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Article

Rockfall Hazard Evaluation in a Cultural Heritage Site: Case Study of Agia Paraskevi Monastery, Monodendri, Greece

by
Spyros Papaioannou
1,
George Papathanassiou
1,* and
Vassilis Marinos
2
1
Department of Geology, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
2
School of Civil Engineering, National Technical University of Athens, 15772 Athens, Greece
*
Author to whom correspondence should be addressed.
Geosciences 2025, 15(3), 92; https://doi.org/10.3390/geosciences15030092
Submission received: 27 January 2025 / Revised: 23 February 2025 / Accepted: 3 March 2025 / Published: 7 March 2025
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Abstract

:
Rockfall is considered the main geohazard in mountainous areas with steep morphology. The main objective of this study is to assess the rockfall hazard in the cultural heritage site of the Monastery of Agia Paraskevi, Monodendri, in northern Greece, where a recent rockfall event occurred, destroying a small house and the protective fence constructed to protect the Monastery of Agia Paraskevi. To evaluate the rockfall potential, engineering geological-oriented activities were carried out, such as geostructurally oriented field measurements, aiming to simulate the rockfall path and to compute the kinetic energy and the runout distance. In addition, using remote sensing tools such as Unmanned Aerial Vehicles (UAVs), we were able to inspect the entire slope face and detect the locations of detached blocks by measuring their volume. As a result, it was concluded that the average volume of the expected detached blocks is around 1.2 m3, while the maximum kinetic energy along a rockfall trajectory ranges from 1850 to 2830 kJ, depending on the starting point (source). Furthermore, we discussed the level of similarity between the outcomes arising from the data obtained by the traditional field survey and the UAV campaigns regarding the structural analysis of discontinuity sets.

1. Introduction

Rockfall phenomena are classified as a type of landslide and are considered a significant hazard in mountainous areas that poses substantial risks to individuals, structures, infrastructure, and the surrounding environment [1,2,3,4,5]. Several authors [1,6,7,8] stated that rockfalls increase in frequency due to the effects of climate change. Rockfalls continue to pose a significant threat to the safe functioning of transportation infrastructure, creating dangerous conditions that can lead to road and railway damage and even fatalities [9,10,11,12,13]. It has been shown that even small amounts of rockfall can cause substantial damage and disrupt traffic, especially on railways [14].
A rockfall is a fragment of rock that becomes detached from a vertical or near-vertical cliff through sliding, toppling, or falling [15]. Rockfalls can vary in size and can travel at speeds ranging from a few meters per second to as much as 30 m per second [16,17]. Larger-scale rockfalls are classified as rock avalanches and cliff falls and typically occur on slopes with geological conditions that favor instability [18]. The movements of blocks during the falling process are in most cases unpredictable and highly influenced by the topography, the geological material, and the volume and shape of the detached block [19,20]. The exceedance of a specific threshold value of rainfall, earthquakes, freeze-thaw episodes, and human activities are considered the main preparatory and triggering factors for a rockfall [6,20].
The protection of urban areas and cultural heritage sites from rockfall events is one of the main goals for engineering geologists and engineers who work in this field. Characteristic examples of relevant studies on landmark cultural heritage sites were performed all over the world (e.g., Italy, Iraq, Greece, Peru) [21,22,23,24,25,26,27]. In recent years, remote sensing technologies e.g., Unmanned Aerial Vehicles (UAVs), have been widely employed in various geoscience fields [28], including high-resolution topographic surveys for identifying past earthquakes [29], volcanology [30], and flood event monitoring [31]. UAV technology has significantly advanced rockfall mapping and hazard assessment by enabling high-resolution data acquisition in inaccessible or hazardous areas. UAV-derived photogrammetry and 3D models of cliff faces facilitate detailed geostructural and geomechanical analyses, improving the identification of discontinuities and rockfall source zones [32]. It should be mentioned that the identification of rock mass discontinuities can be achieved using several tools and techniques such as the DCS (Discontinuities Classification and Spacing), which is an open-source Python tool version 3.8 [33]; the analysis of color changes in a 3D point cloud [34]; artificial neural network (ANN) algorithms [35]; and the DSE (Discontinuity Set Extractor) developed by [36], among others. In addition, remote sensing techniques, such as UAVs, can be used to quickly and consistently measure discontinuity properties in inaccessible areas, enhancing rockfall assessments by providing comprehensive trajectory simulation and susceptibility assessment datasets. Consequently, the increased spatial accuracy enables a more reliable assessment of rockfall hazards and impact zones, ultimately strengthening risk mitigation strategies.
For the purposes of this study, we investigated the rockfall hazard in the cultural heritage site of the Monastery of Agia Paraskevi, which was constructed in a mountainous area in Monodendri, Epirus, northwestern Greece. Having carried out a geological–geotechnical survey, it was feasible to perform a kinematic analysis and assess the main type of rockfall failure. In addition, using a 3D model, developed based on UAV-derived data, we were able to assess the mean volume of the detached blocks and the main parameters, i.e., kinetic energy, bouncing height, and runout distance, of rockfall trajectories. In addition, we measured the orientation of the discontinuity using both the geological compass and the semi-automatic procedure performed in the open-source software Discontinuity Set Extractor (DSE) [36], accessible at https://personal.ua.es/en/ariquelme/dse-discontinuity-set-extractor.html (accessed on 16 February 2025), with the aim of investigating whether there is any similarity in the results between these two methods.

