Tectonophysics 578 (2012) Contents lists available at SciVerse ScienceDirect. Tectonophysics. journal homepage:

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1 Tectonophysics 578 (2012) Contents lists available at SciVerse ScienceDirect Tectonophysics journal homepage: Assessments of serious anthropogenic land subsidence in Yunlin County of central Taiwan from 1996 to 1999 by Persistent Scatterers InSAR Hsin Tung, Jyr-Ching Hu Department of Geosciences, National Taiwan University, Taipei, Taiwan article info abstract Article history: Received 30 November 2010 Received in revised form 9 August 2012 Accepted 9 August 2012 Available online 18 August 2012 Keywords: Land subsidence PSInSAR Precise leveling Groundwater pumping Natural hazards Anthropogenic ground subsidence due to massive pumping of groundwater is one of the severe environmental hazards in Taiwan. The Yunlin County located in the southwestern coastal region of Taiwan is one of the most counties with serious land subsidence because of the agricultural needs. In certain areas of the region, the subsidence rate reaches as much as 14.3 cm/yr. The severe land subsidence gives rise to the risk of flood hazard and damage of infrastructures in this area. We represented a Persistent Scatterers InSAR (PSInSAR) results deduced from 1996 to 1999 time span for monitoring of land subsidence in this area. The PSInSAR results show that Baojhou, Tuku and Yuanchang Townships reveal a maximum subsidence rate of about 7.8 cm/yr along the LOS and Lunbei Township located on the northern Yunlin reveals a subsidence rate of 3.5 cm/yr, which is quite coincident with the precise leveling result. This result has proven that the effective reduction of labor and cost could be achieved by using this technique on monitoring land subsidence in Yunlin County Elsevier B.V. All rights reserved. 1. Introduction The anthropogenic activities of excessive groundwater utilization in agriculture, aquaculture, industry and urban area give rise to serious land subsidence. Consequently, land subsidence could result in environmental hazards such as exhaustion of groundwater resources, damage of infrastructures, increase of risks of inundation and inland sea water intrusion (Abidin et al., 2001; Amelung et al., 1999; Chen et al., 2007; Hou et al., 2005; Hsieh et al., 2011; Hung et al., 2011; Motagh et al., 2007; Osmanoglu et al., 2010; Phien-wej et al., 2006; Teatini et al., 2005; Wang et al., 2011). In Taiwan, groundwater has been abundantly used as an alternative to surface water, especially in the southwestern coastal region where the deficiency of surface water resources is severe due to the high water demand from aquacultural and industrial utilization in Taiwan (Hsu, 1998). From a tectonic viewpoint, Taiwan is situated along the ongoing collision boundary between the Eurasian Plate and the Philippine Sea Plate (Fig. 1a) with a convergence rate of about 8 cm/yr (Hu et al., 2001; Lin et al., 2010). Most of the western coastal plains are considered to be in the foreland basin of the Taiwan orogen (Lin and Watts, 2002). Although the morphology of western Taiwan is flat, the subsurface deformation is characterized by various subsiding or uplift rate in response to the incipient development of fold-and-thrust belt (Huang et al., 2006a). However, anthropogenic ground subsidence induced by heavy withdrawal of Corresponding author. Tel.: ; fax: address: jchu@ntu.edu.tw (J.-C. Hu). underground water has resulted in environmental hazard and potential risk in Taiwan which the severe land subsidence of about 1 to 10 cm/yr was observed in several counties from 2002 to 2006 (Fig. 1b, data from Water Resource Agency, Department of Economics). Particularly in the Choshui River alluvial fan where the Yunlin section of the Taiwan High Speed Rail (THSR) had been constructed through the central of subsidence area which might pose a serious threat of its operation (Chang and Wang, 2006; Hwang et al., 2008). From 1992 to 2007, a severe cumulative land subsidence larger than 110 cm was observed at Mailiao, Lunbei and Baojhon Townships (Fig. 2). From 1996 to 1998, the center of land subsidence was located in Lunbei and Baojhon Townships with a subsidence rate of 7 8 cm/yr (Fig. 3a). However the center of land subsidence changed to Tuku and Yuanchang counties and almost covered the THSR (Fig. 3b). It is believed that the land subsidence is caused by a deformation of clay or sand layers by compression, accompanied by heavy withdrawal of underground water near the coastal regions of Yunlin County (Liu et al., 2004). A lot of efforts have been done in managing groundwater over-extraction and land subsidence in the coastal area (Hsu, 1998; Tang and Tang, 2006). Seasonal effects of land subsidence occurring in the study area had been estimated using a regression analysis of a series of weekly GPS height solutions. The average rate of ground subsidence in this area over the period of was 3 cm/yr (Chang and Wang, 2006). Based on the data collected at the piezometer, the variation of land subsidence rate appeared to be associated with an unstable underground water level, which drops gradually during winter and either remains constant or rises during summer time. Consequently, land subsidence rates vary considerably from 1.5 cm/yr for the summer time to 9.0 cm/yr for the /$ see front matter 2012 Elsevier B.V. All rights reserved.

