lut

valyzian

Morphometric parametersTopographic changes

dyredaraariandad ment rate of the toe) were estimated. Deformation structures (scarps, cracks, folds)fferent times were also mapped; some of such structures represent precursors of

activity (e.g. rock-fall, ow) and geometry (e.

detectiows (eowth.fected

Remote Sensing of Environment 115 (2011) 32373248

Contents lists available at ScienceDirect

Remote Sensing o

w.esliding processes for hazard assessment purposes (e.g., Baldo et al.,2009; Chen et al., 2006; Dewitte et al., 2008; Haneberg et al., 2009;Jaboyedoff et al., 2010; McKean & Roering, 2004; Miliaresis et al., 2005;Ventura & Vilardo, 2008). In landslide studies, the use of high resolutionDigital Terrain Models (DTM) allows to detect and analyze the spatialdistribution of landforms related to the sliding activity, e.g. cracks,scarps, and folds. Multi-temporal, high resolution DTMs present theadvantage of monitoring the evolution of the landslide's phenomenaadding value to more classical techniques such as airphotos ormultispectral digital imagery, GPS, DInSAR (Herrera et al., 2009; Hilley

mechanisms, (b) ow rate, (c) identication of landslide expansionareas (e.g. crown area) or accretion/accumulation areas (e.g. toe zone),and (d) formation of ridges and natural dams.

In this study, we present the results of a multi-temporal airborneLiDAR survey (four aerial missions in the 20062010 period) of theactive Montaguto landslide (Fig. 1; Southern Apennines, Italy). This isone of larger and complex landslides in Europe and its activitycompromises the functionality and safety of the Pan-European RailCorridor infrastructure connecting the Tyrrhenian and Adriatic coastsof Italy (SS90 road and Benevento-Foggia railway line; Fig. 1a). Theet al., 2004; Mackey & Roering, 2011; Metternstudies based on LiDAR-derived DTMs orphotogrammetric data on unstable terrain fboundary changes and on the estimate of the v

Corresponding author. Tel.: +39 06 51860221.E-mail address: guido.ventura@ingv.it (G. Ventura).

0034-4257/$ see front matter 2011 Elsevier Inc. Aldoi:10.1016/j.rse.2011.07.007by instability phenomenastimate the volumes ofonitor the evolution of

Roering (2004), landslide studies usually avoid the information revealedby internal deformation features. These features are of primaryimportance because may provide new insights on (a) the slidingallows to (a) identify weakness zones, (b) eremoved or accumulated material, and (c) mSpatio-temporal analysis

1. Introduction

The use of airborne LiDAR (lightremote sensing of gravity-controlled avalanches) is undergoing a fast grresolution 3D information on terrains aftopographic features change with time due to articial drainage and earth handling/removal work weredetected. The LiDAR-derived information allows us to decipher the kinematics of the landslide. The resultsprovide new insight on the use of airborne LiDAR in the monitoring strategies of gravity-controlled processes.

2011 Elsevier Inc. All rights reserved.

on and ranging) in the.g. landslides, lava ows,The acquisition of high

removedmaterial (Baldo et al., 2009; Bull et al., 2010; Burns et al., 2010;Chen et al., 2006; Corsini et al., 2009; Jaboyedoff et al., 2010; Joyce et al.,2009;Kasai et al., 2009). Comparatively little attentionhas beendevotedto the characterization of the ow kinematics and detailed analyses oftopographic changes are virtually lacking. According to McKean andicht et al., 2005). Recentcombined LiDAR and

ocuses on the landslideolume of accumulated or

analysis of DTMLiDAR data alloin different tim(c) estimate the(d) determine a(e) detect zonesow), (f) identifthe retreat rate o

l rights reserved.g., channelized ow) are recognized and zones whoseLandslideMonitoring impending instability processes or give information on the mechanism of emplacement. Various types ofAirborne LiDAR of the crown and advancemaffecting the landslide in diTracking and evolution of complex activeLiDAR data: The Montaguto landslide (So

