Post on 28-Jan-2023
Use of differential SAR interferometry in monitoring and
modelling large slope instability at Maratea (Basilicata, Italy)
Paolo Berardinoa, Mario Costantinib, Giorgio Franceschettic, Antonio Iodicec,Luca Pietranerab, Vincenzo Rizzod,*
a IREA-CNR, Via Diocleziano 328, 80124 Naples, ItalybTelespazio S.p.A. Via Tiburtina 965, 00156 Rome, Italy
cUniversita di Napoli ‘‘Federico II’’, Dipartimento di Ingegneria Elettronica e delle Telecomunicazioni, Via Claudio 21, 80125 Naples, ItalydCNR-IRPI, Via Cavour Roges di Renda, 87030 Cosenza, Italy
Received 9 September 2001; received in revised form 29 March 2002; accepted 10 May 2002
Abstract
Differential Synthetic Aperture Radar (SAR) interferometry (DiffSAR) allows, in principle, to measure very small
movements of the ground and to cover in continuity large areas, so that it can be considered as a potentially ideal tool to
investigate landslides and other slope instability. In this paper, we explore the use of this technique to improve our knowledge of
the slope instability of a well-investigated area (the Maratea Valley), affected by continuous slow movements, producing an
impressive ‘‘Sackung’’-type phenomenon, which poses several unanswered questions.
In particular, by using this technique, we analyse the time evolution of ground movements from 1997 to 2000. The slope
movements during this same time interval have also been monitored in the past by using other techniques, such as electronic
distance-meter (EDM) and GPS measurements: GPS and DiffSAR results are here compared.
In our implementation of the DiffSAR technique, the problem of decorrelation noise is faced by using a phase unwrapping
approach that allows to process sparse data, and the impact of atmospheric artefacts is reduced by performing a temporal
analysis of the deformations observed in successive interferograms. In this study, we also show that it is possible to perform a
temporal analysis of continuous slow landslide movements by using a limited number of ERS SAR data sets and low-precision
topographic information.
All the acquired data (EDM, GPS and DiffSAR) are consistent and allow a kinematic model of instability within the
investigated time interval to be proposed. A map of slopes subject to different velocities of vertical displacements was
delineated, modifying previous knowledge. Within the valley, progressive and almost linear displacements over time were
confirmed.
D 2002 Elsevier Science B.V. All rights reserved.
Keywords: SAR interferometry; GPS; Landslide monitoring; Ground surface deformation
1. Introduction and motivations
Differential Synthetic Aperture Radar (SAR) inter-
ferometry (DiffSAR) is a powerful technique that
0013-7952/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved.
PII: S0013 -7952 (02 )00197 -7
* Corresponding author. Tel.: +39-984-835515; fax: +39-94-
835319.
E-mail address: Rizzo@irpi.cs.cnr.it (V. Rizzo).
www.elsevier.com/locate/enggeo
Engineering Geology 68 (2003) 31–51
allows us, in principle, to measure very small move-
ments of the ground over time (e.g. Franceschetti and
Lanari, 1999). Accordingly, it can also be fruitfully
applied in the field of geological hazards (e.g. sub-
sidence, landslides, earthquakes, volcanic activities),
both for scientific studies and risk assessment and
monitoring (Massonnet et al., 1993, 1995; Goldstein
et al., 1994; Achache et al., 1995; Carnec et al., 1996).
In addition, almost continuous monitoring of large
areas of the Earth’s surface has been made possible by
the availability of data from remote sensing satellites,
such as ERS-1/2.
The rationale of the interferometric technique is
simple. The phase difference of two complex SAR
images of the same scene is related to the scene
topography and to ground displacement. The topo-
graphic phase term can be easily eliminated if orbit
data and topographic information (for instance, an
aerophotogrammetric Digital Elevation Map, DEM)
are available. The remaining phase term is propor-
tional to the ground displacement (projected along the
SAR line of sight). The accuracy of the measurement
is of the order of fractions of the employed electro-
magnetic wavelength: accordingly, at microwave fre-
quencies, the accuracy can be of the order of fractions
of a centimeter.
Some promising applications of the DiffSAR tech-
nique to landslide monitoring have been recently
presented (Fruneau et al., 1996; Rott et al., 1999;
Rizzo and Tesauro, 2000). However, these studies also
pointed out that landslide analysis via SAR techniques
suffers from some important well-known limitations
of the interferometric technique: decorrelation noise
caused by random temporal variations of terrain
reflectivity and atmospheric noise related to random
fluctuations of the atmospheric refraction index. In
particular, due to decorrelation noise, ‘‘good-quality’’
data are often available only on sparse stable areas
(e.g. urban areas and/or isolated buildings, rocks, arid
terrain, other stable objects).
In order to overcome the abovementioned prob-
lems, one possibility is to apply the technique of
‘‘permanent scatterers’’ (PS) (Ferretti et al., 2001),
that allows to obtain very high-accuracy (in the order
of the millimeter) displacement measurements over
isolated stable points in the scene. The PS technique
has been successfully employed to study landslide
phenomena (Ferretti et al., 2001). However, this
technique requires a large number of SAR acquisi-
tions. In addition, it loses one of the main advantages
of the SAR sensors with respect to GPS, i.e. the
possibility to monitor a large area, and not only a
limited number of points. Therefore, it is worth
exploring a different approach to obtain displacement
maps of wide areas, although with slightly lower
accuracy (of the order of half a centimeter in coherent
areas). In our approach, the problem of decorrelation
noise is faced by using a phase unwrapping technique
that allows to process sparse data (Costantini and
Rosen, 1999), and the impact of atmospheric noise
is reduced by performing a temporal analysis of the
deformations observed in successive differential SAR
interferograms, as already utilised in monitoring sub-
sidence and volcanic phenomena (Costantini et al.,
2000a,b).
In this paper, we test the use of this method for
investigating slope instability in Maratea: a large
unstable area covering more than 4 km2 and involving
the entire valley (Guerricchio et al., 1986; Rizzo,
1997). For this area, electronic distance-meter
(EDM) and GPS measurements are available, so that
a comparison is possible and is actually performed.
