10th Asian Regional Conference of IAEG (2015)

High resolution satellite multi-temporal interferometry for detecting and monitoring
landslide and subsidence hazards
Janusz WASOWSKI(1), Fabio BOVENGA(2), Raffaele NUTRICATO(3), Davide Oscar
NITTI(3), Maria Teresa CHIARADIA(4), Spartak KUCAJ(5) and Boris STRATI(5)(1) National Research Council–Research Institute for Geo-hydrological Protection, Bari, Italy
E-mail: j.wasowski@ba.irpi.cnr.it(2) National Research Council–ISSIA Institute, Bari, Italy, (3) GAPsrl, c/o Polytechnics-University of Bari, Italy,(4) Department of Physics, Polytechnics of Bari, Italy, (5) Polytechnic University of Tirana, Albania
Abstract
With the increasing number of radar satellites and improved data processing tools,
multi-temporal interferometry (MTI) can considerably enhance our capabilities ofmonitoring landslide and subsidence hazards. MTI provides long-term (years), regular(weekly-monthly), precise (mm) measurements of ground displacements over large areas(thousands of km2), combined with high spatial resolution (up to 1-3 m) and possibility ofmulti-scale (regional to site-specific) investigations using the same series of radar images.
To highlight the great potential of high resolution MTI we discuss application examples
from two seismically active regions prone to land instability: i) Albania, including the large
plain area occupied by the city of Tirana and nearby scarcely populated mountains, and ii)
Haiti, including the Port-au-Prince metropolitan area, with coastal and mountain zones
destabilized by the 2010 Mw 7.0 earthquake. It is shown that MTI can provide very useful
results in a wide range of geomorphic, climatic and vegetation environments.
Keywords: landslide, subsidence, hazard, detection, monitoring, satellite interferometry
1.

Introduction

In particular, satellite MTI offers outstanding
surveying capability of land instability (e.g., Hooper
et al., 2012; Bally, 2014; Tomás et al., 2014;
Wasowski and Bovenga 2014a,b). The users of MTI
can rely on the unique strengths of the technique:a. Large area coverage (thousands of km2)
together with high spatial resolution (1-3 m) of
the new radar sensors e.g., COSMOSky-Med,
TerraSAR-X (Table 1) and multi-scale
investigation option (regional to site-specific);b. Very high precision (mm-cm) of surface
displacement measurements only marginallyinfluenced by bad weather;c. Regular, high frequency (days-weeks) of
measurements over long periods (years);d. Retrospective studies using long-period (>20
years) archived radar imagery.
Here we illustrate the great potential of highresolution MTI for the detection and monitoring oflandslide and subsidence hazards by presenting
application examples from two areas located indifferent geomorphic, climatic and vegetation
settings: Central Albania (Tirana County plain andmountains) and Haiti (coastal and mountain regions).

In situ investigations and monitoring of areas
prone to landslide or subsidence are expensive,
typically conducted only after the ground failure, andlimited in terms of spatial and temporal coverage.
Therefore, the use of complementary, cost-effective
or economically sustainable approaches to hazard
detection and assessment is an important issue.
Different remote sensing techniques can be used
to measure ground surface displacements linked toland instability (e.g., Bally, 2014). These include
ground-based radar interferometry (e.g., Lowry et al.,2013), air-borne and terrestrial LiDAR (e.g.,
Monserrat and Crosetto, 2008; Jaboyedoff et al.,2012), air- and space-borne image matching (e.g.,
Kääb, 2002; Raucoules et al., 2013).
In this work we solicit a widespread applicationof advanced satellite MTI, with emphasis on early
detection of ground displacements via long-term(years) monitoring. MTI approach is cost-effective
and can deliver large quantities of useful information
for scientists and land-use managers involved inlandslide and subsidence hazards mitigation.

1

10th Asian Regional Conference of IAEG (2015)

Table 1 Selected characteristics of principal Synthetic Aperture Radar (SAR) sensors; future missions shown ingrey (updated after Wasowski and Bovenga, 2014a)
Satellite
missionWaveResolution
length Life status Az./Range
(cm)
(m)

RepeatCycle(days)

Swath
width (km)

Max.
Vel.
(cm/yr)

Incident
Angle(degree)C-bandERS-1/2

5.6

19922001≈ 6 / 24

35

100

14.6

23

ENVISAT

5.6

20032010≈ 6 / 24

35

100

14.6

1944

RADARSAT-1

5.5

1995

≈ 83024

45 (fine)
100 (Strip)
200 (Scan)

