integration of terrasar-x and palsar psi for detecting ground deformation
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This article was downloaded by: [Moskow State Univ Bibliote]On: 06 December 2013, At: 02:57Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
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Integration of TerraSAR-X and PALSARPSI for detecting ground deformationHengxing Lan a b , Xing Gao a , Hongjiang Liu a , Zhihua Yang a &Langping Li aa State Key Laboratory of Resources and EnvironmentalInformation System (LREIS) , Institute of Geographic Sciencesand Natural Resources Research, Chinese Academy of Sciences ,Beijing , 100101 , Chinab Department of Civil and Environmental Engineering , Universityof Alberta , Edmonton , Alberta , Canada , T6G 2W2Published online: 23 Apr 2013.
To cite this article: Hengxing Lan , Xing Gao , Hongjiang Liu , Zhihua Yang & Langping Li (2013)Integration of TerraSAR-X and PALSAR PSI for detecting ground deformation, International Journalof Remote Sensing, 34:15, 5393-5408, DOI: 10.1080/01431161.2013.789570
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International Journal of Remote Sensing, 2013Vol. 34, No. 15, 5393–5408, http://dx.doi.org/10.1080/01431161.2013.789570
Integration of TerraSAR-X and PALSAR PSI for detecting grounddeformation
Hengxing Lana,b*, Xing Gaoa, Hongjiang Liua, Zhihua Yanga, and Langping Lia
aState Key Laboratory of Resources and Environmental Information System (LREIS), Institute ofGeographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing100101, China; bDepartment of Civil and Environmental Engineering, University of Alberta,
Edmonton, Alberta, Canada T6G 2W2
(Received 11 October 2011; accepted 21 March 2013)
In connection with the detection of various spatial- and temporal-scale ground set-tlements, an integrated persistent scatterer interferometry (PSI) approach is discussedusing multi-source, multi-temporal, and multi-resolution synthetic aperture radar (SAR)data. Based on the comprehensive analysis of characteristics of available radar sensors,two remote-sensing SAR data sets were selected: 1 m resolution X-band TerraSAR-Xand 10 m resolution L-band Advanced Land Observing Satellite (ALOS) phased arrayL-band SAR. ‘Tianjin Binhai New Area’ has become one of the most important eco-nomic centres in China, and one of its fast-developing urban areas, Tanggu, was selectedas the study area. PSI processing was conducted on both data sets. Substantial valida-tion was performed for PSI results from both data sources using levelling measurement.The overall good agreement confirmed the ground deformation maps derived from bothdata sets. Integration of PSI results appears to be a potentially significant contributionto solving the problems related to common spatial and temporal gaps when using sin-gle-type data sets. Application of both data sets revealed the capability of integratedPSIs to measure ground deformation with strong temporal and spatial variation, therebyimproving the interpretation of ground deformation characteristics which increases theconfidence of hazard assessment and provides some insight into complex underlyingmechanisms.
1. Introduction
Ground subsidence is a geological phenomenon of lowered land surface caused by eithernatural (e.g. dewatering) or human activities (e.g. loading due to engineering construc-tion). It is a worldwide geological hazard that affects almost all countries since its causativefactors (e.g. pumping, mining, underground water, engineering construction, and cavities)are effective in all countries but with different consequences. Statistics show that at least60 countries or regions globally are suffering from severe land subsidence hazard (e.g.Mexico City in Mexico, San Joaquin Valley, Long Beach, and Huston in the USA, Tokyoand Osaka in Japan, Bangkok in Thailand, Po River Delta and Venice in Italy, Cheshire inthe UK, Wairakei in New Zealand, and Latrobe Valley in Australia). Nearly 100 cities inChina have encountered severe damage to infrastructure, historical monuments, and per-sonal properties due to land subsidence (Zheng, Wu, and Hou 2002; Xue, Zhang, and Ye
*Corresponding author. Email: [email protected]
© 2013 Taylor & Francis
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2003). Some coastal urban areas like Tianjin, where surface elevation is lower than sealevel, are frequently at high risk of becoming submerged by ocean tides (Fan, Li, and Guo2007; Jiang, Lin, and Cheng 2011).
