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LAND SUBSIDENCE MONITORING USING PERSISTENT SCATTERER InSAR
(PSInSAR) IN KELANTAN CATCHMENT
Ami Hassan Md Din12, Mohd Nadzri Md Reba2, Kamaludin Mohd Omar1, Mohamad Rashidi bin Md.Razli1
and Noradila Rusli 3
1
Geomatic Innovation Research Group (GIG), Faculty of Geoinformation and Real Estate, Universiti Teknologi
Malaysia, 81310 Johor Bahru, Johor, Malaysia.
Email: [email protected], [email protected], [email protected]
2
Geoscience and Digital Earth Centre (INSTEG), Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia.
Email: [email protected]
3
Faculty of Architecture, Planning and Surveying, Universiti Teknologi MARA, 40450 Shah Alam,
Selangor Darul Ehsan, Malaysia..
Email: [email protected]
KEYWORDS: Land Subsidence, Groundwater Extraction, Persistent Scatterer InSAR
ABSTRACT: Land subsidence is defined as a gradual settling or sudden sinking of the Earth’s surface due to
movement of earth materials. Common cause of land subsidence from human activity is groundwater extraction. In
Kelantan catchment, groundwater is a vital resource for urban and rural residential, agricultural and commercial water
users. Approximately 70% of the total domestic water supplies in Kelantan are from groundwater. Therefore, this study
aims to monitor land subsidence due to groundwater extraction using Persistent Scatterer (PS) InSAR. The specific
study areas are limited to three prominent groundwater extractions in Kelantan, i.e. Pintu Geng, Tanjung Mas and
Tumpat. PSInSAR is an extension to the conventional InSAR techniques, which addresses and overcomes the major
limitations of repeat pass SAR interferometry (i.e., temporal and geometrical decorrelation and variations in
atmospheric conditions). PSInSAR technique requires only selective pixels which are stable in phase throughout the
acquisition time period of the images. Practically, phase stable (PS) pixels are represented by the static ground object
such as buildings, roads, bare rocks, bridges and so on. Stanford Method for Persistent Scatterers (StaMPS) was used to
identify the PS points and extract deformation signals even in the absence of bright scatterers. This study applies 17
ERS-2 scenes in the descending tracks from April 1996 to February 2011. To verify the deformation rate of the study
area, GPS measurements collected at GETI station was compared. The hydrogeological map by the Minerals and
Geoscience Department Malaysia (JMG) was used to support in analysis. The results found that the land subsidence rate
at Pintu Geng, Tanjung Mas and Tumpat is about -1.78 mm/yr, -2.39mm/yr and -1.87mm/yr respectively. Extensive
land subsidence due to groundwater extension is existed in the study area and thus this concludes that the PSInSAR
application is suitable for monitoring land subsidence.
1. INTRODUCTION
Land subsidence is a natural phenomenon expected to occur in some places that experiencing groundwater extraction.
Kelantan uses groundwater for 70% of total water usage (Akademi Sains Malaysia, 2009). Several areas in Kota Bharu,
Kelantan have been experienced this phenomenon due to they used well or boring to extract the groundwater for their
daily usage. Over-exploitation groundwater causes compression of the interbedded layers of clay and silt within the
aquifer system. Besides, it can also cause depletion of surface water, degraded water quality, high pumping costs, etc.
(Ashrafianfar et al., 2009). This awareness led the researcher to investigate potential of monitoring the land subsidence,
particularly in Kota Bharu, Kelantan.
The utilization of groundwater in Kota Bharu started way back since 1935 (SMHB, 2000). Groundwater contributes
about 90% of the total demand for drinking water in Kelantan (Suratman, 1997). The demand for groundwater
increased 2.5% per year and for the year 2010 the production demand for the whole state was estimated at 146 Ml/d,
(Suratman, 2012). Figure 1 shows the total groundwater production in Kelantan from1990-2010. In general, The
civilians dig into private wells and draw water from a shallow aquifer while the Water Supply Department (Kelantan)
extracts the groundwater from the deeper aquifer. If excessive groundwater is extracted, it is afraid that land subsidence
occurs. Thus, this study aims to monitor land subsidence due to groundwater extraction using Persistent Scatterer (PS)
InSAR.
