Advancing Airport Land Subsidence Monitoring Through

Time-Series InSAR Technology

Anuphao Aobpaet

, Intira Thanomsin, Chanikan Yodya

and Suchanpong Obnam

Department of Civil Engineering, Faculty of Engineering, Kasetsart University, Bangkok 10900, Thailand

Keywords: InSAR, Time-series InSAR, Land Subsidence Monitoring, Airport.

Abstract: The airport is a pivotal infrastructure project, serving as a hub for parking, transporting, and maintaining

aircraft carrying passengers, freight, and cargo. However, the substantial usage of the airport leads to the

challenge of land subsidence, necessitating ongoing monitoring and assessment. This study focuses on

monitoring land subsidence at Suvarnabhumi Airport, Thailand's premier international airport catering to

passengers and aircraft. In a lowland area with soft soil, applying advanced technology becomes imperative

for continuous monitoring and analysis of subsidence over time. Employing InSAR Time-Series technology,

researchers processed data from Sentinel-1 satellites spanning October B.E. 2017 to December 2023 to

analyse the evolving conditions at Suvarnabhumi Airport. Results reveal that the most significant subsidence

occurs in the Runway and Taxiway areas, with values ranging between -9.1 and 5.1 millimeters. per year.

This subsidence is likely attributed to the constant heavy air traffic on these surfaces. Continuous monitoring

and evaluation are crucial to planning effective maintenance. InSAR technology is valuable for monitoring

land subsidence or displacement, alleviating data constraints and streamlining operational processes.

1 INTRODUCTION

An airport is a critical infrastructure connecting air

and ground transportation systems and facilitating

links between national and global economies

(Pornpiboon, 1997; Chimtawan, 2005). Runways and

taxiways experience continuous use and are subject to

heavy loads and temperature fluctuations, leading to

wear, deformation, and subsidence, posing risks to

safety and infrastructure integrity.

Suvarnabhumi Airport, located in Samut Prakan

Province, is in a soft soil zone on the lower Chao

Phraya floodplain (Department of Mineral Resources,

2016). The area's geological structure comprises low-

density soft sedimentary soils, making them

unsuitable for supporting heavy loads. Groundwater

extraction has exacerbated subsidence, with

approximately 28–100 mm/year rates before airport

operations (Srisompong, 2008). Post-construction

monitoring indicates continued subsidence at 20–30

mm/year in the apron and runway areas. Ongoing

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monitoring and evaluation are essential to mitigate

potential damage.

Traditional subsidence monitoring methods, such

as benchmarks and GNSS-based elevation surveys,

provide high accuracy but are limited in spatial

coverage and costly for large-scale monitoring

(Kheerinarat, 2020; Eiaurattanawadi, 2023). InSAR

technology, which uses phase differences in SAR

satellite images, offers a cost-effective and efficient

alternative for detecting land movement over large

areas (Piyamarat, 2022). Time-series InSAR further

enhances this by providing average subsidence rates

in millimeters per year using widely available satellite

imagery without requiring field installations.

This research applies time-series InSAR to

analyse Sentinel-1 satellite images (October 2017 to

December 2023) to monitor subsidence at

Suvarnabhumi Airport, Thailand's largest airport.

Subsidence is achieved through maps created in

QGIS, which provide insights into subsidence patterns

for improved infrastructure management and safety.

Aobpaet, A., Thanomsin, I., Yodya, C. and Obnam, S.

Advancing Airport Land Subsidence Monitor ing Through Time-Series InSAR Technology.

DOI: 10.5220/0013359500003935

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2025), pages 179-186

ISBN: 978-989-758-741-2; ISSN: 2184-500X

179

1.1 Scope of the Research

1.1.1 Study Area

Samut Prakan Province is located along the Chao

Phraya River, at the river’s endpoint just above the

Gulf of Thailand, between latitudes 13-14 degrees

north and longitudes 100-101 degrees east. The

province covers an area of approximately 1,004

square kilometers, or about 627,557 rai, in central

Thailand, about 29 kilometers southeast of Bangkok.

The general landscape is predominantly lowland

plains, with the Chao Phraya River flowing through

the center, dividing the province into western and

eastern parts (Samut Prakan Province, n.d.; Office of

Project Administration, Royal Irrigation Department,

2018; Department of Mineral Resources, 2016).

