remote sensing

Article InSAR Reveals Land Deformation at and , between 2011 and 2017 with COSMO-SkyMed Data

Alex Hay-Man Ng 1,2 ID , Hua Wang 1,*, Yiwei Dai 1, Carolina Pagli 3, Wenbin Chen 1, Linlin Ge 2, Zheyuan Du 2 and Kui Zhang 4 1 Department of Surveying Engineering, University of Technology, Guangzhou 510006, China; [email protected] (A.H.-M.N.); [email protected] (Y.D.); [email protected] (W.C.) 2 School of Civil and Environmental Engineering, UNSW Australia, Sydney 2052, Australia; [email protected] (L.G.); [email protected] (Z.D.) 3 Department of Earth Sciences, University of Pisa, Pisa 56100, Italy; [email protected] 4 School of Communication Engineering, Chongqing University, Chongqing 400044, China; [email protected] * Correspondence: [email protected]; Tel.: +86-135-7001-9257

 Received: 23 April 2018; Accepted: 23 May 2018; Published: 24 May 2018 

Abstract: Subsidence from groundwater extraction and underground tunnel excavation has been known for more than a decade in Guangzhou and Foshan, but past studies have only monitored the subsidence patterns as far as 2011 using InSAR. In this study, the deformation occurring during the most recent time-period between 2011 and 2017 has been measured using COSMO-SkyMed (CSK) to understand if changes in temporal and spatial patterns of subsidence rates occurred. Using InSAR time-series analysis (TS-InSAR), we found that significant surface displacement rates occurred in the study area varying from −35 mm/year (subsidence) to 10 mm/year (uplift). The 2011–2017 TS-InSAR results were compared to two separate TS-InSAR analyses (2011–2013, and 2013–2017). Our CSK TS-InSAR results are in broad agreement with previous ENVISAT results and levelling data, strengthening our conclusion that localised subsidence phenomena occurs at different locations in Guangzhou and Foshan. A comparison between temporal and spatial patterns of deformations from our TS-InSAR measurements and different land use types in Guangzhou shows that there is no clear relationship between them. Many local scale deformation zones have been identified related to different phenomena. The majority of deformations is related to excessive groundwater extraction for agricultural and industrial purposes but subsidence in areas of subway construction also occurred. Furthermore, a detailed analysis on the sinkhole collapse in early 2018 has been conducted, suggesting that surface loading may be a controlling factor of the subsidence, especially along the road and highway. Roads and highways with similar subsidence phenomenon are identified. Continuous monitoring of the deforming areas identified by our analysis is important to measure the magnitude and spatial pattern of the evolving deformations in order to minimise the risk and hazards of land subsidence.

Keywords: subsidence; Persistent Scatterer Interferometry; TS-InSAR; Guangzhou; Foshan

1. Introduction Many large deltas in the world have been experiencing subsidence because of soil consolidation and compaction, water extraction, and underground tunnelling driven by the rapid economic and population growth [1,2]. These deltas accommodate a large percentage of the human population, hence, significant subsidence can cause serious problems to buildings and infrastructures, generating tension cracks. Moreover, deltas are low-lying plains where even small ground subsidence can

Remote Sens. 2018, 10, 813; doi:10.3390/rs10060813 www.mdpi.com/journal/remotesensing Remote Sens. 2018, 10, 813 2 of 23 significantly increase the risk of flood hazards and seawater intrusion. Therefore, continuous and accurate monitoring of ground subsidence at the major cities in these deltas is crucial to assess the level of risk. The Greater Delta (GPRD) is one of the main hubs of China’s economic growth; it is also one of the largest urban agglomerations and fastest-growing urban regions of the world and is located on alluvial and lacustrine deposits. In this study, the land stability of Guangzhou and Foshan, two of the main mega-cities in the GPRD, are investigated. The studied area was built over a network of canals in the Pearl River flood plain. The majority of the man-made infrastructures in the study area are less than one metre above sea level (area with white colour in Figure1). Subsidence in Guangzhou and Foshan has occurred for many years, mainly caused by sediment loading and compaction, and groundwater extraction due to concentrated urban development [3–5]. For example, tens of buildings in Jinshazhou at Guangzhou were damaged by the subsidence caused by underground construction of the high-speed railway underground project, resulting in an economic loss of 30 million RMB (currency of People’s Republic of China) [6]. In addition, the development in these areas is much faster than its drainage infrastructure could support. This, together with the continually increasing sea level rise and ground subsidence, puts the local population under a greater risk of environmental problems, such as flooding and saltwater intrusion. In recent years, Guangzhou has frequently suffered from urban flooding. On 7 May 2010 and 31 March 2014, extreme rainfall caused inundations and severe transport chaos [7]. The frequent flooding and ground collapses in Guangzhou have also caused huge economic losses and endangered the lives of citizens [4,6,8]. Systematic analysis of the land subsidence in Guangzhou and its neighbour, Foshan, is, therefore, required for subsidence risk assessment and mitigation. The capability of InSAR for land deformation mapping has already been demonstrated in many applications, for example surface displacements monitoring during and after tunnels excavations [9–11], and groundwater exploitation [12,13]. The radar interferometry time-series technique (TS-InSAR), an extension of the conventional InSAR technique, has been used for mapping surface deformation for many years [14–18], because it is capable of obtaining accurate measurements of surface deformation rates over large areas and at high spatial and temporal resolutions. TS-InSAR is also much more cost-effective than conventional ground-based surveying techniques [19–24]. Subsidence events in Guangzhou and Foshan between 2006 and 2011 have been well studied using TS-InSAR techniques. Zhao et al. [25] utilised 10 C-band ENVISAT ASAR images acquired between 2007 and 2008 to measure the subsidence in Guangzhou, and showed that the maximum subsidence rate was −26 mm/year. Wang, et al. [26] demonstrated that most of the region in Guangzhou and Foshan was stable between 2006 and 2009 based on the TS-InSAR result from 19 ENVISAT ASAR images. Zhao, Lin, and Jiang [25] suggested that the subsidence was caused by brittle hydrogeological failures and underground excavations. Chen, Lin, Zhang, and Lu [6] analysed the ENVISAT ASAR data acquired between 2006 and 2010 and concluded that anthropogenic activities, such as subway construction and groundwater extraction, should be primarily responsible for the local subsidence. Wanget al. [5] conducted a study using both ENVISAT ASAR and L-band ALOS PALSAR data and found that the newly excavated subway tunnels (Lines 2, 3, 6 and GuangFo) correspond to areas subsiding at an average rate of more than −8 mm/year between 2007 and 2011. They suggested that the maximum subsidence along the GuangFo line ranged from −5.2 mm to −23.6 mm and its ground loss ratio ranged from 1.5% to 8.7% between 2008 and 2011. These studies have provided information about the patterns of subsidence that are useful to improve the urban planning in Guangzhou. This study aims to investigate the deformation in Guangzhou and Foshan between 2011 and 2017 using X-band COSMO-SkyMed (CSK) SAR data, which can provide much higher resolution than the C-band Envisat and L-band ALOS satellites previously used in the region. More importantly, the previous studies focused on the subsidence period between 2006 and 2011, the comprehensive subsidence phenomenon at Guangzhou and Foshan is not known after 2011. It is worth noting that significant developments in the subway system in Foshan and Guangzhou have been implemented Remote Sens. 2018, 10, 813 3 of 23

Remote Sens. 2018, 10, x FOR PEER REVIEW 3 of 23 since 2011. Foshan and Guangzhou have up to 10 metro lines covering a total length of 309 km and 167 stations asas ofof AugustAugust 2017, 2017, which which is is a a large large jump jump from from the the length length of 250.8of 250.8 km km and and 153 153 stations stations at the at theend end of 2011. of 2011. The subsidence The subsidence is one aspectis oneconnected aspect connected to the rapid to urbanthe rapid development urban development in Guangzhou in Guangzhouand Foshan, and which Foshan, may leadwhich to may serious lead economic to serious loss economic and threaten loss and human threaten life. human Hence, life. up-to-date Hence, up-to-datesubsidence informationsubsidence information is very important is very because importan it is usefult because for assessing it is useful the riskfor assessing of subsidence-related the risk of subsidence-relatedincidents that may occurincidents in the that near may future. occur in the near future.

Figure 1. ((aa)) SRTM SRTM (Shuttle (Shuttle Radar Radar Topography Topography Mission) Mission) digital digital elevation elevation model model of of the the study study area. area. The white colour represents the flood-prone flood-prone area (elevation lower than than 1 1 m); and ( b)) the 10 subway lineslines in Guangzhou and Foshan, and the selected areas for detailed analysis.

A numbernumber of of researchers researchers have have already already demonstrated demonstrated that the up-to-datethat the up-to-date displacement displacement information informationobtained from obtained SAR data from can be SAR useful data for can detecting be useful the rapidly for detecting developing the surface rapidly deformations developing such surface as in deformationsareas subject to such collapse. as in Nof,areas et subject al. [27] to demonstrated collapse. Nof, that et CSK al. [27] data demonstrated can be used to that detect CSK the data precursory can be usedsubsidence to detect occurring the precursory a few months subsidence before sinkholeoccurring collapse a few inmonths Israel. beforeChang sinkho and Hanssenle collapse [28] inanalysed Israel. Changthe SAR and data Hanssen between [28] 1992 analysed and 2011the SAR at a data shopping between mall 1992 in and Heerlen, 2011 at the a shopping Netherlands mall and in Heerlen, showed thea dramatic Netherlands increase and in showed vertical deformationa dramatic increase in the last in fewvertical years deformation preceding thein sinkholethe last few formation. years preceding the sinkhole formation. The authors then argued that early detection of sinkhole deformations potentially leading to collapses is possible using InSAR monitoring. Therefore, recent subsidence data at Guangzhou and Foshan is very important for assessing the risk of future sinkholes

Remote Sens. 2018, 10, 813 4 of 23

The authors then argued that early detection of sinkhole deformations potentially leading to collapses is possible using InSAR monitoring. Therefore, recent subsidence data at Guangzhou and Foshan is very important for assessing the risk of future sinkholes formation, in particular as a sinkhole formed in Foshan on 7 February 2018, which resulted in the deaths of eight people.

