IEEE Geoscience and Remote Sensing Magazine - March 2020 - 104

LANDSLIDE-INDUCED DAMAGE
TO BUILDINGS AT RISK
As previously pointed out, ad hoc InSAR data-processing
methods allow generation of point measurements localized
with meter (rarely, submeter) accuracy, for which deformation times series can be derived with centimeter- or millimeter-order accuracy over time intervals of several years. This
information-implemented in an innovative procedure that
properly combines the DInSAR-retrieved displacements with
conventional ground-based monitoring data, geotechnical
criteria, and structural damage estimation methods-can
specifically concur with the geometric and kinematic characterizations of landslides that are slowly moving on preexisting sliding surfaces as well as in the vulnerability analysis of
facilities (e.g., buildings) at risk.
Here, we present a procedure that aims to enhance the use
of DInSAR data in the prediction of landslide-induced damage to buildings at risk. DInSAR technology offers many attractive features
that can be fruitfully exploited in the
SAR Data
Exploitation
framework of landslide risk analysis. Among these is the possibility to
Landslide
Interferometric
Field Data
DEM
Inventory Map
Processing
access the deformation time series,
from which the intensity parameter
can be derived, at a fine resolution for
Geometric and Kinematic
DlnSAR
Characterization of
the detailed analysis of single built
Velocities
Landslides
structures of interest, distributed over
wide areas and over temporal intervals spanning many years to decades.
Figure 5 provides a four-phases proceCumulative
Exposed
Built-Up
Settlement in
dure for the implementation of a DInElement
Area Map
the Observation
SAR data exploitation method aimed
(i.e., Building)
Period
at the detection and monitoring of
landslide-affected urban areas as well
as the analysis and forecasting of the
vulnerability of buildings at risk.
Phase 1 deals with the geometDInSAR-Retrieved
In Situ
Damage
Differential
Intensity
Damage
ric
and kinematic characterizations
Level
Settlement
Survey
of landslides. In particular, the assessment of surficial displacements
induced by slope movements and
the delineation of both the geomeCause-Effect
try and depth of the sliding surface
Relationships
are key aspects for the characterStructural
Probabilistic
ization of landslide mechanisms.
Typology
Model
These have been addressed in the
Fragility
Curves
scientific literature with specific
Regression
procedures that combine landslide
Model
inventor y maps (resulting f rom
geomorphological investigations),
Vulnerability
Curve
superficial GPS and deep ground
displacements (e.g., inclinometer
readings), and surficial displacement data acquired from spaceborne
FIGURE 5. The proposed procedure for DInSAR data exploitation in detection and monitoring
radar sensors with reference to sinof urban areas affected by slow-moving landslides, along with their geometric and kinematic
gle landslides [127]-[129].
characterizations and analysis of the vulnerability of buildings at risk.
Phase 4

Phase 3

Phase 2

Phase 1

movement toward the sensor when measured on the ascending orbit, whereas it proceeds far away from the sensor
along the descending orbit.
However, the system has low sensitivity along the sensor
orbit. Indeed, since the available sensors have a quasipolar
orbit, any deformation occurring along the north-south
direction has a very small LOS projection [30]; in this case,
the LOS sensitivity can be recovered only on slopes introducing a significant vertical component. Based on the availability of measurements from two opposite orbits, over the
same period of observation, from the linear combination
of the two LOS measurements, it is possible to estimate the
vertical and west-east components.
Figure 4 shows a schematic representation of the decomposition of the ground deformation component in
the two viewing geometries of the SAR sensor (ascending
and descending).

104

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE

MARCH 2020
IEEE Geoscience and Remote Sensing Magazine - March 2020

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