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Article

UAV and Airborne LiDAR Data for Interpreting Kinematic Evolution of Landslide Movements: The Case Study of the Montescaglioso Landslide (Southern Italy)

by
Roberta Pellicani
1,2,*,
Ilenia Argentiero
1,
Paola Manzari
3,
Giuseppe Spilotro
1,2,
Cosimo Marzo
4,
Ruggero Ermini
1 and
Ciro Apollonio
5
1
Department of European and Mediterranean Cultures, University of Basilicata, Via Lanera, 75100 Matera, Italy
2
National Research Council, CNR-IREA, Via Amendola 122/D, 70125 Bari, Italy
3
Italian Spatial Agency, ASI, Via del Politecnico, 00133 Rome, Italy
4
Italian Spatial Agency, ASI, Contrada Terlecchia, 75100 Matera, Italy
5
Dipartimento di Ingegneria Civile, Ambientale, del Territorio, Edile e di Chimica, Politecnico di Bari, 70125 Bari, Italy
*
Author to whom correspondence should be addressed.
Geosciences 2019, 9(6), 248; https://doi.org/10.3390/geosciences9060248
Submission received: 15 April 2019 / Revised: 12 May 2019 / Accepted: 27 May 2019 / Published: 3 June 2019
(This article belongs to the Section Natural Hazards)
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Abstract

:
Airborne remote sensing systems are increasingly used in engineering geology and geomorphology for studying and monitoring natural hazardous scenarios and events. In this study, we used two remote sensing monitoring techniques, i.e., light detection and ranging (LiDAR) and unmanned aerial vehicles (UAV) to analyze the kinematic evolution of the Montescaglioso landslide (Basilicata, Southern Italy), a large rain-triggered landslide that occurred in December 2013. By comparing pre- and post-event LiDAR and UAV DEMs and UAV orthomosaics, we delineated landslide morphological features and measured horizontal displacements and elevation change differences within landslide body. Analysis of two subsequent post-events digital terrain models (DTMs) also allowed the evaluation of the evolutionary behavior of the slope instability, highlighting no signs of reactivation. The UAV-derived digital surface models (DSMs) were found consistent with the LiDAR-DTMs, but their use was in addition highlighted as highly effective to support geomorphic interpretations and complement LiDAR and field-based data acquisitions. This study shows the effectiveness of combining the two UAV-LiDAR methodologies to evaluate geomorphological features indicative of the failure mechanism and to interpret the evolutionary behavior of the instability process