20.4

2050

RADARSAT-2

5.5

2007

≈3/3
≈8/8
≈ 26 / 25

24

10 (Spot)
40 (Strip)
200 (Scan)

20.4

2050

Sentinel-1

5.6

2014-2024

205

6, 12

250

85

3046

RADARSAT
Constellation
Mission (3 Sat)

5.5

2018-2026

550

3, 12

30350

163,2

2055

44

75

48.7

35

46

4070

46.8

860

149.2

870

L-bandJ-ERS

23.5 19921998

18

ALOS
PALSAR

23.6 20062011 ≈ 5 / 788

ALOS PALSAR-2

1/3
22.9 20142019 310/310
100/100

14

25 (Spot)
5070(Strip)
350 (Scan)

SAOCOM (2 Sat)23.5 20152021

8, 16

20150268

2050

2,4,8,16

10 (Spot)
40 (Strip)
200 (Scan)

17.7
35.4
70.7
141.4

2060

11

10 (Spot)
30 (Strip)
100 (Scan)

25.7

2055

28

5 (Spot)
30 (Strip)

10.4

2045

1050
X-bandCOSMO-SkyMed(4 Sat)≈ 2.5 / 2.5
1.0 / 1.0

3.1

20072014

TerraSAR-X

3.1≈ 3.3 / 2.8
20072018
1.0 / 1.0

KOMPSAT-5

3.2

2013-2018

3/3
1/1

COSMO-SkyMed-2(2 Sat)

3.1

2016

13

TerraSAR-X-NG

3.1

2015

1040
11
520 (Spot)(constel. 1024 (Strip)
with PAZ ) 50400 (TOPS)

0.25302

2050

10th Asian Regional Conference of IAEG (2015)

2.

Background information on MTI

The standard MTI products include: i)
geo-located radar targets; ii) map or optical image
with overlaid average annual displacement velocitiesof targets; iii) displacement time series of each target.
In recent years Google EarthTM tools and associatedoptical imagery are being increasingly used to display
distribution and movement rates of radar targets (cf.
Bovenga et al., 2012).

MTI is based on processing of long temporalseries of synthetic aperture radar (SAR) images(typically >15) to remove the atmosphericdisturbance, and on the selection of radar targets on
the ground that provide a backscattered phase signal
coherent in time (e.g., Hooper et al., 2012). The
majority of radar targets correspond to human-madeobjects (e.g., buildings and other engineered
structures), as well as to rock outcrops and bareground. To make distance measurements between thesatellite sensor and the target, phase difference
images (interferograms) are generated using radar
images acquired for the study area during successive
satellite passes. Detailed information on applied
satellite interferometry and MTI is available inreview articles published in the engineering geology
literature (e.g., Colesanti and Wasowski, 2006;
Wasowski and Bovenga, 2014a and references
therein).
An important limitation of MTI is that thedisplacements are measured in one dimension along
satellite slant range or Line of Sight (LOS), withincidence angles varying between about 20°-50°(Table 1), and it is nearly impossible to retrieve
movements in the radar satellite flight direction(azimuth), i.e., approximately north-south (Fig. 1). In
addition, it is very difficult to measure high velocity
displacements e.g., exceeding few tens of cm/year(Table 1) and strong non-linear deformations. For adetailed discussion of MTI technical limitations anddata interpretation issues, as well as guidelines on
how these can be mitigated, the reader is referred to a
recent article by Wasowski and Bovenga (2014a).

3.

Satellite data and MTI processing

3.1 Radar data
In this study we used two stacks of highresolution (3 m) COSMO-SkyMed (CSK) radar
images. For Albania we had 39 CSK images covering
the period May 2011 - June 2014. For Haiti weobtained about 100 CSK images covering the period
June 2011 – August 2013. Technical specifications of
CSK sensor are given in Table 1.
3.2 MTI processing
Our results are based on the application of the
SPINUA (Stable Point INterferometry overUnurbanized Areas) MTI algorithm. This Persistent
Scatterers Interferometry-like algorithm, originallydeveloped for detection and monitoring of radartargets in non- or scarcely-urbanized areas (Bovenga
et al., 2005), has been updated in order to increase its
flexibility also for the applications in densely
urbanized areas, as well as to guarantee proper
processing of high-resolution X-band data from the
new generation radar sensors such as CSK (Bovenga
et al., 2012).
The quality of SPINUA processing has been
confirmed in many applications in the last several
years. In particular, the results obtained through
SPINUA have been cross-compared with thosederived by using other MTI techniques (Wasowski etal., 2007; Wasowski and Bovenga, 2014a), andvalidated by using in situ measurements from both
GPS/GNSS and leveling (Bovenga et al., 2013).
4.