Study Area

The Monastery of Agia Paraskevi is located in Zagori (geographic coordinates 39°53′12.03″ N, 20° 45′17.66″ E) in northwestern Greece, near the entrance to the Vikos Gorge in the Region of Epirus, along a vertical cliff of limestone over 100 m high (Figure 1a–c). The geological environment is predominated by thin- to medium-bedded karstified limestone. The limestone is characterized as fresh to slightly weathered and fractured.
This site is considered one of the most important tourist attractions in the area since it combines both religious monuments and hiking activities. However, as is shown in Figure 2, rockfall phenomena are frequently reported, with the most recent occurring a few years ago, in October 2021, after a heavy rainfall of >80 mm in 24 h, as per data provided by the National Observatory of Athens recorded at the meteorological stations Aspraggeloi, Tsepelovo, and Papigo at distances of 10, 6, and 9.5 km, respectively, from Agia Paraskevi Monastery. As a consequence, the wire mesh that was placed for rockfall protection was totally destroyed, luckily without any fatalities (Figure 2). This rockfall protecting netting was placed one year earlier in July 2020, after small rockfall events (max 1 m3) that occurred in March 2020 and caused damage to structures 60 and 120 m from the Monastery of Agia Paraskevi (Figure 2). It is important to point out that according to witnesses, small earthquakes of magnitude Mw5.4 and Mw4.6 occurred on 15 October 2016 (https://www.seismicportal.eu/eventdetails.html?unid=20161015_0000089 accessed on 16 February 2025) and 22 July 2021 (https://www.seismicportal.eu/eventdetails.html?unid=20210722_0000196 accessed on 16 February 2025) at an epicentral distance of 12 km and 4 km from the study area and at a depth of 10 km and 20 km, respectively, which may have reduced the strength of the discontinuities, acting as preparatory factors.

2. Materials and Methods

2.1. Methodology

The methodology typically used to assess rockfall hazard in a given area is divided into several stages (Figure 3), including (i) an engineering geology field survey consisting of geostructural and geomechanics-oriented measurements and subsequent data processing, (ii) a kinematic analysis aiming to assess the failure type mechanism, (iii) simulation of the rockfall path, and (iv) an assessment of key parameters, such as the kinetic energy and bounce height of the detached rocks (e.g., [19,20]). Both parameters depend on the initial volume, the release height, and the path topography, i.e., trajectory. In the last decade, remote sensing techniques, such as light detection and ranging (LiDAR) (e.g., [28,37,38]) and structure from motion (SfM) image processing techniques based on UAV surveys, have also been used for the evaluation of relevant hazards. The photogrammetric survey is often carried out using remotely piloted aircraft systems (RPASs), as they offer an efficient and cost-effective remote sensing platform for rockfall studies. RPASs allow for the quick acquisition of high-resolution imagery, even in hard-to-reach areas [20]. However, it is crucial for engineering geologists to validate the reliability of the RPAS-derived data and compare them with those obtained by traditional geological compass-related surveys.
The first phase of this methodology focuses on the in situ characterization of the rock mass, relying on data gathered from the field survey. Key parameters, such as the rock mass structure, discontinuity quality, joint spacing, and filling material, are quantified and described to evaluate the overall quality of the rock mass. Having classified the rock mass and defined critical parameters of its structure, i.e., the identification of the discontinuity sets and their orientation, kinematic analysis and rockfall hazard evaluation can be performed [20]. Kinematic analysis is a method employed to evaluate the stability of a rock slope through relevant information such as the identification of the slope’s discontinuities, the dip and dip direction of the discontinuities, the dip and dip direction of the slope, and the parameters of shear strength, i.e., cohesion and angle of friction, of the rock mass composing the slope, among other parameters [39].

