PERS_1-14_Flipping - page 12

12
January 2014
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
sensors that are normally in polar
orbits, airborne SARs can be used to
image the target from versatile flight
directions. This enables calculation
of the 3-D surface displacement
field and improves estimation of
parameters that characterize the
deformation source (e.g., depth,
location, shape) (Jung
et al
., 2011;
Lundgren
et al
., 2013).
The use of ground-based radar
systems also has significantly
increased in recent years (e.g., Luzi
et al
., 2006; Werner
et al
., 2012).
Ground-based radar systems are
becoming a reliable tool to map
ground surface displacement with
high spatial resolution of centimeters
to meters and unprecedented
temporal resolution of minutes.
This capability promises to provide
additional insights into processes
responsible for rapid deformation
during volcanic eruptions, landslides,
dam failures, etc.
TanDEM-X — A Testbed for
Innovative Technologies
The DLR TerraSAR-X tandem
mission for DEM measurements
(TanDEM-X) (Table 1) deserves
special mention here. TanDEM-X is
a new high resolution constellation
InSAR mission that relies on an
innovative flight formation of two
X-band TerraSAR-X satellites to
produce InSAR-derived DEMs on
a global scale with an accuracy
better than the SRTM flown in 2000
(Krieger
et al
., 2007). In addition,
TanDEM-X enables precise mapping
of ocean and river currents by fusing
two SAR images steered in the
along-track direction (e.g., Romeiser
et al
., 2013). The resulting product
will be invaluable for monitoring
extreme waves and ocean hazards.
Furthermore, TanDEM-X will
provide data to assess the utility
of new methods for characterizing
landscapes and monitoring their
changes, including bi-static multi-
angle SAR imaging, digital beam
formation, and polarimetric InSAR.
Conclusion
InSAR is one of the fastest growing
fields in remote sensing and Earth
sciences. Timely observations of
precise land surface topography
and time-transient surface changes
from InSAR are accelerating the
development of models for volcanic
eruptions, earthquake displacements,
landslides, and land subsidence, and
also providing new tools to better
characterize natural resources.
Multi-interferogram techniques now
allow monitoring of the safety and
stability of critical infrastructure
at fine resolution, and offer the
capability to image the 3-D structure
of vegetation on a global scale for
improved resource management. With
more operational SARs available in
the near future, InSAR, coupled with
state-of-the-art data fusion and data
mining techniques, will continue
to address and provide solutions to
many scientific questions related to
natural and man-made hazards and
to natural resource management.
Acknowledgements
We thank Stefan Gernhardt
from DLR for providing imagery
shown in Figure 1, USGS Volcano
Hazards Program and NASA Earth
Surface and Interior Program
(11-DESSDT11-0024) and Research
Grants Council of Hong Kong SAR
(PolyU 5381/13E) for funding support,
and Google Earth for Figure 1a. We
are grateful for constructive review
and editing by Dan Dzurisin from
USGS and Jie Shan from Purdue
University.
References
Amarsaikhana, D., H.H. Blotevogelb,
J.L. van Genderenc, M.
Ganzoriga, R. Gantuyaa, B.
Nerguia, 2010. Fusing high-
resolution SAR and optical
imagery for improved urban land
cover study and classification,
International Journal of Image
and Data Fusion
, 1, 83-97.
Bechor, N.B.D., and H.A. Zebker,
2006. Measuring two-dimensional
movements using a single
InSAR pair,
Geophysical
Research Letters
, 33, doi:
10.1029/2006GL026883.
Berardino, P., G. Fornaro, R. Lanari,
and E. Sansosti, 2002. A new
algorithm for surface deformation
monitoring based on small baseline
differential SAR interferograms,
IEEE Trans. Geosci. Remote Sens.
,
40, 2375–2383.
Biggs, J., Wright, T., Lu, Z., and
Parsons, B., 2007. Multi-
interferogram method for
measuring interseismic
deformation: Denali Fault, Alaska,
Geophysical Journal International
,
173, 1165-1179, 2007.
Datcu, M., M., R. King, L. Gueguen,
P. Marchetti, 2012. Image
information mining for EO
applications,
International
Journal of Image and Data
Fusion
, 3, 199-201.
Ferretti, A., C. Prati, and F. Rocca,
2001. Permanent scatterers in
SAR interferometry,
IEEE Trans.
Geosci. Remote Sensing
, 39, 8-20.
Ferretti, A., A. Fumagalli, F. Novali,
C. Prati, F. Rocca, and A.,
Rucci, 2011. A New Algorithm
for Processing Interferometric
Data-Stacks: SqueeSAR,
IEEE
Transactions on Geoscience and
Remote Sensing
, 49, 3460-3470.
Gernhardt, S., X. Cong, M. Eineder,
S. Hinz and R. Bamler, 2012.
Geometrical Fusion of Multitrack
PS Point Clouds,
IEEE Geoscience
and Remote Sensing Letters
, 9,
38-42.
Hensley, S., and others, 2005. Status
of a UAVSAR Designed for
Repeat Pass Interferometry for
Deformation Measurements,
IEEE MTT-S International
Microwave Symposium Digest
, 3,
1453-1456.
Hooper, A., P. Segall, and H. Zebker,
2007. Persistent scatterer
interferometric synthetic aperture
radar for crustal deformation
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