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Groundwater Extraction (Ml/d)
160
140
120
100
80
60
40
20
0
Year
Figure 1. Total groundwater production in Kelantan 1990 – 2010 (Suratman, 2012)
2. MATERIALS AND METHOD
2.1 Concept of Persistent Scatterer (PS) InSAR
The precision of InSAR measurement has been greatly improved in the recent years with the development of new
methods for time series analysis such as Persistent Scatterer (PS) InSAR (Ferretti et al., 2001; Hooper et al., 2004). This
advanced algorithm method is originally an extension to the conventional InSAR model that was developed by Ferretti
et al. (1999) and later patented as the Permanent Scatterer Technique by Politecnico of Milano (Ferretti et al., 2001). PS
InSAR deals with both the decorrelation and atmospheric delay errors of conventional InSAR, and is able to provide a
dynamic map of the deformation field at millimetre level on a high spatial density grid of phase stable radar targets
(Hooper, 2006).
PS InSAR technique does not use every image pixel, but only chooses particular pixels based on their phase stability
throughout the time period of the images. In the real world, these phase stable pixels reflect objects such as buildings,
roads, bare rocks, bridges and so on. As a result, the PS InSAR implementation has been mostly limited to applications
that have many bright scatterers, which are normally man-made structures. These man-made structures are usually
tended to be angular and frequently yield very efficient reflectors that dominate the background scattering (Hooper,
2006; Agram, 2010).
The concept of PS InSAR is clearly illustrated in Figure 2. The figure illustrates the point scatterers contributing to the
phase of one pixel in an image and the plots below represent the phase simulations for 100 iterations. The persistent
scatterer in Figure 2b is three times brighter than the sum of the smaller scatterers. The image decorrelation could be
decreased to zero if the phase of a pixel were defined by only one point scatterer. Therefore, all radar images of the area
can create usable interferograms. Although this is rarely the case, there are pixels where one scatterer dominates the
echo and which behave like so-called point scatterers, so that decorrelation is greatly decreased. This is the model for a
PS pixel (Hooper, 2006).
Figure 2. Phase simulations for (a) a distributed scatterer
pixel and (b) a persistent scatterer pixel (Hooper, 2006)
Nonetheless, PS InSAR requires a larger number of SAR images than conventional InSAR in order to choose
statistically reliable PS points and as well as to estimate more reliable atmospheric phase (Warren, 2007). From all the
image pairs, a series of interferograms where all images are refered to a similar master scene can be generated. Hence,
the atmospheric delay signal is able to be estimated and removed by filtering the resulting phase time series obtained for
each of the PS pixels (Hooper, 2006).
Currently, the PS InSAR technique are already extensively applied in volcanic and crustal deformation monitoring
(Hooper and Pedersen, 2008; Paganelli and Hooper, 2008; Perski et al., 2008), urban subsidence monitoring (Ferretti et
al., 2000; Worawattanamateekul et al., 2003), subsidence monitoring due to mineral or gas extraction (Kemeling et al.,
2004; Cuenca and Hanssen, 2008; Ketelaar, 2009) and also to verify individual building stability (Ferretti et al., 2000;
Colesanti et al., 2003; Farina et al., 2006).
2.2 Datasets Used
In this study, 17 SAR images from ERS-2 satellite mission were requested from the European Space Agency
(ESA).Kota Bharu is located at the north-eastern part of Peninsular Malaysia, and it lies near the mouth of the Kelantan
River. The city of Kota Bharu is close to the Thai border. 17 ERS-2 scenes from descending satellite tracks are used for
PS InSAR processing for this area. The data span covers from April 1996 to February 2011. Details on each satellite
image that was used in this study are presented in Table 1. As illustrated in Figure 3, a bin of 30 km by 30 km area is
cropped for PS InSAR processing for the area of Kota Bharu.
Table 1. List of ERS-2 SAR data and its related information for Kota Bharu, Kelantan
ID
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
MISSION
ERS-2
ERS-2
ERS-2
ERS-2
ERS-2
ERS-2
ERS-2
ERS-2
ERS-2
ERS-2
ERS-2
ERS-2
ERS-2
ERS-2
ERS-2
ERS-2
ERS-2
SENSOR
AMI/SAR
AMI/SAR
AMI/SAR
AMI/SAR
AMI/SAR
AMI/SAR
AMI/SAR
AMI/SAR/
AMI/SAR
AMI/SAR/
AMI/SAR
AMI/SAR
AMI/SAR
AMI/SAR
AMI/SAR
AMI/SAR
AMI/SAR
DATE
19960406
19960511
19960824
19961102
19971018
19990814
20000205
20010609
20050129
20080712
20080920
20081129
20100821
20100925
20101030
20101204
20110212
ORBIT
5025
5526
7029
8031
13041
22560
25065
32079
51117
69153
70155
71157
80175
80676
81177
81678
82680
FRAME
3484
3484
3484
3484
3484
3484
3484
3484
3484
3484
3484
3484
3484
3484
3484
3484
3484
TRACK
161
161
161
161
161
161
161
161
161
161
161
161
161
161
161
161
161
PASS
D
D
D
D
D
D
D
D
D
D
d
D
D
D
D
D
D
Figure 3. ERS-2 satellite image
covering Kota Bharu (left). The red
square indicates the cropping area
(bin) for Kota Bharu, 30 km by 30
km. Further details on PS InSAR
analysis around Kota Bharu are
demonstrated by Google Earth (right)
Besides, hydrogeological map of Kelantan state (see Figure 4) was taken from a Department of Mineral and
Geoscience, Malaysia as secondary data was used to determine the tabulation of groundwater well in Kelantan, i.e.
