Land subsidence characteristics of Bandung Basin as revealed by ENVISAT ASAR and ALOS PALSAR interferometry

Remote Sensing of Environment 154 (2014) 46–60

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Remote Sensing of Environment

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Land subsidence characteristics of Bandung Basin as revealed by ENVISATASAR and ALOS PALSAR interferometry

Linlin Ge a,⁎, Alex Hay-Man Ng a, Xiaojing Li a, Hasanuddin Z. Abidin b, Irwan Gumilar b

a Geoscience and Earth Observing Systems Group (GEOS), School of Civil & Environmental Engineering, The University of New South Wales (UNSW), Sydney, Australiab Geodesy Research Division, Faculty of Earth Science and Technology, Institute of Technology Bandung (ITB), Bandung, Indonesia

Please cite this article as: Ge, L., et al., Laninterferometry, Remote Sensing of Environme

⁎ Corresponding author.E-mail address: [email protected] (L. Ge).

http://dx.doi.org/10.1016/j.rse.2014.08.0040034-4257/© 2014 Elsevier Inc. All rights reserved.

a b s t r a c t
a r t i c l e i n f o
Article history:Received 6 February 2014Received in revised form 15 June 2014Accepted 2 August 2014Available online xxxx

Keywords:Bandung BasinSubsidenceRadar InterferometryInSAR

In thiswork, land subsidence in Bandung Basin, Indonesia between2002 and 2011wasmapped using the C-bandENVISAT ASAR and L-band ALOS PALSAR data. Twenty four ALOS PALSAR and thirty ENVISAT ASAR imagesacquired between 2002–2008 and 2007 – 2011, respectively, were analysed. Several locations that wereexperiencing land displacement were identified including Cimahi, Dayeuh Kolot, Rancaekek, Solokan Jeruk andGedebage. The subsidence rates in the basin range from −240 mm/year to 40 mm/year. The comparisonbetween the ALOS PALSAR and ENVISAT ASAR measurements, which showed good agreement, suggested thesubsidence inmany areas of the basin were steady between 2002 and 2011, except south Bandung. Accelerationof subsidence in south Bandung was observed between the measurements from the two datasets, likely as aresult of increasing population and industries. The subsidence in the basin was expected mainly as a result ofexcessive groundwater extraction and soil consolidation caused by surficial loading. GPS survey data collected be-tween 2002 and 2010were used to validate the ALOS PALSAR and ENVISAT ASARmeasurements. Good correlationswere observed between InSAR and GPS measurements. The comparison between the land subsidencemeasurements and the groundwater level information showed there were some correlations betweenthem. For every 1 m reduction in groundwater level per annum, ground subsidence of 20-23 mm perannum has been observed. The comparison results suggested that there was no evidence showing the aquiferin the basin is being recovered.

© 2014 Elsevier Inc. All rights reserved.

1. Introduction

The land subsidence phenomenon in several areas in Bandung Basin,Indonesia, is well known for many years (Abidin et al., 2006). It has ledto cracking of buildings and infrastructures, changes in river flow paths,increased extent of flooding areas and destruction to local groundwatersystems. Cracking, tilting or general damage observed for the buildings,houses and other infrastructure in Bandung Basin as a result of landsubsidence has been widely reported (Abidin, Gumilar, Andreas,Murdohardono, & Fukuda, 2013b). The indirect effects of subsidencethrough worsening of other hazards have also been observed inmany areas and are considerably more severe than the direct effects.One example was the increase in flooding extent caused by continuingsubsidence. Cisaranten Kidul is frequently experiencing flood andTegullar (near the City of Bandung) is prone to flooding. Causes of thissubsidence include excessive groundwater extraction, excessiveconstructions, natural consolidation of alluvium soil, and tectonicmovement. Mapping the long term displacement over BandungBasin is important for enhancing the knowledge of the land subsidence

d subsidence characteristicsnt (2014), http://dx.doi.org/1

phenomena. Subsidence phenomena in the basin has been studiedusing GPS surveys since 2000 (Abidin et al., 2006). Abidin et al. (2008)demonstrated that the land subsidence do not always have a positivecorrelation with the registered volume of groundwater extractionaround the corresponding GPS stations. Abidin et al. suggested that themain reason was because of the large number of unregistered wells.Abidin et al. (2009) later showed a strong correlation between therates of groundwater level lowering with the rate of land displacementin several locations in the Basin. To better understand the correlationpattern between land subsidence and groundwater level in the BandungBasin, more geodetic and hydrogeological data are needed. Satellitesynthetic aperture radar interferometry (InSAR) technique can henceplay an important role to complement theGPS for providing the geodeticdata in area where the GPS survey points are not available.

InSAR has proven to be an effective technique for land subsidencemeasurement due to its precision, spatial coverage and resolution.The use of InSAR for land deformationmapping has been demonstratedin many applications, for example, glacier movement (Kumar,Venkataramana, & Høgda, 2011), volcanic activity (Lanari, Lundgren,& Sansosti, 1998), crustal movement (Zebker, Rosen, Goldstein,Gabriel, & Werner, 1994; Zhang, Ng, Ge, Dong, & Rizos, 2010),urban subsidence (Hui, Fulong, & Qing, 2011; Osmanoglu, Dixon,

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47L. Ge et al. / Remote Sensing of Environment 154 (2014) 46–60

