Interferometric capabilities of ALOS PALSAR and its utilization

Ryoichi Furuta(1), Masanobu Shimada(1) , Takeo Tadono(1) , and Manabu Watanabe(1)

(1) EORC, JAXA, Office Tower X23F, Harumi Triton Square, Harumi Island, 1-8-10, Harumi, Chuo-ku, Tokyo,

104-6023, Japan

ABSTRACT

JAXA’s new land observation satellite, the Advanced Land Observing Satellite (ALOS) will be launched in 2006.

ALOS will carry L-band SAR (PALSAR). It can obtain InSAR/DInSAR products through repeat pass data acquisition

in 46-day cycles. PALSAR observation modes were screened to six modes out of 132, combining 18 different off-nadir

beams for strip SAR, and five observation modes Fine Beam Single (FBS), Fine Beam Dual (FBD), Direct

Transmission (DT), SCANSAR, and polarimetry. The six modes are FBS of 21.5°, FBS of 34.3°, FBS of 41.5°, FBD of

41.5°, SCANSAR (short burst), and POL of 21.5°. PALSAR can provide global coverage three times per year in

FBS/FBD mode and once a year in ScanSAR mode. From this acquisition, we will provide Digital Elevation Models

(DEMs) by InSAR processing. We will also derive global and local area deformation maps with the DInSAR

processing.

Application of the DInSAR technique has expanded to various fields. Its utilization in disaster monitoring and

mitigation is anticipated. Monitoring of crustal deformation of pre- and post-earthquake provides scientific knowledge

of earthquake mechanisms. Monitoring landslides and subsidence can mitigate severe disasters and support decision

making. Moreover, InSAR processing provides highly accurate and fresh DEMs of disaster areas, supports deformation

detection, and gathers information of the disaster area topography. This information assists in disaster relief activities.

DEMs produced by InSAR processing will be useful in the fields of topology, geology and resource surveying.

To clarify the utilization of the InSAR/DInSAR technique of ALOS PALSAR, it was applied to various studies using

JERS-1 SAR. ALOS PALSAR overview and its strategy, as well as examples of InSAR/DInSAR products, are

introduced.

1 INTRODUCTION

JAXA scheduled the launch of the Advanced Land Observing Satellite, ALOS [1], [2], [3], [4], [5], on 19 January 2006.

ALOS will carry two optical sensors, PRISM and AVNIR-2, and one active microwave radar sensor, PALSAR.

PALSAR is an L-band SAR that can obtain information from the surface with a highly vegetated area with 24 cm wave

length. Previous works by JERS-1 L-band SAR Interferogram and Differential Interferogram demonstrated good

results due to high vegetation penetration, especially in the mountains. It is very useful for detecting surface

deformation in mountainous areas, especially slow landslide movement [6] [7]. In previous works, L-band SAR

exhibited high potential to detect surface deformation, providing much scientific knowledge to geophysical scientists.

This paper introduces an overview of JAXA’s new Earth observation satellite, ALOS. The interferometric capabilities

of ALOS PALSAR are introduced using the case study of InSAR/DInSAR analysis for surface movement detection by

JERS-1 L-band SAR.

2 OVERVIEW OF ALOS SATELLITE

Figure 1 depicts an image of the ALOS satellite. ALOS is one of the largest satellites in the world, with a mass of 4

tons and 7 kW electric power generated by the 23 m solar array paddle. Its orbit is sun synchronous with an altitude of

691.65km. The cycle repeats every 46 days. A sub-cycle of two days is provided via pointing function of AVNIR-2. The

characteristics of the ALOS satellite are presented in Table 1.

ALOS has the following five mission objectives.

i) Cartography

ii) Regional observation

iii) Disaster monitoring

iv) Resources Surveying

v) Technology Development for future satellites

Because of the large number of catastrophes occurring in recent years, disaster monitoring has a higher priority than

other mission objectives. JAXA joined the international disaster charter in February 2005. ALOS sensors are expected

to monitor disasters immediately worldwide.