Guido Ventura a,, Giuseppe Vilardo b, Carlo Terranoa Istituto Nazionale di Geosica e Vulcanologia, Via di Vigna Murata 605, 00143 Roma, Itab Istituto Nazionale di Geosica e Vulcanologia, sezione Osservatorio Vesuviano, Via Diocle

a b s t r a c ta r t i c l e i n f o

Article history:Received 3 March 2011Received in revised form 4 July 2011Accepted 7 July 2011Available online 6 August 2011

Keywords:

A multi-temporal LiDAR stuDigital TerrainModels acquiof selected morphometric panalysis of the temporal vlandslide. The landslide bouremoved and/or accumulate

j ourna l homepage: wwandslides by multi-temporal airbornehern Italy)b, Eliana Bellucci Sessa b

o 328, 80124 Napoli, Italy

of an active landslide at Montaguto (Italy) is presented. Four LiDAR-derivedonMay 2006, July 2009, April 2010 and June 2010 are used. The interpretationmeters (surface roughness, residual topographic surface) and the statisticaltions of such parameters allowed the reconstruction and tracking of thery monitoring was achieved and zones of uplift and subsidence, volumes ofaterial, and average rates of vertical and horizontal displacement (retreat rate

f Environment

l sev ie r.com/ locate / rses derived by the post-processing of multi-temporalw us to (a) delimit the boundary of the landslidees, (b) recognize zones of uplift and subsidence,volumes of removed and/or accumulated material,verage rates of vertical and horizontal movements,characterized by different types of activity (e.g. fall,y major and minor scarps, ridges, cracks and estimatef the crown, and (g) detect zones in which articial

Mon guto landslide, SS90 road and BeneventoFoggia railway. Symbols: circle=wind tower,e a a. The landslide boundary is representative of the area affected by the mass movementsth al rnated clays and marls, Ps=Pliocene clays and sandy clays, i=clays and sandy clay with

intercalated calcarenites, limestones, shists and jasper, Qt=Quaternary uvial deposits, Q= cent alluvial sediments, red line with arrow=strike and dip of strata, red line withe als reported.

3238 G. Ventura et al. / Remote Sensing of Environment 115 (2011) 32373248modications of the topography due to material handling occur. The

barbs=fault. The different sectors of the landslide S1, S2, S3 and S4 discussed in the text arFig. 1. a) Digital color orthophoto acquired on March, 29 2010 showing the location of thestars=fountains, cross=springs. b) Location and geological map of the Montaguto landslidbetween May and June 2010. Symbols: bcd: Miocene calcarenitic and limestone breccias wiresults shed light on the natural and man-induced processes thataffect the studied terrain.

2. Geological setting

The Montaguto landslide (ML; Fig. 1) is located along the axis ofthe Neogene Southern Apennine fold-and-thrust belt, which repre-sents the accretionary wedge of the southwestward subduction of theAdriatic domain below the Italian peninsula (Doglioni, 1991;Malinverno & Ryan, 1986). From Pleistocene times, the axial sectorof the belt is affected by a NE-SW oriented extension with thedevelopment of NWSE striking normal faults (Vezzani et al., 2010and reference therein). ML has a length of about 3 km and rangein width between 45 m and 420 m, and covers an area of about0.5 km2 (Fig. 1). ML emplaces on a south facing slope of an 870 m a.s.l.high, EW elongated ridge, and mainly develops within a NNWSSEstriking synform with a nucleus constituted of Pliocene clays andsandy clays (Fig. 1b). These Pliocene deposits overlie Miocenecalcarenitic and limestone breccias with alternated clays andmarls. The geometry of the landslide is characterized by a crown at840 m a.s.l. destabilizing the Miocene deposits outcropping on alocally southwestward dipping slope, while the main landslide bodylies within a NNWSSE striking valley roughly orthogonal to the EWelongated relief. A NNWSSE striking,WSWdipping fault scarp affectsthe right side of this valley and delimits the eastern side of thelandslide body (Fig. 1b). The toe zone is located at about 415 m a.s.l.,at the mouth of the valley, and partly lls an EW striking intra-mountain basin crossed by SS90 road and the BeneventoFoggiarailway line (Fig. 1a). The Montaguto slide initiated in 2003 in an areawherewater springs occur at different altitudes. Thewater table in thelandslide body is at 2 m below the topographic surface in the centralzone and 1520 m in the toe zone, as revealed by drilling survey datatareteReoFig. 2. LiDAR-derived hillshade map of ML (March 29, 2010). The lines show thelandslide boundary at different times. The different sectors of the landslide S1, S2, S3and S4 discussed in the text are also reported.