Aims of the present work are:
(a) to delimit and to map areas with notable vertical
displacement (well detected by DiffSAR methodol-
ogy, due to the low, i.e. 23j, look angle of the ERS
antenna);
(b) to perform a temporal analysis of slow slope
movements by using a limited number of ERS SAR
data sets and low-precision topographic information;
(c) to observe the activity of the Maratea Sack-
ung area and of external relief, where the EDM
monitoring stations and the relative reference sys-
tems are placed, and where questions on the role of
tectonic activity on the Sackung development are
open.
The paper is organized as follows. In Section 2, the
area under study is described, and available EDM and
GPS data are presented. In Section 3, the employed
DiffSAR technique is described in detail. Obtained
DiffSAR results are presented and discussed in Sec-
tion 4, where a comparison with GPS data is also
performed, and a kinematical model based on the
integration of EDM, GPS and DiffSAR data is pro-
posed. Finally, some concluding remarks are reported
in Section 5.
P. Berardino et al. / Engineering Geology 68 (2003) 31–5132
Fig. 1. Geology and interpretative scheme of the instability at Maratea Valley. (a) and (b) show large instability phenomena, particularly
developed on the eastern flank of the valley, where Sackung morphology is present and where the geological sequence is characterised by the
tectonic overlapping of carbonate rocks on clayey flysches. The schemes in (c) and (d) point out a structural opening of the valley and complex
geological processes, partly tectonic and partly gravitational. In particular, the lower part of the Maratea Valley (sector C) is subject to a deep
and slow flow–slide, with movements at ‘‘constant rate’’ (in the order of 2–3 cm year� 1); the crests of the eastern valley flank are subject to
prevalently horizontal tectonic movements having opposite direction to the local gravitational slope movements; intermediate slopes, where
there are lateral spreads (sector A) and the Sackung (sector B), are subject to the interaction of both these processes, with discontinuous
movements. In legend: 1—Carbonate rocks (Verbicaro Unit); 1a—as before, dislocated by gravitational and tectonic processes; 2—Clayey
flysch (Crete Nere Formation); 3—Carbonate rocks (Alburno–Cervati Unit); 3a—as before, dislocated by gravitational and tectonic processes;
4—Pleistocene detritus and breccias; 5—Eluvium; 6—Holocene alluvial fan; 7—Boundaries between masses having different velocity and
rupture processes; 8—Main morphostructural dislocations; 9—Thrust; 10—Directions of slope movement; 11—spring; 12—geological section
or seismic line.
P. Berardino et al. / Engineering Geology 68 (2003) 31–51 33
2. Description of the study area and available data
2.1. Geological and morphostructural setting
The Maratea Valley (Basilicata) is a tectonically
active area, affected by large complex gravitational
phenomena and impressive Sackung-type morpholo-
gies (Fig. 1a and b). The asymmetric valley has a
triangular shape, which could be divided into sectors
marked by different stratigraphy (Fig. 1c and d):
Sector A—a large area of the valley (Piano Campo)
formed by dislocated units of the Mesozoic calcare-
ous–dolomitic formations (belonging to ‘‘Bulgheria–
Verbicaro’’ tectonic unit), covered by alluvial fan, and
fragmented in aligned contiguous steps to the valley
border; Sector B—south-eastern flank of the valley
including the relief is built by the same carbonate
formations, but without detrital covers; it is affected
by impressive crest sagging and trenches, interpreted
as Sackung-type phenomena; Sector C—the remain-
ing part of the valley with outcrops of clayey for-
mations and shallow detrital covers, characterised by
landslide morphology and divided in minor sectors,
following the main structural dislocation (Fig. 1d;
Rizzo, 1997). Several springs are present along the
western border of Sectors A and B, prevalently along
the contact with the Sector C.
The complex morphostructural setting of the area
has been influenced by Pleistocene extensional tecton-
ics. Structural dislocations probably characterised by a
main transcurrent component have first allowed the
superimposition of the Bulgheria–Verbicaro unit on
the ‘‘Crete Nere’’ formation and now are facilitating the
opening of the valley (Fig. 1c and e). The ‘‘Crete Nere’’
argillitic flysch, assuming a plastic behaviour due to
high water content, is in the presence of tectonic
stresses and constitutes a ‘‘lubricant’’ for deep gravita-
tional movements (Fig. 1e).
A relation between active tectonic and slope move-
ments was hypothesised by Rizzo (1997) on the basis
of bathymetric and high-resolution seismic lines real-
ised on the sea-floor of the Maratea Valley: upper
Pleistocene–Holocene morphological, structural and
depositional setting shows in front of the valley a
structure marked along its western flank by a marine
canyon (Colantoni et al., 1997).
2.2. Previous investigation and GPS monitoring
The data on ground surface movements in the
investigated area were collected through long-term
annual monitoring, which began in 1983 (Fig. 2).
The movements were monitored through distance
variation between benchmarks and control stations
by the use of an electronic distance-meter (EDM); in
1987 and 1996, this network was updated. Based on
these data and morphostructures, a first schematic
mapping and modelling of slope instability was pro-
posed in 1996 (Rizzo, 1997).
Other data on slope movements (Figs. 2 and 3)
were collected through short-term or continuous
monitoring over different single sites within the
valley by the use of electrical extensometers and
crack gauges (Guerricchio et al., 1988) and by
inclinometric soundings (Rizzo, 1997; Rizzo and
Limongi, 1997).
Furthermore, between 1997 and 1999–2000–
2001, GPS data were acquired (Rizzo, 2001) on a
separated network which, nevertheless, shared a num-
ber of points with the EDM data. In June 1997, a
network of 42 benchmarks was set up; this grid was
used for differential static GPS monitoring, (Table 1).