Fig. 1. Image of ALOS

3 OVERVIEW OF ALOS PALSAR AND ITS INTERFEROMETRIC CAPABILITIES

PALSAR is an L-band SAR capable of day or night observation regardless of cloud cover. It can penetrate a dense

vegetation canopy with its 24 cm wavelength signal. The capacity to observe highly vegetated areas is helpful in

monitoring disasters and developing damage maps of large areas. It also enables producing Digital Elevation Models

(DEMs) using the InSAR technique in highly vegetated mountain and forest areas. Moreover, its powerful equipment

can survey natural resources and lineaments, and detect surface deformation such as crustal deformation, subsidence,

and landslide movement. PALSAR can analyze InSAR/DInSAR every 46 days due to the ALOS orbit revisit time.

Figure 2 provides an image of the PALSAR antenna. The antenna is 3.1 m high and 8.6 m wide. Figure 3 displays the

Table. 1. Characteristics of ALOS satellite

Launch Date JFY2005

Launch Vehicle H-IIA

Launch Site Tanegashima Space Center

Spacecraft Mass Approx. 4 tons

Generated Power Approx. 7 kW (at End of Life)

Design Life 3 -5 years

Sun-Synchronous Sub-Recurrent

Repeat Cycle: 46 days (Sub Cycle: 2 days)

Altitude: 691.65 km (at Equator)

Orbit

Inclination: 98.16 deg.

Attitude Determination Accuracy 2.0 x 10-4 deg.(with GCP)

Position Determination Accuracy 1m (off-line)

Data Rate 240 Mbps (via Data Relay Technology Satellite)

120 Mbps (Direct Transmission)

Onboard Data Recorder Solid-state data recorder (90Gbytes)

PALSAR observation, and the PALSAR characteristics are cited in Table 2. PALSAR has 132 observation modes

composed by combining variable off-nadir angle and full polarization. PALSAR observation modes were screened to

six modes out of 132, combining 18 different off-nadir beams for strip SAR and five observation modes of Fine Beam

Single (FBS) of 21.5°, 34.3°, and 41.5°; Fine Beam Dual (FBD) of 41.5°; Direct Transmission (DT); ScanSAR (short

burst); and Polarimetry of 21.5°. We considered the sensitivity to observation targets and similarity to JERS-1 SAR in

selecting the following observation modes.

i) 21.5° off-nadir angle has higher sensitivity for oil-spill detection; 34.3° has similarity to JERS-1 SAR; and 41.5°

reduces geometric distortion.

ii) HH polarization is the mode reference because of high penetration and similarity to JERS-1 SAR.

iii) HH+HV polarization exhibits good sensitivity to vegetation structure.

iv) full-polarization of 21.5° becomes the representative.

v) five-beam ScanSAR short burst that covers a 350 km swath, facilitating 120 Mbps co-activation with the mission

instruments.

All 132 observation modes will be employed for disasters, which is why disaster monitoring has high priority through

the ALOS mission.

The interferometric capabilities of PALSAR are based on its revisit time and the perpendicular baseline of its orbit. The

satellite position and attitude is affected by the accuracy of InSAR and DInSAR. ALOS has adopted a highly accurate

position and attitude decision system to acquire absolute data for interferometric analysis. JAXA EORC will generate

Fig. 2. Image of PALSAR antenna Fig. 3. Observation image of PALSAR

Table. 2. Characteristics of PALSAR

Mode Fine ScanSAR Polarimetric

Center Frequency 1270 MHz (L-band)

Chirp Bandwidth 28MHz 14MHz 14MHz, 28MHz 14MHz

Polarization HH or VV HH+HV or

VV+VH

HH or VV HH+HV+VH+VV

Incidence angle 8 ~ 60 deg. 8 ~ 60 deg. 18 ~ 43 deg. 8 ~ 30 deg.

