A Case Study of Three Gorges Area, China

remote sensing

Article Wide-Area Landslide Deformation Mapping with Multi-Path ALOS PALSAR Data Stacks: A Case Study of Three Gorges Area, China

Xuguo Shi 1, Mingsheng Liao 1,2, Menghua Li 1, Lu Zhang 1,2,* and Cory Cunningham 3

1 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China; [email protected] (X.S.); [email protected] (Mi.L.); [email protected] (Me.L.) 2 Collaborative Innovation Center for Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China 3 SAIT Polytechnic, 1301-16 Avenue NW Calgary, Calgary, AB, T2M 0L4, Canada; [email protected] * Correspondence: [email protected]; Tel.: +86-27-6877-8395; Fax: +86-27-6877-8229

Academic Editors: James Jin-King Liu, Yu-Chang Chan, Zhong Lu and Prasad S. Thenkabail Received: 30 November 2015; Accepted: 29 January 2016; Published: 6 February 2016

Abstract: In recent years, satellite synthetic aperture radar interferometry (InSAR) has been adopted as a spaceborne geodetic tool to successfully measure surface deformation of a few well-known landslides in the Three Gorges area. In consideration of the fact that most events of slope failure happened at places other than those famous landslides since the reservoir impoundment in 2003, focusing on a limited number of slopes is insufficient to meet the requirements of regional-scale landslide disaster prevention and early warning. As a result, it has become a vital task to evaluate the overall stability of slopes across the vast area of Three Gorges using wide-coverage InSAR datasets. In this study, we explored the approach of carrying out joint analysis of multi-path InSAR data stacks for wide-area landslide deformation mapping. As an example, three ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar) data stacks of neighboring ascending paths covering the area along the Yangtze River from Fengjie to Zigui were analyzed. A key problem to be solved is the separation of the tropospheric signal from the interferometric phase, for which we employed a hybrid description model of the atmospheric phase screen (APS) to improve APS estimation from time series interferograms. The estimated atmospheric phase was largely correlated with the seasonal rainfall in the temporal dimension. The experimental results show that about 30 slopes covering total areas of 48 km2 were identified to be landslides in active deformation and should be kept under routine surveillance. Analyses of time series displacement measurements revealed that most landslides in the mountainous area far away from Yangtze River suffered from linear deformation, whereas landslides located on the river bank were destabilized predominantly by the influences of reservoir water level fluctuation and rainfall.

Keywords: slow moving landslide; InSAR; Three Gorges; displacement; water level; rainfall

1. Introduction Landslides including rock falls and debris flows are one of the most destructive geological hazards, accounting for around 80% of geological disasters [1]. In the Three Gorges area landslides are a major threat to navigation, people’s lives and properties. Wide areas of the Three Gorges are recognized as being at high risk, particularly areas near the bank of the Yangtze River [2]. The significant increase of the water level causes overland flooding in many areas in the Three Gorges which significantly affects the stability of the slopes. Additionally, periodical fluctuation of the water level and seasonal rainfall

Remote Sens. 2016, 8, 136; doi:10.3390/rs8020136 www.mdpi.com/journal/remotesensing Remote Sens. 2016, 8, 136 2 of 14 can make the situation even worse. Therefore, the detection and mapping of active slopes is a critical task for landslide disaster prevention and developing early warnings. The Chinese government has spent many resources to find effective methods for landslide monitoring and reinforcement in the Three Gorges area [3]. Among the tools employed in landslide deformation monitoring, Synthetic Aperture Radar Interferometry (InSAR) as an important complement to traditional techniques has been applied to landslide investigation in the Three Gorges area. Serious decorrelation caused by dense vegetation coverage makes the application very difficult [4,5]. Advanced InSAR methods such as Persistent Scatterers InSAR (PSI) [6] and small baselines subset InSAR (SBAS) [7,8], proposed to overcome the limitations of decorrelation and atmospheric influence, were also used in landslide detection in Three Gorges area. Sparse point-like targets identified on landslides caused by decorrelation significantly affect the interpretation with TerraSAR-X and ENVISAT ASAR data [9]. Successful applications with advanced InSAR methods such as PSI, Quasi Permanent Scatterers (QPS) and SBAS were achieved on single landslides such as the Huangtupo landslide [10–13] and Fanjiaping landslide [14]. Until now, successful InSAR-based studies have focused only on small areas such as the Huangtupo and Fanjiaping landslides. This may be a result of two important reasons: (1) the dense vegetation coverage within the Three Gorges area causes serious decorrelation, leaving a limited number of sparse point-like targets and therefore resulting in unreliable measurements over large regions; (2) dynamic variation of the heterogeneous atmospheric condition across the vast area cannot be simply estimated using spatial and temporal filters, which can further reduce the accuracy of landslide deformation measurements. However, it remains insufficient to focus on just a few known landslides that have been closely monitored with traditional techniques such as GPS and extensometer. Identification of unknown active slopes is more important for risk management and government action, thus it is a main focus of this study. Furthermore, time series analysis will also be extremely helpful in evaluating the potential driving factors on slope instability. In order to perform large-area landslide deformation mapping, the problems of decorrelation and atmospheric disturbances must be overcome. To mitigate the decorrelation, L-band PALSAR data of longer wavelength are used rather than C-band or X-band SAR data to ensure better coherence in mountainous areas covered by dense vegetation [15]. To deal with the problem of atmospheric disturbances, the Stanford Method for Persistent Scatterers (StaMPS) SBAS, developed by Hooper [8] for natural terrain displacement monitoring, was employed for time series analysis. In particular, since the unfavorable atmospheric phase screen (APS) induced by tropospheric path delay in mountainous areas usually consists of two components, namely stratified APS which is correlated with topography and turbulent APS which is independent of topography, a hybrid description model of APS was used to estimate APS from time series interferograms in the StaMPS SBAS method. In this study, we investigated an approach for carrying out wide-area landslide deformation mapping with multi-path satellite SAR data stacks. Three ALOS PALSAR datasets acquired from adjacent paths were analyzed to detect landslides in the Three Gorges area along the Yangtze River. Deformation patterns of these landslides detected were characterized and the impacts of driving factors were evaluated qualitatively. Reservoir water level fluctuation and rainfall have been considered two major driving factors for slope instability [16], and correlations between water level/rainfall and displacements are analyzed and discussed.