Differential synthetic aperture radar (SAR) interferometry has been reported as a suit-able technique for operational monitoring of land subsidence (Strozzi et al. 2001). It hasbeen utilized in detecting ground deformation in Hong Kong (Ding et al. 2004) and on theChinese mainland (Wang et al. 2004). In regard to the limitation of handling atmosphericdistortion with traditional differential interferometric synthetic aperture radar (DInSAR)techniques, the more advanced persistent scatterer interferometry (PSI) has now ‘come ofage’ and is currently the predominant technique for mapping and monitoring coherent sub-sidence displacements at a very high level of accuracy (Wegmuller et al. 2004; Teatiniet al. 2006; Ferretti et al. 2007; Strozzi, Teatitni, and Tosi 2009; Righini et al. 2011).It has demonstrated very convincing capability in monitoring low-grade ground subsi-dence, while limitations still remain in regard to fast non-uniform deformation monitoring(Raucoules et al. 2009; Wegmuller et al. 2010). It takes full advantage of a large variety ofradar data from a number of spaceborne SAR sensors. Compared with the early EuropeanRemote Sensing (ERS)-type SAR sensor, recently introduced high-resolution SAR sen-sors including the Advanced Land Observing Satellite (ALOS) phased-array L-band SAR(PALSAR), TerraSAR-X, Cosmo-Skymed, and RadarSat2 suggest a more promising appli-cation to ground surface deformation monitoring (Eineder et al. 2009; Ng et al. 2009;Crosetto et al. 2010; Herrera et al. 2010).
In fast-developing urban areas, there exists a significant demand for deformation infor-mation that meets the requirement of spatial and temporal resolution for safety assessment.Existing SAR sensors have varying radar configurations in terms of wavelength, incidenceangles, polarization, geometric and radiometric resolution, spatial coverage, and temporalrevisit frequency (Baghdadi et al. 2009). Data archive availability and performance priceratio are two important factors that need to be taken into account for individual researchcases. Therefore, multi-temporal data from different SAR sensors have both advantagesand disadvantages in regard to assessing ground deformation.
Compared with L-band (∼30 cm) ALOS PALSAR, TerraSAR-X is characterized byshorter SAR wavelength (∼3 cm), higher spatial resolution (up to 1 m), and shorter revisitinterval (11 days) (Crosetto et al. 2010). PSI with TerraSAR-X achieves very high accuracyby reducing the complexity of the phase wrap and unwrapping procedure (Crosetto et al.2010; Wegmuller et al. 2010). It displays excellent capabilities in monitoring and sam-pling ground deformation with high spatial and temporal resolution. TerraSAR-X’s shortwavelength has low sensitivity to surface roughness, which is satisfactory for monitoringthe surface of urban features (Auer et al. 2010; Crosetto et al. 2010). However, it shows agreater degree of limitation in regard to monitoring surfaces with vegetation than PALSAR,as longer L-band radar is better adapted to penetrating vegetation. In addition, it has muchsmaller spatial coverage (e.g. 5 × 10 km for Spotlight mode). The nominal swath width forPALSAR is usually greater than 50 km at a spatial resolution of around 10 m. The expenseper scene, however, is much higher than for ALOS PALSAR. In spite of its lower spatialand temporal resolution, ALOS PALSAR has shown a significant improvement in regardto the applicability of PSI in monitoring surfaces with high deformation rates such as coal-mining areas, and in retrieving sufficient deformation information over forests (Wegmulleret al. 2010).
Consequently, different SAR sensors may supplement each other to reduce the spatialand temporal gap, which is hard to resolve using a single SAR sensor. It is in our inter-ests to determine the most suitable radar configuration by integrating multiple SAR sensors
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(e.g. TerraSAR-X and PALSAR) for better characterization of urban ground surface defor-mation. Wegmuller et al. (2010) indicated that SAR interferometry could play an importantrole in the integrated monitoring concept. This article focuses on the potential of integratinghigh-resolution Spotlight TerraSAR-X and PALSAR PSI to resolve complex deformationcharacteristics in rapidly developing urban and rural areas. For the selected study site, sub-stantial validation and interpretation were conducted to assess the improvement gained inunderstanding tempo-spatial deformation characteristics by this integrated approach.
2. Study area
The area investigated in ‘Tianjin Binhai New Area’ in China (Figure 1) includes bothurban and rural parts. ‘Tianjin Binhai New Area’ has become one of the most importanteconomic centres in China, covering a large coastal area east of Tianjin. The widely dis-tributed unconsolidated marine and fluvial deposits render this area at great risk from landsubsidence. Engineering geological conditions are diverse, including medium–high com-pressibility and medium-strength layer, high compressibility and low-strength layer, andmedium–low compressibility and high-strength layer.
Land subsidence in this area was first observed in 1959 (Xue, Zhang, and Ye 2003).Between that time and 2006, high spatial deformation gradients were present and deforma-tion was non-uniform. Both natural processes such as consolidation settlement and tectonicmovement and human activities such as groundwater mining, construction of high-risestructures, and ocean reclamation are responsible for land subsidence in the study area.