MTI application example from Albania

4.1 Environmental setting
The area of interest is in the central region of
Albania, a country that lies in the western part of theBalkan Peninsula on the eastern coast of the AdriaticSea (Fig. 2). Albania belongs to the Dinarides (thesouthern extension of the Alpine belt) and its territoryis predominantly mountainous and hilly. The coastal
lowlands to the west and intermountain plainsrepresenting less than 25% of the land (Meco andAliaj, 2000).
Regarding the geological setting, the study area
can be divided into the western part (lowland), which
belongs to the Periadriatic Foredeep Basin, and the“External Albanides” (mountains) to the east (Fig. 2).

Fig. 1 Two radar acquisitions during successive
satellite passes: R = sensor-target geometrical distance,h = target elevation, dh = target displacement, and θ =incidence angle (modified after Wasowski andBovenga, 2014a).

3

10th Asian Regional Conference of IAEG (2015)

Fig. 2 Line of sight (LOS) velocity of radar targets (color dots) in Central Albania (cf. inset figure), including
the plain of Tirana and the mountains to NE; velocity scale saturated to ±10 mm/year. Reddish to yellowish
targets move away from the satellite and denote subsidence in Tirana (cf. inset graph with displacement timeseries of a target from SW part of the city) and slope movements in the mountains. White rectangle indicatesunstable slope shown in Fig. 3. The background image is from Google EarthTM.

Fig. 3 Unstable slope detected in the mountain range east of Tirana. Reddish to yellowish dots (radar targets)indicate landslide activity and slope deformations and/or instability of slope debris; velocity scale saturated to±10 mm/year. Inset graphs show displacement time series of two representative targets.

4

10th Asian Regional Conference of IAEG (2015)

The Foredeep includes Miocene and Pliocene
molasse and Quaternary (mainly alluvial) sediments,
while flysch sequences and carbonates represent thetwo predominant lithologies in the mountain range(Meço and Aliaj, 2000).
Albania belongs to one of the most seismically
active regions of Europe – the Western Balkans. At
present, the lowlands and coastal regions of western
Albania are characterized by active thrust faulting,while active extension on normal faults occurs in themountains in the east of the country (Meço and Aliaj,
2000).
Albania belongs to the Mediterranean climaticzone characterized by hot and dry summers, but the
average annual rainfall is high and extremely
variable, from about 700-800 mm in the SE coastal
areas to about 3000 mm in the mountains in the NEpart of the country (Meço and Aliaj, 2000). Winterrepresents the most rainy season of the year, withsnow precipitation common in the interior mountains.

related to the presence of dense vegetation (trees),very limited number of human-made objects (e.g.
houses) that can act as coherent targets, and the snow
cover in winter period in the Albanian mountains.
Nevertheless, the radar data processing provided
useful results even in such a harsh mountainous
environment for MTI applications. In particular, we
identified over 10 clusters of moving targets, which,as illustrated below, are indicative of landslide activity
or slope instability.
4.3 Local scale landslide monitoring
We found no information in the English
literature about slope failures in the mountains ENEof Tirana. However, active erosion and landslides canbe easily recognized on high resolution Google
EarthTM imagery (Fig. 2).
Signs of active landsliding are evident in Fig. 3,
which also shows the distribution and average annual
velocity of radar targets. The measured velocities arevery low, from few to about 20 mm/year (cf.
representative displacement time series in Fig. 3).
One group of moving targets coincides with theupper part of a landslide body, which deflected the
local stream; thus the very slow target motion reflects
the post-failure activity of the slide. The
displacements of other groups of targets may indicate
the movements of slope debris and/or incipient slope
deformations. Clearly, in situ inspection would be
helpful in this case.

