Digital photogrammetric analysis and electrical resistivity tomography for investigating the Picerno landslide (Basilicata region, southern Italy)

Geomorphology 133 (2011) 34–46

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Geomorphology j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / g e o m o r p h

Digital photogrammetric analysis and electrical resistivity tomography for investigating the Picerno landslide (Basilicata region, southern Italy) Claudia de Bari a, b, Vincenzo Lapenna a, Angela Perrone a,⁎, Claudio Puglisi c, Francesco Sdao b a b c

Istituto di Metodologie per l'Analisi Ambientale, CNR, Tito Scalo (PZ), Italy Dipartimento di Strutture, Geotecnica e Geologia Applicata all'Ingegneria, Università degli Studi della Basilicata, Potenza, Italy Dipartimento Ambiente, Cambiamenti climatici e Sviluppo sostenibile, ENEA, Centro Ricerche Casaccia, Roma, Italy

a r t i c l e

i n f o

Article history: Received 7 April 2010 Received in revised form 22 April 2011 Accepted 13 June 2011 Available online 28 June 2011 Keywords: Roto-translational slide Aerial photos Analytical and digital photogrammetry ERT DEM difference

a b s t r a c t Digital photogrammetric analysis and Electrical Resistivity Tomography (ERT) techniques were applied to evaluate the volume of material involved in a complex roto-translational slide occurred in the territory of Picerno (Basilicata region, southern Italy). Analytical and digital photogrammetric techniques facilitated a multi-temporal analysis of aerial photos for the years 1997, 2004 and 2006. In order to identify different geomorphologic features (scarps, terraces and trenches) of the landslide and their development, the analytical and digital photo interpretation was performed at the maximum scale of 1:5000. Geological and geomorphological surveys were carried out to verify photo-interpretation results. Digital Elevation Models (DEMs) for 1997, 2004 and 2006 were produced by applying the Grid Adaptive method. The differential DEMs (2006–1997; 2006–2004; 2004–1997) for the most dangerous part of the landslide allowed us to recognize the areas affected either by deposition or erosion and also estimate any altitudinal changes in each geomorphologic unit. To detect the sliding surface and estimate the thickness of the sliding material, several transversal and longitudinal ERT profiles were obtained. The electrical images of subsurface supported by stratigraphical data from boreholes were integrated with the information from the DEMs. The altitudinal changes and the sizes of the source and accumulation areas allowed us to estimate the volume of material involved in the mass movement. The fusion of data from different sensors allows us to gather indications on the surface and subsurface characteristics of the landslide providing useful information for landslide mitigation activities. Such an approach can help both to improve our knowledge and overcome the drawbacks of each methodology. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Landsliding is the most common geomorphological hazard in Italy. The Italian Landslide Inventory developed within the IFFI project reports about 470,000 landslides affecting an area of about 20,000 km 2, corresponding to 6.6% of Italy (Trigila et al., 2008). Many of these landslides affect residences. Northern Italy is characterized by the highest density of landslide casualties followed by southern and central Italy. In southern Italy, the most abundant landslide casualties are recorded in the regions of Campania, Calabria, Basilicata and Sicily due to their geological, geomorphological and climatic conditions (Salvati et al., 2010). Among these regions Basilicata exhibits the highest density of landslides, with more than 27 landslide areas every 100 km2 (Canuti et al., 2002). This high density is related to clayey materials, extreme rainfall events, deforestation, intense urbanization, and industrialization (Gullà and Sdao, 2001). Landslides are often the reactivation of old

⁎ Corresponding author. Tel.: + 39 0971427282. E-mail address: [email protected] (A. Perrone). 0169-555X/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.geomorph.2011.06.013

movements and are characterized by a high complexity and variability in space and time. Due to such a complexity, only the integration of different methodologies for getting ground-based and airborne/satellite data can provide useful information for landslide mitigation (Perrone et al., 2006; Castellanos Abella and Van Westen, 2008; Kawabata and Bandibas, 2009). This paper applies a multidisciplinary approach based on the integration of aerial and ground-based methods for the investigation of a landslide in the Italian Southern Apennine near the village of Picerno (Basilicata region). To obtain more information the landslide was studied through analytical and digital photogrammetry, geomorphologic and geological surveys, 2D Electrical Resistivity Tomography (ERT) and a borehole survey. The aerial photointerpretation (Farrow and Murray, 1992) was applied to define the geometric characteristics of the landslide surface. Multi-temporal analyses were performed to discuss slope evolution. High resolution Digital Elevation Models (DEMs), generated through the digital aerial photogrammetry, were used to estimate the volume of the material involved in the movement. Geomorphologic and geological surveys (Farrow and Murray, 1992; Heipke, 1995) were performed