2 H. Tung, J.-C. Hu / Tectonophysics 578 (2012) Fig. 1. (a) Topography, bathymetry and main geological units in Taiwan. Rectangle indicates the study area. The large red indicator arrow notes direction and convergence rate of Philippine Sea Plate relative to Eurasia Plate. Major thrust faults with triangles are on the upthrust side. (b) Average land subsidence area in Taiwan during 2002 to Black lines are the administrative boundaries. Data from Water Resource Agency, Department of Economics. 127

3 128 H. Tung, J.-C. Hu / Tectonophysics 578 (2012) Fig. 2. Cumulative land subsidence in Yunlin County by precise leveling from 1992 to Maximum cumulative land subsidence >110 cm occurred in Baojhon, Tuku and Yuanchang Townships. Red line indicates Taiwan High Speed Rail (TSHR). Data from Water Resource Agency, Department of Economics. winter time (Chang and Wang, 2006). In addition, the annual subsidence rate is deeply influenced by annual rainfall. An annual subsidence rate of greater than 3 cm/yr is considered significant in the Choshoui River alluvial fan. Based on the six leveling surveys carried out in 2000, 2002, 2003, 2005, 2006, and 2007, the maximum annual subsidence rate was 9.5 cm/yr in 2002, 12.2 cm/yr in 2003, 11.6 cm/yr in 2005, 10.1 cm/yr in 2006 and 8.2 cm/yr in 2007, respectively (Hung et al., 2010). The subsidence rate was highest in 2003 due to a serious drought that occurred in Taiwan which led to excessive pumping of groundwater. According to the report of Hung and Liu (2007), the total area with a subsidence rate >3 cm/yr was 1600 km 2 in The total area with a subsidence rate >3 cm/yr was reduced to 803 km 2 in 2007 after the efforts of mitigations in land subsidence since However, the Changhua and Yunlin areas still suffer with the problems of heavy land subsidence. Recent study using multiple sensor of multi-level compaction monitoring well demonstrates that the aquifer-system compaction occurs mostly below depths >200 m and could occur at the depth greater than 300 m (Hung et al., 2010). Although the observations from GPS measurements, precise leveling and monitoring wells provided robust monitoring of land subsidence hazard, however spatial coverage is quite sparse due to these point observations, thus the low spatial density over large areas could be improved by the detection of surface deformation revealed by differential SAR interferometry (D-InSAR). The InSAR technique has proven to be capable of measuring topographic and crustal deformation at fine space resolution of tens of meters over wide coverage (e.g., Buckley et al., 2003; Bürgmann et al., 2000; Ding et al., 2004; Huang et al., 2006b, 2009; Massonnet and Feigl, 1998; Pritchard and Simons, 2002; Wright, 2002; Yen et al., 2008). However, temporal and spatial decorrelations of radar signal have prevented this technique from more frequent utilization. Besides, the accuracy of InSAR measurements may also be significantly reduced by atmospheric phase artifacts that are difficult to be removed from SAR interferograms (e.g., Buckley et al., 2003; Ding et al., 2004, 2008). Thus persistent scatterer (PS) technique has been proposed to improve the applicability of radar interferometry when applied to detect long-term ground deformation with tracking the signals of discrete point-wise targets (Berardino et al., 2002; Ferretti et al., 2000, 2001; Hooper et al., 2004; Kampes and Hanssen, 2004; Liu et al., 2008; Mora et al., 2003). In this paper, we use PSInSAR technique to deduce the perturbation and obtain land subsidence motion around a section of THSR in Yunlin County in central Taiwan by the natural targets recognized from a time series of SAR interferograms. Thus 33 ERS-1 and ERS-2 images (Table 1) acquired from 1996 to 1999 are employed to improve the monitoring density and to further characterize the land subsidence in the area of interest. 