4.2 Regional scale detection of land instability
Figure 2 provides a wide-area overview of MTI
results for the Central Albania region. The SW
portion of the study area is marked by a high density
cluster of radar targets corresponding to themetropolitan area of Tirana (about 40 km2). Althougha general stability of the capital city territory isapparent, there are several groups of moving radartargets, which indicate a more or less localized
ground displacements. The movements are awayfrom the satellite sensor (downward) and denote theoccurrence of subsidence phenomena. The averagerate of deformation is low, only seldom exceeding 10
mm/year (cf. displacement time series in Fig. 2).
Although detailed geological data are notavailable at present, it is apparent that some of theunstable sites detected in Tirana can be linked to
ground settlement processes occurring in the newly
developed (last 10 years) areas of the city. The
presence of semi-linear deformation trends (Fig. 2)
and the location of the unstable sites on flat groundnear the river network suggests that settlements are
induced by gradual loading of alluvial (compressible)
sediments by the new engineering constructions(mainly houses).
The origin of some subsiding areas could also
be related to the increased ground water withdrawal
in Tirana. Indeed, in recent years the city has not only
experienced huge urban expansion into the new areas(with the construction of formal and informal
housing), but the population in the Tirana area hasmore than doubled from about 250,000 inhabitants in1991 to 600,000 in 2008 (World Bank, 2013).
The high density of radar targets (over1000/km2) in the Tirana region is in sharp contrast
with the relative scarcity of targets in the mountainous
area to the NE and E of the city (Figure 2). This can be

5.

MTI application example from Haiti

5.1 Background information
The example of Haiti is of interest, because highresolution X-band MTI applications are rare intropical regions (Wasowski and Bovenga, 2014b).
This stems from the difficulty of obtaining good MTI
results in areas with dense and rapidly growing
vegetation.
Haiti occupies the western part of the island of
Hispaniola located in the Caribbean Sea (Fig. 4). It
has mostly humid tropical climate with hightemperatures throughout the year and two rainyseasons: April–June and August-October (seehttp://www.britannica.com/EBchecked/topic/251961/
Haiti/54458/Climate).
The vegetation cover is typically dense with the
exception of the intensely urbanized areas like that ofPort-au-Prince, the capital city of Haiti (Fig. 4). Also
the mountainous regions are much vegetated (Fig. 5).
The study area covers a large plain and coastal
zones (including the metropolitan region of
Port-au-Prince) and the mountains bordering theplain to the NW and SE (Fig. 4). While
Miocene-Paleocene age rocks predominate in themountains, the plain contains abundant Quaternaryand Pliocene alluvial deposits (Lambert et al., 1987).

510th Asian Regional Conference of IAEG (2015)

Fig. 4 Line of sight (LOS) velocity of radar targets (color dots) in the metropolitan area of Port-au-Prince, Haiti,
and the surrounding regions (red rectangle in inset figure indicates coverage of satellite imagery); velocity scale
saturated to ±10 mm/year. Reddish to yellowish dots represent targets moving away from the satellite sensor anddenote significant (up to few cm/year) subsidence affecting coastal areas. White arrow indicates location oflandslide shown in Fig 5. The background image is from Google EarthTM.

Fig. 5 Deep-seated landslide (center of image) and local instabilities along National Route N°3 of Haiti. Clustersof reddish to yellowish radar targets indicate the landslide motion and localized instabilities affecting the slope
and road embankments; velocity scale saturated to ±10 mm/year. White circle marks position of landslide target
whose displacement time series is shown in inset graph.

6

10th Asian Regional Conference of IAEG (2015)

5.2 Regional scale detection of land instability
Figure 4 provides a wide-area overview of MTI
results for the large plain and coastal zones occupiedby the Greater Port-au-Prince area and the city of
Carrefour, and for the nearby mountains. Overall, theamount of available information is impressive, withover two and a half million measurement points andtheir average density exceeding 1700/km2.
However, the distributions of radar targets in thelowland and mountain regions are different, becauseof the sharp differences in the density of urbanizationand vegetation cover.
In particular, the extremely high density of radartargets in the metropolitan area of the Haitian capital(on the order of 10,000/km2) allowed the detection anddetailed spatial delimitation of subsidence processes
affecting river deltas and coastal areas of thePort-au-Prince and Carrefour region (Fig. 4). The over
two year long time series of radar target displacements
showed that the subsidence movements locally exceed
few cm/year (Nutricato et al., 2013).
In the mountains, away from large urban centers,
the amount of obtained MTI information was lower
and spatially variable. Nevertheless, thanks to the highresolution of CSK radar imagery and the presence ofhuman-made structures dispersed in vegetated terrain(Fig. 5), the density of targets in many rural areas
resulted suitable for slope instability detection. Notethat some areally limited clusters of moving radartargets are not visible in Fig. 4, because its scale is too
small.

creeping landslide movement. Significantly, thedisplacement rates of targets on the slide are
significantly greater than those of other nearby
moving targets. The latter move at velocities typicallybelow 10 mm/year. In situ checks indicated that thesemoving targets denote localized instabilities affecting
the slope, road embankment (including gabions) andisolated human-made structures (Fig. 5).
The above example demonstrates the capabilityof high resolution (3 m) MTI to provide site-specificinformation on landslide activity even in vegetatedsettings. It is also evident that with such resolution
radar imagery one can also use MTI to monitor
stability of roads in landslide prone areas.
6.

