Interferometric capabilities of ALOS PALSAR and its...
Transcript of Interferometric capabilities of ALOS PALSAR and its...
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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