Evaluation of rockfall hazard based on UAV technology and 3D Rockfall Simulations

In this study, the rockfall hazard in Hacıabdullah village located in the Central Anatolia region of Turkey was assessed with three-dimensional (3D) rockfall analyses based on unmanned aerial vehicle (UAV) technology using RAMMS (Rockfall software). With several rockfall disasters experienced in the village, the �nal event occurred in 2008, and several houses were evacuated due to rockfall risk after this event. A total of 17 hanging blocks with fall potential were identi�ed and block dimension measurements were performed during �eld studies. In order to assess the rockfall hazard in the study area, digital surface model (DSM) data were obtained using high-resolution images obtained by UAV. According to dimensional values, the geometric and volumetric features of each rock were assessed close to reality with the RAMMS 3D rockfall modeling program. As a result of 3-D rockfall modelling, the maximum kinetic energy, maximum velocity, and maximum jump height of the falling blocks are reached to 3476 kJ, 23.1 m/s, and 14.57 m, respectively. The shape and volume of the blocks, as well as the slope features, rocks display differences in their runout distances after falls. A rock block with equant geometry has a runout distance of 53.1-126.9 m, whereas a rock block with �at or long geometry has a runout distance of 34-122.9 m. Rocks that do not move very far from the source area are; in other words, where the free-fall process is dominant, may signi�cantly damage the roads. However, rolling blocks, in other words, blocks which can travel long distances from the source area, have a potential to cause great damage at the settlement areas, roads and trees. According to the hazard map, R6, R12, R13, R14, R15, R16, and R17 blocks involve high and moderate levels of risk for settlement units. R1, R4, R7, R8, R9, and R10 blocks show that the majority of them involve low risk, while a small portion is a moderate risk.


Introduction
Rockfalls, which are very di cult to predict in terms of occurrence time and pattern are one of the most dangerous natural disasters experienced on earth (Varnes 1978, Evans and Hungr 1993, Liu et al. 2021).
Falling rocks exert signi cant damage on roads, settlement units, and infrastructure, in addition to causing great risk for human life (Tanarro and Munoz 2012, Lu et al. 2019, Utlu et al. 2020a).Due to the enormous speed of falling blocks, rockfalls may rapidly and suddenly occur at different angles, in nearly all conditions and different rock groups (Fityus et al. 2013, Singh et al. 2016, Gül et al. 2016).The source area of rockfalls is mostly vertical or almost vertical rock faces (Varnes 1978, Evans andHungr 1993).
Generally, rockfalls modeling carried out by two-dimensional (2D) and three-dimensional (3D) rockfalls modeling software helps to understand of the dynamics of the rockfalls (Schober et al. 2012, Kim et al. 2015).These software have different algorithms and basis (Ansari et al. 2018).Among these models especially 2D models had been used actively, until recently.However, 3D rockfall models, which have emerged with the development of computer technologies, are much more preferred currently (Guzzetti et al. 2002, Lan et al. 2007, Bartelt et al. 2016a, Sellmeier andThuro 2017).3D models provide a great advantage in understanding rockfall dynamics compared to 2D models.The most important of these is to observe the rockfall trajectories on 3D surfaces, as well as to solve the hazard and risk problems such as the intersection of the transportation corridors, settlements area, infrastructure at critical points in a quantitative way (Kim et al. 2015, Dorren andKühne 2016).Besides, the development of remote sensing technology and remote sensing platforms such as UAV, Lidar, Radar technologies allows obtaining a 3D high-resolution surface/elevation model in a short time.In rockfall studies, 3D models offer great advantages in hazard and risk studies, especially with the use of high-resolution data.For example, simulating the desired number and shape of rock blocks and determining the possible trajectories of the modeled results are some of them (Abellán et al. 2006, Frattini et al. 2013, Kim et al. 2015, Li and Lan 2015).
Rockfall events frequently occur in Turkey, at different locations and elevations, in settlement units founded in both mountainous areas and adjacent to high slopes.Rockfall are the most frequent natural disasters after earthquakes, landslides, and oods in Turkey (