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to verify the results of photo-interpretation, to better define the landslide morphology, to understand the type and state of movement in relation to preexisting landslide bodies, and classify the lithology of material involved in the movement. Finally, the Electrical Resistivity Tomography (ERT) method (Griffiths and Barker, 1993; Loke and Barker, 1996), characterized by a high spatial resolution and a relatively fast field data acquisition with low costs, was used to estimate the geometry of the investigated landslide body (e.g. the depth of sliding surfaces and the thickness of the material involved in the slide). ERT results were successfully compared with borehole data and made it possible to extrapolate the borehole 1D information in 2D. Although the chosen methodologies have frequently been used for investigating landslides (Baldi et al., 2002, 2005; van Westen and Getahun, 2003; Perrone et al., 2004; Agnesi et al., 2005; Lapenna et al., 2005; Sdao et al., 2005; Federici et al., 2007; Sdao and Simeone, 2007; Caniani et al., 2008; Naudet et al., 2008; Perrone et al., 2008; Sass et al., 2008; Prokešová et al., 2010), this paper can be considered one of the first examples of their integrated application. The fusion of data from different sensors allows us to gather information on the surface and subsurface characteristics of the landslide and can help both to improve our knowledge and overcome the drawbacks of each methodology. Moreover, a detailed geometrical reconstruction of the landslide could facilitate better mitigation activities. 2. The study area The Basilicata region can be considered as a natural outdoor laboratory to apply and test different methodologies for the investigation of the complex geometry of landslides. Indeed, Basilicata is one of the areas mostly affected by landslide risk in southern Italy. This high landslide density is related to conditions such as prevailing clayey materials, morphological setting of slopes, and extreme rainfall events (Polemio and Sdao, 1999; Piccarreta et al., 2004) as well as human

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activity such as cave excavation (Lazzari et al., 2006), deforestation (Boenzi and Giura Longo, 1994) and urbanization. Many landslide events have been historically triggered off by extreme rainfall or snowmelt. The study area (Fig. 1) is located in the west of the Basilicata region and on the south-eastern slope of Li Foi Mountain (1355 m a.s.l.) near the S. Loja Basin along the axial zone of the Lucanian Apennine. The area is characterized by a landslide affecting the terrain of the Pignola–Abriola facies (calcareous-silica-marly series) of the Lagonegro Unit II (Scandone, 1972). This facies is composed of different lithological formations (Fig. 2) whose names in order of sedimentation are: the Siliceous Schist (Upper Triassic–Jurassic) outcropping along the slope of Monte Li Foj mountain, the Galestrino Flysch (Lower Cretaceous), the Red Flysch (Upper Cretaceous–Lower Miocene) and the Corleto Perticara Formation (Upper Eocene– Lower Miocene) (Pescatore et al., 1988; Gallicchio et al., 1996). The most recent terrains in the area are characterized by the debris outcropping at the toe of the mountain. The 240-m-thick Siliceous Schist formation is constituted by red and greenish shales with a typical rupture cleavage called “pencil cleavage”, red and green jaspers embedded with layers of radiolarites and flint, and manganese jaspers. The Galestrino Flysch (about 200 m thick) consists of alternating black claystones and siliceous marls, calcilutites and marly limestones, marls and leaf clay. The Red Flysch formation made of jasper, siliceous claystones, calcarenites, red and green marls is in eteropic succession with the Corleto Perticara formation (Selli, 1962). This is the most compact limestone deposit of the “Argille Varicolori” formation and is constituted by calcarenites, calcilutites and whitish marly limestone. The investigated landslide occurred in March 2006 after intense snowfalls and involved the terrains of the Corleto Perticara, Red Flysch and Galestrino Flysch formations. It is 600 m long and 230 m wide with an altimetry range varying between 1072 m a.s.l., at the main

Fig. 1. Location of the Picerno landslide in the west of the Basilicata region (southern Italy) on the south-eastern slope of the Li Foi Mountain (coordinate system Gauss-Boaga Roma 40).

