cause a destabilization of schists. Both effects ultimately re-
sult in decompaction and shearing that is ultimately followed
by gravitational downslope movement of rock masses as shal-
low landslides mainly. A number of occurrences are observed
in deconsolidated alluvial sediments and near contact areas
with granitic and gneiss units where pronounced susceptibil-
ity is likely caused by differences in rock consolidation rather
than direct weathering effects.
Therefore, we are certain that the topographic instability
caught by the atmospherically corrected
DInSAR
analysis is
genuine and caused by the combined effects of (a) morpho-
logical instability along steep walls, and (b) seasonal factors.
Conclusions
In this work, we studied the application of
DInSAR
to forecast
localized landslides over a test site in the eastern coastal area
of Uljin County, South Korea. The primary focus of our work
was to develop a methodology to address error components in
DInSAR
measurements without conventional
InSAR
time series
analyses such as
PS
and
SBAS
, which require multiple
SAR
im-
age acquisitions. The error components are usually produced
by (a) the heterogeneous distribution of water vapor of a study
area, and (b) a base
DEM
with insufficient grid resolution to
represent steep and high frequency/high-relief topography.
Gravitational surface creep which can be considered as a
prelude of landslide events were screened by the error terms.
Since
StaMPS
time series analysis, employed as a suppressor
of
DInSAR
error terms, does not produce sufficiently detailed
information about deformation due to the insufficient number
of
InSAR
pairs, we constructed an external error map through
a combination of spaceborne radiometers and high-resolution
DEM
. Some agreement was observed between the deformation
values by error-corrected
DInSAR
analysis and the
LEFs
such
as slope, vegetation and geological context. All results have
shown that
DInSAR
must be applied with a suitable atmospher-
ic error correction and reliable
DEM
in order to accurately
detect the pre-movement of terrain as a precursor to landslide
events over steep local slopes. Considering that landslide
events are commonly associated with seasonal weather condi-
tions, our approach using two-pass
DInSAR
with improved
atmospheric and topographic corrections is proposed as an
effective indicator of locations prone to show potential mass
wasting. If the high quality
APS
and fine resolution base
DEM
,
as for instance extracted by a lidar campaign, are introduced
together for two-pass error-regulated
DInSAR
processing, it
will be of superior use with respect to observation density,
processing cost and accuracy, when compared to time series
analysis. Especially, we conclude that an error-correction
method with
APS
only but without a high-accuracy base
DEM
may result in under- or over-estimation of landslide potential
by reasons stated in this study.
For the next steps of this study, approaches to quantitative-
ly classify landslide risks employing
DInSAR
measurements
(especially with L-
SAR
pairs and
LEF
maps) will be implement-
ed as shown in Chen
et al
. (2010). Meanwhile, techniques
to address the complicated atmospheric effects from
DInSAR
outputs will be further explored.
Acknowledgements
This study was kindly supported by the 2017 Research fund
of the University of Seoul for YunSoo Choi. Research by
Stephan van Gasselt was supported by the Korean Brain Pool
scheme (No. 162S-2-3-1757). The H/W system for this study
was provided by the University of Seoul equipment procure-
ment grant.
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