The reference station was located outside of the
valley, on the carbonate slope west of Fiumicello, that
is not subject to gravitational instability (Fig. 2). In the
first campaign, two Trimble 4000SSi receivers equip-
ped with M L1/L2 ground plain antenna were
employed. The campaigns of November 1999 and
March 2000 used compensation mode and similar
equipment. GPS measurement information are col-
lected in Tables 1 and 2. The obtained results are
shown in Table 3. A new GPS network, located
outside the landslide area (on the relief constituting
the EDM monitoring reference system) was set up in
Fig. 2. Global Positioning System (GPS) and Electronic Distance Meter (EDM) monitoring networks used to measure the surface movements
(beginning 1983). The GPS measurements refer to the reference station (station 0), the stable slopes of the western flank of the valley, whereas
the EDM measurements refer to reliefs not affected by slope the gravitational processes (reference slope of Monte Crivo and Monte S. Biagio)
of the eastern flank of the valley. The figure also shows the location of instrumentation used for the measurement of deformations, both at
surface (by crack-gauges), and in depth (by inclinometric soundings); the results are shown in Fig. 3.
P. Berardino et al. / Engineering Geology 68 (2003) 31–5134
Fig. 3. Movements monitored in the flow–slide area of Fig. 1c, by various measurement systems and with various time intervals. The data show
a continuous gravitational process, tending to ‘‘constant rate’’, apparently little influenced by the pluviometric conditions. (a) Displacements of
benchmarks labelled Vr in Fig. 2, obtained by using an Electronic Distance Meter (EDM) from the ‘‘Madonna degli Ulivi’’ (Vs) Station (yearly
monitoring, beginning 1983). (b), (c) and (d): movements of inclinometric tubes (respectively, for tubes 1, 2 and 3 in Fig. 2), calculated at the
head (quadratic shape) and on the sliding plane (triangles) with respect to the bottom. (e) Fissure opening in a building in the old town centre,
continuously measured by an electrical crack-gauge. (f) Monitoring of the spreading of a carbonate block, continuously monitored by a long-
base invar-wire electrical extensometer anchored to the scarps of Monte S. Biagio. The graph below shows monthly rain trend in the period of
the various monitoring.
P. Berardino et al. / Engineering Geology 68 (2003) 31–5136
2000 in order to gather in the future a complete picture
of the instability processes.
2.3. Available data and state of knowledge
On the basis of these investigation, the previously
proposed scheme (Rizzo, 1997) was confirmed and
simplified as follows (Fig. 1d; Rizzo, 2001; Rizzo and
Leggeri, 2001):
The Sector C shows an almost constantly increas-
ing rate of movement and deformation, following the
gravitational directions, gathered both in surface and
in depth (Fig. 3), although the velocity is not spatially
homogeneous, and is therefore different from point to
point (Rizzo, 1997; Toni et al., 2001).
This kind of rupture could be seen as a constant
deep flow in high saturated clays, due to peculiar
hydrogeological condition (Cotecchia et al., 1990),
slightly influenced by rain and probably partially
influenced by tectonic (Rizzo, 1997). The depth of
flow masses is still not well known, and its study is in
progress; relevant shear plain was located in the first
50 m (Rizzo and Limongi, 1997).
In the Sectors A and B, the instability of the
Sackung and other fragmented carbonate formations
shows little and irregular movements, such as a spread
due to deep creep, that could be influenced by tectonic
activity (Rizzo, 2001). This question is still open and
first data on a new GPS network, located outside the
landslide area, confirms a role of tectonic movement
on the landslide instability and Sackung reactivation,
as proposed in Fig. 1c (Rizzo and Leggeri, 2001).
3. Employed DiffSAR technique
In this section, we describe the procedure that
allows to obtain the time evolution of ground dis-
placement corresponding to each pixel of the imaged
scene. First of all, SAR acquisitions of the considered
scene to be used in the study must be selected. In
order to make topographic phase suppression easier,
Table 1
Date and characteristics of the SAR and GPS acquisitions
Dates of SAR
acquisitions
Spatial
baseline B? (m)
Temporal
baseline (days)
Percentage of selected
points (coherence>0.3) (%)
13-08-1997 31-12-1997 30 140 33.11
31-12-1997 15-04-1998 15 105 46.74
13-08-1997 15-04-1998 45 245 30.34
15-04-1998 02-09-1998 110 140 30.85
02-09-1998 09-06-1999 2 280 33.17
02-09-1998 14-07-1999 6 315 35.28
09-06-1999 14-07-1999 4 35 52.46
09-06-1999 05-01-2000 6 210 32.05
14-07-1999 05-01-2000 10 175 29.53
Dates of GPS Work conditions (static differential; 2 h standing at rover station and minimum a week at reference station)
monitoringUtilised equipment Compensation
June 1997 N.2 Trimble 4000 with M Ll/L2
ground plain antennae
without
November 1999 N.2 Trimble 4000 and N.1 Trimble 4400
SSi with M L1/L2 ground plain antennae
on triangular grid
March 2000 N.3 Trimble 4400 SSi, N.1 Trimble 4000
with M L1/L2 ground plain antennae, N.1
Turborogue at reference station
on triangular grid
Instrumental
accuracy
in vertical: 1 + 0.1 cm for each km
of base length
