004) and theWeyburn–Midale project carried out in Saskatchewan, Canadaduring 2000–2011 (White 2011). Another large-scale commer-cial project is Snøhvit, which has been in a full operation since2008, in an offshore gas field with carbondioxide injection inBarents Sea (Maldal and Tappel 2004). The potential of salineaquifer formations for storing CO2 was tested in the In SalahCO2 storage project in Algeria during 2004–2009 (Rutqvistet al. 2010; Ramirez and Foxall 2014). Other demonstrationinitiatives were performed in the Australian Ottway Basin Pilotproject (Cook 2014), in the United States Frio project (Kharakaet al. 2006), K-12B in Netherlands (Van der Meer 2013), Na-gaoka in Japan (Sato et al. 2011), Ketzin in Germany (Zhanget al. 2012), and Quest in Canada (Bourne et al. 2014).

In every CCS project, the monitoring program is designedbased on particular site characteristics and risk assessments. Ap-propriate monitoring techniques are selected and implementedto address the requirements and needs of the individual storagesite. The critical task for each monitoring program is to demon-strate that the storage is safe and effective. CCS sites are moni-tored to (1) recognize the site characteristics in the preinjectionphase, collect information about its natural conditions, stability,and long-term dynamics according to site selection criteria; (2)measure geomechanical parameters related to the operationalphase, such as geomechanical deformation produced by reser-voir pressure increase; (3) validate postinjected site behaviorwith model assumptions (Benson et al. 2004; Chadwick 2010).Monitoring techniques focus on the detection of quantitativeanomalies in comparison with predictive storage models, therecognition of which might be indicative of CO2 leakage fromthe storage reservoir. Reservoir pressure, CO2 plume location,ground deformation above the injection zone, and CO2 leakageare all monitored during the injection phase (Rackley 2010).Various techniques are used for ground deformation monitoringand CO2 leakage tracking, including tiltmeter measurements,groundwater sampling, soil gas sampling, and infrared (IR) at-mospheric measurements. Advanced remote sensing techniquessuch as DInSAR and LiDAR are still not widely used for CCSmonitoring purposes. Although DInSAR has been known forthe past 2 decades, the technology has been applied experimen-tally in only 2 commercial sites (Rutqvist et al. 2010; Vascoet al. 2010; Lubitz et al. 2013). Vertical deformation rates ofabout 5 mm/year were successfully detected by DInSAR at theIn Salah site (Rutqvist et al. 2010; Vasco et al. 2010).

In Samsonov et al. (2015), we performed elastic and poro-elastic analytical modeling of ground deformation due to carbondioxide sequestration and compared the magnitude of modeleddeformation with the magnitude of observed deformation. In thisstudy, we performed detailed site characterization analysis ofthe Aquistore storage site, using ground deformation data previ-ously used in Samsonov et al. (2015), and polarimetric SAR andLIDAR data and optical imagery. We combined RADARSAT-2DInSAR and airborne LiDAR surface data to evaluate the stabil-

ity of anthropogenic and natural features. The high-resolutionand high-precision deformation rates for selected sites werecombined with LiDAR to create geomorphometric profiles forgathering dynamic information about the terrain. To detect landuse and land cover condition for interpretation of geodynamicprocesses, we used RADARSAT-2 quad polarization (quad-pol)SAR and RapidEye Constellation optical imagery.

STUDY AREAThe Aquistore site (Figure 1) is located in an historical coal

mining area on the southern flank of the Souris River near Es-tevan, Saskatchewan, Canada. Large open-pit coal mining wasconducted in this area from 1892 until the 1990s. After comple-tion of mining, the land was reclaimed to a depth of ∼20 m–25 m(MacKenzie 2003).1

Around the Aquistore site, there are 2 earth-filled dams:Boundary Dam, built in 1957 along with the Boundary Reservoiron the Long Creek River; and Rafferty Dam, built on the SourisRiver in 1991. The Boundary Dam Power Station uses waterfrom the Boundary Reservoir for cooling, which is the reasonwhy it is the only water body in Saskatchewan that does notfreeze during the winter. A 10-kilometer diversion channel wasbuilt between Boundary and Rafferty Dams to prevent flooding.Although the estimated flow capacity of the diversion channel is60 m3/s, diversion in excess of 45 m3/s causes excessive erosionof the rock structure discharging into the Rafferty Reservoir2 .

There are many small surface water bodies in the area. Someof them are permanent and others dry out in the summer. Sea-sonal groundwater level changes due to recharge caused by snowmelting and early spring rainfall (Van der Kamp and Maathuis1991). Quaternary glacial drift deposits are relatively thin, rang-ing from 0 m to 10 m in thickness and are composed mainlyof tills and clays (Anderson et al. 1997). The area is domi-nated by morainal and glaciofluvial deposits3 .The near-surfacegeology consists of the shallow bedrock unit called the Pa-leocene Ravenscrag formation, which is about 170 m–180 mthick. Ravenscrag is composed of interbedded sandstone, clay-stone, siltstone and includes significant coal seams, with localcarbonaceous, kaolinitic concretionary and calcareous zones(Beaton et al. 1991). Surface geology is strongly affected byprevious pit mining. Around the Aquistore site, there are 2 dis-tinct lignite seams within 40 m of the surface, the shallowerEstevan seam and the deeper Boundary seam (SaskatchewanDept. of Environment 2006). The Estevan seam has been

1https://sites.google.com/site/geomorphologyandmammals/home/effects-of-mining (accessed 10 June 2015); http://esask.uregina.ca/entry/estevan.html(accessed 10 June 2015).