Concluding discussion

Land instability hazards related to landslide andsubsidence processes affect most countries in theworld and represent a global problem. This and thecontinuous growth of the world population, withurbanization of areas susceptible to failure, combinedwith the increasing climate variability, imply greater
vulnerability and risk.
However, extensive in situ monitoring efforts arein most situations unaffordable. The recent literatureon satellite interferometry applied to engineering
geology problems (e.g., Cigna et al., 2014; Tomás etal, 2014; Wasowski and Bovenga, 2014a,b andreferences therein), as well as the case studies
presented in this work, show that thanks to thewide-area coverage of satellite imagery (tens of
thousands of km2) MTI represents a cost-effective
monitoring technique. When combined with a highspatial resolution (1-3 m) and improved re-visit
frequency (days-weeks) of the new radar sensors(Table 1), the millimeter precision MTI surveying canprovide enormous amounts of high quality
information about ground surface displacements
occurring in areas susceptible to slope or subsidence
hazards.
Finally, we expect that with regular globe-scale
coverage and freely available imagery, new radar
satellite background missions such as the European
Space Agency's Sentinel-1 will foster an even wider
and more efficient use of MTI in ground hazard
investigations.

5.3 Local scale landslide monitoring
Figure 5 shows an example of a landslide andlocalized instabilities detected from MTI results. The
slide is about 300 m long and 200 m wide and has its
toe eroded by an ephemeral stream. The slope cuts
along the National Route N°3 passing around theunstable area expose stratified sandstones andlimestones. The site is located in the Montagnes duTrou D’Eau, a moderate elevation range distant few
tens of kilometers from Port-au-Prince.
No information is available on the origin of themain failure, but the examination of “historical”
optical images in Google EarthTM shows that the
Triano landslide predates the 2010 7Mw Haiti
earthquake. The slide could be quite old (decades ormore) considering the subdued geomorphic
expression, dense vegetation cover and the presenceof small human dwellings. The latter belong to thevillage of Triano and, together with the adjacent road,are the sources of radar targets identified on thelandslide and in its surroundings.
The LOS velocities of the radar targets on theslide are similar, averaging around 15 mm/year (Fig.
5). This together with the overall geomorphic
expression of the failed slope are indicative of a deep

Acknowledgements
Project carried out using CSK® Products© of the
Italian Space Agency(ASI), delivered under a license
to use by ASI. The data for Albania were obtained
within the project "Studio su instabilità del terreno
sull’area di Tirana (Albania)”. We thank colleagues of
the Centre National dell'Information Géo-Spatiale CNIGS, Haiti for help in acquiring thematic data andthe assistance in the field.

7

10th Asian Regional Conference of IAEG (2015)

Referencesinterferometry. Engineering Geology, Vol. 166,
pp. 160–169.
Meço S., and Aliaj S. (2000): Geology of Albania.
GebruderBorntraeger,
Berlin(ISBN
978-3-443-11028-4). 246p.
Monserrat, O., and Crosetto, M. (2008): Deformation
measurement using terrestrial laser scanning data
and least squares 3D surface matching. ISPRS J.
Photogram. Remote Sensing, Vol. 63, pp.
142-154.
Nutricato, R., Wasowski, J., Chiaradia, M.T., Piard, B.
E., and Mondesir P. (2013): Multi-temporalinterferometricmonitoringofgrounddeformations in Haiti with COSMO-SkyMed
HIMAGE data - Abstract. AGU Fall Meeting
2013, San Francisco, USA, 9-13 Dec. 2013.
Raucoules D., de Michele, M., Malet, J.P. and UlrichP.
(2013):
Time-variable
3D
grounddisplacements from high resolution synthetic
aperture radar (SAR): application to La Valettelandslide (South French Alps). Remote Sensing
of Environment, Vol. 139, pp. 198-204.
Tomás, R., Romero, R., Mulas, J., Marturià, J.J.,
Mallorquí, J.J., Lopez-Sanchez, J.M., Herrera, G.,
Gutiérrez, F., González, P.J., Fernández, J.,
Duque, S., Concha-Dimas, A., Cocksley, G.,
Castañeda, C., Carrasco, D., and Blanco P.
(2014): Radar interferometry techniques for the
study of ground subsidence phenomena: a review
of practical issues through cases in Spain. Environ.
Earth Sciences, Vol. 71, pp. 163-181.
Wasowski J., Bovenga F. (2014a): Investigatinglandslides and unstable slopes with satellite Multi
Temporal Interferometry: Current issues andfuture perspectives. Enginering Geology, Vol.
174, pp. 103–138.
Wasowski J., Bovenga F. (2014b): Remote Sensingof Landslide Motion with Emphasis on Satellite
Multitemporal Interferometry Applications: An
Overview. In T. Davies Ed. Landslide Hazards,
Risks and Disasters. pp. 345-403.
http://dx.doi.org/10.1016/B978-0-12-396452-6.0
0011-2 Elsevier Inc.
Wasowski, J., Casarano D., Bovenga, F., Conte, D.,
Nutricato, R., Refice,, A. Berardino P., Manzo,
M., Pepe, A., Zeni, G., and Lanari, R. (2007): A
comparative analysis of the DInSAR results
achieved by the SBAS and SPINUA techniques:a case study of the Maratea valley, Italy.
Proceedings of the Envisat Symposium, April
23-27, 2007, Montreux,
Switzerland. ESA
Special Publication SP-636, July 2007, CD.
World Bank. (2013): Albania-Tirana City
Development Strategies Project. Washington DC;
World Bank.
http://documents.worldbank.org/curated/en/2013/05/18136205/albania-tirana-city-development-str
ategies-projec.