(by Trimble) in horizontal: 0.5 + 0.1 cm for each
km of base length
P. Berardino et al. / Engineering Geology 68 (2003) 31–51 37
Table 2
WGS coordinates relative to the March 2000 campaign (the benchmarks with blank cells were destroyed)
Results of GPS campaign conducted from 20 to 25 March 2000
WGS84 coordinates at reference station
x 4710295,991 lat (dms) N 40j 00V12,664Wy 1323003,504 elon (dms) E 15j 41V19,374Wz 4078378,756 wlon (dms) W 344j 18V40,626W
ht (m) 146,1940
Coordinate difference between reference and rover stations (RS)
RS dx (m) dy (m) dz (m) distance (m) dh (m) Azimuth (dms) forward
MAR1 � 128,425 1,998,270 � 443,033 2,050,818 34,737 107j 13V33,209855WMAR2 � 202,088 2,667,520 � 450,814 2,712,883 114,283 104j 36V56,335938WMAR3 � 179,138 2,400,660 � 437,538 2,446,773 84,359 105j 12V39,820661WMAR4 1521 2,251,247 � 664,766 2,341,759 53,387 112j 14V30,555003WMAR5 249,375 2,037,846 � 961,339 2,266,975 � 11,562 123j 18V39,051549WMAR6 55,099 1,870,242 � 685,161 1,992,558 � 12,106 116j 20V19,971580WMAR7 286,614 1,976,295 � 1,007,687 2,236,811 � 26,654 125j 18V39,499746WMAR8 474,715 1,628,924 � 1,144,631 2,046,687 � 47,984 135j 16V28,800690WMAR9
MA10 409,494 1,059,365 � 881,717 1,437,833 � 45,215 140j 45V19,400616WMA11 � 54,391 2,749,806 � 618,866 2,819,111 132,265 109j 02V02,255330WMA12 40,882 1,120,692 � 519,999 1,236,132 � 71,869 120j 04V33,245220WMA13 � 190,792 3,010,693 � 500,663 3,057,995 161,806 104j 58V08,829688WMA14 � 246,335 2,303,395 � 330,413 2,339,975 83,479 102j 22V19,185358WMA15
MA16 � 380,338 2,685,222 � 227,329 2,721,535 130,163 98j 34V44,449124WMA17 � 251,334 3,350,973 � 444,965 3,389,717 223,613 103j 07V13,421785WMA18 � 116,360 3,099,375 � 572,294 3,153,916 189,066 106j 42V29,758646WMA19
MA20 98,095 2,914,821 � 761,721 3,014,303 187,155 112j 29V27,231835WMA21 � 24,407 3,084,967 � 663,819 3,155,673 195,095 109j 04V51,239920WMA22
MA23 � 809,741 2,633,654 314,594 2,773,226 151,200 84j 06V25,088304WMA24
MA25
MA26 � 107,077 2,987,916 � 585,102 3,046,547 164,540 107j 14V02,428241WMA27 � 1,046,161 2,591,791 635,488 2,866,300 174,498 76j 10V 26,307833WMA28 � 1,241,614 2,528,654 1,003,283 2,990,363 253,765 68j 23V 22,341637WMA29
MA30 � 939,747 2,690,518 467,068 2,887,934 165,168 80j 35V03,971734WMA31 � 123,021 3,537,897 � 596,767 3,589,983 259,484 106j 09V00,470594WMA32 130,571 3,100,039 � 840,465 3,214,603 198,949 113j 11V26,980553WMA33 � 609,574 2,783,512 21,445 2,849,558 141,445 91j 48V56,234005WMA34 � 343,936 2,605,008 � 258,449 2,640,294 120,354 99j 33V27,544847WMA35 � 310,494 2,582,859 � 294,251 2,618,043 117,405 100j 37V28,396191WMA36 � 22,283 1,383,952 � 453,023 1,456,382 � 20,819 113j 12V19,814751WMA37 411,106 1,655,908 � 1,010,149 1,982,786 � 2875 131j 35V08,583167WMA38 � 1,209,599 2,659,290 855,331 3,044,144 209,353 71j 56V21,316074WMA39 � 1,397,718 3,056,743 1,143,109 3,550,211 338,198 69j 59V25,850068WMA40 � 1,615,336 3,205,725 1,491,427 3,887,202 432,674 65j 46V44,226765WMA41 � 1,355,440 3,447,166 1,092,427 3,861,732 417,668 73j 43V12,677252WMA42 � 929,371 3,618,644 448,267 3,762,879 353,431 85j 34V03,660213WMAT1 � 403,001 3,063,325 � 144,765 3,093,109 245,009 97j 20V26,843961W
P. Berardino et al. / Engineering Geology 68 (2003) 31–5138
we choose acquisitions whose orbits are enclosed
within a cylinder whose diameter is about 100 m.
All SAR images are then co-registered, so that homol-
ogous pixels in all different images correspond to the
same ground point; subsequently, interferograms are
generated by taking pairs of images. From each
interferogram, a displacement map is obtained. Then,
displacement maps are properly combined to obtain a
complete analysis of time evolution of ground dis-
placements.
Table 3
Differences in WGS coordinates, obtained in June 1997, November 1999 and March 2000 GPS campaigns
Bench June 1997/November 1999 June 1997/March 2000 November 1999/March 2000
Mark number d1(x) mm d1( y) mm d1(z) mm d2(x) mm d2( y) mm d2(z)mm d2� d1(x) mm d2� d1( y) mm d2� d1(z) mm
MAR1 � 93 � 32 � 41 � 112 � 28 � 10 � 19 4 31
MAR2 � 106 � 1 4 � 126 4 3 � 20 5 � 1
MAR3 � 100 � 11 � 37 � 118 � 16 � 20 � 18 � 5 17
MAR4 � 87 � 20 � 31 � 94 � 22 � 17 � 7 � 2 14
MAR5 � 45 � 27 � 13 � 52 � 23 � 55 � 7 4 � 42
MAR6 � 63 � 43 17 � 84 � 24 � 47 � 21 19 � 64
MAR7 � 55 � 66 14 � 63 � 61 3 � 8 5 � 11
MAR8 � 46 � 64 � 24 � 53 � 70 � 39 � 7 � 6 � 15
MAR9 – – – – – – – – –
MA10 � 46 � 17 5 � 55 � 15 � 8 � 9 2 � 13
MA11 � 89 � 5 � 13 � 101 14 � 46 � 12 19 � 33
MA12 � 46 � 10 � 11 � 59 � 11 11 � 13 � 1 22
MA13 � 86 37 � 71 � 104 37 � 66 � 18 0 5
MA14 � 113 � 39 8 � 131 � 43 � 26 � 18 � 4 � 34
MA15 – – – – – – – – –
MA16 � 42 � 7 33 � 59 � 10 � 10 � 17 � 3 � 43
MA17 � 28 8 � 37 � 41 13 1 � 13 5 38
MA18 � 128 25 � 23 � 137 27 � 60 � 9 2 � 37
MA19 – – – – – – – – –
MA20 � 39 37 � 34 � 37 33 � 25 2 � 4 9
MA21 � 61 66 � 21 � 69 83 � 80 � 8 17 � 59
MA22 – – – – – – – – –
MA23 � 111 � 83 � 51 � 125 � 101 � 60 � 14 � 18 � 9
MA24 � 122 � 98 � 39 – – – – – –
MA25 – – – – – – – – –
MA26 � 75 44 � 73 � 76 17 � 79 � 1 � 27 � 6
MA27 � 32 � 84 � 39 � 42 � 89 � 17 � 10 � 5 22
MA28 1 � 13 � 15 � 1 � 15 � 35 � 2 � 2 � 20
MA29 – – – – – – – – –
MA30 � 93 � 90 � 7 � 111 � 95 � 11 � 18 � 5 � 4
MA31 � 31 15 � 31 � 42 28 � 5 � 11 13 26
MA32 � 56 19 � 34 � 69 47 � 97 � 13 28 � 63
MA33 � 47 � 6 � 50 � 75 � 7 � 74 � 28 � 1 � 24