2https://www.wsask.ca/Lakes-and-Rivers/Water-Conveyance-and-Flood-Controls/ (accessed 10 June 2015).

3http://www.infomaps.gov.sk.ca (accessed 10 June 2015)

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FIG. 1. Location of Aquistore site in Saskatchewan, Canada. RapidEye image with nominal resolution of 5m acquired on 20120812(YYYYMMDD) provides overview of Aquistore site and its surrounding area. Aquistore area is outlined in yellow. The lettersymbols and name are the following: RD – Rafferty Dam, ESTEVAN – location of Estevan community, SR – Souris River, RBDCH– Rafferty-Boundary Diversion Channel, BDPS – Boundary Dam Power Station, LCR – Long Creek River, A – Aquistore injectionsite, BD – Boundary Dam, BDR – Boundary Dam Reservoir. The RGB RapidEye color composite image was created from thefollowing electromagnetic spectra: 440-510nm (Blue), 520-590nm (Green) and 630-685nm (Red). PCI Geomatics software wasused to create RGB image, which was orthoreactified, enhanced by applying adaptive filtering and resampled using nearestneighbor interpolation technique to UTM 13N map projection.

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extensively strip-mined, and the preexisting overburden hasbeen replaced with mine tailings.

Mesozoic strata contains siltstones, sandstones, and thicktransgressive shales. Paleozoic strata consist primarily of car-bonates, evaporates, and minor shales. The rocks that are tar-geted as a carbondioxide storage reservoir are the deepest forma-tions within the Williston Basin overlying the Precambrian base-ment (Norford et al. 1994). These are the Cambro-OrdovicianDeadwood and Ordovician Winnipeg Formations, which cre-ate a very deep saline geologic package known as a flow unit(Bachu and Stewart 2002). These rocks provide the best targetsfor geologic storage of carbon dioxide because they are highlyporous and permeable, have huge volumes, and are overlainby effective barriers and geologic seals that can impede CO2

migration.The Aquistore site lies within an intraplate seismotectonic

zone and is characterized by very low levels of seismicity. Re-activation of Precambrian basement faults has been suggested asthe source of natural seismicity events in the area (Bakun et al.2011). The largest known earthquake in the area was registeredin 1909 with a magnitude of 5.5 (Bakun et al. 2011) and anepicenter located approximately 200 km west of Estevan(Horner and Hasegawa 1978). This location falls on a trajec-tory of earthquake epicenters that correlate with well-recognizedfault systems (e.g., Hinsdale, Weldon–Brockton–Froid), plac-ing the nearest fault system with associated natural seismicity200 km to the west of the Aquistore site. Most of the seismicityin the area is related to mining activities. A link has been estab-lished between potash extraction and 2 earthquakes of magni-tude 2.3 and 3.0, respectively (Gendzwill et al. 1982). Oil field-related water injection has been ongoing in the Weyburn–Midaleto the northwest of the Aquistore site for the past 50 years withno demonstrable impact on the induced seismicity in the region.

DATAThree remote sensing datasets were collected for the project:

optical, satellite SAR, and airborne LiDAR data. Details of eachdataset are described in the following sections.

RapideyeTo characterize ground conditions and anthropogenic fea-

tures of the Aquistore area, we used a RapidEye Constellationoptical image acquired on August 12, 2008 with the nominalground resolution of 5 m (Figure 1). Each of RapidEye’s 5satellites contains identical sensors capable of collecting 5-bandcolor imagery with daily revisit times.4

To produce an RGB image, we used 3 of 5 available bands:440 nm–510 nm, 520 nm–590 nm, and 630 nm–685 nm.

4http://www.blackbridge.com/geomatics/upload/blackbridge/RapidEye%20Constellation.pdf

LidarLiDAR technology can create high-accuracy, precise, dig-

ital elevation models (DEMs) for the large areas, particularlyvaluable in the areas where recent topographical or geomorpho-logical maps are not available or ground-based mapping is notpossible (as, e.g., remote arctic areas; Committee of FloodplainMapping Technologies 2007). We used airborne LiDAR datacollected over the Aquistore site to map topography and creategeomorphometric profiles used for geodynamic analysis.

The Leica ALS70 LiDAR system was used to acquire dataduring 2 acquisition flights dated November 11 and 12 of 2011,and covering ∼80 km2 centered on the CO2 injection well(Figure 2). LiDAR data was acquired with a pulse rate repe-tition frequency of 130,000 kHz, scanning frequency of 40 Hz,and flying speed of 300 km/hr. The flying altitude was 1550 mabove ground level, and the scanning angle was 50 degrees witha side overlap of 50 %. This system configuration allowed thecreation of a point cloud with a density of 2.2 pts/m2.

Data was acquired over a calibration site flown with 2 passesin opposite directions, before and after the flight. Any errorthat was observed in the altitude of the aircraft (roll, pitch,and heading) was corrected. In order to statistically quantifythe accuracy, LiDAR data was compared with independentlysurveyed ground points. Geodetic survey was acquired using aGPS-mounted truck, which collected data while driving on anopen road. The kinematic positions on the road were postpro-cessed from a nearby base station.