Suvarnabhumi Airport (IATA: BKK, ICAO:

VTBS) is located on Debaratana Road and Burapha

Withi Expressway, approximately at kilometer 15, in

the subdistricts of Nong Prue and Racha Thewa, Bang

Phli District, Samut Prakan Province. It lies about 31

kilometers east of Bangkok and covers an area of

approximately 22,000 rai. As Thailand's largest

airport and the tenth largest globally, Suvarnabhumi

Airport opened for commercial service on September

28, 2006. It serves as Thailand’s primary airport,

featuring modern design and advanced technology,

providing excellent service across all areas (Airports

of Thailand Public Company Limited, n.d.).

Currently, Suvarnabhumi Airport has two runways,

capable of handling 68 flights per hour. A third

runway, 4,000 meters in length, is under construction

on the airport's west side to accommodate increasing

air traffic in the future and to ensure capacity during

maintenance closures of Runways 1 and 2. The third

runway will increase the airport’s capacity to 94

flights per hour. In optimal conditions with entire

runway and taxiway operations, it will support an

average of 800-1,000 flights per day (Suvarnabhumi

Airport Construction Project Management Office,

n.d.; AEC Consortium Group, 2020). Thus, the

airport is a vital infrastructure component that

influences the future of the aviation industry,

symbolising economic growth and facilitating

international business operations.

1.1.2 Duration of Implementation

This study uses Sentinel-1 satellite images from

October 2017 to December 2023 for analysis based

on Synthetic Aperture Radar (SAR) technology.

Figure 1: Suvarnabhumi Airport Layout

(Suvarnabhumi

Airport Construction Project Management Office, n.d.).

2 PRINCIPLES AND THEORY

2.1 Sentinel-1 Satellite Data

The Sentinel-1 satellite, part of the European Space

Agency’s (ESA) Copernicus program, began

operations in 2014 and includes Sentinel-1A and

Sentinel-1B satellites (Bunyapoluk, 2022). However,

Sentinel-1B cannot transmit data back to Earth due to

an issue with its power supply (Eiaurattanawadi,

2023). Sentinel-1 utilises a radar imaging system that

uses microwave signals from a satellite-based energy

source and transmits them to Earth at an oblique angle

(GISTDA, 2021). It operates in the C-band with a

frequency of 5.405 GHz, providing images with

varying spatial resolutions and swath widths, as

detailed in Table 1. For this research, the

Interferometric Wide-Swath Mode (IW) is selected,

covering a 250-kilometer swath and offering an

image resolution of 5 x 20 meters in range and

azimuth directions. Additionally, it operates in dual

polarisation mode with an incidence angle ranging

from 31 to 46 degrees (European Space Agency,

n.d.).

Figure 2: SAR Operating Mode (European Space Agency,

n.d.).

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180

Table 1: SAR Operating Mode Under Sentinel-1 Satellite

(European Space Agency, n.d.).

Modes Swath

(

)

Spatial resolution

(

)

Stri

(

)

80 5 x 5

Interferometric

Wide Swath (IW)

250 5 x 20

Extra-Wide Swath

(EW)

400 20 x 40

Wave (WV) 20 x 20 5 x 5

The Sentinel-1A satellite operates in a near-polar,

sun-synchronous orbit at 693 kilometers, with an

inclination angle of 98.18 degrees and a 12-day repeat

cycle. Equipped with a C-SAR sensor, it provides

data under all weather conditions, day and night. The

satellite requires precise orbital control to ensure

accurate InSAR measurements for land and maritime

monitoring, emergency management, and

infrastructure analysis.

2.2 Interferometric Synthetic Aperture

Radar (InSAR)

InSAR (Interferometric Synthetic Aperture Radar)

technology is an advancement of SAR technology,

which combines Synthetic Aperture Radar (SAR)

images with wave interferometry technology

(Aobpaet & Trisirisatayawong, 2012). The working

principle of InSAR involves analysing phase

differences between two or more SAR images.

Synthetic Aperture Radar is a radar system that

produces high-resolution images (Piromthong, 2015).

When images of the same area taken at different times

are compared, the resulting phase differential indicates

surface movement (Geoscience Australia, n.d.;

Chelbi, Khireddine, & Charles, 2011). The difference

due to movement creates discrepancies between the

two images. The phase difference allows the study of

deformation patterns in various forms of land changes

(Chaitawee, 2015). However, while InSAR

technology can operate in all weather conditions, both

day and night, a significant limitation is the signal

distortion caused by the atmosphere, which leads to

phase measurement errors (Lu, Kwoun, & Rykhus,

2007).