1. Introduction

The use of appropriate technologies for landslide mapping and monitoring is of paramount importance in reducing risk of landslide disaster. There are many available techniques (field-based geodetic, geotechnical, geophysical techniques, and remote sensing), but the selection of the most appropriate, or combination of different techniques, depends on multiple factors, such as areal extent, geostructural features, and velocity rate [1,2,3,4]. In the last years, airborne remote sensing solutions are increasingly used in a wide range of applications thanks to the technological development of platforms and sensors and their improved efficiency in data acquisition [5,6].
For this reason, numerous applications of light detection and ranging (LiDAR) and unmanned aerial vehicles (UAVs) have been carried out in different fields, such as: (a) civil engineering [7,8], (b) geomorphology [9,10,11,12,13], (c) cultural heritage [14,15,16], (d) ecology [17,18], (e) hydrology [19,20], etc.
The use of LiDAR and UAVs has proved to be very useful in the risk assessment, monitoring, early warning systems, and post event phase of natural hazards such as landslides, earthquakes [21,22,23], volcanic eruptions [24,25], and meteorological events [26,27,28]. In unstable areas, the use of LiDAR and UAVs allows the acquisition of high-resolution imagery and 3D information on ground surface, with many advantages with respect to classical field-based techniques (i.e., total stations, GPS, terrestrial LiDAR scanning). Furthermore, both techniques can be efficiently adopted for a rapid assessment and mapping of phenomena in post-event phase, and therefore to gather information on their magnitude, spatial distribution, and temporal evolution, as well as provide images in unprecedented detail, in dangerous and unreachable areas [6,12]. The multitemporal acquisition and processing of high-resolution digital terrain models (DTM) of landslides allows identification of morphological changes and weakness zones, estimation of volumes of removed or accumulated materials, and monitoring of the kinematic evolution of the instability process [29,30].
Airborne LiDAR is widely used to generate high-resolution DTMs for terrain analysis, instability phenomena assessment [29], small landslide susceptibility assessment [31], and post-failure landslide mechanisms [1]. Nowadays, this technology is very efficient and accurate. Indeed, a detailed planning flight allows for excellent results to be obtained during data acquisition, and therefore to derive very high resolution DTMs, in the order of decimeter. Furthermore, if combined with ground surface imagery, it offers various advantages, such as orthophoto generation, providing more detailed information of the instability phenomenon. Due to elaborate programming and processing phases, airborne LiDAR is not appropriate for near real-time monitoring of instability phenomena and, due also to the cost of this technique, multitemporal LiDAR surveys are not commonly used for analyzing the kinematic evolution of landslides [1,6,8,31,32].
The UAV platforms are flexible and adaptable in various contexts and ensure access to remote and dangerous areas without risks for pilots and technicians. UAV systems with the miniaturization of on-board equipment and, in particular, equipped with standard digital camera and GPS allows for the collection of multitemporal sets of very high-resolution images, in relation to flight configuration, climatic conditions, and surface texture [33]. Moreover, the use of image post-processing procedures, such as structure from motion (SfM) and multi-view stereopsis (MVS) [34,35,36,37,38], allows for conversion of hundreds of overlapping images into 3D point clouds, DTMs, and orthomosaics with centimeter resolution [39]. In addition, thanks to the repeatability of measures, the cost-effectiveness, the timesaving data acquisition and processing, and the performance improvement, these photogrammetric products are increasingly used for multitemporal surveys and for managing of an emergency following a landslide occurrence. Nevertheless, the use of UAVs could be limited by weather conditions, especially in emergencies, and flight regulations [6,28,40,41,42,43].
In this paper, a combined geomorphological and topographical analysis of the Montescaglioso landslide (Basilicata, Southern Italy), based on integrating two remote sensing monitoring techniques, i.e., LiDAR and UAV, is presented. The aim of this study is to interprete the landslide kinematics, by evaluating the geomorphological features of failure mechanism and the evolutionary behavior of landslide in the post-event phase, indicative of the activity state. The interpretation of LiDAR and UAV survey products was supported by a wide dataset deriving from a traditional, geotechnical and topographical, measurement campaign. In particular, the multitemporal airborne LiDAR surveys were performed pre-event in July 2013 and post-event in December 2013 and November 2016. Firstly, the two 2013 LiDAR DTMs were compared in order (i) to detect the morphological features of the landslide, in terms of topographic surface changes; (ii) to evaluate horizontal displacements of well recognizable points within the landslide body; and (iii) to model the spatial distribution of the elevation differences. Subsequently, four UAV surveys were performed, from December 2013 to February 2014, to produce a high-resolution digital surface model (DSM) and an orthomosaic. The first was used to map the morphological features of the landslide, in combination with December 2013 LiDAR DTM, and the second was used for the visual inspection of the failure kinematics and terrains in critical points not otherwise accessible, such as the landslide toe. Finally, a comparison between the two post-event LiDAR DTMs, aimed at a visual interpretation of morphological changes of the landslide during the three years after the first movement and a quantification of elevation differences within the unstable area, was carried out.

2. The Montescaglioso Landslide

2.1. Landslide Setting

The studied landslide is located along the south-western (SW) hillslope of Montescaglioso (Figure 1), a small town near Matera (Basilicata region, Southern Italy). The morphology of this area is characterized by faulted monoclines produced by an intense and widespread distensive tectonics towards the south-east (SE) and by fluvial processes (Figure 1). The hillslope affected by the landslide ends in an alluvial plane, at an elevation of about 60 m a.s.l.
In particular, the whole Montescaglioso area is affected by widespread instability [44,45], mostly resulting from active distensive tectonics concentrated in the SW sector (the left side of the Bradano river valley), where very ancient landslides are located [46]. This morphology contributes to an anisotropy of the mass related to strength and permeability, which will be very relevant in the kinematics of landslides.
The stratigraphic sequence of the hillslope affected by instability and surrounding area emerged from several boreholes, carried out previously and subsequently of the landslide event, consists of (i) clayey substrate (“Argille Subappennine” formation), outcropping at the top of the town (at an elevation of about 350 m a.s.l.) and continuously sloping towards the river Bradano alluvial plain (at an elevation of about 60 m a.s.l.); (ii) regressive detrital sequence (“Sabbie di Monte Marano” formation); and finally (iii) terraced deposits constituted by marine conglomerates.
Hydrological and hydrogeological conditions of the SW hillslope of Montescaglioso are characterized by two main natural drainage networks, “Capoiazzo” and “Cinquebocche” streams (Figure 1) and by a continuous cover of variable depth, consisting of the remains of the old coarse sediments (belonging to the Sabbie di Monte Marano” formation) or new debris (marine conglomerates, but also due to human activities) over the impermeable clayey basement, belonging to the “Argille Subappennine” formation. This continuous bedrock constitutes the bed of a continuous aquifer, hosted in the coarse covers. The continuous flow of groundwater over geological times has led to an increased level of permeability just above the clayey bed. Moreover, the study area, over the last 50 years, has been affected by diffuse urban changes (e.g., urbanization, increase in craft activities, road construction) which have modified substantially the natural drainage networks and have produced, consequently, significant changes in the hydrologic balance (mainly the runoff and infiltration rate) with an increase of the infiltration component.
The Montescaglioso landslide occurred on 3 December 2013 after heavy rainstorm. The landslide triggered after 56 h of continuous rain, from 30 November to 2 December. This intense meteorological event caused during the three consecutive days 11, 125, and 21 mm/day of rainfall, respectively. The large failure started moving along the SSW-facing slope of Montescaglioso municipality and caused severe failures along the road (“Montescaglioso-Piani Bradano”) connecting Montescaglioso to SP175 provincial road and damaged some buildings for residential and productive use, such as supermarket and marble industry (Figure 2). The landslide extends from at an elevation of about 200 m in the main crown area to 100 m of elevation at the toe, located in Capoiazzo stream, with a total length of about 1000 m, a maximum width of 850 m, a areal extent of about 420,000 square meters and a mobilized soil volume of about 8 million cubic meters.
Despite the wide extension of the landslide body, no casualties occurred.