Wdowinski, Cabral-Cano, & Jiang, 2011; Tosi, Teatini, & Strozzi,2013), permafrost deformation (Chen, Lin, Zhou, Hong, & Wang,2013), fluid sequestration into the subsurface (Teatini et al., 2011;Vasco et al., 2010) and underground mining activity (Ge, Chang, &Rizos, 2007; Ng, Chang, Ge, Rizos, & Omura, 2009; Ng et al., 2010).The use of InSAR for monitoring land deformation over the basin hasbeen studied since 2006 (Abidin et al., 2011). Because of the strongtemporal decorrelation due to vegetation cover at south-east BandungBasin, L-band SAR data acquired by JERS-1 and ALOS are often used. In2013, Chaussard et al. (Chaussard, Amelung, Abidin, & Hong, 2013) haveapplied the time-series analysis methods to map the area using theALOS PALSAR data acquired from 2007 to 2009. Because of the relativelyshort life span of the ALOS satellite, the temporal coverage with the dataused in these studies are often limited. The information obtained fromALOS datamay not be able to clearly explain the causes of the subsidence.Abidin, Gumilar, Andreas,Murdohardono, and Fukuda (2013b) comparedthe GPS derived rates of land subsidence in several locations in the basinwith the rates of groundwater level lowering. The comparison periodwasbetween 2000 and 2010 which had only little temporal overlap for thearchived ALOS PALSAR data (typically 2007 to 2011). For this reason,the C-band ENVISAT ASAR, with archived data dating back to 2002 atBandung Basin, can be used to complement the ALOS PALSAR results inorder to providemuch larger temporal coverage for the subsidence study.

The C-band data with shorter wavelength usually exhibit moretemporal decorrelation than the L-band data. The land use in south-eastBandung Basin is mainly for agriculture and plantation purposes wherestrong temporal decorrelation is expected for the C-band ENVISAT ASARdata. The capability of the conventional InSAR time-series analysismethods (Ferretti, Prati, & Rocca, 2001; Kampes, 2006; Ng, Ge, Li, &Zhang, 2012b) aim to identify the point-wise stable scatterer (PS), usuallymade up of manmade structure, is greatly limited in such areas. Recently,several studies have used amplitude-based algorithms for the adaptivefiltering of InSAR stacks to preserve the phase information of naturalstructures in the observed area (Ferretti et al., 2011; Parizzi & Brcic,2011). The pixels identified from such an algorithm are known to bethe potential distributed scatterer (DS). The DS often correspond toimage pixelswithmany neighbouring pixels that share similar reflectivityvalues, as these pixels belong to the same object. The DS and PS can beprocessed together using the conventional InSAR time-series analysismethods (Ferretti et al., 2011; Hooper, 2008). The joint processing of PSand DS has significantly enhanced the capability of conventional InSARtime-series analysis methods for deformation mapping over the ruralarea. In order to further maximise the number of pixels over theSouth-East part of the basin, a region growing approach has beendeveloped to iteratively increase the number of measurement pointsduring the process.

In this study, time-series analysis was applied to both ENVISAT ASAR(2002 to 2008) and ALOS PALSAR (2007 to 2011) data in order to mapthe land subsidence at Bandung Basin between 2002 and 2011. ThePALSAR and ASAR results were compared with the GPS data as well asthe groundwater reduction rate in order to obtain more comprehensiveknowledge on the land subsidence related issues at the basin.

2. Study Area

Bandung Basin is located inWest Java province, Indonesia. The basinis a large intra-montane basin surrounded by volcanic mountains andhighlands (Fig. 1). The total area of the basin is approximately2,340 km2, which consists of three administrative units: Bandungmunicipality, the surrounding Bandung regency and part of Semdangregency.

The central part of the basin mostly consists of urban and industrialareas and has an altitude of about 650 – 700 m. It is a plain with adimension of about 40 km in east–west direction and about 30 km innorth–south direction. The basin is surrounded by up to 2,400 m high

Please cite this article as: Ge, L., et al., Land subsidence characteristicsinterferometry, Remote Sensing of Environment (2014), http://dx.doi.org/1

late Tertiary and Quaternary volcanic ranges (Dam, Suparan, Nossin, &Voskuil, 1996).

The Bandungmunicipality, located at the central part of the basin, isthe third largest city and second largest metropolitan area in Indonesia.The area of the municipality is approximately 81 km2 in size. Thepopulation of the municipality increased dramatically in the pastfew decades, from nearly 1 million in 1961 to 5.9 million in 2003. Theincrease in both population and the expansion of manufacturing andtextile industries in the basin (Bandungmunicipality and its surroundingarea) led to significant increase in water demand. This resulted inincreasing groundwater extraction in the basin.

In the basin, Cibeureum Formation is the main aquifer, whileKosambi Formation as aquitard, and Cikapundung Formation andseveral formation as a bed rock (Hutasoit, 2009). Fig. 2 shows theaquifer profile of Bandung Basin (highlighted in Fig. 1) with thedepth and thickness of the three formations. On the basis of itshydraulic characteristics and its depth, the multi-layer aquifer configura-tion of the Bandung Basinmay be simplified into two systems (Soetrisno,1996): shallow aquifers (Kosambi aquifer within upper 40 m belowground surface) and deep aquifers (Cibeureum aquifer and theunderlying Cikapundung aquifer at between 40 – 250mbelow surface).These aquifers are composed of volcanic products from the volcaniccomplexes that bordered this basin, and lake sediments that weredeposited when the central part of the basin was a lake. The lake wasfully formed about 50,000 years ago, and was drained away about16,000 years ago (Dam et al., 1996).

Groundwater from shallow aquifer is mostly used for domesticpurpose while the deep water counterpart is often used by the regionalwater company or by private firms such as textile industries,manufactur-ing companies and hotels (Braadbaart & Braadbaart, 1997). The ground-water level in the plain decreased rapidly due to the large amount ofgroundwater extraction that led to land subsidence. Wirakusumah andDanaryanto (2004) found that the piezometric head tended to decreaseprogressively at intensive groundwater withdrawal areas. As a result, adepression of the piezometric head has been formed in South Cimahi(maximum depth N 100 m below soil surface), Dayeuhkolot (maximumdepth 64 m), Rancaekek – Cimanggung (maximum depth 73 m),Majalaya (maximum depth 46 m), and in some places in Bandung City.

