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LAND SUBSIDENCE EVALUATION USING INSAR TIME SERIES ANALYSIS IN BANGKOK METROPOLITAN AREA Anuphao Aobpaet (1) , Miguel Caro Cuenca (2) , Andy Hooper (2) , Itthi Trisirisatayawong (3) (1) GISTDA-EOC, 165 Chalongkrung Rd., 10520 Bangkok, Thailand, Email: [email protected] (2) Delft University of Technology, Kluyverweg 1, 2629 HS Delft, Netherlands, Email: [email protected], [email protected] (3) Chulalongkorn University, Phyathai Road, 10330 Bangkok, Thailand, Email: [email protected] ABSTRACT There has been increasing interest over the last few years in the use of radar interferometry for land subsidence evaluation/monitoring. InSAR time series algorithms such as PSI and small baseline methods offer the possibility to measure ground displacements to a degree of accuracy comparable to traditional geodetic technique such as leveling. This preliminary work reveals the potential of time series analysis to detect more than 300,000 pixels as monitoring points with the maximum subsidence rates roughly 15 mm/year in eastern central Bangkok. The number of monitoring points provided by InSAR is three orders of magnitude greater than those available from the leveling network. The denser and more evenly distributed coherent pixels alleviate an inherent problem of leveling which are spatially biased to accessible areas such as buildings or bridges along major public roads. We generated interferograms from Radarsat-1single look complex images in fine beam mode with DORIS and applied the StaMPS combined PSI and small baseline algorithm. Leveling data of the same period were used to verify the results. In our analysis, we integrated InSAR and leveling data with the same spatial and temporal reference to compare the results and describe the subsidence phenomenon. We find that combining the different characteristics of leveling and InSAR data leads to more efficient monitoring. 1. INTRODUCTION Bangkok metropolitan area has suffered from land subsidence induced by groundwater extracting for long time. This deformation signal was first reported by [1] and then investigated by [2] [3], but surface subsidence was not determined quantitatively until early 1978 [4]. Other relevant works are for example [4], [5], [6], [7]. Land subsidence problem in Bangkok has been annually monitored by the Royal Thai Survey Department based on the result of leveling survey including more than 300 benchmarks installed throughout the metropolitan area. Since the late 1980s, groundwater pumping had decreased by government’s laws reducing the deformation to -5 to -10 mm/year [6]. However, the highest subsidence rate up to -120 mm/year in 1981 had been recorded in the eastern area [5]. In 2002, the maximum subsidence rate was -30 mm/year in the southeast and southwest industrial zones. Currently, the city is still subsiding with a rate that was estimated to be -20 mm/year. Nevertheless, the proximity to the coast and the continuous sea level rise make Bangkok to be under high flooding risk. Monitoring is therefore a primary task that is mostly carried out using conventional leveling, with the limitation that this technique provides with sparse measurements. On the other hand, spaceborne radar interferometry is an alternative tool that provides with surface deformation data at a low cost with high revisiting time and large area coverage. Satellite interferometry has already been applied to study the deformation in Bangkok. [8] reported that the maximum subsidence rate was -30 mm/year in the southeast and southwest alongside Chao Phraya River during the time spanning from February 1996 to October 1996. Land subsidence in Bangkok is a long term process, and mostly slow (-5 to -10 mm/year). Therefore, the factors such, geometrical and temporal decorrelation, and the atmospheric phase are the major limitations of traditional InSAR measurements. Other methods _____________________________________________________ Proc. ‘Fringe 2009 Workshop’, Frascati, Italy, 30 November – 4 December 2009 (ESA SP-677, March 2010)

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Page 1: LAND SUBSIDENCE EVALUATION USING INSAR TIME SERIES ANALYSIS IN BANGKOK METROPOLITAN … · 2018-05-15 · LAND SUBSIDENCE EVALUATION USING INSAR TIME SERIES ANALYSIS IN BANGKOK METROPOLITAN

LAND SUBSIDENCE EVALUATION USING INSAR TIME SERIES ANALYSIS IN

BANGKOK METROPOLITAN AREA

Anuphao Aobpaet(1)

, Miguel Caro Cuenca(2)

, Andy Hooper(2)

, Itthi Trisirisatayawong(3)

(1) GISTDA-EOC, 165 Chalongkrung Rd., 10520 Bangkok, Thailand, Email: [email protected] (2) Delft University of Technology, Kluyverweg 1, 2629 HS Delft, Netherlands, Email: [email protected],

[email protected] (3) Chulalongkorn University, Phyathai Road, 10330 Bangkok, Thailand, Email: [email protected]