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Fig. 2. Geological map of the slope involved in the Picerno landslide showing the main geological units. The area where the reactivation occurred mainly involves terrains belonging to the Corleto Perticara, Red Flysch and Galestrino Flysch formations.

crown, and 978 m a.s.l. at the toe. It is classified as a complex retrogressive roto-translational slide and represents the reactivation of an old mass movement. The landslide slid on a road near some farmers' houses which had to be evacuated. Due to the movement, counter slope terraces filled with stagnant water formed. The road lowered by about 12 m, thus modifying the surface flow condition. Transversal and radial cracks were evident in the accumulation zone of the landslide.

and geomorphological surveys increase the resolution in the identification of superficial features. The electrical resistivity tomography provides useful information on the subsurface structure of the landslide body especially when calibrated by stratigraphic borehole data.

3. Methodologies

The analytical and digital photogrammetry was carried out using 1:19,000 aerial photos for the years 1997 and 2004 and 1:14,000 photos for 2006. The analytical aerial photo interpretation was performed for each year on a maximum scale of 1:5000 to locate geomorphological features (scarps, terraces and trenches) as well as erosional and depositional zones.

The proposed approach is based on the integration of different aerial and field survey techniques. The analytical and digital photogrammetry facilitates both a synoptic view of the phenomenon and the extraction of elevation data from the area. The field geological

3.1. Photogrammetry

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To identify any topographic changes in the slope and evaluate its morphological evolution, a multi-temporal analysis was performed on the stereo pairs by using digital photogrammetry with the same reference system so as to avoid wrong interpretations (Pesci et al., 2004). The digital acquisition for the 1997 aerial photo was carried out with an AGFA scanner at 1800 dpi resolution. The digital acquisition for the other aerial photos was conducted with a photogrammetric scanner at 1639 dpi for 2004 and 1290 dpi for 2006. Each strip of the aerial photos was processed by using the Socet Set software version 5.4.2 (SoftCopy Exploitation Tool Set; LH Systems LLC, 1999). The standard procedure for automatic image orientation in digital photogrammetry includes interior orientation, relative orientation, point transfer in aerial triangulation (tie point generation), absolute orientation (registration into a defined reference system) and point extraction (Heipke, 1997). Interior orientation was performed by using camera parameters (calibrated focal length, principal point position, calibrated fiducial mark position, and radial lens distortion). Relative orientation for each stereo pair was obtained by using nine tie points common to both images; while absolute orientation to generate a stereoscopic model with the Gauss Boaga – ROMA 40 reference frame was carried out by defining x,y,z coordinates of 10 ground control points (GCPs) for the 1997 stereo pair, nine GCPs for 2004 and 10 GCPs for 2006. The coordinates of these GCPs have been extracted from the 1:5000 Basilicata technical map (CTR). The GCPs are natural and anthropogenic elements easily identifiable on both the CTR and the photos. They are located in a stable area outside the landslide during the time interval considered. The process of aerotriangulation (relative and absolute orientations) provided very small cumulated errors (Table 1), which means that the stereographic alignment and georeferencing of the stereo pairs have been successful, and as a result reliable high-resolution DEMs can be generated. The accuracy of orientation steps mainly depends on the camera-object distance (aerial photo scale), image quality and resolution (digital camera characteristics and scanner resolution), and the quality of GPS surveys to detect suitable GCPs. 3.2. Extraction and comparisons of DEMs The preliminary steps required to generate DEMs (internal, relative and absolute orientations in a defined reference system) were carried out. A DEM was generated by using the software module Automatic Terrain Extraction (ATE – SOCET. SET 5.4.1) in which adaptive and un-adaptive methods can be chosen to calculate height levels. By applying the former, which consists of a deduction mechanism to generate in an adaptive way the strategy of image matching based on field data (Ionescu et al., 2008), a 1 × 1 m grid model of the area around the landslide was generated. For each stereo-pair also 10 × 10 and 5 × 5 m grid models and orthophotos with a 0.5 m resolution were extracted. This made it possible to define landslide body shapes at a high spatial resolution (Baldi et al., 2002). The DEM generation gave very small linear and circular errors (LE and CE) characterized by the same order of magnitude for each year. LE gives an estimate of the overall accuracy of a DEM (it assumes that X and Y are correct and Z is compared), while CE represents horizontal positional accuracy. The obtained LE and CE values (Table 2) were Table 1 Mean errors of the parallaxes (pixel) in the relative orientation and x, y, z in the absolute orientation. Year