Horizontal accuracy of LiDAR data is 45 cm and fundamentalvertical accuracy on flat, hard surfaces is 30 cm. Point cloud datawas used to create a high-resolution digital terrain model of 1 m× 1 m grid spacing, stored in 32-bit GeoTIFF format. In furtherprocessing, we used a terrain model resampled to 5 × 5 mresolution in order to reduce processing times.

Radarsat-2 SARThe C-band RADARSAT-2 data was collected during

06/05/2012—10/03/2014 (in format MM/DD/YYYY) in or-der to observe ground deformation at the Aquistore site. TheRADARSAT-2 revisit period is 24 days, but here we useddata from 4 RADARSAT-2 tracks, 2 ascending and 2 de-scending. Such an acquisition configuration allowed us to in-crease the image frequency to 6 days, on average, in order toachieve nearly continuous temporal SAR coverage. The follow-ing RADARSAT-2 data was acquired (Table 1): ascending anddescending Spotlight (SLA 18 and SLA 12) with a swath widthof 18 km × 8 km, very high 1.6 m × 0.8 m range-azimuth pixelspacing, incidence angles of 43◦ and 39◦, respectively, and HHpolarization; ascending and descending wide ultra-fine (U7W2)images with a swath width of 50 km × 50 km, high 1.6 m ×2.8 m range-azimuth spatial resolution, incidence angle of 35◦,and HH polarization.

To distinguish land use and land cover over the Aquistorearea and to analyze seasonal surface changes, 2 descending

278 CANADIAN JOURNAL OF REMOTE SENSING/JOURNAL CANADIEN DE TELEDETECTION

FIG. 2. LiDAR DEM of Aquistore site acquired on November 11 and 12, 2011. The bar at the bottom left shows altitude abovesea level in meters. Points P1-P9 as red diamonds were selected for MSBAS-DInSAR time series analysis; REF denotes MSBASreference point. Black lines (A-H") denote profiles used for integretation of MSBAS-DInSAR and LiDAR data. Monitoringstations NE1, NE2, SE1, SE2, SE3, SITE, SW1, NW1, and NW2 are shown as black diamonds.

fine quad-polarization (Fine Quad-Pol28) images from May 9,2014 and August 13, 2014 were acquired with a swath width of25 km × 25 km, 4.7 m × 5.1 m range-azimuth spatial resolution,an incidence angle of 47◦, and HH, HV, VV, VH polarizationmodes. RADARSAT-2 datasets were obtained in single-look-complex (SLC) format, point target lookup table (LUT), withprecise orbital parameters.

METHODOLOGYThis study focused on mapping and monitoring ground con-

ditions at the Aquistore site by integrating satellite SAR andairborne LiDAR data to obtain information about topography,morphology, and recent dynamics of the surface behavior.Ground surface characterization of the area was done usingRapidEye and RADARSAT-2 Spotlight intensity maps, airborne

TABLE 1RADARSAT-2 satellite data used in the study: beam-mode, geometry, pixel spacing in meters in range and azimuth directions,

respectively, resolution, and swath width in kilometers

Beam-mode Geometry Pixel Spacing, rg, az [m] Resolution Swath Width [km]

SLA18 Ascending 1.6 × 0.8 Very High 18 × 8SLA12 Descending 1.6 × 0.8 Very High 18 × 8U7W2 Ascending 1.6 × 2.8 High 50 × 50U7W2 Descending 1.6 × 2.8 High 50 × 50Fine Quad-Pol28 Descending 4.7 × 5.1 Medium 25 × 25

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FIG. 3. RADARSAT-2 SLA 18 SAR intensity image in the grey tone acquired on August 23, 2014. Light color coding representsstrong back-scaterring, e.g., man-made structures, and dark color coding represents low back-scattering, e.g., water. Regionsselected for detailed analysis are numbered I, II, and III, and outlined in yellow, purple, and blue. Other symbols are as in Figure 2.

LiDAR and RADARSAT-2 Quad-Pol data shown in Figures 1–4.Ground deformation was computed from RADARSAT-2images using MSBAS-DInSAR technology developed bySamsonov et al. (2011) and Samsonov and d’Oreye (2012).Topography, morphology, and recent ground dynamics weredetermined based on RADARSAT-2 interferometry and Li-DAR data across the selected regions experiencing high grounddeformation (Figure 10).

LiDAR DataTo obtain quantitative land surface information, we used 24

2.4 km × 2.4 km sheets of bare Earth DEM merged and re-sampled on a 5 m × 5 m grid in ESRI ArcMap and stored inGeoTIFF format. To explore the Aquistore site elevation char-acteristics, we produced color composition using the GenericMapping Tool5 (GMT) topo palette in the range of 530 m–593 mand recognized terrain elevation and morphological forms asvalleys, plain, channel, raised areas, slopes, mining tailings.Using R6 and GMT software (Figures 2 and 10), we createdgeomorphometric profiles of the 9 selected sites characterizedby the highest MSBAS-DInSAR deformation rates.

5http://gmt.soest.hawaii.edu/

6https://www.r-project.org/

SAR IntensitySAR intensity images are sensitive to surface rough-

ness, slope, moisture content, and dielectric properties of thebackscattering material (Chan and Peng 2003) and, thus, can beused for ground surface characterization (e.g., Lu and Dzurisin2014).

