PREVENTION AND MANAGEMENT OF HYDROGEOLOGICAL …geo.geoscienze.unipd.it/sites/default/files/Fiaschi_Simone_XXIX_2.pdf · advantages in the study of ground displacements but not in

Scuola di Dottorato in Scienze della Terra, Dipartimento di Geoscienze, Università degli Studi di Padova – A.A. 2014-2015

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PREVENTION AND MANAGEMENT OF HYDROGEOLOGICAL DISASTERS: BENEFIT-

COST ANALYSIS OF GEOLOGICAL HAZARD MITIGATION

Ph.D. candidate: SIMONE FIASCHI, II course

Tutor: Dr. MARIO FLORIS

Cycle: XXIX

Abstract

The monitoring of geological hazards with conventional ground-based techniques is usually expensive and time consuming.

One of the best way to overcome to this problem is to use remote-sensing techniques that allow us to reduce operating costs

and time, covering large portion of territory. Differential Synthetic Aperture Radar Interferometry (DInSAR) is one of the best

tool to monitor and study ground displacement in a cost-effective way. In this work, we applied DInSAR to the subsidence

affecting the city of Ravenna, Italy; the sinkholes in the Dead Sea area in Jordan; the detection of instabilities in Cuba.

The aim is to give an overall view of the DInSAR techniques potential when applied to the study of different geological

hazards. This can be useful to show to local authorities that DInSAR techniques can be fully integrated as part of territory

planning activity in the framework of geological hazard risk management and mitigation.

Full Report

In recent years, DInSAR remote sensing techniques resulted very effective for the detection and

monitoring of different geological hazards such as landslides, subsidence, sinkholes, earthquakes and

volcanic activity. DInSAR uses the difference in phase length of two radar waves coming from two

acquisitions made in different times to evaluate displacements on the ground (Figure 1). Thanks to this

technique, it is possible to detect ground motions with millimetric accuracy in very large areas and in a

very short amount of time in comparison with the classical ground-

based techniques such as GPS and spirit levelling. In this project, we

apply DInSAR to three different case studies to see what the

capabilities of this technique in various environments are.

Furthermore, we want to show to local authorities that using

DInSAR as part of geological hazards and territory monitoring

activities is more convenient than rely only on ground-based surveys

methods. For this purpose, at the end of our research activity we will

perform a benefit-cost analysis comparing the expenses in terms of

money and time of DInSAR and standard ground based survey, e.g.

GPS. In the first case study, we report the results obtained from the

monitoring of more than twenty years of subsidence activity

affecting the city of Ravenna. We exploit three different SAR image

datasets acquired by ERS-1/2, ENVISAT and TerraSAR-X

satellites, covering the 1991-2014 period. The produced

displacement maps show an almost linear trend of deformation for

the entire period that indicates a steady state of deformation velocities. This depicts the strict relationship

between the subsidence phenomenon and the ongoing extraction activity that are taking place in particular

in the industrial area and along the coastline. The second case study concerns the study of different form

of displacements occurring along the coastline of the Dead Sea in Jordan. In particular we focus on the

detection of sinkholes phenomena in the Gohr Al Hadita area and the displacements, both subsidence and

uplift, occurring in the Lisan Peninsula. For this purpose, we exploit ALOS-Palsar, ERS-1/2, ENVISAT

and COSMO-SkyMed SAR images. The total period covered goes from 1991 to 2013. The processing

activity results very challenging due to different aspects. In particular, one of the main problems is related

to the very steep topography that characterize this territory, added to the lack of a high resolution Digital

Elevation Model (DEM). An in-depth discussion of this subject is reported in the Dead Sea case study

chapter. The last case study is related to a new research activity that started during the visiting period at

the Rosenstiel School of Marine and Atmospheric Science (RSMAS), University of Miami. The aim of

Figure 1: working principle of SAR

interferometry (from T.R.E. Europa)


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this research activity is to detect ground displacement in the entire Cuba territory using ALOS-Palsar

data. In the actual scientific literature there are no papers regarding the study of Cuba’s territory with

DInSAR techniques, so this is a completely new topic. In this report, we give an overview of this ongoing

research topic.