Range Resolution 7 ~ 44m 14 ~ 88m 100m (multi look) 24 ~ 89m

Observation Swath 40 ~ 70 km 40 ~ 70 km 250 ~ 350km 20 ~ 65km

Bit Length 5 bits 5 bits 5 bits 3 or 5bits

Data rate 240Mbps 240Mbps 120Mbps, 240Mbps 240Mbps

NE sigma zero < -23dB (Swath Width 70km)

< -25dB (Swath Width 60km)

< -25dB < -29dB

S/A > 16dB (Swath Width 70km)

> 21dB (Swath Width 60km)

> 21dB > 19dB

Radiometric accuracy scene :1dB / orbit :1.5 dB

DEM with InSAR technology and crustal deformation maps with DInSAR technology to distribute high-level products

and research products. DInSAR analysis is applied to surface deformation monitoring such as volcanic activities,

subsidence and landslide, allowing for immediate monitoring of disasters that occur anywhere in the world.

4 CASE STUDY OF SURFACE DEFORMATION DETECTION BY DInSAR TECHNIQUES

4.1 DInSAR analysis of surface deformation due to earthquakes

Surface deformation due to earthquakes results in crustal deformation as well as subsidence and landslide. This section

introduces crustal deformation and subsidence due to seismic motion [8]. Figure 4 provides results of time series crustal

deformation detection of the Mid Niigata Prefecture Earthquake in 2004 by DInSAR. The data was acquired by

RADARSAT-1 C-band SAR on 7 September 2004, 1 October 2004, 25 October 2004, and 17 November 2004. All data

were processed by SIGMA-SAR processor developed by JAXA EORC [9], [10]. Results indicate that the crustal

deformation was produced by the main shock of an earthquake on 23 October 2004, and its maximum deformation was

16.8 cm. Time series analysis detected -2.8 cm of crustal deformation produced by an aftershock using data of 25

October 2004 and 17 November 2004. Unfortunately, this analysis cannot detect crustal deformation in the mountains

because the area is highly vegetated and has a long perpendicular baseline. The PALSAR solve these problems.

Interestingly, L-band SAR can detect crustal deformation before and after an earthquake. The information derived from

time series DInSAR results provides scientific knowledge of seismic motion. In this earthquake, liquefaction

phenomena occurred around Nagaoka. In particular, subsidence occurred after liquefaction due to dissipation of ground

water pressure. Figure 5 displays the DInSAR detected subsidence due to liquefaction in a rice paddy field. DInSAR

analysis revealed a maximum subsidence of 3 cm, and it is possible to quickly recognize the subsidence area. The aerial

photo in Figure 5 was taken during a field investigation after the earthquake. Evidence of sand “boiling” confirmed that

liquefaction occurred in this rice paddy field.

7 Sep. – 1 Oct. 1 Oct. – 25 Oct. 25 Oct. – 17 Nov.

-1.4cm +1.4cm

Pre-EQ Main shock Aftershock

©SIGMA-SAR JAXA/EORC

Stable state Deformed by main shock Deformed by aftershock

7 Sep. – 1 Oct. 1 Oct. – 25 Oct. 25 Oct. – 17 Nov.

-1.4cm +1.4cm-1.4cm +1.4cm

Pre-EQ Main shock Aftershock

©SIGMA-SAR JAXA/EORC

Stable state Deformed by main shock Deformed by aftershock

Fig. 4. Result of crustal deformation detection of the mid Niigata Prefecture Earthquake in 2004 by time series of

DInSAR. Data was acquired by RADARSAT-1 C-band SAR on 7 September 2004, 1 October 2004, 25 October 2004,

and 17 November 2004.

Fig. 5. Result of subsidence detection by DInSAR. Upper left image shows result of DInSAR and lower left image

shows enlarged view of subsidence area. Subsidence occurred after liquefaction was able to confirm from aerial photo

(right image).