2. Study Area and Test Datasets

2.1. Three Gorges Area The Three Gorges Reservoir refers to the upstream region of the Yangtze River involved in the water impoundment of the Three Gorges Project within the Chongqing municipality and Hubei Province. The Three Gorges is a smaller region spanning from Fengjie County in Chongqing municipality to Yichang city in Hubei Province, as shown in Figure1. This region lies in the transition Remote Sens. 2016, 8, 136 3 of 14

Remote Sens. 2016, 8, 136 3 of 14 zone from the second to the third topographic step of China, with more than 70% being mountainous areas.transitionRemote The Sens. maximum 2016zone, 8 ,from 136 elevation the second in our to the study third area topo isgraphic greater step than of 2000China, m. with more than 70% being3 of 14 Constructionmountainous areas. of the The Three maximum Gorges elevation Dam was in finishedour study in area 2006, is greater and the than reservoir 2000 m. water level rose to 175 mtransition forConstruction the firstzone time from of in thethe 2009. Threesecond According Gorges to the Damthird to was thetopo operationalfinishedgraphic instep 2006, schemeof China,and the of with reservoir the more Three waterthan Gorges 70% level Reservoir,being rose largetomountainous seasonal 175 m for water areas.the levelfirst The time fluctuationsmaximum in 2009. elevation occurAccording annuallyin our to study the in operationalarea three is greater periods: scheme than a rise 2000of the from m. Three 145 mGorges to 175 m betweenReservoir, earlyConstruction Octoberlarge seasonal of andthe Three earlywater Gorges November,level fluctuations Dam awas decrease finishedoccur annually from in 2006, 175 in and mthree tothe 156periods: reservoir m between a waterrise from earlylevel 145 rose January m to 175 m for the first time in 2009. According to the operational scheme of the Three Gorges and April,to 175 m and between a further early decrease October fromand early 156 November, m to 145 m a betweendecrease from May 175 and m mid-Juneto 156 m between [17]. During early the JanuaryReservoir, and large April, seasonal and a water further le veldecrease fluctuations from 156occur m annuallyto 145 m in between three periods: May and a rise mid-June from 145 [17]. m flooding season (June to September) when heavy rainfall occurs, the water level is maintained at Duringto 175 m the between flooding early season October (June and to early September) November, when a decrease heavy fromrainfall 175 occurs, m to 156 the m waterbetween level early is 145 mmaintainedJanuary [17]. Theand at waterApril, 145 m and level[17]. a Thefurther of thewater upstreamdecrease level of fromthe Yangtze upstream 156 m River to Yangtze 145 and m betweenRiver the weekly and May the cumulativeweeklyand mid-June cumulative rainfall [17]. in FengjierainfallDuring during inthe Fengjie the flooding period during season from the 2007period(June to tofrom 2011 September) 2007 are to plotted 2011 when are in plottedheavy Figure rainfall 2in. Figure occurs, 2. the water level is maintained at 145 m [17]. The water level of the upstream Yangtze River and the weekly cumulative rainfall in Fengjie during the period from 2007 to 2011 are plotted in Figure 2.

Figure 1. Geographic coverage of the Three Gorges area, with red rectangles indicating our Figure 1. Geographic coverage of the Three Gorges area, with red rectangles indicating our test areas. test areas. Figure 1. Geographic coverage of the Three Gorges area, with red rectangles indicating our ManyManytest landslide areas. landslide disasters disasters have have happened happened in this area, area, such such as as the the Xintan Xintan landslide landslide in 1985 in 1985[2] [2] and theand Qianjiangping the Qianjiangping landslide landslide in in 2003 2003 [[18].18]. The The wide wide distribution distribution of landslides of landslides can be can attributed be attributed in Many landslide disasters have happened in this area, such as the Xintan landslide in 1985 [2] in largelarge part part to to a combinationa combination ofof lithologicallithological composition, composition, planar planar geological geological structures, structures, and andclimate climate conditionsand the Qianjiangping [2]. The huge landslide water levelin 2003 fluctuations [18]. The wide since distribution the initial impoundmentof landslides can in be2003 attributed make the in conditions [2]. The huge water level ﬂuctuations since the initial impoundment in 2003 make the conditionslarge part toin athis combination area more ofcomplex. lithological The composition,Qianjiangping planar landslide geological occurred structures, a few days and after climate the conditions in this area more complex. The Qianjiangping landslide occurred a few days after the impoundmentconditions [2]. [18].The huge water level fluctuations since the initial impoundment in 2003 make the impoundmentconditionsThe climate [in18 this]. in area this areamore is complex. categorized The as Qianjiangping subtropical monsoon landslide with occurred a hot and a few rainy days summer. after theAs Theweimpoundment can climate see from in [18]. thisFigure area 2, isthe categorized rainfall is mainly as subtropical concentrated monsoon in summer with with a hotweekly and cumulative rainy summer. As werainfall canThe see at climate Fengjie from Figurein reaching this 2area, the more is rainfallcategorized than 150 is mainlymm. as subtropical Intuitively, concentrated monsoon seasonal in withrainfall summer a hot could withand introduce rainy weekly summer. seasonal cumulative As rainfallatmosphericwe atcan Fengjie see from signals reaching Figure into moreInSAR2, the than rainfallmeasurements 150 is mm. mainly Intuitively,and concentrated should be seasonal removed in summer rainfall accordingly. with could weekly introduce cumulative seasonal atmosphericrainfall at signals Fengjie into reaching InSAR more measurements than 150 mm. andIntuitively, should seasonal be removed rainfall accordingly. could introduce seasonal atmospheric signals into InSAR measurements and should be removed accordingly.