We used a RADARSAT-2 SLA 18 intensity image thatwas spatially averaged (i.e., multilooked), orthorectified andgeocoded (Figure 2). Spotlight beam mode is programmed to il-luminate areas of interest over longer aperture times, producinga superior azimuth resolution of 0.8 m.

Three rectangles (I, II, III, marked in Figure 2) indicate thefollowing regions: Rafferty–Boundary Channel, Boundary DamPower Station, and Rafferty–Boundary outlet. These regionswere selected for in-depth, combined MSBAS-DInSAR andLiDAR analysis.

SAR PolarimetryTo distinguish land use and land cover and to investigate sea-

sonal surface changes, we used full polarimetric RADARSAT-2Fine Quad-Pol 28 data. We performed processing of 2 imagesacquired on May 9, 2014 and August 13, 2014 to obtain po-larimetric information for spring and summer, respectively. TheFreeman–Durden decomposition was applied to identify scat-

280 CANADIAN JOURNAL OF REMOTE SENSING/JOURNAL CANADIEN DE TELEDETECTION

FIG. 4. Freeman-Durden polarimetric decomposition of RADARSAT-2 Quad-Pol28 images acquired on May 9, 2014 (a) andAugust 13, 2014 (b) over Aquistore site. Surface changes are outlined with yellow and named A-O.

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FIG. 5. RADARSAT-2 horizontal east-west (a) and vertical up-down (b) MSBAS-DInSAR linear deformation maps during June2012–October 2014. The bar in the bottom left corner shows the MSBAS-DInSAR velocity rate in centimeters per year and wasclipped to 0.5 cm/year deformation map (a) and to 1 cm/year for deformation map (b) to achieve clear visual effect. Red indicatesmaximal heave and magenta shows maximal subsidence. Spotlight intensity image was used as a background of the composition.Other symbols are as in Figure 2.

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FIG. 6. Region I (Rafferty–Boundary Channel) from Figure 3. RADARSAT-2 SLA18 intensity image with a legend descriptionas for Figure 3 (a), LiDAR DEM with a legend description like figure 2 (b), MSBAS-DInSAR horizontal east-west (c) and verticalup-down (d) deformation maps during June 2012–October 2014 with the legend description as for Figures 5a and 5b, respectively.

tering characteristics of land features. The Freeman–Durden de-composition distinguishes 3 scattering mechanisms: (1) volumescattering from randomly oriented, very thin scatterers—mainlytall vegetation, canopies, or bushes; (2) even or double-bouncescattering from a pair of orthogonal surfaces, e.g., trunk-groundinteraction or manmade objects such as buildings; and (3) oddor single-bounce scattering from a slightly rough surface suchas open soil, short vegetation, grassy fields. Detailed descrip-tion of the polarimetric decomposition method can be found inFreeman and Durden (1998). This technique determines the pre-dominant scattering mechanisms of the natural scatterers suchas forest, cropland, and bare soil.

For processing, we used RADARSAT-2 Polarimetric Tool-box version 6.3.2. We applied a radiometric calibrationRange-Doppler model for the quad-pol dataset and performedFreeman–Durden decomposition with a window size of 5 pixels× 5 pixels. We saved results in a GeoTIFF format with RGBrepresentation of 3 components geocoded to the World Geode-tic System (WGS84). Double-bounce, volume, and odd-bouncescatterers were color coded in red, green, and blue, respectively.

The color black in the RGB image represents water surfaces(Figure 4).

SAR InterferometryDInSAR is a geodetic tool for measuring ground defor-

mation with millimeter precision and high spatial resolutionand coverage (Massonnet and Feigl 1998; Rosen et al. 2000).Classical DInSAR is applied to calculate interferogram from 2SAR images (master and slave) acquired from the same posi-tion at different times. There are several limitations of classi-cal DInSAR, such as, sparse temporal resolution and inabilityto separate horizontal and vertical components of deformationfrom the line-of-sight measurements. That is the reason whymore advanced multitemporal processing approaches were de-veloped, including Permanent Scatterer Interferometry (PSin-SARTM; Ferretti et al. 2001), Small BAseline Subset (SBAS;Berardino et al. 2002), Persistent Scatterer Interferometry (PSI,e.g., Hooper et al. 2004), and Multidimensional Small Base-line Subset (MSBAS; Samsonov et al. 2011; Samsonov and

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FIG. 7. Region II (Boundary Dam Power Station) from figure 3. RADARSAT-2 SLA18 intensity image with a legend descriptionas for Figure 3 (a), LiDAR DEM with a legend description like Figure 2 (b), MSBAS-DInSAR horizontal east-west (c) andvertical up-down (d) deformation maps during June 2012–October 2014 with the legend description like for Figures 5a and 5b,respectively.

d’Oreye 2012). The MSBAS technique utilizes repeatedly ac-quired SAR data from multiple sensors or beams to producetime series of ground deformation in east–west and up–downdirections.