2.2. Technical Approach

Considering the steep morphology of the rock slope, we performed a UAV campaign in October 2021 in order to be able to develop an accurate and detailed 3D model. UAV imagery was collected using a DJI Phantom 4 Pro V2.0 equipped with a 1-inch 20-megapixel sensor and an FC6310S camera of 8.8 mm in focal length and a manually adjustable aperture from F2.8 to F11. The UAV captured images, under manual control, in frontal mode, where photos are taken at a slightly oblique angle to the rock face. The overlap among the images ranged between 70 and 80%, compensating for the steep and complex terrain [40].
These 180 images were processed with AgisoftTM Metashape software v. 1.5.2 (Agisoft LLC., St. Petersburg, Russia), generating a dense point cloud dataset containing 19.05 million points with a ground resolution of 1.56 cm/pix and a reprojection error of 0.685 pix. Further processing of the point cloud data enabled the production of a detailed Digital Elevation Model (DEM) of 6.24 cm/pix and 287 points/m2 density. The 3D point cloud and corresponding model were created by intersecting matched features from the overlapping, offset images using the structure from motion (SfM) image processing technique [41]. The generated 3D point cloud was then used for a semi-automated structural analysis, enabling the extraction of information about the discontinuity sets, such as orientation and joint spacing [42,43,44,45].
Figure 4 shows the slope face of the study area (120 m high and 230 m length) and the sites where measurements of rock mass properties were conducted. In particular, site D was investigated by a traditional field survey, i.e., geological compass, while sites A, B, and C were analyzed based on the generated 3D model (point cloud).

2.3. Dataset Creation

In this study, we used a geological compass to collect data (more than 60 measurements) regarding the orientation of the discontinuities in terms of dip and dip direction. The joint compressive strength (JCS) and the joint roughness coefficient of the joints (JRC) were estimated using a Schmidt hammer and a profilometer, respectively. Finally, the filling material and the weathering conditions of the joint surfaces were also assessed. After measuring the orientation of the joints in the field, the dip and dip direction of the dominant discontinuities were determined using Dips v.8 software (Rocscience, Toronto, ON, Canada).
Given the steep morphology of the area and limited access to only the lower part of the rock face, we decided to collect data along a 20 m scanline (site D, Figure 4). While the conducted structural analysis has limitations in representing the entire rock face, it is important to emphasize that an engineering geological field survey is essential for rock mass characterization. This traditional approach is critical to accurately assess key parameters such as joint roughness and weathering, which cannot be reliably assessed by remote sensing techniques alone [20].
To detect the discontinuity sets and measure their orientation in the studied area, the generated point cloud was analyzed using the SfM-based Discontinuity Set Extractor (DSE) methodology. This semi-automated analysis was performed using the open-source software DSE (developed by [36]). The DSE is described as a supervised classification of discontinuity sets, designed to calculate the normal vector for each point based on a set of “knn” neighbors, along with its corresponding pole on a stereonet plot [36]. Applying a PCA algorithm, a test is conducted based on the tolerance concept η, which is defined as the ratio of the third eigenvalue to the total sum of the eigenvalues.
η = λ 3 λ 1 + λ 2 + λ 3
Following the recommendations provided by [36], if η exceeds a defined threshold, the examined set of points is considered non-planar and discarded. On the other hand, if it is below the threshold, the set is treated as a potentially planar surface. Afterward, the Kernel Density Estimation is applied in order to compute the density function, considering the number of principal planes and their orientations as specified by the user [42]. As an outcome of this procedure, it is feasible to assign a set to each point when the angle between the point’s normal and principal plane is smaller than a threshold value defined by the user [46]. In our case, we used the default parameters suggested by the developer, i.e., knn = 30, η = 0.2, and cone = 30°.
Based on the developed 3D model, we manually measured the volume of blocks that are likely to have been detached from the rock face, i.e., source areas. In order to achieve this, we use the open-source software CloudCompare™ (available at https://www.danielgm.net/cc/ accessed on 23 February 2025). The objective was to determine the location and dimensions of the source areas from where it is believed that blocks were detached and fell and then compute the relevant volume. This approach was achieved on the basis of the methodology suggested by [46,47]. The results obtained from the application of the presented approach at sites A, B, C, and D are described in the following sections.
Finally, 2D rockfall trajectories were simulated using Rocfall v.7 software developed by Rocscience aiming to evaluate critical parameters, i.e., the kinetic energy and bounce height of the boulder. Rocfall v.7 software performs a probabilistic simulation of rockfalls and can be used to design and evaluate the effectiveness of mitigation measures [48]. In general, a detached rock descends rapidly through the air by falling, bouncing, and rolling [49], while its trajectory is mainly determined by the slope geometry and the properties of the underlying geological formation.

