Understanding Land Subsidence Along the Coastal Areas of Guangdong, China, by Analyzing Multi-Track Mtinsar Data

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Article Understanding Land Subsidence Along the Coastal Areas of Guangdong, China, by Analyzing Multi-Track MTInSAR Data

Yanan Du 1 , Guangcai Feng 2, Lin Liu 1,3,* , Haiqiang Fu 2, Xing Peng 4 and Debao Wen 1 1 School of Geographical Sciences, Center of GeoInformatics for Public Security, Guangzhou University, Guangzhou 510006, China; [email protected] (Y.D.); [email protected] (D.W.) 2 School of Geosciences and Info-Physics, Central South University, Changsha 410083, China; [email protected] (G.F.); [email protected] (H.F.) 3 Department of Geography, University of Cincinnati, Cincinnati, OH 45221-0131, USA 4 School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China; [email protected] * Correspondence: [email protected]; Tel.: +86-020-39366890

Received: 6 December 2019; Accepted: 12 January 2020; Published: 16 January 2020

Abstract: Coastal areas are usually densely populated, economically developed, ecologically dense, and subject to a phenomenon that is becoming increasingly serious, land subsidence. Land subsidence can accelerate the increase in relative sea level, lead to a series of potential hazards, and threaten the stability of the ecological environment and human lives. In this paper, we adopted two commonly used multi-temporal interferometric synthetic aperture radar (MTInSAR) techniques, Small baseline subset (SBAS) and Temporarily coherent point (TCP) InSAR, to monitor the land subsidence along the entire coastline of Guangdong Province. The long-wavelength L-band ALOS/PALSAR-1 dataset collected from 2007 to 2011 is used to generate the average deformation velocity and deformation time series. Linear subsidence rates over 150 mm/yr are observed in the Chaoshan Plain. The spatiotemporal characteristics are analyzed and then compared with land use and geology to infer potential causes of the land subsidence. The results show that (1) subsidence with notable rates (>20 mm/yr) mainly occurs in areas of aquaculture, followed by urban, agricultural, and forest areas, with percentages of 40.8%, 37.1%, 21.5%, and 0.6%, respectively; (2) subsidence is mainly concentrated in the compressible Holocene deposits, and clearly associated with the thickness of the deposits; and (3) groundwater exploitation for aquaculture and agricultural use outside city areas is probably the main cause of subsidence along these coastal areas.

Keywords: Land subsidence; MTInSAR; coastal areas; Guangdong; deformation time-series

1. Introduction Guangdong Province is located on the South China Sea coast and has the longest coastline of any province in China, approximately 4300 km. Along this coastline, several large agglomerations (e.g., the Pearl River Delta (PRD) and Chao Shan Plain (CSP)), harbors (Zhujiang, Zhanjiang, and Shantou), and a wide range of aquaculture and agricultural areas are clustered, controlling the primary economic activity and ecological environment. Over the past few decades, this coastline has experienced rapid growth in both population and economy. The PRD has undergone a shift from an agricultural-based economy to an industrial- and technological-based economy, making the PRD Asia’s fourth-largest economy [1,2]. To satisfy the needs of urban residents and export trade, an increasing amount of land is exploited through the expansion of aquaculture land use and reclamation of seashore. Among these exploitations, large-scale freshwater aquaculture has made Guangdong Province one of the

Remote Sens. 2020, 12, 299; doi:10.3390/rs12020299 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 299 2 of 20 top three aquaculture areas in China [3], which has contributed to the increasing aquaculture export. However, accumulated anthropogenic activities along the coastline have caused land subsidence, which is monitored by in situ and InSAR measurements [4–11]. As the majority of these measurements are focused on the PRD, deformation monitoring for the entire coastline of Guangdong Province is not available. Moreover, in addition to the direct damage caused by anthropogenic activities, continued coastal subsidence can also accelerate relative sea-level rise [12–14], groundwater salination [15], infrastructure damage [7]. Therefore, comprehensive monitoring over the whole Guangdong coastline is essential to help elucidate the spatial-temporal evolution of subsidence, and causal analysis is needed to uncover the mechanisms of subsidence. Doing so can potentially contribute to more effective prevention and mitigation of disasters and protection of the ecosystem and economy. Preliminary subsidence monitoring along the Guangdong coastal area is focused on several primary cities, such as Zhanjiang [6], and is based on in situ measurements (Global positioning system (GPS) and leveling). However, in situ measurements are costly when the surveying area is large and can provide only point measurements, which can hinder the interpretation of subsidence patterns due to low spatial coverage. In contrast, the InSAR technique, a widely used method for deformation monitoring, can provide deformation measurements at a meter-scale spatial resolution with millimeter accuracy [16–18]. During the evolution of InSAR algorithms, multi-temporal InSAR (MTInSAR) techniques have been developed and successfully applied in large-scale deformation time series monitoring [9,16,19–22] due to their superiority over differential InSAR (DInSAR) in the suppression of spatial and temporal decorrelation and atmospheric effects. By using the MTInSAR technique, a range of deformation monitoring results were generated and analyzed along the Guangdong coastal area, for example, land subsidence in Guangzhou [8,10,23], Shenzhen [9,11,24], Macau [5], and the Lianjiang Plain [7]. Although these studies have obtained 2D deformation, the interest areas are mainly concentrated over urban areas of the PRD, and these areas have steady backscatters. Areas outside the PRD are rarely monitored except for some in situ measurements. In addition to the limitation of coverage, these studies have mainly focused on illustrating the effectiveness of MTInSAR methods, and only simple qualitative analyses of land subsidence causes were presented. For example, land subsidence in Guangzhou and Shenzhen was caused by subway construction [8,25], and building damage in Macau and Lianjiang Plain was due to land reclamation and groundwater exploitation [5,7]. The majority of these land subsidence events were related to anthropogenic activities that occurred in urban areas, such as subway excavation and seashore reclamation. However, as mentioned above, several activities outside urban areas are in progress, such as the extensive exploitation of agricultural and aquaculture lands, which is a common cause of large land subsidence. For example, centimeter-scale subsidence rates caused by groundwater exploitation for agricultural use in Indonesia [26,27], Mexico [28,29], and the Mekong Delta [30] have been reported. Nevertheless, land subsidence in aquaculture-dominated areas, which is dominant the Guangdong coastline, has received far less attention than land subsidence in agricultural-dominated areas [31,32]. Moreover, quantitative analysis between subsidence and land use is rarely conducted except for some qualitative analysis based on the visual interpretation of optical images [26–28,33]. Hence, quantitative analysis of land subsidence along the Guangdong coastal area can help us to gain a better understanding of subsidence causes and provide guidance for land planning. To fill these research gaps, the following tasks were conducted: (1) Two MTInSAR processing techniques, SBAS-InSAR and TCPInSAR, were adopted to generate the deformation along the whole Guangdong coastal area, with long-wavelength L-band images. The relative accuracy of InSAR results was validated within the overlapped areas of adjacent tracks, and the precision was validated by the collected GPS measurements. (2) The mechanism of land subsidence was analyzed with a focus on the quantitative relationship between land use and geology. Remote Sens. 2020, 12, 299 3 of 20

2. Geological Setting and Datasets Remote Sens. 2020, 12, x FOR PEER REVIEW 3 of 20 2.1. Geological Setting GuangdongGuangdong is is bounded bounded by by the the South South China China Sea to Sea the to south; the thesouth; elevation the elevation of Guangdong of Guangdong decreases fromdecreases north from to south north (Figure to south1). (Figure In particular, 1). In particular, in the coastal in the areas, coastal the ar terraineas, the is terrain flat, with is flat, an averagewith an average elevation of 101.2 m and a maximum elevation of 1493 m. Several major rivers, such as the elevation of 101.2 m and a maximum elevation of 1493 m. Several major rivers, such as the Jianjiang Jianjiang River (JJR), Tanjiang River (TJR), Pearl River (PR), Hanjiang River (HJR), and Rongjiang River (JJR), Tanjiang River (TJR), Pearl River (PR), Hanjiang River (HJR), and Rongjiang River (RJR), flow River (RJR), flow across the coastal areas and merge into the sea (i.e., the cyan lines in Figure 2). Two across the coastal areas and merge into the sea (i.e., the cyan lines in Figure2). Two deltas, the PRD and deltas, the PRD and Chaoshan Plain (CSP), evolved from river valleys (the PR for the PRD; the HJR Chaoshan Plain (CSP), evolved from river valleys (the PR for the PRD; the HJR and RJR for the CSP) by and RJR for the CSP) by sediment alluviation and land reclamation over tens of thousands of years. sediment alluviation and land reclamation over tens of thousands of years. The coastal area has a humid The coastal area has a humid subtropical climate, accompanied by frequent typhoons and heavy subtropical climate, accompanied by frequent typhoons and heavy rainfall during July to October. rainfall during July to October. The Quaternary deposits are mainly distributed along the coastline and include four series: the The Quaternary deposits are mainly distributed along the coastline and include four series: the Lower Pleistocene (Q1), Middle Pleistocene (Q2), Upper Pleistocene (Q3) and Holocene (Q4). The Lower Pleistocene (Q1), Middle Pleistocene (Q2), Upper Pleistocene (Q3) and Holocene (Q4). The Holocene deposits are mainly concentrated in the PRD, CSP and Leizhou Peninsula (LZP), as marked Holocene deposits are mainly concentrated in the PRD, CSP and Leizhou Peninsula (LZP), as marked by the light yellow shading in Figure1. The sedimentary thickness of Q4 in the PRD ranges from by the light yellow shading in Figure 1. The sedimentary thickness of Q4 in the PRD ranges from 5 m 5 m to 60 m, as shown in Figure2[ 34]. The sedimentary thicknesses in LZP and CSP, ranging from to 60 m, as shown in Figure 2 [34]. The sedimentary thicknesses in LZP and CSP, ranging from 5 m 5to m 40 to m 40 and m 42 and m 42to 132 m to m, 132 respectively, m, respectively, are generate are generatedd from the from measurements the measurements of a series ofof aboreholes series of boreholes[35–39], which [35–39 are], which represented are represented by black hollow by black circles hollow in Figure circles 2. in Figure2.

