RELATIONSHIP BETWEEN TOPOGRAPHIC RELIEF AND GROUND DEFORMATION AT CEPHALONIA ISLAND (GREECE)

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1 RELATIONSHIP BETWEEN TOPOGRAPHIC RELIEF AND GROUND DEFORMATION AT CEPHALONIA ISLAND (GREECE) Maurizio POSCOLIERI 1, Michael FOUMELIS 2, Issaak PARCHARIDIS 2, Sergio CAMIZ 3 1 CNR Institute of Acoustics and Sensors Rome, Italy. maurizio.poscolieri@idasc.cnr.it. 2 Department of Geography, Harokopio University of Athens, El. Venizelou 70, Athens, 17671, Greece. mfoumelis@hua.gr, parchar@hua.gr. 3 Mathematics Department Guido Castelnuovo, La Sapienza University of Rome, Italy, sergio.camiz@uniroma1.it. ABSTRACT Relief is a major element for portraying a landscape, while even slight changes in its shape are frequently identified as a result of deformation. Both are strictly related to the morphological setting of a given area that is often represented as a raster data set, the Digital Elevation Model (DEM). In particular, morpho-units can be discriminated by applying classification methods to topographical attributes, usually derived by processing DEMs, while deformation rates are pinpointed by elaborating Synthetic Aperture Radar (SAR) data by means of Interferometric techniques. The main goal of this work is to relate the morphological information gathered by classifying terrain units of Cephalonia Island (Greece), on the basis of a DEM, with the observed deformation resulting from SAR interferometry, not disregarding the geo-structural setting of the area. KEYWORDS Relief, DEM, Multidimensional data analysis, Deformation, SAR Interferometry 1. INTRODUCTION A landscape can be strongly characterized by the relief representing its major component, while even small changes on its shape are revealed by deformation. Both these terrain features are strictly connected to the height setting of a given area, often represented as a raster data set, i.e. the Digital Elevation Model (DEM). By processing a DEM, it is possible to derive topographical attributes, such as slope, aspect, elevation gradients, and others, which are then used as input for applying classification techniques to categorize landforms. Conversely, deformations are discriminated by elaborating a dataset of Synthetic Aperture Radar scenes on the basis of Interferometric techniques often considering a DEM in the calculation. The main purpose of this study is to define possible relationships between morphological information, collected by classifying terrain units on the basis of a DEM, and deformation conditions resulting from SAR Interferometry, considering also the geological and structural setting of that area. As test area the Cephalonia Island (Greece) was selected, due to its location in the Ionian Sea, one of the most seismically active Mediterranean zones. The methodological approach for the morphometric classification is based on a relatively new technique (Camiz et al., in press; Poscolieri, 2010), based on statistical multidimensional analysis of local elevation gradients, extracted considering each pixel of the DEM and its nearest neighbours. On the other hand, repeat-pass Differential SAR Interferometry (DINSAR) has been used since 1992 (Massonnet et al., 1993, Zebker et al., 1994) by the scientific community to further study and understand specific ground deformation hazards. Stacking of differential interferograms (Parcharidis et al., 2006) aims at combining several differential interferograms in order to extract their common information; the most basic procedure is to compute linear combinations (generally sums or averages) of interferograms. Interferometric stacking is useful in overcoming shortcomings of conventional DINSAR such as atmospheric influences. 2. GEO-STRUCTURAL SETTING Cephalonia (fig. 1) is one of the Ionian Islands in Western Greece, located at the North-Western part of a narrow convergence zone between two continental masses, the African and Eurasian plates. To the South, the Ionian basin is still being subducted under the Aegean domain of the Eurasian plate, whereas to the North, a continental collision occurred between the Apulian microplate and the

Hellenic foreland. Those two domains are linked by a major right-lateral, NE-SW trending, transform fault (Cephalonia Transform Fault), which is located offshore, West of Cephalonia.