ABSTRACT

There has been increasing interest over the last few years in the use of radar interferometry for land subsidence evaluation/monitoring. InSAR time series algorithms such as PSI and small baseline methods offer the possibility to measure ground displacements to a degree of accuracy comparable to traditional geodetic technique such as leveling. This preliminary work reveals the potential of time series analysis to detect more than 300,000 pixels as monitoring points with the maximum subsidence rates roughly 15 mm/year in eastern central Bangkok. The number of monitoring points provided by InSAR is three orders of magnitude greater than those available from the leveling network. The denser and more evenly distributed coherent pixels alleviate an inherent problem of leveling which are spatially biased to accessible areas such as buildings or bridges along major public roads. We generated interferograms from Radarsat-1single look complex images in fine beam mode with DORIS and applied the StaMPS combined PSI and small baseline algorithm. Leveling data of the same period were used to verify the results. In our analysis, we integrated InSAR and leveling data with the same spatial and temporal reference to compare the results and describe the subsidence phenomenon. We find that combining the different characteristics of leveling and InSAR data leads to more efficient monitoring.

1. INTRODUCTION

Bangkok metropolitan area has suffered from land subsidence induced by groundwater extracting for long time. This deformation signal was first reported by [1] and then investigated by [2] [3], but surface subsidence was not determined quantitatively until early 1978 [4]. Other relevant works are for example

[4], [5], [6], [7]. Land subsidence problem in Bangkok has been annually monitored by the Royal Thai Survey Department based on the result of leveling survey including more than 300 benchmarks installed throughout the metropolitan area. Since the late 1980s, groundwater pumping had decreased by government’s laws reducing the deformation to -5 to -10 mm/year [6]. However, the highest subsidence rate up to -120 mm/year in 1981 had been recorded in the eastern area [5]. In 2002, the maximum subsidence rate was -30 mm/year in the southeast and southwest industrial zones. Currently, the city is still subsiding with a rate that was estimated to be -20 mm/year. Nevertheless, the proximity to the coast and the continuous sea level rise make Bangkok to be under high flooding risk. Monitoring is therefore a primary task that is mostly carried out using conventional leveling, with the limitation that this technique provides with sparse measurements. On the other hand, spaceborne radar interferometry is an alternative tool that provides with surface deformation data at a low cost with high revisiting time and large area coverage. Satellite interferometry has already been applied to study the deformation in Bangkok. [8] reported that the maximum subsidence rate was -30 mm/year in the southeast and southwest alongside Chao Phraya River during the time spanning from February 1996 to October 1996. Land subsidence in Bangkok is a long term process, and mostly slow (-5 to -10 mm/year). Therefore, the factors such, geometrical and temporal decorrelation, and the atmospheric phase are the major limitations of traditional InSAR measurements. Other methods

_____________________________________________________ Proc. ‘Fringe 2009 Workshop’, Frascati, Italy, 30 November – 4 December 2009 (ESA SP-677, March 2010)

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that employed time series analysis, namely persistent scatterers interferometry (PSI) [9], [10] and small baselines (SB) techniques [11], have been recently developed to overcome these limitations. They have provided remarkable results showing that they are advanced tools for surface deformation monitoring. [12] applied PSI technique for deformation estimate in Bangkok (16 and 10 interferograms) using ERS1/2 data which covered the time period of 1992-2000 with sufficient accuracy (S.D. about 6-8 mm/year) for subsidence planning purpose. The subsidence rates were classified in 3 levels which are high (> 20 mm/year), medium (> 10 mm/year), and low (< 10 mm/year) in the small area located southeast of Bangkok In our study case, we present the results of applying both PSI and SB techniques to 14 Radarsat-1 images covering Bangkok Metropolitan area, Thailand. In particular, we processed the data using the Stanford Method for Persistent Scatterers (StaMPS) [13] to analyze the data acquired from 23 October 2005 to 02 October 2009 in ascending orbit to determine line-of-sight (LOS) displacements. For the SB processing, we selected those interferogram combinations whose temporal baselines and geometrical distance were small to increase the coherent areas. The results were combined by the application of a multi-temporal InSAR approach (MTI) [14] that uses both, PSI and small baseline methods to improve spatial unwrapping. Finally, we compared the results with conventional leveling survey data to describe the subsidence phenomenon.