3239G. Ventura et al. / Remote Sensing of Environment 115 (2011) 32373248(Commissariato di Governo della Regione Campania, 2006). Thethickness of the slide deposits also increases from 5 m in the centralzone to 20 m at the toe. In summary, the depth of the water tablebelow ML roughly corresponds to the thickness of sliding material, asalso observed in other landslides, e.g. at the VillervilleCricqueboeuflandslide (France; Lissak et al., 2010). Sag ponds occur in the centraland upper zone of ML (Fig. 1a). At present, the slide is articiallydrained in the upper and middle-lower zones, while the slidingmaterial is removed from the toe. All these actions are under thesupervision of the National Civil Protection Department.

On the basis of its morphological and lithological features, ML maybe divided in 4 main sectors (Fig. 1b):

Sector 1 (S1) includes the uppermost part of the slide, which,following the nomenclature by Glade et al. (2005), basedon Varnes, (1978), includes the crown, main scarp andhead.

Sector 2 (S2) represents the displaced material within the ENEWSW striking, 50 to 70 m wide valley.

Sector 3 (S3) is 70 to about 200 m wide and it is characterized bymaterial lling the abovementionedNNWSSE striking valley.

Sector 4 (S4) represents the lowermost part of the landslide andincorporates the toe accumulation zone. This sector is locatedat the mouth of the NNWSSE striking valley which containsthe Pan-European Rail Corridor infrastructures.

S1 is underline by Miocene terrains (calcarenitic and limestonebreccias with alternated clays and marls), whereas Pliocene clays andsandy clays outcrop in S2, S3 and S4.

Fig. 3. Spatial distribution of surface roug3. Data acquisition and pre-processing

The landslide analysis is based on multitemporal high-resolutionLiDAR data sets collected using different systems operating atfrequencies from 50 to 135 kHz on board of a twin-engine aircraftPartenavia P68. There were four LiDAR ight missions over the MLarea (May 2006, July 2009, April 2010 and June 2010), and wereaccompanied by a single ight mission with digital aerial camera inorder to acquire high resolution color orthophoto.

The aerial LiDAR survey consisted of several ight lines, side lappedof 2025%, aimed to longitudinally cover the whole landslide area, withadditional crossingight lines to provide a proper coverage on the steeptopography (e.g., crown zone) and to ensure at least 3 points per squaremeter over thewhole areaof investigation (MLand surroundings areas).

The laser instrument calibration phases were performed in differenttimes and in two different ground calibration sites (Grumento Nova andCapodichino airports) using head runaway signs from intensityinformation and topographic control points located on the ground andinfrastructures to check the horizontal and vertical accuracy. Precisemeasurements of the roll, pitch and heading of the aerial platform wasperformed by on-board inertial measurement unit (IMU-Applanix)during both calibration phases and operational missions over ML.

The on board GPS system recorded from high to very high PDOP(Precision Dilution of Precision) values during all ight operations overthe investigated area, and the four missions were performed in theafternoon avoiding the direct reection of sunlight on the landslideooded areas increasing the GPS accuracy due to low ionosphericactivity.

hness in S1 and S2 in different times.