FigureFigure 1. 1.Data Data coverage coverage andand geologygeology formationformation alongalong thethe coastline of Guangdong Province. ( (aa)) Blue Blue solidsolid and and red red dotted dotted rectangles rectangles representrepresent thethe collectedcollected syntheticsynthetic aperture radar (SAR) (SAR) images images and and LandsatLandsat optical optical images,images, respectively.respectively. TheThe blackblack starsstars represent the GPS stations used, used, and and the the gray gray lineslines represent represent faults. faults. ( b(b)) The The locationlocation ofof thethe studystudy area.area.

2.2.2.2. Datasets Datasets 2.2.1. SAR Datasets and Landsat Datasets 2.2.1. SAR Datasets and Landsat Datasets To generate the surface motions of the whole Guangdong coastal area, we collected 253 archived To generate the surface motions of the whole Guangdong coastal area, we collected 253 archived SAR image datasets acquired from 2007 to 2011 by L-band ALOS1/PALSAR satellites, including SAR image datasets acquired from 2007 to 2011 by L-band ALOS1/PALSAR satellites, including 16 adjacent orbits covering an area of approximately 49,000 km2 (represented by blue rectangles in Figure 1). Table 1 gives the imaging parameters of each orbit, including the number, time span, and systematic parameters. An average of 12 acquisitions per frame was used during the 3.5-year period.

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16 adjacent orbits covering an area of approximately 49,000 km2 (represented by blue rectangles in Figure1). Table1 gives the imaging parameters of each orbit, including the number, time span, and systematic parameters. An average of 12 acquisitions per frame was used during the 3.5-year period. In addition to the active remote sensing datasets, we also collected 8 optical Landsat images (represented by red dotted rectangles in Figure1, see Table2), aiming to generate land classiﬁcation maps. Although a 2-year time span between SAR images and Landsat images exists, however, the changing rate of the agricultural land, forest land, and aquaculture land are only about 8.29%, 2.09%, and 2.99% respectively, according to the Statistics Bureau of Guangdong Province, see Table S1 [40]. Moreover, Landsat 8 image has a superior spectral resolution compared with Landsat 5 and 7, which is launched on 2013 [41,42].

Table 1. SAR data and their imaging parameters.

Heading Incidence Angle Time Span Master Track_Frame Polarization Scenes (◦) (◦) (yyyymmdd) Image 452 HH 10.5 38.7 18 20070713–20110308 20091018 − 453_450 HH 10.6 38.7 5 20070614–20090201 20081217 − 453_460 HH 10.6 38.7 5 20070730–20090201 20081217 − 454 HH 10.5 38.7 13 20061229–20101009 20080101 − 455 HH 10.5 38.7 7 20070115–20090120 20080118 − 456 HH 10.5 38.7 15 20070201–20101228 20071220 − 457 HH 10.6 38.7 8 20070706–20100714 20091011 − 458 HH 10.6 38.7 14 20061205–20091213 20070723 − 459 HH 10.6 38.7 22 20061222–20110102 20090211 − 460_430 HH 10.6 38.7 21 20070711–20110119 20090716 − 460_440 HH 10.6 38.7 12 20070711–20110119 20080111 − 461 HH 10.6 38.7 19 20070125–20110205 20090802 − 462 HH 10.6 38.7 7 20061227–20101122 20080214 − 463 HH 10.6 38.7 8 20070113–20080718 20071016 − 464 HH 10.6 38.7 15 20070130–20101226 20090622 − 465 HH 10.6 38.7 9 20070216–20090106 20080104 − 466_410 HH 10.6 38.7 9 20070305–20100729 20080723 − 466_400 HH 10.7 38.7 13 20070305–20110129 20091211 − 467_390 HH 10.6 38.7 11 20061220–20100630 20071223 − 467_400 HH 10.6 38.7 11 20061220–20100630 20071223 − 467_410 HH 10.6 38.7 11 20061220–20100630 20071223 −

Table 2. Landsat 8 data and their parameters.

Acquisition Time Cloud Content Path Row (yyyymmdd) (%) 20131023 0.03 119 42 20131023 0.11 119 43 20130407 0.02 120 43 20131201 0.08 120 44 20131005 0.02 121 44 20141015 0.17 122 44 20131231 0.86 122 45 20150416 0.72 123 45 20141013 0.37 124 45 20131026 0.27 124 46

2.2.2. In Situ Datasets In addition to the remote sensing datasets, in situ datasets were collected in this study, including time-series datasets of 7 GPS stations and a dataset of Quaternary deposit thicknesses derived from boreholes (represented by black stars in Figure1 and black circles in Figure2, respectively). The GPS data were used to validate the InSAR-derived results, and the borehole data were adopted to generate the 2D deposit maps (Figure2). Remote Sens. 2020, 12, 299 5 of 20 Remote Sens. 2020, 12, x FOR PEER REVIEW 5 of 20

FigureFigure 2. 2.Sedimentary Sedimentary thicknessthickness map of the Leizhou Pe Peninsulaninsula (LZP), (LZP), Pearl Pearl River River Delta Delta (PRD) (PRD) and and ChanshaoChanshao Plain Plain (CSP). (CSP). The The cyancyan ﬁlledfilled lineslines represent the primary rive riversrs along along the the coastline. coastline. The The black black hollowhollow circles circles represent represent thethe locationslocations of the sediment depth depth measurements measurements collected collected from from boreholes. boreholes. TheThe purple purple circles circles represent represent the thecity city alongalong thethe coastalcoastal areasareas of Guangdong Province.

3.3. Methodology Methodology MTInSARMTInSAR is is developed developed basedbased onon DInSAR,DInSAR, aiming to extract extract the the deformation deformation from from mixed mixed phases phases ofof series series didifferentialﬀerential interferograms.interferograms. Th Thatat is, is, the the residual residual topographic topographic phase phase (𝜑 (ϕtopo),), deformation deformation phasephase ( ϕ(𝜑de f ),), orbit orbit phase phase ((ϕ𝜑orb),), atmospheric atmospheric phasephase ( 𝜑ϕaps)) andand noise ( (𝜑ϕnoi) constitute the the differential diﬀerential interferometricinterferometric phase phase ( ϕ(𝜑Dint).). n o 𝑊𝜑 =𝑊𝜑 +𝜑 +𝜑 +𝜑 +𝜑 1)(1) W ϕDint = W ϕtopo + ϕde f + ϕorb + ϕaps + ϕnoi (1)