Figure 2. Water levels of upstream Yangtze River and weekly cumulative rainfall in Fengjie from 2007 to 2011. FigureFigure 2. Water 2. Water levels levels of upstream of upstream Yangtze Yangtze River River and and weekly weekly cumulative cumulative rainfall rainfall in in Fengjie Fengjie fromfrom 2007 to 2011.2007 to 2011. Remote Sens. 2016, 8, 136 4 of 14

Remote Sens. 2016, 8, 136 4 of 14 2.2. Datasets 2.2. Datasets Three ascending paths, path 462, path 463 and path 464, of the ALOS PALSAR datasets were Three ascending paths, path 462, path 463 and path 464, of the ALOS PALSAR datasets were collected over the Three Gorges area, spanning from the end of 2006 to early 2011. There are overlaps collected over the Three Gorges area, spanning from the end of 2006 to early 2011. There are overlaps betweenbetween adjacent adjacent paths paths (Figure (Figure1). 1). A A similar similar look angle angle of of approximately approximately 34 degrees 34 degrees was applied was applied in in thesethese three three paths paths and and they they areare allall inin thethe same flight flight direction. direction. The The images images acquired acquired by fi byne finebeam beam dual-polarizationdual-polarization (FBD) (FBD) mode mode are are oversampled oversampled to match the the pixel pixel spacing spacing of the of thefine fine beam beam single single polarizationpolarization (FBS) (FBS) mode mode for cross-mode for cross-mode interferometry. interferometry. The SRTMThe SRTM (Shuttle (Shuttle Radar Radar Topography Topography Mission) DEMMission) (Digital DEM Elevation (Digital Model) Elevation of 1-arc-second Model) of 1-arc-second resolution coveringresolution ourcovering study our area study was area employed was in the generationemployed in of the single generation look differential of single look interferograms differential interferograms and geocoding. and Thresholdsgeocoding. Thresholds of 1000 days of and 1000 m1000 are days used and for temporal1000 m are and used perpendicular for temporal baselines and perpendicular separately during baselines interferogram separately during generation. As a result,interferogram 40 interferograms, generation. As 37 a interferograms result, 40 interfer andograms, 36 interferograms 37 interferograms were and produced 36 interferograms from path 462, were produced from path 462, 463 and 464 datasets, respectively, for the final time series analysis 463 and 464 datasets, respectively, for the final time series analysis (Figure3). (Figure 3).

FigureFigure 3. Interferograms 3. Interferograms used used for for small small baselines baselines analysis (a (a) )path path 462, 462, (b) ( bpath) path 463 463and and(c) path (c) path464. 464. The circlesThe circles represent represent the the images images and and solid solid lines lines represent represent the interferograms. interferograms.

3. Methodology3. Methodology

3.1. Small3.1. Small Baselines Baselines Subset Subset InSAR InSAR Analysis Analysis The small baseline subset method was initially proposed to overcome the problem of The small baseline subset method was initially proposed to overcome the problem of decorrelation decorrelation by making full use of the interferograms with both small temporal baselines and short by makingperpendicular full use baselines of the interferograms [7]. Since then it with has bothbeen frequently small temporal applied baselines in displacement and short monitoring perpendicular in baselinesrural [areas7]. Since[11,19–22]. then Compared it has been with frequently the original applied method inwhich displacement can operate monitoring with separated in rural areassubsets [11,19– 22of ].SAR Compared acquisitions, with theisolated original interferograms method which are not can allowed operate within StaMPS separated SBAS subsets analysis. of SAR acquisitions,Interferograms isolated with interferograms a temporal baseline are not and allowed perpendicular in StaMPS baseline SBAS below analysis. given Interferograms thresholds were with a temporalfirst generated baseline and to maximize perpendicular the coherence baseline and below then given grouped thresholds by common were master first generated or slave images to maximize to the coherenceform subsets. and Separated then grouped subsets by were common connected master by or interforgrams slave images with to formlarge subsets.baselines Separated to ensure the subsets weretemporal connected continuity. by interforgrams with large baselines to ensure the temporal continuity. SARSAR amplitude amplitude information information was wasinitially initially exploitedexploited to to exclude exclude bad-quality bad-quality pixels pixels based based on on amplitude dispersion values [8]. The pixels with a slow decorrelating filtered phase (SDFP) that are amplitude dispersion values [8]. The pixels with a slow decorrelating filtered phase (SDFP) that widely distributed in natural terrain and have maintained good coherence during a short time are widelyinterval distributed are identified in and natural analyzed terrain to monitor and have surface maintained displacements good coherence[8]. Phase characteristics during a short are time intervalused are to identifiedselect the SDFP and pixels. analyzed The spatially-co to monitorrrelated surface phase displacements component [and8]. spatially-uncorrelated Phase characteristics are usedlook to select angle the error SDFP are estimated pixels. The from spatially-correlated the wrapped phase. phase The spatially-correlated component and spatially-uncorrelatedcontribution to the lookinterferometric angle error are phase estimated of a pixel, from due theto spatially-correlated wrapped phase. signals The spatially-correlated including height error, contribution orbit error to the interferometricand ground displacement, phase of a etc. pixel,, is estimated due to spatially-correlated by the bandpass filtering signals of includingsurrounding height pixels error,in the orbit errorfrequency and ground domain. displacement, A spatially-uncorrelatedetc., is estimated look by angle the bandpasserror, which filtering is mainly of surrounding composed of pixels in thespatially-uncorrelated frequency domain. height A spatially-uncorrelated error, is then estimated look through angle its error,correlation which with is the mainly perpendicular composed of spatially-uncorrelatedbaseline. The residual, height after error,removal is thenof these estimated two terms through from the its wrapped correlation phase, with gives the an perpendicular estimate of the decorrelation noise γ [8]. baseline. The residual, after removal of these two terms from the wrapped phase, gives an estimate of the decorrelation noise γ [8].

1 N ? γ “ exp ´1pψ ´ ∆ψˆu ´ ∆ψq (1) N ˇ ˇ ˇi“1 ! )ˇ ˇÿ ˇ ˇ r ˇ ˇ ˇ Remote Sens. 2016, 8, 136 5 of 14 where N is the interferograms, ψ is the wrapped phase, ∆ψˆu is the spatially-uncorrelated look angle error and ∆ψ is the spatially-correlated term. Once the SDFP pixels were selected according to γ, three-dimensional phase unwrapping was carried out onr all the pixels [23]. The unwrapped phase on a given pixel can be expressed as:

ϕ “ ϕd ` ϕT ` ϕa ` ϕo ` ϕn (2) where ϕ is the unwrapped phase, ϕd, ϕT, ϕa, ϕo and ϕn are the phase components due, respectively, to surface displacements, topographic error, atmospheric disturbance, inaccurate orbit information and noise. In the StaMPS SBAS analysis, these different components were estimated by iterative ﬁltering with consideration of their identifying characteristics in spatial-temporal domains. Phase ramps due to orbital errors are spatially correlated and should be removed preferentially before the estimation of other terms. Orbital phase ramps caused by inaccurate ephemeris records in interferograms can be corrected using a bilinear or biquadratic model [19]. Usually, ALOS PALSAR data with large perpendicular baselines suffer considerably from orbital phase ramps. In most cases, a full second-order polynomial phase ramp correction method should be sufﬁcient [24]. Therefore, a biquadratic model was used to correct the phase ramps in our study.