2 Hellenic foreland. Those two domains are linked by a major right-lateral, NE-SW trending, transform fault (Cephalonia Transform Fault), which is located offshore, West of Cephalonia. Cephalonia is built up mainly by alpine Mesozoic-Cenozoic sedimentary rocks that belong to the external units of the Hellenides and have taken part in the Hellenide fold-and-thrust belt during Neogene. On top of them, younger Plio-Quaternary sediments lay uncomformably, located mainly to the W-SW side of the island (Underhill, 1989). Alpine stage deformation resulted in generating major and minor thrust faults, trending generally NW-SE. These older structures are being crosscut by later NE-SW faults, which in some cases exhibit a significant right-lateral movement. Based on lithology, similar structural features, and common evolution during upper Quaternary, four major tectonic blocks were distinguished on the island (Lagios et al., 2007; Chousianitis 2009). These blocks are: Figure 1: Google Earth image of Cephalonia island and regional location map. I. Erissos peninsula block (Northern part of the island); II. Paliki peninsula block (Western part of the island); III. Ainos block (central and South-Eastern part of the island); IV. Argostoli block (South and South-Western part of the island). Each of the above major blocks consists of several subordinate blocks and is flanked by a major thrust fault. Most subordinate blocks have more or less distinct geological features, on account of difference in geological evolution, in some stage; yet, some of them were unified in Late Quaternary and comprised the four major blocks of the island. 3. MORPHOMETRIC DATA AND CLASSIFICATION METHOD In order to define quantitatively the geomorphologic characteristics of the Cephalonia Island, as aforementioned, a procedure, based on the analysis of local morphological setting, has been applied to geomorphometric data. They were gathered by processing a raster DEM (20m/pixel ground resolution) produced by digitizing the contour lines from topographic maps on a 1:50,000 scale. This geomorphometric method is based on the application of a multidimensional data analysis procedure to an 8-layers stack made by topographic gradients measured along the 8 azimuth orientations of each DEM pixel neighbourhood. Such an approach allows to quickly estimating the spatial distribution of different types of slope steepness, permitting discrimination of areas characterized by similar local geomorphologic setting. Hence, changes in shape, orientation and steepness are highlighted, emphasizing the impact of erosion and tectonic processes on the overall relief.

3 As classification method was chosen an approach taking into account a sequence of statistical multidimensional techniques known as Tandem Analysis Tandem analysis This term (first used by Arabie and Huber, 1994) refers to the sequence of an exploratory factor analysis followed by a hierarchical classification of units, based on the distance among them on a selected principal space. The method belongs to the so-called exploratory data analysis techniques (Camiz, 2001) as a cognitive model able to suggest a possible structure of the data, based on the search for ordination gradients as factors that influence the variation of the data and classifications that allow the partition of the units according to possible sub-populations in respect to the chosen factors. The method was introduced in the 70's (Lebart et al., 1977; Bellacicco and Labella, 1979) and as such implemented in several software packages, including SPAD (Lebart et al., 1999), but has been severely criticized by Arabie and Huber (1994) among others, since, if the units of a data table are set according to a given classification, such a procedure may not detect the true classes of this natural partition, as a Discriminant Analysis could do. Nevertheless, Tandem Analysis revealed useful to search for possible partitions of the sample at hand compatible with the selected factors. Such partitions, albeit not necessarily natural, may contribute to identify homogeneous classes of units in respect to the factors, this way synthesizing the data structure (Lebart et al., 1995). Indeed, despite the criticism, the Tandem Analysis is still broadly used in exploratory context. Given the quantitative nature of our data, we applied first Principal Component Analysis (PCA; Lebart et al.,, 1995) based on the correlation matrix, and then a Hierarchical Ascendant Classification (HAC; Lebart et al., 1995; Gordon, 1999) based on the Euclidean distance on the selected factor space and using the Ward s (1963) criterion to aggregate the classes. As the variance of the units' coordinates on the factors equals the corresponding eigenvalue, it must be remarked that classes tend to be more separated according to the first factors, where distances among units are larger, than according to the following, where distances are smaller. In the use of such a procedure, two choices are left to the user: the number of factors to take into account for the classification and the level at which to cut the hierarchy to obtain a partition. It is interesting to observe that both choices may be done after the analysis, thus based on the examination of the obtained results. In our case, the PCA results were so clear, that no specific method was needed. To cut the hierarchy, we chose a level followed by an important raise in the within groups inertia. Indeed, the very large number of units in our case prevented the use of a HAC since the beginning, so that we applied first a K-means algorithm, to get 500 classes and then on these classes a HAC was performed. Eventually, once decided the suitable number of classes, another K-means was run, in order to get the classes more compact. This way, the obtained partition may result better than applying only a K-means algorithm that would depend too heavily upon the starting random configuration. Figure 2: The eight morphological gradients on the first principal plane of PCA.