3240 G. Ventura et al. / Remote Sensing of Environment 115 (2011) 32373248Differential corrections of the GPS trajectory of the aircraft werecalculated for all the four ight missions from a same local basestations belonging to the National geodetic network IGM 95 ETRF2000 located at 411148N, 150813E. After the acquisition phase,laser rangender data have been computed in conjunction withrecordings from the onboard differential Global Positioning System(DGPS) and inertial measurement unit (IMU), producing geocoded x,y, z coordinates of all the LiDAR points (Baltsavias, 1999). The postprocessing software (Terrascan) indicates a vertical accuracy of 12 cmand planimetric accuracy at 24 cm; these values were not exceeded inany of the four acquisitions.

Extraneous low and high points, e.g. multipath and fog, respec-tively, were eliminated from each data set, and four ltered bare earthmodels were generated from the raw point clouds by removing lowvegetation, trees and buildings (Axelsson, 1999). Automated lteringremoved up to about 8090% of the above-ground features reliably,and the last 10% were removed by manual editing supported by highresolution orthophoto imagery.

A regular 1 by 1 m grid Digital Terrain Model (DTM) was createdfrom LiDAR bare-earth points cloud by means of the inverse distanceweighted (IDW) interpolation method with a 3 m search radius.Because of the LiDAR data sets were collected at different times usingdifferent systems, an evaluation of the data was performed todetermine the point density/spatial resolution. A comparison of the

Fig. 4. Spatial distribution of surface rheight differences (Miliaresis & Paraschou, 2005) was made betweenthe different data sets in areas of overlap located away from thelandslide area, where the topography was relatively subdued, andaway from man-made features (cultivated elds). Following theapproach of Bull et al. (2010), hundreds of stable check points (man-made features) were selected over the ML surrounding area onbuildings, roads, railroad, and fresh water pipe lines; the elevationdifferences between the four LiDAR data sets have been computed(Crane et al., 2004) and resulted to be 0.01 m0.01 (20092006),0.258 m0.12 (2010 April2006); 0.475 m0.15 (2010June2006). These values have been used to correct the offset betweenmultitemporal LiDAR data.

4. Morphometric parameters and analytical methods

The methodological approach involves the quantitative descriptionof the landslide topography, the photo-interpretative analysis, andthe study of some morphometric parameters. Photo-interpretation ofshaded relief maps and geomorphometric imagery (surface roughness(Figs. 3, 4, and 5), and residual topographic surface (Figs. 6, 7, and 8))allow the recognition and mapping of landslide phenomena. A range ofmethods has been developed for the denition, calculation, andapplication of the surface roughness (e.g. Cavalli et al., 2008; Frankel &Dolan, 2007; Glenn et al., 2006; Grohmann et al., 2007, 2010; Jenness,

oughness in S3 in different times.

Fig. 5. Spatial distribution of surface roughness in S4 in different times.

Fig. 6. Spatial distribution of Residual Topographic Surface (RTS) in S1 and S2 in different times. Black=positive residuals (crests, levees); white=negative residuals (channels,gullies).

3241G. Ventura et al. / Remote Sensing of Environment 115 (2011) 32373248

Fig. 7. Spatial distribution of Residual Topographic Surface (RTS) in S3 in different times. Black=positive residuals (crests, levees); white=negative residuals (channels, gullies).

Fig. 8. Spatial distribution of Residual Topographic Surface (RTS) in S4 in different times. Black=positive residuals (crests, levees); white=negative residuals (channels, gullies).

3242 G. Ventura et al. / Remote Sensing of Environment 115 (2011) 32373248

3243G. Ventura et al. / Remote Sensing of Environment 115 (2011) 323732482004; McKean & Roering, 2004). Here, the surface roughness wasestimated in ArcMap 9.3 (ESRI Inc.) using the DEM Surface Tool(Jenness, 2010),which calculates this parameter for eachcell as the ratiobetween the surface area (S=real) and theat area (A=plan) of squarecells (Hobson, 1972; Jenness, 2004; Li et al., 2005). In this approach, atareas have values close to 1, whilst S/A increases asymptotically toinnity as S increases. With respect to other methods, which consideronly one parameter (e.g., the standard deviation of slope), the methodby Jenness (2010) includes areas and slope, being S inversely relatedto the cosine of slope. Because of the surface roughness is a scale-dependent parameter (Grebby et al., 2010; Grohmann et al., 2010), ithas been estimated using different moving window sizes. Due to theprevailing short wavelength of theML elements (see Section 5.1), these