2 2 ϕo “ a0 ` a1 ¨ x ` a2 ¨ y ` a3 ¨ xy ` a4 ¨ x ` a5 ¨ y (3) where ai (i= 0, 1,..., 5) are the coefﬁcients used in orbital error phase estimation; x and y are the coordinates in azimuth and range direction on a SDFP pixel. Then phase ramps were estimated over all interferograms using the SDFP pixels. Typically, the deformation signal will leak into the orbital errors when there are displacements distributed in large areas, as in the case of earthquakes. If this occurs, these areas with displacements should be masked out before performing orbital error estimation. The 1-arc-second SRTM DEM was used to remove the topographic phase in our study. The topographic changes since 2000 cannot be neglected, especially in developing countries such as China [25]. There is a linear relationship between topographic error and baseline.

4π B ϕ “ ∆h (4) T λ Rsinθ B, ∆h, λ, R, θ stand for perpendicular baseline, topographic error, radar wavelength, slant range and incidence angle, respectively. Thus, the topographic error can be accurately estimated by using a least squares approach with multi-baseline interferograms. Interferograms with long temporal baselines sensitive to displacements were excluded from topographic error estimation. According to previous studies [19,21,26], atmospheric delays in mountainous areas are normally composed of stratiﬁed APS and turbulent APS. Usually there is a linear relationship between phase delays introduced by stratiﬁed APS and terrain elevation.

ϕa “ ϕs ` ϕa_r (5)

ϕs “ b ¨ h (6) where ϕs, ϕa_r represent the phase delays introduced by the stratified APS and residual, respectively; b is the coefficient used to represent the relationship between elevation and phase delays caused by stratified APS; and h is the elevation. A hybrid description model of the atmospheric phase screen (APS) was incorporated into StaMPS SBAS analysis to improve APS estimation from time series interferograms. The phase delays introduced by stratified APS were estimated by a least squares procedure using all the unwrapped SDFP pixels. Then the atmospheric phase residuals were estimated with temporally high-pass and spatially low-pass filters. Afterwards, time series deformation can be inverted using singular value decomposition (SVD) [7,8]. Remote Sens. 2016, 8, 136 6 of 14

AnRemote iterative Sens. 2016, 8 procedure, 136 was employed to ensure the estimation accuracy of each6 of term 14 in Equation (2). Since unwrapping errors are more prevalent in interferograms with longer perpendicular baselines,estimated the estimatedwith temporally DEM errorhigh-pass using and Equation spatiall (4)y low-pass was reduced filters. before Afterwards, phase unwrappingtime series to improvedeformation the unwrapping can be inverted accuracy. using Re-estimationsingular value decomposition of each term in(SVD) Equation [7,8]. (2) was carried out with the newlyAn unwrapped iterative procedure phase. This was procedure employed canto ensure be repeated the estimation until the accuracy change ofof the each residual term in is less than aEquation target threshold. (2). Since unwrapping errors are more prevalent in interferograms with longer perpendicular baselines, the estimated DEM error using Equation (4) was reduced before phase 3.2. Integrationunwrapping of to Multi-Path improve the Displacement unwrapping Measurements accuracy. Re-estimation of each term in Equation (2) was carried out with the newly unwrapped phase. This procedure can be repeated until the change of the Sinceresidual our is less objective than a target is to detectthreshold. potential landslides across the wide area of the Three Gorges Reservoir, the resultant displacement maps derived from the three paths of ALOS PALSAR data separately3.2. Integration should beof Multi-Path further integrated Displacement into Measurements one map. Usually, an assumed stable point is chosen as a reference pointSince our by whichobjective all is the to otherdetect parameters potential landslides are estimated across [ 7the,27 wide]. In thisarea study, of the differentThree Gorges reference pointsReservoir, were used the inresultant our process displacement for each maps path. deri Sinceved nofromin-situ the threedata paths was available,of ALOS PALSAR reference data points locatedseparately at Badong should and be the further Fengjie integrated city council into one building map. Usually, were used. an assumed The Badong stable point reference is chosen point as was useda for reference both path point 462 by and which path all 463. the The other Fengjie parameters reference are pointestimated was used[7,27]. to In calibrate this study, the different SBAS results fromreference path 464. points Biases were between used in path our process 463 and for the each other path. two Since paths no in-situ in the data line-of-sight was available, (LOS) reference direction points located at Badong and the Fengjie city council building were used. The Badong reference are estimated and corrected as [27] did based on the average displacement rate differences in the point was used for both path 462 and path 463. The Fengjie reference point was used to calibrate the overlapping area with respect to the Badong reference point. SBAS results from path 464. Biases between path 463 and the other two paths in the line-of-sight It(LOS) is worth direction noting are estimated that besides and corrected a displacement as [27] did rate based map on in the the average LOS direction, displacement generally rate a displacementdifferences map in the along overlapping the slope area direction with respect can to also the beBadong obtained reference by a point. simple projection. However, here the displacementIt is worth noting rate mapthat inbesides the along-slope a displacement direction rate map was in not the available LOS direction, for two generally reasons: (1)a the LOS deformationdisplacement map could along be projected the slope todirection the slope can directionalso be obtained if the landslide by a simple is aprojection. translational However, landslide basedhere on boththe displacement geomorphological rate map andin the DEM along-slope models direction [28], but was we not have available no idea for of two the reasons: speciﬁc (1) landslide the categoryLOS ofdeformation each active could slope be detected;projected to (2) the the slope slopes direction along if the the Yangtzelandslide Riveris a translational are mostly landslide south-north orientedbased while on both the geomorphological LOS deformation and is DEM primarily models sensitive [28], but we to have the deformation no idea of the inspecific the east-west landslide and category of each active slope detected; (2) the slopes along the Yangtze River are mostly south-north vertical directions, hence the projection between the LOS and slope direction would be inaccurate. oriented while the LOS deformation is primarily sensitive to the deformation in the east-west and 4. Experimentalvertical directions, Results hence and the Analyses projection between the LOS and slope direction would be inaccurate.