3. Data Availability

3.1. SAR

In order to map the displacement over Bandung Basin, 30 ENVISATASAR data and 24 ALOS PALSAR scenes acquired from 13 December2002 to 16 December 2008 and 14 January 2007 to 12 March 2011,respectively, were analysed in this study. Both datasets were acquiredin ascending orbit but with different look angles (23° for ENVISATASAR dataset and 34.4 ° for ALOS PALSAR dataset). The ENVISAT data(Track 411, Frame 7050) were acquired in VV polarisation while theALOS data (Path 436, Frame 705) were acquired in HH. The baselineinformation for the two datasets is shown in Fig. 3.

3.2. GPS

For monitoring land subsidence of small magnitude, the idealpositioning accuracy should be at the mm level. In order to achieve thislevel of accuracy, the GPS static survey method based on dual-frequencycarrier phase data processing should be implemented, with stringentmeasurement and data processing strategies (Leick, 2004). In this study,GPS surveys data from six epochs conducted in July 2002, June, 2003,June 2005, August 2008, July 2009 and July 2010 were used. Surveys atall stations were carried out using dual-frequency geodetic-grade GPSreceivers. For all of the GPS surveys the length of sessions was between10 and 12 hours. The data were collected with a 30 second intervalwith the elevation mask set to 15° for all stations. The surveys were



Fig. 1. Bandung City and its location in West Java, Indonesia. The groundwater profile line A-A’ is shown in the Bandung elevation map.

48 L. Ge et al. / Remote Sensing of Environment 154 (2014) 46–60

Please cite this article as: Ge, L., et al., Land subsidence characteristics of Bandung Basin as revealed by ENVISAT ASAR and ALOS PALSARinterferometry, Remote Sensing of Environment (2014), http://dx.doi.org/10.1016/j.rse.2014.08.004

image of Fig.�1


Fig. 2. The aquifer profile A-A' (in Fig. 1) of Bandung Basin (after Wirakusumah, 2006).


mainly carried out by the staff and students from the Department ofGeodesy and Geomatics Engineering, Institute of Technology Bandung(ITB) (Abidin, Andreas, Gumilar, Sidiq, & Fukuda, 2013a; Abidin et al.,2008). The GPS station PSCA (Fig. 4) with known coordinates was se-lected as the reference point with known coordinates and it was as-sumed to be stable. Coordinate of the PSCA station with a fixedheight monumentation was fixed to ITRF 2000 epoch 1998. All themonitoring points were established at benchmarks and are measuredusing tripod and tribrach. The data were processed using the softwareBernese 5.0 (Beutler et al., 2007). The primary purpose of the GPSsurveys was conducted for land subsidence monitoring only, that is,the relative heights with respect to a stable point should to be obtained.Therefore the radial processing mode was used instead of a networkadjustment mode. In this case, the relative ellipsoidal heights of allstations are determined relative to PSCA. The standard deviations ofGPS-derived relative ellipsoidal heights were at several mm level(Abidin et al., 2008; Gumilar et al., 2014). For the data processing, afinal precise ephemeris (SP3) was used. The effects of troposphericand ionospheric biases were reduced by the differencing process andthe use of dual-frequency observations. The residual tropospheric biasparameters for individual stations were estimated to further reducethe tropospheric effects. The algorithms for the tropospheric parameterestimation used can be found in Beutler et al. (2007). In processing of

Fig. 3. Baseline information for the ENVISAT ASAR and ALOS PALSAR datasets. The centresof the connecting lines are the master scenes for the ALOS PALSAR dataset and theENVISAT ASAR dataset.


GPS baselines, most of the cycle ambiguities of the phase observationswere successfully resolved.

Since some of the GPS stations were either missing, damaged ordestroyed, some GPS stations were not surveyed at some epochs.Therefore the GPS stations with reasonable temporal coverage relativeto the SAR acquisitions were selected for analysis (i.e. data from atleast three epochs available during the SAR acquisition periods) (seeTable 1). In this study, GPS observations from 20 and 13 stations wereselected for analysis with the ALOS PALSAR and ENVISAT ASAR data,respectively. Location of GPS stations selected for this study is shownin Fig. 4.

3.3. Groundwater Level

Groundwater levels from eleven water wells in Bandung Basinobtained between 2002 and 2010, were used in this study to investigatethe relationship between land subsidence and reduction of groundwaterlevel. Water wells with groundwater data of at least 3 and 2 samplesavailable during the ENVISAT ASAR andALOS PALSAR acquisition periods,respectively, were selected for analysis. A more relaxing threshold waschosen for ALOS PALSAR because of the relatively short lifetime of thesatellite. The lack of groundwater data after 2009was also another reason.In this study, eight and tenwaterwellswere selected for the analysiswiththe ALOS PALSAR and ENVISAT ASAR data, respectively. Location of waterwells selected for this study is shown in Fig. 4.

4. Methodology

Land displacement in Bandung was mapped using the GEOS-ATSA (Advance Time Series Analysis) software. GEOS-ATSA is a soft-ware developed in the GEOS group at the University of New SouthWales for InSAR time-series analysis. It was developed based onGEOS-PSI (Ng, Ge, Li, & Zhang, 2012b) with an extension modulefor identifying the DS candidates and extracting their optimalphase values at each acquisition. Once the DS candidates and theiroptimal phase values were identified and estimated, the DS and PScandidates were then jointly processed based on the workflowused in GEOS-PSI.

Due to the lack of different viewing geometry, the deformation inBandung Basin was assumed mainly in vertical direction and hencethe LoS (Line-of-Sight) displacement measured was directly projectedinto the vertical direction assuming that there was no differentialdisplacement in the horizontal direction.


image of Fig.�2

image of Fig.�3


Fig. 4. Locationmap of the water wells and GPS stations used in this study. The name of the GPS stations is indicated by the black letters. The water wells are labelled with the white numbers.


4.1. Interferogram generation

A single reference (master) image was selected for the generation ofinterferogram stack. The master images acquired on 19 July 2008 and 7December 2004 for ALOS PALSAR andENVISATASARdatasets, respective-ly, were chosen in this study because its perpendicular and temporalbaselines were relatively short. The differential interferograms weregeneratedwith respect to a selectedmaster image using the conventional2-pass DInSAR (Differential InSAR) approach (Ge, Ng, Wang, & Rizos,2009; Massonnet et al., 1993). The topographic phase in each

Table 1GPS surveys for land subsidence monitoring in the Bandung Basin.