2. Study Area Guangzhou and Foshan are located in a humid subtropical climate zone controlled by the Asian monsoon, characterised by wet summers with high temperatures and high humidity and cool dry winters. The mean annual temperature is around 22–23 ◦C. The annual rainfall is about 1700 mm. The lengthy monsoon season, with average monthly rainfall between 166 and 284 mm, spans from April through September, during which the cities frequently experience floods. The landform of the study area is relatively flat (Figure1), with altitudes of most area less than 50 m. Guangzhou is located in the Guangdong Depression Belt of the South China fold system. There are more than 40 faults and 30 folds. The boundary of the tectonic depression is marked by two active faults, the Guang-Cong Fault and the Guang-San Fault [29]. The two faults have divided Guangzhou into three geological units: the Zengcheng Hilly area, the Guanghua Basin area, and the Southern Plain area. According to the Guangdong Geological Bureau reports [4,5,29], there are three geological formations, i.e., soft, loose deposits, buried karst, and other types of bedrock in this area. The first two geological units are subject to subsidence, thereby threatening the safety of the surrounding buildings. The bedrock consists of different types of granite [30] widespread throughout the Zengcheng Hilly area and the northern Guanghua Basin. The weathered granitic gneiss is very sensitive to moisture alternation and has complex geotechnical characteristics that create difficulties during construction and, hence, significantly limit the progress of the urban development. The northern and western portions of Guangzhou city are built over limestone terrains, where rock corrosion by fluid circulation with fracture formations and the karst caves develop [4]. A high rate of annual rainfall can favour ground collapse in the karst areas. The collapse of karst-cave roofs is common during the rainy season due to enhanced dissolution when the underground water table rises and the water flow rate increases. The karst-cave roofs can also collapse because of the overburden following the rainy season when the underground water table drains.

3. Dataset and Methodology

3.1. Dataset In this study, we analysed 86 X-band CSK HIMAGE SAR images in stripmap mode collected from 20 May 2011 to 27 January 2017 over the study area. The CSK images are acquired in ascending orbits with spatial resolution of 3 m in both range and azimuth. Based on previous studies [4,26], most of the surface deformation in the study area are expected to be focused on relatively small areas with a radius of a few kilometres and with a magnitude of the deformation of less than ~20 mm/year. Another focus of this paper is to study the urban areas of Guangzhou and Foshan where the temporal decorrelation is not an issue compared to areas with dense vegetation cover. X-band SAR products, which are sensitive to small deformations, are, hence, the preferred choice rather than C-band and L-band SAR data. The reference image acquired on 9 June 2013 was chosen as it minimises the perpendicular and temporal baselines of the series of interferograms (Figure2). The perpendicular baseline varies between −1069 m and 1317 m and the time-period of observations spans 2079 days (approximately 5.7 years). Remote Sens. 2018, 10, 813 5 of 23 Remote Sens. 2018, 10, x FOR PEER REVIEW 5 of 23

Figure 2. CSK SAR dataset (orbit direction: ascending; imaging mode: HIMAGE; polarisation: HH; Figure 2. CSK SAR dataset (orbit direction: ascending; imaging mode: HIMAGE; polarisation: HH; incidence angle: 29.5°; look direction: right). The black solid circle at zero perpendicular baseline incidence angle: 29.5◦; look direction: right). The black solid circle at zero perpendicular baseline represents the master image. represents the master image.

3.2. Methodology 3.2. Methodology In this study, TS-InSAR analysis is conducted in order to derive the deformation times-series In this study, TS-InSAR analysis is conducted in order to derive the deformation times-series from the multi-temporal CSK SAR data. The TS-InSAR software, GEOS-ATSA (Advance Time-Series from the multi-temporal CSK SAR data. The TS-InSAR software, GEOS-ATSA (Advance Time-Series Analysis), is used. GEOS-ATSA, the extension of GEOS-PSI, is a software developed in the GEOS Analysis), is used. GEOS-ATSA, the extension of GEOS-PSI, is a software developed in the GEOS group at the University of New South Wales for InSAR time-series analysis which has been proven group at the University of New South Wales for InSAR time-series analysis which has been proven to to be an efficient and reliable tool for mapping land deformation [20,21,31]. The single master be an efficient and reliable tool for mapping land deformation [20,21,31]. The single master approach approach mode is chosen [15], in which 85 differential interferograms are generated with respect to mode is chosen [15], in which 85 differential interferograms are generated with respect to the selected the selected master image acquired on 9 June 2013. The differential interferograms are generated master image acquired on 9 June 2013. The differential interferograms are generated using the using the conventional two-pass differential InSAR method [32,33], where the topographic phase conventional two-pass differential InSAR method [32,33], where the topographic phase components components are removed using the one arc-second Shuttle Radar Topography Mission (SRTM) are removed using the one arc-second Shuttle Radar Topography Mission (SRTM) digital elevation digital elevation model (DEM) [34]. model (DEM) [34]. After generating the interferograms, the coherent distributed scatterer (DS) and persistent After generating the interferograms, the coherent distributed scatterer (DS) and persistent scatterer scatterer (PS) selection process is conducted. For DS selection, the DeSpecKS algorithm is used to (PS) selection process is conducted. For DS selection, the DeSpecKS algorithm is used to reduce the reduce the speckle and decorrelation noise over the statistically homogeneous areas [14]. The phase speckle and decorrelation noise over the statistically homogeneous areas [14]. The phase linking linking algorithm [35] is then applied to estimate the optimal phase value of the DS at each algorithm [35] is then applied to estimate the optimal phase value of the DS at each acquisition. acquisition. Only the pixels that pass the goodness-of-fit test (γ > 0.4) are retained for further Only the pixels that pass the goodness-of-fit test (γ > 0.4) are retained for further processing [14]. processing [14]. For PS selection, all pixels with amplitude dispersion index (DA) lower than 0.25 are For PS selection, all pixels with amplitude dispersion index (D ) lower than 0.25 are selected [15]. selected [15]. A A reference triangular network is then constructed based on the reliable DS (γ > 0.65) and PS A reference triangular network is then constructed based on the reliable DS (γ > 0.65) and PS (DA (D < 0.25) with a maximum arc length of 1.5 km. The modelled parameters (i.e., deformation and < A0.25) with a maximum arc length of 1.5 km. The modelled parameters (i.e., deformation and topographic error) are then computed using the approach discussed in Ng et al. [31]. Due to the large topographic error) are then computed using the approach discussed in Ng et al. [31]. Due to the large amount of data available, a more relaxing ensemble phase coherence threshold can be used to assess if amount of data available, a more relaxing ensemble phase coherence threshold can be used to assess the estimated parameters fit the modelled parameters [36]. The estimated data with ensemble phase if the estimated parameters fit the modelled parameters [36]. The estimated data with ensemble phase coherence higher than 0.6 are used for further analysis. The modelled parameters of the isolated DS coherence higher than 0.6 are used for further analysis. The modelled parameters of the isolated DS (γ > 0.4) and PS (DA < 0.25) are estimated from the parameters of the reference network using the (γ > 0.4) and PS (DA < 0.25) are estimated from the parameters of the reference network using the adaptive estimation strategy [31]. adaptive estimation strategy [31]. The TS-InSAR measurements are relative to a reference point/area, which is often chosen in a The TS-InSAR measurements are relative to a reference point/area, which is often chosen in a stable point/area. Unfortunately, ground truth measurements are not available to us to identify a stable point/area. Unfortunately, ground truth measurements are not available to us to identify a stable point. However, previous studies in this area [4,5,26] have shown that the subsidence in this stable point. However, previous studies in this area [4,5,26] have shown that the subsidence in this region is localised to small areas. Therefore, we assumed that the broad region is stable and we fixed region is localised to small areas. Therefore, we assumed that the broad region is stable and we fixed a virtual reference point rather than a fixed point/area. We initially selected our reference point in a virtual reference point rather than a fixed point/area. We initially selected our reference point in an an area that is believed to have no deformation (black diamond in Figure3)[ 4,5,26]. We obtained the area that is believed to have no deformation (black diamond in Figure 3) [4,5,26]. We obtained the displacement rate map for each pixel relative to the selected reference point, then we calculated the displacement rate map for each pixel relative to the selected reference point, then we calculated the mode of the displacement rates and we recalculated all our maps relative to this value. The mode is mode of the displacement rates and we recalculated all our maps relative to this value. The mode is used here instead of the mean or median in order to avoid the bias due to the local-scale subsidence

Remote Sens. 2018, 10, 813 6 of 23

Remote Sens. 2018, 10, x FOR PEER REVIEW 6 of 23 Remote Sens. 2018, 10, x FOR PEER REVIEW 6 of 23 used here instead of the mean or median in order to avoid the bias due to the local-scale subsidence (Figure(Figure 4 4).4).). If IfIf a aa reference referencereference point, point,point, or oror a a virtuala virtualvirtual point pointpoint from frfromom a small aa smallsmall area, area,area, is used, isis used,used, there therethere is a isis possibility aa possibilitypossibility that thatthat the thereferencethe referencereference point/area point/areapoint/area may maymay correspond correspondcorrespond to a previously toto aa prevpreviously unknowniously unknownunknown ground ground subsidenceground subsidencesubsidence zone. This zone.zone. potential ThisThis potentiallimitationpotential limitationlimitation is overcome isis overcome byovercome this method byby thisthis of method selectingmethod ofof a virtualselectingselecting reference aa virtualvirtual point referencereference based onpointpoint the based displacementbased onon thethe displacementratedisplacement distribution raterate in distributiondistribution the broad region. inin thethe broadbroad region.region.

FigureFigure 3.3. ((aa()a) CSK)CSK CSK subsidencesubsidence subsidence ratesrates rates (2011–2017)(2011–2017) (2011–2017) andand and ((bb)) ( theirbtheir) their standardstandard standard deviationsdeviations deviations forfor GuangzhouGuangzhou for Guangzhou andand FoshanandFoshan Foshan overlaidoverlaid overlaid onon thethe on averageaverage the average intensityintensity intensity image.image. image. ThThee levellinglevelling The levelling derivedderived derived meanmean meansubsidencesubsidence subsidence raterate atat rate 6363 benchmarksatbenchmarks 63 benchmarks betweenbetween between 20052005 andand 2005 20122012 and areare 2012 highlightedhighlighted are highlighted byby ththee blackbyblack the circles.circles. black circles.TheThe samesame The colourcolour same barbar colour isis usedused bar forisfor used thethe subsidencesubsidence for the subsidence raterate ofof bothboth rate measurements.measurements. of both measurements. TheThe negativenegative The signsign negative inin thethe figure signfigure in indicatesindicates the figure subsidence,subsidence, indicates whilesubsidence,while thethe positivepositive while sign thesign positive indicatesindicates sign uplift.uplift. indicates TheThe purplepurple uplift. lines Thelines purple representrepresent lines thethe represent districtdistrict boundary. theboundary. district The boundary.The blackblack diamondThediamond black signsign diamond indicatesindicates sign thethe indicates referencereference the pointspoints reference ususeded points toto comparecompare used to withwith compare thethe levellinglevelling with the andand levelling ENVISATENVISAT and measurements.ENVISATmeasurements. measurements.

((aa)) ((bb))

FigureFigure 4.4. TheThe distributiondistribution ofof ((aa)) thethe InSAR-derivedInSAR-derived subsidencesubsidence raterate andand ((bb)) thethe subsidencesubsidence raterate differenceFigure 4. betweenThe distribution the CSK and of ( athe) the levelling InSAR-derived measurements. subsidence rate and (b) the subsidence rate difference between the CSK andand thethe levellinglevelling measurements.measurements.