20 km1050
Aerial photograph
Figure 1. Study area and data set coverage. The core area of Tianjin Binhai New Area, the cityof Tanggu, is covered by both TerraSAR-X and PALSAR. Compared with high-resolution aerialphotography, TerraSAR-X shows much higher capability in discriminating fine urban features thanPALSAR.
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Over-exploitation of groundwater causes rapid lowering of underground water levels which,in turn, increases the effective stress on overlying soils resulting in unrecoverable consol-idation of soil and final ground subsidence. The maximum total local settlement reachedwas 3.25 m, and many parts of the area have sunk below sea level. In the 1970s, intensivegroundwater exploitation (100 million m3 year−1) caused sinking of the ground by about100 mm year−1, and by the early 1980s, this figure exceeded 200 mm year−1. Groundsubsidence threatens the safety of buildings, water supply, and drainage systems in urbanareas, and also weakens the capability of resisting floods and storm surges. For example,storm surges on 19 August 1985 and 1 September 1992 in Tianjin caused a direct eco-nomic loss of 100−400 million dollars. Since 1986, when a series of control plans wereimplemented on groundwater extraction, the average land subsidence rate has decreased toaround 20 mm year−1 (Zheng, Wu, and Hou 2002; Xue, Zhang, and Ye 2003; Fan, Li, andGuo 2007).
3. Data sets and methods
3.1. Data set
In the ‘Tanjin Binhai New Area’, great variation is seen in surface features in both urbanand rural parts. Taking into account the parameters of various SAR sensors available andthe particular characteristics of land subsidence in ‘Tianjin Binhai New Area’, we adoptedthe following strategies to select the optimal radar images: (1) the data should cover anadequate spatial zone of the research area and have sufficient temporal duration; (2) spa-tial resolution should meet the demands of subsidence monitoring at various spatial scales;both azimuth resolution and range resolution should be as high as possible for more accu-rate urban attribute classification and subsidence monitoring to meet the requirements ofengineering applications; (3) temporal resolution should meet the processing demands ofPSI for urban land subsidence; the revisit periods of radar data should be uniformly dis-tributed and as short as possible to reduce the difficulties in phase unwrapping; and (4) radardata of high quality need to be acquired to fit the natural environmental characteristics ofthe research area (i.e. precise orbital parameters, stable imaging quality, and low level ofnoise).
Based on the radar image archives available for the research area, two data sourceswere acquired: 11 scenes of Spotlight-mode single polarization TerraSAR-X data with anextremely high spatial resolution of 1 m and 22 scenes of ALOS PALSAR data with anapproximate 10 m spatial resolution (19 scenes of fine-beam double (FBD) polarizationand 3 of fine-beam single (FBS) polarization). Their spatial coverage is shown in Figure 1.Comparison of the two simulated SAR images with high-resolution optical aerial photogra-phy indicates the higher capabilities of TerraSAR-X imagery in discriminating fine groundfeatures than PALSAR. It can also be seen that PALSAR has much larger spatial coveragethan TerraSAR-X and has potential for monitoring land subsidence on a larger regionalscale.
The characteristics of TerraSAR-X SAR and ALOS PALSAR images from the studyarea are listed in Tables 1 and 2, respectively. Both data sets were acquired in single-look complex (SLC) format using the same track direction, enabling PSI processing. The11 scenes of Spotlight-mode TerraSAR-X data acquired have very high spatial resolution,with pixel spacing of 0.45 m in slant range and 1.0 m in azimuth but short temporal cover-age from 29 December 2007 to 22 June 2008. Much longer temporal coverage was shownby the ALOS PALSAR image stack from 20 May 2006 to 8 October 2009.
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Table 1. Characteristics of TerraSAR-X SAR images used in the study area.
ID Date Mode Track LookResolution
(range)Resolution(azimuth) Polarization
Incidenceangle (◦)
1 29 December 20072 22 February 20083 4 March 20084 15 March 20085 26 March 20086 6 April 2008 Spotlight Asc Right 0.45 m 1 m VV 40.677 28 April 20088 9 May 20089 31 May 200810 11 June 200811 22 June 2008
Table 2. ALOS PALSAR images stack.
