ALOS PALSAR D-InSAR for Land Subsidence

ALOS PALSAR D-InSAR for land subsidencemapping in Jakarta, Indonesia

Luhur Bayuaji, Josaphat Tetuko Sri Sumantyo, and Hiroaki Kuze

Abstract. Differential synthetic aperture radar interferometry (D-InSAR) is a technique capable of detecting land surface

deformation. In this research, we use Advanced Land Observing Satellite (ALOS) Phased Array L-band SAR (PALSAR)

data to investigate land subsidence in Jakarta during 2007 and 2008. It is found that four northern areas in the city exhibit

clear indications of land subsidence. The location of the centre of subsidence is estimated, and the subsidence volume is

evaluated for each area using the unwrapping method as a further process of using D-InSAR results. The subsidence

depth and volume around the centre are estimated to be 10–22 cm and 0.2 6 105 – 4.2 6 105 m3, respectively, in the study

time period. Comparison with ground survey data indicates that the D-InSAR analysis gives reliable estimates of the

subsidence in an urban environment.

Resume. L’interferometrie differentielle radar a synthese d’ouverture (D-InRSO) est reconnue comme une technique

permettant de detecter des deformations de surface. Dans cette recherche, on utilise des donnees PALSAR (« Phased

Array L-band SAR ») d’ALOS (« Advanced Land Observing Satellite ») pour etudier les affaissements de terrain a

Djakarta au cours des annees 2007 et 2008. On a trouve quatre zones situees dans le nord de la ville qui affichaient des

signes evidents d’affaissement. Pour chacune de ces zones, on estime la localisation du centre de la subsidence et on evalue

le volume de celle-ci en utilisant la methode de deroulement de phase comme procedure de mise en valeur des resultats D-

InRSO. La profondeur et le volume de la subsidence du sol autour du centre sont estimes respectivement a 10–22 cm et

0,2 6 105 – 4,2 6 105 m3 durant la periode couverte par l’etude. Une comparaison avec des donnees de releves de terrain

indique que le produit de l’analyse D-InRSO donne une estimation fiable de la subsidence du sol dans un environnement

urbain.

[Traduit par la Redaction]

Introduction

Differential synthetic aperture radar interferometry (D-

InSAR) is a technique useful for accurately detecting the

ground displacement or land deformation in the antenna

line-of-sight (slant-range) direction using synthetic aperture

radar (SAR) data taken at two separate acquisition times

(Stramondo et al., 2006; Tralli et al., 2005). The D-InSAR

method is complementary to ground-based methods such as

levelling and global positioning system (GPS) measure-

ments, yielding information in a wide coverage area even

when the area is inaccessible (Raucoules et al., 2007).

The area studied in the present work is Jakarta, the

capital city of Indonesia. The data from the Phased Array

L-band Synthetic Aperture Radar (PALSAR) onboard the

Advanced Land Observing Satellite (ALOS) are used to

observe the land subsidence during 2007 and 2008, and

affected areas are detected with a spatial perspective. Since

the launch of ALOS in 2006, the PALSAR data have been

applied to several subsidence studies (Onuma and Ohkawa,

2009; Wang and Allen, 2008). To the best of our knowledge,

this report is the first case in which the urban area of

Jakarta, a tropical city with nearly 8 million inhabitants, is

studied by means of D-InSAR using ALOS PALSAR data.

The satellite-derived estimates of the subsidence depth are

compared with the results of a previous GPS survey (Abidin

et al., 2007) and with results of our ground survey

conducted in 2009.

Study area

Jakarta is located between 5u489300 and 6u249000S

latitude and 106u339000 and 107u009000E longitude, in the

northern part of West Java Province. The city consists of

five regions, covering an area of about 652 km2. The area is

relatively flat: topographical slopes range between 0u and 2uin the northern and central part and are up to 5u in the

southern part. The elevation of the southernmost area is

about 50 m above sea level, with the other areas being lower

(Figure 1A).

Jakarta, located in the Jakarta basin, has the following

five main landforms: alluvial, marine origin, beach ridge,

swamp (including mangrove), and former channel (Abidin

et al., 2007). It is known that the Jakarta basin is filled with

Received 14 July 2009. Accepted 17 February 2010. Published on the Web at http://pubservices.nrc-cnrc.ca/cjrs on 3 September 2010.