1997

2004

2006

Scale factor

19,000

19,000

14,000

Parallaxis error (pixel) x error (m) y error (m) z error (m)

0.831 0.353 0.347 0.347

0.709 0.322 0.213 0.213

0.892 0.679 0.520 0.244

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Table 2 Comparison of DEM accuracy. (A) Mean quadratic errors in the orientation phase: linear and circular errors (LE and CE). (B) Accuracy obtained by applying a law variance propagation to the Normal Case formula. σPξ = error concerning horizontal parallax measurement. σh = root mean square error in elevation h. (C) Accuracy of DEMs obtained by adopting the empirical formula proposed by Kraus and Waldhäusl (1994). Year

1997

2004

Scale factor

19,000

19,000

14,000

0.43 0.75 14 0.59 0.29 0.44

0.42 0.90 15 0.63 0.29 0.44

0.39 0.73 19 0.58 0.21 0.32

(A)

CE (m) LE (m) σPξ (μm) σh (m) σh (m) per a = 0.1 σh (m) per a = 0.15

(B) (C)

2006

compared with the accuracy (σh) obtained from the two relations. One is based on a theoretical approach: the law of variance propagation: 2

σh =

h σPξ c B

ð1Þ

where h = relative flight height; c = focal length of the camera; B = photobase = distance between central points on subsequent photograms; and σPξ = horizontal parallax measurement error. The other is the rule of thumb suggested by Kraus and Waldhäusl (1994): σh =

a h 1000

ð2Þ

where a = empirical constant (0.1–0.15). The errors calculated with the Socet Set software range between 0.4 and 0.9 m, while the errors calculated with Eqs. (1) and (2) are smaller (0.2–0.6). The difference may be due to the error caused during the orientation of stereo pairs. However, the theoretical and empirical values are comparable. Since the magnitude of morphological changes induced by sliding events was some ten meters in xy direction and some meters in z direction, the DEM error can be neglected. The area selected for the DEM comparison shows various landforms in steep to smooth terrains including the most seriously damaged area during the 2006 reactivation, with a high possibility of future dangerous reactivation. The spatial analysis tools of ArcGIS have facilitated a comparison between the DEMs of 1997, 2004 and 2006 to identify deformation on the slope. The analysis of differential DEMs makes it possible to identify the main deformation phenomena occurring on the slope during the considered time interval (1997– 2006). A first differential DEM was created by subtracting the DEM of 1997 from that of 2006. A second differential DEM was created by subtracting the 2004 DEM from that of 2006 in order to focus on the 2006 reactivation event. 3.3. Geological and geomorphological surveys Geological and geomorphological field surveys are often carried out after an aerial photo interpretation in order to confirm it (Agnesi et al., 2005; Schrott and Sass, 2008). They also help to identify geological and geomorphological features not visible at a synoptic scale. Geological surveys allow us to specify the lithological nature of moved material, identify the presence of structural elements (e.g. faults) that can trigger landslides and measure the dip of beds which also affects landslides. Geomorphological surveys allow us to identify trenches, secondary scarps and other elements that reflect the degree of landslide activity. They can also help to locate areas characterized by a high water content where increased pore water pressure could

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cause re-activation. In the study area geological and geomorphological surveys were performed at a detailed scale (ca. 1:5000) to obtain exhaustive geological and geomorphological maps (Figs. 2 and 3). The lithology and stratigraphy of the landslide area were also studied through a borehole survey carried out by the Civil Protection (see Fig. 4 for location). 3.4. Electrical resistivity tomography The ERT method was applied in order to depict the geometric features of the landslide and estimate the thickness of the material mobilized. Two different electrode arrays (dipole–dipole and Wenner–Schlumberger) and a multi-electrode system with 32 electrodes connected to a geo-resistivimeter Syscal R2 were used for data acquisition. The dipole–dipole array configuration was chosen for its depth of penetration and good resolution in identifying lateral heterogeneities, and the Wenner–Schlumberger array for its robustness and low sensitivity (Dahlin and Zhou, 2004). Resistivity data were acquired along five profiles, one longitudinal and four transversal to the landslide body (Fig. 4) with an electrode spacing of 20 m for the former and 10 m for the latter. The apparent resistivity data were inverted by using the RES2DINV algorithm of Loke and Barker (1996) based on the smoothness constrained least squared inversion with a quasi-Newton optimization technique.