𝑊 where is a phase wrap operation. To generate high-precision deformation rate and time- whereseries,W MTInSARϕDint is ais phase mainly wrap focus operation. on the eliminat To generateion of high-precision atmospheric deformationphase and decorrelation rate and time-series, noise MTInSARbased on iswrapped mainly or focus unwrapped on the elimination phases. In ofthis atmospheric case, i.e., coastal phase areas and decorrelationwith large coverage noise basedand onserious wrapped temporal or unwrapped decorrelation, phases. multi-master In this case, MTIn i.e.,SAR coastal with areasa large with multilooking large coverage number and (3 seriousand 8 temporallooks in decorrelation,the range and multi-master azimuth directions, MTInSAR respectively) with a large is multilooking adopted. Specifically, number (3 andconsidering 8 looks in the the rangecomplicated and azimuth data directions,conditions, respectively) such as frames is adopted. with Specifically,limited SAR considering image numbers the complicated and serious data conditions,atmospheric such effects as frames in the coastal with limited area, two SAR kinds image of MTInSAR numbers andare adopted. serious atmospheric The first is SBAS-InSAR effects in the coastalbased area,on unwrapped two kinds phases, of MTInSAR which are is adopted.appropriate The for first most is SBAS-InSAR situations. The based second on unwrapped method is an phases, arc- whichbased ismethod, appropriate TCPInSAR, for most which situations. is based on The wrapped second phases method and is anaims arc-based to eliminate method, the influence TCPInSAR, of whichserious is basedatmospheric on wrapped effects. phases Considering and aims the todata eliminate processing the influence efficiency, of the serious latter atmospheric is adopted in eff theects. Consideringcase that the the SAR data image processing number effi isciency, limited the and latter the isatmospheric adopted in theeffects case are that serious the SAR (tracks image 453, number 457, is462 limited and 463), and thesee atmospheric the comparison effects between are serious pixel-based (tracks SBAS-InSAR 453, 457, 462 and arc-based 463), see theTCPInSAR comparison in betweenFigure S1. pixel-based After generating SBAS-InSAR deformation and arc-based over the TCPInSAR 16 orbits, in the Figure average S1. Aftervelocity generating maps in deformation the line of oversight the (LOS) 16 orbits, direction the are average mosaiced velocity after maps removing in the the line relative of sight offsets (LOS) in direction the overlapped are mosaiced areas by after a removinglinear polynomial the relative fitting offsets [43], in theconsidering overlapped the areasapproximate by a linear incident polynomial angle (38.7°, fitting see [43], Supplementary considering the approximateTable S1). Moreover, incident angle the precision (38.7◦, see of Supplementary the InSAR results Table is S1).evaluated, Moreover, including the precision relative of precision the InSAR resultscalculation is evaluated, by the velocity including difference relative precision in the overla calculationpped area by the of velocityadjacent diorbitsfference and in analysis the overlapped of the areaabsolute of adjacent precision orbits with and respect analysis to ofGPS the measuremen absolute precisionts. Next, with the respectland-use to classification GPS measurements. maps along Next, thethe land-use coastal areas classification are obtained maps based along on the select coastaled areasoptical are Landsat obtained images. based Finally, on the the selected deformation optical Landsatfeatures images. are observed, Finally, the and deformation the causes features of the are deformation observed, and are the analyzed causes of thequalitatively deformation and are analyzedquantitatively. qualitatively The flowchart and quantitatively. is shown in Figure The flowchart 3. is shown in Figure3.

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Figure 3. Flowchart of data processing. Figure 3. Flowchart of data processing.

SBAS-InSAR, developeddeveloped byby BerardinoBerardino etet al. al. [ 16[16]] and and improved improved by by researchers researchers in in recent recent years, years, is is a pixel-baseda pixel-based approach approach that that has has been been widely widely used used forfor wide-rangingwide-ranging deformationdeformation monitoring [16,26]. [16,26]. In thisthis paper,paper, GAMMA GAMMA software software is usedis used to generate to generate the unwrapped the unwrapped differential differential interferometric interferometric phases. SARphases. images SAR of images each track of each are first track coregistered are first coregist with a masterered with image, a master as shown image, in Table as shown1. Interferograms in Table 1. withInterferograms a given spatial with and a given temporal spatial baseline and thresholdtemporal (850baseline m and threshold 800 days respectively),(850 m and aiming800 days to reducerespectively), the influence aiming of to decorrelation reduce the influence and digital of decorrelation elevation map and (DEM) digital error elevation on parameter map (DEM) estimation. error Afteron parameter interferograms estimation. selection, After traditional interferograms DInSAR selection, with a multilook traditional operation DInSAR (three with looks a multilook and eight looksoperation in range (three and looks azimuth and eight direction looks respectively) in range and is azimuth applied direction to generate respectively) the unwrapped is applied phases, to includinggenerate the flatten unwrapped and topographic phases, phase including removing, flatten goldstein and topographic filtering, MCF phase phase removing, unwrapping. goldstein The orbitfiltering, error MCF is removed phase unwrapping. with a polynomial The orbit fitting. error is Then,removed pixels with with a polynomial a mean coherence fitting. Then, above pixels 0.6 willwith be a mean selected coherence for the followingabove 0.6 processingwill be selected procedures, for the following including processing residual topographic procedures, estimation, including atmosphereresidual topographic elimination, estimation, deformation atmosphere time series elimin calculation,ation, deformation and average time velocity series calculation. calculation, Here, and weaverage used velocity a modified calculation. method to Here, reduce we the used influence a modi offied residual method topography to reduce causedthe influence by the correlationof residual oftopography the perpendicular caused by baseline the correlation of ALOS1 of the in perpen time dimensiondicular baseline [44]. A of spatial ALOS1 filtering in time anddimension a temporal [44]. filteringA spatial are filtering used to and eliminate a temporal the atmosphere filtering are with used a to window-size eliminate the of 500atmosphere m and two with years a window-size respectively. Aof bootstrap500 m and with two ayears parameter respectively. of 500 A is bootstrap adopted to with generate a parameter the uncertainty of 500 is adopted of the average to generate velocity the fitting,uncertainty and pixelsof the whose average uncertainty velocity exceedsfitting, and three pixels times whose the standard uncertainty deviation exceeds (std) three are deleted. times Wethe alsostandard deleted deviation those pixels (std) whose are deleted. coherent We observation also deleted number those is belowpixels twowhose thirds coherent of the totalobservation number. numberUnlike is below SBAS-InSAR, two thirds TCPInSARof the total number. is a method proposed based on the wrapped phase of arcs [Unlike21,45,46 SBAS-InSAR,]. It selected temporarily-coherenceTCPInSAR is a method points proposed (TCPs) based and on the the unknown wrapped parameters, phase of arcs i.e., deformation[21,45,46]. It rate, selected orbit phase,temporarily-coherence residual topographic po phase,ints (TCPs) can be and calculated the unknown without phase parameters, unwrapping i.e., operation.deformation TCPInSAR rate, orbit proposed phase, aresidual local Delaunay topographic networks phase, with can a uniformbe calculated sampling without in the rangephase andunwrapping azimuth directions,operation. TCPInSAR aiming to improve proposed the a arclocal density Delaunay of the networks whole image. with a Unknown uniform sampling parameters in arethe solvedrange and with azimuth an orbit directions, errors joint aiming model to [46 improve] and a phase the arc ambiguity density of detection the whole model image. [21 ]Unknown based on phaseparameters difference are solved of arcs. with Here, an weorbit used errors a sampling joint model spacing [46] ofand 700 a mphase in both ambiguity directions. detection The average model velocity[21] based and on residual phase difference topography of arcs. of arcs Here, are solvedwe used with a sampling an ambiguity spacing detection of 700 m method, in boththat directions. is, arcs withThe average residuals velocity over a and given residual threshold topography are deleted of arcs (1.5 are rad solved is used with in an this ambiguity case). After detection generating method, the parametersthat is, arcs of with each residuals arc, the parameters over a given in eachthreshold pixel areare calculateddeleted (1.5 based rad onis anused L1 in norm this least-squares case). After generating the parameters of each arc, the parameters in each pixel are calculated based on an L1 norm least-squares estimation. The nonlinear deformation is obtained after spatial-temporal filtering