No. Scene Mode Path no. CENFLM no. Date
1 ALPSRP074180770 FBD 445 770 16 June 20072 ALPSRP080890770 1 August 20073 ALPSRP087600770 16 September 20074 ALPSRP107730770 1 February 20085 ALPSRP114440770 18 March 20086 ALPSRP154700770 19 December 20087 ALPSRP181540770 21 June 20098 ALPSRP188250770 6 August 20099 ALPSRP049820770 FBD 446 770 31 December 200610 ALPSRP090080770 3 October 200711 ALPSRP096790770 18 November 200712 ALPSRP123630770 20 May 200813 ALPSRP130340770 5 July 200814 ALPSRP137050770 20 August 200815 ALPSRP143760770 5 October 200816 ALPSRP157180770 5 January 200917 ALPSRP163890070 20 February 200918 ALPSRP170600770 7 April 200919 ALPSRP197440770 8 October 200920 ALPSRP017000760 FBS 449 760 20 May 200621 ALPSRP023710760 5 July 200622 ALPSRP030420760 20 August 2006
3.2. Methods
Preprocessing of both data sets was conducted. The TerraSAR-X and ALOS PALSARscenes acquired on 15 March 2008 and 18 November 2007, respectively, were used as SLCreference geometry. Geocoding was performed on the multi-look intensity image of thereference scene. We also transformed the digital elevation model heights to SAR geometry,which were shown in SLC co-registration. Offsets for the co-registered SLSs were below0.05 SLC pixels in TerraSAR-X, and 0.2 SLC pixels in ALOS PALSAR, which showedvery fine co-registration.
A number of PSI approaches have been reported (Ferretti, Prati, and Rocca 2000;Ferretti, Prati, and Rocca 2001; Werner et al. 2003; Hooper et al. 2004; Kampes 2005).
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The Interferometric Point Target Analysis (IPTA) module is utilized in this study. It hasmany successful applications (Werner et al. 2003; Wegmuller et al. 2004, 2010; Strozziet al. 2006; Strozzi, Teatitni, and Tosi 2009; Teatini et al. 2005, 2006). A number of stepsare conducted including persistent scatterer (PS) candidates selection, differential inter-ferograms of PS generation, phase unwrapping and interpretation; model refinement usingregression analysis; and model interpretation. The very short baseline and temporal intervalof each TerraSAR-X pair facilitates the phase unwrapping and interpretation, while moreefforts were taken in processing the PALSAR data set due to its longer temporal interval andlower spatial resolution. Additional refinement processes are required for ALOS PALSAR,for example, image focusing, co-registration, height and deformation rate refinement andresampling between low (FBD) and high (FBS) bandwidth acquisitions (Sandwell et al.2008; Baghdadi et al. 2009).
Both PSI models were calibrated by removing the atmospheric phase from the residualphase. Iteration was conducted several times to achieve a refined PSI model and to includeas many high-quality PS points as possible (Lan et al. 2012). A high-quality referencepoint was selected using an interactive pointwise phase regression analysis. The referencepoint was located in a relative stable area where the phase standard deviation was moreor less independent of the location. Spatial filtering was performed on the reference pointto reduce phase noise. The following important results can be obtained using refined PSIprocessing: (1) the time series of the deformation, (2) average displacement rates over theperiod observed, and (3) topographic and deformation uncertainty estimation.
4. Results
4.1. TerraSAR-X and PALSAR PSI results
IPTA was performed on both TerraSAR-X and PALSAR image stacks. High-quality persis-tent scatterers were identified using spatial and temporal regression analysis, and the resultsfrom both PSI models are shown in Figure 2.
The co-registered TerraSAR-X images in this research cover an area of 41.7 km2
(5.3 km × 7.9 km), while ALOS PALSAR images cover a much larger area of over3000 km2 (58.3 km × 64.5 km). The right-hand map in Figure 2 shows the PSIresults in the area covered by both data sets. A total of 40,120 high-quality persistentscatterers were identified using TerraSAR-X data, accounting for the density of nearly1000 points/km2, which is 100 times the density of PSs extracted from ALOS PALSAR(around 10 points/km2). The spatial distribution of these persistent scatterers is veryheterogeneous, with large spatial gaps found due to the distribution of the Haihe River.
The results derived from PALSAR PSI shown on the left side of Figure 2 facilitatecharacterization of surface deformation at high spatial scale, consisting of both urban andrural parts. A number of rapid-subsidence troughs were identified around southeast DongliDistrict, Jinnan District, Wuqi County, and Tianjin City, the settlement rate in these regionsexceeding 50 mm year−1. This spatial distribution is comparable with historical recordsand a field survey carried out in 2010. A number of instances of ground and infrastructuraldamage due to subsidence were detected in various areas where the PSI results revealedrapid settlement.