2. Geological setting and data 2.1. Geological and hydrological background The study area is located in central Taiwan island (Fig. 1), which is a zone of active continental deformation located at the plate boundary zone of the Eurasian Plate (EUP) and the Philippine Sea Plate (PSP) (Lin et al., 2010). The PSP moves towards the northwest with respect to the stable EUP (Penghu Islands) at a rate of 82 mm/yr and the polarity of subduction between the EUP and PSP is flipped near the central part of Taiwan (Lin et al., 2010). Yunlin County is one of the important agricultural production regions located in the southwestern coastal region of Taiwan where the irrigated area is up to 123,000 ha and the agricultural water consumption reaches approximately 90% of all available water resources in the Choshui River Basin (Zhang, 2005). Due to there is no sufficient surface water supply, the groundwater becomes another necessary source for water consumption. Choshui River is the longest river in Taiwan. Choshui River alluvial fan covers a total area of 2000 km 2 and bounded by Wu River in

4 H. Tung, J.-C. Hu / Tectonophysics 578 (2012) Fig. 3. Land subsidence in Yunlin County revealed by precise leveling from 1996 to (a) Maximum land subsidence of 7 8 cm/yr from 1996 to 1998 occurred in Mailiao, Lunbei and Baojhon Townships. Red line indicates Taiwan High Speed Rail (TSHR). (b) The maximum land subsidence areas with a rate of 7 8 cm/yr shift to Tuku and Yuanchang Townships from 1998 to Data from Water Resource Agency, Department of Economics. north, Pekang River in south, western Foothills in east and Taiwan Strait in west (Fig. 4a). The sediments in Choshui River alluvial fan originate from the rock formation in western Foothills and Central Range, which includes metamorphic quartzite, slate, shale, sandstone and mudstone (Fig. 4a). The thickness of the sediments is about m, and the mean grain size shows a tendency to decrease from east to west. Thus, near the head the alluvial fan mainly compose of gravel and coarse sand, meanwhile the soil and fine sand are mainly

5 130 H. Tung, J.-C. Hu / Tectonophysics 578 (2012) Table 1 Descending orbit data processed for the Yunlin area (track: 232, frame: 3141). Baseline is relative to (orbit: 23767). Orbit Date Sensor Baseline (m) F DC (Hz) /01/31 ERS /02/01 ERS /03/06 ERS /04/11 ERS /03/07 ERS /04/10 ERS /05/15 ERS /06/20 ERS /05/16 ERS /08/29 ERS /12/12 ERS /02/20 ERS /01/16 ERS /03/27 ERS /05/01 ERS /06/05 ERS /07/10 ERS /08/14 ERS /11/27 ERS /01/01 ERS /02/05 ERS /03/12 ERS /05/21 ERS /06/25 ERS /07/30 ERS /09/03 ERS /11/12 ERS /12/17 ERS /01/21 ERS /04/01 ERS /02/25 ERS /05/06 ERS /06/10 ERS in the toe of the alluvial fan. Due to frequent flooding and channel migration along the Choshui River, the complex inter-bedded or lens-structural clay, fine sand, medium fine sand, coarse sand and gravel layers are found in the unconsolidated formation in the flood plan (Fig. 4b). Liu et al. (2001, 2004), proposed that the Choshui River alluvial fan can be divided into four marine sequences (aquitard I aquitard IV) and four non-marine sequences (aquifer I aquifer IV) at a depth of 0 to 300 m (Fig. 4c) Precise leveling measurements In order to monitor the subsidence rate, the government has carried on the leveling measurements in this area for more than two decades. According to the precise leveling measurement maintained by the Industrial Technology Research Institute, the serious subsidence rate is well known to occur along the southwestern coast of Yunlin County, and the average subsidence rate is about 10 cm/yr before The most subsidence area transfer happened in inside Yunlin County with a maximum land subsidence of 7 8 cm/yr during the period of 