2.3 Time-Series InSAR

The analysis using Time-series InSAR provides a

sufficient density of checkpoints to resolve issues

related to sparse checkpoints. It also addresses the

limitations of using such points to monitor

displacement or subsidence. As a result, this

technique offers high accuracy and precision.

Persistent Scatterer Interferometry (PS-InSAR) is

a technique that uses phase data from Synthetic

Aperture Radar (SAR) images to analyse changes in

land displacement over time or across specific periods.

It relies on the consistent and permanent backscatter

(Permanent Scatterer, PS) of radar signals, which are

transmitted to objects and reflected to the satellite

antenna. The method generates multiple Differential

Interferogram pairs, each referenced to a master image

for image matching (Chaitawee, 2015). This study

processes time-series radar images using the Persistent

Scatterers method, another approach in the Time-

series InSAR technique.

Persistent Scatterers InSAR

Persistent Scatterers InSAR (PSInSAR) is an

advanced remote sensing technique developed from

InSAR to address issues related to the lack of data

correlation and signal distortions caused by the

atmosphere (Piromthong, 2015). It utilises the

backscatter of microwave signals from prominent and

permanent reflectors (Permanent Scatterers, PS),

which reflect off objects and return to the signal

receiver, resulting in backscattering values for each

pixel. The amplitude and phase of each pixel are

vector sums of the backscattering from various

scatterers. Over time, any changes in these objects,

regardless of the cause, lead to changes in the

amplitude and phase of the pixel, indicating

movement, as shown in

Figure

(Sricharoenpramong,

2015; Hooper, Segall, & Zebker, 2007). This

technique is, therefore, suitable for monitoring

subsidence at airports.

Figure

: Simulation of Scattering Characteristics

Distributed Scatterer Pixel

(

b) Persistent Scatterer Pixel

(Yhokha, Goswami, & Chang, 2018).

2.5 Stanford Method for Persistent

Scatterers (StaMPS)

StaMPS is a software used for InSAR processing with

the Persistent Scatterers (PS) method, developed to

Advancing Airport Land Subsidence Monitoring Through Time-Series InSAR Technology

181

work even in areas without human-made structures or

regions experiencing unstable deformation (Hooper,

Bekaert, & Spaans, 2013). The core of the PS

technique involves identifying PS pixels and using

only the values from the selected pixels for

displacement processing. In the early stages of the PS

technique, amplitude values were primarily used for

filtering, which limited its use to areas with bright

scatterers, such as regions with many structures.

Another limitation was that a model of surface

displacement had to be known beforehand, or the

surface needed to exhibit stable movement to provide

reliable results. However, StaMPS uses amplitude

and the positional correlation of phase values for PS

pixel filtering. This allows StaMPS to identify PS

pixels even in areas with few structures. It enables it

to work in regions with non-stable displacement

without prior knowledge of the surface displacement

rate (Chaitawee, 2015).

3 METHODOLOGY

3.1 Preparation of Satellite Image Data

The data used in this study consists of images from

the Sentinel-1 satellite in the ascending orbit, which

moves from south to north over the Earth's surface,

covering the area of Suvarnabhumi Airport, Samut

Prakan Province, as shown in Figure 4. The dataset to

be used includes 75 Synthetic Aperture Radar (SAR)

satellite images recorded from October 25, 2017, to

December 29, 2023, covering 7 years. These images

are captured in the ascending orbit, with L1 Single

Look Complex (SLC) data, operating in VH and VV

polarisation modes, and recorded in Interferometric

Wide (IW) mode. The coordinate system used is

WGS 1984, in Path 172 and Frame 1222, ensuring all

images overlap perfectly for subsequent analysis.

Figure 4: Scope of Ascending Orbit Satellite Images

Covering the Study Area.

3.2 Data Processing

Interferometric processing (InSAR) relies on the

phase difference between two SAR images, referred

to as the master image and the slave image, from the

same area. Creating the interferogram uses the SNAP

software to select the master image from the 75

images available. The image from October 21, 2020

(Subswath: IW2, Polarization VV, Burst 6-8) was

chosen as the master image for reference and image

matching, as shown in Figure 5. Then, the Co-

registration algorithm aligned the sub-images from

nearby areas, ensuring their coordinates were

consistent (Laohudomchoke, 2023).