3. Results

In this section, the findings from the field surveys, both traditional and UAV-based, are presented. Starting with the results of data processing obtained during both types of surveys, afterward, we are going to present the dominant type of failure, the estimated volumes of the detached blocks, and, finally, the computation of the basic parameters of rockfall trajectories.

3.1. Traditional Field Survey at Site D

This analysis identified three main sets of discontinuities, B1, J1, and J2, listed in Table 1. Based on 31 Schmidt hammer tests of a mean value of 45 rebounds for bedding and joint surfaces, we estimated the JCS as 110 MPa, while the JRC was assessed as 8. In general, there is no infilling material in the discontinuities. Adopting a value of 27° of the basic angle of friction (φb) for limestone as a conservative scenario for wet conditions, following the recommendations provided by [50], we estimated the friction of angle φ = φb + i = 36°, where i = JRClog (JCS/σn). The produced stereographic diagram is presented in the following Figure 5.

3.2. Structural Analysis Based on UAV-Campaigns

3.2.1. Site A

The 3D examined area of site A has approximate dimensions of 4 m in length and 4 m in height, covering a total area of 16 m2. The classified point cloud contains 70,109 points. The discontinuity sets at site A, as they were semi-automatically extracted based on the DSE, are three (3), and their orientations are listed in the following Table 2, while the density of the normal vector’s poles is shown in Figure 6.

3.2.2. Site B

The dimensions of the 3D studied area of site B are approximately 16 m in length and 4.5 m in height, covering an area of 72 m2. The number of points of the classified point cloud is 147,011. The discontinuity sets at site B, as they were semi-automatically extracted based on the DSE, are three (3), and their characteristics are listed in the following Table 3, while the density of the normal vector’s poles is shown in Figure 7.

3.2.3. Site C

The area of site C covered an area of 35 m2, approximately 17.5 m in length and 2 m in height. A total of 147,076 points were used to develop the 3D model and classified point cloud. The discontinuity sets at site C, as they were semi-automatically extracted based on the DSE, are three (3), and their characteristics are listed in the following Table 4, while the density of the normal vector’s poles is shown in Figure 8.

3.2.4. Comparing Traditional and 3D-Based Characterization of Rock Mass at Sites A, B, C, and D

Having measured the orientations of the main discontinuity sets with the geological compass at site D and with the DSE at sites A, B, and C, it was decided to attempt to compare their characteristics in order to examine if there is any similarity among them (Table 5).
It can be primarily stated that three discontinuity sets, bedding and two joints, were identified at site D based on the analysis of the data provided by the geological compass as well as at the three sites A, B, and C extracted by the point-cloud-based approach. The orientation of the bedding based on the geological compass measurements is relatively similar to that extracted by the DSE and characterized by low-angle, almost horizontal, surfaces. The J1 discontinuity set was also identified, as a high-angle joint with a dip direction to the east. Finally, the discontinuity set J2 is characterized as a high-angle surface, almost vertical, with a dip direction similar to the natural rock face (Figure 9). Considering that even geological compass measurements can vary in the same location, we can state that the differences between compass and UAV-derived data are not so significant, and in inaccessible or high-risk areas, the latter approach could be used as a complementary method.

3.3. Type of Failure Mechanism—Kinematic Analysis Based on Data Derived by Geological Compass

The next step in this work was to examine and define the main type of failure mechanism based on the outcome arising from the kinematic analysis. To achieve this, we took into account the slope face orientation as 77°/131° and the orientation of discontinuity sets obtained by the geological compass-derived measurements. Regarding the parameters of the shear strength of discontinuities, we adopted the scenario of φ = 36° and cohesion c = 0. As a result, it is shown that it is unlikely that a planar (Figure 10a) or wedge (Figure 10b) failure as a mechanism can be generated in the area. In addition, it is unlikely that toppling (Figure 10c) phenomena can be generated by considering the mean orientation of the J2 discontinuity set. However, as can be seen in Figure 10d, considering the results from the traditional analysis i.e., geological compass, it was concluded that 8% (2 out of 24) of the joints grouped at discontinuity set J2 can generate toppling failures like the one reported on October 2021. This percentage, resulting from the kinematic analysis performed in the software Dips by Rocscience, consisted of almost vertical joints of similar strike with a slope face that dips into the slope with a dip direction of ~310–320°.