4. Results 4.1. Surface characteristics of the slope The integration of the results from the aerial photogrammetry and the geological/geomorphological field surveys allowed us to better define the superficial geometry of the study area (Fig. 3). The results indicate the same type of movement for most of the ancient and recent landslides. The mass movements along the slope can be subdivided into two categories following Cruden and Varnes's (1996) classification: inactive and active landslides. The mass movements reflect clayey lithology and local climatic conditions. The geomorphological features shown in Fig. 3 include major and minor scarps, creeping phenomena, several tensional fractures and counter-slopes in the active landslide bodies, confirming the activity of some parts of the slope. The slides in the center have a main scarp with a circular form, secondary scarps, structural highs due to swelling, and counter slopes defining morphological depression where small short-lived lakes are sometimes found. In the western part of the area close to the central mass movements, there are small landslide bodies that are morphologically similar to the central landslides but inactive at present. Some of them show little evidence of movement such as new scarps, road cracks and inclined trees and lampposts. On the contrary, on the eastern side of the active landslides, there is an ancient inactive

Fig. 3. Geomorphologic map of the study area obtained by integrating aerial photo interpretation and a field survey. The red dashed lines highlight the active areas of the landslide occurred in March 2006.

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Fig. 4. Geomorphologic map of the investigated active part of the landslide reporting the ERT profiles and the S1 and S2 boreholes.

earth-flow. A slow movement in the lowest part was confirmed by GPS surveys with Leica Geosystem equipments, performed in December 2006, September 2007, October 2008 and February 2009. The scarp of the main landslide in the upper slope is vegetated and seems to be stable. In the same way the oldest scarps are completely eroded and obliterated. The definition of surface characteristics of the investigated slope, obtained by integrating photogrammetry, geological and geomorphological surveys, allowed us to identify active and potentially unstable areas. The reactivation of the latter could significantly affect residences and infrastructures. Therefore, we focused on the area of the slope at a higher reactivation risk (Fig. 4). 4.2. Topographic changes The analysis of differential DEMs facilitated the identification of the main deformation phenomena occurring on the slope during the considered time interval (1997–2006). A first differential DEM was created by subtracting the DEM of 1997 from that of 2006. This time interval highlights the 2006 reactivation event. It must be specified that the landslide subdivision into source and accumulation zones is based on a geomorphologic photo interpretation and surveys, therefore, it does not result from any algorithm related to DEMs. In the source area there is a retrogressive movement of the main scarp and the amount of scarp retreat can be approximated con-

sidering the thickness of “collapsed” terrains. The differential DEM (1997–2006) evidences a moderately distributed collapse in the source and flow truck zones of the landslide (down to − 6 m), and a rise (up to 3 m) in the lower part of the slope, at the toe of the landslide (Fig. 5a). The distribution of deposition and lowering zones in the 2006–2004 map (Fig. 5b) differs from that in the 2006–1997 map (Fig. 5a). The negative difference is smaller maybe because in 1997 the source area of the landslide was located higher than in 2004. This supports the presupposition that between 1997 and 2004 small movements deformed the surface. The main and secondary scarps and the counter dipping terraces are well delineated in both maps. However, the 2004–1997 map does not show such features but only small and spread accumulation areas (Fig. 5c). This suggests the absence of roto-translational slides before 1997, even if previous aerial photos in 1956 highlighted the presence of scarp and depression zones in the investigated area. 4.3. Subsurface characteristics of the slope 4.3.1. Stratigraphy The lithology of the S1 borehole (a 27 m-deep core drilling; Fig. 6) is mainly clayey for the first 16 m but more consolidated down to 27 m. As for the S2 borehole, which is a core loss drilling, the upper 15 m of material consists of less consolidated clay than the lower material down to 51 m. The S1 borehole is situated in the accumulation area, and S2 in

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Fig. 5. Differential DEMs obtained from the subtraction between (a) 2006–1997 DEMs; (b) 2006–2004 DEMs; and (c) 2004–1997 DEMs. In (a) and (b) a layer of the main geomorphological characteristics is laid upon differential DEMs.

the upper part of the landslide (Fig. 4). The first 15–16 m of the S1 core is the landslide material composed of detrital clay marl deposits with pebbles, interbedded with marly limestones. Below 16 m more

consolidated clay can be found down to the bottom of the borehole. Two water tables are found in the S1 borehole at about 14 and 25 m, respectively. The S2 stratigraphical data show a change in lithology at

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Fig. 6. Stratigraphic columns of the S1 and S2 direct soundings performed in the area by the local office of the Civil Protection.