Figure 5: Image Matching of Slave Using the Master Image

as a Reference.

The algorithm for processing time-series satellite

data works as follows: first, Sentinel-1 satellite

images are downloaded. Once completed, the SNAP

software is used to read and convert the data by

performing image matching between the Master and

Slave images to create the interferogram. Then, a PS

candidate file is created (using the command

mt_prep_snap), and MATLAB is used to process the

data with the Persistent Scatterers method in StaMPS

to identify PS pixels. Phase unwrapping is performed

to obtain complete phase values, and atmospheric

noise is corrected using a linear process in TRAIN.

Finally, time series plots are generated (Ladawadee,

2022). The workflow for the algorithm to determine

subsidence rates can be illustrated in the flowchart

shown in Figure 6.

3.3 Displaying Data on the Subsidence

Map

Using MATLAB, the application of time-series

InSAR for monitoring subsidence at Suvarnabhumi

Airport allows for calculating displacement

(Deformation) and coordinates (Latitude and

longitude). The displacement data can then be

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Figure 6: Flowchart of the Algorithm Workflow.

displayed on a map in QGIS, as shown in Figure 9.

This is done by creating a data layer and defining

coordinates along the axis, followed by classification

to display the frequency of the data. The resulting

data is shown as points, with varying colors

representing the different subsidence values across

the study area, as depicted in Figure 7.

Figure 7: Results of Subsidence Analysis from MATLAB.

3.4 CORS Station

This research used data from one CORS station,

BPLE, operated by the Department of Lands. This

station is located at the Samut Prakan Land Office,

Bang Phli branch, as shown in Figure 8. It provides

automatic GNSS satellite signal surveying data under

the supervision of the Royal Thai Survey Department.

The data was collected from 2021 to 2023 and

processed to display horizontal and vertical

displacement. The results are presented in positional

data (Latitude, Longitude, Height), which can be used

as a reference for comparing subsidence trends

alongside time-series InSAR analysis using Sentinel-

1 satellite images from the same period. The GNSS

data were obtained through high-precision single-

point positioning, calculated by GPS surveying using

the online AUSPOS service, developed by

Geoscience Australia, a government agency. This

service offers free online GPS data processing. The

data processing utilises MicroCosm software, using

high-precision satellite orbit and IGS high-precision

satellite clock corrections. The processing can only be

done with static survey data using dual-frequency

receivers in RINEX format (Receiver Independent

Exchange Format). The accuracy of the results may

depend on the quality of the receiver, the survey

duration, and the distance from the reference station

(Sukwimonsaree et al., 2011).

Figure 8: Position of BPLE CORS Station.

Figure 9: A subsidence rate map of Suvarnabhumi Airport,

focusing on the runways and taxiways that require special

attention, derived from time-series InSAR processing.

Advancing Airport Land Subsidence Monitoring Through Time-Series InSAR Technology

183

4 RESULTS

4.1 Checking the Subsidence Value

The study on the application of time-series InSAR for

monitoring subsidence at Suvarnabhumi Airport,

using 75 Sentinel-1 satellite images from October

2017 to December 2023, detected 44,343 PS pixels

indicating subsidence and uplift across the airport

area. The analysis of subsidence values in the

ascending satellite orbit revealed land subsidence

ranging from -9.1 mm/year (subsidence) to 5.1

mm/year (uplift), as shown in Figure 9. These

subsidence values are within the normal range for

airport design in typical lowland areas, allowing

subsidence of up to 300 to 450 mm over 10 years

without causing operational or safety issues

(Srisompong, 2008).

As shown in Figure 13, the runway and taxiway

areas have a clear difference in subsidence compared

to other areas. For example, areas marked with orange

to red indicate subsidence ranging from -9.1 to -4 mm

per year, showing more significant subsidence than

other locations. These areas should, therefore, be

closely monitored and inspected. However, time-

series InSAR provides preliminary information about

subsidence.

4.2 Verification of Time-Series InSAR

Technique

The processing using the Persistent Scatterer

Interferometry (PSI) time-series technique to monitor

subsidence at Suvarnabhumi Airport, specifically in

the runway and taxiway areas (Runway 2), referenced

at coordinates Lat 13.6637, Lon 100.7536, was

conducted within a 100-meter radius. As shown in

Figure 10, the airport's cumulative subsidence trend

is increasing. The graph in Figure 11 illustrates daily

cumulative subsidence values in millimeters. The

detailed time-series measurements of PS deformation

reveal a clear subsidence pattern.