All of the persistent scatterers were grouped into three classes indicating differ-ent hazard levels: high subsidence rate (>50 mm year−1), medium subsidence rate(20−50 mm year−1), and low subsidence rate (<20 mm year−1). Overall, this area isdominated by low subsidence, while local spatial variation in subsidence exists. BeijiangHarbour showed a relatively rapid subsidence trend with the maximum annual value
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Levelling contours
mm year–1
PALSAR PSsTerraSAR-X PSs
Subsidence rate
High (>50 mm year–1)
Medium (20–50 mm year–1)
Low (<20 mm year–1)
30
20
40
50
0 1 2 km
5 10 20 km
Figure 2. Average deformation map derived from ALOS PALSAR PSI (left) and TerraSAR-X PSI(right). The contours from levelling measurement are overlaid on the left-hand PALSAR image. ThePS legend for PALSAR PSI results on the left is the same as that for TerraSAR-X PSI on the right. Thelower-left map shows coloured ALOS PALSAR PSs in the subset interested by the TerraSAR-X dataset. The right-hand map also shows the distribution of PSs extracted from PALSAR for comparison.
reaching 100 mm at a number of sites. Several abnormal differential non-uniform set-tlements were located in Bohai Petroleum New Village and Donggu Petroleum NewVillage.
4.2. Validation of PSI monitoring results
A significant effort was made to evaluate the accuracy of PSI monitoring results derivedfrom both PALSAR and TerraSAR-X by comparing them with levelling results. In total,50 operational levelling survey points in the study area were covered by PALSAR butonly three by TerraSAR-X. Since the levelling reading was performed only once per year,making it impossible to conduct a comparison with TerraSAR-X PSI results, detailed com-parison was performed between levelling results and PALSAR PSI results. The validationof TerraSAR-X PSI results was evaluated by comparing these with PALSAR results at theiroverlaid region.
The persistent scatterers extracted by ALOS PALSAR data show an average annual set-tlement of 17.2 mm from 2006 to 2009. Levelling surveying data show an average annualrate of 18.83 mm at similar spatial and temporal range to PALSAR data, which suggests anannual error of less than 2 mm by PALSAR PSI. The deformation contour maps derivedfrom both levelling surveys and PALSAR also show good consistency. Comparison of totalsubsidence for the period 2006−2009 between several levelling points and their adjacentpersistent scatterers is shown in Table 3, and it confirms good agreement between theremote PALSAR PSI and in situ levelling surveys.
Deformation time series are available for every PS point, so that pointwise deformationhistory can be examined. Deformation time series comparisons between PALSAR results
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Table 3. Comparison of PS monitoring by results from PALSAR and levelling point monitoring,2006−2009.
Levelling ID Levelling (mm) PS result (mm) Difference (mm) Difference (%)
Tianjin-1044 95.9 94.1 1.8 1.88Bu-846 106.3 109.7 −3.4 3.20JC-884 107 112.8 −5.8 5.42Bu-1695 77.1 78.9 −1.8 2.33Bu-1225 92.6 88.4 4.2 4.54JC-736 67.6 71.3 −3.7 5.47JC-623 57.4 52.1 5.3 9.23Average 86.3 86.78 −0.5 0.58
(a)0
–20
–40
–60
–80
Subsid
ence (
mm
)
Date
Levelling-Bu-846
Levelling
0 2 4 8 km
PS-Monitoring
10-Oct-2006 6-Aug-2007 1-Jun-2008 28-Mar-2009 22-Jan-2010 18-Nov-2010
–120
–100
(b) 0
Subsid
ence (
mm
)
Date
Levelling-Tianjin–1044
Levelling
0 2 4 8 km
10-Oct-2006 6-Aug-2007 1-Jun-2008 28-Mar-2009 22-Jan-2010 18-Nov-2010
PS-Monitoring
–10
–20
–30
–40
–50
–60
–70
–80
–90
–100
(c)
Date
10-Oct-2006 6-Aug-2007 1-Jun-2008 28-Mar-2009 22-Jan-2010 18-Nov-2010
–10
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–50
–60
–70
–80
–90
0
Subsid
ence (
mm
)
Levelling-bu-1695
PS-Monitoring
Levelling
0 2 4 8 km
Figure 3. Comparison of time series deformation between ALOS PALSAR PSI results and levellingmeasurements: levelling points Bu-846 (a), Tianjin-1044 (b), and Bu-1695 (c).
and levelling measurements were conducted at various sites within the study area. Therepresentative points of the PSI results located in the near neighbourhood of the selectedlevelling points were determined, then both levelling and PSI values were plotted againsttime. 12 December 2006 was specified as the temporal reference since the levelling mea-surements are usually taken at the end of each year, and thus the reading of both levellingmeasurements and PSI results was set to zero at this reference date. Comparison of timeseries deformation for a number of locations is shown in Figure 3.