Case studies

1) Ravenna, Italy

Since the last century, the entire Emilia Romagna territory is affected by land subsidence of both natural

and anthropic origin (Teatini et al., 2005). One of the most affected areas is the city of Ravenna where the

anthropic component of the land settlement is much higher than the natural one. Here, the subsidence

started in the 1940s with the heavy exploitation of the underground natural resources, mainly water and

methane gas. In the 1980s the Municipality, in order to reduce the subsidence affecting the area, slowly

decreased the water extraction activities (Gambolati et al., 1999). This affectively reduced the subsidence

rates but did not stop the problem. In fact, the actual subsidence rate (> -2cm/y) is lower than in the past

but is still higher than the natural one (-2mm/y) (Gambolati et al., 1991). This represent a serious risk for

the territory and in particular along the coastline where the subsidence effect is added to the mean see

level rise increasing the flooding risk (Carbognin et al., 2002).

The monitoring of the subsidence is conducted exploiting three different SAR datasets: 60 ERS-1/2, 47

ENVISAT and 30 TerraSAR-X images. ERS-1/2 and ENVISAT data were already processed during the

first year of PhD but the results are now improved re-processing part of the data using finer parameters. In

the work done during this year the monitoring activity is completed adding the TerraSAR-X dataset and

extending the covered period up to 2014. TerraSAR-X presents different features in respect to the ERS-

1/2 and ENVISAT (Table 1): first of all, it works with the X-band, which is shorter than the L-band of

ERS-1/2 and ENVISAT resulting in higher ground resolution (3m vs 20m of ERS-1/2 and ENVISAT)

and higher accuracy but in lower penetrating power (Figure 2).

Table 1: main features of the different SAR datasets

Figure 2: penetrating power of the different RADAR bands (from SARscape guidebook)

Secondly, the orbit repeating time of the satellite, i.e. how frequently the images over the same area are

taken, is three times higher in respect to the other satellites and consequently the changes in the terrain

features between two images are low, resulting in better overall coherence. All these features bring great

advantages in the study of ground displacements but not in all terrain conditions. In particular, the X

SATELLITE PERIOD BAND GROUND RESOLUTION REVISITING TIME (DAYS)

ERS-1/2 1991-Dic/2000 C 20m 35

ENVISAT 2003-2010 C 20m 35

TerraSAR-X 2012-2014 X 3m 11


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band, and consequently the higher resolution, is great in detecting small features such as roads, small

building and outcrops, but because of its lower penetrating power its effectiveness over even low-

vegetated territories is quite low. In our case, the X band worked perfectly and allowed us to have an even

more spatial data density in respect to ERS-1/2 and ENVISAT. All the processing are carried on with the

Small Baseline Subset (SBAS) technique (Berardino et al., 2002), which relies on the combination of

image pairs that have both small temporal intervals and very close orbits between every acquisition.

Thanks to this technique, it is possible to obtain high spatial coherence, high data density and more

effective error reduction. The obtained results for each of the three datasets (Figure 3) show clearly what

the capabilities of this technique are. Even if the obtained spatial coverage is quite good, it is limited only

at the urbanized areas such as the Ravenna’s city centre, its industrial area, the dock and part of the

coastline. One of the limits of this technique, in fact, is related to how the features on the ground maintain

the coherence over time. In particular terrain conditions, such as cultivated land or wetlands, the changing

in the territory features can be not only seasonal but can happen even weekly or daily causing the

complete loss of coherence in a short time. Consequently, no data can be acquired in those areas.

Adopting advanced processing methods it is possible to overcome to this problem and obtain results even

over low coherence areas, but to the detriment of the data accuracy. In this work we prefer to obtain more

reliable results with less spatial coverage instead of completely cover the area but introducing high level

of uncertainties.

Figure 3: total cumulated displacements for each period. Subsidence in red, uplift in blue, stable areas in white and green. In the blue

circle, the location of “Lido di Dante”

Figure 3 shows the cumulated total displacement maps in millimeters for each of the three periods: 1992-

2000 for ERS-1/2, 2003-2010 for ENVISAT and 2012-2014 for TerraSAR-X. The colour bar from red to

blue represents the displacement values. Negative values, in red, represent ground moving far from the

satellite, i.e. subsidence. On the contrary, positive values on the blue side represent motion towards the

satellite, the uplift. The range going from white to light blue is considered stable because is included in

the technique range of error (± 1,5mm/y). In the analysed periods there are no uplift areas as we expect

from the type of phenomenon occurring in the area. Most of the Ravenna’s city centre lies over stable

areas and only two localized subsidence areas are detected in the Southern end of the City. As expected,

most of the subsidence is occurring in the industrial/Porto Corsini area to the North-East and along the

coastline to the East. In the industrial area, the maximum displacement rates are of -140mm for the period

1992-2002 and of -147mm for 2003-2010. The high level of subsidence is caused by intense extraction of

water in the industrial area and of methane gas from the off-shore pumping platform near Porto Corsini.