4.1. Mean4. Experimental Displacement Results Rate and Map Analyses The4.1. Mean obtained Displacement displacement Rate Map rate map in the LOS direction over the Three Gorges area from Fengjie County toThe Zigui obtained County displacement is shown rate in Figuremap in4 .the A LO totalS direction of 1,775,238 over the points Three were Gorges identiﬁed area from in our analysis,Fengjie covering County an to areaZigui of County 4800 km is shown2 from in Fengjie Figure County 4. A total to of Zigui 1,775,238 County points along were the identified Yangtze in River. Approximatelyour analysis, 96% covering of the an points area of show 4800 displacements km2 from Fengjie rates County between to Zigui´10 County mm/year along and the 10Yangtze mm/year, whichRiver. indicated Approximately the overall 96% stability of the of poin the studyts show area. displacements About 30 slopes rates withbetween total −10 areas mm/year of 48 kmand2 were identiﬁed10 mm/year, as being which in active indicated deformation. the overall stability of the study area. About 30 slopes with total areas of 48 km2 were identified as being in active deformation.

FigureFigure 4. Mean 4. Mean displacement displacement rate rate map map of Threeof Three Gorges Gorges area area generated generated from from three threeALOS ALOS PALSARPALSAR data stacks.data Three stacks. selected Three selected areas are areas highlighted are highlighted by dashed by dashed rectangles. rectangles. Remote Sens. 2016, 8, 136 7 of 14

Remote Sens. 2016, 8, 136 7 of 14 The distribution of landslides in Fengjie is strongly connected with the orientation of synclinal stratigraphicThe distribution structures. of landslides The stratum in whichFengjie initiates is strongly the instabilityconnected iswith located the orientation at the transitional of synclinal zone whichstratigraphic is steeply structures. dipping The in thestratum elevated which regions initiates and the gently instability dipping is located in the lower at the regions transitional [29]. zone As a whichfamous is karst steeply region, dipping stratigraphic in the elevated formations regions in the an westd gently of Fengjie dipping mainly in the consist lower of regions Jurassic [29]. red beds,As a whilefamous formations karst region, in the stratigraphic east mostly formations consist of Triassicin the west carbonate of Fengjie rock [mainly1]. Approximately consist of Jurassic half of red the beds,detected while active formations landslides in the in oureast study mostly area consist are distributed of Triassic incarbonate Fengjie Countyrock [1]. (Figure Approximately5). The SDFP half pixelsof the withdetected positive active values landslides in Figure in our5 are study located area on are west-facing distributed slopes. in Fengjie This is County reasonable (Figure since 5). most The SDFPmovements pixels werewith inpositive the downslope values in direction. Figure 5 According are located to on [29 ],west-facing there are many slopes. landslides This is distributedreasonable sincealong most the banks movements of the Yangtze were in River, the anddownslope it seems thatdirection. most ofAccording them are stableto [29], and there only are six activemany landslides weredistributed detected along along the the banks river of judging the Yangtze from River, our results. and it Theseems slopes that alongmost of the them Yangtze are stable River andare generallyonly six active facing landslides north-south, were which detected is insensitive along the for river InSAR judging measurements. from our results. Nevertheless, The slopes local variationsalong the inYangtze slope orientation River are allow gene InSARrally tofacing detect north-south, some movements, which suchis insensitive as the landslide for InSAR where measurements.P4 is located. According Nevertheless, to a previouslocal variations study in in Wanzhou, slope orientation located in allow the west InSAR of the to Threedetect Gorges some movements,area, slopes ofsuch active as the landslides landslide concentrate where P4 withinis located. the intervalAccording from to a 16 previous to 26 degrees study [ 30in ].Wanzhou, Here the locateddetected in active the west landslides of the in Three Fengjie Gorges are also area, distributed slopes of on active gentle landslides slopes with concentrate angles varying within from the 15interval to 30 degrees.from 16 to 26 degrees [30]. Here the detected active landslides in Fengjie are also distributed on gentle slopes with angles varying from 15 to 30 degrees.

Figure 5. Mean displacement rate map measured at Fengjie.Fengjie. P1, P1, P2, P3, P4 are points used for time series analysis shown in later sections.

Figure 6 shows the displacement rate over the area of Wushan and three active landslides were Figure6 shows the displacement rate over the area of Wushan and three active landslides were detected. The landslide susceptibility map given by [31] shows that most of the landslide-prone detected. The landslide susceptibility map given by [31] shows that most of the landslide-prone areas areas were distributed around the south of the town of Wushan. All three landslides are located were distributed around the south of the town of Wushan. All three landslides are located within the within the areas prone to landslides according to the susceptibility map [31]. However, those areas areas prone to landslides according to the susceptibility map [31]. However, those areas with steep with steep slopes of more than 50 degrees seem generally stable, according to our results. Most of slopes of more than 50 degrees seem generally stable, according to our results. Most of the areas along the areas along the bank of the Yangtze River are stable according to our InSAR results, which is the bank of the Yangtze River are stable according to our InSAR results, which is consistent with the consistent with the susceptibility map. susceptibility map. Figure 7 shows the mean displacement rate map covering areas around Badong and Zigui. Figure7 shows the mean displacement rate map covering areas around Badong and Zigui. According to the landslide susceptibility map [31,32], the Badong blocks consisting of the Badong According to the landslide susceptibility map [31,32], the Badong blocks consisting of the Badong formation are prone to landslides. About five active landslides were detected within Badong formation are prone to landslides. About ﬁve active landslides were detected within Badong County, County, among which the Huangtupo landslide was the most famous that has been studied using among which the Huangtupo landslide was the most famous that has been studied using the InSAR the InSAR method [10–14]. The mean displacement velocity of the Huangtupo landslide is about method [10–14]. The mean displacement velocity of the Huangtupo landslide is about 10~15 mm/year. 10~15 mm/year. The active Huangtupo landslide has forced the government to change the relocation plan of the town of Badong. The Fanjiaping landslide that is located at the south bank of the Yangtze River and approximately 8 km east of the town of Badong was another one in active Remote Sens. 2016, 8, 136 8 of 14

The active Huangtupo landslide has forced the government to change the relocation plan of the townRemote of Badong. Sens. 2016 The, 8, 136 Fanjiaping landslide that is located at the south bank of the Yangtze River8 of 14 and Remote Sens. 2016, 8, 136 8 of 14 approximately 8 km east of the town of Badong was another one in active deformation [9,14,33]. The deformation [9,14,33]. The displacement rate of the Fanjiaping landslide can reach nearly displacementdeformation40 mm/year rate and[9,14,33]. of is the possibly Fanjiaping The correlateddisplacement landslide with cantherate water reachof the level nearly Fanjiaping fluctuations 40 mm/year lands andlide andrainfall, can is possibly whichreach willnearly correlated be with40 theanalyzed mm/year water in level theand following ﬂuctuationsis possibly sections. correlated and rainfall, with the which water will level be fluctuations analyzed in and the rainfall, following which sections. will be analyzed in the following sections.

