Monitoring Vertical Deformations of the Coastal
City of Palu After Earthquake 2018 Using ParallelSBAS
Argo Galih Suhadha
Aeronautics and Space Research
Organization (LAPAN), National
Research and Innovation Agency
(BRIN)
Jakarta, Indonesia
agsuhadha@gmail.com
Atriyon Julzarika
Aeronautics and Space Research
Organization (LAPAN), National
Research and Innovation Agency
(BRIN)
Jakarta, Indonesia
verbhakov@yahoo.com
Mohammad Ardha
Aeronautics and Space Research
Organization (LAPAN), National
Research and Innovation Agency
(BRIN)
Jakarta, Indonesia
mohammad.ardha@lapan.go.id
Farikhotul Chusnayah
Aeronautics and Space Research
Organization (LAPAN), National
Research and Innovation Agency
(BRIN)
Jakarta, Indonesia
farikho14@gmail.com
Abstract—Surface deformation is a phenomenon that can
occur both naturally and by human intervention. Remote
sensing known can be used to obtain information regarding
surface deformation. Various methods have been developed,
such as DInSAR, TS-NSAR, SBAS, and PSBAS. The PSBAS
method is on the geohazard exploration platform issued by
Terradue. One of the objectives of this research is to determine
whether the PSBAS method can see the condition of surface
deformation after the hammer earthquake in Central Sulawesi
in 2018 using a data range from 2019-2021. Based on the
research results, it can be seen that several points after the
hammer earthquake experienced subsidence and uplift. Where
the highest subsidence is -3.893 cm and the highest uplift is 3.935
cm. then the accuracy check results show that areas
experiencing subsidence correlate with urban areas related to
groundwater extraction. In contrast, uplift areas correlate with
the uplift location being at the collision zone of the Palu-koro
fault facing the Makassar fault. The result proves that PSBAS
in Geohazard Exploration Platform cloud computing-based
could get information about surface deformation.
Keywords—vertical
Sentinel-1
deformation,
Palu,
P-SBAS,
GEP,
I. INTRODUCTION
Surface deformation is an un-periodic and irreversible
phenomenon related to the relative displacement of adjacent
parts of the surface crust caused by seismic and tectonic
activities, including earthquakes, landslides, volcanic,
tsunami, and anthropogenic effects, including land
subsidence and mining [1]–[3].
In deformation detection, remote sensing is a well-known
method for long data continuity, complete data coverage, and
minimal operator interaction. As a non-terrestrial method,
remote sensing can monitor deformation more precisely than
the terrestrial method [4]–[6].
Differential Interferometry Synthetic Aperture Radar
(DInSAR) is a typical remote sensing technique to generate
deformations of earth surface utilize microwave sensor that
permits measurement precision until centimeter-level
precision [7]. DInSAR was initially designed to monitor
single event deformation phenomena. The principle of
XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE
DInSAR is comparing two different SAR images, and if there
is a change on the Earth's surface with the related location,
the sensor will change the location of the point and consider
the phase-shift to generate an interferogram [8], [9].
Furthermore, DInSAR has limitations in phase decorrelation
and signal propagation delays in the atmosphere.
On the other hand, time series-InSAR (TS-InSAR) was
developed to overcome the limitations of conventional
DInSAR so that it can be effectively used to observe surface
deformation in urban areas. Urban areas have natural and
artificial objects such as large rocks, bridges, or roads with a
high spatial density to strengthen and stably scattered, and
these objects are commonly called persistent scatterers [10],
[11]. TS-InSAR is developed to detect pixel coherence based
on the number of Signal to Noise Ratios (SNR) to estimate
and minimalize phase error in the interferogram so can be
obtained mean Line of Sight (LoS) velocity number [7], [12],
[13]. TS-InSAR can separate based on the selection of image
pairs on their baseline, first using a large baseline to consider
the return signal information with a single master data called
Permanent Scatterer InSAR (PSInSAR), then using a small
baseline and using spatial and temporal characteristics of
Conventional DInSAR to produce more than one subset
acquisition is known as the Small Baseline Subset (SBAS)
[7], [14]–[16]. If PSInSAR uses one master, then SBAS
consists of several subsets where each subset has a different
master (multiple masters).
Recently, the SBAS approach has been effectively
exploited to investigate different scenarios, such as
earthquakes, volcanoes, landslides, and anthropogenicinduced land movements like mining and groundwater
extraction [17]–[22]. SBAS algorithm has been developed for
parallel processing called P-SBAS, which exploits computing
infrastructures and cloud computing environments [23]–[26].
Some work of SBAS has been done using Sentinel-1
because of the availability and free access of this satellite
imagery. Moreover, Sentinel-1 is the European Space
Agency satellite that continues the mission of ERS-1/2 to
provide C-band SAR data [27]. Sentinel-1 has four
acquisition modes, namely StripMap (SM), Interferometric
Wide (IW), Extra Wide (EW), and Wave [28]. IW mode is
the standard mode from Sentinel-1 appropriate to
interferometric applications because this mode has a broad
swath and spatial resolution to support operational missions
to monitor the Earth's surface [29] routinely.