2.2. Landslide Field Surveys and Monitoring

An intensive plan of surveys and monitoring activities was immediately activated after the landslide triggered in order to reconstruct the landslide kinematics, and some of these are still in place, within both the unstable area and in the surroundings, for monitoring the evolution of landslide movement. In this section, the major results of several techniques, which were used to monitor morphological modifications caused by the landslide, are presented. We do not attempt a systematic description of all monitoring techniques (device type and location, data elaboration, graphs, and numerical results, etc.) because it would be beyond the purpose of the paper. A summary of the main activities and results is provided in Table 1.
First, a geomorphological map was compiled by the CNR-IRPI (National Research Council-Research Institute for Hydrogeological Protection) research by means of field surveys aided by visual analysis of post-event terrestrial and helicopter photographs [46,47]. Main and minor scarps, uphill-facing scarps, cracks, and ponds were mapped as the main geomorphological features (Figure 2). In order to evaluate the activity state of the landslide after the movement of 3 December 2013, the evolution of superficial and deep displacements was monitored, immediately after the event, by several research groups [45,46,47,48,49,50,51,52,53,54,55].
Stratigraphical and hydrogeological analyses [52,53] were performed by using data deduced from a considerable database of boreholes and wells covering the landslide area and the surrounding urbanized territory (Figure 2). Moreover, TDR (time-domain reflectometer) monitoring, gamma-log measures, and borehole video inspections were performed with the aim to identify the location of sliding surface, since the previous inclinometer monitoring had not provided relevant information about this. In particular, the main indications were derived from the boreholes located in the middle-lower part of the landslide body, where the stratigraphic logs and TRD measures highlighted anomalies at a depth of 37–38 m, compatible with a deep shear disorder and congruent also with the stratigraphic border between the sedimentary cover and blue clays. In upper boreholes, the failure surface was considered coinciding with the top of the subappennine clay layers. Finally, a surface displacement monitoring was carried out, continuously from January 2014 to April 2014 and discontinuously until March 2016, by means of single-beam lasers, characterized by millimeter accuracy for controlling the landslide movement at a selected point [53]. Two targets located in the lower part of the hillslope, inside and outside of the unstable area, were used for evaluating the trend of displacement rates. It emerged in the absence of significant displacements for the target inside the landslide and negligible millimeter displacements after relevant rainfalls for the target outside the landslide [52].
The geometrical and kinematic characteristics of landslide movement were detected mainly using topographic, inclinometer, and remote sensing monitoring (Table 1). The results of all the monitoring techniques were coherent. The landslide kinematics was characterized by different directions of movement and temporal succession. In general, a first movement involved the main body of landslide, as also detected in the geomorphological map by field evidences, with displacements exceeding 10 m with SSW main direction and a second minor and retrogressive phenomenon was triggered northeast of the unstable area with a dominant SSE direction. After the first initiation of the landslide, none or negligible displacements (in the order of few millimeters) were recorded. Therefore, the landslide immediately after the paroxysmal event showed a state of equilibrium that was characterized by the absence of deformative and displacement trends.
Although several datasets were collected during the last five years using remote and in situ landslide monitoring to characterize the landslide area, this study is mainly focused on the use of LiDAR and UAV surveys for a better understanding of the landslide kinematics and evolution immediately after the 2013 triggering event and the current activity state.