GPS surveyepochs

Survey period Observation points

1 11-14 July 2002 PSCA, BNJR, CMHI, CPRY,DHYK, GDBG, KPO1, KPO2,MJL1, MJL2, RCK1, RCK2,UJBR

2 1-3 June 2003 Same as in Survey 13 24-27 June 2005 Same as in Survey 1, plus

M13L4 21-23 August 2008 PSCA, ANTP, BNJR, CKPD,

CMH1, CMH2, CPRY, DHYK,GDBG, KPO1, KPO2, M13L,MJL1, MJL2, NP01, NP21,P142, RCK1, RCK2, UJBR,2430, 2431

5 26-29 July 2009 Same as in Survey 4, exceptRCK2

6 29-31 July 2010 Same as in Survey 5


interferogram was removed using the three arc-second Digital ElevationModel (DEM) generated by the Shuttle Radar Topography Mission(SRTM) (Rodriguez et al., 2005).

4.2. DS candidate identification and optimal phase estimation

The procedure used to obtain DS candidates and their optimal phasemainly followed the procedure used in SqueeSAR, developed by Ferrettiet al. (2011). In this study, theDS candidateswere calculated on pixel-by-pixel basis. For each image pixel, the two-sample Kolmogorov-Smirnov(KS) tests were first applied to all surrounding pixels within the definedwindows, centred on the image pixel, to identify the neighbouring pixelsthat were statistically homogenous. The defined space adaptive filter(Ferretti et al., 2011) was then applied to the image pixel and itsneighbouring pixels that were statistically homogenous to reduce thespeckle and decorrelation noise. In order to make use of all possibleinterferograms to estimate the optimum phase value of the imagepixel at each acquisition period, the "Phase Linking" algorithm developedby Guarnieri and Tebaldini (2008) was employed. To decide if the imagepixel should be considered as DS candidate, the goodness of fit test wasapplied. In this study, all pixels that passed the goodness of fit test areconsidered as the DS candidates. The goodness of fit test can be expressedby (Ferretti et al., 2011):

γp ¼ 2N2−N

ReXN

n¼1

XN

k¼nþ1

eiϕnk e−i θn−θkð Þ

where γp is the goodness of fit to assess the quality of the optimal phasevalues estimated for an image pixel, N is the number of SAR images


image of Fig.�4



available, ϕnk is the spatially filtered interferometric phases by image nand image k, and θn is the optimal estimate of the image n.

4.3. Persistent scatterer candidate selection

PS is a pixel that is dominated by a point-wise backscattering objectand its phase is coherent during the data acquisition period. The indexused to estimate the phase stability of the pixels and to detect PScandidates was the amplitude dispersion index (DA) (Ferretti et al.,2001). The SAR images were first calibrated by an empirical SARcalibration method before the calculation of amplitude dispersionindex for each pixel. The calibration was carried out in two steps(Cassee, 2004): (1) the amplitude observations of each SAR imagewas corrected using the calibration constant provided; (2) anothercalibration factor per image were calculated based on the modes ofthe potentially stable pixels (i.e. pixels with low amplitude dispersionindex) of each image. Once the images were calibrated, the calculationfor the amplitude dispersion index of each pixel was performed.

4.4. Reference network construction and initial displacement modelestimation

The DS and PS candidates were combined for analysis. The reliablecandidates were used to construct the reference network (ALOS:γp N 0.9 or DA b 0.1; ENVISAT: γp N 0.75 or DA b 0.25). The networkwas constructed based on Delaunay triangulation network. Themaximum distance of connection between any two points was limitedat 1.5 km. The displacement rate difference and the DEM error differencebetween any two points of the triangulation networks were estimatedusing the Integer Least Squares (ILS) estimator with Least-squaresAMBiguity Decorrelation Adjustment (LAMBDA) method (Kampes,2006). The estimates with "ensemble phase coherence" (Ferrettiet al., 2001) less than 0.75 for ENIVSAT ASAR data and 0.8 for ALOSPALSAR data were assumed poorly fitted with the model and henceremoved from further analysis.

To compute the displacement rate and DEM error at each point inthe triangulation network, spatial integration process was appliedto the estimates (parameter difference) from the LAMBDA method.M-estimator is a robust estimator which can make adjustments inthe estimates to take into account some of the outlier in the data itself.The spatial integration and outlier detection processes was applied tothe displacement rate difference andDEMerror difference independentlyusing the M-estimator. The outlier (parameter difference between thetwo points) were removed after the integration process and the isolatedpixels resulted from the outlier removal were also removed from thereference network.

4.5. Inclusion of pixels into the network

To extend the spatial coverage of themeasurement points obtained,the less reliable candidates (ALOS: γp N 0.75 or DA b 0.25; ENVISAT:γp N 0.3 or DA b 0.25) were added into the reference network usingthe adaptive estimation strategy (Ng, Ge, Li, & Zhang, 2012b). The strategyis a region growing technique, which prioritises the isolated pixels basedon its quality and distance from the network and adds them into thenetwork iteratively. Due to the lack of measurement points identifiedin the ENVISAT ASAR results, the last iteration of the adaptive estimationstrategy was repeated 10 times in order to maximise the spatial extentand distribution of measurement points.

Fig. 5 shows the displacement rate generated using 1) the adaptiveestimation strategy with repetition of the last iteration for 10 timesand 2) the conventional point inclusion approach (i.e. all isolated pixelswere added into the reference network without iteration, see Kampes(2006) and Ketelaar (2009)). A significant enhancement in spatial extentwith the adaptive estimation strategy can be observed especially at thesouth-east part of the basin (i.e. the lower-right part in the figure).


The number of measurement points has increased from 1,377,200 to2,762,416 which is about 100% improvement.