1996 to 1999 (Fig. 3). At the same time, the maximum cumulative subsidence along the coastal area has decreased from 5 to 7 cm/yr to 3 5 cm/yr observed in Taishi and Sihhu townships. In addition, the precise leveling data measured after 1999 indicate that the maximum subsidence center is located inside Yuanchang and Tuku Townships in Yunlin County with a rate of 7 8 cm/yr SAR datasets In this case, 33 C-band (radar wavelength of 5.6 cm) radar images collected by the ERS-1 and ERS-2 satellites of the ESA along descending orbits from track 232 between January 1996 and June 1999 were used to form 32 interferometric pairs (Table 1). The interferometric processing is carried out with the Diapason software developed by the Centre National d'etudes Spatiales (CNES). The external DEM used to remove the topographic component of the interferometric phase is the free distribution DEM from NASA's SRTM mission. In particular, the Delft Institute for Earth-Oriented Space Research (DEOS) precise orbital data was injected in the interferometric processing to further remove the orbital uncertainties. 3. Persistent scatterer analysis The Persistent Scatterers (PS) InSAR is an advanced technique in comparison with conventional InSAR technique, which addresses to overcome the problems of decorrelation for generating a time series of phase changes without atmospheric and DEM residual effects by computing only on sparsely distributed PSs which are pixels coherent over long time series. This technique has been developed in the late 1990s by A. Ferretti, F. Rocca, and C. Prati of the Technical University of Milan (POLIMI). The first algorithm to find out the PS pixels was brought up by Ferretti et al. (2000, 2001), and trademarked it as the Permanent Scatterer technique. After that, similar processing algorithms have since been developed by Crosetto et al. (2003) and Kampes (2005). Besides, the SBAS (Small Baseline subset) technique developed by Berardino et al. (2002) and StaMPS (Stanford Method for Persistent Scatterers) developed by Hooper et al. (2004) have the same idea with that of PS-InSAR technique but with different names. Persistent scatterers technique uses the largest contributor signal (i.e. bridges, buildings) as the signal of a resolution element which we call persistent scatterers. In the time series, these PS points are stable enough that could refer the information in the whole area. The steps of the persistent scatterers technique can be processed as follows: 1) the appropriate master image selection, 2) differential interferograms formation, 3) stable point target selection, and 4) linear deformation and DEM residual extraction Master image choosing and PS identification The first step is to choose one SAR image that minimizes the sum decorrelation, i.e., maximizes the sum correlation, of all the interferograms in the whole data set. The correlation depends on four terms: temporal interval (T), perpendicular spatial baseline (B ), Doppler centroid frequency baseline (F DC ) and thermal noise (Hooper, 2006; Zebker and Villaseno, 1992): ρ total ¼ ρ temporal ρ spatial ρ doppler ρ thermal T B 1 f T c 1 f F B c 1 f DC F c DC ρ temporal where ρ denotes correlation and superscript c denotes the critical value which means the limit of producing a useable interferogram. For ERS data, T=5 years, B c c =1100 m, F DC =1380 Hz, and ρ thermal is a constant value. In the processes of PSInSAR, the algorithm is established based on the PS points selected. Thus, the selections of PS points are quite important. We used spatial coherence method to pick the point targets. In order to assure the phase is stable enough, the pixel exhibits coherence always greater than the threshold in all data set will be discriminated. All these pixels with this character will be selected as PS candidates. After picking up the PS candidates, we only analyze these pixels in the following steps. It is important to point out that the PS candidates we selected by the above-mentioned algorithms were the primary result, not the final PS points. In the following steps, we will reduce the points with bad correction to the real deformation to get the real PS points. ð1þ