15 m between unconsolidated and compact clay deposits. The water table in this borehole is about 10 m deep. 4.3.2. Geoelectrical results To detect subsoil geometry, five ERTs with the Wenner–Schlumberger configuration, one longitudinal and four transversal to the landslide body, were carried out with an electrode spacing of 20 m for

the longitudinal one and 10 m for the transversal ones (Fig. 4). All the ERTs show a small range of resistivity values (2–70 Ωm) indicating a weak discontinuity in the electrical properties. The high resistivity values (N 20 Ωm) for the first 20 m of subsoil could reflect more dislocated and porous material. The very low resistivity values (10 Ωm) in the deepest part could be related to clayey material or a higher water content. Furthermore, the ERTs indicate a possible

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sliding surface at a depth of ca. 20 m, facilitating the volume calculation of moved material. The 60 m-deep longitudinal A–A′ ERT (Fig. 7) crosses two main scarps: the old one at ca. 180 m from the origin of the profile, and the main scarp at 280 m. The former is totally eroded and is not visible at the surface. Highlighted in the ERT is a semi vertical discontinuity between the highly resistive material (20–80 Ωm) and the conductive one (4–20 Ωm) at the old scarp which can be associated with the rupture surface of a deep ancient landslide. The main scarp of the landslide is the object of this study. The high resistivity values at the NW side of A–A′ profile could represent the material uninvolved in the movement. Shallow (20-m thick) material with resistivity of 10–30 Ωm corresponds with the reactivated landslide body. All the 50-m deep transversal ERTs (B–B′ to E–E′; Fig. 8) show the same distribution as the resistivity values. In particular, the central part is characterized by a resistive material (30–80 Ω m) with a lenticular shape that could be associated with the slide. The deepest part of the ERTs shows conductivity values associated either with clayey material not involved in the slide or a higher water content. The landslide body can be delineated from the interpretation of all the ERTs in order to estimate the sliding surface and the thickness of the material moved. The tomographies A–A′ (Fig. 7) and B–B′ (Fig. 8) were also compared with the stratigraphical data from the boreholes. This showed a good correlation of the resistivity discontinuities with the lithological boundary between detrital material and more compact clay at the sliding surface as well as with water table depth (Fig. 6). 4.4. Mass volume estimation The total volume of the Picerno landslide was assessed. Usually, the total volume of a landslide or material displaced (Vtot) can be estimated by using a simple formula (Cruden and Varnes, 1996) based on the ellipsoid equation (Beyer, 1987): Vtot

1 = π Dr Wr Lr 6

ð3Þ

where Dr is the depth of the rupture surface, and Wr and Lr are respectively the width and length of the dormant landslide. After the

movement, the volume of the material displaced or reactivated (Vd), can be computed by assuming that the depth of the displacement Dd is the same as Dr and the width and length of the displaced area measured on a map or an orthophoto (Wd and Ld) (WP/WLI, 1990). The final formula of the reactivated landslide Eq. (4) is given by:

Vd =

1 π Dr Wd Ld 6

ð4Þ

The ellipsoid formula considers a regular shape without any topographical elevation changes; this approximation is not realistic and, in order to obtain the true volume of the displaced material the topographical changes in the collapsed and accumulation zone have to be considered. Accordingly, the collapsed and accumulated material volumes were added to the ellipsoid volume (Fig. 9). The ellipsoid volume was computed by obtaining the Dr value from the comparison between the ERTs and stratigraphical data, and measuring Wd and Ld from the topographical map and the orthophoto. Then the landslide area was subdivided into the collapsed zone with the main and secondary scarps and the accumulation zone. The mass volume of these two zones was estimated by multiplying the average thickness of the moved material obtained by the differential DEMs for the area of each zone. Then, a volume for the material collapsed (Vc), and a volume for the accumulated material (Va) were obtained (Fig. 9). The differential DEMs of Fig. 5a (2006–1997) and Fig. 5b (2006–2004) highlight the main reactivation event (i.e. Picerno landslide 2006) and they were analyzed to estimate Vc and Va and then assess the amount of mass wasting showing the mass balance between collapsed and accumulation zones (Vx), Vx = Va −Vc

ð5Þ

which can be considered as the result of sheet and rill erosion and anthropogenic slope deformation. Errors in volume based on the highest DEM errors (see Table 2B,C) were calculated and range between 2.7% and 4.1% of Vtot. Vx is always negative (Table 3), reflecting that soil erosion due to rainfall and intensive farming.