3.4. Estimation of the Volume of the Detached Blocks and Simulation of Rockfall Trajectories

After visual inspection during the field survey, a 3D point cloud analysis was performed using CloudCompare™, aiming to determine the location and volume of the detached blocks. As is shown in Figure 11, it was feasible to detect thirty sites considered to be source areas of blocks that had already detached and fallen. The dimensions and the volume of the blocks were manually measured based on the tools provided by CloudCompare™ (Table 6). The estimated average volume V of the blocks was 1.18 m3, while the maximum volume was estimated to equal 3.3 m3, larger than the blocks mapped in the area. This can be attributed to the fact that i) the block was not detached as a single boulder from the slope face but had instead been fragmented into two or more smaller blocks beforehand, and/or ii) it detached as a single block and broke into smaller pieces upon descent. In addition, according to the field survey, the maximum volume of the fallen blocks was estimated to be 1.5 m3.

3.5. Simulation of Rockfall Trajectory

The final step in the rockfall hazard assessment is the simulation of the trajectories of the detached blocks identified using the 3D approach. The determination of these rockfall trajectories in this study was achieved by considering the slope geometry extracted from the data provided by the developed point cloud. The aim of this analysis is to assess the critical parameters of the rockfall, such as kinetic energy and impact height, and to evaluate the runout distance along the path between the Monastery of Agia Paraskevi and the entrance to the Vikos Gorge, located at the base of the rock face.
Figure 12 shows traces of the trajectories, representing a free fall and secondarily bouncing or rolling locally. These slope profiles were extracted from the georeferenced 3D point cloud via the CloudCompareTM software. Based on the information provided by the point cloud, the release points of the detached blocks in these models were defined as a seeder line, e.g., rock blocks detached along a line instead of a point.
One of the most important parameters that must be defined along the rockfall path is the tangential coefficient of restitution (Rt) and the normal coefficient of restitution (Rn). For the purposes of this study, we considered the values suggested by Rocfall software for hard bedrock and for bedrock with little soil or vegetation [51,52].
The result of the simulation (Figure 13) indicates that the detached blocks can reach the path and threaten people, and, consequently, the rockfall risk cannot be neglected. Regarding the maximum total kinetic energy of the rockfall at the location of impact with the path, it was estimated to be 2831 kJ, 1850 kJ, and 1933 kJ for trajectories 1, 2, and 3, respectively (Table 7).
Having estimated the values of kinetic energy, preliminary proposed protection measures, i.e., barriers, were attempted in order to examine their effectiveness. As can be seen in Figure 13, the results of simulations indicated that the safety of visitors on the path between the monastery and the entrance to Vikos Gorge can be guaranteed by the construction of a 6 m long barrier with a maximum capacity of 2000 kJ in the area of trajectory tr3 and 3000 kJ in the area of trajectory tr1, while along the trajectory tr3, a combination of a 4 m length and 1000 kJ capacity barrier and a second one of 6 m in length and 2000 kJ capacity at a lower elevation is proposed.