L. Bayuaji,1 J.T. Sri Sumantyo, and H. Kuze. Center for Environmental Remote Sensing, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba263-8522, Japan.

1Corresponding author (e-mail: [email protected]).

Can. J. Remote Sensing, Vol. 36, No. 1, pp. 1–8, 2010

E 2010 CASI 1

marine Pliocene and Quaternary sand and delta sediments,

with thicknesses of up to 300 m (Delinom et al., 2009) and a

high possibility of consolidating. Figure 1B shows the

geological information for the study area, which is mostly

dominated by alluvial deposits. There are 13 natural and

artificial (for supplying public water) rivers flowing through

the city, which has a humid tropical climate with annual

rainfall varying between 1500 and 2500 mm and is

influenced by monsoons. The nighttime population is

around 8 million, which increases to 11 million during

business hours because many people commute to Jakarta

from satellite cities. The population (residence) density in

the five districts was between 9600 and 23 000 inhabitants

per square kilometre in 2000, and the most recent statistics

for 2009 indicate that the value is between 12 000 and

19 000 inhabitants per square kilometre (Dinas Kependu-

dukan dan Pencatatan Sipil Provinsi DKI Jakarta, 2009).

The occurrence of land subsidence in Jakarta was

recognized by a Dutch surveyor as early as 1926 (Abidin

et al., 2005). Scientific investigations started in 1978, and a

continuous investigation using levelling measurement was

conducted during 1982–1999 (Djaja et al., 2004). The

measurement using GPS was also undertaken during

1997–2005 (Abidin et al., 2007); however, its extension to

a long-term and wide-area measurement would impose

considerable effort and cost. The present study uses the

ALOS PALSAR data to detect land subsidence and

estimate subsidence volume in the time period from 2007

to 2008. The methodology of D-InSAR and subsequent

phase unwrapping is used for this purpose.

Figure 1. (A) Map of Jakarta basin. (B) Geological map of Jakarta basin.

Vol. 36, No. 1, February/fevrier 2010

2 E 2010 CASI

D-InSAR

In SAR interferometry (InSAR), the phase data of SAR

images are analyzed to derive the local topography (original

InSAR) or detect and quantify the ground displacementthat has occurred in the slant-range direction between the

two acquisitions (D-InSAR) (Rosen et al., 2000). The phase

difference between an InSAR data pair wInt,P1{P2

� �can be

expressed as follows (Raucoules et al., 2007):

wInt,P1{P2~wdisp,P1{P2

zwatm,P1{P2zwnoise,P1{P2

zwtopo,P1{P2zwflat,P1{P2

ð1Þ

where wdisp,P1{P2, watm,P1{P2

, wnoise,P1{P2, wtopo,P1{P2

, and

wflat,P1{P2refer to the phase difference originating from

ground displacement along the slant range, atmosphericeffect, noise from the radar instrument and temporal

deceleration, topographic height information, and the

assumption of ideally flat earth terrain, respectively. In the

process of extracting the ground displacement, the topo-

graphic wtopo,P1{P2

� �and flat earth wflat,P1{P2

� �phase

differences should be removed using digital elevation model

(DEM) data and precise satellite orbital data, respectively. The

result of this process is generally called D-InSAR, which

estimates the ground displacement in the slant-range direction.

By assuming that land deformations have occurred only

in the vertical direction and the incidence angle is

approximately the same as the sensor off-nadir angle, the

ground displacement in the vertical direction, Dz, can be

derived as (Curlander and McDonough, 1991)

Dz~Dslcosh ð2Þ

where Dsl is the slant-range change caused by ground

displacement, and h is the incidence angle. The incidence

angle in this study is assumed to be 34.3u for all pixels in the

target area.

Data and processing software

A series of SAR interferograms are computed from

ALOS PALSAR fine-beam single-polarization (FBS) data

taken on three different acquisition dates (31 January 2007,

3 February 2008, and 5 November 2008). The data have the

same observation parameters: reference system for planning

(RSP) number 437, path number 7050, and an off-nadir

angle of 34.3u. Among the three pairs generated from these

Table 1. ALOS PALSAR pair and baseline information.

Pair Date 1 Date 2

Interval

observation

time (weeks)

Perpendicular

baseline (m)

1 20070131 20080203 52 220

2 20080203 20081105 39 840

3 20070131 20081105 92 618

Note: Dates are given as year, month, and day (e.g., 20070131 denotes31 January 2007).