The time series deformation of corresponding PSI results matches well with levellingresults, and the subsidence rates extracted from PSI agree with the levelling measurement
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at different stages. Point-to-point variability of a few millimetres was shown for the PSIresults, which usually show a slightly higher subsidence value at the end of the measure-ment period. PSI results are capable of capturing the effect of environmental changes due totheir finer temporal scale as compared with levelling measurements. Various deformationstages can be identified by PSI monitoring. For example, deformation PSs near levellingpoint Bu-846 show three main periods: acceleration at the initial stage, virtual stability byslight movement fluctuation, and acceleration again at the end stage. The correspondinglevelling point shows almost linear movement.
PSs near levelling point Bu-1695 are characterized by fast movement in the first periodfollowed by a reduction in speed. There is a large discrepancy between levelling and PSIresults, which might be related to an unwrapping error or more possibly to uncompensatedsmall-scale atmospheric phase distortion (Wegmuller et al. 2010) which is significant incoastal areas. Smaller discrepancies observed may also be related to the fact that the ref-erencing points for PSI and levelling are not identical, or possibly to spatial and temporalvariation.
Table 4 shows the comparison of average annual deformation rate between TerraSAR-Xand PALSAR for various regions. An order of several millimetres difference was observed,as expected, since the spatial and temporal coverage of both data sets cannot be preciselymatched. A large discrepancy was noted in the region of Yujiapu, probably due to intensiveengineering construction taking place in the latter half of 2008. Nevertheless, the smalldifference seen over the whole region indicates that regional subsidence characteristics canbe captured by both TerraSAR-X and ALOS PALSAR with good agreement.
It should be also noted that the location of PSs varies between sensors and often doesnot correspond to levelling points. Although comparison between PS and levelling pointsis based on the distance between those points, spatial variation in PSs should be taken intoaccount when validating results since strong spatial variability in motion can be a factor incertain areas. For example, a damaged house could be affected by differential deformationvarying from several centimetres to a few metres (see Figure 6). Areas with lower spatialdeformation variation are favourable for validation of PSI results.
Overall, the accuracy achieved by both TerraSAR-X and PALSAR was satisfactory,considering that subsidence is rapid and non-uniform in many parts of the study area. It isto be welcomed that the spatial and temporal distribution of ground deformation and itsvariation was captured by both PSI models.
4.3. Spatiotemporal characteristics of ground subsidence
Several urban sites were selected to evaluate the potential of integration of different PSImodels in characterizing ground subsidence. To visualize the changes in the typical urban
Table 4. Comparison of average annual subsidence between TerraSAR PSs and PALSAR PSs.
RegionTerraSAR-X (mm
year−1)PALSAR (mm
year−1)Difference (mm
year−1)
Bohai Petroleum Village −11.2 −15.1 3.9Donggu Petroleum Village −12.0 −15.7 3.7Beijiang Harbour −26.0 −20.3 −5.7Nanjiang Harbour −12.4 −16.2 3.8Yujiapu −33.6 −12.6 −21Overall −15.6 −16.5 0.9
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0
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Bohai
Bohai
10-Oct-2006 6-Aug-2007 1-Jun-2008 28-Mar-2009 22-Jan-2010
10-Oct-2006 6-Aug-2007 1-Jun-2008 28-Mar-2009 22-Jan-2010
Date
Date
PALSAR–ID97070
TerraSAR–ID62248
PALSAR–ID111804
TerraSAR–ID117655
0
TerraSAR–X PSs PALSAR PSsSubsidence rate
High (>50 mm year–1)
Medium (20–50 mm year–1)Low (<20 mm year–1)
1 2 km
Subsid
ence (
mm
)S
ubsid
ence (
mm
)
0
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–100
Figure 4. Comparison of time series deformation between results from TerraSAR-X PSI andPALSAR PSI.
land deformation process, the time series deformation curves derived from both PSI resultsfor typical PS points in Bohai Petroleum New Village and Donggu Petroleum New Villageare shown in Figures 4 and 5, respectively. The time series deformation of PSs derivedfrom TerraSAR is plotted against long-term series deformation of corresponding PSsderived from PALSAR. The time-deformation curves obtained are comparable over thesame monitoring period (December 2007 to June 2008).
Integration reveals the temporal variation in urban land subsidence processes. Uniformsubsidence behaviour is usually shown by short-term TerraSAR-X PSI results, while long-term PALSAR results reveal markedly non-linear subsidence characteristics. Subsidencerates tended initially to remain constant before varying dramatically after June 2008.