MA34 � 110 17 � 51 � 141 10 � 28 � 31 � 7 23
MA35 � 110 2 � 32 � 146 4 � 8 � 36 2 24
MA36 � 86 � 43 � 7 � 88 � 50 � 27 � 2 � 7 � 20
MA37 � 46 � 43 � 5 � 53 � 53 � 22 � 7 � 10 � 17
MA38 16 � 38 � 6 8 � 34 � 33 � 8 4 � 27
MA39 � 20 � 22 � 14 � 9 � 21 � 17 11 1 � 3
MA40 0 � 7 � 14 � 14 � 5 � 22 � 14 2 � 8
MA41 � 9 � 9 3 � 32 � 8 � 3 � 23 1 � 6
MA42 � 11 � 11 12 � 31 � 8 3 � 20 3 � 9
MAT1 � 16 � 7 � 23 � 35 � 9 12 � 19 � 2 35
P. Berardino et al. / Engineering Geology 68 (2003) 31–51 39
3.1. Generating a displacement map
In this subsection, we describe the procedure to
obtain a displacement map from an interferometric
pair. First of all, the topographic phase term must be
suppressed. In this study, we use a DEM of the area
under analysis, provided by the Italian ‘‘Istituto Geo-
grafico Militare’’ (IGM), and orbit data provided by
European Space Agency (ESA) along with SAR data.
The DEM pixel size is 20� 20 m, its vertical accuracy
is about 7–10 m and is topography dependent. After
the topographic phase term suppression, we obtain the
‘‘differential interferogram’’.
At this point, unwrapping the interferometric phase
(i.e. obtaining the correct phase from its restriction to
the interval �k,k) is a necessary step to measure
without ambiguity ground deformation. Use of tempo-
ral baselines of the order of several months causes
considerable decorrelation noise in some areas (partic-
ularly, in vegetated areas). Even when only some of the
data are corrupted, it may be impossible to unwrap the
good-quality phase in the remaining points. To over-
come this problem, the phase unwrapping operation
should be performed only on the subset of ‘‘good’’ data,
selected according to a given criterion. For instance,
this criterion can be based on the correlation coefficient
(or coherence) map (Franceschetti and Lanari, 1999) of
the interferometric pair. In general, however, the
selected data will no longer be distributed on a regular
rectangular grid, and it will not be possible to use
traditional phase unwrapping techniques. A new gen-
eralized technique, presented in Costantini and Rosen
(1999) is able to deal with sparse data, i.e. data on an
irregular grid, and can be therefore used in these cases.
Accordingly, in this method, phase unwrapping is
preceded by the selection of ‘‘good’’ points from the
differential interferogram. We select the points of the
interferograms for which the measured correlation
coefficient is greater than a prescribed threshold.
Choice of the threshold and of the size of the window
used to calculate the correlation coefficient (from the
single look complex image pair) is a delicate matter: we
verified that, for ERS data, a 3� 15 pixel window size
(corresponding to a final resolution cell of about
60� 60m) and a correlation coefficient threshold equal
to 0.3 seem to be a good choice. In fact, these values
provide a good compromise between phase ‘‘quality’’
(i.e. phase noise level) and number of selected points.
After the pixel selection, we obtain a finite set S of
irregularly distributed points in the plane. This set
represents the points for which reliable wrapped phase
data are available. The phase unwrapping algorithm
described in Costantini and Rosen (1999) and Cos-
tantini et al. (2000a) is then applied to this set of
points.
Once phase unwrapping has been performed, phase
values are converted to displacement values by multi-
plying by k/4p. The measured displacements are the
projections of actual displacements along the SAR
line of sight. The unwrapped phase values are known
up to an additive constant, different for each inter-
ferometric pair. To resolve this indetermination, it is
assumed that a given point (or, more conveniently, a
given area) in the scene is motionless. If this reference
point is not actually still, all obtained displacements
are relative to the reference point.
After determination of the phase-additive constant,
displacement values for all the irregularly distributed
points of S are available. A displacement map over the
regular original grid can be finally obtained by per-
forming a simple interpolation.
3.2. Evaluating the time evolution of ground displace-
ment
In this subsection, we describe the procedure used
to obtain the displacement time evolution for all the
points of the considered scene.
Displacement maps relative to different time inter-
vals can be combined to obtain the displacement as a
function of time. This is achieved by solving, for each
point P of the scene, the following linear system of
equations:
sðtj;PÞ � sðti;PÞ ¼ smeasðti; tj;PÞ ; i; j ¼ 1; . . . ; n ;
ð1Þ
where ti is the time of the ith acquisition, n is the total
number of acquisitions, smeas (ti, ti; P) is the displace-
ment measured over the map obtained from the pair of
SAR images acquired at times ti and tj and s(ti; P) is
the (unknown) displacement that occurred in the time
interval between the first and the ith acquisition. Once
the system (1) has been solved with respect to the
unknowns s(ti; P), the displacement values corre-
P. Berardino et al. / Engineering Geology 68 (2003) 31–5140
Fig. 4. Map of coherence loss (above) and of vegetation intensity (below). The pixels having a coherence higher than 0.65 are coloured and
show the obtained displacements.