6 H. Tung, J.-C. Hu / Tectonophysics 578 (2012) Fig. 4. (a) Regional geological map of the Choshui River alluvial fan superimposed with 40 m digital elevation model. H: Recent; Q: Pleistocene to Pliocene; PM: Pliocene to Miocene; OE: Oligocene to Eocene; MI: Miocene. (b) Two hydrogeological profiles of the Choshui River alluvial fan. Profile lines are indicated in Fig. 4a. (c) Conceptual hydrogeological profile of the Choshui River alluvial fan at the profile b b. (a) Modified from CGS (1999), (b) modified from Liu et al. (2004), and (c) modified from Liu et al. (2004). The mean coherence value of the 33 coherence values is calculated for each pixel. We consider a pixel as a PS candidate if the mean coherence value of the pixel is larger than the given threshold of 0.3, and thus resulting in 5605 PS points for the study area (Fig. 5). After selection of all the PSs, we establish a triangular irregular network (TIN) as shown in Fig. 5 from the identified PSs by the Delaunay triangulation method (Liu et al., 2009) Extraction of linear deformation When producing a conventional DInSAR result by two SAR images, the phase can be written as the sum of four terms: ϕ diff ¼ ϕ mov þ ϕ topo error þ ϕ atm þ ϕ noise ð2þ

7 132 H. Tung, J.-C. Hu / Tectonophysics 578 (2012) Fig. 5. The distribution of 5605 PSs and generated Delaunay triangular irregular network. All PSs are marked by red solid circles and triangular irregular network marked by green lines. where ϕ mov ϕ topo error ϕ atm ϕ noise phase change due to movement of the pixel in the range distance; residual topographic phase due to misfit with the DEM; phase due to atmospheric artifacts; noise term due to temporal and spatial decorrelation, thermal noise and coregistration errors. Here, the movement term (ϕ mov ) has two contributions: linear movement and nonlinear movement, the linear movement only correlates with velocity, and the DEM error is proportional to the perpendicular spatial baseline: ϕ mov ¼ ϕ linear þ ϕ nonlinear ¼ 4π λ v T þ ϕ nonlinear ϕ topo error ¼ 4π λ B ε r sinθ : ð4þ Because the phase of individual pixel has an infinite solution (the phase is wrapped), the Delauney triangulation is then being used to connect all the PSs. This kind of triangulations relates all the neighboring pixels of irregularly gridded data generating nonoverlapped triangles. Differential operation of the interferometric phase values along each side (referred to as connection hereafter for simplicity) of the triangles is carried out to reduce the effects of spatially correlated errors such as the residual atmospheric effects and orbital errors. Thus, the phase difference between two PSs (x a,y a ) and (x b,y b )ona connection can be expressed as: δϕ diff ¼ δϕ ε þ δϕ mov þ δϕ atm þ δϕ noise : For the whole data set, it becomes as: ð3þ ð5þ where x, y position coordinates of the pixel; T i temporal baseline of the ith interferogram; β nonlinear component of velocity; a atmospheric artifacts; n noise. Using the information we already know in Eqs. (2) and (3), a model with linear movement and DEM error can be assumed as: δϕ model ðx a ; y a ; x b ; y b ; T i Þ ¼ 4π λ T i ½ v modelðx a ; y a Þ v model ðx b ; y b ÞŠ þ 4π λ BT ð i Þ : ½ rt ð i Þ sinðθ i Þ ε modelðx a ; y a Þ ε model ðx b ; y b ÞŠ ð7þ For the 32 interferograms in this study, we can form 32 observation equations for each connection based on Eq. (7), where there are two unknowns (i.e., Δh