IEEE Geoscience and Remote Sensing Magazine - March 2020 - 112
D1
Building Damage Level
D2
D3
D4
D5
0
DInSAR-Derived ∆ (cm)
DInSAR-Derived ∆ (cm)
0
D0
1
2
3
4
T_B2
T_C
T_D
Standard Deviation
Average
5
6
D0
D1
Building Damage Level
D2
D3
D4
D5
1
2
3
4
T_C
T_D
Standard Deviation
Average
5
6
(a)
(b)
FIGURE 11. The building damage level versus DInSAR-derived T for (a) RC and (b) masonry buildings in the Lungro urban area (modified
1
5
0.8
4
0.6
3
D1
µD
P (Damage Level ≥ Di ∆)
from [38]).
D2
0.4
2
D3
D4
0.2
1
Vulnerability Curve
Data
D5
1
0
0
1
2
3
4
DInSAR-Derived ∆ (cm)
(a)
5
6
0
1
2
3
4
DInSAR-Derived ∆ (cm)
(b)
5
6
FIGURE 12. The empirical (a) fragility and (b) vulnerability curves for masonry buildings interacting with slow-moving landslides in the
Lungro urban area (modified from [38]).
hydromechanical properties of the involved soils, position of the building within the unstable area, foundation
typology, and main features of the superstructure). Another application of the procedure at the municipal scale
was carried out by Peduto et al. [21] for the analysis of
damage to buildings on the unstable slopes of El Papiol,
Spain, via TerraSAR-X PSI data.
A similar approach at medium scale (1:25,000) was tested by Peduto et al. [38] in another area of approximately
557 km2 that included 21 municipalities of the Benevento
province (Campania region, southern Italy). In this case,
the elements exposed to landslides were identified with
vulnerable areas (i.e., building aggregates in landslide-affected areas) to which the equivalent cumulative displacement (the intensity parameter derived from DInSAR data)
and an equivalent damage were associated to derive both
fragility and vulnerability curves.
112
CONCLUSIONS
This article presented a review of the methodology for applying SAR to landslide risk, with emphasis on the monitoring of buildings. Processing schemes for the derivation
of deformation measurements from satellite data at small
(rough-resolution) and large (HR) scales were addressed.
Procedures for the use and interpretation of the results
with reference to landslide detection and kinematic characterization, as well as for the evaluation of the effects on
buildings, were also described. The aim was to provide an
end-to-end reference that addresses the data use procedure,
from satellite data collection to the final exploitation for
quantitative risk evaluation.
As mentioned in the existing literature, SAR is experiencing a golden era. Future small-satellite SAR constellations
are planned to deliver VHR images with daily revisit time.
This will provide interesting perspectives for technological
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
MARCH 2020
IEEE Geoscience and Remote Sensing Magazine - March 2020
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