Study Area
Hacıabdullah village located in Niğde province in Central Anatolia which is within a valley formed by volcanic rocks on the east slopes of the Melendiz stratovolcano and is an area where intense rockfall events occur (Fig. 1a-c).Rockfalls are mostly experienced on high-slope areas located north of the village (Fig. 1d) and the village is located under a steep slope with an inclination of 90°.The inclination of the terrain where rockfall events occur vary between 23 and 90° and the area has a convex pro le.The steep slope which is the rockfall source in the study area is composed of volcanic rocks containing vertical cooling joints triggering many rockfall events not only in the past but also recently (Fig. 2).The study area has semi-arid climate features shown by the letters "BSk" according to the Köppen-Geiger climate classi cation (Öztürk et al. 2017).Freeze-thaw processes occurring especially during the winter season, gravity on high slope areas, and the general lithology of the study area containing volcanic rocks with heavily jointed structure play dominant roles in triggering rockfall events.

UAV Survey and Photogrammetric processing
In this study, a DJI Phantom 4 Advanced UAV was used to produce high-resolution orthophoto and digital surface models for rockfall modeling.The general owchart followed when producing this data is given in Fig. 3.
Preparations for ight height, scanning width and stereo image matching proportions were completed with Pix4dcapture software for the study area.With the aim of producing DSM and orthophoto data with good quality and sensitivity, 40 ground control points (GCP) were placed with D-GPS before the ight.
Five parallel ights were made at 100 meters height, and 621 stereo aerial photographs were obtained with 70% overlap ratio (Fig. 4a-b).Processing of these images was performed using the Pix4d trial version.Eventually, point cloud data with a 3.03 cm resolution were obtained after the photogrammetric processing of orthophoto images with low (1.4 cm) RMSE error (Fig. 4c-d).

Evaluation of the quality of elevation data of DSMs
There are some errors when DSM data is obtained using different platforms.These errors are caused by the structure of the land surface and land use/cover features.
One of these errors is the vertical error rate (Ajayi et al. 2017, Coveney andRoberts 2017).Ground control points (GCPs) and checkpoints (CPs) are used to x these errors during collecting stereo images by UAV.
In order to understand the vertical accuracy error rate, CP data is also collected in addition to the GCP data.Determining the vertical accuracy rate in DSM data is important for the performance of the study and the precision of the results (Tamminga et al. 2015).
In this respect, using 35 checkpoints in the study area, the vertical accuracy rate of the DSM data root mean square error (RMSE, Formula 1), mean square error (MSE, Formula 2), mean absolute deviation (MAD, Formula3) and mean absolute tested with percentage error (MAPE, Formula 4) (Tamminga et al. 2015, Akturk and Altunel 2019).These formulas are expressed as, The results obtained according to the speci ed formulas are given in Table 1.Accordingly, the error rates of the DSM data are MAD: 0.169, MSE: 0.066, RMSE: 0.258, MAPE: 0.01 cm.Accordingly, DSM data was obtained with a low vertical accuracy error rate.2018).Discontinuities were performed in 3 part of the high slope source zone in this study (Fig. 5) and discontinuities were analyzed based on UAV-3D Point cloud data using qFacet plugin that allows to extract characteristics of the rock surface such as planar facets and their orientation and distance (Dewez et al. 2016, Valkaniotis et al. 2018).The mean dip angel and dip direction for the study area gives a similar result based on high-density point cloud datasets (Fig. 5. 1a-2b-3c).Besides, the discontinuity results obtained from 3 different locations gave very similar values.Accordingly, the obtained orientations generally refer to the areas corresponding to the vertical cooling cracks due to the lithology of the area.The general values obtained accordingly show that there are 3 main discontinuity surfaces represents Fig. 5b-c-d and the mean dip direction (°) of these surfaces is between 190-208°, on the other hand, the dip angle (°) values are between 33-35° (Fig. 5 and Table 2).