4.2 Monitoring landslide movement by DInSAR

The advantages of applying the DInSAR technique to landslide monitoring have been clarified by previous research [6],

[7]. To understand the capability of DInSAR to monitor landslide movement, JERS-1 L-band SAR Differential

Interferometry was applied to the Takisaka landslide, Niigata, Japan. This is one of largest landslides in Japan,

extending 2.1 km to north to south and 1.3 km east to west. Figure 6 presents an aerial photo of the Takisaka landslide.

Dense vegetation at the Takisaka landslide test site was confirmed by the aerial photo. Data analysis of the Takisaka

landslide was acquired by JERS-1 L-band SAR from September 1992 to October 1998. In this analysis, the data used

was acquired in spring and autumn because of its good coherence and synchronization with GPS observations from

1995 to 1998. Figure 7 exhibits the status of the analytical pairs. Figure 8 present the results of time series DInSAR of

the Takisaka landslide. Stable ground is green, and other colors represent the amount of deformation. The figure

indicates that significant deformation occurred in 1995 and 1998. To confirm quantitative recognition, the DInSAR

result was compared with the observed GPS data. In DInSAR analysis, the unwrapping of the processed phase is

important for highly accurate detection of surface deformation. In this case, GPS data was used to correct phase

unwrapping because a dense GPS network was installed in this test site. Figure 9 compares the DInSAR result and the

GPS observation result and demonstrates that DInSAR correlates well with the GPS data, with a correlation coefficient

of 0.97.

Takisaka Landslide

Rain station

Aganoriver

JR Bansai route

Takisaka Landslide

Rain station

Aganoriver

JR Bansai route

Takisaka Landslide

Rain station

Aganoriver

JR Bansai route

Fig. 6. Aerial photo of the Takisaka landslide.

Jan-92 Jan-93 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99

1992/9/2 1994/9/201995/4/28

1995/10/211996/4/14

1996/7/111997/11/7

1998/9/11

Bp=461.4m

Bp=69.2mBp=-850.6m

Bp=676.9mBp=552.4m

Bp=577.4mBp=329.0m

Jan-92 Jan-93 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99

1992/9/2 1994/9/201995/4/28

1995/10/211996/4/14

1996/7/111997/11/7

1998/9/11

Bp=461.4m

Bp=69.2mBp=-850.6m

Bp=676.9mBp=676.9mBp=552.4mBp=552.4m

Bp=577.4mBp=577.4mBp=329.0mBp=329.0m

Fig. 7. Analytical pairs of DInSAR.

To decide how to mitigate landslide disasters, it is important to understand what triggers a landslide. To understand this,

the DInSAR result is compared with observed precipitation data. Figure 10 clearly indicates that the large deformation

corresponded with the heavy rain season. Finally, we can state the following about this landslide.

i) Approximately 400 mm of precipitation per month for two consecutive months will cause a landslide.

ii) Over 500 mm precipitation in a single month will cause a landslide.

Moreover, to evaluate the depth of slide surface at the center of the landslide block, Maruyama et al. [11] proposed the

following equation where depth of slide surface is proportional to the length of landslide block.

1992/9/2 1994/9/20 1995/4/28 1995/10/21 1996/4/14 1996/7/11 1997/11/7 1998/9/11

-5.9cm +5.9cm

©SIGMA-SAR, JAXA/EORC1km

N Landslide movement monitoring of Takisaka Landslideby JERS-1 SAR Differential Interferometry

1992/9/2 1994/9/20 1995/4/28 1995/10/21 1996/4/14 1996/7/11 1997/11/7 1998/9/111992/9/2 1994/9/20 1995/4/28 1995/10/21 1996/4/14 1996/7/11 1997/11/7 1998/9/11

-5.9cm +5.9cm-5.9cm +5.9cm

©SIGMA-SAR, JAXA/EORC1km1km

NN Landslide movement monitoring of Takisaka Landslideby JERS-1 SAR Differential Interferometry

Fig. 8. Time series DInSAR results of Takisaka landslide. Data was acquired by JERS-1 L-band SAR from 1992 to

1998.