4.6. Atmospheric error estimation

The residual phases for each interferogram, calculated by subtractingunwrappedmodelled phase from the differential interferometric phase,were unwrapped with the sparse Minimum Cost Flow (MCF) method(Costantini & Rosen, 1999). The phase due to atmospheric artefactswas considered due to two components: topography related andnon-topography related. The two componentswere estimated separately.The relationship between the measurement points' elevation and theirunwrapped residual phase for each interferogram were estimated usingthe M-estimator (Ng et al., 2012a). The estimated relationship wasexpected to be the topography related atmospheric signal. Once thetopography related atmospheric signals for all interferograms wereestimated, the phase components due to the topography relatedatmospheric signalwere removed from the unwrapped residual phases.The non-topography related atmospheric signals were estimated fromthe resultant phases based on the low pass filtering operation in spatialdomain and high pass filtering operation in the temporal domain(Ferretti et al., 2001).

4.7. Estimation of non-linear components of the displacement

The residual phases were calculated by removing the phasecontribution due to modelled parameters and atmospheric signalsfrom the differential phase of each interferogram. The residual phaseswere expected to contain two components: non-linear displacementand error. The residual phases were unwrapped using the sparse MCFmethod (Costantini &Rosen, 1999) and then low-passfiltering operationsin spatial domainwere carried out to estimate the unwrapped phase. Thefiltered residual phases were converted to the non-linear displacement.

5. Results

The subsidence rate maps generated from the ALOS PALSAR andENVISAT ASAR data are shown in Fig. 6 and Fig. 7, respectively. Thereference points for the two datasets were selected near the referenceGPS station PSCA. The displacement measurements obtained from theInSAR analysis were the relative displacement with respect to thereference point. The total number of measurement points obtainedfrom the ALOS PALSAR dataset was 7,948,688, of which 120,307were PS points. There were 2,746,789 measurement points obtainedfrom the ENVISAT ASAR dataset, of which 15,627 were PS points.The density of the measurement points was 3,522 points/km2 and1,916 points/km2 for ALOS PALSAR and ENVISAT ASAR, respectively.The range of subsidence rate obtained from ALOS PALSAR data wasbetween −240 mm/year and 30 mm/year, while the ENVISAT ASARcounterpart was between −210 mm/year and 40 mm/year. Althoughthe time spans of the two datasets only had limited overlap, the displace-ment patterns were very similar. This suggests that many areas wereexperiencing continuous subsidence over the 9 years period. Both resultssuggested that there were uplift behaviour outside or near the boundaryof the basin. Although it is possible there is elastic uplift over these areas, itis likely that the reference point, which was assumed to be stable, wasactually sinking. Fig. 8 shows the distribution of subsidence rates betweenthe ALOS PALSAR and ENVSIAT ASARmeasurements. The results showeda good agreement between both datasets with correlation of 0.93suggesting the subsidence rate in the basin were relatively steady. Thestandard deviation of the subsidence rate difference was 12 mm/year.The difference observed between the twomeasurements could be causedby four main factors: (1) poor temporal overlap between the twodatasets; (2) possible seasonal/non-linear displacement occurrence;(3) error in InSAR processing; (4) errors in geolocation (mismatchbetweenmeasurements in horizontal plane); (5) differentmeasurement



Fig. 5.ENVISAT ASAR LoS displacement rate maps for Bandung Basin overlaid on the average intensity image generated using (upper) one-off point inclusion process (i.e. withoutiteration) and (lower) adaptive estimation strategy.

Please cite this article as: Ge, L., et al., Land subsidence characteristics of Bandung Basin as revealed by ENVISAT ASAR and ALOS PALSARinterferometry, Remote Sensing of Environment (2014), http://dx.doi.org/10.1016/j.rse.2014.08.004


image of Fig.�5


Fig. 6. Subsidence rate map (13 December 2002–16 December 2008) generated with ALOS PALSAR data for Bandung Basin. The reference point is highlighted by the black cross. The fourrapidly subsiding zones shown in Fig. 9 are highlighted by the purple squares.


precision due to different sensors (L-band vs. C-band). Moreover, theENVISAT ASAR measurements tended to saturate for displacement ratehigher than −180 mm/year (Fig. 8).

Based on the InSAR results obtained, several large subsidence bowlswere observed at Cimahi, Dayeuh Kolot, Rancaekek, Solokan Jeruk andGedebage sites. The subsidence could reach as large as −240 mm/year.Several studies (e.g. Abidin et al., 2009; Braadbaart & Braadbaart, 1997)have suggested that land subsidence in several locations in the basinwas caused by excessive groundwater extraction. The shallow-waterextraction was mainly used for domestic purposes, while groundwaterin the deep aquifers was extracted for industrial and agriculturalpurposes, for example, textile industries, manufacturing companies andhotels. The InSAR results showed that significant subsidence occurredmostly in the textile industry area, where large volumes of groundwaterare usually extracted. The groundwater levels in those areaswere expect-ed to dramatically reduce due to excessive groundwater extraction. Infact, the spatial land subsidence patterns matched well with those conesof depression discussed in Wirakusumah and Danaryanto (2004) exceptthe one in Majalaya. The land subsidence in Majalaya, with relativelylow piezoemtric head depth (maximum of 46 m below soil surface),was much lower than those in the other cones of depression. Althoughtherewas no clear subsidence pattern observed inMajalaya, a subsidencebowl was observed in Solokan Jeruk, a town near Majalaya. Fig. 9 showsthe optical satellite images (©Bing Maps) over the rapidly subsidingzones. Large buildings with white or gray roofs were related to the


industrial land use. It can be seen that most subsidence bowls werelocated over the industrial area except the one at Dayeuh Kolot, wherethe land use over Dayeuh Kolot was amixture of residential and industri-al. The land usemap in Suhari and Siebenhüner (1993) indicated that theshallow aquifers were the main groundwater sources at the south-eastpart of the basin with low productivity. It can be seen in Fig. 9D that theland subsidence rate obtained from the InSAR results over these areaswere relatively lower except the subsidence bowl at the industrial areain Solokan Jeruk. This suggested that the main cause of the rapid landsubsidence might be excessive groundwater extraction for industrialpurpose (i.e. deep-water extraction). Alternatively, sincemost subsidencebowls were found near or at the industrial land use area, it is likely thatthe soil consolidation caused by surficial loading might also played animportant role to the land subsidence.