Pintu Geng, Tanjung Mas and Tumpat.
Figure 4. Hydrogeological Map of Kelantan 2008 Edition
2.3 Persistent Scatterer Framework and Procedure
In this study, the Stanford Method for Persistent Scatterers (StaMPS) method is applied for quantification of land
subsidence using time series analysis. StaMPS is a multi-temporal (time series) PS InSAR processing developed by
Hooper (2006) and it is capable monitoring of non-urban areas (Hooper et al., 2004; Hooper et al., 2007; Hooper,
2008). StaMPS uses DORIS, Delft Object-oriented Radar Interferometric Software (Kampes et al., 2003) software
package developed by the Delft Institute for Earth-Oriented Space Research (DEOS), Delft University of Technology to
form differential interferograms.
The input data for the interferometric processing steps in DORIS is Single-Look Complex (SLC) images. In SLC
format, the SAR data collects the dispersed energy into a single pixel for the target point. This contrasts the raw SAR
data, where the dispersed energy of target point is spread in azimuth and range (Leighton, 2010). Each complex
observation data in SLC format can be converted into amplitude and phase observations. Basically, the interferogram
can be computed by the complex multiplication of the observations in each resolution pixel from two coregistered SAR
images, a master and a slave image. This will produce the interferometric phase differences where the actual
observations of earth surface deformation or land subsidence can be estimated.
After reading and cropping the SLC data, the SAR images are oversampled by a factor 2 before performing
coregistration and formation of interferograms. By performing the oversampling with a factor 2, it avoids aliasing in the
complex multiplication of the SAR images for both datasets (master and slave). Oversampling, or correlation between
adjacent pixel values, allows interpolating new points between every neighbouring pixel. Hence, the coordinates of an
oversampled image can be determined at sub-pixel level with respect to the original sampling rate. This means that the
target point in the radar coordinate system can be more precisely determined in an oversampled image (Arikan et al.,
2010).
In this study, the master image acquisition has been chosen based on stack coherence. The aim of the master selection
using stack coherence is to minimise the total decorrelation of all the interferograms. Concurrently, this approach is able
to maximise the total correlation of the interferometric stack based on the perpendicular baseline ( ), temporal
baseline ( ), and the mean Doppler centroid frequency difference (
) (Zebker and Villasenor, 1992; Hooper, 2006).
The acquisition geometries per track (perpendicular baseline as a function of temporal baseline) are illustrated in Figure
5 for the study area.
Figure 5. PS network for each study area (track). Each black
circle is a SAR image and each edge (baseline) is a SAR
interferogram. PS interferogams are all connected to a single
master scene
Next is the coregistration step. The coregistration is a crucial step and fundamental in interferogram generation.
Coregistration of images is to ensure that the images fit to each other for both the master and slave images in the subpixel level. If the precision of the image’s coregistration is not sufficient, it deteriorates the PS selection process and
decreases the PS phase observation precision. Consequently, all SLC images have to be resampled in an adaptive way
to the master (a reference image), i.e., by applying the offset values indicated by the coregistration grid to each pixel (in
sub-pixel accuracy). The resampling is performed by computing the offsets between corresponding pixels in the two
SLCs (reference and resampled images) and by calculating a rotation and skew matrix that registers the resampled SLC
image to the reference image (Aly, 2006).
After resampling, the master and slave images should be two identically sized complex arrays of values. If the size of
master and slave images is different, it means the slave crop does not include the entire master crop. This may be due to
a problem with coregistration for that particular slave. Subsequently, the interferogram is computed by complex
multiplication of each pixel from the master with the complex conjugate of the corresponding pixel in the resampled
slave image (Leighton, 2010). Then, in order to obtain only a phase value due to deformation of surface, the
unnecessary phase contribution of topography is refined using Digital Elevation Model (DEM). In this work, the DEM
model from the global Shuttle Radar Topography Mission (SRTM) was used for topography removal.