and Δv) to be estimated. Then, use the following model coherence function to perform maximization γ (Ferretti et al., 2000): γ model ¼ 1 N X N i¼0 h exp j δϕ diff ð T i i Þ δϕ model ðt i Þ where N is the number of interferograms. This function is equal to 1 when the adjustment to the data is perfect, and zero in case of total decorrelation. Once the maximization process has been done for each connection, all the connections with coherence below a threshold are rejected. A least mean square process is necessary to obtain absolute values for each point. Δv estimated ðx a ; y a ; x b ; y b Þ ¼ ½v model ðx a ; y a Þ v model ðx b ; y b ÞŠ maximize γ ð9þ Δε estimated ðx a ; y a ; x b ; y b Þ ¼ ½ε model ðx a ; y a Þ ε model ðx b ; y b ÞŠ maximize γ ð8þ δϕ diff ðx a ; y a ; x b ; y b ; T i Þ ¼ 4π λ T i ½vx ð a ; y a Þ vðx b ; y b ÞŠ þ 4π λ BT ð i Þ ½ rt ð i Þ sinðθ i Þ ε ð x a; y a Þ εðx b ; y b ÞŠ þ½βðx a ; y a ; T i Þ βðx b ; y b ; T i ÞŠ þ½ax ð a ; y a ; T i Þ aðx b ; y b ; T i ÞŠ þ½nx ð a ; y a ; T i Þ nðx b ; y b ; T i ÞŠ ð6þ γ denotes the overall coherence (OC) of a connection (γ [0,1]); p M is the number of interferometric pairs (33 in this case); j ¼ ffiffiffiffiffiffiffi 1 ; and εk is the residual. In practice, both Δε and Δv are determined by searching a predefined solution space to maximize the OC value. It should be noted that phase unwrapping for ambiguity resolution, a challenging task in conventional InSAR data processing, can be

8 H. Tung, J.-C. Hu / Tectonophysics 578 (2012) Table 2 Comparison of land subsidence revealed by precise leveling and SRD rate of PSInSAR at 11 benchmarks located in the study area. Township Station name Elevation (1996/10) Elevation (1998/02) Elevation (1999/11) Leveling average subsidence rate (cm/yr) PSInSAR subsidence rate along LOS (cm/yr) A Mailiao Fengan Elementary School B Mailiao Township Office C Mailiao Work Station D Lunbei Dayou Elementary School E Erhlun Youche Elementary School F Taishi Anshi Temple G Sinsing Elementary School H Taishi Work Station I Baojhong Longyan Farm J Douliou Government of Yunlin County K Yuanchang Yuanchang Cemetery L Jhongsiao Elementary School Residual (cm/yr) avoided through optimizing the objective function. In addition, the OC value is a good quality measure of the phase measurements at the PS points. The larger an OC value is, the higher the accuracy of the estimated parameters is. Our study has shown that Δε and Δv can be estimated accurately for a connection when its γ is greater than 0.7. Therefore only connections with γ greater than 0.7 are used for analysis. 4. Results and discussion 4.1. Results of land subsidence revealed by PSInSAR We select the master image using Eq. (1), and then generate 32 interferograms with the master image (1997/06/05). According to the dense vegetation in the study area, the 32 interferograms reveal less information of phase difference even the temporal baseline is small; most of the interferograms are full of perturbation that we are not able to observe any information of land subsidence. Based on early repeat leveling results measured by ITRI, we chose You-Che Elementary School as the reference point with the minimum elevation change. After processing the data using the algorithm for linear deformation estimation, there are 4566 points remained from the initial 5605 points according to the quality test. However, the linear deformation measurement shows a similar pattern both with precise leveling even the PS density is low (2.68 PS/km 2 ). The slant range deformation rate of PSs relative to the benchmark at You-Che Elementary School in Erhlun (see Table 2) can be observed in Fig. 6. The uplift rate up to 2 cm/yr and subsidence rate up to about 8 cm/yr are observed in the study area. Most areas with severe subsidence are located