4. Discussion

Worldwide, cultural heritage sites are considered places of significant indirect value for both religious and cultural reasons. Considering also the high number of visitors, it can be concluded that the vulnerability and corresponding risk in these areas is high. In Greece and other countries, many of these sites have been built in mountainous areas with steep rocky slopes, creating unavoidable exposure to rockfall. Therefore, in order to reduce the risk, it is mandatory to study the geological phenomenon, i.e., rockfall, and to evaluate the relevant hazards that could threaten the monument and its visitors. It should be noted that rockfall models involve uncertainties due to the complex terrain and rock behavior, since block shape, surface roughness, and friction are hard to quantify.
This study aims to evaluate the rockfall hazard on a rock slope located above the Agia Paraskevi Monastery and the natural entrance of the Vikos Gorge in the Region of Epirus, Northwest Greece. In order to achieve this, we used geological compass- and UAV-derived data to define the characteristics of the discontinuity sets; the latter are based on a semi-automated procedure in order to extract the orientation and spacing of discontinuities.
By comparing the results of the traditional field survey with those based on the developed 3D point cloud, an acceptable difference in the measured orientations was found. Considering the limitations of field surveys in regions characterized by steep topography, it can be stated that the utilization of point cloud approaches offers a viable solution. These approaches facilitate the identification of the orientation of discontinuity sets and the classification of rock mass. The deviation in dip angle and dip direction of discontinuities should be expected between traditional and UAV-derived measurements, and accordingly, an engineering geologist with a solid background in structural geology should evaluate these deviations and decide whether they should be included in the next stage of investigation, i.e., kinematic analysis. Nevertheless, the use of new technologies is strongly recommended since it can provide a great deal of data that could not be obtained using traditional methods alone.
In the context of evaluating rockfall hazard and its critical parameters, the developed 3D model of the slope face was employed for the detection and estimation of the volume of potentially unstable blocks. This procedure can be performed using the relevant tool in CloudCompare™, while the accuracy of the obtained block dimensions strongly depends on the quality of the point cloud. Though the fact that the study area is part of the recognized UNESCO Global Geopark of Vikos-Aoos, and consequently only low-intensity actions are permitted, this study shows that one of the most efficient measures to eliminate rockfall risk is the construction of barriers. In addition, scaling is proposed as the primary mitigation measurement that should be implemented in advance to reduce the risk.
Finally, we would like to point out that in the near future, further actions must be taken for a holistic evaluation of rockfall hazards. These actions must include the organization of systematic multitemporal monitoring of the rock face, the control of the capacity and effectiveness of the constructed mitigating measures, and the communication of the relevant hazard and risk to the local population and visitors to this area.

5. Conclusions

The cultural heritage site of the Monastery of Agia Paraskevi, Epirus, Greece, can be considered an example of the successful combination of conventional and “new technology” techniques in a challenging environment for evaluating rockfall hazards. The former method was applied at the lower part of the rock face while the latter one was used on inaccessible zones of the rock face, aiming to define the geometrical characteristics of the discontinuities (bedding and joints).
This study shows that on slightly fragmented and slightly weathered rock mass without vegetation, the UAV-derived data can be analyzed in the CloudCompare™ and Discontinuity Set Extractor (DSE) software, providing reliable information regarding the identification of joints’ orientation and the volume of fallen blocks.
Having simulated the rockfall trajectories, it is concluded that rockfall phenomena threaten the path between the Monastery of Agia Paraskevi and the entrance to Vikos Gorge. The proximity of the path used by gorge visitors to the rock slope and the nature of the rockfall, i.e., free fall, indicate the need for the construction of mitigation measures.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/geosciences15030092/s1.

Author Contributions

Conceptualization, S.P. and G.P.; methodology, S.P.; software, S.P. and G.P.; validation, S.P., G.P. and V.M.; investigation, S.P. and G.P.; data curation, S.P., G.P. and V.M.; writing—original draft preparation, S.P.; writing—review and editing, G.P. and V.M.; supervision, G.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The dataset of field measurements (dip and dip direction of the discontinuities) is provided as Supplementary Materials.

Acknowledgments

We would like to thank the reviewers for their constructive comments, which helped us to improve the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
UAV Unmanned Aerial Vehicle
DSE Discontinuity Set Extractor
SfMStructure from motion
LiDARLight Detection and Ranging
RPARSRemotely Piloted AiRcraft Systems
DEMDigital Elevation Model
JCSJoint Compressive Strength
JRCJoint Roughness Coefficient
DOMDigital Outcrop Model
ANNArtificial Neural Network