4. GEOMORPHOMETRIC ANALYSIS The PCA of the eight gradients gave two principal components that explain up to 93.75% of the total inertia, with nearly equal values (49.78 and 43.98% respectively).

4 4. GEOMORPHOMETRIC ANALYSIS The PCA of the eight gradients gave two principal components that explain up to 93.75% of the total inertia, with nearly equal values (49.78 and 43.98% respectively). The distribution of the gradients on the plane spanned by the first two factors (fig. 2) shows that the directions of maximum gradients variation are the ENE-WSW and NNW-SSE. The prevailing aspect is towards West, as it results on its position once projected on the factors plane. The K-means clustering based on 500 random classes, eventually gave 207 non-empty classes, on which the HAC was applied. According to the withingroups inertia values, the partition in 18 classes was chosen. On this another K-means was run to further optimize it. Figure 3: Geomorphometric classification map of the Cephalonia island area, issued from Tandem analysis method. The resulting classification map was set up assigning each class a given colour shade, in order to accurately evaluate the spatial distribution of different local morphological settings (fig. 3), to be subsequently compared with the geo-structural setting. Then, for each class mean and standard deviation of the eight layers were computed: these layers were represented by the elevation differences with respect to each reference DEM s pixel. Moreover, to perform a correct geomorphic interpretation of the classification results and to verify the accuracy of the corresponding map, slope and aspect values, relative to Cephalonia Island, were calculated from the same elevation matrix. Next, mean and st.dev. of height, slope and aspect, relative to all the 18 classes, were computed and compared with the 8-layers statistics earlier obtained directly from the classified thematic layers, allowing a morphological interpretation of the classes (table 1). The examination of the statistics of the 18 classes shows that the aspect plays a more significant role in discriminating classes than the slope. In fact, plotting the classes mean and st.dev. values of slope and aspect (fig. 4), it is possible to verify that the classes aggregate according to three main levels of slopes. Class 1 corresponds to nearly flat areas, classes 2, 3, 4, 5, 6, 10, 11 and 15 exhibit average slope values between 9.6 and 20, and classes 7, 8, 9, 12, 13, 14, 16, 17 and 18 are characterized by slope means between 28.6 and 33. Table 1 shows that, except for the first class, covering nearly 9% of the island and corresponding to quite flat areas, the other classes encompass 1 to about 3% of the whole region. Most classes exhibit sharp orientations: Class 5 faces West; class 11 and 12 East; class 13 South; classes 3, 15 and 16 SW; classes 4 and 14 SE; class 6 NW; classes 2 and 10 NE. No class appears clearly facing North.

5 Table 1: Classes obtained by processing the topographic gradients of the Cephalonia island on the basis of Tandem Analysis. Each class, of which the covered surface is pointed out, is provided with a morphologic interpretation and the mean values of both main landform attributes and topographic gradients with respect to the central pixel of a 3x3 kernel (shown in Red if negative and in Blue if positive). Figure 4: Plot of slope vs. aspect for all the 18 classes of the Cephalonia island area, issued from Tandem analysis.

5. SAR INTERFEROMETRY Earlier results from available ENVISAT ASAR images, covering the period from June 2003 to November 2008, highlighted uplift in the Southern and Western part of Cephalonia Island.

interferometric software. ALOS PALSAR is an active Earth s observation system that uses L band microwave radiation, providing coherent interferograms over long temporal separation.