3. Data Collection and Methodology

The LiDAR and UAV surveys, carried out at different times, were used for producing high-resolution DTMs and orthophotos of the landslide area.
The multitemporal airborne LiDAR survey was carried out in a pre-event aerial mission (July 2013) and two post-event missions (7 December 2013 and 29 November 2016). The pre-event airborne LiDAR survey was commissioned by Basilicata Region and used for producing five-meter DTMs of the whole region, available on the geoportal of Basilicata Region [56].
The post-event airborne LiDAR surveys were commissioned by the municipality of Montescaglioso to collect data about the landslide evolution and used for producing one-meter DTMs (summary in Table 2).
In order to cover the landslide four acquisitions campaigns in four dates between 21 December 2013 and 5 February 2014 were planned. The meteorological conditions during these four dates were from sunny to sparse clouds and wind speed always less than 15 km/h (summary in Table 3).
The elaboration of images was performed using Agisoft’s PhotoScan software, that uses SfM techniques for the reconstruction of the scene [57] on the basis of a large number of overlapped photos. A summary of LiDAR and UAV products is provided in Table 4.
Firstly, the two 2013 high-resolution LiDAR DTMs, (i.e., jul2013DTM (resampled at 1 m) and dec2013DTM), were compared in order (i) to detect the morphological features of the landslide, in terms of topographic surface changes; (ii) to evaluate horizontal displacements of well recognizable points within the landslide body; and (iii) to model the spatial distribution of the elevation differences.
The first analysis was carried out by depicting the two DTMs as stretched greyscale images, which highlight morphological disturbances much more than a hill-shade image [59]. The visual comparison of the images revealed the exact geometry of the landslide body, the main geomorphological features, and aided to reconstruct the kinematic evolution of the landslide. The horizontal displacements were obtained quantitatively by selecting about 30 GCP, corresponding mainly with structures, quite uniformly distributed in the landslide area, and by outlining, for each point, the displacement vector that represents the distance and direction, at that point of the landslide movement along a straight line from the initial position (in jul2013DTM) to the final position (in dec2013DTM). Finally, the elevation change within the landslide body was evaluated by using a GIS (geographic information system) algorithm for calculating the differences among raster. In particular, the differences between dec2013DTM and jul2013DTM made it possible to identify the spatial distribution (cell by cell) of the vertical displacement rate in the unstable area.
Subsequently, the UAV survey, performed from 21 December 2013 to 5 February 2014, produced high-resolution DSM and orthophotos (Figure 3), which were used, respectively, for mapping the landslide morphological features, in support of December 2013 LiDAR DTM, and for better interpreting the failure kinematics at critical points not otherwise accessible, such as the landslide toe. Finally, a comparison between the two post-event LiDAR DTMs (i.e., dec2013DTM and nov2016DTM) that was aimed at individuating any sign of landslide movement evolution three years after the first movement was carried out. This analysis was based on a visual interpretation of morphological changes of the landslide and on a quantification of elevation differences within the unstable area.