Fig. 7. 2D ERT along a longitudinal section of the landslide body (A–A′) using a Wenner–Schlumberger array. The interpretation considering the resistivity contrasts was confirmed by the stratigraphic data from boreholes S1 and S2.

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Fig. 8. 2D ERTs along transversal sections (B–B′, C–C′, D–D′, E–E′) using a Wenner–Schlumberger array. The resistivity contrasts enabled the recognition of some surface and subsurface features. For B–B′ features based on ERT were confirmed by stratigraphic data from borehole S1.

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Fig. 9. Schematic drawing explaining the approach for the volume estimation (modified after Corsini et al., 2009).

The real volume of the studied 2006 reactivation was obtained only from the volume of material lost from the collapsed area or that increased in the accumulation area (VMT in Table 4). 5. Discussion and conclusions A multi-disciplinary approach based on geomorphologic, geologic, photogrammetric and geoelectrical investigations was applied to study the surface and subsurface characteristics of a roto-translational slide in the Basilicata region. Because the roto-translational slide is the main type of mass movement in Italy (about 32.5% of the total number of landslides – ISPRA, 2008), it has been studied using various techniques. However, different techniques have been used separately for the investigation of landslides, providing only limited information. In particular, satellite differential interferometric SAR (Squarzoni et al., 2003; Catani et al., 2005; Guzzetti et al., 2009), aerial photogrammetry (Baldi et al., 2008; Dewitte et al., 2008; Prokešová et al., 2010) Table 3 Accumulated and collapsed volumes for different years which compare the most recent surface features with the oldest. Landslide 1997–2006

(m3)

Accumulated volume Collapsed volume Net volume

0.009 × 106 − 0.113 × 106 − 0.104 × 106

Landslide 2004–2006 Accumulated volume Collapsed volume Net volume Landslide 1997–2004 Accumulated volume Collapsed volume Net volume

(m3) 0.021 × 106 − 0.045 × 106 − 0.023 × 106

and Lidar (Baldo et al., 2009; Kasai et al., 2009) were often applied techniques in order to monitor areas affected by ground deformation, characterize landslide morphology and activity, and gather information on the displacement amount and rate. Standard in-situ techniques, such as boreholes and inclinometric measurements (Lollino et al., 2002; Calvello et al., 2008), were used in order to reconstruct the subsoil geological setting and obtain information on the displacement rate of the slide material. Modern techniques such as geophysical ones were applied to reconstruct the subsoil geological setting in 2D or 3D (Perrone et al., 2008; Travelletti et al., 2010). It is clear that the integration of data from different techniques provide useful information to characterize the geometry and the temporal evolution of landslides. This work represents one of the first contributions of such integration based on aerial photogrammetry, geological and geomorphological surveys and geophysical measurements, in order to characterize the roto-translational earthflow and estimate the volume of the material involved in the movement. A landslide volume estimation requires the knowledge of some important parameters such as the size of the landslide body, the thickness of the slide material and the altitudinal changes due to

Table 4 Volume estimation of the studied reactivation event quantifying the volumes of each components. Vd: volume of the displaced material from the Cruden and Varnes equation (WP/WLI, 1990); Vtot: volume of the displaced material after the movement; Vc: volume of the “collapsed” material; Va: volume of the “accumulated” material obtained from the three differential DEMs (modified after Corsini et al., 2009). DEMs difference