Phase rotation : Considered

y = 0.9877x + 0.8784R2 = 0.9747

-200

-150

-100

-50

0

50

100

150

200

-200 -150 -100 -50 0 50 100 150 200

GPS displacement (cm)

DIn

SA

R d

ispl

acem

ent (

cm)

Phase rotation : Considered

y = 0.9877x + 0.8784R2 = 0.9747

-20

-15

-10

-5

0

5

10

15

20

-20 -15 -10 -5 0 5 10 15 20

GPS displacement (cm)

DIn

SA

R d

ispl

acem

ent (

cm)

Phase rotation : Considered

y = 0.9877x + 0.8784R2 = 0.9747

-200

-150

-100

-50

0

50

100

150

200

-200 -150 -100 -50 0 50 100 150 200

GPS displacement (cm)

DIn

SA

R d

ispl

acem

ent (

cm)

Phase rotation : Considered

y = 0.9877x + 0.8784R2 = 0.9747

-20

-15

-10

-5

0

5

10

15

20

-20 -15 -10 -5 0 5 10 15 20

GPS displacement (cm)

DIn

SA

R d

ispl

acem

ent (

cm)

Phase rotation : Considered

y = 0.9877x + 0.8784R2 = 0.9747

-20

-15

-10

-5

0

5

10

15

20

-20 -15 -10 -5 0 5 10 15 20

GPS displacement (cm)

DIn

SA

R d

ispl

acem

ent (

cm)

Fig. 9. Comparison between DInSAR results that unwrapped based on GPS data and observation result of GPS

network.

0

200

400

600

800

1000

Jan-92 Dec-92 Dec-93 Dec-94 Dec-95 Dec-96 Dec-97 Dec-98Year/Month

prec

ipita

tion

(mm

)

Rain_Nishiaizu Rain_Takisaka

Fig. 10. Comparison between DInSAR result and observed monthly precipitation data.

d=0.105+0.084L (1)

Here, d (m) is the depth of slide surface at the center of landslide block, and L (m) is the length of landslide block.

From this equation, the depth of the slide surface of this landslide was estimated to be 78 m to 100 m. It closely agrees

with the estimated depth of the slide surface by field investigation.

The conclusions of these case studies provide us scientific knowledge as well as important information of measures to

mitigate disasters.

5 CONCLUSIONS

ALOS will be launched on 16 January 2006 carrying L-band SAR, PALSAR, and two optical sensors, AVNIR-2 and

PRISM. Observation modes of PALSAR were screened by five observation modes, FBS, FBD, DT, ScanSAR and

Polarimetry. PALSAR has capabilities to repeat pass interferometry every 46 days. ALOS will be useful for providing

highly accurate digital elevation models (DEMs) and deformation monitoring, as well as disaster monitoring and

hazard prevention. FBS and FBD will provide global coverage three times a year, and ScanSAR, once a year. EORC

JAXA will generate InSAR DEMs and deformation maps.

In previous work, L-band SAR demonstrated good performance, i.e. crustal deformation detection, subsidence

detection and landslide monitoring. This paper introduced a case study of DInSAR to detect crustal deformation and

subsidence caused by earthquakes. Landslide monitoring by DInSAR was also introduced. These results were

compared to several field investigation results and confirmed that DInSAR is capable of detecting surface deformation

and that L-band SAR has significant advantages. All results provided scientific knowledge, as well as important

information of how to mitigate disasters. It will be possible to achieve highly accurate DEMs and DInSAR results with

ALOS PALSAR.

ACKNOWLEDGMENTS

The purchase of the RADARSAT-1 SAR data was supported by the Remote Sensing Technology Center of Japan.

Copyright of RADARSAT-1 SAR data was provided by the Canadian Space Agency, Canada Centre for Remote

Sensing, and processed and distributed by Radarsat International. The aerial photograph of the Mid Niigata Earthquake

was provided by the Geophysical Survey Institute, Japan. Field investigation data of Takisaka landslide was provided

by the Ministry of Land Infrastructure and Transport.

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