Fig. 9. a)Mapof the scarps, cracks and crownboundary in the eastern, uppermost sector ofS1 in different times deduced by the combined analyses of data reported in Figs. 2, 4 and 7.b) Map of the folds in S3 in different times deduced by the combined analyses of datareported in Figs. 2, 4, and 7. The shaded relief images are from the 2010.04 DTM.tests have shown that the 33 mmoving window better describes thegeomorphic features of interest.

In order to further highlight the ne scale ML landforms, maps ofthe residual topographic surface were also produced. The residualtopographic surface (RTS; Figs. 6, 7, 8) (Glenn et al., 2006; Grebbyet al., 2010) was derived by separating the large scale topographyfrom the ne scale variability by considering a 99 pixel movingwindow. RTS of each DTMwas calculated by subtracting the 9 m DTMfrom the 1 m DTM. The analysis of the surface roughness and RTSmaps was assisted by the visual interpretation of the landslide fromdigital ortho-imagery.

5. Results and discussion

The recognition of areas characterized by clear evidence ofmorphological changes or by development of longitudinal leveeswhose positions mark the landslide margins allowed us to accuratelydelineate the evolution of the landslide boundary over the 20062010period (Fig. 2).

Results indicate that a total area of 0.48 km2wasaffectedby themassmovement with a progressive increase of the ML surface from May2006 to June 2010 (May 2006=0.367 km2; July 2009=0.396 km2;April 2010=0.424 km2; June 2010=0.427 km2). This increase of theML surface is mainly due to (a) the downslope migration and lateralspreading of the toe into the main, EW striking intra-mountain basinwhere the SS90 road and the BeneventoFoggia railway line are located(Fig. 1a), and (b) the progressive upslope migration of the ML crownin S1.

5.1. ML morphological and structural features

DTMs and morphometric landscapes representations (Figs. 2, 35and 68) highlight distinctive morphological features related todifferent lithology and/or dynamic processes. S1 is mainly character-ized by a disorganized distribution of the surface irregularities (e.g.Figs. 3 and 6). This part of ML reects the fall of dm- to m- sizedcalacarenitic and limestone rock fragments from the crown steepslope, and minor grain-ows. Some scarps and cracks affected zonesthat, in the May 2006 LiDAR survey, were located outside the crownarea, and, in the successive surveys, become part of S1 (Fig. 9a). Thesescarps and cracks detected in May 2006 are structural discontinuitieswithin the Miocene limestone, and represent precursors of the crownretreat processes. NWSE striking, sub-parallel scarps were alsodetected within the S1 zone at the base of the crown in 05.2006(Fig. 3). These scarps were progressively covered by debris accumu-lated at the base of a new, major scar formed in the central part of S1in 2009 (Fig. 3). In S2, the ML central body is bounded by NESWstriking, parallel ridges (levees) (Figs. 3 and 6). Ridges have beenobserved in other landslides and have been related to the lateralpressure of the moving earthow on the lateral walls of the channel(Kelsey, 1978). The S3 topography is signicantly smoother than thatof S1 and S2. Longitudinal grooves delimit a central area characterizedby the presence of lobe-like and compressive structures (folds)(Figs. 4 and 7). The position of the outcropping lobes, some of whichare partly overlapping, remains stable over time. In the upper part ofS3, the landslide width decreases from 2006 to 2010 so marking anarrowing of the transportation track. In the lower part of S3, thefold bending and the local landslide width increase as the localslope decreases (Fig. 9b). Such morphological features indicate theemplacement of individual ow units before 2006 and suggest,according to McKean et al. (2004) and Evans and De Graff (2005), anon-Newtonian behavior of the moving material, which consists ofsaturated clays and sandy clays. One-dimensional spectral analysishas been applied in order to obtain information about the magnitudeof the dominant surface periodicities (folds) in S3. Specically,