Figure 6. Mean displacement rate map of Wushan. FigureFigure 6. 6.Mean Mean displacement displacement rate rate map map of of Wushan. Wushan.

Figure 7 Mean displacement rate map covering areas around Badong and Zigui. P5 and P6 are points FigureFigureused 7. Meanin 7 time Mean displacement series displacement analysisrate shown rate map map in coveringlater covering sections. areas arou aroundnd Badong Badong and and Zigui. Zigui. P5 and P5 andP6 are P6 points are points usedused in time in time series series analysis analysis shown shown in in later later sections. sections. 4.2. Measurement Consistency Evaluation 4.2. Measurement Consistency Evaluation In order to evaluate the consistency of the results, a quantitative analysis of the differences withinIn theorder overlapping to evaluate areas the from consistency different of paths the resuwas lts,performed. a quantitative Figure analysis8 displays of the the difference differences in within the overlapping areas from different paths was performed. Figure 8 displays the difference in Remote Sens. 2016, 8, 136 9 of 14

4.2. Measurement Consistency Evaluation

RemoteRemoteIn Sens. Sens. order 2016 2016 to, ,8 8, evaluate,136 136 the consistency of the results, a quantitative analysis of the differences99 of of 14 14 within the overlapping areas from different paths was performed. Figure8 displays the difference indisplacementdisplacement displacement rate rate rate values values values between between between adjacent adjacent adjacent paths. paths. paths. The The The standard standard standard deviations deviations deviations of of the the of thedisplacement displacement displacement rate rate ratedifferencesdifferences differences on on the onthe thecommon common common pixels pixels pixels between between between path path path 464 464 464 and and and path path path 463, 463, and and path pathpath 463 463463 and and pathpath 462462 462 areare are 4.74.74.7 mm/yearmm/year mm/year and and 4.94.9 4.9 mm/year,mm/year, mm/year, respectively.respectively. PossiblePossible factorsfactors whichwhich which causedcaused caused thethe the disparitydisparity disparity betweenbetween between differentdifferentdifferent pathspaths paths overover over thethe the overlappingoverlapping overlapping areasareas areas havehave have beenbeen been analyzedanalyzed analyzed inin in detaildetail detail inin in [27[27]. [27].]. InIn In brief,brief, brief, thethe the mainmain main causescausescauses were werewere identiﬁed identifiedidentified as: as:as: (1) (1) A(1) slight AA slightslight difference differencedifference in incidence inin incidenceincidence angle angle andangle satellite andand satellitesatellite heading headingheading angle caused angleangle bycausedcaused the different by by the the different observationdifferent observation observation geometries geometries geometries at corresponding at at corresponding corresponding points on points differentpoints on on paths.different different In ourpaths. paths. case, In In thisour our differencecase,case, this this difference wasdifference found was was to reach found found a to maximumto reach reach a a maximum maximum of more than of of more more 2 degrees. than than 2 2(2) degrees. degrees. The observation (2) (2) The The observation observation time series time time of threeseriesseries paths ofof threethree are different, pathspaths areare resulting different,different, in different resultingresulting temporal inin differentdifferent samplings temporaltemporal of the landslidesampsamplingslings progression. ofof thethe landslidelandslide Since theprogression.progression. progression Since Since of landslides the the progression progression is usually of of landslides nonlinear,landslides samplingis is usually usually timenonlinear, nonlinear, differences sampling sampling may causetime time differences inconsistenciesdifferences may may incausecause mean inconsistencies inconsistencies velocity estimation. in in mean mean (3)velocity velocity The SRTMestimation. estimation. DEM (3) acquired(3) The The SRTM SRTM in 2000 DEM DEM was acquired acquired used inin in our2000 2000 process was was used used and in in groundourour process process surface and and changes ground ground could surface surface introduce changes changes DEM could could uncertainties. intr introduceoduce DEM DEM This uncertainties. uncertainties. could cause theThis This estimation could could cause cause of thethe the displacementestimationestimation of of velocitythe the displacement displacement to be inaccurate, velocity velocity in to to addition be be inaccurate, inaccurate, to causing in in addition addition a mismatch to to causing causing of points a a mismatch mismatch between adjacentof of points points paths.betweenbetween (4) adjacent adjacent There are paths. paths. some (4) (4) active There There landslides are are some some active inactive the landslides overlappinglandslides in in areas.the the overlapping overlapping In our case, areas. areas. the In numberIn our our case, case, of landslidesthethe number number in of theof landslides landslides overlapping in in areasthe the overlapping overlapping is no more thanareas areas three, is is no no somore more their than than contributions three, three, so so their totheir this contributions contributions disparity may to to bethisthis limited. disparity disparity may may be be limited. limited.

FigureFigureFigure 8.8. 8.The The The distributiondistribution distribution ofof of displacementdisplacement displacement raterate rate differencedi differencefference forfor for thethe the overlappingoverlapping overlapping areasareas areas betweenbetween between ((a a()a) path )path path 464464464 andand and pathpath path 463463 463 datasets;datasets; datasets; ( b( b()b) path )path path 463 463 463 and and and path path path 462 462 462 datasets. datasets. datasets.

FigureFigureFigure 9. 9.9. TimeTimeTime series seriesseries displacement displacementdisplacement of ofof point point P5 P5 alongalong thethe slopeslopeslope directiondirectiondirection locatedlocatedlocated atatat thethethe HuangtupoHuangtupoHuangtupo landslide.landslide. landslide.

Unfortunately,Unfortunately, wewe havehave nono in-situin-situ measurementmeasurement datadata inin thethe ThreeThree GorgersGorgers area,area, soso itit isis impossibleimpossible toto evaluateevaluate thethe absoluteabsolute accuracyaccuracy ofof ouourr results.results. ToTo evaluateevaluate thethe consistencyconsistency ofof timetime seriesseries measurements, measurements, time time series series displacements displacements from from path path 462 462 and and path path 463 463 at at point point P5 P5 (refer (refer to to FigureFigure 7) 7) located located on on the the Huangtupo Huangtupo landslide landslide were were illustrated illustrated in in Figure Figure 9. 9. The The Huangtupo Huangtupo landslide landslide isis aa translationaltranslational landslidelandslide [34].[34]. TheThe LOSLOS dispdisplacementslacements werewere transformedtransformed ontoonto thethe downslopedownslope direction.direction. Slight Slight discrepancies discrepancies can can be be observed observed between between the the measurements measurements from from path path 462 462 and and path path 463,463, forfor whichwhich thethe reasonsreasons mightmight bebe thethe samesame asas discusseddiscussed above.above. OnOn thethe otherother hand,hand, thethe Remote Sens. 2016, 8, 136 10 of 14

Unfortunately, we have no in-situ measurement data in the Three Gorgers area, so it is impossible to evaluate the absolute accuracy of our results. To evaluate the consistency of time series measurements, time series displacements from path 462 and path 463 at point P5 (refer to Figure7) located on the Huangtupo landslide were illustrated in Figure9. The Huangtupo landslide is a translational landslide [34]. The LOS displacements were transformed onto the downslope direction.