(m3) 0.005 × 106 − 0.085 × 106 − 0.081 × 106

2006–1997 2006–2004

Mass displacement

Mass transfer

Mass wasting

Total volume

Vd

VMT = max of |Vc| or |Va|

Vx = − Vc + Va

Vtot = Vd + |Vc| + |Va|

≈1.028 ×106 m3 ≈1.028 ×106 m3

0.113× 106 m3 0.045× 106 m3

−0.104 ×106 m3 −0.023 ×106 m3

1.150× 106 m3 1.095× 106 m3

C. de Bari et al. / Geomorphology 133 (2011) 34–46

sliding and erosion. Only the integrated use of different techniques can provide all such information. One of the techniques we used, the analytical and digital photogrammetric multi-temporal analysis, made it possible to define the geomorphological features of the landslide, monitor its dynamic processes and quantify topographic changes, as indicated by some previous studies carried out in the Apennines and Alps (Mora et al., 2003; Rapisarda, 2007; Prokešová et al., 2010). The subsurface investigation techniques have focused on a particular zone of the landslide reactivated in March 2006. Despite the low resistivity contrasts due the lithological nature of the investigated area, the ERT technique allowed us to estimate the subsurface structure of the landslide body, especially the sliding surface. The results obtained, calibrated by using borehole data, have confirmed the applicability of the ERT technique for investigating landslides in the resistive terrains of the Alps (Bruno and Marillier, 2000; Mauritsch et al., 2000; Godio and Bottino, 2001; Lebourg et al., 2005; Godio et al., 2006) and less resistive terrains of the Apennines (Lapenna et al., 2003; Perrone et al., 2004; Lapenna et al., 2005; Jongmans and Garambois, 2007). The integration of the results from the different methodologies permitted the accurate estimation of the volume of moved material. In particular, the volume of mass transfer was calculated for two different time intervals: 0.113 × 10 6 m 3 for 1997–2006 and 0.045 × 10 6 m 3 for 2004–2006. The integration of methodologies allowed us to overcome the drawbacks of each of them. Although digital photogrammetry products including differential DEMs give information on the volume of material moved from the source area, that of accumulation, and that of material lost due to erosion, they do not give any information on the thickness of displaced material. On the other hand, the ERT method provides information on the subsurface structure of the landslide body such as the depth of the sliding surface and thickness of the slide material, but it does not give any information on altitudinal changes. The proposed approach can be considered a more complete tool for investigating landslides. It could give a useful support for the management and mitigation of landslide risk. Acknowledgements This paper is a part of the PhD thesis by Claudia de Bari submitted to the University of Basilicata. Some data are from the Morfeo (MOnitoraggio e Rischio da Frana mediante dati EO) project. The authors wish to thank Geocart Ltd Potenza and Geotec Ltd Matera for providing aerial photos. The authors would also like to thank Roberta Prokesova, Tomas Panek and the Editor for their constructive and detailed suggestions useful for improving this paper. References Agnesi, V., Camarda, M., Conoscenti, C., Di Maggio, C., Diliberto, I.S., Madonia, P., Rotigliano, E., 2005. A multidisciplinary approach to the evaluation of the mechanism that triggered the Cerda landslide (Sicily, Italy). Geomorphology 65, 101–116. Baldi, P., Cenni, N., Fabris, M., Zanutta, A., 2008. Kinematics of a landslide derived from archival photogrammetry and GPS data. Geomorphology 102, 435–444. Baldi, P., Fabris, M., Marsella, A., Monticelli, R., 2005. Monitoring the morphological evolution of the Sciara del Fuoco during the 2002–2003 Stromboli eruption using multi-temporal photogrammetry. ISPRS J. Photogramm. Remote Sensing 59, 199–211. Baldi, P., Bitelli, G., Carrara, A., Zanutta, A., 2002. Detecting landslide long term movements by differential photogrammetry. Proceedings of the European Geophysical Society, XXVII General Assembly, 21-26 April, Nice, France. Baldo, M., Bicocchi, C., Chiocchini, U., Giordan, D., Lollino, G., 2009. LIDAR monitoring of mass wasting processes: the Radicofani landslide, Province of Siena, Central Italy. Geomorphology 105, 193–201. Beyer, W.H. (Ed.), 1987. Handbook of Mathematical Sciences, 6th ed. CRC Press, Boca Raton, Florida. 860 pp. Boenzi, F., Giura Longo, R., 1994. La Basilicata: i tempi, gli uomini e l'ambiente. Bari, Edilpuglia. 250 pp. Bruno, F., Marillier, F., 2000. Test of high-resolution seismic reflection and other geophysical techniques on the Boup landslide in the Swiss Alps. Surveys in Geophysics 21, 333–348.

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