2. STUDY AREA AND DATA DESCRIPTION

The region of interest in this process is the city of Bangkok (center latitude: 13° 33' 44.37"N, center longitude: 100° 38' 47.76"E) with a size of about 1500 km2 of 2500 km2 scene covering. In this area, the terrain is flat with an average height 0.5 to 1.50 meters above mean sea level and mass of permanent structure correspondingly. In this contribution, we employed 14 Radarsat-1 images in F1N beam mode with azimuth resolution 8.9 m. and range resolution 6.0 m. They were acquired over Bangkok during the period of October 2005 and October 2009 in ascending orbit (Path-Row: 2-60). All production requests were submitted through a Product Generation System (PGS) interface at GISTDA earth observation center to retrieve from the archive and placed into a large server computer. It was used to process data for Single Look Complex (SLC) product in CEOS format which consists of five files containing various descriptive records. The files

are Volume Directory file, SAR Leader file, SAR Data file, SAR Trailer file, and Null Volume Directory file. Single Look Complex data performed interpolation of the slant range coordinates, and corrected for satellite reception errors, includes latitude/longitude positional information.

Figure 1. The location of test area, Bangkok

Metropolitan, covering an area about 1500 km2

Figure 2. Radarsat-1SAR, Bangkok, Thailand

Each image pixel is represented by complex I and Q numbers to maintain the amplitude and phase information [15]. In addition, Single Look Complex data retains the optimum resolution available for each beam mode, which makes it suitable for interferometric processing. A SRTM Digital Elevation Model (DEM) with 3-arcsecond geographical resolution (90m) and 10-meter height accuracy is used as the external DEM in this process.

Chao Phraya River

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We set master images acquired in 2008-07-27, and their baselines lists were shown in the “Tab. 1”. Table 1. Temporal and Perpendicular Baseline (PS)

3. INSAR TIME-SERIES ANALYSIS

In this study, we applied PSI and small baseline (SB) approaches on the region of Bangkok in October 2005 to October 2009 using StaMPS/MTI (Stanford Method of PS/Multi-Temporal InSAR). The PS analysis of StaMPS uses primarily spatial correlation of the phase to identify phase-stable pixels, as opposed to temporal correlation, and it does not assume any approximate model of displacements (such as linear or periodic). A requirement is that the displacement gradients in space and time should not be steep for proper unwrapping. In addition, The SB analysis aims to detect pixels whose phase decorrelates little over short time intervals. Finally the results are combined in the StaMPS/MTI approach to improve phase cycle estimations (phase unwrapping), and the spatial sampling of the signal of interest, [14]. StaMPS/MTI uses a 3D unwrapping algorithm, which is advantageous over 2D algorithms because insufficient spatial density of persistent or coherent pixels can be compensated by unwrapping in time [13]. 3.1 The PSI Technique

The PSI technique [9], [10], employed time series of radar images to detect potential coherent measurement points for deformation in the area of interest. It is an extension and more advantage than conventional InSAR to overcome the problems of temporal and geometrical decorrelation. A master image is chosen base on the favorable geometry related to all others images including high coherence and possibly minimum atmospheric disturbances.

Once coregistering master and slave images, a series of interferograms is constructed, which also uses the most precise orbit information available. An evaluation of interferometric phase differences in time is done to obtain the potential PS points. Finally, temporally coherent of natural reflectors in SAR images are detected due to their correlated phase behavior over time. Then, the displacement of each individual PS point is estimated by the technique. 3.2 Small Baselines technique

The Small Baselines technique relies on an appropriate combination of multiple small baseline interferograms [11]. Interferograms having mutual small baselines combinations are created based on the available of image. However, this can produce different subsets of InSAR pairs connected in time and separated by large baselines. The generation of a linear model will increase the sampling rate and allow the use of all acquisitions included in the different small baseline subsets. While the usual approach to analyze phase differences in classical InSAR processing is to set a coherence threshold to reduce phase noise and preserve spatial resolution. The small baselines method searches to ease phase unwrapping by means of selecting small baselines interferograms and filtering the phases. It creates a network of interferograms to estimate heights and deformation with respect to one single master image.

Figure 3. Plot Small Baselines

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4. RESULTS AND DISCUSSIONS

4.1 Differential Interferograms

We processed data for a region of Bangkok with StaMPS/MTI software by using 14 single look complex (SLC) images. 13 interferograms for PS were used to identify persistent and coherent pixels. For the SB approach, we computed 40 interferograms, and the wrapped phase was corrected for spatially-uncorrelated look angle error and noise associated with the master image. Unwrapping errors affect our results due to the sub-optimal number of images in the time-series and also from orbital errors of Radarsat-1which has no on-board GPS since the fringe patter visible in all interferograms. “Fig. 4” shows the unwrapped phases with removed orbital trend. They contain the deformation and the atmospheric component of the phase. The lack of images, in particular in 2005, makes difficult to remove atmospheric artifacts.