SlightRemote discrepanciesSens. 2016, 8, 136 can be observed between the measurements from path 462 and path 463,10 of for 14 which the reasons might be the same as discussed above. On the other hand, the measurements of pathmeasurements 462 were referenced of path 462 with were respect referenced to 4 January with respect 2010 whileto 4 January the measurements 2010 while the of pathmeasurements 463 were referencedof path 463 with were respect referenced to 21 January with respect 2010. Thisto 21 could January also 2010. be an This important could also cause be for an the important discrepancies cause infor displacement the discrepancies differences in displacement between the two differen paths.ces Additionally, between the phase two unwrapping paths. Additionally, artifacts induced phase byunwrapping different datasets artifacts might induced also by be differen considered.t datasets might also be considered.

4.3.4.3. EffectEffect ofof SeasonalSeasonal RainfallRainfall on on APS APS Variation Variation PrecedingPreceding thethe analysisanalysis ofof thethe timetime seriesseries resultsresults ofof SDFT SDFT pixels, pixels, thetheinfluence influenceof of atmospheric atmospheric turbulenceturbulence toto our our displacement displacement estimationestimation waswas discussed.discussed. InIn aa mountainousmountainous areaarea suchsuch asas thethe Three Three GorgesGorges region region along along the the Yangtze Yangtze River, River, the the phase phase distortions distortions induced induced by by atmospheric atmospheric turbulence turbulence are are veryvery significant.significant. FigureFigure 1010aa givesgives thethe phasephase signalsignalinduced inducedby byatmospheric atmosphericturbulence turbulence ononpoint pointP2 P2 forfor each each slave slave image image with with respect respect to to the the master master image. image. Strong Strong correlation correlation can can be be observed observed between between atmosphericatmospheric signal signal and and seasonal seasonal rainfall. rainfall. More More specifically, specifically, we can we see can that see the that seasonal the seasonal rainfall causedrainfall largecaused atmospheric large atmospheric artifacts inartifacts the rainy in seasonsthe rainy while seasons the effect while is the relatively effect is small relatively in dry seasons.small in Ondry theseasons. other On hand, the water other levelhand, fluctuation water level might fluctuatio also contributen might also to thecontribute amount to of the water amount vapor of inwater the Threevapor Gorges in the area,Three whereby Gorges area, the large whereby water the level large fluctuation water level in the fluctuation Three Gorges in the area Three might Gorges result area in amight change result in water in a change vapor concentration. in water vapor However, concentratio comparedn. However, to the compared effect of rainfall to the oneffect atmospheric of rainfall turbulence,on atmospheric it seems turbulence, that the effectit seems of waterthat the level effect fluctuation of water islevel relatively fluctuation minor. is relatively minor. TheThe differencesdifferences amongamong estimationsestimations ofof APSAPS withwith ororwithout withoutstratified stratifiedAPS APScorrections corrections arearevery very smallsmall for for pixels pixels with with heights heights close close to to the the reference reference point. point. For For pixels pixels with with large large elevation elevation differences differences withwith respect respect to to the the reference reference point, point, if theif the atmospheric atmosphe signalric signal is not is not properly properly estimated estimated and removed,and removed, the seasonalthe seasonal effects willeffects remain will in remain the displacement in the signaldisplacement and significantly signal and hinder significantly the interpretation hinder of thethe results.interpretation Figure 10ofb the shows results. the timeFigure series 10b displacementsshows the time signalseries afterdisplacements removing signal the spatially-correlated after removing the atmosphericspatially-correlated signal atmospheric with and without signal stratified with and APS without correction. stratified We APS can corre see thatction. without We can stratified see that APSwithout correction, stratified part ofAPS the correction, APS signal stillpart remains of the in APS the timesignal series still displacements, remains in whilethe time the results series appeardisplacements, to be more while reasonable the results after appear correction. to be more reasonable after correction.

FigureFigure 10. 10.( a()a Estimated) Estimated atmospheric atmospheric signal signal on P1on and P1 impactand impact factors: factors: water levelwater (unit: level m) (unit: and rainfallm) and (unit:rainfall mm), (unit: the mm), dashed the linedashed represents line represents the temporal the temporal location location of the master of the image.master image. (b) Time (b series) Time displacementsseries displacements on P1 with on P1 and with without and without stratiﬁed stratified APS correction. APS correction.

4.4. Impact Factors for Landslide Activities According to our results, most of the landslides have a nearly linear displacement trend as shown in Figure 11a, particularly landslides located farther away from the Yangtze River. While small variations in the displacement trend might be occurring throughout the entire time span, the influence of water level variation is not obvious and can be neglected on these landslides. Considering Figure 11b, the displacements of P3 can be divided into two periods. The displacement of P3 is very small with almost a linear rate before August 2008. Acceleration of displacement occurred during the second period, but the displacement trend is still linear. The Remote Sens. 2016, 8, 136 11 of 14

Remotedisplacement Sens. 2016, 8 of, 136 P3 is very small with almost a linear rate before August 2008. Acceleration11 of 14 of displacement occurred during the second period, but the displacement trend is still linear. The change changein displacement in displacement rate between rate between these two these periods two might periods be triggered might be by triggered the consecutive by the rainfallconsecutive in the rainfallsummer in of the 2008. summer of 2008.

FigureFigure 11. 11. RelationshipRelationship between impact factorsfactors andand displacementsdisplacements on on (a ()a P2;) P2; (b ()b P3;) P3; (c) ( P4;c) P4; (d) ( P6.d) P6. The Themeasurement measurement units units are meterare meter for waterfor water level level and and millimeter millimeter for rainfallfor rainfall separately. separately.