Bally, P. Ed. (2014): Satellite Earth Observation for
Geohazard Risk Management - The Santorini
Conference - Santorini, Greece, 21-23 May 2012.
ESA Publication STM-282
doi:10.5270/esa-geo-hzrd-2012
Bovenga, F., Refice, A., Nutricato, R., Guerriero, L.,
and Chiaradia, M.T. (2005): SPINUA: a flexible
processing chain for ERS / ENVISAT long term
Interferometry. Proceedings of ESA-ENVISAT
Symposium, September 6-10, 2004, Salzburg,
Austria. ESA Sp. Publ. SP-572, April 2005, CD.
Bovenga, F., Wasowski, J., Nitti, D.O., Nutricato, R.,and
Chiaradia,
M.T.
(2012):
Using
Cosmo/SkyMed X-band and ENVISAT C-band
SAR Interferometry for landslide analysis.
Remote Sensing of Environment, Vol. 119, pp.
272-285.
Bovenga, F., Nitti, D. O., Fornaro, G., Radicioni, F.,
Stoppini, A., and Brigante, R. (2013): Using
C/X-band SAR interferometry and GNSS
measurements for the Assisi landslide analysis.
International J. Remote Sensing, Vol. 34(11), pp.
4083–4104.
Cigna, F., Jordan, H., Bateson, L., McCormack, H.,
and Roberts, C. (2014): Natural andAnthropogenic Geohazards in Greater London
Observed from Geological and ERS-1/2 and
ENVISAT Persistent Scatterers Ground Motion
Data: Results from the EC FP7-SPACE PanGeo
Project. Pure Appl. Geophys., 1-31.
Colesanti, C., and Wasowski, J. (2006): Investigatinglandslides with space-borne Synthetic Aperture
Radar (SAR) interferometry. Engineering
Geology, Vol. 88(3-4), pp. 173–199.
Hooper, A., Bekaert, D., Spaans, K., and Arıkan M.T.
(2012): Recent advances in SAR interferometry
time series analysis for measuring crustal
deformation. Tectonophysics, Vol. 514–517, pp.
1-13.
Jaboyedoff, M., Oppikofer, T., Abellan, A., Derron,
M.-H., Loye, A., Metzger, R., and Pedrazzini A.
(2012): Use of LIDAR in landslide
investigations: a review. Natural Hazards, Vol. 61,
pp. 5-28.
Kääb, A. (2002): Monitoring high-mountain terrain
deformation from repeated air- and spaceborne
optical data: examples using digital aerial
imagery and ASTER data. ISPRS J. Photogram.
and Remote Sensing, Vol. 57, pp. 39–52.
Lambert, M. L, Gaudin, J. and Cohen, R. (1987):
Carte Geologique D'Haiti, Feuille Sud-Est:
Port-au-Prince, 1 :250,000, IMAGEO-CNRS.
Lowry B., Gomez F., Zhou W., Mooney M.A., Held
B., Grasmick J. (2013): High resolution
displacement monitoring of a slow velocity
landslideusing
groundbased
radar

8