longitudinal cross-sections were extracted from the four RTS maps

conned to a channel, and S4, which incorporates the toe accumu-lation zone, exhibit intermediate surface roughness and RTS values(Fig. 11). In addition to the spatial changes in the roughness andRTS values in the different ML sectors, the temporal pattern of theseparameters also varies. In general, the variation range of these twoparameters decreases or maintain roughly constant in the different

phy prole in S3 at different time (see procedure in the text). Power spectra illustrate howsed in the text are marked by arrows and numbers, respectively.

3244 G. Ventura et al. / Remote Sensing of Environment 115 (2011) 32373248(Figs. 68). Such one-dimensional residual elevation proles, withmean elevation equal to zero, were low-pass ltered with a 9 pointsmoving-average lter and power spectra were calculated (Fig. 10).Spectral analysis evidences four peaks at about 2.5, 3.5, 6, and 15 m forwavelengths less than 20 m. Such peaks are stable in the May 2006June 2010 time interval. At wavelengths larger than 20 m, two majorpeaks occur at 40 and 70 m in May 2006. In the 2009.072010.06period, such peaks shift to 45 m and 180 m. As a result, the smaller-scale S3 periodicity remains stable over time, whereas the larger scaleperiodicity increases, mainly between 2006.05 and 2009.07. Such anincrease cannot be due to shallow (surface) processes, and could berelated to basal sliding (shear), i.e., to movements at the contactbetween landslide materials and the bed rock. According to resultsfrom the vertical displacement analysis (Section 5.3), the net effect of

Fig. 10. Results of the one-dimensional spectral analysis of longitudinal residual topograthe longer wavelength peaks vary with time. The peaks and wavelengths (in m) discusthis process is a subsidence.In S4, a rather complex distribution of the relief irregularities

occurs (Figs. 5 and 8). Fold-like structures prevail in 2006 and 2009,whereas longitudinal grooves form in the 2010.042010.06 period.This morphological evolution indicates the formation of channelsby gully erosion. The interpretation of Figs. 2, 5 and 8 indicates thatthe toe front advanced and expanded downslope from 2006.05 to2010.04, and stopped when the articial draining of the landslide andearth removal activities started. Terraces due to material handling atthe ML toe were recognized in 2010.04 and 2010.06 (Figs. 2 and 5).

5.2. Temporal and spatial variability of the ML morphometricparameters

Statistical analysis of the surface roughness and RTS maps aresummarized in Fig. 11, where box-plot diagrams representative of thepixel distribution in each sector of the landslide of the four DTMs arereported.

The larger variability of the surface roughness and RTS values(Fig. 11) occurs in the uppermost part of the slide (S1), where thecrown surface and scarps transversely oriented to the slidingmovement are located (Figs. 3 and 6). The lower variability of thesetwo parameters occurs in the central body of the landslide (S3), wherethe large scale surface shows a gentle slope (Figs. 2 and 4) and thelandslide surface is characterized by small amplitude folds (Fig. 10)and lobe-like structures. S2, which represents the zone of the slideFig. 11. Box-plot diagrams: a) surface roughness; b) residual topographic surface.Horizontal bars within boxes represent the median, the tops and bottoms of the boxesrepresent the 75th and 25th quantiles, and the whiskers represent the range excludingoutliers. Outliers were considered values whose total areal extent is lower than 0.01%.

3245G. Ventura et al. / Remote Sensing of Environment 115 (2011) 32373248sectors between 2006.05 and 2010.06 (Fig. 11), even if a local increaseis recognized in S3 and S4 between 2006.05 and 2009.07. Such localincrease may indicate a slowdown of ow and/or translation activityin the central-basal part of ML, whereas the general decrease inroughness and RTS values could be related to runoff erosion, asobserved in other landslides (McKean & Roering, 2004). Therecognized post-2009.07 decrease of the values of these parametersin S4 is mainly due to earth removal activity and creation of sub-horizontal, terraced surfaces.