Fig. 10 shows the subsidence time-series obtained by ALOS PALSARand ENVISAT ASAR dataset over several points. It was found that theland was subsiding linearly in many areas, especially the industrialland use area (i.e. KPO1, KPO2, MUL2 and GDBG). Since groundwaterwas mainly extracted from semi-confined/confined aquifers throughthe deep wells in these areas, the effect of seasonal recharge was notobvious.

Although the subsidence in the basin was found relatively steadybetween the ALOS PALSAR and ENVISAT ASAR results, it was foundthat there was an acceleration of subsidence in the south Bandung(i.e. south Margahayu, Soreang, etc.). In the past decade, economic


image of Fig.�6


Fig. 7. Subsidence rate map (14 January 2007–12 March 2011) generated with ENVISAT ASAR data for Bandung Basin. The reference point is highlighted by the black cross.

Fig. 8. Subsidence rate distribution of the data obtained from ALOS PALSAR and ENVISATASAR.



growth in South Bandung has been significant, particularly in the townof Soreang as the capital of Bandung regency, and in severaladjacent areas such as the districts of Margahayu, Ketapang andBandjaran. Amongst these districts, the greatest change in subsidencerate was found in south Margahayu. In fact, the number of industriesin Margahayu, an industrial complex, has increased from 17 unitsto 43 units since 1991 (Tetuko Sri Sumantyo, Shimada, Mathieu, &Abidin, 2012). An increase in the number of employees, who settlearound the industrial sites, was boosted by the increasing number ofindustries. The population has risen from 102,400 in 2002 to 122,300in 2011. Excessive amount of groundwater extraction was expected asa result of the increase in industry and population over the area whichled to acceleration of subsidence.

6. Disucssions

6.1. Comparison between InSAR measurements and GPS measurements

In order to assess the precision of InSARmeasurements, quantitativeanalysis of the subsidence rate differences between the InSAR and GPSmeasurements was performed. Since the InSAR measurement pointsand the permanent marks for GPS surveys were unlikely to be at theexact same locations due to SAR scattering nature as well as theuncertainty in geolocation, the procedures used in Ng, Ge, Li, Abidin,


image of Fig.�7

image of Fig.�8


Fig. 9. Subsidence rate map (13 December 2002–16 December 2008) generated with ALOS PALSAR data overlaid on optical satellite image (©Bing Maps) at the four areas highlighted inFig. 6. The colour bar for the subsidence rate map is same as the one in Fig. 6. The location of GPS stations and water wells are indicated by the red star and blue circle, respectively.


et al. (2012a) were applied to match the InSAR measurement points andthe GPS stations. First, a map with 100 m × 100 m grid was created. Sec-ond, the location of the InSARmeasurement points and GPS points corre-sponding to the map grid was assigned. Third, the InSAR measurementpoints at the same grid of the corresponding GPS stations were identifiedand selected. Forth, the average displacement value for all InSAR mea-surement points at the same grid (corresponding to the GPS stations)was calculated. Finally, the InSAR and GPS measurements were com-pared. The displacements of both measurements obtained at each gridwere assumed to be from the same object, or at least they had acommon deformation signal.

Fig. 11 shows the comparison between the InSAR and the GPSderived linear subsidence rate for all GPS stations. Strong correlationsbetween the InSAR and GPSmeasurementswere observed. The correla-tion between the ALOS PALSAR and GPS measurements was 0.87 whilethe correlation of ENVISAT ASAR and GPS measurements was 0.96. Thestandard deviation of difference between InSAR and GPS measurementwas 22 mm/year and 13 mm/year for ALOS PALSAR and ENVISAT ASAR,respectively. It was found that ENVISAT ASARmeasurements showed ahigher correlation and lower standard deviation of difference than theALOS PALSAR measurement. This is due to the fact that ENVISAT ASARhas shorter wavelength (C-band data) and larger image stack available.Moreover, there were more GPS measurements available for thecomparison during the ENVISAT ASAR acquisition period.

There were several reasons for the difference between InSAR and GPSmeasurements in this study: possible existence of non-linear subsidenceprocess; possible existence of horizontal displacement; errors in GPS


and InSAR measurements; errors in geolocation (mismatch betweenmeasurements in horizontal plane); lack of GPS surveys epochs availablebetween the SAR acquisition periods (non-adequate temporal sampling).

Amongst the thirteen and twenty GPS stations used for the compari-son with the ENVISAT ASAR and ALOS PALSAR data, respectively, elevenof them were used for both datasets (see Section 3.2). Fig. 10 shows themeasured deformation histories for 7 of the 11 stations. Four of the select-ed stations, KPO1 (Maragahayu), KPO2 (Maragahayu), MUL2 (Majalaya),and GDBG (Gedebage), were located in the textile industry areas wherelarge subsidence were expected to occur due to excessive groundwaterextraction. Fig. 10 shows that the four stations (KPO1, KPO2, MUL2 andGDBG) were experiencing rapid subsidence between 2002 and 2011.The other three stations (RCK1, UJBR and CPRY) were selected in areaswith smaller land displacements. In the four stations thatwere experienc-ing rapid deformation, itwas observed that the ground surfacewas fallinglinearly along with time where the seasonal displacement trends werenot noticeable. Unlike the four stations mentioned, some non-lineardisplacements were observed for the other three stations.