The detailed description of DInSAR processing and the per-pendicular baseline vs. timespan plot for each beam can be foundin Samsonov et al. (2015). We performed standard DInSAR pro-cessing of each of 4 beam modes (Table 2) separately using theGAMMA Remote Sensing software (Wegmuller and Werner1997). SLA 18 and SLA 12 data was multilooked in range-azimuth by a factor of 3 × 12, and U7W2 data was multilookedby factor of 4 × 3 to produce approximately similar resolutions.The multilooking procedure improved the signal-to-noise ratiosufficiently for proper phase unwrapping. A single master wasselected for each dataset, and the remaining slave images wereresampled to the master geometry. The topographic phase wasreconstructed from airborne LiDAR DEM resampled to 5 m ×5 m resolution and removed. Differential interferograms werefiltered using adaptive filtering based on local fringe spectra

(Goldstein and Werner 1998) and unwrapped with a minimumcost flow (MCF) method (Costantini 1998). Orbital errors werecorrected using a procedure implemented in GAMMA softwarethat refines the baseline by measuring phase values vs. topo-graphic heights for a set of coherent, automatically selectedpixels. Interferograms were interpolated to increase the spatialcoverage affected by temporal decorrelation (Samsonov, Beavenet al. 2011) and geocoded. Finally, all interferograms were re-sampled to a common spatial grid with a resolution of 5 m × 5 musing GMT scripts. Although it was feasible to produce defor-mation products with higher than 5 m × 5 m spatial resolution,it seemed unnecessary.

Any error in the DEM used to remove topographic effectspropagates to surface deformation measurements. The altitudeof ambiguity (ha) is the amount of topographic signal requiredto generate 1 interferometric fringe after the interferogram flat-tening (Massonnet and Feigl 1998). We calculated altitudes ofambiguity for DInSAR data over the study area ha as a functionof wavelength (λ), incident angle (θ ), satellite altitude (H), and

284 CANADIAN JOURNAL OF REMOTE SENSING/JOURNAL CANADIEN DE TELEDETECTION

FIG. 8. Region III (Boundary Dam outlet) from Figure 3. RADARSAT-2 SLA 18 intensity image with a legend description likefor Figure 3 (a), LiDAR DEM with a legend description like Figure 2(b), MSBAS-DInSAR horizontal east-west (c) and verticalup-down (d) deformation maps during June 2012–October 2014 with the legend description like for Figures 5a and 5b, respectively.

perpendicular baseline B⊥ (e.g., Chan and Peng 2003):

ha = λHtanθ

2B⊥ . [1]

RADARSAT-2 data has the following parameters: λ = 5.6 cm,θ = 43º, H = 860 km, and for the worst case scenario weassumed a perpendicular baseline of 500 m. This resulted in thealtitude of ambiguity of 45 m. Since the accuracy of LiDARdata is 30 cm, the maximum error due to incorrectly removed

topography does not exceed 0.3/45 of a fringe, which is lessthan 0.1 cm, and can be assumed negligible.

MSBAS-DInSARIn order to overcome limitations of the classical DInSAR

techniques we utilized the advanced MSBAS-DInSAR method-ology (Samsonov et al. 2011; Samsonov and d’Oreye 2012;Samsonov et al. 2014) that combines multiple DInSAR data

TABLE 2RADARSAT-2 satellite data used for MSBAS-DInSAR processing: beam-mode, time span, azimuth (�), incidence angle (�),

number of available SAR images (N), and number of produced interferograms (M)

Beam-mode Time span � [◦] � [◦] N M

SLA18 06/05/2012–09/23/2014 351 43 25 184SLA12 07/16/2012–09/16/2014 −170 39 20 156U7W2 06/19/2012–08/20/2014 349 35 18 110U7W2 06/15/2012–10/03/2014 −170 35 17 81Total 06/05/2012–10/03/2014 80 531

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FIG. 9. MSBAS-DInSAR time series for points P1–P9 with the highest observed displacement in centimeters for the time spanJune 2012–October 2014. Gaps in time series are caused by exclusion of RADARSAT-2 images dated December 2012–March2013 and February 2014 from the MSBAS processing.

into a single dataset with improved parameters such as lowernoise and higher temporal resolution. Assuming that individualascending and descending datasets overlap in time, the MSBAS-DInSAR produces vertical and horizontal time series with un-interrupted temporal coverage.

The MSBAS-DInSAR methodology uses highly coherent in-terferograms with small spatial and temporal baselines. Theprocessing that includes K sets of independently acquired SARdatasets is presented in matrix form as

⎛⎜⎜⎝

A1

A2

. . .

Ak

⎞⎟⎟⎠

⎛⎝ VN

VE

VU

⎞⎠ =

⎛⎜⎜⎝

�1

�2

. . .

�k

⎞⎟⎟⎠ , or AVIos = �obs . [2]

Here, matrix A consists of time intervals between consecutiveSAR acquisitions (Samsonov 2010), vector V={VN,VE,VU}represents unknown velocities in the North–South (VN),East–West (VE) and Up–Down (VU) directions that have tobe determined, and vector � represents observed DInSAR in-terferograms; Equation (2). Because of the near-polar orbitalconfiguration, Equation (2) can be modified by excluding a

term responsible for the north–south motion VN (Samsonov andd’Oreye 2012).

A solution for Equation (2) deformation rates between se-quential SAR acquisitions is computed by applying the sin-gular value decomposition (SVD) and Tikhonov regularization(Tikhonov and Arsenin 1977),

(A

λI

)(VE

VU

)=

(�

0

), [3]

where I is identity matrix Equation (3), and λ is a regularizationparameter that can be found using, for instance, the L-curvemethod (Hansen and O’Leary 1993). The 2-dimensional de-formation time series are reconstructed from the deformationrates and then the 2-dimensional mean deformation rates arecomputed by linear regression of the time series.