WaterWater levellevel ﬂuctuationsfluctuations and and rainfall rainfall are are the twothe mosttwo most important important impact impact factors offactors slope stabilitiesof slope stabilitiesin the Three in the Gorges Three region Gorges for slopesregion nearfor slopes the river near [16 the]. The river upstream [16]. The water upstream level of water Three level Gorges of Threeand rainfall Gorges recordsand rainfall from records Badong from and Badong Fengjie and were Fengjie used were to investigate used to investigate the relationship the relationship between betweendisplacements displacements and water and level water ﬂuctuation/rainfall. level fluctuation/rainfall. Since theSince temporal the temporal sampling sampling rate of rate ALOS of ALOSPALSAR PALSAR is very is low, very the low, relationship the relationship between between displacements displacements and water and level/rainfall water level/rainfall was analyzed was analyzedby visual inspection.by visual inspection. As we can seeAs from we Figurecan see 11 c,from the landslidesFigure 11c, located the atlandslides P4 experienced located stepwise at P4 experienceddisplacements. stepwise The landslides displacements. in the ThreeThe landslid Gorgeses areas in the with Three stepwise Gorges displacement areas with progression stepwise displacementmainly undergo progression two main mainly processes: undergo the slidingtwo main process processes: and the the dormant sliding process process and [16 ].the During dormant the processsliding process,[16]. During the landslidethe sliding moves process, at inconsistent the landslide rates, moves whereas at inconsistent during the rates, dormant whereas stage, during shear thestrength dormant has beenstage, recovered shear strength and stability has been is achievedrecovered for and some stability duration is achieved of time. for some duration of time. There are generally four periods of movement at P4. Among the four periods, period II could be caused by the decline of the water level when the rainfall is relatively small. Periods I, III and IV could be caused by the joint effect of water level decline and rainfall. During these three periods, the rainfall was relatively low at first while the fast decline of the reservoir water level triggered the movement by changing the pressure difference between the groundwater inside the slope and the reservoir waterbody outside [35]. The following heavy rainfall caused the displacement to accelerate, especially during period I when the weekly rainfall reached more than 150 mm. It is also worth noting that a lag effect of water level may be present. For periods III and IV, although heavy rainfall followed when the decline of the water level ceased, the lag effect of the water level decline might also have contributed to the movement at the end of periods III and IV. Remote Sens. 2016, 8, 136 12 of 14

There are generally four periods of movement at P4. Among the four periods, period II could be caused by the decline of the water level when the rainfall is relatively small. Periods I, III and IV could be caused by the joint effect of water level decline and rainfall. During these three periods, the rainfall was relatively low at ﬁrst while the fast decline of the reservoir water level triggered the movement by changing the pressure difference between the groundwater inside the slope and the reservoir waterbody outside [35]. The following heavy rainfall caused the displacement to accelerate, especially during period I when the weekly rainfall reached more than 150 mm. It is also worth noting that a lag effect of water level may be present. For periods III and IV, although heavy rainfall followed when the decline of the water level ceased, the lag effect of the water level decline might also have contributed to the movement at the end of periods III and IV. The movement of P6 located at the Fangjiaping landslide was fairly complicated, as shown in Figure 11d. There may be a constant displacement trend that exists during the entire progression of the Fangjiaping landslide; nevertheless, we can ﬁnd three periods with where accelerations occur. Periods I and II may be mainly caused by the reservoir water level decline. Although the weekly rainfall at the end of period I reached more than 180 mm, no acceleration was observed during the following process. The movement of periods II and III might be accelerated by the water level decline and the following movements at the end of these two periods might be caused by the rainfall and lag effect of the water level decline.

5. Conclusions In this paper, we investigated the stability of slopes from Fengjie to Zigui within the Three Gorges area by time series InSAR analysis. InSAR data with its large coverage and millimeter accuracy is suitable for displacement monitoring in the vast Three Gorges area. However, we have to overcome decorrelation effects and atmospheric disturbances to achieve good results. To avoid decorrelation as much as possible, L-band SAR data with longer wavelengths are preferred. To overcome atmospheric disturbances, a hybrid description model of atmospheric phase screen (APS) is necessary to improve APS estimation from time series interferograms, particularly in mountainous areas such as the Three Gorges region. The estimated atmospheric phase was mainly correlated with the seasonal rainfall in the temporal dimension. About 30 slopes covering areas of 48 km2 in total were identiﬁed to be active with slopes ranging from 15 to 30 degrees. Through time series analysis, active landslides far away from the Yangtze River exhibit a linear displacement trend. Rainfall plays a key role in the change of the displacement trend. For those active landslides located near the river, rapid water level decline and consecutive rainfall are potentially two key factors to increase slope instability. It is worth noting that the limitations of the InSAR method can make it blind to several active phenomena. InSAR methods applied to polar-orbit satellite SAR datasets are only sensitive to displacement in the LOS direction, while they hardly detect movements parallel to the ﬂight direction, such as the north-south direction in our study. Geometric distortions such as shadow and obscuring landscape features can also make many slopes invisible in SAR images [22]. Descending and ascending datasets can be combined to increase the effective coverage of InSAR measurements. Additionally, for fast-moving landslides, a large displacement gradient might go beyond the upper detectability of InSAR methods, which would make interferometric phase observation completely fail to measure these displacements. An effective tool for fast-moving landslide detection is the pixel offset tracking method with high-resolution SAR data. Successful applications have also been achieved on the Shuping landslide in the Three Gorges area with high-resolution TerraSAR-X data [22,35–37]. A combination of the InSAR method and pixel offset tracking methods can be carried out with high-resolution SAR datasets in our future work.

Acknowledgments: This work was ﬁnancially supported by the National Key Basic Research Program of China (Grant No. 2013CB733205 and No. 2013CB733204) and the National Natural Science Foundation of China (Grant No. 61331016 and 41271457). The PALSAR datasets were provided by Japan Aerospace Exploration Agency Remote Sens. 2016, 8, 136 13 of 14

(JAXA) through the ALOS-RA4 project (PI1247, PI1440). Ziyun Wang from Wuhan University was appreciated for helping to collect the water level data of the upstream of Yangtze River. Author Contributions: Xuguo Shi processed ALOS PALSAR data, interpreted the results and wrote the original manuscript. Mingsheng Liao supervised the research. Menghua Li contributed to some processings of the InSAR images. Lu Zhang wrote the manuscript and interpreted the results. Cory Cunningham proofread and edited the manuscript. All authors have read and approved the final manuscript. Conflicts of Interest: The authors declare no conflict of interest.

References

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