Figure 2. Coherence image (A1, B1, C1) and differential SAR interferogram (A2, B2, C2)

obtained from each pair.

Canadian Journal of Remote Sensing / Journal canadien de teledetection

E 2010 CASI 3

data, the last pair is the most accumulative one corres-

ponding to the longest interval time of about 92 weeks.

Details of each interferogram pair (interval time and

perpendicular baseline) are summarized in Table 1. ForD-InSAR processing, we use the JAXA SIGMA-SAR

software (Shimada, 1999) to obtain the interferogram by

utilizing the DEM of the Jakarta area. The DEM was

obtained from the Shuttle Radar Topography Mission

(SRTM) with a grid resolution of 90 m.

The Goldstein–Werner filtering process was applied three

times to the noisy interferogram, with one iteration eachprocess, to remove noise and smooth the interferogram

(Goldstein and Werner, 1998). The coefficient in the filtering

process is 0.2. The resulting D-InSAR interferogram is in the

Figure 3. (A) D-InSAR interferogram of Jakarta (observation interval 20070131–20081105).

P1–P4, subsidence points. (B–D) BP1–BP4, CP1–CP4, and DP1–DP4 denote enlarged D-

InSAR interferogram of every point observation derived from each data pair.


4 E 2010 CASI

form of phase cycles, each cycle being correlated to ground

displacement along the slant-range direction. In the case of

ALOS PALSAR, the wavelength is 23.6 cm (L band), and

hence each cycle in the interferogram represents a ground

displacement of 11.8 cm. The final result was projected onto

the universal transverse Mercator projection (zone 48S) with

a pixel resolution of 20 m. Subsequently, the phase

unwrapping was carried out to derive the subsidence depth

from the interferogram. Precision ground-control points

(GCPs) obtained using GPS and JAXA SIGMA-SAR

software were used to obtain the unwrapping result. The

slant-range subsidence depth (phase unwrapping result) was

converted to vertical subsidence depth using Equation (2).

The volume estimation of land subsidence around each centre

was calculated by summing the product of pixel area and

subsidence depth. In addition to the D-InSAR study, we

conducted a ground survey of the study area on 28 January

and 3 February 2009.

Results and discussion

The coherence and filtered D-InSAR interferogram of

each pair are shown in Figure 2. The coherence patterns of

Figure 4. Results of unwrapping process. Two-dimensional, three-dimensional, and contour

plots for subsidence points P1–P4.

Table 2. Information on observation points.

Point Name Area specification

Subsidence coverage

area (km2)

Maximum subsidence

depth (cm)aSubsidence volume

estimation (6105 m3)a

P1 Mutiara Residential; port; recreation resort 1.7 14 0.9

P2 Cengkareng Settlement 4.4 22 4.2

P3 Glodok Trading 7.5 13 3.7

P4 Cakung Industrial 0.5 10 0.2

aDuring January 2007 and November 2008.


E 2010 CASI 5

the first and third pairs (A1 and C1in Figure 2) are almost the

same, and good coherence is seen in most parts of the study

area, but in the second pair (B1 in Figure 2) coherence is lost

in the northeastern part of the study area, which is mostly

vegetated (paddy fields and crops). Although the second pair

has the shortest acquisition time interval, its perpendicular

baseline is the largest (Table 1), and this may have affected

the coherence result. The interferogram image generally

shows clear (noisy) interferogram patterns in the area with

high (low) coherence. The results in A2 and C2 of Figure 2

exhibit similar patterns for the most part, except the number

of fringe cycles. It is noticeable, on the other hand, that some

fringes that are remarkable in A2 and C2 of Figure 2 do not

appear in B2 of Figure 2, even in the high-coherence area.

This is presumably due to the atmospheric effect represented

by the term watm,P1{P2in Equation (1) during the data

acquisition time, although more detailed analysis cannot be

carried out at this time because of limitations of data

availability. Nevertheless, the results in Figure 2 (especially

C2) show four separate areas with interferograms indicative

of subsidence effects. The following analysis focusses on these

areas, which are indicated by points P1–P4 in Figure 3A.

Figures 3B–3D show the enlarged differential SAR

interferogram from every data pair (three interferograms).