Along the coastline, we find maximum values of -99mm for 1992-2002 and -137mm for 2003-2010. One

<


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of the most affected areas is “Lido di Dante” which is located South East of Ravenna along the coastline

(Figure 3, in blue circle). Here, the damage produced by the coastline regression are so heavy that every

few years the Municipality tries to reduce the effects of the subsidence replenishing the beach with send

and placing artificial marine artificial barriers, but the results do not endure (Antonioli et al., 2007). Even

in this case, Lido di Dante is very close to an off-shore methane gas pumping platform.

The TerraSAR-X dataset shows results in accordance with the other two datasets: the maximum rates for

the two years period 2012-2014 are -64mm in the industrial area and -29mm along the coastline. These

values are lower in respect to the two previous periods because in this case we are analysing a much

shorter dataset, only two years compared to the eight and seven years of ERS-1/2 and ENVISAT, but the

respective velocity ratios are very close and reach 2-

3cm/y.

After a first look at the displacement maps, we perform

some time series analyses in order to understand the

displacements trends over time and to check the

goodness of our results. We choose four control points

in different location of the study area (Figure 4). We

choose the points following these criteria: are well

distributed over the study area, possibly are not located

over large buildings (large buildings may attenuate the

subsidence effects with their structural strength), do not

represent outlayers (i.e. points with extremely high

values associated with computing errors). As example,

here we report only the time series for the points

associated with the ENVISAT dataset for the period

2005-2010 (Table 2). All the four points show an

almost linear trend of deformation. Points E2 and E3

are located over stable areas with velocities of around -

2mm/y. Points E1 and E4 are over areas of higher

subsidence and their velocities are respectively -5mm/y

and -9mm/y. All four trends show low noise level

indicating the good reliability of the results.

Table 2: displacements time series of the four selected points for the period 2005-2010 (ENVISAT dataset)

1a) Future activities

Future activities planned for the Ravenna case study can be summarized as follow:

Figure 4: location of the control point chosen to perform the

time series analysis


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In order to complete the stack of TerraSAR-X images going from 2012 to 2014, we acquired other

23 images that will fill the temporal gap we had in the data. We will re-process the full stack of 53

(30 old, 23 new) images and compare the obtained results in term of accuracy and noise level in

the time series. We expect that the new processing will produce more “clean” and reliable data

thanks to the higher number of images available.

We are acquiring also the new generation Sentinel-1 data that can be downloaded free of charge

from the European Space Agency (ESA) DataHub website. Sentinel-1 is a new ongoing satellite

mission that acquires SAR images over very large areas (250x60 km) every 12 days. This allows a

continuous monitoring of very large areas that can be useful to keep track of the evolution of

ground movement phenomena affecting the territory. For the Ravenna area, we will process the

Sentinel-1 data from April 2014 to today to evaluate if the ground displacement rates are the same

as in the past or are changing over time. One of the factors to consider is how to store both the raw

data and the processing results since one single Sentinel-1 SAR image can weight up to 8giga.

On 19 May 2014 we started a GPS campaign in a selected area of the industrial area of Ravenna.

The aim of this campaign is to provide monitoring data that can be used as reference to validate

the new TerraSAR-X data that the DLR is acquiring for us as part of a research proposal we made

in collaboration with: University of Padova Dep. of Civil, Architectural and Environmental

Engineering; University of Pavia Dep. of Earth and Environmental Science; University of Naples

Dep. of Earth, Environment and Resources Science; INGV Osservatorio Vesuviano division.

Every 22 days DLR will acquire one High Resolution Spotlight image, with 1m of resolution, and

one Staring Spotlight with around 40cm of resolution for a total of 20 images for each acquisition

mode. Since these images cover just a very small area (around 6x4 km), we decided to choose a

“test” area where we can test the capabilities of these new data (Figure 5). In the first campaign

(Figure 6) we materialized 12 well distributed GPS points and we acquired the first set of data that

is used as reference for the next acquisitions. We will perform one campaign every month (if

possible) for a total of 20 campaigns. Once all the SAR data are available, we will perform the

DInSAR processing and compare the results with the acquired GPS data.