AA hybridhybrid atmosphericatmospheric modelmodel isis assumedassumed here,here, wherebywhereby thethe atmosphericatmospheric artefactsartefacts areare assumedassumed A hybrid atmospheric model is assumed here, whereby the atmospheric artefacts are assumed toto bebe mademade upup ofof twotwo components:components: topography-relatedtopography-related andand non-topography-relatednon-topography-related artefacts.artefacts. TheseThese to be made up of two components: topography-related and non-topography-related artefacts. twotwo componentscomponents areare estimatedestimated separatelyseparately basedbased onon ththee approachapproach usedused inin NgNg etet al.al. [37].[37]. Finally,Finally, thethe These two components are estimated separately based on the approach used in Ng et al. [37]. Finally, non-linearnon-linear displacementdisplacement componentscomponents ofof thethe DSDS anandd PSPS areare estimatedestimated usingusing thethe residualresidual phasephase afterafter the non-linear displacement components of the DS and PS are estimated using the residual phase thethe correctionscorrections ofof modelledmodelled parameters,parameters, atmospheriatmosphericc signals,signals, andand orbitorbit signals.signals. TheThe displacementdisplacement after the corrections of modelled parameters, atmospheric signals, and orbit signals. The displacement time-seriestime-series areare derivedderived byby combiningcombining thethe linearlinear displacementdisplacement componentcomponent andand thethe non-linearnon-linear displacementdisplacement component.component. DisplacementsDisplacements areare measuredmeasured byby SARSAR alongalong thethe raradardar line-of-sightline-of-sight (LoS)(LoS) direction,direction, therefore,therefore, measurementsmeasurements fromfrom multiplemultiple viewingviewing geometriesgeometries areare neededneeded toto derivederive thethe displacementsdisplacements inin vertical,vertical, easting,easting, andand northingnorthing directionsdirections [23,38].[23,38]. However,However, sincesince onlyonly thethe imagesimages fromfrom thethe CSKCSK ascendingascending

Remote Sens. 2018, 10, 813 7 of 23 time-series are derived by combining the linear displacement component and the non-linear displacement component. Displacements are measured by SAR along the radar line-of-sight (LoS) direction, therefore, measurements from multiple viewing geometries are needed to derive the displacements in vertical, easting, and northing directions [23,38]. However, since only the images from the CSK ascending orbit are used in this paper, and given that the dominant deformation in Guangzhou and Foshan is expected to be in the vertical direction [4,5], the displacement observed in the LoS direction is directly projected onto vertical direction with the assumption of zero horizontal displacements.

4. InSAR Results The subsidence rate map generated using CSK data is shown in Figure3. The total number of pixels (i.e., DS and PS pixels) is 3,772,305, corresponding to a pixel density of approximately 2441 pixels/km2. The distribution of subsidence displacement rates with a mean of −0.4 mm/year and standard deviation of 1.8 mm/year is shown in Figure4a. The subsidence rates range from −35 mm/year (subsidence) to 10 mm/year (uplift). The subsidence areas are mainly found in the south and southwest directions, where no clear uplift is observed. The subsidence rate at approximately 99.7% of the measurement points is between −15 mm/year to 15 mm/year. The pixels are evenly distributed (~3800 points/km2) in the central part of the map mainly because coherence is kept in the dense urban areas. Relatively sparse pixels (~2150 points/km2) are obtained in the northern and southern parts of the study area, because of vegetation cover outside the main urban areas, causing some decorrelation. The levelling data obtained between 2005 and 2012 are compared to the CSK-derived subsidence rates to investigate whether the assumption that most areas in the region are stable in time is correct. The levelling data from [26] is used for the comparison. The levelling data was collected in three campaigns with 105, 8 and 113 surveying benchmarks in 2005, 2009 and 2012, respectively, within the CSK coverage. The subsidence rate at each benchmark is computed with relative to the earlier campaign. The levelling data show that most of the area is stable between 2005 and 2012 with a mean subsidence rate of 0.6 mm/year and a standard deviation of 1.7 mm/year. Although the temporal overlap between the measurements is not perfect, it can be expected that most areas that were stable between 2005 and 2012 would remain stable between 2011 and 2017, assuming that no sudden deformations started since 2012 and that the two measurements agree well with each other. A quantitative analysis of the differences between subsidence rates obtained from both measurements is performed. It is worth noting that the TS-InSAR measurement points (MPs) and levelling benchmarks are often at different locations. In order to match the identified MPs and the benchmarks, the following procedure is applied: (1) a grid with spatial resolution of 30 m × 30 m is created; (2) the location of the MPs corresponding to the grid is assigned; (3) the location of the benchmarks corresponding to the grid is identified; (4) multiple MPs within the same grid cell are assumed to be representative of the same deformation signal, hence, the displacement values within each cell are calculated by averaging all the MPs corresponding to the grid cell; (5) common pixels between the measurements are identified; and (6) a reference point is selected from one of the common pixels (see Figure3) and the relative subsidence rate at each common pixel with respect to the reference point is calculated. There are 63 benchmarks considered as common pixels after the above procedure, while the remaining levelling benchmarks are either located near the edge of the CSK boundary or in the area with low coherence (i.e., no measurement points nearby). The subsidence rate at the 63 levelling benchmarks from [26] is overlaid on the CSK-derived subsidence rate map (circle with black outline in Figure3). The histogram of the subsidence rate difference between the two measurements is shown in Figure4b. It can be seen that the two sets of measurements are in good agreement with a mean difference of 0.2 mm/year and standard deviation of 1.7 mm/year. Two separate TS-InSAR analyses are conducted using the CSK images acquired between 2011 and 2013 (45 images), and those acquired from 2013 to 2017 (42 images). The two results are compared Remote Sens. 2018, 10, 813 8 of 23 to investigate the temporal behaviour of the subsidence in Guangzhou and Foshan. The subsidence rate maps for the first and second sets are shown in Figure5. The results show that the displacement pattern and magnitude of the three displacement maps (2011–2017, 2011–2013, and 2013–2017) are in broad agreement. However, a number of areas, which appears to deform at different rates, are observed, including areas such as the centre and southern part of , the western part of Chancheng district, the northern part of , and the southwest part of Haizhu. For example, the southern part of Nanhai district at Foshan appears to be deforming in Figure5a, but it is relatively stable in Figure5b. Figure6 shows the deformation time series of a point in this area. RapidRemote Sens. deformation, 2018, 10, x FOR overPEER REVIEW−11.3 mm/year, occurred between 2011 and mid-2012,8 butof 23 waned after mid-2012, suggesting that such an area is not affected by stable, linear subsidence (see discussion is relatively stable in Figure 5b. Figure 6 shows the deformation time series of a point in this area. for furtherRapid detail). deformation, Interestingly, over −11.3 themm/year, temporal occurred variation between of2011 displacement and mid-2012, ratesbut waned observed after mid- in regions characterised2012, suggesting by subsidence that such may an be area the is result not affected of the by rapid stable, urban linear developments subsidence (see and discussion sudden, for episodic changesfurther in the detail). groundwater Interestingly, level. the temporal variation of displacement rates observed in regions Thecharacterised CSK measurements by subsidence are may compared be the result to of the the ENVISAT rapid urban measurements, developments and which sudden, were episodic generated based onchanges 19 ENVISAT in the groundwater ASAR images level. acquired between 2006 and 2009 [26]. Since the spatial resolution The CSK measurements are compared to the ENVISAT measurements, which were generated of CSK and ENVISAT data is different, the CSK result is resampled at the same grid spacing as the based on 19 ENVISAT ASAR images acquired between 2006 and 2009 [26]. Since the spatial resolution ENVISATof CSK result and for ENVISAT comparison. data is different, The comparison the CSK result procedure is resampled here at isthe similar same grid to spacing the approach as the used for the levellingENVISAT result data. for The comparison. reference The point comparison is selected procedure in the here same is similar area (Figureto the approach3), but used a grid for with a spatial resolutionthe levelling ofdata. 90 The m × reference90 m is point used is instead.selected in There the same are area 106,511 (Figure common 3), but a grid points withafter a spatial the above procedure.resolution Figure of7 90 shows m × 90 am broad is used agreementinstead. There between are 106,511 the common CSK and points ENVISAT after the above subsidence procedure. rate maps Figure 7 shows a broad agreement between the CSK and ENVISAT subsidence rate maps with a mean with a mean subsidence difference of 0.1 mm/year and standard deviation of 1.8 mm/year between subsidence difference of 0.1 mm/year and standard deviation of 1.8 mm/year between the two the twomeasurements. measurements.

Figure 5. CSK subsidence rate maps from (a) 2011–2013 and (b) 2013–2017 for Guangzhou and Foshan Figure 5. CSK subsidence rate maps from (a) 2011–2013 and (b) 2013–2017 for Guangzhou and Foshan overlaid on the average intensity image; (c) The difference in subsidence rate between (a) and (b). The overlaidblue on colour the average represents intensity increased image; in subsidence, (c) The and difference the red colour in subsidenceindicates the subsidence rate between has eased. (a) and (b). The blueThe colour red rectangle represents highlighted increased one of in the subsidence, areas with la andrge difference the red colourin subsiden indicatesce rate as the an subsidenceexample; has eased. Theand ( redd) district rectangle information highlighted overlaid one on the of CSK the areasaverage with intensity large map. difference in subsidence rate as an example; and (d) district information overlaid on the CSK average intensity map.

Remote Sens. 2018, 10, 813 9 of 23 Remote Sens. 2018, 10, x FOR PEER REVIEW 9 of 23 Remote Sens. 2018, 10, x FOR PEER REVIEW 9 of 23

Figure 6. InSAR-measured displacement series at the area in Nanhai district. FigureFigure 6.6. InSAR-measuredInSAR-measured displacementdisplacement seriesseries atat thethe areaarea inin NanhaiNanhai district. district.