It can be seen that the displacement time-series obtained fromthe ALOS PALSAR, ENVISAT ASAR and GPS measurements matchedreasonably well in this study. Most uncertainties were found in thefirst two GPS epochs for station RCK1 and CPRY and the first threeGPS epochs for station UJBR. Abnormal measurements were foundin these epochs with sudden uplift of land obtained from the GPSmeasurements. It was suspected that there might be some errors (e.g.GPS antenna height was not recorded correctly) in GPS measurementsfor those epochs at these stations. Since the temporal resolution of the


image of Fig.�9


Fig. 10. ALOS PALSAR, ENVISAT ASAR and GPS subsidence time-series at the 7 GPS stations.


GPS surveys for this study was low compared to the SAR acquisitions, itwas difficult to understand the real problemwith the GPSmeasurementsbased on the limited information available.

The InSARmeasurements, with higher temporal resolution (Fig. 10)than the GPS surveys, were able to providemore information about theseasonal displacement trends in the basin. This information can beuseful for the hydrogeological assessment, groundwater extractionmanagement, and aquifer storage and recovery management. Thegoodmatch between both techniques suggested that themeasurementobtained from both techniques can be combined for better analysis ofland subsidence in both spatial and temporal domains in the future.For example, a more detailed displacement time series can be ob-tained by combining the displacement values obtained from bothtechniques.


6.2. Correlation between land subsidence and the groundwater levelreduction

In order to understand the effect of groundwater reduction on theground surface elevation in Bandung Basin, the land subsidence obtainedfrom InSAR and GPS measurements were compared with the change ingroundwater level between 2002 and 2010 at several deep water wells(see Table 2). The same procedures used in previous section were ap-plied to match the InSAR measurement points and the water wells (in-stead of GPS stations). Unfortunately, this approach was notsuitable for matching GPS stations and water wells. This was becauseboth the GPS stations and thewater wells were too sparse in spatial dis-tribution. To match the GPS stations and the water wells, the nearestGPS station for each water well was searched. It was found that the


image of Fig.�10


Fig. 11. Comparison between (left) ENVISAT ASAR-derived linear subsidence rate (December 2002 - December 2008) and GPS-derived linear subsidence rate (November 2001 – July2009) at the GPS survey points; (right) ALOS PALSAR-derived linear subsidence rate (January 2007 - March 2011) and GPS-derived linear subsidence rate (June 2005 – July 2010) atthe GPS survey points. The negative rate indicated the ground surface subsided.


difference in displacement rates between the InSAR measurements atthewaterwells and theGPSmeasurements at their nearest GPS stationstended to increase as their distances increase. A standard deviation ofthe displacement rate difference of 43 mm/year has been observed ifall water wells have been used in the analysis without consideringtheir distances to the water wells. The standard deviation was signifi-cantly improved to 16 mm/year if a distance threshold of 1 km is ap-plied at each water well. Hence the water wells and their nearest GPSstations that were over 1 km away from each other would not beanalysed in this study. As result, there were only 5 out of 11 waterwells remained for the analysis with the GPS data. In contrast, morethan 8 water wells (10 for ALOS PALSAR) were selected for the case ofInSAR measurements. This demonstrated that the InSAR could be an ef-fective tool for providing land deformation information at the waterwells in Bandung Basin.

The scatter plots for ALOS PALSAR, ENVISATASAR andGPSmeasuredsubsidence rate against the annual groundwater level reduction rate areshown in Fig. 12. Some correlation between the rates of groundwaterlevel reductionwith the rates of land subsidencewas observed. A corre-lation of 0.85, 0.77 and 0.73was observed between the rate of reductionin groundwater level and the ground surface subsidence rate obtainedby ALOS PALSAR, ENVISAT ASAR and GPS measurements, respectively.


Table 2Water level drawdown rate and the InSAR-derived subsidence rate at each water well.

Waterwells

Water level(2006–2009)m/year⁎

ALOSmm/year⁎⁎

Water level(2003–2008)m/year⁎⁎

ENVISATmm/year⁎

1 −2.9 −97 −3.2 −792 −0.03 −26 −0.3 −463 0 −77 −0.2 −814 −0.1 −51 −0.9 −635 −0.6 −696 −0.3 −48 −0.4 −607 −3.4 −127 −3.4 −1478 −0.4 −26 −0.3 −409 −1.1 −5410 −1.8 −88

⁎ The negative water level drawdown rate represented the groundwater level declined.The water level drawdown rate are derived from the groundwater data published inAbidin, Gumilar, Andreas, Murdohardono, and Fukuda (2013b).⁎⁎ The negative rate of ground elevation change represented that the land subsided.

The lowest correlation was observed for the GPS measurements. Onepossible reason was the distance between the GPS stations and thewater wells were relatively further apart (within 1 km). Therefore thedisplacements observed in the GPS stations might not reflect the truedisplacements at the water wells. The slope of the regression line forthe three scatter plots agreed reasonably well. Both the ENVISAT ASARand GPS measurements suggested that for every 1 m reduction ingroundwater level per annum, it would induce 23 mm subsidence inground surface per annum. A slightly lower land subsidence rate withrespect to groundwater level reduction rate was found for ALOSPALSAR. For the ALOS PALSAR measurement, every 1 m reduction ingroundwater level per annum induced a 20mmdrop in ground surfaceper annum. It is worth noting that the magnitude of land subsidencecaused by lowering of groundwater level is also influenced by other fac-tors, such as the different geological structures and solid compressibilityat the observed locations.

To identify the correlation between the groundwater level reductionand land subsidence at each water well, the land displacement andgroundwater level time-series were analysed. Fig. 13 shows the landdisplacement and groundwater level time-series at four water wells.These water wells were located in Cimahi, Dayeuh Kolot and Banjaran,where there are many textile industries. In general, the land subsidenceand the reduction in groundwater level agreed well with each other.Clear correlations could be observed between them in most waterwells. However, the correlation for the water wells (3: Luen Fung) inDayeuh Kolot was relatively poor. The groundwater level tended to bestable after 2005 but significant land subsidence was still observed.Sincemeasurements from InSAR andGPSwere consistent in this period,themeasurements obtainedwere expected to be reliable. Dayeuh Kolotis located inside the Kosambi formation in themiddle of Bandung Basin,which is mainly composed of relatively young and soft lake sediments,consisting of clay, silt and loose sand (Abidin, Gumilar, Andreas,Murdohardono, & Fukuda, 2013b; Dam et al., 1996). The continuousconsolidation at the non-elastic clay layers, which has the character ofhysteresis, might have resulted in continuous land subsidence eventhe groundwater level had become stable.