Remote Sens. 2020, 12, 299 7 of 20 Remote Sens. 2020, 12, x FOR PEER REVIEW 7 of 20 estimation.for the residual Thenonlinear phase. The deformation final deformation is obtained time after series spatial-temporal is generated by filtering combining for the the residual linear phase.and Thenonlinear final deformation deformation. time series is generated by combining the linear and nonlinear deformation. Usually,Usually, 3D3D deformation, that that is, is, deformation deformation in inboth both the the vertical vertical and and horizontal horizontal directions, directions, is iscalculated calculated by by the the combination combination of of ascending ascending and and descending descending images, images, which which can can help help us us infer infer hazard hazard mechanisms.mechanisms. However,However, in in this this case, case, only only descending descending SAR imagesSAR images were collected. were collected. Furthermore, Furthermore, according toaccording the existing to the research, existing theresearch, horizontal the horizontal deformation deformation in the coastal in the areascoastal is areas not obvious is not obvious [47]. Hence, [47]. weHence, assume we assume that the that horizontal the horizontal displacement displacement is negligible, is negligible, deformation deformation in the in the vertical vertical direction direction can 𝑑 =𝑑 /𝑐𝑜𝑠𝜃 𝑑 𝑑 becan generated be generated with withdv = dlos/cosθ, where, wheredv and dandlos are the are displacement the displacement in the in vertical the vertical and and LOS) directions,LOS) directions, and θ andis the 𝜃 incidence is the incidence angle. angle. The velocity The velocity values values referred referred to in theto in following the following sections sections are in theare LOSin the direction LOS direction unless otherwise unless otherwise specified. specified. In addition In toaddition the deformation to the deformation monitoring monitoring techniques, antechniques, object-oriented an object-oriented nearest neighbor nearest classification neighbor method classification based onmethod multiresolution based on segmentationmultiresolution and supervisedsegmentation classification and supervised is also classification adopted for is generating also adopted the land-usefor generating classification the land-use map. classification The details of themap. object The classificationdetails of the method,object clas whichsification are beyondmethod, the which scope are of beyond this study, the scope can be of found this study, in previously can be publishedfound in previously research [48 published]. As described research in [48]. Section As described2.2.1, because in Section of the 2.2.1, low changebecause rate of the of land-uselow change and therate superiority of land-use of and Landsat the superiority 8, the comparison of Landsat between 8, the comparison subsidence between and land-use subsidence classification and land-use map is appropriate,classification which map is can appr helpopriate, us understand which can thehelp mechanism us understand of land the subsidence.mechanism of The land accuracy subsidence. of the The accuracy of the classification map is compared with a 30-m resolution product (with an overall classification map is compared with a 30-m resolution product (with an overall accuracy of 97.29% within accuracy of 97.29% within the range of [20°–30°N, 60°–120°E]) obtained in 2015 derived from the the range of [20◦–30◦N, 60◦–120◦E]) obtained in 2015 derived from the National Science & Technology National Science & Technology Infrastructure of China [49,50]. The overall accuracy of the Infrastructure of China [49,50]. The overall accuracy of the classification map in this study is about 89.5%. classification map in this study is about 89.5%. 4. Validation of InSAR Results 4. Validation of InSAR Results To quantitatively evaluate the accuracy of the InSAR-derived results, relative and absolute accuracyTo quantitatively methods are adopted evaluate [51 the–53 ].accuracy The former of th ise also InSAR-derived called inner precision,results, relative which isand defined absolute as the velocityaccuracy di methodsfference inare the adopted overlapping [51–53]. areas The of former adjacent is also tracks, called while inner the precision, absolute accuracy which is isdefined defined as as the velocity difference in the overlapping areas of adjacent tracks, while the absolute accuracy is the consistency with in situ measurements. Here, we used both methods to verify the deformation defined as the consistency with in situ measurements. Here, we used both methods to verify the accuracy. Because the occurrence frequency of the large-scale terrain deformation in the coastal areas deformation accuracy. Because the occurrence frequency of the large-scale terrain deformation in the is very low, the relative accuracy is calculated by the velocity consistency in the overlapping areas coastal areas is very low, the relative accuracy is calculated by the velocity consistency in the between adjacent orbits after a 1st order polynomial operation [43]. The histograms of the average overlapping areas between adjacent orbits after a 1st order polynomial operation [43]. The histograms velocity difference between adjacent orbits are shown in Figure4, and the mean and std values are of the average velocity difference between adjacent orbits are shown in Figure 4, and the mean and shown in Table3. It is obvious that the reference precision of velocity (std value of velocity di fference) std values are shown in Table 3. It is obvious that the reference precision of velocity (std value of is generally less than 3.1 mm/yr, with the exception of several values approximate to 4 mm/yr. The velocity difference) is generally less than 3.1 mm/yr, with the exception of several values approximate causes of these high values may be the limited number of SAR images [54]. to 4 mm/yr. The causes of these high values may be the limited number of SAR images [54].

FigureFigure 4.4. Accuracy of InSAR- InSAR-derivedderived results. results. (a ()a )The The average average deformation deformation rate rate difference difference in in the the overlappingoverlapping areasareas of adjacent tracks. Blue Blue bars bars repr representesent the the histograms histograms of of the the average average rate rate difference, difference, andand blackblack lineslines representrepresent the normal distribu distributiontion fitting fitting of of these these rate rate differences; differences; (b (b) )The The average average deformationdeformation raterate didifferencefference betweenbetween the InSARInSAR results and the GPS GPS station station results. results.

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Table 3. Mean and standard deviation (std) of the average deformation rate difference in the Tableoverlapping 3. Mean andareas standard of adjacent deviation tracks. (std) of the average deformation rate difference in the overlapping areas of adjacentAdjacent tracks. Tracks Mean Std Adjacent Tracks Mean Std AdjacentT452-T453_450 Tracks Mean−0.8 Std3.1 T460_430-T461 Adjacent Tracks Mean 0.2 2.4 Std T452-T453_450T452-T453_460 0.8−0.9 3.13.1 T460_440-T461 T460_430-T461 1.8 0.2 4.3 2.4 − T452-T453_460T453_450-T454 0.9−1.0 3.14.0 T460_440-T461 T461-T462 0.1 1.8 2.8 4.3 − T453_450-T454 1.0 4.0 T461-T462 0.1 2.8 T453_460-T454 − −0.4 3.9 T462-T463 −0.06 3.1 T453_460-T454 0.4 3.9 T462-T463 0.06 3.1 T454-T455 − 0.4 2.2 T463-T464 −0.5 4.2 T454-T455 0.4 2.2 T463-T464 0.5 4.2 T455-T456T455-T456 0.030.03 2.42.4 T464-T465 T464-T465 −0.020.02 2.4 2.4 − T456-T457T456-T457 1.0−1.0 2.62.6 T465-T466 T465-T466 −0.10.1 3.3 3.3 − − T457-T458 0.3 2.2 T466-T467_390 1.5 4.0 T457-T458 0.3 2.2 T466-T467_390 −−1.5 4.0 T458-T459 0.4 2.0 T466-T467_400 0.2 3.4 − T459-T460_440T458-T459 0.80.4 3.62.0 T466-T467_400 T466-T467_410 −0.020.2 3.4 3.1 T459-T460_440 0.8 3.6 T466-T467_410 −−0.02 3.1 In addition to the statistical offsets in the overlapped areas, we also compared the velocity and In addition to the statistical offsets in the overlapped areas, we also compared the velocity and deformation time series with the data collected from GPS stations, as shown in Figures4b and5. It is deformation time series with the data collected from GPS stations, as shown in Figures 4b and 5. It is noted that the velocity comparison between InSAR and GPS is in the vertical direction. The correlation noted that the velocity comparison between InSAR and GPS is in the vertical direction. The ofcorrelation InSAR and of GPS InSAR velocities and GPS is 0.71, velocities confirming is 0.71, the highconfirming precision the ofhigh InSAR precision results. of Moreover, InSAR results. the std ofMoreover, deformation the timestd of series deformation between time the twoseries measurements between the two is also measurements calculated, is as also shown calculated, in Figure as5 . Theshown GPS in stations Figure FOMO5. The GPS and stations HKSL areFOMO selected and HKSL as reference are selected stations as reference in tracks stations 460 and in tracks 459 for 460 the deformationand 459 for time-series the deformation comparison, time-series respectively, comparison, due torespectively, their different due acquisition to their different dates of acquisition SAR image seriesdates (see of TableSAR image1) and series di fferent (see coverage.Table 1) and The different resultsshow coverage. that theThe std results of the show deformation that the std time of seriesthe fordeformation stations DSMG, time HKFN,series for HKKT, stations HKLT, DSMG, and HKFN, HKST areHKKT, 5.5 mm, HKLT, 8.2 mm,and HKST 6.8 mm are 8.7 5.5 mm mm, and 8.2 8.7 mm, mm, respectively.6.8 mm 8.7 Themm millimeter-leveland 8.7 mm, respectively. accuracy ofThe the millimeter-level deformation series accuracy also provesof the deformation the reliability series of the InSAR-derivedalso proves the results. reliability of the InSAR-derived results.

FigureFigure 5. 5.Deformation Deformation time time series series comparison comparison between between InSAR InSAR and and GPS GPS results. results. Blue Blue dots dots represent represent the GPSthe time GPS series,time series, and red and triangles red triangles represent represent the InSARthe InSAR time time series. series.

5. Results The surface displacement map of the Guangdong coastal areas derived from MTInSAR is calculated and shown in Figure6. Positive values (yellow colors) represent movement toward the satellite (uplift). Remote Sens. 2020, 12, x FOR PEER REVIEW 9 of 20 Remote Sens. 2020, 12, x FOR PEER REVIEW 9 of 20

5. Results Remote Sens. 2020, 12, 299 9 of 20 The surface displacement map of the Guangdong coastal areas derived from MTInSAR is calculated and shown in Figure 6. Positive values (yellow colors) represent movement toward the satellite (uplift). Negative values (purple colors) indicate movement away from the satellite Negativesatellite values(uplift). (purple Negative colors) values indicate (purple movement colors) indicate away from movement the satellite away (subsidence). from the satellite Selected (subsidence). Selected areas with concentrated land subsidence rates in excess of 20 mm/yr and areas(subsidence). with concentrated Selected areas land subsidencewith concentrated rates in la excessnd subsidence of 20 mm rates/yr and in excess discrete of areas 20 mm/yr with gentleand discrete areas with gentle deformation are analyzed to grasp the features and characteristics of deformationdiscrete areas are with analyzed gentle todeformation grasp the featuresare analyzed and characteristics to grasp the features of surface and deformation. characteristics Three of surface deformation. Three obvious subsidence areas in LZP, PRD, and CSP are identified. In obvioussurface subsidencedeformation. areas Three in LZP,obvious PRD, subsidence and CSP are areas identiﬁed. in LZP, In PRD, addition, and CSP a land are use identified. classiﬁcation In addition, a land use classification map of the coastal areas of Guangdong Province is also generated mapand of shown the coastal in Figure areas 7. ofAquaculture, Guangdong agriculture, Province is urban, also generated forest, and and water shown are in extracted Figure7. based Aquaculture, on the agriculture,and shown urban,in Figure forest, 7. Aquaculture, and water are agriculture, extracted urban, based onforest, the and collected water Landsat are extracted images. based on the collected Landsat images.