4. Landslide Kinematic Interpretation: Results and Discussion

The comparison between jul2013DTM (Figure 4a) and dec2013DTM (Figure 4b) revealed the main morphological features of landslide body. In particular, multiple scarps and deep fractures were recognized, which also emerged by the field surveys carried out immediately after the landslide movement. By overlapping the dec2013DTM with the morphological features extracted from the geomorphological map produced by CNR-IRPI [47] (Figure 4c), it emerged that the surface deformations observable from dec2013DTM are not completely overlapping and comparable with these tracks. These discrepancies could be evaluated more clearly in the right sector of the central part of the landslide body (Figure 4d–f). In particular, in the geomorphological map, main and minor scarps, uphill-facing scarps, distensive cracks, and cracks with evidence of strike-slip movement were recognized, also thanks to the field surveys. This differentiation among the geomorphological features is more difficult to obtain only by means of the visual interpretation of a stretched greyscale DTM image. Nevertheless, the use of a georeferenced high-resolution image allows a more reliable localization of the surface deformation tracks, as compared with the use of visual transposition techniques of the tracks on pre-existing maps.
From the visual analysis of dec2013DTM in combination with the field observations, three main scarps, and therefore three parts of the landslide body, were recognized (Figure 5a), unlike other interpretations [49,54] which had recognized only two main body. The first, crossing and cutting diagonally the “Montescaglioso-Piani Bradano” road, in the lower part of the landslide, was characterized by a shear surface with an exposed front of about 5 m. It caused the failure and toppling of the road support wall (scarp no.1 in Figure 5b). The second was located in the middle part of the landslide area, corresponding to the destroyed supermarket. In addition, in this case, it involved transversally the previous mentioned road, with the formation of a trench about 5 m deep (scarp no.2 in Figure 5b). Finally, the latter scarp was individuated in the upper left-hand sector of the unstable area (scarp no.3 in Figure 5b). The accumulation area of the landslide was recognized downslope of the confluence point of the Cinquebocche ditch inside the Capioazzo ditch, which corresponds with the track of a tectonic structure resulting from previous (in the late Pleistocene) distensive tectonics. The landslide translation, in this part, caused the occlusion of the stream bed, its lifting, and the formation of water ponds (Figure 5a). The first movement started, almost certainly, from the lower scarp. This is attested by some evidence of instability noted a few times before the parossistic event. In particular, oblique cracks on the “Montescaglioso-Piani Bradano” roadway which correspond to the next shear surface, and an uplift on the terrain plain a few meters at east of the road which correspond to the door of a henhouse, were observed.
A better interpretation of landslide kinematics was derived by combining the morphological evidences that emerged from the dec2013DTM with the quantitative analysis of horizontal displacements and the spatial distribution of elevation differences within the landslide body. The displacement values, recognized for about 30 points within the landslide (Figure 5b), ranged from 2.5 m in the upper part to 17.6 m in the middle right-hand sector of landslide body. In particular, the lowest values found correspond to the upper left-hand scarp of the landslide (scarp no.3), where the vectors are slightly north-north west to south-south east oriented (Figure 5c). Mean values, from 9 m to 11 m, were measured immediately downslope of the middle scarp at the left side (Figure 5d). While highest values surveyed correspond to the middle sector at the right side (Figure 5d) and the downslope, scarp no.1, of the landslide body in the left-hand stretch (Figure 5e). It was not possible to detect well recognizable GCP at the landslide toe. The orientation of the displacement vectors in the central and lower sectors of the landslide is variable, northeast to southwest, in the right-hand sector, and north-to-south in the left-hand sector. The morphological features, observed and mapped, combined with the surficial displacement directions highlight the coalescence of several lump movements inside the overall landslide body.
The elevation differences between the 2013 LiDAR DTMs have provided spatially distributed information on vertical displacements. This analysis highlighted a widespread lowering of the topographic surface in the upper and central part of the unstable area and a significant uplift at the landslide toe, along the left bank of the Capoiazzo ditch (Figure 6a). The elaboration of elevation differences also provided useful information on the landslide geometry by highlighting the exact boundaries of the unstable area. A maximum ground surface lowering of 10 m, corresponding to the trenches formed in the right sector down the second scarp, and a maximum uplift of about 20 m, at the right side of the accumulation area in corresponding to the Capoiazzo ditch, were recorded. This trend was also recognized by Amanti et al. 2014 [48]. By combining the horizontal displacements with the elevation change map, it was observed that the highest horizontal displacements correspond to vertical displacements in the order of about 1–2 m (Figure 6b). Therefore, the landslide movement is mainly characterized by a strong horizontal component, especially in the upper and central part of the landslide.
Definitely, the landslide shows the evidences of a progressive failure, characterized by a rigid translation of a first lower plate, with main direction of movement towards SSW. This movement has created the conditions for a distensive propagation of the failure towards the upper part of the slope, Consequent translation of the other two bodies was accompanied by the formation of several secondary scarps, distensive cracks, and trenches. In particular, the mobilization of these other two portions of the landslide body has produced thrust on the crown of the first body, which obliterated the scarp of the first mobilized landslide portion, not clearly visible from the LiDAR DTM. The landslide stopped on the left bank of the Capoiazzo ditch, with compressive and shearing zones and kinematics mainly trascurrent, and subsequently lifting, of about 20 m.
The DSM obtained by UAV flight is consistent with the LiDAR DTM obtained after the landslide event. Since the elaboration of the LiDAR data generates a DTM (Figure 7a), whereas, the elaboration of the UAV data gives a DSM (Figure 7b), the difference between the two sets of data can be used to highlights some less evident or uncertain structures occurring in the area. This difference between the UAV-DSM and LiDAR-DTM is overall useful, in the case of unvegetated or uncovered areas, to support the interpretation of LiDAR data, in particular, in areas where geomorphological features are less evident or further spatial information are needed due to the presence of depressions, for example local water-filled depressions (Figure 8a,b).
Moreover, the orthophotos analysis allowed the interpretation of failure kinematics in critical and not accessible areas, thus offering a tool for a panoptic view of the landslide for planning further investigations on particular areas. The large dimension of the landslide body, the overlap of main morphological lineation with water drainage channels and ditches, and an impressive amount of mud everywhere have not allowed in situ inspection of remote points, in particular at the toe of the landslide.
Considering the landslide accumulation area (Figure 8a), Figure 8b shows an area characterized by low and sparse vegetation, the landslide toe and a local water-filled depression that can be observed on the DSM by UAV only. Figure 8c shows the difference between the UAV-DSM and LiDAR-DTM of the selected area. The image of the difference highlights the geomorphological features of the unvegetated areas and supports the interpretation of the LIDAR data.
In this area, the orthophotos and subsequent detailed photos by UAV highlighted that the unbalanced horizontal component of load for the landslide body generated a passive failure [60], and its kinematic was revealed as transcurrent, rather than as inverse fault which more commonly happens (Figure 8d).
Finally, the comparison between two LiDAR DTMs, performed in December 2013 (dec2013DTM) and November 2016 (nov2016DTM), provided useful information on the movement evolution three years after the landslide event. This analysis was carried out by means of visual interpretation of the morphological features of the unstable area in the two DTM hillshade images (Figure 9a,b), in order to individuate the main morphological changes, and by producing the map of elevation variations that occurred in the landslide body (Figure 9c). As highlighted in Figure 9b, the main morphological modifications of the landslide area are concentrated along the borders. In particular, erosional processes have shaped and slightly obliterated scarps and trenches, especially in the upper portion of the area (blue arrows in Figure 9b); while in the lower part, the Capoiazzo stream has eroded and incised the landslide toe (red box in Figure 9b). Nevertheless, in this area, an incision along the eastern bank of the Piani-Bradano road, immediately downslope of the already damaged section, is evident in the nov2016DTM (red arrow in Figure 9b). Finally, man-made modifications, carried out in the months following the event in order to restore severely damaged road sections or private areas, also emerge from the DTM comparison (blue boxes in Figure 9b). These morphological changes are confirmed by the spatial distribution of elevation differences between two DTMs (Figure 9c). In effect, the main uplift zones coincide with the areas affected by man-made restoration works, while the main lowering areas are located along the Capoiazzo ditch and at the landslide toe. Overall, this last analysis highlights an evolutionary behavior of the landslide which has not been very considered. No signs of movement reactivation have been highlighted.