5.3. Vertical and horizontal displacements

Elevation difference maps were produced by subtraction (Baldoet al., 2009; Dewitte et al., 2008) of the LiDAR-derived DTMs with theaim to estimate the vertical displacement during the 2006.052010.06time interval. The difference maps corresponding to successive DTMsare shown in Fig. 12; Table 1 summarizes the total volumetric changesand the associated errors estimated on the basis of the verticaldisplacements computations, the pixel dimension, and the verticaland horizontal accuracy of the LiDARdata. In addition, the production ofmaps of the vertical ground displacement rate (Fig. 13) provides afurther characterization of ML. Our LiDAR data and results from SARimages collected between 1992 and 2001 (Vilardo et al., 2009) do notevidence signicant subsidence or uplift episodes outside the MLarea. As a result, the uplift and subsidence zones evidenced in Fig. 12and discussed in the following are related to the ML dynamics and tothe activities performed by the National Civil Protection Department onthe ML body.

Fig. 12.Maps of the vertical ground displacements within landslide inferred from the compc) from April 2010 to June 2010; d) from May 2006 to June 2010. The net volume changesFigs. 12 and 13 clearly show the complexity of ML, which ischaracterized by distinct, alternating uplift and subsidence zones. Inthe 2006.052010.04 period, the crown area is mainly affected bysubsidence (rock-fall and grain-ows), whereas uplift (accumulation)occurs in the lower zone of S1. Subsidence related to ow processescharacterizes the upper zone of S2, whereas uplift related to owaccumulation affects the lower zone. Between 2006.05 and 2009.07,the upper zone of S3 was characterized by prevailing accumulationfrom ows originated in S2, whereas the lower zone was affected bysubsidence related to the sliding within a channel (Fig. 12a). Thissubsidence surface extended in the upper zone of S4 between 2009.07and 2010.04 (Fig. 12b), in an area earlier characterized by uplift. Aspreviously reported, the toe surface in S4 was mainly affected byaccumulation processes between 2006.05 and 2010.04 (Fig. 12a, b)and by subsidence due to the articial removal of material in 2010

arison between DEMs: a) from May 2006 to July 2009; b) from July 2009 to April 2010;on the ML area are also reported for the different time periods.

Table 1Change in volume computed for different time intervals based on LiDAR-derived DEMs.

Time interval Erosion (m3) Deposition (m3) Volume balance (m3)

2009 July2006 May

494.00014.000 448.00014.000 46.00028.000

2010 April2009 July

422.00017.000 253.00012.000 169.00029.000

2010 June2010 April

391.00024.000 47.0006.000 344.00030.000

2010 June2006 May

935.00021.000 357.00010.000 578.00031.000

3246 G. Ventura et al. / Remote Sensing of Environment 115 (2011) 32373248(Fig. 12c). Data from Fig. 12 and Table 1 show that the differencebetween the uplifted and subsided volumes is negative in all theconsidered time intervals. This indicates a general deation of thelandslide. The vertical displacement rate reveals, however, that theuplift rate was almost constant between 2006.05 and 2010.04 in S1,S2, and S3, whereas the subsidence rate increased in the same period(Fig. 13). Such increasemainly occurred in S1 (crown zone) and in thenorthern part of S4. In the 2010.042010.06 time interval, a large partof the landslide was subsiding, with the larger rates in S4, wherematerial removal and articial drainage activities concentrate. As aresult, the spatial arrangement, i.e. alternation of subsidence anduplift zones between 2006.05 and 2010.04 reects (a) rock-fall andow in a channel, and (b) accumulation processes. The post-2010.04deformation eld largely results from man-induced activities. Mate-rial removal explains the subsidence observed at the toe in S4, whilesliding and runoff erosion processes, possibly associated to a decreasein porosity due to articial draining, could justify the generalsubsidence (deation) observed in S2 and mainly in S3. However,the lack of porosity determinations does not allow us to furtherconstrain this last hypothesis. The ground displacement rate map ofFig. 13 details themodications of the topography due to the activitiesperformed by the National Civil Protection Department: the emptyingof a pond in S1, the reopening of a road crossing the slide in S2, thedrainage activities in S3, the material removal from the toe arealocated north of the railway, and the accumulation of that material intwo areas located south of the railway and at the eastern tip of the toe.