This paper aims to exploit P-SBAS from Geohazards
Exploitation Platform (GEP) to monitor vertical deformation
in coastal cities, especially in Palu city after the 2018
earthquake using Sentinel-1 IW mode from 2019-2021.
II. DATA AND METHOD
A. Study Area
Sulawesi has highly active seismic caused by the collision
zone due to the meeting of three plates (triple junction): the
Indian-Australian Plate moving relative to the north, the
Pacific Ocean Plate moving close to the west, and the
Eurasian Plates, which is relatively stationary, and is
portrayed by rapid rotations of small blocks revealed by both
geographical and kinematics considers [29]–[31]. Sulawesi
and the surrounding area have several major geological
structures (faults) which are generally still active. The main
geological structures include the Palu-Koro Fault, Matano
Fault, Poso Up Fault, Gorontalo Fault, Tujaman (Trench)
North Sulawesi. The main faults also result in local faults,
folds, and depressions in various places [31]–[34].
Palu, a coastal city in central Sulawesi, experienced a
large earthquake (Mw 7.5) on September 28, 2018. This
earthquake going with another hazard, including tsunami,
landslides, and liquefaction, caused minor damage in the
western part of Sulawesi, including two coastal regencies;
Palu city, the central city of the central Sulawesi province,
and Donggala regency [35]. Based on this event, we are
curious to monitor vertical deformation after this earthquake
to find out the movement of the Palu Koro fault. The study
area location showed in figure 1.
Sentinel-1 is a SAR satellite imagery from the European
Space Agency (ESA), an ongoing mission from the ESA
project with ERS-1, ERS-2 ENVISAT, and Canada mission
in RADARSAT-1 and RADARSAT-2 [27]. This satellite is a
piece of the Copernicus mission, which forms Sentinel-1,
Sentinel-2, Sentinel-3, Sentinel-4, Sentinel-5, and Sentinel5p. The Sentinel-1 mission comprises two satellites with
polar orbits, operational during the day and night, using Cband sensors [28]. C-band sensor in radar satellite has a
wavelength (λ) of 5,66 cm, which allows the sensor to obtain
backscattering from the subject's surface and, at the same
time, get additional backscattering from inside the object
[36]. The acquisition of Sentinel-1 consists of several
methods, including Strip map (SM), IW, Extra Wide (EW), and
Wave (WV), each of which has its swath width and resolution
(Table. 1).
Table 1. Specification of Sentinel-1 acquisition modes.
Mode
Swath
Width
Resolution
Pixel Spacing
(Range ground
x azimuth)
(Range ground x
azimuth)
Strip map (SM)
80 km
1.7 x 4.3 m to 3.6
x 4.9 m
1.5 x 3.6 m to 3.1
x 4.1 m
Interferometric
Wide (IW)
250 km
2.7 x 22 m to 3.5
x 22 m
2.3 x 14.1 m
400 km
7.9 x 43 to 1.5 to
43 m
5.9 x 19.9 m
2.0 x 4.8 m and
3.1 x 4.8 m
1.7 x 4.1 m and
Extra Wide (EW)
Wave (WV)
20 km
2.7 x 4.1 m
Source: [28].
Interferometric Wide (IW) is a Sentinel-1 data acquisition
mode suitable for interferometry applications due to swath
width and spatial resolution, specifically for monitoring
deformation. Besides, the availability of image data can be
obtained free [37].
C. Methodology
SBAS method can reduce the baseline decorrelation
problem of conventional DInSAR due to the magnitude of the
perpendicular baseline between the two observations and
avoiding temporal decorrelations that occur due to changes in
the return signal due to changes in vegetation or snow
between the two observations.
a)
b)
Fig. 1. Maps of the study area; a) Palu, the source location of the 2018
earthquake b) location of the Palu Koro traverse fault line.
B. Materials
The data used as inputs of P-SBAS is Interferometric Wide
(IW) mode of Sentinel-1 Single Look Complex (SLC) that
images related to the same track and acquired with the same
modes.
SBAS was developed to assess the deformation of the
Earth's surface by utilizing multiple small baseline subsets,
which were combined to obtain unwrapped differential
interferograms [15], [38], [39]. The SBAS-DInSAR
algorithm was demonstrated by [40] to overcome
atmospheric artifacts and topographical errors in the
interferogram collection and obtain time-series displacement
information. The selection of SAR image pairs is determined
based on the small spatial/ temporal baseline to achieve the
small baseline.
P-SBAS algorithm generates a displacement time series
of Sentinel-1 using a parallelization processing chain. The
available sentinel-1 data burst is considered a series of
separate images to be independently processed. The
workflow of P-SBAS is shown in figure 2.