4.3 Comparison of Subsidence Trends

The results obtained from processing RINEX data

from 2021 to 2023 for station BPLE, reported as

positional data including Latitude (X), Longitude (Y),

and Height (Z), reveal movement in three directions:

X, Y, and Z.

This research focuses on vertical displacement

(Z) or land subsidence. From data processing, the

height of station BPLE on January 25, 2021 (initial

date), was -18.319 meters, and on December 29, 2023

Figure 10: The points within the 100-meter inspection

radius on Runway 2, located at coordinates Lat 13.6637,

Lon 100.7536, are measured in the Line of Sight (LOS).

Figure 11: The cumulative subsidence trend from 2017 to

2023 within a 100-meter radius on Runway 2, located at

coordinates Lat 13.6637, Lon 100.7536.

(final date), it was -18.390 meters. A height

difference of 0.071 meters, equivalent to 71

millimeters, was observed from 2021 to 2023. Over

the three years of processing, this indicates a land

subsidence rate of 71 millimeters or 23.7 millimeters

per year. As shown in Figure 12, when compared to

the subsidence rates obtained using the InSAR

technique at Suvarnabhumi Airport, which range

from -9.1 to -5.1 millimeters per year, the results

demonstrate an increasing trend in land subsidence

that is consistent with the overall pattern.

The subsidence rate trends derived from InSAR

time-series processing and the permanent GNSS

station BPLE, under the Department of Lands, from

2021 to 2023, exhibit a consistent pattern. The

subsidence rate increases over time. The subsidence

in the Suvarnabhumi Airport area is lower due to soil

improvements and specialised construction

techniques designed to support massive loads. In

contrast, station BPLE, located approximately 10

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Figure 12: The height statistics and subsidence trends of

station BPLE.

kilometers from the airport, shows a higher

subsidence rate despite being in a nearby area.

5 CONCLUSIONS

Time-series InSAR technology is an effective method

for monitoring airport subsidence. This study

analysed Sentinel-1 satellite data using the Persistent

Scatterer (PS) technique from October 2017 to

December 2023, utilising 75 ascending orbit SLC

images. The analysis identified 44,343 monitoring

points with subsidence rates ranging from -9.1 to 5.1

mm/year, where negative values indicate subsidence

and positive values indicate uplift. Runway and

taxiway areas exhibited significant cumulative

subsidence due to continuous air traffic use.

GNSS data from the BPLE station (10 km from

Suvarnabhumi Airport) recorded a subsidence rate of

23.7 mm/year (2021–2023), aligning with InSAR-

derived trends. Differences in subsidence values

reflect varying construction techniques and land use.

Still, both highlight Samut Prakan's susceptibility to

subsidence due to its soft soil and impacts from

urbanisation and groundwater extraction.

The findings underscore the importance of

continuous subsidence monitoring to mitigate risks.

InSAR technology proves valuable in engineering

surveys, offering preliminary insights into ground

stability, reducing fieldwork, and optimising time and

costs. Thus, it is a practical tool for infrastructure

management and hazard prevention.

ACKNOWLEDGEMENTS

The authors thank the European Space Agency (ESA)

for providing the Sentinel-1 satellite images from the

Copernicus program from 2017 to 2023, distributed

by ASF DAAC and the SNAP (Sentinel Application

Platform) software. I also thank Prof. Andy Hooper

from the School of Earth and Environment,

University of Leeds, for the StaMPS (Stanford

Method for Persistent Scatterers) software. Thanks to

MathWorks for providing MATLAB (Matrix

Laboratory) and to the software development team

behind QGIS (Quantum GIS), which was used for

processing and visualising data with the time-series

InSAR technique. I would also like to thank the Royal

Thai Survey Department (RTSD) and the Department

of Lands (DOL) for providing the RINEX Data.

Finally, I would like to thank the AUSLIG Online

GPS Processing Service (AUSPOS) for providing

free GNSS data processing software. This research

has received funding support from the NSRF via the

Program Management Unit for Human Resources &

Institutional Development, Research and Innovation

[grant number B11F670110]

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