P. Berardino et al. / Engineering Geology 68 (2003) 31–51 41
sponding to the SAR acquisition times ti (i = 1,. . .,n)are available for each point of the scene.
After combining displacement maps, an atmos-
pheric artifact occurring in correspondence to a given
acquisition only affects the displacement value corre-
sponding to the time of the considered acquisition and
does not apply to other displacement values (although
it affects all the displacement maps corresponding to
pairs including the considered acquisition). The same
property holds for effects of inaccuracies in the topo-
graphic phase term subtraction due to errors in the
orbit data of a given acquisition. Hence, measured
displacement at each acquisition time can be modeled
as the sum of the true displacement value, atmos-
pheric noise and topographic noise. Therefore, it is
reasonable to fit measured displacements at different
times to a proper function of time. For the case
considered in this paper, a constant rate displacement
can be assumed (e.g. Fig. 3).
4. Results and discussion
4.1. SAR results and their interpretation
The method described in Section 3 has been used
to analyse instability in the Maratea Valley. In partic-
ular, we have considered seven ERS-2 descending
orbit SAR acquisitions covering a time interval span-
ning from August 1997 to January 2000. Due to the
dense vegetation cover (Fig. 4), a very low correlation
is obtained for pairs with high temporal spacing, so
that nine interferograms have been generated from
these acquisitions (some pairs were too decorrelated to
be used). Dates of employed SAR acquisitions and
interferograms are reported in Table 1, along with
percentages of selected points over each interfero-
gram, i.e. point where coherence is larger than 0.3.
Two of the nine unwrapped interferograms are
shown in Fig. 5. These nine interferograms have been
combined according to the procedure of Section 3.2,
Eq. (1), so that a complete space–time analysis of
displacements has been performed. The displacement
map corresponding to the entire considered time
interval is shown in Fig. 6. The displacement map
of Fig. 6 is not an unwrapped interferogram; in fact, it
is the result of the combination of all obtained
interferograms, performed on a pixel-by-pixel basis
according to Eq. (1). Furthermore, all obtained dis-
placements must be meant as relative to a reference
point placed outside the Maratea Valley, a few kilo-
meters to the NW (c.f. Fig. 6), in an area known to be
not affected by slope instability. In addition, all move-
ments are projected along the sensor line of sight,
which forms an angle of about 23j with the vertical
direction and is oriented approximately from east to
west. The horizontal resolution of the map is of
60� 60 m. Fig. 6 shows also the result of GPS
measurements over the time interval from June 1997
to March 2000. It is clear from visual inspection that
the overall spatial distribution of displacements
reported in Fig. 6 is consistent with previous knowl-
edge (Rizzo, 1997), as well as with the recent results
obtained by GPS measurements (Rizzo, 2001; Fig.
1d). A more detailed comparison of SAR and GPS
data is reported in Section 4.2. We underline that a
complete history of time displacement for each pixel
can be obtained by means of the DiffSAR procedure.
As an example, measured displacements of some
points, selected in order to analyse zones with differ-
ent kinematics, are plotted as a function of time in Fig.
7. Again, these displacements have been obtained by
using the procedure of Section 3.2. In particular, Fig.
7a and b refer to the points outside the valley (Fig. 6):
the first one shows a point on the west of the valley
(the reference point of GPS measurements, see bench-
mark 0 in Fig. 2); the second shows several points
around the valley (points 1, 2 and 3). These points
were expected to be unaffected by significant move-
ments, and this expectation is confirmed by DiffSAR
measured displacements that show regular ‘‘ups and
downs’’ between �1 and +1 cm, probably due to
atmospheric refraction index seasonal fluctuations and
to the marked decorrelation noise: in fact, these points
Fig. 5. Unwrapped interferograms obtained from the following acquisition pairs: 09-06-1999 14-07-1999 (A); 09-06-1999 05-01-2000 (B).
Points whose coherence is less than 0.3 and sea area are reported in black. Note the difference, in terms of contained information, between these
single interferograms and the displacement map of Fig. 6, obtained by combining all available interferograms via the algorithm described in
Section 3.2. For reference purposes, the SAR amplitude image (C) is also reported. The white frames approximately represent the area of the
Maratea valley, considered in Fig. 6.
P. Berardino et al. / Engineering Geology 68 (2003) 31–5142
have a low correlation coefficient in all the employed
interferograms. Fig. 7c and d are referred to other
points placed within the valley and showing different
amount of displacements: points 4, 5, 6 and 7 in Fig.
7c and points 8 and 9 in Fig. 7d, belonging to more
and less active areas, respectively. The data clearly
show a linear dependence of displacement on time, as
expected.
4.2. Comparison with GPS measurements
GPS measurements are available from an initial
network of 43 ground points (Fig. 2, Table 3) and
refer to the period from June 1997 to March 2000
(Tables 1 and 2). However, comparison between GPS
and DiffSAR data requires some attention because of
the following issues.
First of all, the GPS reference point has turned out
to be a low-coherence point on the SAR interfero-
grams, so that we had to choose a different reference
point for DiffSAR measurements (Fig. 6). In making
this choice, we verified that the two reference points
of the two measurement systems do not significantly
move with respect each other and as regard the line of
sight direction. As a matter of fact, as shown in Fig.
7a, DiffSAR measurements over the GPS reference
point only show small fluctuations around zero that
may include both actual small movements and decor-
relation noise.
Another issue to be faced is that GPS and DiffSAR
measurements refer to different time intervals. How-
ever, considering the linearity of displacements with
time (Fig. 3), for comparison purposes, the DiffSAR
measurements were corrected in order to refer to the
same time interval as the GPS ones.