Figure 6. Average surface deformation map along the coastline of Guangdong. Yellow and purple FigureFigure 6. 6.Average Average surfacesurface deformationdeformation mapmap along the co coastlineastline of of Guangdong. Guangdong. Yellow Yellow and and purple purple shading represent surface motion toward and away from the satellite, respectively. Seven insets with shadingshading represent represent surface surface motionmotion towardtoward andand away fr fromom the the satellite, satellite, respectively. respectively. Seven Seven insets insets with with obvious deformation rates are selected: (A) Hailing Bay, (B) the estuary of the Moyang River, (C,F) obviousobvious deformation deformation ratesrates areare selected:selected: ( (AA)) Hailing Hailing Bay, Bay, ( (BB)) the the estuary estuary of of the the Moyang Moyang River, River, (C (,CF,)F ) forest areas in tracks 463 and 457, respectively, (D) the estuary of Guanghai Bay, (E) the petrochemical forestforest areas areas in in tracks tracks 463 463 andand 457,457, respectively,respectively, (D) the estuary estuary of of Guanghai Guanghai Bay, Bay, (E (E) )the the petrochemical petrochemical area of Daya Bay, and (G) the estuary of the Haungjiang River. areaarea of of Daya Daya Bay, Bay, and and ( G(G)) the the estuary estuary of of thethe HaungjiangHaungjiang River.River.

Figure 7. Land-use classiﬁcation map. The red, green, blue, purple and yellow shading represent urban, forest, water, aquaculture and agricultural land uses. Remote Sens. 2020, 12, x FOR PEER REVIEW 10 of 20

RemoteFigure Sens. 2020 7. ,Land-use12, 299 classification map. The red, green, blue, purple and yellow shading represent10 of 20 urban, forest, water, aquaculture and agricultural land uses.

5.1. Subsidence in the Leizhou Peninsula 5.1. Subsidence in the Leizhou Peninsula The LZP is is located located in in southwest southwest Guangdong Guangdong Provin Province.ce. Crops Crops are grown are grown in more in morethan 90% than of 90% the ofpeninsula the peninsula (see the (see yellow the yellowareas in areas Figure in 7), Figure making7), makingthe LZP thethe LZPgranary the granaryfor Guangdong for Guangdong Province. Province.Following Following the SBAS-InSAR the SBAS-InSAR procedure, procedure, the result in the the result LZP inis shown the LZP in is Figure shown 8. in The Figure high8 subsidence. The high subsidence(>20 mm/yr) (> 20in mmLZP/yr) is inconcentrated LZP is concentrated in the two in thebays two (Zhanjiang bays (Zhanjiang Bay and Bay Leizhou and Leizhou Bay) Bay)and andthe thesouthwest southwest of Donghai of Donghai Island, Island, as outlined as outlined by the by black the black dotted dotted circle circle in Figure in Figure 8. The8. Thesubsidence subsidence area 2 areais approximately is approximately 251.5 251.5 km2 km, with, with an anaverage average subsidence subsidence rate rate of of 15.1 15.1 mm/yr, mm/yr, and and the maximummaximum subsidence rate of 58.058.0 mmmm/yr/yr occursoccurs onon DonghaiDonghai Island.Island. The location of thesethese subsidencesubsidence bowls is consistent withwith the the trend trend of middleof middle groundwater groundwater decline decline [4]. In addition[4]. In addition to the high to ratesthe ofhigh subsidence, rates of 2 somesubsidence, other subsidencesome other subsidence bowls with bowls areas with over areas 4 km over, located 4 km along2, located the along western the coastline,western coastline, are also observed.are also observed. The corresponding The corresponding optical imagesoptical images are also are given, also showinggiven, showing that obvious that obvious subsidence subsidence bowls arebowls located are located in areas in with areas aquaculture with aquaculture land use. land Moreover, use. Moreover, the deformation the deformation time-series time-series of two selected of two pointsselected in points aquaculture in aquaculture areas are areas shown are in shown Figure 8inb,c, Figure with 8b,c, an approximately with an approximately linear trend. linear Discrete trend. subsidenceDiscrete subsidence with an averagewith an rateaverage of 7.0 rate mm of/yr 7.0 is presentedmm/yr is inpresented inland LZP,in inland such asLZP, the such subsidence as the areassubsidence of Suixi areas and of Leizhou, Suixi and with Leizhou, a nonlinear with a deformation nonlinear deformation time-series time-series trend, as shown trend, inas Figureshown8 e.in InFigure addition 8e. In to addition subsidence to subsidence in agricultural in agricultural and aquaculture and aquaculture areas, small-scale areas, small-scale displacement displacement (with an average(with an subsidence average subsidence rate of 9.8 mmrate/ yr)of is9.8 also mm/yr) observed is also in urbanobserved areas in of urban Zhanjiang areas city, of Zhanjiang especially incity, its industrialespecially areasin its (seeindustrial inset Fareas in Figure (see 8inseta,d). F In in addition Figure 8a,d). to the In subsidence addition areas,to the asubsidence small-scale areas, uplift a (~3small-scale mm/yr) isuplift observed (~3 mm/yr) in Dengjiaolou, is observed which in is Dengjiaolou, consistent with which the results is consistent of traditional with measurementsthe results of fromtraditional tectonic measurements activity [47]. from tectonic activity [47].

Figure 8.8. Average(a) Average surface surface deformation deformation map map of Leizhou of Leizhou Peninsula Peninsula (LZP). (LZP). Black Black solid solid circles circles are arethe theselected selected subsidence subsidence bowls. bowls. Insets Insets (A) (A) to to(F) (F) are are the the corresponding corresponding optical optical images images of of the selectedselected subsidence bowls. The The black black dotte dottedd circle represents the primary subsidence areas with aa notablenotable subsidence rate.rate. P1P1 and and P2 P2 are are two two selected selected points points located located inaquaculture in aquaculture lands, lands, P3 located P3 located in urban in urban land, andland, P4 and located P4 located in agricultural in agricultural land. (land.b–e) are(b– thee) are corresponding the corresponding time series time of series the four of the selected four selected points. points.

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5.2.5.2. Subsidence Subsidence inin thethe PearlPearl RiverRiver DeltaDelta TheThe spatial-temporal spatial-temporal characteristiccharacteristic ofof subsidence in the PRD PRD is is much much different different in in magnitude magnitude and and rangerange than than that that of of thethe LZP,LZP, as as shownshown inin FigureFigure9 9.. ThisThis resultresult indicatesindicates that the large-scale large-scale subsidence subsidence areasareas with with an an average average raterate inin excessexcess ofof 3535 mmmm/yr/yr reach a total area of 263.37 km km22, ,in in comparison comparison to to 15.1615.16 km km2 in2 in LZP LZP during during the approximatethe approximate period. period. The subsidence The subsidence area in area the PRD in the is mainly PRD distributedis mainly alongdistributed the Pearl along River the tributary Pearl River (see Figuretributary9). Two(see large-scaleFigure 9). subsidenceTwo large-scale bowls subsidence are observed bowls along are the Modaomenobserved along channel the Modaomen (outlined by channel B1 and (outlined B2 in Figure by 9B1), and with B2 average in Figure subsidence 9), with average rates of subsidence 32.0 mm /yr 2 2 andrates 25.1 of mm 32.0/yr, mm/yr respectively, and 25.1 and areasmm/yr, of 265.43respectively, km2 and and 387.32 areas km of2, respectively.265.43 km Theand statistical387.32 km area, isrespectively. calculated after The kriging statistical interpolation area is calculated due to the after temporal kriging decorrelation interpolation of thedue surrounding to the temporal water. decorrelation of the surrounding water. Subsidence bowls with low subsidence rates are also Subsidence bowls with low subsidence rates are also observed along the other three channels: the observed along the other three channels: the Hengmen channel, Bingqili channel, and Jiaomen Hengmen channel, Bingqili channel, and Jiaomen channel. In addition, local subsidence areas with channel. In addition, local subsidence areas with notable rates are distributed along the coastline, notable rates are distributed along the coastline, especially in reclamation areas (which are outlined especially in reclamation areas (which are outlined between the blue and red lines during the period between the blue and red lines during the period from 1984 to 2011 in Figure9). For example, an from 1984 to 2011 in Figure 9). For example, an artificial island located in Nansha district and a artificial island located in Nansha district and a reclamation area located in Jinwan district (see dotted reclamation area located in Jinwan district (see dotted purple circles in Figure 9), have maximum purple circles in Figure9), have maximum subsidence rates of 71.9 mm /yr and 150.9 mm/yr, respectively. subsidence rates of 71.9 mm/yr and 150.9 mm/yr, respectively. Notably, some small-scale uplift is Notably, some small-scale uplift is found in mountainous areas (see the black dotted circles in Figure9). found in mountainous areas (see the black dotted circles in Figure 9). This uplift may be caused by This uplift may be caused by elevation-related atmospheric effects or residual topography. Besides elevation-related atmospheric effects or residual topography. Besides the average deformation map, the average deformation map, deformation time-series of four selected points are also generated (see deformation time-series of four selected points are also generated (see Figure 9b–e). The linear trend Figure9b–e). The linear trend is found in both agricultural and aquaculture areas (see Figure9f–i). is found in both agricultural and aquaculture areas (see Figure 9f–i).