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Figure 1. (a) Map showing the location of the study area in the northwestern part of Greece and the two earthquake epicenters (red and orange stars for the Mw 5.4 and Mw 4.6 earthquakes, respectively); (b) photo showing the Agia Paraskevi Monastery and the steep slope of limestone (photo taken on 21 October, 2021; (c) morphology of the Vikos Gorge showing the location of the Agia Paraskevi Monastery (produced from Google Maps, 2025).
Figure 1. (a) Map showing the location of the study area in the northwestern part of Greece and the two earthquake epicenters (red and orange stars for the Mw 5.4 and Mw 4.6 earthquakes, respectively); (b) photo showing the Agia Paraskevi Monastery and the steep slope of limestone (photo taken on 21 October, 2021; (c) morphology of the Vikos Gorge showing the location of the Agia Paraskevi Monastery (produced from Google Maps, 2025).
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Figure 2. (a,b) Rockfalls at a distance of 20 and 60 m, respectively, from the Monastery of Agia Paraskevi. The image below (a) shows the destroyed rockfall protection netting from the rockfall event in October 2021.
Figure 2. (a,b) Rockfalls at a distance of 20 and 60 m, respectively, from the Monastery of Agia Paraskevi. The image below (a) shows the destroyed rockfall protection netting from the rockfall event in October 2021.
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Figure 3. Flowchart of the methodology applied for the purposes of this study. (SfM: structure from motion; UAV: Unmanned Aerial Vehicle; DSE: Discontinuity Set Extractor; DS: Discontinuity Set).
Figure 3. Flowchart of the methodology applied for the purposes of this study. (SfM: structure from motion; UAV: Unmanned Aerial Vehicle; DSE: Discontinuity Set Extractor; DS: Discontinuity Set).
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Figure 4. Rock slope of the study area delineating the sites where traditional (site D) and UAV-based structural analysis (sites A, B, and C) were carried out. In addition, the location of the Monastery of Agia Paraskevi and the entrance to Vikos Gorge are shown.
Figure 4. Rock slope of the study area delineating the sites where traditional (site D) and UAV-based structural analysis (sites A, B, and C) were carried out. In addition, the location of the Monastery of Agia Paraskevi and the entrance to Vikos Gorge are shown.
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Figure 5. Stereographic projection (lower hemisphere, equal area) of discontinuity poles and main sets resulting from geological compass-derived data (site D).
Figure 5. Stereographic projection (lower hemisphere, equal area) of discontinuity poles and main sets resulting from geological compass-derived data (site D).
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Figure 6. Site A. Stereonet of the density of the normal vector’s poles developed based on the Discontinuity Set Extractor (DSE).
Figure 6. Site A. Stereonet of the density of the normal vector’s poles developed based on the Discontinuity Set Extractor (DSE).
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Figure 7. Site B. (a) Stereonet of the density of the normal vector’s poles developed based on the Discontinuity Set Extractor (DSE). (b) Three-dimensional view of the discontinuity sets defined by the DSE; bedding is shown in blue, J1 in red, and J2 in green. It should be noted that the export of the DSE provides a chromatic identification of the discontinuity sets without indicating the orientation of each joint.
Figure 7. Site B. (a) Stereonet of the density of the normal vector’s poles developed based on the Discontinuity Set Extractor (DSE). (b) Three-dimensional view of the discontinuity sets defined by the DSE; bedding is shown in blue, J1 in red, and J2 in green. It should be noted that the export of the DSE provides a chromatic identification of the discontinuity sets without indicating the orientation of each joint.
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Figure 8. Site C. Stereonet of the density of the normal vector’s poles developed based on the Discontinuity Set Extractor (DSE).
Figure 8. Site C. Stereonet of the density of the normal vector’s poles developed based on the Discontinuity Set Extractor (DSE).
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Figure 9. Stereographic projection (lower hemisphere, equal area) of discontinuity poles and main sets based on geological field survey (site D) and UAV-derived data (Sites A, B, and C). The green color indicates the bedding, the red color indicates J1, and the orange color indicates J2.
Figure 9. Stereographic projection (lower hemisphere, equal area) of discontinuity poles and main sets based on geological field survey (site D) and UAV-derived data (Sites A, B, and C). The green color indicates the bedding, the red color indicates J1, and the orange color indicates J2.
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Figure 10. Kinematic analysis for (a) planar sliding, (b) wedge failure, (c) toppling phenomena, and (d) toppling for vertical joints of similar strike with a slope face that dips into the slope.