Figure 7. (a) ENVISAT ASAR subsidence rate map (2006–2009) for Guangzhou and Foshan overlaid Figure 7. (a) ENVISAT ASAR subsidence rate map (2006–2009) for Guangzhou and Foshan overlaid onFigure the average 7. (a) ENVISAT intensity ASAR image subsidence(generated rateusing map the (2006–2009)TS-InSAR results for Guangzhou from Wang, and Yu, Foshan and Jiang overlaid [26]); on the average intensity image (generated using the TS-InSAR results from Wang, Yu, and Jiang [26]); andon the (b) average subsidence intensity rate difference image (generated between using the CSK the and TS-InSAR the ENVISAT results frommeasurements Wang, Yu, at and the Jiang common [26]); and (b) subsidence rate difference between the CSK and the ENVISAT measurements at the common pixels.and (b ) subsidence rate difference between the CSK and the ENVISAT measurements at the commonpixels. pixels. 5. Discussion 5.5. DiscussionDiscussion 5.1. General Land Displacement Pattern and Evolution 5.1.5.1. GeneralGeneral LandLand DisplacementDisplacement PatternPattern andand EvolutionEvolution The TS-InSAR results show similar patterns in Figures 3 and 5, indicating that most deforming TheThe TS-InSARTS-InSAR resultsresults showshow similarsimilar patternspatterns inin FiguresFigures3 3and and5, 5, indicating indicating that that most most deforming deforming areas are experiencing continuous subsidence over the six-year period from 2011 to 2017. Moreover, areasareas areare experiencingexperiencing continuouscontinuous subsidencesubsidence overover thethe six-yearsix-year periodperiod fromfrom 20112011 toto 2017.2017. Moreover, the general agreement between the CSK subsidence rate map (2011–2017) and ENVISAT ASAR thethe generalgeneral agreementagreement betweenbetween thethe CSKCSK subsidencesubsidence raterate mapmap (2011–2017)(2011–2017) andand ENVISATENVISAT ASARASAR subsidence rate map (2006–2009) also indicate that most deformations have been ongoing at least subsidencesubsidence rate rate map map (2006–2009) (2006–2009) also also indicate indicate that that most most deformations deformations have have been been ongoing ongoing at least at sinceleast since 2006. Subsidence rates from different time periods are similar in most of these actively 2006.since Subsidence2006. Subsidence rates from rates different from different time periods time areperiods similar are in similar most of in these most actively of these deforming actively deforming areas, suggesting that many of the areas are deforming at approximately constant rates. areas,deforming suggesting areas, thatsuggesting many of that the many areas of are the deforming areas are at deforming approximately at approximately constant rates. constant rates. Apart from the two regional scale land zones in the south and south-west part of the study area, ApartApart fromfrom thethe twotwo regionalregional scalescale landland zoneszones inin thethe southsouth andand south-westsouth-west partpart ofof thethe studystudy area, most deformation phenomena observed in this study are local. The deformation zones identified by mostmost deformationdeformation phenomena observed observed in in this this study study are are local. local. The The deformation deformation zones zones identified identified by this study are mainly in the western part of , the southern part of , and this study are mainly in the western part of Panyu district, the southern part of Haizhu district, and theby thiswestern study part are of mainly Liwan in district the western in Guangzhou, part of Panyu and the district, western the part southern of Chancheng part ofHaizhu district,district, Beijiao the western part of in Guangzhou, and the western part of Chancheng district, Beijiao townand the (northern western part part ofof Shunde Liwan district), district inand Guangzhou, the eastern part and of the Nanhai western district part ofin ChanchengFoshan (see Figures district, town (northern part of Shunde district), and the eastern part of Nanhai district in Foshan (see Figures 1Beijiao and 3). town (northern part of Shunde district), and the eastern part of Nanhai district in Foshan (see1 and Figures 3). 1 and3).

Remote Sens. 2018, 10, 813 10 of 23

5.2. Groundwater Subsidence

AccordingRemote Sens. 2018 to, Xu10, x[ FOR39], PEER there REVIEW are over 6000 groundwater wells in Foshan, and many of them 10 of extract23 waters from shallow aquifers, at depths of only ~5 m from the surface. Moreover, illegal groundwater extractions5.2. Groundwater are very Subsidence common in industrial areas [39]. A similar scenario is expected in Guangzhou. Therefore,According groundwater to Xu extraction[39], there isare expected over 6000 to groundwater be one of the wells main in causes Foshan, of and subsidence many of inthem Foshan and Guangzhou.extract waters from shallow aquifers, at depths of only ~5 m from the surface. Moreover, illegal Thegroundwater groundwater extractions subsidence are very phenomenacommon in industrial found in areas Guangzhou [39]. A similar and Foshanscenario areis expected very different in fromGuangzhou. those identified Therefore, in groundwater other large extraction cities such is expe ascted Beijing to be one (China) of the main and causes Bandung of subsidence (Indonesia). in Foshan and Guangzhou. Groundwater over-extraction in those cities has led to subsidence over large spatial scales (tens of The groundwater subsidence phenomena found in Guangzhou and Foshan are very different kilometres) [19,21,31] while many, but focused, deformation patterns of approximately a few kilometres from those identified in other large cities such as Beijing (China) and Bandung (Indonesia). radiusGroundwater have been observedover-extraction in Guangzhou in those cities and has Foshan. led to subsidence The groundwater over large withdrawal spatial scales in (tens the Beijingof area iskilometres) mainly at [19,21,31] depths of while 20–200 many, m [40 but,41 ],focused, whereas deformation the groundwater patterns abstraction of approximately with depth a few ranges fromkilometres 40–60 m are radius common have been for domesticobserved in use Guangzhou [42]. Therefore, and Foshan. one explanationThe groundwater is that withdrawal the depth in of the exploitedthe Beijing aquifers area in is Guangzhoumainly at depths and of Foshan 20–200 arem [40,41], shallow, whereas hence, the the grou relativelyndwater narrowabstraction extent with of the deformationdepth ranges zones. from 40–60 m are common for domestic use [42]. Therefore, one explanation is that the Figuredepth of8 theshows exploited areas aquifers of subsidence in Guangzhou that and are Foshan possibly are causedshallow, by hence, groundwater the relatively exploitation. narrow The rapidlyextent of deforming the deformation region zones. in Figure 8a is located at Sanshan Yidong Industrial Zone at Nanhai Figure 8 shows areas of subsidence that are possibly caused by groundwater exploitation. The District where subsidence rates of up to −23 mm/year are observed. The industrial zone is dense with rapidly deforming region in Figure 8a is located at Sanshan Yidong Industrial Zone at Nanhai District factories of different types, including factories for moulds, toy, footwear, metalware, etc. These factories where subsidence rates of up to −23 mm/year are observed. The industrial zone is dense with factories typicallyof different require types, large including amount offactories water duringfor moulds the, productiontoy, footwear, cycle metalwar and ite, is,etc. therefore, These factories likely that the observedtypically require subsidence large isamount related of towa groundwaterter during the production pumping. cycle Figure and8b it shows is, therefore, subsidence likely that over an ecologicalthe observed farm at subsidence Chancheng is related District to withgroundwate deformationr pumping. rates Figure of up 8b toshows−30 subsidence mm/year over around an the farm.ecological Since groundwater farm at Chancheng is commonly District with used deformation for irrigation rates purposes, of up to −30 subsidence mm/year around from groundwaterthe farm. exploitationSince groundwater is likely occurring is commonly in this used area. for The irriga subsidencetion purposes, rate of subsiden Beijiao Townshipce from groundwater in the Northern Shundeexploitation District, is is likely a well-known occurring in signal this area. [26] The (Figure subsidence8c). The rate peak of Beijiao deformation Township rate in observedthe Northern here is −21 mm/year,Shunde District, which is a is well only-known slightly signal lower [26] than (Figure at the8c). otherThe peak two deformation places. Although rate observed the deformation here is −21 mm/year, which is only slightly lower than at the other two places. Although the deformation rate is lower, the spatial extent of the signal is the largest. Moreover, this region is a combination rate is lower, the spatial extent of the signal is the largest. Moreover, this region is a combination of of agricultural,agricultural, residential, residential, andand industrialindustrial developments. developments. Considering Considering many many vegetated vegetated areas areas in this in this townshiptownship where where no measurementno measurement points points could could bebe obtained, it it can can be be concluded concluded that that the themaximum maximum subsidencesubsidence rate rate could could be manybe many times time largers larger than than the the reported reported here.here. Possible causes causes of of the the subsidence subsidence in Beijiaoin includeBeijiao include groundwater groundwater extraction, extraction, lack lack of bearing of bearing capacity capacity in in the the buildings buildings foundations, and and the unstablethe unstable geological geological conditions. conditions.

Figure 8. Cont.

Remote Sens. 2018, 10, 813 11 of 23 Remote Sens. 2018, 10, x FOR PEER REVIEW 11 of 23

Figure 8. InSAR-measured displacement rate map between 2011 and 2017 at possible groundwater Figure 8. InSAR-measured displacement rate map between 2011 and 2017 at possible groundwater subsidence areas. (a) Sanshan Yidong Industrial Zone at Nanhai District; (b) Pengcheng new village subsidence areas. (a) Sanshan Yidong Industrial Zone at Nanhai District; (b) Pengcheng new ecological farm at Chancheng District; and (c) Beijiao Township in Northern Shunde District. These village ecological farm at Chancheng District; and (c) Beijiao Township in Northern Shunde District. areas are located at point 1, 2 and 3, respectively in Figure 1. The background images are the Sentinel- These2 true areas colour are located images acquired at point on 1, 21 November and 3, respectively 2017. in Figure1. The background images are the Sentinel-2 true colour images acquired on 1 November 2017. 5.3. Subsidence Observed along the Subway/Railway Lines 5.3. SubsidenceThe surface Observed subsidence along the caused Subway/Railway by tunnel Linesexcavation during subway construction has been Thereported surface between subsidence 2006 and 2011 caused [4,5,29]. by tunnelAlthough excavation some of these during subsidence subway phenomena construction can still has be been observed in the CSK results, many of the previously-identified subsidence zones do not appear in the reported between 2006 and 2011 [4,5,29]. Although some of these subsidence phenomena can still CSK result, especially for the Guangzhou (GZ) Subway , 5, 8, and APM (Zhujiang New Town be observed in the CSK results, many of the previously-identified subsidence zones do not appear Automated System), likely because most of the subway lines in Guangzhou were in thealready CSK result, completed especially and operational for the Guangzhou before 2011. (GZ) Subway Line 1, 5, 8, and APM (Zhujiang New Town AutomatedA number People of subway Mover lines System), have been likely constructe becaused between most of 2011 the and subway 2017, including lines in Guangzhou the GZ Line were already7 and completed the extensions and of operational the GuangFo before (GF) Line 2011. 1 and GZ . This study is, hence, focused on these Anew number subsidence of subway patterns. lines have been constructed between 2011 and 2017, including the GZ Line 7 and the extensionsAccording ofto thethe GuangFo news reported (GF) Line in March 1 and GZ2016, Line cracks 6. This in studythe buildings is, hence, with focused gaps onof these newapproximately subsidence patterns. 1 cm were found near the Nanzhou station at GZ [43]. Figure 9 shows that the Accordingsubsidence tonear the that news location reported (highlighted in March 2016,by the cracks red cross in the in buildings the figure) with is gapsapproximately of approximately −8 mm/year. Subsidence over −15 mm/year has also been observed surrounding the Guangzhou South 1 cm were found near the Nanzhou station at GZ Line 2 [43]. Figure9 shows that the subsidence near Railway station. As the construction works for these lines were completed long before the CSK that location (highlighted by the red cross in the figure) is approximately −8 mm/year. Subsidence acquisitions (especially for ), it is likely that the deformation is caused by the weight of the − over buildings,15 mm/year given hasthat alsothe deformation been observed areas surrounding are covered by the dense Guangzhou buildings. South Railway station. As the construction works for these lines were completed long before the CSK acquisitions (especially for line 3), it is likely that the deformation is caused by the weight of the buildings, given that the deformation areas are covered by dense buildings.