All the points are in the northern part of the city, where

geological formations are mostly alluvium and sand bars

(see Figure 1B). The northern part has very high human

activity, though its population density on an official basis is

low compared with that of southern districts. The harbor,

airport, warehouse, industrial, and trading areas are

established in this region, as well as the spread of slum

areas. Thus, there is a large number of commuters and

unregistered inhabitants. The slum area, covering up to 20%

of the total residence area in Jakarta, causes various

problems both socially and environmentally (Media Indo-

nesia, 2009).

Figure 4 shows the unwrapping image of each point in two-

and three-dimensional representations. The points P1–P4 can

be characterized with different land type–usage as explained

in the following. The result of GPS measurement conducted

by Abidin et al. (2007) also indicated land subsidence effects

for all four points. Point 1 (P1), Mutiara, is a luxury residence

area, tourism resort, and seaport built on a beach reclama-

tion area. The development of a new residential area started

in September 2006 and covers an area of 0.11 km2. Point 2

(P2), Cengkareng, is a settlement area that covers more than

23 km2. Since 2005, flat housing has been widely developed in

this region to relocate the slum dwellers. The international

airport and industrial area were built nearby. Point 3 (P3),

Glodok, is the largest trading region in Jakarta and covers a

wide area of more than 7 km2, with a large number of people

commuting to this area every day. Point 4 (P4), Cakung, is an

industrial area in the northeast part of Jakarta.

The maximum subsidence rates found during the time

span of the study are 8.0, 12.6, 7.4, and 5.7 cm/year for P1,

P2, P3, and P4, respectively. The maximum subsidence rate

and the subsidence volume estimation are shown in Table 2.

Figure 5 shows a comparison of vertical subsidence rates

between the previous GPS measurement during the period

1997–2005 (Abidin et al., 2007) and the D-InSAR results

during the period 2007–2008. The subsidence rates from

both periods are alike despite the differences in the applied

techniques and observation duration. The variability among

the four points (P1–P4) can possibly be traced back to

mechanisms such as excessive groundwater extraction, load

and building construction, and consolidation of alluvial soil.

During 2002 and 2005, the groundwater level in the Jakarta

basin lowered by 1–6 m below sea level on average, since the

water supply provided by the government only covers 30%

of the public demand (Delinom et al., 2009).

Figure 6 shows pictures taken at every point during our

ground survey in 2008, indicating the effects of subsidence that

had appeared in surface construction. P1-1 in Figure 6 shows a

dam in the Mutiara area (P1) built by a housing developer and

the government to prevent flooding due to tides. Wall cracking

and subsiding surfaces are also seen in P1-2 in Figure 6. In the

Cengkareng area (P2), many houses in settlement areas have

sunk beneath the road and land-surface levels as shown by P2-

1 and P2-2 in Figure 6. A large number of traders and

customers visit the centre of Glodok (P3) trading areas. The

well-constructed and well-maintained trading buildings did

not show any serious damage, but smaller houses suffered

seriously as seen in P3-1 and P3-2 in Figure 6, and P4 shows a

cracked brick fence around the industrial area in Cakung.

Conclusion

We have shown that the application of the differential

synthetic aperture radar interferometry (D-InSAR) technique

to Advanced Land Observing Satellite (ALOS) Phased

Figure 5. Chart of subsidence rate taken from GPS measure-

ments (199712–200509) and maximum subsidence from the D-

InSAR result (200701–200811).


6 E 2010 CASI

Array L-band SAR (PALSAR) data can reveal detailed

conditions of land subsidence in the urban area of Jakarta.

Most of the subsidence occurred in the northern part of the

city during the time interval between 2007 and 2008, although

this part of the city has the lowest population density of all

the city regions. The industrial district, reclamation area,

trading centre area, international airport, and seaport were

built in this region. It has been found that the subsidence

occurred in separate regions with different types of land use.

Despite the noise in the SAR interferogram, presumably due

to atmospheric effects, the centres of subsidence have been

successfully located, and estimates have been made of the

area affected by subsidence and subsidence volume.

The L-band, D-InSAR method using the ALOS PAL-

SAR data has produced reasonable results of urban

subsidence in a wide area as compared with ground-based

Figure 6. Field photographs of observation areas. The arrows indicate the effect of

subsidence occurrence.


E 2010 CASI 7

global positioning system (GPS) measurements. This ability

is beneficial for the creation of temporal urban subsidence

maps for further studies. Continuous information about

subsidence volume will be useful for urban maintenance and

development as one of a number of important factors forplanning and construction work.