Figure 10. Kinematic analysis for (a) planar sliding, (b) wedge failure, (c) toppling phenomena, and (d) toppling for vertical joints of similar strike with a slope face that dips into the slope.
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Figure 11. Overview of the areas from where rock blocks had been detached (source areas), depicted with red color.
Figure 11. Overview of the areas from where rock blocks had been detached (source areas), depicted with red color.
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Figure 12. Traces of the trajectories (tr1, tr2, and tr3) used to determine the parameters of the rockfall. The path is shown as yellow dashed line and trajectories as red.
Figure 12. Traces of the trajectories (tr1, tr2, and tr3) used to determine the parameters of the rockfall. The path is shown as yellow dashed line and trajectories as red.
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Figure 13. Two-dimensional rockfall trajectories, tr1, tr2, and tr3, in the area between the Monastery of Agia Paraskevi (northeast of Monodendri village) and the entrance to the Vikos Gorge. Yellow color refers to the clean hard bedrock,green color to bedrock with little soil or vegetation and red color to rockfall trajectories. The location of suggested barriers is also shown as a black-color line segment.
Figure 13. Two-dimensional rockfall trajectories, tr1, tr2, and tr3, in the area between the Monastery of Agia Paraskevi (northeast of Monodendri village) and the entrance to the Vikos Gorge. Yellow color refers to the clean hard bedrock,green color to bedrock with little soil or vegetation and red color to rockfall trajectories. The location of suggested barriers is also shown as a black-color line segment.
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Table 1. Discontinuity sets and their characteristics in terms of dip and dip direction resulting from the field-based survey at site D.
Table 1. Discontinuity sets and their characteristics in terms of dip and dip direction resulting from the field-based survey at site D.
S/NSetDip/Dip Direction
1Β Bedding5°/160°
2J1 Joint82°/068°
3J2 Joint88°/154°
Table 2. Discontinuity sets and their characteristics in terms of dip and dip direction at site A.
Table 2. Discontinuity sets and their characteristics in terms of dip and dip direction at site A.
S/NSetDip/Dip Direction
1Β Bedding23°/144°
2J1 Joint77°/081°
3J2 Joint86°/150°
Table 3. Discontinuity sets and their characteristics in terms of dip and dip direction at site B.
Table 3. Discontinuity sets and their characteristics in terms of dip and dip direction at site B.
S/NSetDip/Dip Direction
1Β Bedding08°/162°
2J1 Joint76°/088°
3J2 Joint83°/141°
Table 4. Discontinuity sets and their characteristics in terms of dip and dip direction at Site C.
Table 4. Discontinuity sets and their characteristics in terms of dip and dip direction at Site C.
S/NSetDip/Dip Direction
1Β Bedding08°/135°
2J1 Joint85°/093°
3J2 Joint88°/148°
Table 5. Comparison of major discontinuity levels (dip/dip direction) defined by the DSE at sites A, B, and C, as well as by the geological compass at site D.
Table 5. Comparison of major discontinuity levels (dip/dip direction) defined by the DSE at sites A, B, and C, as well as by the geological compass at site D.
SetField Survey MeasurementsDSE
Site DSite ASite BSite C
Dip/Dip DirectionDip/Dip DirectionDip/Dip DirectionDip/Dip Direction
B05°/160°23°/144°08°/162°08°/135°
J182°/068°77°/081°76°/088°85°/093°
J288°/154°86°/150°83°/141°88°/148°
Table 6. Volumes of fallen blocks detected on the rock slope.
Table 6. Volumes of fallen blocks detected on the rock slope.
S/NVolume V (m3)S/NVolume V (m3)S/NVolume V (m3)
10.42110.70210.28
20.07120.49220.10
30.95132.53230.59
40.44140.74240.16
52.77150.52251.37
62.33160.54262.89
70.66171.10270.50
80.88180.43283.33
90.59192.07290.98
101.17203.20302.51
Table 7. Outcome of the trajectory simulations executed by RocFall software.
Table 7. Outcome of the trajectory simulations executed by RocFall software.
TrajectoryLength (m)Block Weight (kg)RtRnTotal Kinetic Energy (kJ)
tr115039780.990.532831
tr217039780.990.531850
tr318139780.990.531933
0.850.35
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Papaioannou, S.; Papathanassiou, G.; Marinos, V. Rockfall Hazard Evaluation in a Cultural Heritage Site: Case Study of Agia Paraskevi Monastery, Monodendri, Greece. Geosciences 2025, 15, 92. https://doi.org/10.3390/geosciences15030092

AMA Style

Papaioannou S, Papathanassiou G, Marinos V. Rockfall Hazard Evaluation in a Cultural Heritage Site: Case Study of Agia Paraskevi Monastery, Monodendri, Greece. Geosciences. 2025; 15(3):92. https://doi.org/10.3390/geosciences15030092

Chicago/Turabian Style

Papaioannou, Spyros, George Papathanassiou, and Vassilis Marinos. 2025. "Rockfall Hazard Evaluation in a Cultural Heritage Site: Case Study of Agia Paraskevi Monastery, Monodendri, Greece" Geosciences 15, no. 3: 92. https://doi.org/10.3390/geosciences15030092

APA Style

Papaioannou, S., Papathanassiou, G., & Marinos, V. (2025). Rockfall Hazard Evaluation in a Cultural Heritage Site: Case Study of Agia Paraskevi Monastery, Monodendri, Greece. Geosciences, 15(3), 92. https://doi.org/10.3390/geosciences15030092

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