Fig. 2. P-SBAS workflow. Blackline shows sequential processing steps, and red shows parallel processing steps (Source: [23]).
P-SBAS results displacement in Line of Sight that is the
displacement that occurs along the line of observation of the
sensor to the target so that it is necessary to convert it into
actual deformation or called vertical deformation using the
equation [5]:
fault, making this location almost happened uplift throughout
2019-mid 2021.
𝑽𝒆𝒓𝒕𝒊𝒄𝒂𝒍 𝒅𝒊𝒔𝒑𝒍𝒂𝒄𝒆𝒎𝒆𝒏𝒕 = 𝑳𝒐𝑺 / 𝒄𝒐𝒔 (𝜽)
where 𝜽 is Sentinel-1 incidence angle.
III. RESULTS AND DISCUSSION
For P-SBAS processing, we used Sentinel-1 IW data from
the same track in path 134 and frame 595, which covered the
Palu city area. Sentinel-1 images used has an amount of 132
data which is acquired from January 2019 until May 2021.
The results showed in figure 3, where the point is getting
closer to red, indicating that experienced uplift, while the
point is getting closer to blue, indicating subsidence.
Vertical deformations results showed that the settlement
area of Palu city is getting more subsidence where the
maximum subsidence rate is closer to -3.893 cm/year.
Furthermore, the west area of the Palu-koro fault is getting
more uplifting with the uplift rate maximum up to 3.935
cm/year.
Figure 4a shows the sampling locations of time series
vertical deformations in Palu and its surroundings. Point a
and b represent subsidence locations, while points c and d
showed the places of uplift event. Each point is generated to
the line chart that shows the vertical deformation in separate
times besides 2019 until May 2021. In the subsidence
location, we know that in the earlier period (before April
2019), these locations have experienced uplift. The area is
periodically experiencing subsidence until it reaches a
maximum value of -2.55 cm, which occurs around January
2021. The subsidence in the urban city is in line with the
results of [6], [18], [20], [41] that subsidence is triggered by
groundwater extraction and building mass in the urban city.
Uplift phenomenons are interesting to discuss that these
locations are located on the west of Palu koro fault line,
characterized by highlands and land cover still in dense
vegetation. Furthermore, the uplift location being at the
collision zone of the Palu-koro fault facing the Makassar
Fig. 3. Palu city vertical deformation information generated from SBAS
chain using Sentinel-1 SAR data.
a)
Map of time series vertical displacement sampling
b)
-0.93, 119.91 (subsidence)
c)
-0.91, 119.88 (subsidence)
d)
-0.7, 119.73 (uplifted)
e)
-0.82, 119.65 (uplifted)
Fig. 4. Time series vertical deformation of Palu city from 2019-May 2021
Using Google street maps, we observe the location of each
point. Figure 5 shows the street maps of each point
observation. The zones of maximum subsidence (points a and
b) are in a densely populated residential area because they are
in an urban area in Palu. Point a is located in the small bridge,
and b is located in a house.
Meanwhile, the uplift locations are located in the coastal
area of Donggala, which is a highland area with land cover in
the form of vegetation.
Point c
Point a
Point d
Fig. 5. Street maps view of each sampling point of time series vertical
displacement
The previous research found that the sampling locations
(Figure 4) and the spatial distribution of the vertical
deformation of the P-SBAS results (Figure 3) in this study
had the same precision and distributions (Figure 6.). Most of
Palu City has experienced subsidence in several areas.
Point b
[3]
[4]
[5]
[6]
[7]
[8]
[9]
Fig 5. The 3D deformation coseismic field of the Palu earthquake. (a), (b)
and (c) show the east-west, north-south, and vertical components of the
displacements. (Source: [42])
[10]
More than that, due to the limitations of the author's field
data to validate information, the vertical accuracy assessment
of the results of this study can be used as suggestions for
further research.
[11]
IV. CONCLUSIONS
In this research, P-SBAS from the Geohazard Exploitation
Platform has been implemented to monitor vertical
deformations of Palu city. The platform can generate LoS
displacement automatically using cloud computing where
high specifications of user computers do not need. P-SBAS
resulted in LoS displacement, which means converting to
vertical displacement to get actual displacement is needed.
The results show that in 2019- May 2021, Palu city
experienced subsidence in their urban area, whereas Donggala
regency experienced uplift. The subsidence results show their
correlation to the groundwater extraction of the metropolitan
area. The uplift location happened at the collision zone of the
Palu-koro fault facing the Makassar fault. Based on these
results, we know that GEP using cloud computing has
successfully implemented P-SBAS to generate LoS
displacement easier and faster.
[13]
ACKNOWLEDGMENT
The authors would like to thank ESA, Terradue, ADB, and
LAPAN for providing the license of the GEP platform,
training, supporting data, and implementation of this research.
Thanks to google for providing the street maps. We thank
anonymous reviewers. Both authors are the main contributor.
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10.1016/J.TECTO.2019.228216.