The MSBAS-DInSAR processing was performed accordingto the methodology described in Samsonov et al. (2011) andSamsonov and d’Oreye (2012). We utilized 531 interferogramscomputed from 80 RADARSAT-2 images (Table 2) with a meancoherence of greater than 0.4 after filtering. The large number ofinterferograms allowed us to achieve high precision by reducing

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FIG. 10. Geomorphometric profiles of MSBAS-DInSAR horizontal east-west and vertical up-down deformation rates in cm/yearand LiDAR DEM elevation in meters. Black dash line is a zero line for velocity rate in cm/year. Measurement points P1-P9 aremarked in red and schematically located on the LiDAR DEM elevation layer.

the impact of unwrapping and baseline errors and atmosphericnoise.

2D high-pass spatial filtering was applied to remove residualorbital ramps and long wave-length atmospheric noise. Interfer-ogram calibration was performed by subtracting an offset fromeach interferogram calculated at a reference region (REF) thatwas assumed to be stable. The inversion was performed andvertical and horizontal east–west time series were computed.Linear deformation rates were computed by fitting a straightline to the time series.

The 2-dimensional time series were created for points(P1–P9; each point comprising 7 pixels × 7 pixels) at whichthe highest deformation was observed (Figure 9). Winter in-terferograms (from December 2012 to March 2013 and duringFebruary 2014) were excluded from the processing because theywere temporally decorrelated. This produced gaps in the timeseries (Figure 9).

To interpret MSBAS-DInSAR deformation rates, we com-bined interferometric results with LiDAR data resampled to 5 m× 5 m resolution (Figure 10).

A network of 9 paired (i.e., for ascending and descendingacquisitions) corner reflectors was installed around the Aquis-

tore site (NE1, NE2, SE1, SE2, SE3, SITE, SW1, NW1, NW2in Figures 2, 3 and 5–8) in December 2012 and in January2014. The time series for these sites were shown in Samsonovet al. (2015) and are not discussed here. The current versionof MSBAS-DInSAR software is not designed for computingdeformation precisely at corner reflector locations (i.e., pointmeasurements), which will be addressed in future work.

RESULTSResults of ground surface characterization and the monitor-

ing of the Aquistore site are presented in the following subsec-tions.

LiDARUsing DEM computed from airborne LiDAR data, we deter-

mined that the Aquistore site area is characterized by subduedtopography, with the topographic relief ranging from 530 min the Souris river valley to 593 m in the southeast of theBoundary Dam Reservoir area (Figure 2). The average surfaceheight is about 565 m with various morphological undulationsand anthropogenic features locally observed. Such morpholog-ical relief is the result of various glaciofluvial processes and

VOL. 42, NO. 3, JUNE/JUIN 2016 287

open-pit or strip mining. In the DEM, we also observed man-made structures used as flood barriers on both shoulders of theRafferty–Boundary Channel, in mining tailings, and water bod-ies in the natural and the postmining inundated areas.

These results were used to recognize topography, infrastruc-ture, and morphology. Integration of MSBAS-DInSAR and Li-DAR data was used to estimate the stability of the terrain.

SAR IntensityFigure 3 shows a SAR back-scattering intensity map with

bright colors representing strong back-scattering mainly frommanmade structures such as the Boundary Dam Power Sta-tion and surrounding buildings, Boundary Dam, as well asditches. Strong intensity is also observed around water bod-ies produced by the flooded vegetation. Medium-grey tones inthe SAR intensity image correspond to short vegetation suchas grassy fields. Dark and nearly-black tones represent watersurfaces, rivers, intermittent water bodies, wetlands, and in-undated postmining areas. Therefore, simple land cover formssuch as industrial, medium, and tall vegetated areas and waterbodies can be successfully distinguished using SAR intensityimage.

SAR intensity analysis was performed to characterize theAquistore site environment and then to make use of this infor-mation in the complex MSBAS-DInSAR stability analysis.

SAR PolarimetryLand cover changes related to seasonal vegetation growth

and surface moisture and water-level variations were observedin the polarimetric results. To simplify visual interpretation, typ-ical areas were selected in Figure 4 for further analysis. In gen-eral, single-bounce scattering (blue color coding) is predominantin the spring image (Figure 4a) and is produced by scatteringfrom bare soil and short vegetation. Volume scattering (greencolor coding) is predominant in the summer image (Figure 4b)and is produced by scattering from canopy vegetation—e.g.,region (A).

The double-bounce scattering (red color coding) is observedmostly in the spring image and to a lesser degree in the summerimage. It is due primarily to the scattering response from treesand some anthropogenic structures. The change related to sea-sonal vegetation growth is observed around small water basinssouth and east of the test site—regions (B) and (C). Seasonalwater-level variations (black color coding) indicate differencesin water basin boundaries—e.g. regions (D–J).

Many regions are characterized by mixed scattering, forexample, single- and double-bounce scattering in springand volume and double-bounce scattering in summer. Thesechanges are also related to vegetation and surface moisturevariability–region (K; vegetated mining tailing) and region (L).Other examples of changes detected with polarimetric analysiswere observed in regions (M–O).