FigureFigure 9. 9.Average Average surfacesurface deformationdeformation map of the Pearl Ri Riverver Delta Delta (PRD). (PRD). Black Black solid solid circles circles B1 B1 and and B2B2 are are two two primary primary subsidence subsidence bowls bowls with with obvious obvious deformation deformation rates. rates. Blue Blue and and red red lines lines represent represent the coastlinesthe coastlines in 1984 in and1984 2011,and 2011, respectively. respectively. Black Black stars representstars represent the selected the selected GPS stations. GPS stations. Purple Purple dotted circlesdotted indicate circles indicate the two the selected two selected districts, districts, Jinwan Jinwan (J.W.D) (J.W.D) and Nansha and Nansha (N.S.D). (N.S.D). Black Black dotted dotted circles representcircles represent the remaining the remaining topography-related topography-related atmospheric atmospheric eﬀects. effects. Insets Insets (a,b) are(a,b two) are selected two selected urban areas.urban P1 areas. and P1 P2 and are twoP2 are selected two selected points locatedpoints loca in aquacultureted in aquaculture lands, P3lands, and P3 P4 and are P4 two are points two points located inlocated agricultural in agricultural lands. (b lands.–e) show (b– thee) show corresponding the corresponding time series time of series the four of the selected four selected points. (points.f–i) are (f the– correspondingi) are the corresponding optical images optical for images the four for points. the four points.

5.3.5.3. Subsidence Subsidence inin thethe ChaoshanChaoshan PlainPlain TheThe CSP CSP consists consists ofof threethree riverriver alluvialalluvial plains, the Lianjiang River River Plain Plain (LJP), (LJP), Rongjiang Rongjiang River River PlainPlain (RJP) (RJP) andand HanjiangHanjiang RiverRiver PlainPlain (HJP), represented by by purple, purple, black black and and red red dotted dotted circles circles in in

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Remote Sens. 2020, 12, x FOR PEER REVIEW 12 of 20 Figure 10, respectively. The total area of these plains reaches 2600 km2. The displacement results in Figure 10, respectively. The total area of these plains reaches 2600 km2. The displacement results in the CSP are presented in Figure 10 and exhibit a speciﬁc spatial-temporal subsidence feature that, the CSP are presented in Figure 10 and exhibit a specific spatial-temporal subsidence feature that, compared with the results of LZP and PRD, is very large and intensive. As shown in Figure 10, compared with the results of LZP and PRD, is very large and intensive. As shown in Figure 10, a alarge-scale large-scale subsidence subsidence bowl bowl with with an an obvious obvious boundary boundary is is observed observed in in the the LJP, LJP, outlined outlined by by B1. B1. The The 2 areaarea and and average average subsidence subsidence rate rate are 342.21are 342.21 km and km 412 and mm /41yr, respectively.mm/yr, respectively. The maximum The maximum subsidence ratesubsidence reaches 157.2rate reaches mm/yr. 157.2 In addition mm/yr. to In the addition subsidence to the in subsidence the LJP, some in discretethe LJP, small-scalesome discrete subsidence small- areasscale aresubsidence observed areas in the are HJP observed and are in marked the HJP by and black are circlesmarked in by Figure black 10 circles, i.e., B2.in Figure The subsidence 10, i.e., B2. is mainlyThe subsidence concentrated is mainly along theconcentrated western side along of thethe HJP,western and theside maximum of the HJP, subsidence and the ratemaximum reaches 32.9subsidence mm/yr. rate In thereaches RJP, a32.9 small mm/yr. subsidence In the RJP, area a issmall also subsidence generated inarea Jieyang is also city, generated with a in maximum Jieyang subsidencecity, with a rate maximum of 24.2 mm subsid/yr.ence Moreover, rate of four 24.2 points mm/yr. located Moreover, in the mixedfour points agricultural located and in urbanthe mixed areas (seeagricultural Figure 10 andf–i) urban are selected areas (see to generate Figure 10f–i) the deformation are selected time-series, to generate as the shown deformation in Figure time-series, 10b–e. The linearas shown trend in of Figure the deformation 10b–e. The linear time-series trend isof alsothe deformation found. time-series is also found.

Figure 10. Average surface deformation map of the Chaoshan Plain (CSP). Black solid circles B1 and Figure 10. (a) Average surface deformation map of the Chaoshan Plain (CSP). Black solid circles B1 and B2 are two primary subsidence bowls with notable deformation rates. Purple, black and red dotted B2 are two primary subsidence bowls with notable deformation rates. Purple, black and red dotted circles represent the Lianjiang Plain, Rongjiang Plain, and Hanjiang Plain, respectively. Cyan filled circles represent the Lianjiang Plain, Rongjiang Plain, and Hanjiang Plain, respectively. Cyan ﬁlled lines in the inset indicate the three primary rivers, the Lianjiang River (L.J.), Rongjiang River (R.J.) and lines in the inset indicate the three primary rivers, the Lianjiang River (L.J.), Rongjiang River (R.J.) and Hanjiang River (H.J.). P1 to P4 are four selected points located in the mixed agricultural and urban Hanjiang River (H.J.). P1 to P4 are four selected points located in the mixed agricultural and urban land land areas. (b–e) are the corresponding time series of the four selected points. (f–i) are the areas. (b–e) are the corresponding time series of the four selected points. (f–i) are the corresponding corresponding optical images for the four points. optical images for the four points.

5.4.5.4. SubsidenceSubsidence inin Otherother Coastal Coastal Areas Areas InIn addition addition to to the the three three selected selected subsidence subsidence areas, areas, other subsidenceother subsidence areas are areas distributed are distributed discretely alongdiscretely the coastline,along the coastline, especially especially in river estuaries,in river estuaries, for example, for example, Hailing Hailing Bay and Bay the and estuary the estuary of the Moyangof the Moyang River in River track in 463, track represented 463, represented by the insets by the A insets and B A in and Figure B in6, respectively,Figure 6, respectively, the estuary the of Guanghaiestuary of Bay Guanghai in track Bay 461 in (see track inset 461 D (see in Figure inset 6D), andin Figure the estuary 6), and of the the estuary Huangjiang of the River Huangjiang in track 456River (inset in track G in 456 Figure (inset6). G In in addition Figure 6). to In the addition subsidence to the occurringsubsidence along occurring the river along system, the river some system, local subsidencesome local subsidence is also observed is also in observed forest areas, in forest such area as thes, such discrete as the subsidence discrete subsidence bowls in bowls tracks in T463 tracks and T457T463 (seeand theT457 insets (see Cthe and insets F in C Figure and F6 ).in Moreover,Figure 6). Moreover, elongated subsidenceelongated subsidence located in located the industrial in the petrochemicalindustrial petrochemical region is also region observed is also in observed track 458 in (see track inset 458 E(see in Figureinset E6 in). Figure 6).

6. Discussion

6.1. A positive Correlation between Sedimentary Thickness and Subsidence

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6.Remote Discussion Sens. 2020, 12, x FOR PEER REVIEW 13 of 20 6.1. A positive Correlation between Sedimentary Thickness and Subsidence We focused on a geological formation that is usually prone to natural hazards, namely, the HoloceneWe focused series on(Q4), a geologicalto investigate formation the correlation that is usuallyof these pronetwo factors. to natural As shown hazards, in Figure namely, 1, in the Holoceneaddition to series some (Q4), discrete to investigatedistribution thealong correlation the coastline, of these Q4 is twomainly factors. distributed As shown in the LZP, in Figure PRD,1 , inand addition CSP. The to some areas discrete of subsidence distribution have a along distribution the coastline, similar Q4 to isthat mainly of the distributed Q4 deposits, in especially the LZP, PRD, in andareas CSP. with The notable areas ofsubsidence subsidence rates. have To a clearly distribution analyze similar this similarity, to that of we the calculated Q4 deposits, the histograms especially in areasof the with subsidence notable subsidencerate (>15 mm/yr) rates. Towithin clearly and analyze out of areas this similarity, with Q4 deposits; we calculated the results the histograms are shown of thein subsidenceFigure 11a. The rate percentage (>15 mm/yr) along within the Y and axis out is calculated of areas with by the Q4 following deposits; equations: the results are shown in Figure 11a. The percentage𝑝𝑖𝑥𝑒𝑙 along 𝑛𝑢𝑚𝑏𝑒𝑟 the Y axis is𝑤ℎ𝑜𝑠𝑒 calculated 𝑠𝑢𝑏𝑠𝑖𝑑𝑒𝑛𝑐𝑒 by the following equations: 𝑟𝑎𝑡𝑒 >15𝑚𝑚/𝑦𝑟𝑤𝑖𝑡ℎ𝑖𝑛𝑄4𝑎𝑟𝑒𝑎 𝑃𝑒𝑟 = 𝑠𝑢𝑚pixel number 𝑡𝑜𝑡𝑎𝑙 whose subsidence 𝑝𝑖𝑥𝑒𝑙 rate>15mm 𝑛𝑢𝑚𝑏𝑒𝑟/yr within 𝑤𝑖𝑡ℎ𝑖𝑛𝑄4𝑎𝑟𝑒𝑎 Q4 area Rer = (2) 𝑝𝑖𝑥𝑒𝑙Q4 𝑛𝑢𝑚𝑏𝑒𝑟sum total 𝑤ℎ𝑜𝑠𝑒 pixel number 𝑠𝑢𝑏𝑠𝑖𝑑𝑒𝑛𝑐𝑒 within Q4 area 𝑟𝑎𝑡𝑒 >15𝑚𝑚/𝑦𝑟𝑜𝑢𝑡𝑜𝑓𝑄4𝑎𝑟𝑒𝑎 pixel number whose subsidence rate out o f Q area (2) 𝑃𝑒𝑟 = >15mm/yr 4 Rerno Q4 = 𝑠𝑢𝑚 𝑡𝑜𝑡𝑎𝑙 𝑝𝑖𝑥𝑒𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑢𝑡𝑜𝑓𝑄4𝑎𝑟𝑒𝑎 − sum total pixel number out o f Q4 area