The GPS measurements are also affected by errors
(Gili et al., 2000) that can be of the same order or even
higher than DiffSAR ones. As a matter of fact, GPS
precision in horizontal plane could be of about 0.8 cm,
but could be less than 1.3 cm in the vertical direction
(data obtained for our work condition on the base of
the farmer indication). Consider also that vertical
displacements are measured by taking the difference
between three GPS campaigns, and that different (or
no) compensations have been used in the campaigns
(see Tables 1 and 3, Fig. 8). In order to quantify this,
we compared GPS measurements performed in
November 1999 with those performed on March
2000. The differences between the vertical compo-
nents of the two measurements (Table 3), depurated of
the small actual movements predicted over 4 months
(this prediction is based on the constant displacement
rates measured by the June 1997–March 2000 GPS
campaign at every benchmark) are plotted in Fig. 9 for
the GPS ground points. The data show fluctuations in
the order of 2–2.5 cm, having a maximum of 6 cm. In
order to obtain values homogeneous with the DiffSAR
ones, we also projected GPS errors onto the SAR line
of sight. The standard deviation of these projected
errors turns out to be 2.3 cm, which can be considered
as the GPS error (obviously higher than the instru-
mental precision, as consequence of previous assump-
tion and elaboration) along the SAR line of sight.
Unfortunately only very few of the GPS ground
points have a high coherence value on all the inter-
ferograms. More exactly, only for 3 out of 42 GPS
ground points, the average value of coherence is
greater than 0.65. Corresponding results are reported
in Table 4 (points 4, 13 and 17). The difference
between GPS and DiffSAR measurements in these
cases is always smaller than GPS error, thus confirm-
ing the effectiveness of our technique in high coher-
ence areas. With regard to measurements on the other
(i.e. low coherence) points, the difference between
GPS and DiffSAR measurements (see Table 4) has a
mean value equal to � 2.3 cm (DiffSAR underesti-
mates the movements) and a standard deviation equal
to 3.3 cm. These discrepancies can be explained by
considering that in these low-coherence points the
DiffSAR measurements are obtained by interpolating
results obtained in surrounding high-coherence points,
so that they can be considered as average displace-
ment over a rather wide area, whereas GPS measure-
ments are strictly referred to each single ground point.
In addition, as already pointed out, SAR and GPS
measurements have been reported to the same time
Fig. 6. Above: DiffSAR displacement map relative to the period from August 1997 to January 2000, and GPS data acquired in almost the same
time interval (June 1997–March 2000). GPS vectors shown by arrows lie in the horizontal plane, whereas SAR displacements are as projected
along the line of sight (descending orbit, being about 23j from vertical and 104j from the direction north). Below: map showing locations of
DiffSAR reference point and of SAR points (pixels) and GPS measurement points selected for temporal displacement analysis (Fig. 7).
P. Berardino et al. / Engineering Geology 68 (2003) 31–51 45
interval by relying on the constant rate assumption,
and hence, this operation may be slightly affected by
limitations of the assumption. The above results
suggest that DiffSAR accuracy is higher than the
GPS one if the coherence (averaged over all the
interferograms) is greater than 0.65, i.e. in the areas
highlighted in Fig. 4. Such data can be used to
‘‘complement’’ the GPS network. Measurements per-
formed outside these areas must be understood as
spatially averaged measurements, not useful to
retrieve the exact displacement of a particular consid-
ered ground point, but useful to understand the overall
kinematics of the slope instability phenomenon.
4.3. Geological model based on integration of SAR
and GPS data
According to the results of the previous subsec-
tion, integration of GPS and DiffSAR data can lead
to a more complete analysis. Due to the SAR
acquisition geometry, DiffSAR mainly measures the
vertical component of the movement that is not
always accurately estimated by the GPS technique.
Improvement in the understanding of the vertical
component allowed us to obtain a detailed schemati-
sation of the kinematics of the Maratea Valley,
reported in Fig. 10. The area under survey has been
Fig. 7. Displacement versus time for points 1–9 reported in Fig. 6 and for GPS reference pixel (coincident to Station 0 in Fig. 2). (a) The GPS
reference point. (b) A set of stable points outside the valley (point 1: asterisks; point 2: diamonds; point 3: triangles). (c) A set of points within
the valley in the area showing intense vertical displacements (a large strip going from the northern border of the Sackung to Fiumicello on the
coast (point 4: asterisks; point 5: diamonds; point 6:triangles; point 7: squares). (d) Two points within the valley (point 8: asterisks; point 9:
triangles) in the south-eastern corner showing minor vertical displacements.
P. Berardino et al. / Engineering Geology 68 (2003) 31–5146
Fig. 8. Configuration of the GPS compensation networks used in the June 1997, November 1999 and March 2000 campaigns.
Fig. 9. Difference between the vertical component of GPS measurements of November 1999 and March 2000; both with respect to that of June
1997. These differences have been depurated of the small movements predicted over 4 months, supposing that the velocity monitored with GPS
techniques for every single point proceeds ‘‘at constant rate’’. In fact, this procedure, necessary to make the data coherent from the point of view
of the time interval, is not influential since the aforementioned campaigns were almost coeval. It should be noted how the differences in quota
obtained for the various benchmarks are slightly higher than the accuracy known for these measurement systems.