Both water wells (5: Cempaka and 7: Hintex) shown in Fig. 13 werelocated in the industrial area at Cimahi. The water well #5 with muchsmaller changes in piezometric level has experienced much smallerland subsidence. In contrast, water well #7, which was located nearthe centre of the subsidence bowl, has experienced much higher


image of Fig.�11


Fig. 12. Scatter plots for ALOS PALSAR, ENVISAT ASAR andGPSmeasured subsidence rate against the annual groundwater level reduction rate. The negative rate indicated the ground subsided.The negative groundwater reduction rate represented the groundwater level declined. The red numbers beside the red crosses indicate the individual water wells corresponding to Fig. 4.


subsidence. The result suggested that excessive groundwater pumpingwas one of the crucial factors for land subsidence in Cimahi.

Since only some correlation between the rates of groundwater levelreduction with the rates of land subsidence was observed, a direct cor-relation between groundwater level reduction and the land subsidencemay not be necessarily presented. While groundwater extraction is acrucial contributing factor, the comparison result suggested that otherfactors may also play an important role in contributing to land subsi-dence (e.g. soil consolidation caused by surficial loading, natural consol-idation of soil layers and tectonic movements). Unfortunately, becauseof the lack of data available to the authors, detail analysis on other pos-sible contributing factors to land subsidence was not conducted in thisstudy.

It is worth to notice that the water wells at the basin was mostlylocated at the fast subsiding areas. The piezometer static water levelmeasurements could be underestimated because of the displacement(drop) of the well head between readings. This factor was not consideredin this study, but this could be an interesting factor to be considered forfuture studies.

7. Concluding Remarks

This paper presented the long term displacements in Bandung Basinmapped by 30 ENVISAT ASAR and 24 ALOS PALSAR images using GEOS-


ATSA InSAR time-series analysis technique. To overcome the lack ofmeasurement points in the C-band ENVISAT ASAR results, a modifiedpoint inclusion processwas applied. A dramatic improvement in densityofmeasurement points was observed especially at the south east part ofthe basin.

Cross-validation was conducted between the ALOS PALSAR andENVISAT ASAR results. The spatial patterns of the land displacementand their magnitudes between the ALOS PALSAR and ENVISAT ASARresults agreed well with a correlation of 0.93. The InSAR results werecompared to the six epochs of GPS survey conducted between 2002and 2010 at 20 and 13 locations for the ALOS PALSAR and ENVISATASAR data, respectively. Good agreements between the GPS and theInSAR measurements were observed. When comparing with the GPSmeasurements, ENVISAT ASAR measurements, with shorter wavelength,wasmore consistent than the ALOS PALSARmeasurements. The relation-ship between the reduction in groundwater level and land subsidencewas also studied. The measurements obtained from the ALOS PALSAR,ENVISAT ASAR and GPS data were compared to 8, 10 and 5 water wellsrespectively. Some correlation between the average annual reduction ofgroundwater level and land surface was found. GPS measurement, withthe sparsest land subsidence measurement points available, hadthe lowest correlation amongst the three measurements. The InSARtime-series analysis suggested that the land subsidence and thereduction in groundwater level agreed well in general.


image of Fig.�12


Fig. 13. Groundwater level (metres below ground) and land displacement between 2002 and 2011 at some water wells in Bandung Basin. The ALOS PALSAR, ENVISAT ASAR and GPSmeasured displacements are indicated by magenta crosses, black squares, and red circles, respectively. The groundwater level depths are indicated by blue stars.


The InSAR results indicated that several locations in the basin,including Cimahi, Dayeuh Kolot, Rancaekek, Solokan Jeruk and Gedebage,were experiencing continuous subsidence from 2002 to 2011 with landsubsidence as large as 240 mm/year. The good agreement between theALOS PALSAR and ENVISAT ASAR results suggested that the subsidencerates were steady over the Bandung Basin. Several rapidly deformingareas were identified and investigated. It was found that the areas withsignificant subsidence observed were mainly located within or nearthe textile industry areas. The continuous subsidence in these areas wasexpected to be mainly caused by two seasons: (1) severe uncontrolledand over-extraction of groundwater especially through the deep wellsin the industrial areas; (2) soil consolidation caused by surficial loading(e.g. new buildings).

In this study, it was found that every 1 m reduction in groundwaterlevel per annum in Bandung Basin would lead to ground subsidence of20 - 23mmper annum. Although some correlation was found betweenland subsidence and the reduction in groundwater level, there weresome locations that the land surface continued to fall even whenthe groundwater level had become stable. There are two possibleexplanations: (1) despite the excessive groundwater extraction,other factors such as natural consolidation of soil could also contributesignificantly to land subsidence phenomena; (2) the aquifer was stressedandoverexploitedwhich resulted in continuous land subsidence even thegroundwater level had become stable. Further investigation in the futureis necessary to fully understand the actual causes. Nevertheless, therewasno evidence showing the aquifer in BandungBasin is being recovered. It ispossible that the intense extraction rates will not be sustainable inthe near future and might lead to some drastic consequences forthe population and its economy.

Acknowledgement

This research was supported under Australian Research Council'sDiscovery funding scheme (project number DP130101694). The authorswish to thank the Earth Remote Sensing Data Analysis Center (ERSDAC)and the European Space Agency (ESA) for providing the ALOS PALSARand ENVISAT ASAR data, respectively. METI and JAXA retain ownershipof the ALOS PALSAR original data. Finally, the authors would like to


thank the reviewers and editor for their comments which have helpedgreatly to improve the manuscript.

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Land subsidence characteristics of Bandung Basin as revealed by ENVISAT ASAR and ALOS PALSAR interferometry