Engineering activities can influence the temporal variation in urban land subsidence,with many older buildings being torn down. Altered loading conditions resulted in the statusof ground deformation, which is indicated by the time series deformation curve derivedfrom PALSAR PSI.
Despite relatively short temporal coverage, the high density of PSs, as shown byTerraSAR-X, enable us to identify potential or pre-existing subsidence hazards. For exam-ple, a very fast subsidence zone located within one particular community was observed byhigh-density PS extracted from TerraSAR-X. No PALSAR PSs existed in this area, whichis also characterized by uneven settlement leading to damage to buildings. A field surveyconfirmed cracking in a number of buildings (Figure 6).
Beijiang and Nanjiang harbours are two important ports in the study area. They wereconstructed using marine reclamation land. Due to under-consolidated soil, heavy duty
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Figure 5. Urban subsidence monitoring by integration of TerraSAR-X and ALOS PALSAR PSI inDonggu Petroleum New Village.
for cargo storage and transportation, their deformation monitoring is a major concern forthis rapidly developing coastal area. A difference in subsidence characteristics was shownbetween the two towns. Beijiang Harbour has 7708 monitoring PS points, suggesting anannual average subsidence rate of 26.0 mm and maximum settlement rate of 151.8 mm.The 7200 PS points in Nanjiang Harbour have much lower corresponding rates (14.0 and66.4 mm, respectively; Table 5). Large spatial and temporal variation was observed forPSI results (Figure 7). The average displacement rate map shows a number of regionsof rapid subsidence in Beijiang Harbour, while Nanjiang Harbour is more stable. Highspatial deformation gradients were observed in Beijing Harbour. The regions of rapid sub-sidence occur mainly in the area where heavy containers and cargo are stored. The effect ofheavy loading on regional subsidence is likely to disturb the stability of nearby engineeringinfrastructure, such as roads and railway stations, within a range of up to tens of metres(Lan et al. 2012).
Since groundwater exploitation has been restrictively controlled in the study area since1986, it plays a lesser role in the current surface deformation process. The loading andunloading of large amounts of cargo is closely related to the temporal variation in harbourdeformation. Rapid subsidence is likely induced by engineering activity disturbance − forexample, more concentrated loading procedures due to engineering construction, materialtransportation, and storage. Field investigation has also confirmed this finding. In contrastto Beijiang Harbour, Nanjiang Harbour uses conveyor belts and pipelines to transport oiland coal materials, which reduces the effect of loading on the surface. Therefore, NanjiangHarbour has a slower deformation rate than Beijiang.
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Figure 6. Building cracking identification using high-density PS extracted from TerraSAR-X PSI.
Table 5. Comparison of subsidence in Beijiang and Nanjiang harbours.
Zone Harbour Area (km2)Percentage oftotal area (%)
Fast subsidence (>50 mm year−1) Beijiang 0.40 25.3Nanjiang 0.035 2.7
Medium subsidence (20−50 mm year−1) Beijiang 0.27 17.1Nanjiang 0.18 13.8
Slow subsidence (<20 mm year−1) Beijiang 0.91 57.6Nanjiang 1.09 83.5
5. Discussion
Typically, a large image stack (>20 images) is required for optimal PSI processing.However, imagery availability and budget are often big concerns in a project. A numberof issues have been taken into account in PSI processing with reduced data sets. Dataquality, study area, processing strategy, and compensation from other data sources mayhelp improve PSI results. Effective correction for different bias in PSI processing bene-fits substantially from the high quality of TerraSAR-X images (e.g. high geometric andradiometric resolution and high revisit frequency). PSI processing on a reduced stack ofimages is suitable for urban areas, since an infrastructural variation facilitates the detectionof high-quality and high-density persistent scatterer points and phase unwrapping during
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Figure 7. Comparison of subsidence characteristics between Beijiang and Nanjiang harbours. (a)Rapid subsidence region caused by heavy loading of containers. (b) Rapid sinking at a railway sta-tion due to loading of heavy steel cargo. (c) Comparison of average subsidence processes betweenBeijiang and Nanjiang harbours.
processing (Lan et al. 2012). Zhao et al. (2009) used a small stack (10) of advanced syn-thetic aperture radar (ASAR) images in the detection of ground deformation in an urbanarea. Multiple reference scenes in the PSI technique were adopted, facilitating PSI analy-sis using small data stacks. The number of data pairs was substantially increased relatedto coherence values and temporal gaps, and met the statistical requirement for regres-sion processing (Wegmuller and Wiesmann 2003). A stack of interferograms with 56 pairswas created with multi-reference images in our TerraSAR PSI processing, and the resultsconsolidated by model refinement. PALSAR images have larger stacks covering longertemporal periods, and PALSAR PSI results provide important information on adjustmentof parameters for small-stacked TerraSAR PSI processing and its interpretation.