Remote Sens. 2018, 10, 813 12 of 23 Remote Sens. 2018, 10, x FOR PEER REVIEW 12 of 23

Figure 9. InSAR-measured displacement displacement rate rate map map between between 2011 2011 and and 2017 2017 over over ( (aa)) south south part part of of GZ Line 2; (b)) NanzhouNanzhou station;station; ( c(c)) near near Guangzhou Guangzhou South South Railway Railway station. station. The The blue blue line line represents represents the the GZ GZLine Line 2 and 2 theand red the cross red incross (b) representsin (b) represents the location the oflocation subsidence of subsidence incident reported. incident The reported. background The backgroundimages are the images Sentinel-2 are the true Sentinel-2 colour images true colour acquired images on 1acquired November on 1 2017. November 2017.

Both GZ Lines 5 and 6 pass through an island. GZ Line 6 crosses the island in the north–south Both GZ Lines 5 and 6 pass through an island. GZ Line 6 crosses the island in the north–south direction underground, while GZ Line 5 crosses the island through the elevated bridge in the east– direction underground, while GZ Line 5 crosses the island through the elevated bridge in the east–west west direction instead of using underground tunnel [44] (Figure 10). Large deformation with a peak direction instead of using underground tunnel [44] (Figure 10). Large deformation with a peak subsidence rate over −15 mm/year is observed at Jiaokou station (Figure 10b) after 2011. Since the GZ subsidence rate over −15 mm/year is observed at Jiaokou station (Figure 10b) after 2011. Since the GZ Line 5 between Jiaokou and Tanwei stations are connected by a bridge, the deformation is likely Line 5 between Jiaokou and Tanwei stations are connected by a bridge, the deformation is likely caused caused by concentrated urban development and heavy loading inducing sediment compaction (e.g., by concentrated urban development and heavy loading inducing sediment compaction (e.g., cyclic cyclic loading of trains). loading of trains).

Remote Sens. 2018, 10, 813 13 of 23 Remote Sens. 2018, 10, x FOR PEER REVIEW 13 of 23

FigureFigure 10. 10.InSAR-measured InSAR-measured displacement displacement raterate mapmap (2011–2017)(2011–2017) of of ( (aa)) GZ GZ Lines Lines 2, 2, 5, 5, 6, 6, and and GuangFo GuangFo (GF)(GF) Line Line 1; (1;b )( Jiaokoub) Jiaokou and and Tanwei Tanwei stations; stations; and and ( c(c)) Hesha Hesha station.station. The blue, blue, black, black, and and purple purple lines lines representrepresent GZ GZ Lines Lines 2 (north 2 (north part), part), 5, 5, and and 6, 6, respectively. respectively. TheThe redred line represents the the GF GF Line Line 1 1(east (east part). part).

We also analysed an area with varying displacement rates between 2011 and 2017 (highlighted inWe Figure also 5). analysed The region an areaof interest withvarying is located displacement near the Leigang rates station between along 2011 the and GuangFo 2017 (highlighted subway line in Figure1 (GF5). Line The 1), region where of construction interest is located works nearended the in LeigangApril 2010. station Land along settlement the GuangFo phenomenon subway has linealso 1 (GFbeen Line observed 1), where near construction the Leigang works station ended(see Figure in Aprils 6 and 2010. 11c). Land The undergro settlementund phenomenon excavation work has for also beenthe observed first stage near of GF the Line Leigang 1 ended station in April (see 2010, Figures but th6 eand land 11 nearc). The the undergroundstation did not excavationbecome stable work until for theafter first mid stage 2012 of GF(Figure Line 6), 1 suggesting ended in April that the 2010, settlemen but thet duration land near at this the area station is roughly did not 1.8 become years. stable until afterA large mid 2012subsidence (Figure zone6), suggesting is also observed that the betw settlementeen the durationDongping at and this Xincheng area is roughly Dong stations, 1.8 years. theA last large two subsidence stations of zonethe GF is Line also 1 observed extension between (Figure 11). the The Dongping land displace and Xinchengment time Dongseries stations,for the thesubsidence last two stations zone at ofthe the west GF of Line Xincheng 1 extension Dong station (Figure is 11 shown). The in land Figure displacement 12. It can be timeseen seriesthat the for thedeformation subsidence zonepatterns at theconsist west of of 4 Xinchengstages: (1) Dong the land station is stable is shown until in mid-2012; Figure 12 .(2) It rapid can be land seen thatdeformation the deformation has been patterns observed consist from of late 4 stages: 2012 to (1) early the 2013; land (3) is stablethe land until becomes mid-2012; stable (2) again rapid until land deformationmid-2014; and has been(4) rapid observed land deformation from late 2012 has occurr to earlyed 2013;again (3)since the mid-2014. land becomes The displacement stable again time until mid-2014;series seems and (4)to match rapid landthe time deformation of the construction has occurred works again thatsince began mid-2014. in 2012, hence, The displacement the deformation time observed may be related to the construction work. series seems to match the time of the construction works that began in 2012, hence, the deformation In this study we report on one of the limitations of InSAR for mapping subway construction- observed may be related to the construction work. induced subsidence. Figure 13 shows the land deformation along the GZ Line 7, whose construction In this study we report on one of the limitations of InSAR for mapping subway began in April 2013 and ended in February 2016. As shown in Figure 13, very limited measurement construction-induced subsidence. Figure 13 shows the land deformation along the GZ Line 7, points can be obtained from the CSK dataset. This is because of the heavy development and whoseconstruction construction along beganthe GZ in Line April 7 at 2013 the andsame ended time of in underground February 2016. tunnel As excavation. shown in A Figure typical 13 , veryexample limited is measurement the Nancun Wanbo points canstation be obtained(Figure 13 fromb). It the can CSK be dataset.observed Thisthat isthere because are some of the heavymeasurement development points and with construction subsidence alongup to − the14 mm/year GZ Line 7surrounding at the same the time station, of underground but there is a tunnel lack excavation.of measurement A typical points example in the is thecentre Nancun of the Wanbo deformat stationion zones (Figure (i.e., 13 b).near It the can station). be observed This thatissue there is arefound some measurementfor most stations points along with the subsidenceGZ Line 7. up to −14 mm/year surrounding the station, but there is a lack of measurement points in the centre of the deformation zones (i.e., near the station). This issue is found for most stations along the GZ Line 7.

Remote Sens. 2018, 10, 813 14 of 23 Remote Sens. 2018, 10, x FOR PEER REVIEW 14 of 23 Remote Sens. 2018, 10, x FOR PEER REVIEW 14 of 23

FigureFigureFigure 11.11. InSAR-measuredInSAR-measuredInSAR-measured displacementdisplacement displacement raterate rate mapmap map betweenbetween between 20112011 2011 andand and 20172017 2017 overover over ((aa)) GFGF (a )LineLine GF 11 Line (west(west 1 (westpart);part); part);((bb)) thethe ( b sectionsection) the section fromfrom ZumiaoZumiao from Zumiao stationstation station toto KuiqiKuiqi to LuLu Kuiqi station;station; Lu station; ((cc)) thethe sectionsection (c) the from sectionfrom FinancialFinancial from Financial Hi-TechHi-Tech Hi-TechZoneZone stationstation Zone toto station LeigangLeigang to Leigang station;station; station; andand ((dd)) and thethe (sectionsectiond) the section fromfrom LongxiLongxi from Longxi statstationion station toto FinancialFinancial to Financial Hi-TechHi-Tech Hi-Tech ZoneZone Zonestation.station. station. TheThe purplepurple The purple lineline representsrepresents line represents thethe GFGF the LineLine GF 11 Line phphasease 1 phase 11 andand 1 thethe and orangeorange the orange lineline representsrepresents line represents thethe GFGF the lineline GF 11 linephasephase 1 phase 22 (i.e.,(i.e., 2 extensionextension (i.e., extension ofof phasephase of phase 1).1). 1).

Figure 12. InSAR-measured displacement series near Dongping and Xincheng Dong stations (point FigureFigure 12.12. InSAR-measuredInSAR-measured displacement displacement series series near near Dongping Dongping and and Xincheng Xincheng Dong Dong stations stations (point (point ‘O’ ‘O’ in Figure 11). in‘O’ Figure in Figure 11). 11).

Remote Sens. 2018, 10, 813 15 of 23 Remote Sens. 2018, 10, x FOR PEER REVIEW 15 of 23