SubsidenceSubsidence is is mainly mainly located located withinwithin thethe Q4 area, especially especially in in areas areas with with subsidence subsidence rates rates higher higher thanthan 40 40 mm mm/yr/yr (see (see the the blue blue columns columns inin FigureFigure 1111a).a). ToTo quantitatively quantitatively analyze analyze the the correlation correlation of of these these two two factors, factors, we we also also considered considered the the thickness thickness of Q4of depositsQ4 deposits in the in the LZP, LZP, PRD, PRD, and and CSP CSP (see (see the the Q4 Q4 depth depth map map in in Figure Figure2). 2). The The Q4 Q4 thickness thickness maps maps of theof LZPthe LZP and CSPand areCSP generated are generated by a krigingby a kriging interpolation interpolation based onbased the on depths the depths collected collected from borehole from measurements,borehole measurements, which are representedwhich are represented by black circles by black [55]. circles We divided [55]. We the divided thickness the ofthickness Q4 into of several Q4 sections,into several for example, sections, 0for m example, to 10 mwith 0 m to an 10 interval m with of an 5 interval m, 10 m of to 5 50 m, m 10 with m to an 50 interval m with ofan 10interval m, and 50of m 10 to 170m, and m with 50 anm intervalto 170 m of with 20 m. an Additionally, interval of we20 m. calculated Additionally, the average we calculated subsidence the rate average in these sectionssubsidence across rate the in LZP, these CSP sections and PRD. across These the LZP, results CSP are and represented PRD. These by results red diamonds are represented in Figure by 11 redb–d, diamonds in Figure 11b–d, respectively. We can see that an obvious positive correlation between Q4 respectively. We can see that an obvious positive correlation between Q4 thickness and subsidence rate thickness and subsidence rate is observed, that is, the greater the thickness is, the higher the is observed, that is, the greater the thickness is, the higher the subsidence rate, especially in the LZP subsidence rate, especially in the LZP and PRD. However, in the CSP, a positive correlation occurs and PRD. However, in the CSP, a positive correlation occurs when the thickness is larger than 50 m, when the thickness is larger than 50 m, and a negative correlation is generated when the thickness is and a negative correlation is generated when the thickness is low. This phenomenon may be due to the low. This phenomenon may be due to the combined impact of soft soil compaction and groundwater combined impact of soft soil compaction and groundwater recession. recession.

FigureFigure 11. 11.A quantitativeA quantitative relationship relationship between between subsidence subsidence and sedimentary and sedimentary thickness. (thickness.a) Deformation (a) velocityDeformation histogram velocity within histogram (blue bars) within and (blue without bars) (red and bars) without Holocene (red (Q4)bars) deposits. Holocene (b (Q4)–d) Histograms deposits. of(b sedimentary–d) Histograms thicknesses of sedimentary in the LZP, thicknesses CSP, and inPRD. the LZP, White CSP, bars and represent PRD. White the pixel bars numbersrepresent inthe the thicknesspixel numbers bins, and in the red thickness diamonds bins, represent and red the diamon averageds represent deformation the average velocity deformation in the thickness velocity bins. in the thickness bins.

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6.2. Quantitative Correlation between Subsidence and Land-Use Class To analyze the correlation between subsidence and land-use class quantitatively, we generated a land-use classification map based on object-oriented segmentation, as shown in Figure7. Four frequently used classes, agriculture, forest, urban, and water, marked by yellow, green, red, and blue shading are isolated. Notably, the urban land-use class includes manmade constructions, such as buildings and bridges, and bare soil due to their similarity in spectral information. In contrast to the previous classification results [28,33], the specific class of aquaculture is identified due to its wide range and its important role in the economy of Guangdong Province, as mentioned above. The aquaculture class is indicated by purple shading in Figure7. The overall accuracy (OA) of the classification is about 89.5% with a land-use reference map derived from [49,50]. Comparing the subsidence map (Figure3) with the classification map (Figure7), we noticed that areas with notable subsidence rate (>20 mm/yr) are mainly aquaculture and agricultural areas, as indicated by purple and yellow shading in Figure7, respectively. The representative subsidence areas in the aquaculture areas are in the LZP (Figure8) and PRD (Figure9). For example, the relevant optical images of the subsidence bowls in Leizhou Bay and Zhanjiang Bay and the other subsidence bowls along the coastline of the LZP are shown in the insets A–E of Figure8. In the PRD, the majority of the subsidence (>20 mm/yr) is concentrated in aquaculture areas, the total area of which reaches 772.2 km2. However, in the CSP, the subsidence is mainly located in the mixed area with agriculture and urban classes, see the yellow and red shading in Figure7 for the two corresponding subsidence bowls, i.e., B1 and B2 in Figure 10. In addition to these representative areas, aquaculture areas along the coastline are also undergoing subsidence but exhibit different magnitudes of subsidence (see the insets A, B, and D in Figure6). In addition to subsidence in aquaculture and agricultural areas, notable subsidence is also observed in forest and urban areas, for example, discrete subsidence bowls in forest areas of track 463 (inset C in Figure6) and elongated subsidence in the Nansha district of Guangzhou (inset A in Figure9a), petrochemical area of Daya Bay (inset E in Figure6), and Macau airport (inset B in Figure9a). Although the correlation between subsidence and land use is obvious after qualitative comparison, to clarify this correlation, we also give the histograms of subsidence rate in four land-use classes, aquaculture, agriculture, forest and urban, as shown in Figure 12a. Notably, pixels in water areas are ignored because of low coherence. The highest subsidence (>20 mm/yr) is mainly focused in the aquaculture areas, followed by the subsidence in urban areas, agricultural areas, and finally forest (see the red, black, blue, and green lines in the insets of Figure 12b, respectively). The total percentages of the abovementioned subsidence in the four adopted classes are 40.8%, 37.1%, 21.5%, and 0.6%, respectively. Hence, aquaculture-induced groundwater exploitation is the major cause of the notable subsidence in Guangdong Province. Moreover, we also generated the percentage of different subsidence rate sections in each land-use class (see Table4). We can see that slow subsidence mainly occurs in agriculture areas, for example, 44.3% and 34.5% when the subsidence rate is within the range of (0, 5] and (5, 10], respectively.

Table 4. Percentage of subsidence velocity ranges within each land-use class.

Subsidence Rate Land-Use Class (mm/yr) Aquaculture (%) Agriculture (%) Forest (%) Urban (%) (0, 5] 14.0 44.3 13.4 28.3 (5, 10] 26.3 34.5 6.3 32.9 (10, 20] 35.4 25.6 1.4 37.6 >20 40.8 21.5 0.6 37.1 Remote Sens. 2020, 12, 299 15 of 20 Remote Sens. 2020, 12, x FOR PEER REVIEW 15 of 20

Figure 12. Average deformation velocity in the four selected land-use classes. ( b) The enlarged view of the black dotted rectangles in (a).). Red, Red, blue, blue, green green and black lines represent the histograms for aquaculture, agriculture, forest, and urban areas, respectively. The The horizontal axis represents the average deformation velocity. The vertical axis repr representsesents the average velocity frequency in the four land-use classes.