SAR InterferometryHorizontal and vertical RADARSAT-2 deformation maps

(Figures 5a, 5b, 6c, 6d, 7c, 7d, 8c, 8d) and time series of cumu-lative displacement (Figure 9) at the Aquistore site were com-puted from the selected highly coherent interferograms. Forbetter visualization, the horizontal velocity rate was clippedto 0.5 cm/year (Figure 5a), and the vertical velocity rate wasclipped to 1 cm/year (Figure 5b), whereas the measured valuesreached 1 and 2 cm/year, respectively (Figure 9). We observedhorizontal (east–west) and vertical (up–down) deformation dur-ing the time span (Figure 5a and 5b).

We selected 9 points (P1–P9) experiencing the highest defor-mation rates (Figures 5 to 8) to characterize surface deformationtrends in the area during the studied time span. All selected timeseries show either east–west or up–down movements. There are2 gaps in all time series between December 2012–March 2013and in February 2014 due to the exclusion of the particular win-ter images from the analysis. The fastest horizontal east–westdisplacement is observed along the Rafferty–Boundary Chan-nel over a distance of 2 km from point P2 to the location of thecorner reflector NE1. This displacement is directed toward thechannel structure. In particular, eastward motion is observed onthe western flank (P2), and westward motion is observed on theeastern flank of the channel (P3, P4; Figure 5a) suggesting thatit is associated with possible mass movements processes. Thefastest upward motion is observed in the postmining site (P5)and the fastest downward motion is observed at the points (P1)and (P6–P9; Figure 5b) caused by geomorphological processesand industrial operations.

The 2σ or 95 % confidence intervals of the horizontal andvertical deformation rates are 0.2 and 0.3 cm/year correspond-ingly; these values were computed as standard deviations frommeans for the entire deformation rate maps.

Interpretation of MSBAS-DinSAR and LiDAR ResultsRADARSAT-2 deformation rates in cm/year and LiDAR el-

evation in meters were combined for the representative regionsmarked in Figure 2, including the Rafferty–Boundary Channel(I), Boundary Dam Power Station (II), and Rafferty–Boundaryoutlet (III). These regions were chosen based on their as-sociated high MSBAS-DInSAR deformation. The results arepresented in Figures 5–10. Horizontal and vertical time seriesfor selected points with the fastest displacement (P1–P9) arepresented in Figure 9. LiDAR DEM geomorphometric profilesacross regions undergoing fast deformation are presented inFigure 10.

Profile A–A′ directed north–south crosses a small, seasonallyintermittent water body, grassy field, and a former open-pit min-ing site. Consistent subsidence with a rate of about 1 cm/yearis observed at the point (P1; 7 pixel × 7 pixel region; Figures9a, 10a). Subsidence is likely caused by changes in water lev-els, which is also supported by polarimetric analysis in region(E; Figure 4).

288 CANADIAN JOURNAL OF REMOTE SENSING/JOURNAL CANADIEN DE TELEDETECTION

Profiles B–B′ and C–C′ are directed west–east across theRafferty–Boundary Channel. Points (P2) and (P3) are locatedon the eastern and western channel banks, which are up to 5 mhigh and act as flood protective barriers. On the eastward slope,we observe a displacement rate of 0.3 cm/year to the west (P2)and on the westward slope we observe a displacement rate of1.3 cm/year to the east (P3). Horizontal displacements towardthe channel center are accompanied by vertical downward mo-tion for both manmade slopes in profiles B–B′ and C–C′ (Figures9b, 9c, 10b, and 10c). Seasonal heave is observed in these timeseries during summer, whereas subsidence occurs between latefall and early spring.

Profile D–D′ also crosses the Rafferty–Boundary Channel.Point P4 is located on the eastern slope of the channel withelevation of about 3 m. Westward and eastward movementswere observed along the slopes and subsidence on the westwardslope, which is elevated about 5 m (Figures 9d, 10d).

The motions analyzed along profiles from B–B′ to D–D′ (asregion (M) in Figure 4) are likely caused by erosion and seasonalchanges and can be treated as mass movements.

Profile E–E′, directed northwest–southeast, crosses an inun-dated area. Point (P5) located near the main road experiencesa strong uplift of up to 1 cm/year, which is likely produced bygroundwater recharge and the seasonal changes (region (F) inFigure 4, and Figures 9e, and 10e).

Profile F–F′, directed northwest–southeast, crosses a 4-mhigh mine tailings pile where subsidence reaches 1 cm/year(P6) and minor horizontal motion is eastward (Figures 9f and10f). Deformation here can be caused by compaction, water-level changes and erosion (region (H) in Figure 4).

Profile G–G′, directed west–east across mining tailings, andpoint (P7) is located in the ash pond. Fast subsidence of upto 2 cm/year is caused by compaction (region (K) in Figure 4).Seasonal signal, such as uplift during summer, is also noticeable(Figures 9g, 10g).

Profiles H–H′ and H′–H′′ are located near the channel out-let to the Boundary Dam Reservoir (region (L) in Figure 4). Ahigh subsidence rate is observed and seasonal motion is visi-ble during the spring–summer (P8, P9). Undulations of severalmeters height in the LiDAR profile and subsidence of up to2 cm/year in a relatively wide area are observed (9h, 9i, 10h, and10i).

