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An important application of differential SAR interferometry (DInSAR) and Persistent Scatterer Interferometry is landslide detection and monitoring. Several studies have been published, which make use of the entire spectrum of SAR data types available in the last 25 years. This paper describes a procedure to update landslide inventory maps using Sentinel-1 data. The paper briefly discusses the main advantages of the Sentinel-1 SAR data. Then it describes the data analysis procedure used to update landslide inventory maps using interferometric data and a number of additional information layers. The effectiveness of the procedure is illustrated by the results of a study area located in the Molise region, in Southern Italy.
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Oriol Monserrat (1), Michele Crosetto (1), Núria Devanthéry (1), María Cuevas-González (1),
Anna Barra (1), Bruno Crippa (2)
(1) Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Division of Geomatics,
Av. Gauss 7, E-08860, Castelldefels (Barcelona), Spain, Email:;;;;
(2) Department of Geophysics, University of Milan, Via Cicognara 8, I-20129, Milan, Italy,
An important application of differential SAR
interferometry (DInSAR) and Persistent Scatterer
Interferometry is landslide detection and monitoring.
Several studies have been published, which make use of
the entire spectrum of SAR data types available in the
last 25 years. This paper describes a procedure to update
landslide inventory maps using Sentinel-1 data. The
paper briefly discusses the main advantages of the
Sentinel-1 SAR data. Then it describes the data analysis
procedure used to update landslide inventory maps
using interferometric data and a number of additional
information layers. The effectiveness of the procedure is
illustrated by the results of a study area located in the
Molise region, in Southern Italy.
This paper describes a procedure to update landslide
inventory maps using Sentinel-1 interferometric data.
Landslide inventory maps contain information on
landslide activity and are a basic input to landslide
susceptibility and hazard analysis. Several works have
been focused on the use of SAR (Synthetic Aperture
Radar) interferometric data for landslide-related
applications, e.g. see [1-9]. For a review of Persistent
Scatterer interferometry, see [10].
New perspectives in landslide inventory and monitoring
are now opened by the availability of the SAR data of
the Sentinel-1 satellite of the European Space Agency.
This type of SAR data offers very interesting
characteristics. Firstly, Sentinel-1 data are acquired with
a short revisiting cycle of 12 days, which will become
of 6 days with the availability of Sentinel-1B satellite.
This results in a reduced temporal decorrelation.
Secondly, the Sentinel-1 data have a reduced orbital
tube, which implies a reduced geometric decorrelation.
Thirdly, the Sentinel-1 data acquired in the
Interferometric Wide Swath mode, which is the standard
acquisition mode for this sensor, cover wide areas. In
addition, the image acquisitions are in background mode
(images are acquired systematically according to a pre-
defined observation scenario). A fourth and important
aspect is that Sentinel-1 data are available free of charge
to all data users. This represents an important advantage
from the application point of view.
This paper describes the procedure adopted by the
authors to update a landslide inventory using Sentinel-1
data. The effectiveness of the procedure is illustrated by
using results obtained over the Molise region, in
Southern Italy.
The procedure includes two main stages: a simplified
Persistent Scatterer Interferometry analysis and a multi-
layer GIS (Geographic Information System) analysis.
The first stage, which is described in [11, 12], is
performed in the SAR geometry (azimuth and slant
range geometry) using the available interferometric
data. It is done both spatially and temporally with the
aim of detecting areas affected by deformation. The
main output of this analysis is a set of areas potentially
affected by deformation.
The second stage (multilayer GIS analysis) consists in
the integration of the interferometric-derived data with
geological and geomorphological data to interpret and
validate the detected deformation areas. This
information can be used to update pre-existing landslide
inventory maps.
The main steps of the two-stage procedure, see Figure 1,
are briefly discussed below.
- The first step is interferogram generation. Given a
stack of N complex SAR images, we generate the
N-1 consecutive multi-look interferograms.
- The spatial analysis consists in a visual inspection
of the wrapped interferograms to identify spatial
patterns associated with potential deformation
areas. This analysis only provides information on
movements that are fast enough to be observed in
short (typically 12 days) periods. Once the patterns
are detected, the pairwise logic [13] is used to
discard patterns due to other sources, e.g.
topographic errors. The result of this step is a set of
areas potentially affected by deformation.
Figure 1: Flow-chart of the procedure used in this study.
- The third step is a temporal analysis, which consists
in the generation of deformation time series over a
set of coherent pixels. The procedure involves the
phase unwrapping of the interferograms using the
Minimum Coast Flow approach [14]. Only the
pixels with coherence above a given threshold are
unwrapped. The deformation time series are
obtained by a direct integration of the unwrapped
phase values and by transforming the phases into
- The fourth step involves the analysis of the map of
accumulated deformation to search for additional
spatial patterns characterized by slow deformation
rates. The analysis of the time series is done with
respect to a local stable reference to reduce the
atmospheric effects.
- The fifth step is the spatio-temporal analysis. The
potential areas of deformation that were detected in
the previous steps are analysed together with the
time series. This combined analysis is useful to
detect phase unwrapping errors and to confirm or
modify the shape of the detected deformation areas.
- The result of the previous step is the final set of
detected deformation phenomena. This dataset is
then geocoded in order to have it in the same
reference system of the cartographic, geological and
additional information.
- The last step is the multilayer GIS analysis. This
involves a geological and geomorphological
interpretation of the detected areas using different
information layers in a GIS environment: a digital
elevation model, slope, aspect, orthophotos, geo-
lithological maps, existing landslide inventory
maps, etc. At this step, the previously identified
potential deformation areas are confirmed,
discarded or modified and the results are used to
update the existing landslide inventory maps.
The procedure described above was successfully used to
study an area located in the Molise region, in Southern
Italy. The study area is affected by a great number of
landslide phenomena, see for details [15].
The analysis was focused on a single burst of a set of 14
Sentinel-1 Interferometric Wide Swath images (single
polarization VV) acquired with ascending orbits. The
images span a relatively short time period, from
November 2014 to April 2015.
Figure 2 shows some examples of potential deformation
patterns that were identified using a 12-day wrapped
interferogram. Three main patterns are highlighted.
Figure 2: Three potential deformation patterns identified in a 12-day wrapped interferogram.
Figure 3: Accumulated deformation map with some examples of landslides detected using the multilayer GIS analysis.
Figure 3 shows an example of outcome of the multilayer
GIS analysis: a set of confirmed landslides. The border
of each landslide is shown in red. The landslides are
superposed to the accumulated deformation map.
This work has been partially funded by the Spanish
Ministry of Economy and Competitiveness through the
project MIDES (Ref: CGL2013-43000-P).
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... These methods apply traditional thematic maps (geological, topographical, and/or optical images) together with field reconnaissance coupled with InSAR-delivered ground deformation estimates. PSI techniques are mostly used for: (1) landslide phenomena identification Monserrat et al., 2016); (2) landslide boundary verification or modification Reyes-Carmona et al., 2020); (3) landslide velocity and intensity estimation (Bianchini et al., 2012); and (4) activity state assessment (Bianchini et al., 2012Cascini et al., 2013;Del Ventisette et al., 2014;Kalia, 2018). The commonly used methodology in the abovementioned papers applies the PSI matrix approach with diverse SAR sensors such as Envisat (Cascini et al., 2012;Del Ventisette et al., 2014), ALOS-PALSAR , and more recently, Sentinel − 1 Barra et al., 2016;Béjar-Pizarro et al., 2017;Kalia, 2018, Bonì et al., 2020Aslan et al., 2020;Reyes-Carmona et al., 2020;Meng et al., 2020.) ...
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