Figure 5 and 6: on the left, area covered by the Staring Spotlight TerraSAR-X image (red square) and location of the

GPS points (green dots). On the right, acquisition of GPS data at location N°6


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2) Dead Sea, Jordan

The Dead Sea is a hypersaline terminal lake located

between the borders of Israel and Jordan (Figure 7). Since

the 1980’s the potash industries are heavily exploiting the

salty-rich water of the lake, pumping huge amount of

water in the desalinization ponds located in the southern

part of the lake. Furthermore, most of the freshwater that

comes from the Jordan River in the North is derived to

bring potable water to the near cities. The combination of

these factors is the main source of the rapid drop (-

1m/year) in the lake water level that is causing a deep

change in the hydrogeological settings of the entire basin.

The freshwater coming from the near mountain range

now is forced to flow through the underlying evaporate

layers (mostly salt and gypsum) causing their dissolution.

One of the most hazardous phenomena occurring in the

Dead Sea area are the sinkholes, catastrophic collapses

with very rapid evolution. Since the 2000, thousands of

sinkholes appeared all along the coastline in both Israel

and Jordan sides, bringing heavy damage to the territory,

the infrastructures (roads) and the buildings (hotels, resorts, factories). For this study, we want to focus on

one of the most affected areas called Gohr Al Haditah, (GAH) (Figure 8) located near the Southern point

of the Jordanian Dead Sea coastline, at Est of the Lisan Peninsula. GAH is one of the areas where the

sinkholes effects on the territory and artificial structures are most visible (Figure 9). Other geological

hazards that can be found along the Dead Sea are landslides in the Northern-East area and

subsidence/uplift phenomena mostly concentrated in the Lisan Peninsula (Figure 8).

Figure 7: location of the Dead Sea. Israel on the West,

Jordan on the East

Figure 8: the Lisan Peninsula and the Gohr Al Haditah area Figure 9: sinkholes damage in GAH area (Simone Fiaschi, October 2014)


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To detect the different instabilities, we process 9 ALOS-Palsar, 62 ERS-1/2, 32 ENVISAT and 20

COSMO-SkyMed SAR images covering the 1991–2013 period (Table 2).

One of the most important thing to consider while processing SAR data in this kind of territory is the

topography. In fact, one of the mandatory steps in the DInSAR processing chain is the complete removal

of the topographic component in the phase signal, done using high resolution DEMs. This step is very

important especially in areas where the topographic component is dominant. This is one of the cases. The

Dead Sea, in fact, lies in the deepest point on Earth at -400m m.s.l. and is surrounded by very steep rock

faces of few hundreds of meters in height (Figure 8). Unfortunately, to cope with the topography we do

not have available any high resolution DEM (e.g. LIDAR) but only the SRTM DEM with 30m resolution

that is freely available from the USGS Earth Explorer database. This DEM allowed us to remove only

part the topography and the obtained results are not completely reliable.

SATELLITE PERIOD BAND GROUND

RESOLUTION

REVISITING TIME

(DAYS)

ALOS-Palsar 2007-2011 L 10m 46

ERS-1/2 1991-Dic/2000 C 20m 35

ENVISAT 2003-2010 C 20m 35

COSMOSky-Med 2011-2013 X 3m 11

Table 2: main features of the datasets used for the Dead Sea area

Figure 10: rockfall occurred in the main road along the Dead Sea coastline. It is possible to notice the

steep topography that characterizes the area (Simone Fiaschi, October 2014)


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ERS-1/2 processing gave good results, in

accordance to the scientific literature

(Figure 11). High subsidence values are

located at the tip of the Lisan Peninsula

and other phenomena are visible in the

Southern part. The Southern phenomena

have a quite particular circular shape and

are surrounded by areas of low uplift.

What is causing the circular shapes of

depression is still under investigation but

probably are due to the movements of the

salt diapir underlying the peninsula.

ENVISAT results are very close to the

ERS-1/2 ones and present very similar

displacement values and almost equal

data coverage. Even if we are satisfied

with these results, one of the area we are

interested the most, GAH (in red circle,

Figure 11), has not data coverage because

its territory is mostly cultivated and

present low coherence over time. Because

of this gap in the data coverage, we tried

to exploit the COSMO-SkyMed high

resolution data (3m resolution). For this dataset, we focused only on a smaller area of interest that

completely covers GAH. The obtained results (Figure 12) show a great improvement in data coverage

since it is possible to detect few small buildings and even the roads crossing the crop fields. The problems

come out when we analyse the displacements values: here we have maximum values of -280mm/y near

the coastline and +30mm/y in the northwest over the mountain area. Even if high subsidence values can