DISCUSSION AND CONCLUSIONSThe Aquistore CO2 storage site has been characterized using

remote sensing techniques prior to the start of CO2 injectionin order to observe dynamic surface processes and to obtainbackground surface information. We produced and interpreteddeformation maps and time series during the time span of June2012 to October 2014. The results showed horizontal east–westmotion of up to ±0.5 cm/year and vertical motion of up to±1 cm/year. These deformation maps will be used as reference

background maps for ongoing injection and long-term storagemonitoring.

We combined spaceborne MSBAS-DInSAR and airborne Li-DAR data, which allowed us to gather complete temporal andspatial geodynamic image of the Aquistore site. We selected 3representative regions for detailed dynamic analysis. Horizontaland vertical ground deformation was observed on the manmadeslopes along the Rafferty–Boundary Channel, in the postmin-ing area, and around the Rafferty–Boundary Channel outlet tothe Boundary Dam Reservoir. Deformation is caused by variouscomplex processes of anthropogenic and natural origin, suchas the long-term open-pit mining, disturbances in groundwaterlevel, erosion, mine tailings deposition, compaction, swelling ofsediments, and seasonal variations of surface and groundwaterlevels and frost.

We also used RapidEye and SAR intensity data for seasonalchange detection in ground conditions. The Aquistore study siteis relatively stable, with the presence of very slow ground de-formation related to geomorphological and seasonal changesthat do not present a risk for injection CO2 operations. Also,current monitoring informed us about the kind of surface infras-tructure (as, e.g., Rafferty–Boundary Channel or mining tailingstorage) that could be potentially affected by surface deforma-tion related to CO2 operations. Any potential movements of thesurface caused by changes in reservoir pore pressure could bedetected. However, improvements are still required to under-stand the causal links between carbon storage, land response,and the behavior of the plume at large distance from the in-jection well. Examples of modeling and future scenarios of theAquistore site ground behavior can be found in Samsonov et al.(2015).

Constraints impeding DInSAR results around the Aquistoresite could be nonlinear deformation (in space and time) dueto breaks in injection or saturation and vegetation and/or snowcover responsible for low SAR coherence. Artificial corner re-flectors are a solution for the later limitation; however, cornerreflector settlement and related additional motions can disruptthe measurement. For CSS in general, InSAR can be appliedonly in the onshore environment.

LiDAR technology that produces DEMs with centimeter pre-cision (comparing to millimeter precision of DInSAR) can-not be used as a monitoring tool alone. But LiDAR featurediscrimination, terrain profile extraction, and 3D visualizationcan be easily processed for geomorphometric interpretation inany GIS software. However, the cost of LiDAR data is stillhigh.

Advanced multiple MSBAS-DInSAR methodology com-bined with LiDAR data provides strong benefits for grounddynamics detection. Monitoring ground deformation is crucialfor hazard mitigation and risk reduction. In the case of CCS,which has to be carried out responsibly and safely, advancedremote sensing technologies provide powerful solutions withmeasurement precision of 2 mm/year, recurrent temporal cov-

VOL. 42, NO. 3, JUNE/JUIN 2016 289

erage (several to 1 day) of the wide areas, easy access to dataand low cost compared to, e.g., geodetic surveying.

Monitoring of CCS is challenging and represents a signif-icant opportunity to refine new advanced technologies suchas DInSAR or LiDAR. These new solutions integrated withthe established techniques, such as seismic imaging, well log-ging, gravity surveys, GPS, tiltmeters, and piezometers providea complex evaluation of the site.

This study demonstrates the efficient and robust monitor-ing techniques to be used in the future CCS operations, utiliz-ing data from present RADARSAT-2 satellite and forthcomingCanadian RADARSAT Constellation Mission (RCM) plannedto launch in 2018. Canadian RADARSAT-2 satellite with a 24-day revisit cycle offers an expanded range of beam modes togenerate Earth-observation-based products. Simultaneous useof 4 RADARSAT-2 beams allowed us to compute horizontaland vertical surface deformation maps with a precision of betterthan 2 mm/year and 6 days temporal frequency.

Currently, the free access of satellite SAR images is avail-able for scientific users of Cosmo-Sky-Med, TerraSAR-X, andALOS-2 satellites based on prepared proposals and for all usersof, e.g., Sentinel-1 data.

CCS can play a significant role in the reduction of carbondioxide emissions and it requires advanced instrumentation formeasurement, monitoring, and verification. As the CCS mar-ket matures, new tools developed for site selection, innova-tive storage options, monitoring, and environmental impact willrapidly transition to commercial deployment. With an increas-ing number of CCS projects, DInSAR and LiDAR seem to bea promising combination as a surface-monitoring technology.By combining advanced DInSAR and LiDAR technologies, weproduced novel DInSAR-LiDAR mapping products.

The monitoring procedure presented is effective and usefulfor the scale of geomechanical deformation related to the CO2

sites.

ACNOWLEDGMENTSWe thank the Canadian Space Agency for provid-

ing RADARSAT-2 data. The authors also wish to thankthe Petroleum Technology Research Centre (PTRC) andSaskPower. Data were analyzed and plotted using GMT, GNU-PLOT, and R software.

FUNDINGFunding for this research was in part provided by the ecoEN-

ERGY Innovation Initiative Program of Natural ResourcesCanada.

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