Next, the data input for PS InSAR processing in StaMPS is a stack of differential interferograms coregistered to a
selected master interferogram (from DORIS output). For initial PS candidate selection, StaMPS utilises a statistical
relationship between amplitude and phase stability. In this study, the threshold value used for amplitude dispersion is
0.4, which leads to most of the selected pixels not chosen as PS pixels. This amplitude dispersion threshold value
eliminates areas over water and heavily decorrelated pixels in vegetated areas. This PS candidate selection stage is
actually optional, but is able to reduce the processing time and memory requirements by a factor of ten (Sousa et al.,
2011).
Then, phase stability for each PS candidates is estimated using phase analysis. The phase stability is analysed under the
assumption that the deformation signal, atmospheric phase and orbital errors are spatially correlated. Due to the phase
observations are wrapped, only the fractional phase is measured and not the integer number of cycles. The PS
candidates phase observations are filtered using Combined Low-pass and Adaptive Phase (CLAP) strategy and those
with the lowest residual noise are chosen (Hooper, 2006; Sousa et al., 2011).
In this work, RMS threshold for defining the phase stability,
(coherence) is 0.005m. After every iteration, the RMS
of is calculated. If the RMS is bigger than the RMS threshold (0.005m),
do not converge and the interferometric
phase is weighted for next iteration. If the RMS is equal or smaller than the RMS threshold, the solution has converged
and the algorithm stops iterating. Thus, initial PS pixels are selected based on the PS probability square weighting
strategy with consideration of their amplitude dispersion,
and phase stability,
(Hooper et al., 2007). Besides, the
spatially-uncorrelated look angle (SULA) (DEM error) is also estimated in this stage. The threshold for estimating the
DEM error that has been applied in this work is 5m (maximum uncorrelated DEM error). Hence, pixels with
uncorrelated DEM error greater than this threshold will not be picked up.
PS pixels selected in the previous step are kept, and adjacent pixels are dropped as those pixels are due to signal
contribution and deemed too noisy. Normally, the bright scatterer (PS pixel) dominates the other nearby pixels. The
resulting DEM error (SULA) will prevent those adjacent pixels from being selected as PS, if the distance between the
bright scatter and the geometric centre of the adjacent pixel is great enough (Hooper et al., 2007). After dropping
adjacent pixels, the remaining PS pixels are evaluated by their standard deviation and maximum noise. If the noise
standard deviation is smaller than one and the maximum noise is not infinity, the PS point is kept while all others are
removed as noisy pixels (Hooper et al., 2007; Liu, 2012).
Since selected PS phase measurements points are in wrapped between −π and +π, phase unwrapping is therefore
implemented in order to derive continuous deformation values. In this study, 3D phase unwrapping has been performed
(two spatial, as with conventional InSAR, and one temporal). The temporal phase differences for each PS pixel are
calculated and then unwraped spatially from a reference PS pixel using an iterative least square method. PS pixel is also
filtered using Goldstein adaptive phase filter before performing phase unwrapping. This method is convenient to map
slow deformation over time (Hooper, 2006). After phase unwrapping, high-pass filtering can be applied to the
unwrapped data followed by a low-pass filter in order to remove the remaining SCLA errors (look angle, atmosphere
and orbit errors). By subtracting these signals (SCLA) from the PS pixel, it will leave only phase data due to
deformation (land subsidence) and spatially uncorrelated error terms that can be modelled as noise. All the remaining
noise terms are supposed to be substantially reduced (Hooper, 2006; Sousa et al., 2011).
3. RESULTS AND DISCUSSION
3.1 PS InSAR and GPS Vertical Displacement Comparison
It is essential to compare the obtained deformation rates from different data sets. The comparison between PS InSAR
and GPS results are made to estimate the accuracy of PS InSAR measurements. In this study, PS pixels within 300 m
of a GPS station are considered to be on a similar geological setting and PS pixels velocities fitting this criteria are
averaged (Leighton, 2010). The deformation rate standard deviations are computed and statistical outliers are eliminated
before the estimation of the final rate. The final rate is achieved by averaging PS pixels epoch by epoch. The GPS
station used for the comparison is GETI which located at Tumpat, Kelantan. The GETI station is one of the Continuous
Observation Reference Station (CORS) station in Malaysia. This GPS station has been operated since 1999 until now.
The daily GPS data has been processed using Bernese version 5.0. Figure 6 is a plot showing the correlation between
PS InSAR and GPS time series at GETI station. The PS data is represented by red dots and the GPS data is depicted by
blue dots. The black trend line indicates the derived deformation slope velocity. From Figure 6, GPS results
demonstrate better precision, or data repeatability, compared to PS InSAR. Since PS InSAR and GPS data are mostly in
different time spans of the available data and the data noise present for each technique, it leads to difficulty in
estimating the exact accuracy for each technique. However, in general, the velocity pattern of both PS InSAR and GPS
data indicate good agreement for each station. Both techniques give a similar deformation trend, but the subsidence
results provide different rates due to dissimilarity of data span.