5. Conclusions

In this study LiDAR and UAV data were used for interpreting the geomorphological and kinematic evolution of failure mechanism of the Montescaglioso landslide (Basilicata, Southern Italy), a large landslide that occurred on 3 December 2013 after three days of intense rainfalls. LiDAR and UAV surveys, carried out at different times, were used for producing high-resolution DTMs and orthophotos of the landslide area. In particular, the multitemporal airborne LiDAR surveys were performed pre-event in July 2013 and post-event in December 2013 and November 2016.
The UAV-DSM was found coherent with LiDAR DTM and photos from UAV have supported the interpretation of the failure kinematics, especially, in critical (not visible) areas. The landslide shows the evidences of a progressive failure, characterized by a rigid translation of a first lower part, which has created the conditions for a distensive propagation of the failure towards the upper part of the slope, with a consequent translation of other two landslide bodies. Finally, the comparison between the two post-event LiDAR DTMs has shown no evidence for landslide reactivation.
The results of this study show the usefulness of the two combined methodologies to extract and evaluate the geomorphological features indicative of the failure mechanism. The effectiveness of decisions making immediately after the occurrence of a disastrous event is also linked to the ability to obtain exact diagnoses in a very short time. This makes it possible to direct emergency actions in the most correct way. From this point of view, UAV in the case of the Montescaglioso landslide was revealed as an irreplaceable tool. The advantages of using UAV systems, as compared with LiDAR, include the possibility of obtaining a DSM relatively quickly, and in the case of in-depth analysis the possibility of improving the spatial and altitude resolution with additional flights at lower altitudes. The other significant advantage is that in LiDAR DTMs the detailed information visible in the photos acquired by a drone is lost. Overall this detailed information is kept in the mesh and the texture at the end of the 3D reconstruction process. Disadvantages of the UAV as compared with the LiDAR certainly include the smaller surface that can be covered with a flight. The LiDAR acquisition system allows better geo-referencing due to the presence of an RTK-GPS. When the ground is widely covered with vegetation it is not possible to elaborate an accurate DTM of the area. The analysis and the monitoring of evolutionary behavior of the landslide could be further improved by integrating the techniques examined here with other kinds of remote sensing techniques, such as PS-InSAR.

Author Contributions

R.P. conceived the methodological path of the study; R.P., G.S., and R.E. outlined the geological, geomorphological and hydrogeological setting of landslide, and described previous surveys; I.A., C.A., and P.M. performed the state of art; I.A. managed LiDAR data; P.M. and C.M. performed UAV survey and elaborated UAV data; R.P. handled GIS elaborations. All authors analyzed the results and read and approved the final manuscript.