PALSAR and TerraSAR-X utilize different data acquisition schemes, resulting in vari-ation in the possibility of obtaining sufficient data archives, although typically PALSARis usually sufficient in regard to data archives. However, it is difficult to obtain morethan 15 images with the same characteristics (track, frame, mode, etc.), and resamplingof different data sets is sometimes necessary (i.e. resampling FBS to FBD). TerraSAR-Xacquisitions generally have to be planned by the user, and the prerequisite first step of itsstudy is the preprogramming of the images. Therefore, it is somewhat problematic to study‘past’ phenomena. The integration of PALSAR and TerraSAR facilitates characterizationof the evolution of ground deformation from the ‘past’ to the ‘future’.
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Knowledge of the deformation characteristics of a study site is important for properinterpretation of PSI results such as the linearity/non-linearity of the deformation, itsspatial and temporal distribution, and the driving mechanism (Ferretti, Prati, and Rocca2000; Raucoules et al. 2009). In this study area, the average annual deformation rate hasdecreased from a maximum of 200 mm several decades ago to the current 20 mm. Somerural areas covered by PALSAR showed a much higher annual deformation rate, exceeding100 mm. Such rapid deformation may even be severely underestimated as strong corre-lation exists between root mean square error and magnitude of displacement (Raucouleset al. 2009). According to the specifications of Chinese geological disaster prevention,infrastructural subsidence hazard has three classes: high hazard (annual subsidence rate>50 mm), medium hazard (annual subsidence rate 20−50 mm), and low hazard (annualsubsidence rate <20 mm). This classification system was adopted from interpretation ofPSI results in our study site. However the selection of rate criteria should be site based. Fora mining site, an annual deformation rate greater than 50 mm could be considered as mod-erate to slow. Raucoules et al. (2009) summarized deformation characteristics, especiallynon-linear deformation and the limitations of using C-band PSI with the current temporalacquisition capabilities of ERS and Environmental Satellite (ENVISAT). Different defor-mation characteristics might be revealed by different types of SAR data sources based ondifferent bands and configurations. The integration of different data sources improves PSIresult interpretation.
6. Conclusions
PSI processing of sufficiently large archives of PALSAR and TerraSAR-X data over-comes marked phase discontinuity and decorrelation problems commonly observed withthe first generation of satellite SAR data. Both PSI results are comparable with level-ling measurements, and they are capable of generating displacement maps of acceptableaccuracy.
These PSI results demonstrate both advantages and disadvantages in revealing spatialand temporal land subsidence characteristics. Rural areas are usually covered by vegetationof varying density. Studies have shown that ALOS PALSAR PSI facilitates the monitor-ing of ground subsidence in rural areas covered by vegetation and where there is a highlevel of ground deformation, due to its relatively high spatial resolution and longer L-bandwavelength. The spatial distribution characteristics of deformation in rural areas can berevealed accurately by PALSAR PSI due to its larger spatial coverage. TerraSAR-X PSI isrobust in resolving small ground features due to its high spatial and temporal resolution,better temporal phase coherence, shorter X-band wavelength, and high sensitivity to smallsurface displacement. It is better suited for monitoring and mapping deformation of urbanareas and where there is a low level of ground deformation. Therefore, the integration ofboth data sets allows the monitoring of ground deformation with strong temporal variation.
Integration of various SAR data sources helps solve the problem of the constraint oftemporal and spatial coverage inherent in single data sets. The characteristics of lower-resolution PALSAR are suitable for long-term series of land subsidence monitoring on arelatively large spatial scale. Sufficient spatial and temporal coverage facilitates analysisof the effects of long-term regional environmental factors on ground deformation. Higher-resolution TerraSAR-X is favourable for accurate urban subsidence monitoring with largespatial and temporal variation over a small-scale area. The higher number of PS pointsextracted from TerraSAR-X provides better spatial sampling of higher-ground deformation
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gradients. Integration of these PSI models solves the problem of spatial and temporal gapand increases confidence in the interpretation of ground deformation. It also helps in under-standing the complex mechanism of both urban and rural land subsidence and providessupport for practical and sustainable ground deformation monitoring and management.
AcknowledgementsThis research was supported by the National Science Foundation of China (41272354) and NationalKey Technology R&D Programme (No. 2008BAK50B05). The authors also wish to thank TianjinInstitute of Survey and Mapping for their support with high-resolution aerial images, and Spot ImageCN for their support with TerraSAR-X SAR data.
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