be plausible along the coastline due to the rapid

changings the territory is facing, -280mm/y are too

high and seem not realistic. Even in the other

locations the values of displacement are too high and

cannot be explained with any geomorphologic

process. The most reasonable explanation to these

unreliable high values is that with the SRTM DEM

we are not able to remove completely the topography

component in the phase and this is affecting the final

result. In order to cope with the high velocities

problem we are performing some tests to see which

parameters have more influence on our results and to

better define the DEM contribution to the

displacements values. As example, we show the

interferogram 2012/04/04-2012/10/13 (Figure 13)

where are clearly visible (red circles) the extra

fringes due to topographical residuals. These tests are

performed during the six months visiting period at

the Rosenstiel School of Marine and Atmospheric

Science (RSMAS) in Miami. We are using different

processing software that allow us to carry out

specific analyses in each of the processing steps. In

particular, we are using the DORIS and StaMPS free

software developed respectively by the Delft

Figure 11: velocity map obtained processing ERS-1/2 data. In the red

circle, the gap in data coverage over GAH area

Figure 12: velocity map obtained with COSMO-SkyMed

data. The displacement values of few areas are reported

mm/y

mm/y


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University of Technology and by the Leeds University.

2a) Future activities

During the visiting period at RSMAS we

will first try to fix the problems related to

the high deformation velocities we found in

the Dead Sea area and we will re-process

the COSMO-SkyMed and ALOS-Palsar

dataset. Once we are satisfied with the

results we will start analysing both the

interferograms and the velocity maps in

order to detect signals that indicates the

occurrence of sinkholes in GAH area

during the period covered by the datasets.

It will be interesting to see; first if with

these two datasets it is possible to detect

developing sinkholes; second if these

sinkholes signals are actually causing

visible forms of displacement on the

territory. This can be made using high

resolution optical images freely available

on the web (e.g. Google Earth) or asking

the locals to personally check the areas we

are interested in. We have a collaboration

with the Geology Department of the

University of Jordan that can help us with

the surveys in the area if necessary.

3) Cuba

The last case study is related to the detection of the displacements in the whole Cuba Island. This research

started during my visiting period at RSMAS and is performed exploiting hundreds of ALOS-Palsar SAR

images (Figure 14) using the ROI_PAC software developed by the NASA JPL. The activity is at the very

beginning and we are trying to set up the best parameters to obtain the best results possible. Since this

territory has never been studied before with DInSAR techniques and there are no scientific papers about

this topic, it will be very interesting to see what kind of instabilities (if any) we will find thanks to the

ALOS-Palsar data. Subsidence phenomena related to human activities, such as water extraction or

underground mining, can be one of the most probable hazards occurring in the area, in particular in the

Havana area. Only the results of the data processing will confirm or not this assumption. Considering the

great amount of available data and the work to be done, the project will take a lot of time to get a final

result but should be concluded before March 2016.

Figure 13: interferogram generated with the images pair 2012/04/04-

2012/10/13. The extra phase fringes probably generated by

topographical residuals are visible in the red circles


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Figure 14: ALOS-Palsar data coverage over Cuba. Each of blue tiles correspond to a set of data to be processed

individually

Conclusions

In this work, we proved the capabilities of DInSAR applying this technique to three case studies in order

to analyse different geological hazards in areas with peculiar environmental and territorial features. This

technique resulted very useful in the detection and monitoring of the subsidence phenomenon affecting

the Municipality of Ravenna and the obtained results highlighted the areas where the subsidence-related

risk is higher. Along the Dead Sea, we successfully detected different form of displacements but in this

case the results are strongly influenced by the particular topography of the area. This fact draws the

attention on the limits of DInSAR that are mainly related to the local territory features and to the available

data. In particular, in areas where the topography is very steep or where the ground features change very

rapidly over time, such as in vegetated areas or in cultivated land, it is very difficult to get good results

due to decorrelation phenomena. The kind of data available, of course, plays an essential role in DInSAR

processing: for example, as we discussed before, having available a very accurate DEM is necessary to

obtain reliable measurements of ground displacement. It is not just the DEM that influences the goodness

of the results, but even the number of images we can use have a strong effects on many processing

parameters. Among these, the results accuracy and the level of coherence we get from two images

consecutive in time.