P. Berardino et al. / Engineering Geology 68 (2003) 31–51 47
Table 4
Comparison of GPS and DiffSAR measurements
GPS
benchmark
number
GPS
displacement
(mm)
DiffSAR
displacement
(mm)
Zone in
the model
(Fig. 10)
Coherence Linearity GPS-SAR
difference
(mm)
1 � 52 � 54 2 0.3 1 2
2 � 49 � 63 2 0.6 0.9 14
3 � 65 � 63 2 0.5 1 � 2
4 � 52 � 54 3 0.7 0.9 2
5 � 69 � 22 2 0.2 0.7 � 47
6 � 75 � 24 3 0.6 0.8 � 51
7 � 18 � 15 2 0.2 0.5 � 3
8 � 51 � 8 3 0.3 0.4 � 43
9 no � 25 3 0.6 0.8
10 � 28 � 6 3 0.2 0 � 22
11 � 84 � 56 2 0.2 0.9 � 28
12 � 13 � 36 2 0.2 0.8 23
13 � 105 � 83 2 0.7 1 � 22
14 � 73 � 52 2 0.3 0.9 � 21
15 no � 47 2 0.3 0.9
16 � 32 � 65 2 0.3 1 33
17 � 17 � 38 2 0.7 0.8 21
18 � 112 � 46 2 0.6 0.9 � 66
19 no � 14 2 0.4 0.1
20 � 41 � 15 2 0.4 0 � 26
21 � 107 � 27 2 0.6 0.6 � 80
22 no � 58 2 0.3 0.9
23 � 97 � 51 2 0.3 1 � 46
24 no � 69 2 0.3 1
25 no � 69 2 0.3 1
26 � 104 � 56 2 0.6 0.9 � 48
27 � 24 � 75 2 0.2 1 51
28 � 31 � 18 2 0.3 0.3 � 13
29 no 2 2 0.2 0
30 � 47 � 41 2 0.3 1 � 6
31 � 24 � 18 2 0.2 0.6 � 6
32 � 120 � 33 2 0.1 0.7 � 87
33 � 97 � 13 2 0.2 0.8 � 84
34 � 83 � 68 2 0.4 0.9 � 15
35 � 66 � 61 2 0.5 0.9 � 5
36 � 56 � 38 2 0.2 0.9 � 18
37 � 37 � 15 3 0.2 0.7 � 22
38 � 24 � 56 1 0.2 0.7 32
39 � 17 � 9 1 0.2 0 � 8
40 � 25 � 25 1 0.4 0.6 0
41 � 15 � 21 1 0.2 0.7 6
42 � 9 � 26 1 0.3 0.5 17
Displacements are both relative to the period from June 1997 to March 2000 and are projected along the SAR line of sight. GPS error, on the
basis of the data shown in Fig. 9, is 23 mm. The obtained values show good agreement in the pixels at higher coherence.
In the other cases the differences found are due to two different acquisition systems: in part attributable to GPS errors in the vertical direction,
where the SAR data seem to be more reliable; in part to the contribution to the coherence of the various objects in movement present in the SAR
pixels, with respect to the fixed and limited position of the individual GPS benchmark; in part to the difference between SAR and GPS reference
points.
P. Berardino et al. / Engineering Geology 68 (2003) 31–5148
divided into four kinematics typologies: (1) area
subject to very low and irregular movements (about
0.5 cm year� 1); (2) area subject to intense move-
ments, both vertical and horizontal (about 2–4 cm
year� 1); (3) area subject to prevalently horizontal
movements (of similar amount); (4) area external to
the valley and not subject to relevant vertical dis-
placements (and still being monitored by GPS to
control horizontal displacements). In addition, in
Fig. 10, the stable reference area placed on the right
flank of the valley is shown as region 5.
Note that the zone with a marked vertical compo-
nent is wider than previously believed (Toni et al.,
2001): it includes not only the area between Maratea
Village and S. Maria, but also a larger strip that goes
from the northern margin of the Sackung to Fiumi-
cello on the coast (Figs. 1, 2 and 6); where there is the
submarine canyon interpreted in previous work as
Fig. 10. Modelling of the instability at Maratea. The differences from the Fig. 1 scheme are the result of a re-elaboration based on the DiffSAR
data only. Legend: 1—Deep creep zone, characterized by low and irregular gravitational movements (about 0.5 cm year� 1 or less), as a
consequence of opposing tectonic (widening valley) and gravitational movements (creep); 2—Clayey flow: characterised by intense
gravitational movements (both in vertical and horizontal direction of about 2–4 cm year� 1); 3—Flow, as above, but characterised by only
intense horizontal movements; 4—Areas not subjected to gravitational movements, but probably subject to intense tectonic movements; 5—
Stable slopes and utilised as GPS reference. Area 1 near the lower margin of the picture shows the Sackung.
P. Berardino et al. / Engineering Geology 68 (2003) 31–51 49
consequence of tectonic activity (Rizzo, 1997). In
addition, the DiffSAR measurements seem to confirm
the presence of a recently detected movements on the
carbonate slope of the valley, constituting the north-
western border of the flow along the valley, once
assumed to be without movement.
5. Summary and conclusions
In this paper, the use of the DiffSAR technique to
improve our knowledge of the slope instability of a
well-investigated area (the Maratea Valley) affected
by continuous slow movements has been explored.
Advantages of the proposed interferometric proce-
dure are those common to other implementations of
DiffSAR technique, i.e. global coverage and spatial
and (with some limitations) temporal continuity of
displacement measurements. An additional important
advantage of our procedure is that it allows to
mitigate the effects of temporal decorrelation and
atmospheric artifacts. In fact, the study area is
strongly vegetated, so that interferometric coherence
is low in a high percentage of scene points. In spite
of that, the obtained DiffSAR results are consistent
with previous knowledge of the considered phenom-
enon.
Comparison of DiffSAR and GPS vertical dis-
placements has shown that, for high-coherence points,
precision of DiffSAR is higher. Accuracy in low-
coherence areas is less satisfactory, and measurements
in these areas must be understood as spatially aver-
aged measurements; they do not allow to retrieve the
exact displacement of single ground points, but are
useful to understand the overall kinematics of the
instability phenomenon. Hence, obtained results allow
to carry out a complete analysis of the whole valley
and its surroundings.
In particular, we can conclude that:
(a) integration of the GPS and DiffSAR data has
allowed to obtain a new and more detailed picture
of the kinematics within the Maratea Valley; the
valley has been subdivided into four zones with
different ground movement characteristics;
(b) DiffSAR temporal analysis is coherent with the
result of other displacement monitoring networks
showing a deformation at constant rate;
(c) No DiffSAR vertical movements were detected on
the Sackung area in the monitored time interval,
proving a scarce influence of moderate quakes
(September 9, 1998 earthquakes of Castelluccio;
VI–VII MCS; 20 km far from Maratea) on its
stability.
Acknowledgements
This work has been supported by the Italian Space
Agency (ASI), within the project ‘‘Interferometria
differenziale per il controllo delle frane: uso diretto e
integrato con altre tecniche’’, by the financial help of
CNR-GNDCI line 2 (authorisation no. 2465) and POP
Basilicata measure 9.4.
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