The multitemporal analysis of the LiDAR derived DTMs allow us toestimate the average horizontal velocity in three key sectors of ML.Longitudinal cross sections of the ML topography in 2006.05, 2009.07and 2010.04 in the crown (S1) and toe (S4) zones show that the

Fig. 13.Maps of the vertical ground displacement rate within the landslide inferred from th2010; c) from April 2010 to June 2010; d) from May 2006 to June 2010.crown scarp's retreat rate was 53 m/yr between 2009.07 and 2010.04(Fig. 14a). Between 2006.05 and 2010.04, the rate of downslopeadvancement of a major lobe was of 11 m/yr. The toe advancementrate between 2009.07 and 2010.04 was 60 m/yr (Fig. 14b). Theseestimates of the horizontal displacement rate are averaged overrelatively long time intervals, but if LiDAR data are available forshorter time steps on other landslides, these data may be used tosuccessfully determine the ow and/or translation velocity.

6. Conclusions

The main results of our analysis can be summarized as follows:

a) The multi-temporal analysis of LiDAR-derived DTMs of ML allowus to recognize dynamic processes such as rock-fall and ow, andto identify subtle ground deformations and landscape modica-tions due to mass movements and human activities aimed toreduce the landslide motion.

b) The study of the spatial distribution of selected morphometricparameters (residual topographic surface and surface roughness)give information on the ML surface and allow us to recognizestructures (e.g. cracks, folds, groves, ridges) indicative of the owbehavior and ow geometry.

c) The estimate of the volume changes over time consent to identifysectors of the landslide characterized by erosion or deposition andshed light on the emplacement mechanism and its temporalevolution.

d) Subsidence and uplift rates and horizontal velocities may beestimated and allow us to reconstruct the dynamics of landslides.

e differential maps of Fig. 12: a) from May 2006 to July 2009; b) from July 2009 to April

3247G. Ventura et al. / Remote Sensing of Environment 115 (2011) 32373248The outcomes of the analytical approach used here for thecharacterization of ML highlight the relevance of LiDARmulti-temporalanalysis for the study of gravity controlled processes. The monitoringof the effects of the landslide motion may give a support to theimplementation of man-made activities aimed to restore securityconditions of transport infrastructures exposed to landslide risk.Finally, the LiDAR multi-temporal analysis has shown the potentialto support monitoring strategies of complex mass movements byidentifying critical areas.

Acknowledgments

We thank the two anonymous reviewers for the critical andconstructive comments that improved the quality and clarity of themanuscript. In addition, we thank A. Smith for an edit of themanuscript. We also thank the Nuova Avioriprese Srl (Napoli) formaking available the LiDAR data. Discussions with colleagues of

Fig. 14. Cross sections of the ML topography based on the multi-temporal data shown in Figrate (in m/yr) of the crown and the advancement rate of a major ow lobe in S1 are reported(S4) are reported in b).Osservatorio Vesuviano, Universit della Calabria, Universit diNapoli, and INGV were also appreciated. We thank Vincenzo Albanese(Dipartimento della Protezione Civile) for the access permission to thestudy area for the ground surveys. The conclusions of our studymay notrepresent the ofcial view of our institution and/or of Dipartimentodella Protezione Civile on the Montaguto landslide.

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Tracking and evolution of complex active landslides by multi-temporal airborneLiDAR data: The Montaguto landslide (Southern Italy)1. Introduction2. Geological setting3. Data acquisition and pre-processing4. Morphometric parameters and analytical methods5. Results and discussion5.1. ML morphological and structural features5.2. Temporal and spatial variability of the ML morphometric parameters5.3. Vertical and horizontal displacements

6. ConclusionsAcknowledgmentsReferences