Despite these limitations, DInSAR can be adopted by the municipalities or local authorities as part of the

monitoring activity in the framework of the territory management. The operating costs of these

preliminary activities are very low and can be used to find areas where there is the need of further in-

depth geological and geotechnical surveys. The new generation of satellite mission recently launched by

ESA, the Sentinel-1, can bring the monitoring activity to a next level. If fully integrated in semi-

automatized processing chains it will allow to perform continuous monitoring covering very large portion

of territory at one time. In this way, it will easier to detect precursory phenomena and plan in advance the

best mitigation measures to reduce in the most efficient way the related risk.

For the next year activity, part of the work will be spent performing the benefit-cost analysis between

DInSAR and the ground based techniques. For a selected monitoring activity, for example the subsidence

affecting the industrial area of Ravenna, we will compare the overall costs we bore using the remote-


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sensing and a standard technique. The analyses will keep into account the costs of the data/technical tools,

working time, workforce and other secondary expenses. We will also compare the obtained data spatial

distribution in terms of number of points per Km2, and the data accuracy to asses the effective benefits of

using each of the selected techniques. These analyses will allow us to prove that the satellite-based

techniques are essential to monitor the territory at regional and local scales with costs that can be

affordable not only for local authorities by even for small Municipalities.

References

ANTONIOLI, F., SILENZI, F. 2007. Variazioni relative del livello del mare e vulnerabilità delle pianure

costiere italiane, Quaderni della Società Geologica Italiana, 2, pp. 1-29.

BERARDINO, P., FORNARO, G., LANARI, R. and SANSOSTI, E. 2002. A new algorithm for surface

deformation monitoring based on small baseline differential interferograms. IEEE Transactions on

Geoscience and Remote Sensing, 40, 11, pp. 2375–2383.

CARBOGNIN, L., TOSI, L. 2002. Interaction between climate changes, eustacy and land subsidence in

the North Adriatic Region, Italy, Marine Ecology, 23(1), pp. 38-50.

GAMBOLATI, G., RICCERI, G., BERTONI, W., BRIGHENTI, G., and VUILLERMIN, E. 1991.

Mathematical simulation of the subsidence of Ravenna: Water Resources Research, 27, 11, pp. 2899-

2918.

GAMBOLATI G., TEATINI P., TOMASI L., GONELLA M. 1999. Coastline regression of the Romagna

region, Italy, due to natural and anthropogenic land subsidence and sea level rise, Water Resources

Research, 35(1), pp. 163-184.

MORA, O., MALLORQUÌ, J. J., and BROQUETAS, A. 2003. Linear and nonlinear terrain deformation

maps from a reduced set of interferometric SAR images. IEEE Transactions on Geoscience and Remote

Sensing, 41, pp. 2243–2253.

TEATINI, P., FERRONATO, M., GAMBOLATI, G., BERTONI, W., GONELLA, M. 2005. A century of

land subsidence in Ravenna, Italy. Environmental Geology, 47, pp. 831–846.

SUMMARY OF ACTIVITY IN THIS YEAR

Courses:

N. SURIAN, F. FERRARESE. Corso di GIS avanzato. Dipartimento di Geoscienze.

Posters:

S. FIASCHI, D. DIMARTIRE, S. TESSITORE et al.: Monitoring of land subsidence in Ravenna Municipality using two

different DInSAR techniques: comparison and discussion of the results. EGU, 12-17 April 2015, Vienna, Austria.

S. TESSITORE, S. FIASCHI, V. ACHILLI et al.: Statistical elaborations of PS DInSAR data applied to the analysis of the

subsidence affecting the Venetian coast. EGU, 12-17 April 2015, Vienna, Austria.

R. BONÌ, S. FIASCHI, D. CALCATERRA et al.: Characterization of the multi-component driving land subsidence using

Persistent Scatterer Interferometry technique: the Ravenna case of study (Italy). EGU, 12-17 April 2015, Vienna, Austria.


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Publications:

S. FIASCHI et al.: Two decades of subsidence monitored through DInSAR techniques: the Ravenna case study. In Prep.

Other:

Visiting period: 01 September 2015 – 29 February 2016. Exchange scholar at the Rosenstiel School of Marine and

Atmospheric Science (RSMAS), University of Miami, Miami, Florida, USA. Advanced application of DInSAR techniques to

geological hazards.

Field surveys: 19-20 May 2015. Ravenna, Italy. First GPS campaign.

Field surveys: 14-18 December 2014. Dead Sea, Jordan. Second sinkholes monitoring activity.

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PREVENTION AND MANAGEMENT OF HYDROGEOLOGICAL …geo.geoscienze.unipd.it/sites/default/files/Fiaschi_Simone_XXIX_2.pdf · advantages in the study of ground displacements but not in