FigureFigure 13. 13.InSAR-measured InSAR-measured displacement displacement rate rate mapmap (2011–2017)(2011–2017) of of ( (aa)) GZ GZ Line Line 7, 7, GZ GZ Line Line 7 extension 7 extension (under(under construction) construction) and and GF GF Line Line 3 3 (under (under construction); construction); ( b) Nancun Nancun Wanbo Wanbo station; station; and and (c ()c two) two subway subway lines under construction. The background images are the Sentinel-2 true colour images acquired on 1 lines under construction. The background images are the Sentinel-2 true colour images acquired on November 2017. The white boxes in (c) indicate the location of the profile lines B–B’ and C–C’. 1 November 2017. The white boxes in (c) indicate the location of the profile lines B–B’ and C–C’. 5.4. Identification of Potential Areas of Risk 5.4. Identification of Potential Areas of Risk The X-band CSK data, with much finer spatial resolution than C- and L-band satellite data, makesThe X-bandit possible CSK for data,high-resolution with much land finer subsidence spatial resolution monitoring than [22]. C-High-density and L-band measurement satellite data, makesdata, it obtained possible from for high-resolution the CSK data, landis very subsidence useful for monitoring studying the [22 deformation]. High-density of the measurement transport data,infrastructures, obtained from e.g.,the highway, CSKdata, road, israilway, veryuseful and subway. for studying The deformation the deformation information ofthe obtained transport is infrastructures,important to identify e.g., highway, the possible road, risk railway, areas. and subway. The deformation information obtained is importantA massive to identify sinkhole the possible of about risk 30 m areas. wide and 6 m deep, occurred on 7 February 2018 in Foshan [45]A and massive has been sinkhole analysed of about in this 30 mwork. wide The and characteristics 6 m deep, occurred of the subsidence on 7 February signal 2018 observed in Foshan may [45 ] andbe has useful been for analysed identifying in areas this work.at risk of The sinkhole characteristics collapse in of the the future. subsidence The sinkhole signal collapse observed occurred may be usefulnear forthe identifying Jihua Xi road areas and at Foshan risk of No. sinkhole 1 Ring collapse Road Bridge in the intersection, future. The sinkholeand caused collapse at least occurred eight nearcasualties, the Jihua three Xi roadlost and and nine Foshan injured No. people. 1 Ring It Road is likely Bridge that the intersection, sinkhole collapse and caused was caused at least by eight a casualties,water leak three in a lostconstruction and nine site injured of the people. subway It station is likely (GF that line the 2). sinkholeA broken collapse underground was caused pipe or by a a waterchange leak in in ground a construction water flow site paths of the may subway have been station the reason (GF line for 2).water A broken leaking undergroundinto the underground pipe or a changetunnel. in Figure ground 14 water shows flow the pathsfield investigation may have been phot theo at reason the sinkhole for water and leaking the displacement into the underground rate map tunnel.over the Figure region. 14 The shows longitudinal the field subsidence investigation profile photo (A–A’ at in the Figure sinkhole 14) near and the the location displacement of incident rate on Jihua Xi road is shown in Figure 15. It can be seen that the subsidence rate along the profile is over map over the region. The longitudinal subsidence profile (A–A’ in Figure 14) near the location of −8 mm/year. Interestingly, the subsidence rate increases rapidly east of the sinkhole location, where incident on Jihua Xi road is shown in Figure 15. It can be seen that the subsidence rate along the the peak subsidence rate is over −30 mm/year. The explanation for this pattern is uncertain, but it is profile is over −8 mm/year. Interestingly, the subsidence rate increases rapidly east of the sinkhole possibly due to the hinged or sealed failure of the shield tail (at the location of the incident). The location, where the peak subsidence rate is over −30 mm/year. The explanation for this pattern is InSAR-measured displacement series (2011–2017) near the subsidence incident is shown in Figure 16. uncertain,Continuous but subsidence it is possibly with due a rate to theof about hinged −9 mm/year or sealed has failure been ofobserved the shield since tail 2011, (at which the location is long of thebefore incident). the construction The InSAR-measured work for GF displacementline 2 started (i series.e., June (2011–2017) 2014). Therefore, near the the subsidencesubsidence observed incident is shownsince in2011 Figure at the 16 . Continuous Xi road is subsidence likely caused with by other a rate factors, of about e.g.,− surface9 mm/year loading. has Heavy been loading observed sinceof the 2011, ground which can is be long a factor before cont theributing construction to sinkhole work collapse for GF linetogether 2 started with (i.e.,damage June to 2014). the pavement Therefore, theand/or subsidence the pipes observed buried since underneath 2011 at [46]. the JinhuaMoreover, Xi road surface is likely load causedin a saturated by other ground factors, can e.g., push surface the loading.groundwater Heavy flow loading into an of underground the ground cantunnel be [47]. a factor The evolution contributing of land to deformation sinkhole collapse near the together time with damage to the pavement and/or the pipes buried underneath [46]. Moreover, surface load in a

Remote Sens. 2018, 10, 813 16 of 23 saturatedRemote ground Sens. 2018 can, 10, x push FOR PEER the REVIEW groundwater flow into an underground tunnel [47]. The evolution 16 of 23 of land deformationof the collapse near is useful the timeto improve of the the collapse understand is usefuling of to the improve causes of the the understandingsinkhole, but unfortunately of the causes of the sinkhole,the CSKbut data unfortunately acquired after 2017 the are CSK not data available acquired to us. after Nevertheless, 2017 are based not available on the observations to us. Nevertheless, from based onthe theFoshan observations sinkhole, it from is reasonable the Foshan to expect sinkhole, that other it is reasonablelocations with to similar expect conditions that other are locations at risk with similarof conditions sinkhole formation. are at risk of sinkhole formation.

Figure 14. (a) The field investigation photo for the subsidence incident; (b) the InSAR-measured Figure 14. (a) The field investigation photo for the subsidence incident; (b) the InSAR-measured displacement rate map (2011–2017) of GZ Line 2 (under construction); and (c) the field investigation photo displacementnear the location rate map of the (2011–2017) incident. The of GZwhite Line star 2represents (under construction);the location of the and Jihua (c) Xi the road field subsidence investigation photoincident. near the The location box with of white the broken incident. line in The (b) whiteindicates star the representslocation of the the profile location lines A–A’. of the Jihua Xi road subsidence incident. The box with white broken line in (b) indicates the location of the profile lines A–A’.

Figure 15. The longitudinal subsidence profile (A–A’) near the subsidence incident on Jihua Xi road (the white star in Figure 14). The blue square indicates the location of the incident. The red crosses represent the subsidence rate along Jihua Xi road. The blue crosses represent the subsidence rate at the Foshan No. 1 Ring Road Bridge. Figure 15. The longitudinal subsidence profile (A–A’) near the subsidence incident on Jihua Xi road (the white star in Figure 14). The blue square indicates the location of the incident. The red crosses represent the subsidence rate along Jihua Xi road. The blue crosses represent the subsidence rate at the Foshan No. 1 Ring Road Bridge.

1

Remote Sens. 2018, 10, 813 17 of 23 Remote Sens. 2018, 10, x FOR PEER REVIEW 17 of 23 Remote Sens. 2018, 10, x FOR PEER REVIEW 17 of 23

Figure 16. InSAR-measured displacement series near at the location of Jihua road subsidence incident FigureFigure 16. 16.InSAR-measured InSAR-measured displacement displacement series series near near at the at the location location of Jihua of Jihua road road subsidence subsidence incident incident (the white star in Figure 14). (the(the white white star star in Figure in Figure 14). 14). Several areas that are potentially at risk are identified based on the subsidence characteristic for SeveralSeveral areas areas that that are are potentially potentially at riskat risk are are identified identified based based on theon the subsidence subsidence characteristic characteristic for for GF Line 2. For example, the two subway lines, GZ Line 7 extension and GF Line 3, which pass through GFGF Line Line 2. For 2. For example, example, the the two two subway subway lines, lines, GZ GZ Line Line 7 extension 7 extension and and GF GF Line Line 3, which 3, which pass pass through through the Beijiao Township were under construction as the time the manuscript was prepared (construction thethe Beijiao Beijiao Township Township were were under under construction construction as theas th timee time the the manuscript manuscript was was prepared prepared (construction (construction began in mid to late 2016). It can be seen in Figure 13a that both subway lines pass through a number beganbegan in in mid mid to to late late 2016). 2016). It can can be be seen seen in in Figure Figure 13a 13 thata that both both subway subway lines lines pass passthrough through a number a of deformation zones where some of them are deforming with a rate of over −20 mm/year. The numberof deformation of deformation zones zones where where some some of them of them are aredeforming deforming with with a rate a rate of of over over −−2020 mm/year. mm/year. The sections of two subway lines that follow the National Highway Beijing Zhuhai line (highlighted by Thesections sections of of two two subway subway lines lines that that follow follow the the National National Highway Highway Beijing Beijing Zhuhai line (highlighted by the white arrow in Figure 13c) are experiencing rapid deformation. These are similar to the case of bythe white arrow in FigureFigure 1313c)c) areare experiencingexperiencing rapidrapid deformation.deformation. These are similarsimilar toto thethe casecase of GF Line 2, where continuous subsidence occurred before the subway construction started. Hence, of GFGF LineLine 2,2, where where continuous continuous subsidence subsidence occurred occurred before before the the subway subway construction construction started. started. Hence, Hence, surface loading from highway vehicles is expected to be one of the main factors causing subsidence. surfacesurface loading loading from from highway highway vehicles vehicles is expectedis expected to beto onebe one of theof the main main factors factors causing causing subsidence. subsidence. The profile lines in longitudinal (B–B’) and transverse directions (C–C’) near Nan Yuan road on the TheThe profile profile lines lines in longitudinalin longitudinal (B–B’) (B–B’) and and transverse transverse directions directions (C–C’) (C–C’) near near Nan Nan Yuan Yuan road road on on the the National Highway Beijing Zhuhai line (white boxes in Figure 13c) are shown in Figures 17 and 18. NationalNational Highway Highway Beijing Beijing Zhuhai Zhuhai line line (white (white boxes boxes in in Figure Figure 13 c)13c) are are shown shown in inFigures Figures 17 17and and 18 .18. The observed subsidence rate along the longitudinal direction is over −10 mm/year in most area with TheThe observed observed subsidence subsidence rate rate along along the longitudinalthe longitudinal direction direction is over is over−10 − mm/year10 mm/year in mostin most area area with with a peak subsidence approaching −30 mm/year. In addition, the area affected by subsidence along the a peaka peak subsidence subsidence approaching approaching−30 −30 mm/year. mm/year. In In addition, addition, the the area area affected affected by by subsidence subsidence along along the the transverse direction is roughly 200 m wide. Although similar subsidence characteristics were transversetransverse direction direction is roughly is roughly 200 m wide.200 m Although wide. Although similar subsidence similar subsidence characteristics characteristics were reported were reported between these subway lines, there are many more high-rise buildings near Nan Yuan road. betweenreported these between subway these lines, subway there are lines, many there more are high-rise many more buildings high-rise near buildings Nan Yuan near road. Nan Therefore, Yuan road. Therefore, greater loss is expected if a sinkhole would form in these areas. Continuous monitoring in greaterTherefore, loss is greater expected loss if ais sinkhole expected would if a sinkhole form in wo theseuld areas.form in Continuous these areas. monitoring Continuous in thismonitoring area is, in this area is, therefore, very important to minimise the risk of ground collapse during or after the therefore,this area very is, importanttherefore, very to minimise important the to risk minimise of ground the collapse risk of ground during orcollapse after the during underground or after the underground excavation work. excavationunderground work. excavation work.

Figure 17. Longitudinal subsidence profile near Nan Yuan road on the National Highway Beijing Figure 17. Longitudinal subsidence profile near Nan Yuan road on the National Highway Beijing ZhuhaiFigure 17.lineLongitudinal (B–B’ in Figure subsidence 13c). profile near Nan Yuan road on the National Highway Beijing Zhuhai line (B–B’ in Figure 13c). Zhuhai line (B–B’ in Figure 13c).