4.2 Spatial Subsidence Pattern

The PSI and SB methods identified sufficient coherent pixels (318,710 for PS and 60,894 for SB) to enable further processing. After phase unwrapping step and filtering spatially correlated noise, it calculates a mean velocity line-of-sight LOS value

for each PS pixels from 2005 to 2009 with the deformation rates obtained fall in the interval –7.9 mm/year and uplift +6.8 mm/year, relative to the mean estimated value of the scene. Finally, the results are mapped to geodetic coordinate system as shown in “Fig. 5”. For a correct interpretation, one should consider a PSI that is stable and calculate the differences respect to it. Therefore, blue areas mean not absolute uplifting but uplifting respect the mean deformation value. Taking into account that no uplifting is expected, we can consider that the blue areas are sinking slower than mean deformation. In this case, we obtain the center of Bangkok to be subsiding with a rate of ~ -15mm/year. We can also see the estimated deformation field which is not as smooth as expected probably due to unwrapping errors, which can occur when the number of images is small (usually 20 or more are required) and when the interferogram lacks of correlation. We also detected a deformation that seems to relate to the old course of the Chao Phraya River. This can be seen in the center of the image with a snake-like shape, subsiding with -5 mm/year with respect to the surroundings, and is probably due to sediment compaction.

“Fig. 5” also shows that the results were not fully correctly geocoded. However, this could not harm our estimates because of the low topography of the area.

Figure 4. Unwrapped phase with removed orbital trend

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Figure 5. Mean velocity LOS 2005 -2009 (mm/year). A and B show the location of the time series of “Fig. 6”

4.3 Comparison with Ground Leveling

To compare the time series of PSI with leveling, we selected the PS that lie at distance less than 100 m from benchmark and average their velocities. The variability of the deformation of these PS is used to estimate the standard deviation. We referenced both type of measurements to the same area, which is shown as a black circle in “Fig. 5”. We also corrected for the offset produced by different time reference. Therefore, we have leveling and PSI with the same spatial and temporal reference to compare LOS displacements with conventional leveling survey.

“Fig. 6” displays the time series of PSI and leveling in two different places, whose location can be seen in “Fig. 5”. Mostly, the benchmarks are in agreement with PSI results, as seen in “Fig. 6A”. However, we also found some discrepancies, especially for the eastern area, “Fig. 6B”. This is probably because of atmosphere artifacts and unwrapping errors. Further developments are planned to correct for those errors. Unfortunately, the acquisition time of most images do not overlap with leveling periods. Therefore, the comparison of the rates measured by leveling and PSI could not be carried out.

Figure 6. Leveling and PSI time series (Locations of A and B are shown in “Fig. 5”)

5. CONCLUSION AND FUTURE WORK

The PSI technique has been applied to a set of 14 SAR images to detect and characterize a subsidence phenomenon on Bangkok Metropolitan area. The application of a multi-temporal InSAR approach (MTI) that combines both Persistent Scatterer and Small Baseline Subset methods (StaMPS/MTI) successfully detected more than 300,000 pixels that can serve as monitoring points. Deformation rates have been monitored during the period 2005-2009, with velocity a maximum rate of about -15 mm/year. We believe that the estimations are correct for the center of Bangkok where the density of detected PSI was very high. However, unwrapping seems to fail in some parts of eastern area, where jumps in the deformation are seen.

A

B

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We compared these results with leveling estimates to find an overall agreement in the deformation estimates. Therefore, the qualitative comparison of the subsidence rate determined from Radarsat-1 by PSI and leveling surveys showed a strong potential of remote sensing techniques with regard to cost effectiveness, resolution and accuracy. We also discovered a different deformation phenomena not related to water pumping. The ancient course of the river Chao Phraya is subsiding with -5 mm/year with respect to the surroundings. This is probably due to sediment compaction. Further work is planned to continue the monitoring of land subsidence in Bangkok. Radarsat-1 images are being acquired, so they will be appended to the time series improving the results. Another leveling network will be soon included. This should improve the validation of our results.

6. ACKNOWLEDGEMENT

This work was conducted in the framework of the GEO2TECDI project supported by the European Commission Delegation to Thailand. The authors thank Geo-Image Technology Research Unit, Department of Survey Engineering, Chulalongkorn University for facilities support. The Radarsat-1SAR images in this study are supported by Geo-Informatics and Space Technology Development Agency (Public Organization), and Royal Thai Survey Department supplies leveling data. The provision of DORIS for interferogram generation and StaMPS/MTI for PSI processing by TUDelft is also gratefully acknowledged.

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