the inner area of Yunlin County, especially in Baojhou, Tuku and Yuanchang Townships. However, the THSR is just located on the eastern Tuku Township (Fig. 7). For comparison with the leveling data, the incident angles of radar were used to convert the LOS displacement to vertical deformation under the assumption of zero horizontal motion. This assumption has been carried out in previous land subsidence by InSAR (e.g. Amelung et al., 1999; Galloway et al., 1998). The ten benchmarks of precise leveling (Table 2) have been chosen to constrain the vertical deformation rate from PSInSAR converted from SRD rate. The residual of precise leveling and PS-InSAR is in the range from 0.81 to 1.44 cm/yr. In addition, westward horizontal displacements about 1 cm/yr relative to reference point S01R (Fig. 1a) at stable continental margin do contribute to SRD rate with an elongation of 0.38 cm/yr along the Fig. 6. The average rate of along light of sight (LOS) of PS points in the study area superimposed on 40 m DEM. Solid stars are the benchmarks of precise leveling chosen for constrains of PSInSAR results. The negative SRD (Slant Range displacement) rate represents land subsidence and positive ones represent uplift.

9 134 H. Tung, J.-C. Hu / Tectonophysics 578 (2012) Fig. 7. The average vertical displacement map by PSs of the study area from 1996 to Solid rectangles are benchmarks of precise leveling. line of sight to satellite. However, recent study argument that about half of the continuous GPS sites in the Los Angeles basin revealed superposed effects of tectonic motion and deformation associated with fluid pumping (Bawden et al., 2001), thus the evaluation of the components of tectonic motion and deformation associated to groundwater extraction are not taken into account in this study Long-term subsidence rate, shorten subsidence rate From a tectonic point of view, most of the western coastal plains are considered to be in the foreland basin of the Taiwan orogen, the newly initiated blind fault system in the widespread coastal plain. Liew et al. (2004) figured out that the marine incursion of the last interglacial was recognized as between 100 and 150 m below surface level which was widely distributed eastward all the way to the foothills based on sedimentological and paleontological studies from cores in the central part of Choshui fan delta. The study area, underlain by the Mesozoic basement high (Fig. 1a), is the most stable area with a very low subsidence rate of about mm/yr since the last interglacial and a subsidence rate of 2 3 mm/yr since latest Pleistocene (Lai and Hsieh, 2003). Thus the long-term tectonic contribution is the minor component for the subsidence in the study area. 5. Conclusion Although the density of PS is low in Yunlin County due to flourishing vegetation. The PSInSAR result shows the serious subsidence areas located in Baojhou, Tuku and Yuanchang Townships and northern Mailiao, and the most subsidence rate is about 8 cm/yr in this time period in conjunction of Tuku and Yuanchang Townships, which is quite reliable. This PSInSAR result has proven that we can effectively reduce labor and cost by using this technique on observing land subsidence in Taiwan Island. In the future, the available L band ALOS data and X band TerraSAR and COSMO-SkyMed radar satellites could be used to continue surveying land subsidence in Yunlin County. Acknowledgments The comments and suggestions from Editor Fabrizio Storti, guest editor Lionel Siame, Stephane Dominguez, M. Peyret and one anonymous reviewer are deeply appreciated. This study was supported by the National Science Council (project number: M ). We are grateful to the Water Resource Agency, Department of Economics, Taiwan for the leveling data. 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