Remote Sens. 2018, 10, 813 18 of 23 RemoteRemote Sens. Sens. 2018 2018, 10,, 10x ,FOR x FOR PEER PEER REVIEW REVIEW 18 of 23 18 of 23

Figure 18. Transverse subsidence profile near Nan Yuan road on the National Highway Beijing FigureZhuhaiFigure 18. line 18.TransverseTransverse (C–C’ in Figuresubsidence subsidence 13c). profileprofile near near Nan Nan Yuan Yu roadan onroad the Nationalon the National Highway BeijingHighway Zhuhai Beijing Zhuhailine line (C–C’ (C–C’ in Figure in Figure 13c). 13c). 5.5. TS-InSAR Results in Different Land Use Types 5.5. TS-InSAR5.5. TS-InSARThe relationship Results Results in inDifferentbetween Different Landthe Land TS-InSARUse Use Types Types measurements and different land use types in GuangzhouThe Therelationship relationship is analysed between in this the work.the TS-InSAR TS-InSARThe land measurements use meas mapurements in andGuangzhou different and landCitydifferent useis shown types land inin GuangzhouFigureuse types 19. in The land use classification data is digitised from the 2015 Guangzhou land use map (published by Guangzhouis analysed is analysed in this work. in this The landwork. use The map land in Guangzhou use map in City Guangzhou is shown in City Figure is shown19. The in land Figure use 19. Guangzhou land resources and the Planning Committee). The TS-InSAR measurement points The classificationland use classification data is digitised data fromis digitised the 2015 fr Guangzhouom the 2015 land Guangzhou use map (publishedland use map by Guangzhou (published by corresponding to each land use types (and sub-types) are searched and identified. The statistics of Guangzhouland resources land andresources the Planning and the Committee). Planning TheCommittee). TS-InSAR measurementThe TS-InSAR points measurement corresponding points theto eachTS-InSAR land usemeasurements types (and sub-types)at different are land searched use types and (and identified. sub-types) The statisticsare computed of the (Table TS-InSAR 1). corresponding to each land use types (and sub-types) are searched and identified. The statistics of Althoughmeasurements slightly at larger different subsidence land use rates types are (and observed sub-types) in land are computeduse type such (Table as woodland,1). Although cropland, slightly the TS-InSAR measurements at different land use types (and sub-types) are computed (Table 1). andlarger grassland, subsidence the difference rates are observed between their in land subsiden use typece rates such and as woodland, those from cropland,other land and use grassland,types are Althoughsmallerthe difference slightly than 1 sigma betweenlarger (Table subsidence their 1). subsidence Therefore, rates ratesare we observed conclude and those inthat from land no other clearuse type landrelationship usesuch types as betweenwoodland, are smaller the cropland,land than andsubsidence 1grassland, sigma (Table and the1 the). difference Therefore, land use typesbetween we conclude exists their in that Guangzho subsiden no clearu,ce relationshipmainly rates andbecause betweenthose the from subsidence the other land subsidence landhere is use mainly types and are smalleratthe the landthan local use 1 scale. sigma types exists (Table in Guangzhou,1). Therefore, mainly we conclude because the that subsidence no clear here relationship is mainly at between the local scale.the land subsidence and the land use types exists in Guangzhou, mainly because the subsidence here is mainly at the local scale.

Figure 19. Land use map in the study area of Guangzhou City. Figure 19. Land use map in the study area of Guangzhou City.

Figure 19. Land use map in the study area of Guangzhou City.

Remote Sens. 2018, 10, 813 19 of 23

Table 1. Statistics of TS-InSAR measurements for different land use types.

Land Land Use PS Density DS Density Mean Subsidence Subsidence Rate Standard PS + DS PS * DS ** Area (km2) Use Type Sub-Type (point/km2) (point/km2) Rate (mm/year) Deviations (mm/year) Building 1,052,854 48,358 1,004,496 253.6 190.7 3961.7 −0.2 1.8 Low-rise building 402,511 18,611 383,900 115.7 160.8 3317.4 −0.5 2.1 High-rise building 650,343 29,747 620,596 137.8 215.8 4502.7 −0 1.5 Woodland 24,378 866 23,512 28.4 30.5 827.9 −0.8 2.7 Orchard 2088 86 2002 5.4 15.9 370.7 −1.6 2.8 Nursery land 21,400 752 20,648 21.7 34.7 952.3 −0.7 2.7 Mulberry farm 890 28 862 1.3 21.2 653.8 −1.5 3.9 Forest 78,847 3992 74,855 71.7 55.7 1043.5 −0.4 1.9 Bamboo 39,271 1928 37,343 14.5 132.7 2569.6 −0.7 2.1 Shrub 1026 44 982 1.4 31.2 697.3 −0.7 2.4 Tree Forest 38,550 2020 36,530 55.8 36.2 654.8 −0.1 1.7 Crop land 8949 518 8431 14.3 36.2 590.0 −1 2.9 Paddy field 560 8 552 1.8 4.5 307.7 −2 3.6 Dry land 8389 510 7879 12.5 40.8 630.5 −1 2.8 Grassland 23,992 661 23,331 12.4 53.2 1877.5 −1.4 3.6

* TS-InSAR measurement points (final product) with DA < 0.25 are considered as PS. ** DS points are the TS-InSAR measurement points (final product) that are not considered as PS. Remote Sens. 2018, 10, 813 20 of 23

The density of PS and DS points in the different land use types are reported in Table1. These values agree well with the theory, whereby the highest density of PS and DS correspond to areas with buildings, with 190.7 PS/km2 and 3961.7 DS/km2. The density of measurement points observed is slightly higher for high-rise than for low-rise buildings. This is because low-rise buildings are often located in the sparsely-urbanised areas (i.e., fewer buildings) with low population density. The land use types of woodland and cropland have the lowest density points because of crop harvesting and/or re-growth, leading to strong decorrelation. In particular, the paddy fields, a special type of cropland, and orchard fields, a type of woodland, have the lowest point density amongst all land use types. Dry land, another type of cropland, with extremely low moisture content in the soil, appears to have approximately 10 times higher density of PS and DS than the paddy field. It is observed that the land use types of forest and grassland have moderate point densities, likely because they are more stable in time compared to the woodland and cropland. However, very high deviation in point density has been observed over the forest. The point density of bamboo, with 132.7 PS/km2 and 2569.6 DS/km2, is just behind the building counterpart. On the other hand, the point densities of shrub and tree forest are at similar levels as the woodland and cropland. Most areas of the bamboo land use type in Guangzhou has a sparse to intermediate canopy with sparse understory. Since the bamboo does not have branches like other trees do, it is likely that bamboo trunk behaves as a corner reflector. On the other hand, a closed canopy and/or dense understory are expected for most areas in shrub and tree forests. Since X-band signal is unlikely to be able to penetrate through the canopy, therefore, strong temporal decorrelation (due to volume scattering) is expected at these land use types.

6. Conclusions We characterised the surface deformation in Guangzhou and Foshan using time-series analysis of CSK data acquired between May 2011 and January 2017. A total of 86 CSK images have been processed using the TS-InSAR analysis technique to explore the evolution of the surface displacements. The result shows that the displacement rates in these cities are between −35 mm/year to 10 mm/year. We analysed two independent TS-InSAR datasets, using the data acquired between 2011 and 2013, and the data from 2013 to 2017, respectively, to investigate the temporal behaviour of the land subsidence phenomena. The results indicate that linear trends of displacement are appropriate in most areas. The comparison with the ENVISAT and levelling data show that the displacement measurements generally agree with each other well, suggesting that most areas in the region are stable. A few regional scales’, and many local scales’, deformation signals have been identified in the cities. The deformation mainly consists of subsidence as a result of human activities, like excessive groundwater extraction and underground tunnel construction. A number of possible groundwater subsidence zones have been identified mostly in agricultural and industrial areas. The spatial extent of groundwater subsidence found in Guangzhou and Foshan are within a few kilometres, which is because most groundwater was extracted from shallow 5 km depth reservoirs. Many subsidence signals caused by underground excavations, as identified in previous studies have now become stable. However, several new subsidence areas have been identified along the GZ Line 7, the extension of the GF Line 1, and GZ Line 6 that were not identified by previous studies. One of the main limitations for using InSAR to measure the subsidence in areas of subway construction is due to the fact that heavy development and construction on the ground often occurs along the subway line during the same period of tunnelling construction. Hence, it can be difficult to obtain the comprehensive subsidence map because of the lack of measurement points, especially when the districts are developed following the construction of the subway line. A sinkhole collapse occurred on 7 February 2018 in Foshan. Potential areas at risk of sinkhole collapse have been identified based on the study of the subsidence signal preceding the collapse in Foshan. We find that that the subsidence rate along the highway nearby the sinkhole was over −8 mm/year. We argue that the main contribution to this subsidence is the heavy loading (e.g., highway vehicles) on the road rather than underground tunnel excavation. Several areas Remote Sens. 2018, 10, 813 21 of 23 that are potentially at risk of sinkhole collapse have been identified, but further investigation is necessary to improve the understanding of loading-induced subsidence, groundwater subsidence, and underground excavation induced subsidence in these areas to avoid ground collapse incidents during or after the underground construction work. The TS-InSAR measurement at different land use types in Guangzhou has been analysed. No clear relationship between the land subsidence and the land use types has been observed. Therefore, it is believed that the subsidence phenomena in Guangzhou are mainly localised. Moreover, quantitative evaluation of the TS-InSAR measurement point density at different land use types is conducted, showing that the highest density point is found for the land use type of buildings, followed by the grassland and forest with sparse canopy and, finally, the plantation and forest with dense canopy. The data obtained here provide useful information for helping future TS-InSAR studies with X-band data.

Author Contributions: A.H.-M.N. and H.W. conceived the research work; A.H.-M.N. wrote the first draft of the paper; A.H.-M.N. and H.W. analysed the data; Y.D., W.C. and K.Z. contributed to the experiment implementation and results interpretation; and A.H.-M.N., H.W., C.P., Z.D. and L.G. contributed to paper writing and revision. Funding: This research was funded by Natural Science Foundation of Guangdong Province (grant number 2015A030313489), Guangzhou Science and Technology Program (grant number 201510010062) and Guangdong University of Technology startup research grant (grant no. 50010102). Acknowledgments: The authors thank ASI for providing the COSMO-SkyMed data through an open call for science project (ID 453) and ESA for providing the Sentinel-2 data. Conflicts of Interest: The authors declare no conflict of interest.