6.3. Causes of Subsidence in Guangdong Province The causes of surface subsidence are generally su summarizedmmarized as natural compression and artificial artificial activities [[26].26]. The latter is the main main cause of surface surface subsidence in many coastal areas and deltas and includes fluidfluid and and solid solid resource resource exploitation, exploitation, underground underground infrastructure infrastructure construction construction and compaction and compactionof constructions of constructions overlaying the overlaying Holocene the sediment. Holocene In sediment. Guangdong In Province,Guangdong both Province, causes are both involved causes areand involved are thus qualitativelyand are thus analyzedqualitativel iny the analyzed following in the points. following points. 6.3.1. Subsidence Probably Caused by Groundwater Exploitation for Freshwater Aquaculture Use 6.3.1. Subsidence Probably Caused by Groundwater Exploitation for Freshwater Aquaculture Use The areas of subsidence rate > 20 mm/yr along the coastal area occur mainly in the aquaculture The areas of subsidence rate > 20 mm/yr along the coastal area occur mainly in the aquaculture areas, as mentioned in Section 6.2. The aquaculture areas in the LZP and PRD account for 53.3% areas, as mentioned in Section 6.2. The aquaculture areas in the LZP and PRD account for 53.3% of of the entire coastal area (see the purple shading in Figure7). The corresponding subsidence areas the entire coastal area (see the purple shading in Figure 7). The corresponding subsidence areas in in the LZP and PRD are 112.3 km2 and 772.2 km2, respectively. The maximum subsidence rates in the LZP and PRD are 112.3 km2 and 772.2 km2, respectively. The maximum subsidence rates in these these two areas are 58.0 mm/yr and 150.9 mm/yr, respectively. In addition to the subsidence bowls two areas are 58.0 mm/yr and 150.9 mm/yr, respectively. In addition to the subsidence bowls in the in the LZP and PRD, local subsidence bowls are also observed in the aquaculture area, see the insets LZP and PRD, local subsidence bowls are also observed in the aquaculture area, see the insets B, D, B, D, and G in Figure6. The water sources of these aquaculture areas are mainly derived from and G in Figure 6. The water sources of these aquaculture areas are mainly derived from freshwater freshwater to satisfy the water quality required by the aquatic products, such as eel and tilapia [56,57]. to satisfy the water quality required by the aquatic products, such as eel and tilapia [56,57]. Moreover, Moreover, the frequency of freshwater exchange is high, aiming to improve the production and the frequency of freshwater exchange is high, aiming to improve the production and quality of the quality of the products. Hence, the very high requirement for freshwater and the obvious correlation products. Hence, the very high requirement for freshwater and the obvious correlation between between subsidence and aquaculture land use suggest that the exploitation of groundwater for subsidence and aquaculture land use suggest that the exploitation of groundwater for aquaculture aquaculture may be the primary cause of subsidence within these areas. However, we cannot analyze may be the primary cause of subsidence within these areas. However, we cannot analyze the explicit the explicit relationship between subsidence and groundwater exploitation volumes due to the vast relationship between subsidence and groundwater exploitation volumes due to the vast illegal illegal exploitation. Fortunately, deformation time series analysis is commonly adopted to analyze the exploitation. Fortunately, deformation time series analysis is commonly adopted to analyze the subsidence caused by groundwater exploitation [26,28,29,58,59]. From the results in the aquaculture subsidence caused by groundwater exploitation [26,28,29,58,59]. From the results in the aquaculture area (see Figure8b,c and Figure9b,c), the linear trend of the deformation time series is identified. area (see Figures 8b,c and 9b,c), the linear trend of the deformation time series is identified. This lack This lack of seasonal variability is likely caused by the exploitation of groundwater from the confined of seasonal variability is likely caused by the exploitation of groundwater from the confined aquifer aquifer instead of shallow aquifer, resulting in the phenomenon of continual subsidence. instead of shallow aquifer, resulting in the phenomenon of continual subsidence. 6.3.2. Subsidence Probably Caused by Groundwater Exploitation for Agricultural and Residential Use 6.3.2. Subsidence Probably Caused by Groundwater Exploitation for Agricultural and Residential Use Subsidence caused by groundwater exploitation for agricultural and residential use is mainly located in the CSP and the inland area of the LZP [7,60]. Unlike the subsidence that occurs in the PRD, moreSubsidence concentrated caused subsidence by groundwater at very high exploitation rates (maximum for agricultural value of 157.2 and mm residential/yr) and over use large is mainly areas located in the CSP and the inland area of the LZP [7,60]. Unlike the subsidence that occurs in the PRD, more concentrated subsidence at very high rates (maximum value of 157.2 mm/yr) and over large

Remote Sens. 2020, 12, 299 16 of 20

(342.2 km2) are observed in the mixed area of agricultural and residential land use (see B1 in Figure 10) in the CSP. Within the subsidence bowl, the percentages of manmade structures and agricultural lands are 58.6% and 30.3%, respectively, in addition to the small percentage (11.1%) of aquaculture land. Thus, groundwater exploitation for the mixture of residential and agricultural use may be the major cause of subsidence. Similarly, a linear time series instead of a seasonally variable time series is observed (see Figure 10b–e), indicating continued groundwater exploitation for residential use. In addition to the groundwater exploitation, the thick Quaternary deposits in the CSP (with a maximum thickness of 167 m) may accelerate the subsidence rate during groundwater exploitation. In addition, a wide range of slow subsidence is also observed in the agricultural land of the LZP. However, the deformation time series presents an obvious nonlinear characteristic (see Figure8e), reﬂecting the seasonal irrigation in the LZP.

6.3.3. Subsidence Caused by Land Reclamation In addition to the exploitation of groundwater, another dominant cause of surface subsidence is the compaction of the Q4 deposits under the overlying buildings or other infrastructures. These deposits possess a high pore ratio and low strength [61,62], resulting in subsidence due to the compaction of soft soil and aquifers. Moreover, the sea reclamation phenomenon in the PRD is widespread (see the areas between the blue and red lines in Figure9), resulting in the accumulation and compaction of soft soil and ﬁnally leading to subsidence. For example, the deposit thickness map of the PRD in Figure2 shows that the subsidence is mainly concentrated in the areas of soft soil, and the thicker the soft soil, the higher the subsidence rate is, as shown in Figure 11d.

6.3.4. Subsidence Caused by Other Reasons In Zhanjiang and Shenzhen cities, elongated subsidence bowls with maximum subsidence rates of approximately 26.6 mm/yr and 51.4 mm/yr are observed, respectively. The bowls are located in urban areas with dense industries (see inset E in Figure6 and the optical image in inset F of Figure8), indicating groundwater exploitation for industrial use. Because groundwater needs for industry are high and persistent, a decline in groundwater level and continued subsidence are possible (see the corresponding deformation time series of industrial areas in Figure8d). In addition, metro tunnel excavation is a cause of subsidence in urban areas, for example, the excavation of subway line 4 in the Nansha district of Guangzhou (see inset A in Figure). In the LZP, discrete subsidence in a large agricultural area with a subsidence rate < 20 mm/yr is caused by not only groundwater exploitation but also natural compression, such as that due to the chemical erosion of peduncle [63]. In tracks 463 and 458, local subsidence bowls are found in forest areas and may be caused by slope instability (see insets C and F in Figure6), potentially resulting in slow-moving landslides.

7. Conclusions Through the use of two MTInSAR techniques, SBAS-InSAR and TCPInSAR, we obtained a large-scale deformation map for the coastal areas of Guangdong. We also identified three notable subsidence areas in LZP, PRD, and CSP, with maximum subsidence rates approximately 58 mm/yr, 150.9 mm/yr, and 157.2 mm/yr, respectively. The relative precision of the average subsidence velocity in the overlapped areas was generally below 3.2 mm/yr, except for several values ranging from 3.4 mm/yr to 4.3 mm/yr. The root-mean-square error (RMSE) of the velocity and deformation time series calculated between InSAR and GPS results are 1.8 mm/yr and 7.6 mm, respectively. In addition to the subsidence map, a land-use map that identifies five classes (aquaculture, agricultural, urban, forest, and water) along the coastline was generated. The relationship between these two maps showed that a high subsidence rate (>20 mm/yr) mainly occurs in aquaculture areas, followed by urban agricultural and forest areas, accounting for 40.8%, 37.1%, 21.5%, and 0.6% of the total area with this notable subsidence rate, respectively. Subsidence due to land reclamation and manmade Remote Sens. 2020, 12, 299 17 of 20 constructions was also monitored. Additionally, we also found that subsidence mainly occurs in the Holocene deposits, and a positive correlation between subsidence and Holocene deposit thickness is obtained after the interpolation of thickness from the selected borehole data. In addition to the average subsidence velocity, the linear deformation time series in the aquaculture and agricultural areas showed that the groundwater is probably exploited continuously, and the subsidence would continue along the coastal areas if no measures were adopted. The results highlight the aquaculture-induced subsidence, in addition to the more common agricultural-induced subsidence, along these coastal areas.

Supplementary Materials: The following are available online at http://www.mdpi.com/2072-4292/12/2/299/s1. Author Contributions: Conceptualization, Y.D.; methodology, Y.D. and G.F.; software, G.F.; validation, H.F., X.P., and D.W.; formal analysis, L.L.; writing—original draft preparation, Y.D.; writing—review and editing, L.L.; funding acquisition, Y.D. and L.L. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported in part by the National Key Research and Development Program of China under Grants 2018YFB0505500 and 2018YFB0505502, in part by the National Natural Science Foundation of China under Grants 41804003, 41574005 and 41674040. The ALOS1 datasets were supported in part by Japanese Aerospace Exploration Agency (JAXA) through project ER2A2N018. Acknowledgments: We thank Lei Zhang for providing the TCP-InSAR software to generate the deformation results. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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