References

Abidin, H.Z., Andreas, H., Gamal, M., Djaja, R., Subarya, C., Hirose, K.,

Maruyama, Y., Murdohardono, D., and Rajiyowiryono, H. 2005.

Monitoring land subsidence of Jakarta (Indonesia) using leveling, GPS

survey and InSAR techniques. International Association of Geodesy

Symposia, Vol. 128, pp. 561–566.

Abidin, H.Z., Andreas, H., Djaja, R., Darmawan, D., and Gamal, M. 2007.

Land subsidence characteristics of Jakarta between 1997 and 2005, as

estimated using GPS surveys. GPS Solutions, Vol. 12, No. 1, pp. 23–32.

Curlander, J.C., and McDonough, R.N. 1991. Syntetic aperture radar:

systems and signal processing. Wiley-Interscience, Toronto, Ont.

Delinom, R.M., Assegaf, A., Abidin, H.Z., Taniguchi, M., Suherman, D.,

Lubis, R.F., and Yulianto, E. 2009. The contribution of human activities

to subsurface environment degradation in greater Jakarta area, Indonesia.

Science of the Total Environment, Vol. 407, No. 9, pp. 3129–3141.

Dinas Kependudukan dan Pencatatan Sipil Provinsi DKI Jakarta. 2009.

Kepadatan Penduduk per Wilayah Kotamadya [online]. Available from

www.kependudukancapil.go.id [cited 1 July 2009].

Djaja, R., Rais, J., Abidin, H.Z., and Kuntjoro, W. 2004. The land subsidence

of Jakarta metropolitan area. In Proceedings of the 3rd FIG Regional

Conference for Asia and the Pacific, 3–7 October 2004, Jakarta, Indonesia.

International Federation of Surveyors (FIG), Copenhagen. Vol. 3, p. 14.

Goldstein, R.M., and Werner, C.L. 1998. Radar interferogram filtering for

geophysical applications. Geophysical Research Letters, Vol. 25,

pp. 4035–4038.

Media Indonesia. 2009. 20 persen wilayah jakarta pemukiman kumuh

[online]. Available from http://www.mediaindonesia.com/read/2009/05/

10/73939/36/5/20-Persen-Wilayah-Jakarta-Permukiman-Kumuh- [cited

21 January 2010].

Onuma, T., and Ohkawa, S. 2009. Detection of surface deformation related

with CO2 injection by DInSAR at In Salah, Algeria. Energy Procedia,

Vol. 1, No. 1, pp. 2177–2184.

Raucoules, D., Colesanti, C., and Carnec, C. 2007. Use of SAR

interferometry for detecting and assessing ground subsidence. Comptes

Rendus Geoscience, Vol. 339, No. 5, pp. 289–302.

Rosen, P.A., Hensley, S., Joughin, I.R., Li, F.K., Madsen, S.N., Rodriguez, E.,

and Goldstein, R.M. 2000. Synthetic aperture radar interferometry.

Proceedings of the IEEE, Vol. 88, No. 3, pp. 333–382.

Shimada, M. 1999. Verification processor for SAR calibration and

interferometry. Advances in Space Research, Vol. 23, No. 8, p 10.

Stramondo, S., Bignami, C., Chini, M., Pierdicca, N., and Tertulliani, A.

2006. Satellite radar and optical remote sensing for earthquake damage

detection: results from different case studies. International Journal of

Remote Sensing, Vol. 27, No. 20, pp. 4433–4447.

Tralli, D.M., Blom, R.G., Zlotnicki, V., Donnellan, A., and Evans, D.L.

2005. Satellite remote sensing of earthquake, volcano, flood, landslide

and coastal inundation hazards. ISPRS Journal of Photogrammetry and

Remote Sensing, Vol. 59, No. 4, pp. 185–198.

Wang, Y., and Allen, T.R. 2008. Estuarine shoreline change detection using

Japanese ALOS PALSAR HH and JERS-1 L-HH SAR data in the

Albemarle-Pamlico Sounds, North Carolina, USA. International Journal

of Remote Sensing, Vol. 29, No. 15, pp. 4429–4442.


8 E 2010 CASI

ALOS PALSAR D-InSAR for Land Subsidence

Documents

Transcript of ALOS PALSAR D-InSAR for Land Subsidence