Figure 6. PS InSAR and GPS
vertical deformation rate
comparisons. Blue dots
represent GPS and red dots
represent PS InSAR results. PS
InSAR rates are computed by
averaging the velocity epoch by
epoch for all the PS pixels
within 300 m of the related GPS
station
3.2 Land Subsidence Assessments
Figure 7 shows the location that has been chosen earlier for the land subsidence monitoring. Every area has circle
symbols that describe the groundwater well. The red circle indicates inactive well while the blue circle is an active well
where this well is the one that use for domestic usage. The variation of groundwater well tabulation is important in
order to monitor the land subsidence. For presentation of the land subsidence, PS-InSAR results are presented in
graphical model. The analysis was made by comparing the graphical model that generated from PS InSAR and the area
of interest in the hydrogeological map.
Figure 7. The study area. Area A is Tumpat, B is Tg Mas (Kota Bharu) and C is Pintu Geng (Kota Bharu)
3.2.1 Tumpat (Area A)
The total cropping area for Tumpat is 1601.72 acre (see Figure 7). The geological properties of the area are sands, peat,
humus clay and silt. Only one active well detected from the hydrogeological map. In general, as demonstrated in Figure
8 (left), the deformation rates in Tumpat and its surrounding area, from 1996 to 2011, ranges from -1.8 mm/yr
(subsidence) to 0.6 mm/yr (uplift). Besides, Figure 8 (right) shows time series of subsidence rate through years where
the rate is approximately -1.78 mm/yr.
Figure 8. PS InSAR results in Tumpat area from 1996 to 2011. Deformations mean velocity (mm/yr) (left) and plot of
deformation time series for Tumpat Area (right)
3.2.2 Tg Mas (Area B)
The cropping area for Tg Mas is 8797.439 acre (see Figure 7). The geological properties of the area are undifferentiated
of sand, clay, gravel and silt. There are some of active well and one inactive well detected from the hydrogeological
map. By referring Figure 9 (upper), the deformation rates in Tg Mas and its surrounding area, from 1996 to 2011,
ranges from -2.3 mm/yr (subsidence) to 1.4 mm/yr (uplift). From the PS results, the land subsidence is visibly more
dominant in the north part of the study area. While, from the plot of time series at Tg Mas area shows that the rate of
subsidence is estimated at -2.39 mm/yr (lower).
Figure 9. PS InSAR results in Tg Mas area from 1996 to 2011. Deformations mean velocity (mm/yr) (upper) and plot of
deformation time series for Tumpat Area (lower)
3.2.2 Pintu Geng (Area C)
For Pintu Geng study area, the cropping area was 13407.731 acre, which the largest area is chosen in this study. The
geological properties of the area are undifferentiated of sand, clay, gravel and silt. Inactive well is the major tabulation
of well in this area while only some of them are still active well. Based on the Figure 10 (upper), it indicates that the
northern part of Pintu Geng has prominent land subsidence with rates typically around -1.8 mm/yr. The blue areas can
be considered stable at the southern part of Pintu Geng and the rest subsiding. Besides, the time series plot presents in
the Figure 10 (lower) indicates that the subsidence rate at Pintu Geng is about 1.87 mm/yr.
Figure 10. PS InSAR results in Pintu Geng area from 1996 to 2011. Deformations mean velocity (mm/yr) (upper) and
plot of deformation time series for Tumpat Area (lower)
4. CONCLUSIONS
The analyses from the PSInSAR results show that the land subsidence occurred in all three study areas. Areas that
showed the most experienced land subsidence is Tg. Mas with the subsidence rate are at -2.39 mm/yr. Tg Mas area is
closely related to groundwater wells in the vicinity and it has many active ground water wells. The characteristic of the
soil in this area which is from silt and clay may also be potential of land subsidence easy to occur. Based on the
distribution of PSInSAR results, the land subsidence does not only focus on the well, but also occurred around the well
that has common ground features. Hence, the extensive land subsidence due to groundwater extension is existed in the
study area and thus this concludes that the PSInSAR application is suitable for monitoring land subsidence.
ACKNOWLEDGEMENTS
The authors would like to thank to European Space Agency (ESA) for providing ERS-2 SAR images. We are grateful to
the Ministry of Science, Technology and Innovation (MOSTI) for funding this project under the eScience Fund, Vote
Number 04-01-06-SF1092.
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