6 5. SAR INTERFEROMETRY Earlier results from available ENVISAT ASAR images, covering the period from June 2003 to November 2008, highlighted uplift in the Southern and Western part of Cephalonia Island. Implementing the stacking technique, 10 ALOS PALSAR SAR available images, covering the 12/2006 to 04/2010 period, were processed in order to produce interferograms exploiting the GAMMA interferometric software. ALOS PALSAR is an active Earth s observation system that uses L band microwave radiation, providing coherent interferograms over long temporal separation. The interpretation of these data is still in progress. The entire archive of ALOS PALSAR data for the area of interest, consisting of 10 scenes covering the period of about 3.4 yr (12/ /2010), were used to investigate ground displacement rates by means of interferometric stacking. The analysis was performed using the GAMMA software packages. Differential interferometric pairs of relatively small perpendicular baselines ( 2000 m) were used in the analysis. Apart from baseline refinement for the removal of linear phase trends, due to wide orbital tube of the satellite and the absence of precise state vectors, it was considered adequate to compensate for residual quadratic phase components to account for large along-track baseline changes. Results were referred to a GPS station located at Aenos Mt. at the centre of Cephallonia (Lagios et al., 2007; Poscolieri et al., 2006), in order to facilitate future comparisons. High coherence levels were retained even over longer temporal baselines as a result of the observation wavelength (L-band), which in turn provided nearly complete spatial coverage and minimized potential unwrapping errors. This is of great importance when a substantial part of the study area has dense vegetation cover, as Cephalonia is. Although locally significant displacements were recognized for the examined time window (fig. 5), the most interesting feature of the displacement field is the complex pattern of observed uplift and subsidence, signifying the effects of tectonics in one of the most active regions of Greece. Relative displacement rates between subsidence, observed along both the coastal areas and the low relief regions, and uplift within the central part of the island, reach about mm/yr. This measures the vertical deformation during the examined time window. These results are in accordance with the ENVISAT ASAR findings, thus confirming the kind of movements in the region. The interpretation of the pattern of the obtained displacement rates in terms of tectonic analysis is still in progress. Figure 5: Colour coded displacement rates map overlaid on an average SAR image of Cephalonia Island. The black square identifies the selected reference point (Aenos Mt.).

7 6. INTERCOMPARISON RESULTS AND CONCLUSIONS The relation between the classification of terrain units (fig 4) and the displacement rates (fig.5) was studied by computing descriptive statistics of the displacement values in each of the 18 morphometric classes: the results are reported in table 2. Observing the table it results that most classes have a mean value ranging within and Five classes have least values (within and ) and two higher, say and The percentages of positive and negative values do not vary accordingly, but with a similar trend, with a minimum of 16.73% of positive values and a maximum of 40.42: it is only possible to say that classes 12, 9, 14, and 3 are those with lowest mean and highest negative values and the classes 13 and 16 have the highest means and positive values. In conclusion, the unsupervised classification of a multilayer data set extracted from a DEM allowed the automated definition of geomorphic units within the island of Cephalonia. The application of this processing method for the evaluation of similar morpho-units assisted in highlighting the spatial distribution of geomorphologic features and their degree of intensity, providing a valuable new information source for geological applications. A formal test of significant differences among these values (whose implementation is in progress) might help in understanding to what extent a relation exists between geomorphometric features and displacement. This would provide an insight into the impact of relief on slight geological movements. Most important is the fact that by applying exploratory multidimensional data analysis techniques to elevation gradients, it is possible to obtain realistic information that traditional methods may not reveal. Class Number Area (Sq.km) Min. displ. Max. displ. Mean displ. St.dev. displ. % subsidence % uplift Table 2: Statistics about the distribution of displacement values inside the 18 geomorphometric classes. 7. REFERENCES Abdi, H. (2007) Singular Value Decomposition (SVD) and Generalized Singular Value Decomposition (GSVD), in Encyclopedia of Measurement and Statistics (Ed.) N. Salkind, Thousand Oaks, Ca, USA: Sage, pp Arabie, P. and Hubert, L. (1994) Cluster analysis in marketing research, in Advanced methods of marketing research (Ed.) R. J. Bagozzi, London, UK: Blackwell, pp Bellacicco, A. and Labella, A., (1979) Le Strutture Matematiche dei Dati. Milan, It: Feltrinelli. Camiz, S. (2001) Exploratory 2- and 3-way Data Analysis and Applications, Lecture Notes of TICMI, Tbilisi University Press, 2.