Acknowledgments

Authors wish to thank the Basilicata Region and Montescaglioso Municipality for having shared the LiDAR data.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Hillshaded digital terrain models (DTM) of Montescaglioso area, with the location of the landslide.
Figure 1. Hillshaded digital terrain models (DTM) of Montescaglioso area, with the location of the landslide.
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Figure 2. Orthophoto of Montescaglioso area, with the location of the boreholes and main geomorphological features of the landslide. In inserts, photos of the most relevant damages caused by the landslide.
Figure 2. Orthophoto of Montescaglioso area, with the location of the boreholes and main geomorphological features of the landslide. In inserts, photos of the most relevant damages caused by the landslide.
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Figure 3. Orthophoto of the Montescaglioso landslide obtained by the UAV surveys. The red lines represent the planar outline of the landslide body.
Figure 3. Orthophoto of the Montescaglioso landslide obtained by the UAV surveys. The red lines represent the planar outline of the landslide body.
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Figure 4. High-resolution LiDAR DTMs: (a) DTM produced pre-event in July 2013 (jul2013DTM), (b) DTM realized post-event in December 2013 (dec2013DTM), (c) dec2013DTM overlapped with the morphological features extracted from the geomorphological map produced by CNR-IRPI [47]; a detail of eastern side of the landslide body, for highlighting the discrepancies between signs from geomorphological map and visible signs from LiDAR survey: (d) jul2013DTM, (e) dec2013DTM, (f) dec2013DTM overlapped with the geomorphological features.
Figure 4. High-resolution LiDAR DTMs: (a) DTM produced pre-event in July 2013 (jul2013DTM), (b) DTM realized post-event in December 2013 (dec2013DTM), (c) dec2013DTM overlapped with the morphological features extracted from the geomorphological map produced by CNR-IRPI [47]; a detail of eastern side of the landslide body, for highlighting the discrepancies between signs from geomorphological map and visible signs from LiDAR survey: (d) jul2013DTM, (e) dec2013DTM, (f) dec2013DTM overlapped with the geomorphological features.
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Figure 5. Dec2013DTM with indication of the sequence of the mobilization of the three landslide bodies and of the accumulation area (a), Dec2013DTM with horizontal displacement vectors and delineation of the three main scarps (b), detail of displacement vectors downslope the left-hand scarp no.3 of the landslide (c), detail of displacement vectors downslope the middle scarp no.2 (d), detail of displacement vectors downslope the scarp no.1 (e).
Figure 5. Dec2013DTM with indication of the sequence of the mobilization of the three landslide bodies and of the accumulation area (a), Dec2013DTM with horizontal displacement vectors and delineation of the three main scarps (b), detail of displacement vectors downslope the left-hand scarp no.3 of the landslide (c), detail of displacement vectors downslope the middle scarp no.2 (d), detail of displacement vectors downslope the scarp no.1 (e).
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Figure 6. Spatial distribution of the elevation differences, derived by subtracting jul2013DTM from dec2013DTM (a), and overlapping of the spatially distributed elevation differences and horizontal displacement vectors (b).
Figure 6. Spatial distribution of the elevation differences, derived by subtracting jul2013DTM from dec2013DTM (a), and overlapping of the spatially distributed elevation differences and horizontal displacement vectors (b).
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Figure 7. Hillshaded DSM obtained from the UAV surveys and landslide outlines (red lines) (a); image that describes the difference between UAV-DSM and LiDAR-DTM (dec2013DTM) in meters, red lines represent landslide outlines (b).
Figure 7. Hillshaded DSM obtained from the UAV surveys and landslide outlines (red lines) (a); image that describes the difference between UAV-DSM and LiDAR-DTM (dec2013DTM) in meters, red lines represent landslide outlines (b).
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Figure 8. Detail at the landslide toe of the orthophoto realized by UAV (yellow frame) (a), zoom image from a photo (b), zoom of the image difference between DSM-UAV altimetric and LiDAR-DTM (c), and panoramic view (d).
Figure 8. Detail at the landslide toe of the orthophoto realized by UAV (yellow frame) (a), zoom image from a photo (b), zoom of the image difference between DSM-UAV altimetric and LiDAR-DTM (c), and panoramic view (d).
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Figure 9. Comparison between two LiDAR DTMs performed post-event, in December 2013 (dec2013DTM) (a), and November 2016 (nov2016DTM), with the indication of the main morphological modifications within the landslide area (b), and elevation variations occurred in the landslide body until November 2016 (c).
Figure 9. Comparison between two LiDAR DTMs performed post-event, in December 2013 (dec2013DTM) (a), and November 2016 (nov2016DTM), with the indication of the main morphological modifications within the landslide area (b), and elevation variations occurred in the landslide body until November 2016 (c).
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Table
Table 1. Summary of surveys and monitoring activities carried out on the Montescaglioso landslide.
Table 1. Summary of surveys and monitoring activities carried out on the Montescaglioso landslide.
Research GroupActivityResult
CNR-IRPIField surveys and aerial photo interpretationGeomorphological map
CNR-IRPITopographic monitoring with total stationSuperficial displacements of 26 points
DST-UNIFI 1 and CNR-IMAA 2GBInSAR 3Superficial displacements
DST-UNIFI
DST-UNIROMA 4		InSAR with COSMO-SkyMed imagerySuperficial displacements
CNR-IRPIInclinometer monitoringDeep displacements
DiCEM-UNIBAS 5Stratigraphical and hydrogeological analysesStratigraphic sequence, thickness of saturated aquifer
DiCEM-UNIBASTDR monitoring, gamma-log measures and borehole video inspectionsLocation of sliding surface
DiCEM-UNIBASLaser monitoringSuperficial displacements
1 the Department of the Earth Science of University of Firenze, 2 the National Research Council Institute of Methodologies for Environmental Analysis, 3 ground-based interferometric synthetic aperture radar, 4 the Department of Earth Sciences of University of Roma Sapienza, 5 the Department of European and Mediterranean Cultures of University of Basilicata.
Table
Table 2. Summary of airborne light detecting and ranging (LiDAR) information.
Table 2. Summary of airborne light detecting and ranging (LiDAR) information.
LiDAR SummaryTechnical Features
VehicleTwin engine P68B Victor Vulcanair aircraft
Sensors80 Mp resolution aerial camera-Phase One IXA 180
RIEGL LMS-Q680i
Positioning and orientation system
Pulse repetition400 kHz
Scan speed185.2 Km/h
GSD0.9 m
Flight speed180 Km/h
Flight heightAbout 9000 Km
Post-processing softwareRIEGL
Table
Table 3. Summary of unmanned aerial vehicles (UAV) survey information.
Table 3. Summary of unmanned aerial vehicles (UAV) survey information.
UAV SummaryTechnical Features
VehicleExpanded polypropylene motor glider “Bixler”
SensorsSingle board computer Ardupilot
16 Mp resolution camera Canon A2300
Shutter speed1/2000 s
Acquisition rate4 s
Flight speed36 Km/h
Flight height130 m
Number of images1000
Post-processing softwareAgisoft’s PhotoScan
Table
Table 4. Summary of LiDAR and UAV products.
Table 4. Summary of LiDAR and UAV products.
SurveyPre-event LiDAR1st Post-event LiDAR2nd Post-event LiDARUAV
DateJuly 20137 December 2013 (dec2013DTM)29 November 2016 (nov2016DTM)21 December 2013 to 5 February 2014
ProductsOrthophoto
DTM (jul2013DTM)		Orthophoto
DTM (dec2013DTM)		Orthophoto
DTM (nov2016DTM)		Orthophoto
DSM
Orthophoto
Spatial resolution0.2 m0.12 m0.12 m0.5 m
DTM/DSM
Planar accuracy0.2 m *0.3 m *0.3 m *0.5 m
Vertical accuracy0.3 m *0.3 m *0.3 m *1 m
Resolution 5 m *1 m *1 m *
* [58].