References

1. Syvitski, J.P.M.; Kettner, A.J.; Overeem, I.; Hutton, E.W.H.; Hannon, M.T.; Brakenridge, G.R.; Day, J.; Vörösmarty, C.; Saito, Y.; Giosan, L.; et al. Sinking deltas due to human activities. Nat. Geosci. 2009, 2, 681–686. [CrossRef] 2. Bakr, M. Influence of groundwater management on land subsidence in Deltas. Water Resour. Manag. 2015, 29, 1541–1555. [CrossRef] 3. Wang, H.; Wright, T.J.; Yu, Y.; Lin, H.; Jiang, L.; Li, C.; Qiu, G. InSAR reveals coastal subsidence in the Pearl River Delta, China. Geophys. J. Int. 2012, 191, 1119–1128. [CrossRef] 4. Zhao, Q.; Lin, H.; Jiang, L.; Chen, F.; Cheng, S. A study of ground deformation in the Guangzhou urban area with Persistent Scatterer Interferometry. Sensors 2009, 9, 503–518. [CrossRef][PubMed] 5. Wang, H.; Feng, G.; Xu, B.; Yu, Y.; Li, Z.; Du, Y.; Zhu, J. Deriving spatio-temporal development of ground subsidence due to subway construction and operation in delta regions with PS-InSAR data: A case study in Guangzhou, China. Remote Sens. 2017, 9, 1004. [CrossRef] 6. Chen, F.; Lin, H.; Zhang, Y.; Lu, Z. Ground subsidence geo-hazards induced by rapid urbanization: Implications from InSAR observation and geological analysis. Nat. Hazards Earth Syst. Sci. 2012, 12, 935–942. [CrossRef] 7. Huang, H.; Chen, X.; Zhu, Z.; Xie, Y.; Liu, L.; Wang, X.; Wang, X.; Liu, K. The changing pattern of urban flooding in Guangzhou, China. Sci. Total Environ. 2018, 622–623, 394–401. [CrossRef][PubMed] 8. Lyu, H.-M.; Wang, G.-F.; Shen, J.; Lu, L.-H.; Wang, G.-Q. Analysis and GIS mapping of flooding hazards on 10 May 2016, Guangzhou, China. Water 2016, 8, 447. [CrossRef] 9. Strozzi, T.; Delaloye, R.; Poffet, D.; Hansmann, J.; Loew, S. Surface subsidence and uplift above a headrace tunnel in metamorphic basement rocks of the Swiss Alps as detected by satellite SAR interferometry. Remote Sens. Environ. 2011, 115, 1353–1360. [CrossRef] 10. Milillo, P.; Giardina, G.; DeJong, M.; Perissin, D.; Milillo, G. Multi-Temporal InSAR Structural Damage Assessment: The London Crossrail Case Study. Remote Sens. 2018, 10, 287. [CrossRef] 11. Robles, J.G.; Black, M.; Gomar, B.S. Correlation Study between In-Situ Auscultation and Satellite Interferometry for the Assessment of Nonlinear Ground Motion on Crosrail London; Crossrail Learning Legacy Report: London, UK, 2016. Remote Sens. 2018, 10, 813 22 of 23

12. Chaussard, E.; Milillo, P.; Burgmann, R.; Perissin, D.; Fielding, J.F.; Baker, B. Remote Sensing of Ground Deformation for Monitoring Groundwater Management Practices: Application to the Santa Clara Valley During the 2012–2015 California Drought. J. Geophys. Res. Solid Earth 2017, 122, 8566–8582. [CrossRef] 13. Bonì, R.; Cigna, F.; Bricker, S.; Meisina, C.; McCormack, H. Characterisation of hydraulic head changes and aquifer properties in the London Basin using Persistent Scatterer Interferometry ground motion data. J. Hydrol. 2016, 540, 835–849. [CrossRef] 14. Ferretti, A.; Fumagalli, A.; Novali, F.; Prati, C.; Rocca, F.; Rucci, A. A new algorithm for processing Interferometric Data-Stacks: SqueeSAR. IEEE Trans. Geosci. Remote Sens. 2011, 49, 3460–3470. [CrossRef] 15. Ferretti, A.; Prati, C.; Rocca, F. Permanent Scatterers in SAR interferometry. IEEE Trans. Geosci. Remote Sens. 2001, 39, 8–20. [CrossRef] 16. Ferretti, A.; Prati, C.; Rocca, F. Nonlinear subsidence rate estimation using Permanent Scatterers in Differential SAR Interferometry. IEEE Trans. Geosci. Remote Sens. 2000, 38, 2202–2212. [CrossRef] 17. Beradino, P.; Fornaro, G.; Lanari, R.; Sansosti, E. A new algorithm for surface deformation monitoring based on Small Baseline Differential SAR Interferograms. IEEE Trans. Geosci. Remote Sens. 2002, 40, 2375–2383. [CrossRef] 18. Kampes, B.M. Radar Interferometry: Persistent Scatterer Technique, 1st ed.; Springer: Dordrecht, The Netherland, 2006; p. 211. 19. Du, Z.; Ge, L.; Ng, A.H.-M.; Xiaojing, L.; Li, L. Mapping land subsidence over the eastern Beijing city using Satellite Radar Interferometry. Int. J. Digit. Earth 2017, 1–16. [CrossRef] 20. Ng, A.H.-M.; Ge, L.; Li, X. Assessments of land subsidence in the Gippsland Basin of Australia using ALOS PALSAR data. Remote Sens. Environ. 2015, 159, 86–101. [CrossRef] 21. Ge, L.; Ng, A.H.-M.; Li, X.; Abidin, H.Z.; Gumilar, I. Land subsidence characteristics of Bandung Basin as revealed by ENVISAT ASAR and ALOS PALSAR Interferometry. Remote Sens. Environ. 2014, 154, 46–60. [CrossRef] 22. Perissin, D.; Wang, Z.; Lin, H. Shanghai subway tunnels and highways monitoring through Cosmo-SkyMed Persistent Scatterers. ISPRS J. Photogramm. Remote Sens. 2012, 73, 58–67. [CrossRef] 23. Ng, A.H.-M.; Ge, L.; Zhang, K.; Li, X. Estimating horizontal and vertical movement due to underground mining using ALOS PALSAR. Eng. Geol. 2012, 143–144, 18–27. [CrossRef] 24. Chen, F.; Lin, H.; Zhou, W.; Hong, T.; Wang, G. Surface deformation detected by ALOS PALSAR Small Baseline SAR Interferometry over permafrost environment of Beiluhe section, Tibet Plateau, China. Remote Sens Environ. 2013, 138, 10–18. [CrossRef] 25. Zhao, Q.; Lin, H.; Jiang, L. Ground deformation monitoring in Pearl River Delta region with Stacking D-InSAR technique. In Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Monitoring and Assessment of Natural Resources and Environments; International Society for Optics and Photonics: Bellingham, WA, USA, 2008; Volume 7145. 26. Wang, H.; Yu, Y.-P.; Jiang, L.L. Monitoring land subsidence in Guangzhou and Foshan using InSAR. Sci. Surv. Mapp. 2014, 39, 66–71. 27. Nof, R.N.; Baer, G.; Ziv, A.; Raz, E.; Atzori, S.; Salvi, S. Sinkhole precursors along the Dead Sea, Israel, revealed by SAR interferometry. Geology 2013, 41, 1019–1022. [CrossRef] 28. Chang, L.; Hanssen, R.F. Detection of cavity migration and sinkhole risk using Radar Interferometric time series. Remote Sens. Environ. 2014, 147, 56–64. [CrossRef] 29. Ren, D.-J.; Shen, S.-L.; Cheng, W.-C.; Zhang, N.; Wang, Z.-F. Geological formation and geo-hazards during subway construction in Guangzhou. Environ. Earth Sci. 2016, 75, 934. [CrossRef] 30. Chen, M.; Shen, S.L.; Wu, H.N.; Wang, Z.F.; Horpibulsuk, S. Geotechnical characteristics of weathered granitic gneiss with geo-hazards investigation of pit excavation in Guangzhou, China. Bull. Eng. Geol. Environ. 2017, 76, 681–694. [CrossRef] 31. Ng, A.H.-M.; Ge, L.; Li, X.; Zhang, K. Monitoring ground deformation in Beijing, China with Persistent Scatterer SAR Interferometry. J. Geod. 2012, 86, 375–392. [CrossRef] 32. Massonnet, D.; Rossi, M.; Carmona, C.; Adragna, F.; Peltzer, G.; Feigl, K.; Rabaute, T. The displacement field of the Landers Earthquake mapped by Radar Interferometry. Nature 1993, 364, 138–142. [CrossRef] 33. Ge, L.; Ng, A.H.-M.; Wang, H.; Rizos, C. Crustal deformation in Australia measured by Satellite Radar Interferometry using ALOS/PALSAR imagery. J. Appl. Geod. 2009, 3, 47–53. [CrossRef] Remote Sens. 2018, 10, 813 23 of 23

34. Rodriguez, E.; Morris, C.S.; Belz, J.E.; Chapin, E.C.; Martin, J.M.; Daffer, W.; Hensley, S. An Assessment of the SRTM Topographic Products; Report No. JPL D-31639; Jet Propulsion Laboratory: Pasadena, CA, USA, 2005. 35. Guarnieri, A.M.; Tebaldini, S. On the exploitation of target statistics for SAR Interferometry applications. IEEE Trans. Geosci. Remote Sens. 2008, 46, 3436–3443. [CrossRef] 36. Colesanti, C.; Ferretti, A.; Novali, F.; Prati, C.; Rocca, F. SAR monitoring of progressive and seasonal ground deformation using the Permanent Scatterers technique. IEEE Trans. Geosci. Remote Sens. 2003, 41, 1685–1701. [CrossRef] 37. Ng, A.H.-M.; Ge, L.; Li, X.; Abidin, H.Z.; Andreas, H.; Zhang, K. Mapping land subsidence in the Jakarta city, Indonesia using Persistent Scatterer Interferometry (PSI) technique with ALOS PALSAR. Int. J. Appl. Earth Obs. Geoinf. 2012, 18, 232–242. [CrossRef] 38. Fialko, Y.; Simons, M.; Agnew, D. The complete (3-D) surface displacement field in the epicentral area of the 1999 Mw 7.1 Hector Mine Earthquake, California, from space geodetic observations. Geophys. Res. Lett. 2001, 28, 3063–3066. [CrossRef] 39. Xu, M. Foshan Groundwater Resources to be Thoroughly. Guangzhou Daily, 11 January 2006. (In Chinese) 40. Zhang, Y.; Gong, H.; Gu, Z.; Wang, R.; Li, X.; Zhao, W. Characterization of land subsidence induced by groundwater withdrawals in the plain of Beijing city, China. Hydrogeol. J. 2014, 22, 397–409. [CrossRef] 41. Zhu, L.; Gong, H.; Li, X.; Wang, R.; Chen, B.; Dai, Z.; Teatini, P. Land subsidence due to groundwater withdrawal in the Northern Beijing Plain, China. Eng. Geol. 2015, 193, 243–255. [CrossRef] 42. Wangsaatmaja, S.; Sutadian, A.D.; Prasetiati, M.N. A review of groundwater issues in the Bandung Basin, Indonesia: Management and recommendations. Int. Rev. Environ. Strateg. 2006, 6, 425–441. 43. Luo, H.; Li, Y. Residents are Worried about Frequent Cracks Found in Half of the Residential Buidlings in an Area of Haizhu. Guangzhou News Express, 30 March 2016. (In Chinese) 44. Liu, C. Engineering geologic problems of Line 5 and the countermeasures. Urban Mass Transit 2006, 7, 37–39. 45. AFP. Massive Sinkhole the Size of Two Basketball Courts Swallows Eight-Lane Road in a Chinese City, Leaving at Least Eight Dead. Daily Mail, 8 February 2018. 46. Gong, X.-N.; Sun, Z.-J.; Yu, J.-L. Analysis of displacement of adjacent buried pipeline caused by ground surcharge. Rock Soil Mech. 2015, 36, 305–310. 47. Prassetyo, S.H.; Gutierrez, M. Effect of surface loading on the hydro-mechanical response of a tunnel in saturated ground. Undergr. Sp. 2016, 1, 1–19. [CrossRef]

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).