8 Camiz, S., Denimal, J.J. and Poscolieri, M., in press, Comparison of classification methods on geomorphometric data of Peteroa volcano area, Proceedings of the E-ICES 6 Conference, Malargüe, Arg., 4-8 October 2010, 15 p. Chousianitis, K. (2009) Seismic source properties and ground deformation study of the broader area of Cephalonia and Zakynthos Islands using geophysical and space techniques, Doctorate thesis, Department of Geophysics Geothermics, University of Athens, p.280 (in Greek). Gordon, A.D. (1999) Classification, London,UK: Chapman and Hall. Lagios, E., Sakkas, V., Papadimitriou, P., Parcharidis, I., Damiata, B.N., Chousianitis, K. and Vassilopoulou, S., 2007, Crustal deformation in the Central Ionian Islands (Greece): Results from DGPS and DInSAR analyses ( ), Tectonophysics, 444, pp Lebart, L., Morineau, A. and Tabard, N. (1977) Techniques de la descriprion statistique, Paris, Fr: Dunod.. Lebart, L., Morineau, A., Lambert, T. and Pleuvret, P. (1999) SPAD Système Pour l Analyse des Données, Paris, Fr: Cisia-Ceresta. Lebart, L., Morineau, A. and Piron, M. (1995) Statistique Exploratoire Multidimensionnelle, 2 nd edition, Paris, Fr: Dunod. MacQueen, J.B., 1967, Some Methods for classification and Analysis of Multivariate Observations, Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley,USA: University of California Press, 1, pp Massonnet, D., Rossi, M., Carmona, C., Adragna, F., Pelmtzer, G., Feigl, K. and Rabaute T., 1993, The displacement field of the Landers Earthquake mapped by radar interferometry, Nature, 364, pp Parcharidis, I., Lagios, E., Sakkas, V., Raucoules, D., Feurer, D., Le Mouelic, S., King, C., Carnec, C., Novali, F., Ferretti, A., Capes, R. and Cooksley, G., 2006, Subsidence monitoring within the Athens basin (Greece) using space radar interferometric techniques, Earth, Planets and Space Journal, 58, pp Poscolieri, M., Lagios, E., Gregori, G., Paparo, G., Sakkas, V., Parcharidis, I., Marson, I., Soukis, K., Vassilakis, E., Angelucci, F. and Vassilopoulou, S., (2006) Crustal stress and seismic activity in the Ionian archipelago as inferred by satellite- and ground-based observations, Kefallinia, Greece. in Fractal Analysis for Natural Hazards (Eds.) G.Cello and B. D. Malamud, London, UK: Geological Society, Special Publications, 261, pp Poscolieri, M., 2010, Geomorphometry as a value added to land analysis, Proceedings of the E-ICES 5 Conference, Malargüe, Arg, November 2009, pp Ward J.H., 1963, Hierarchical Grouping to optimize an objective function, Journal of American Statistical Association, 58(301), pp Underhill, J.R., 1989, Late Cenozoic deformation of the Hellenic foreland, Western Greece. Geological Society of America Bulletin, 101, pp Zebker, H.A., Rosen, P.A., Goldstein, R.M., Gabriel, A. and Werner, C.L., 1994, On the derivation of coseismic displacement-fields using differential radar interferometry: the Landers earthquake, J. Geophys. Res., Solid Earth, 99 (B10), pp

Continuous Risk Assessment of Structures and Plants in Areas of Ground Deformation Susceptibility by Space-based SAR/ASAR Interferometry

CCN4 Project Continuous Risk Assessment of Structures and Plants in Areas of Ground Deformation Susceptibility by Space-based SAR/ASAR Interferometry Issaak Parcharidis*, Michael Foumelis and Penelope