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Pellicani, R.; Argentiero, I.; Manzari, P.; Spilotro, G.; Marzo, C.; Ermini, R.; Apollonio, C. UAV and Airborne LiDAR Data for Interpreting Kinematic Evolution of Landslide Movements: The Case Study of the Montescaglioso Landslide (Southern Italy). Geosciences 2019, 9, 248. https://doi.org/10.3390/geosciences9060248

AMA Style

Pellicani R, Argentiero I, Manzari P, Spilotro G, Marzo C, Ermini R, Apollonio C. UAV and Airborne LiDAR Data for Interpreting Kinematic Evolution of Landslide Movements: The Case Study of the Montescaglioso Landslide (Southern Italy). Geosciences. 2019; 9(6):248. https://doi.org/10.3390/geosciences9060248

Chicago/Turabian Style

Pellicani, Roberta, Ilenia Argentiero, Paola Manzari, Giuseppe Spilotro, Cosimo Marzo, Ruggero Ermini, and Ciro Apollonio. 2019. "UAV and Airborne LiDAR Data for Interpreting Kinematic Evolution of Landslide Movements: The Case Study of the Montescaglioso Landslide (Southern Italy)" Geosciences 9, no. 6: 248. https://doi.org/10.3390/geosciences9060248

APA Style

Pellicani, R., Argentiero, I., Manzari, P., Spilotro, G., Marzo, C., Ermini, R., & Apollonio, C. (2019). UAV and Airborne LiDAR Data for Interpreting Kinematic Evolution of Landslide Movements: The Case Study of the Montescaglioso Landslide (Southern Italy). Geosciences, 9(6), 248. https://doi.org/10.3390/geosciences9060248

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