Block geometries
In the study area, which is at high risk of rockfall, the source zones of blocks generally correspond to cooling cracks located on steep slopes of cliffs (Figure .2).To identify blocks that have potential falling risks, eld studies were performed and 17 blocks were identi ed (Fig. 6).The height, width, and length were measured for each block and according to these values each rock is modelled in 3D RAMMS: ROCKFALL 1.6.70 module software (Table 3).While 10 of the modeled rock blocks (R1-R2-R4-R6-R7-R10-R11-R15-R16-R17) have equant and real-equant geometry, 5 rocks (R3-R9-R12-R13-R14) have long and real-long geometry and R5-R8 blocks have at geometry (Fig. 7) and forests etc.) as a result of modeling by identifying these blocks and performing the necessary measurements may prevent these hazards and risks (Piacentini and Soldati 2008).
In this study, after determining the geometric and volumetric properties of these blocks with very different shapes and volumes, 3D rockfall analyses were executed with the RAMMS: Rockfall software which is a GIS-based rockfall simulation program (Leine et al. 2014;Bartelt et al. 2016a).In the rockfall modeling process, a range of data inputs are required in addition to modeling obtained linked to DSM data.These inputs include features like plant cover (open, dense, etc.), terrain types (hard, extra hard, medium, soft, medium soft, etc.) and block types ( at, equitant, round, etc.) for blocks or source zone areas to be modelled by the RAMMS program (Vo 2015).For rockfall modeling in the RAMMS program, DSM data with resolution from 1-10 m is su cient.For this reason, DSM data with cm-resolution was rescaled to 1-meter resolution to obtain the format for use in modeling.Considering the volcanic rocks in the eld, the terrain type was selected as hard terrain, covered with very sparse plant cover (Fig. 4c).Besides, the friction parameters (Table 4) were determined based on terrain type that account for; "Rocks jump over ground, includes different size of blocks and absence of the vegetation" (Bartelt et al. 2016b).

Rockfall Hazard Index
Rockfall Hazard Index (RHI) method was employed for the hazard mapping of rockfalls (Crosta and Agliardi 2003).In this method, the kinetic energy, jump height and block count parameters are taken into consideration which was determined through the RAMMS program in this study.These basic parameters are reclassi ed within the scope of hazards.During the reclassi cation, the classi cation values of Crosta and Agliardi (2003) were used.Necessary classi cations for kinetic energy, jump height and block count values are presented in Table 5.For calculation of block count value, the c/(5*n) formula was used and then reclassi cation was performed.As we can see in the formula, c represents rockfall count, and n represents the number of blocks from each cell value.The values obtained as a result of the modeling of the rock blocks were evaluated separately for each block based on RHI.The values obtained were separately calculated for reach rockfall model with the results of classi cation of the 3 parameters combined to identify high-moderate-low hazard areas.

Results
According to the dimension of blocks determined during the eld studies, heights vary from 0.8 to 2.23 m, lengths from 0.96 to 3.2 m, and widths of 1.16 to 2.5 m (Fig. 7).As a result of these dimensions, the volume of blocks was calculated in between 0.46 and 7.96 m 3 , while block mass was calculated to be in a range of 214.48 to 1253 kg.The block shapes were identi ed as equant, real long, real equant, and real at in the RAMMS program, a total of 100 throws for each block at 17 different release locations were modeled and the kinetic energy (kJ), velocity (m/s), jump height (m) and Rockfall Hazard Index were determined.
As a result of the shape and volume of the blocks, as well as the slope features, rocks display differences in their runout distances after falls.Model results showed maximum kinetic energy values (3476.8kJ), seeing that on equant geometry of blocks, while low kinetic energy values (90.6 kJ) seeing that on at and long geometry of blocks (Fig. 8).With 90.6 kJ, the lowest value is found in R5 with at geometry, while the highest value is found in R1 with equant geometry.The rock velocity values vary depending on the topography of the eld and the slope.The velocity values between 8 and 23.1 m/s reach.The maximum and minimum velocities observed in R11 and R13, respectively.In addition to these values, while equant shaped rock blocks exhibit a velocity value between 10-23.1 m/s, at and long blocks show a velocity value between 8.74 and 19.93 m/s.Jump height values range between 1.99 and 14.5 m.
Equant blocks have values ranging from 3.4-14.5m, while long and at blocks have values between 1.99 and 7.8 m.As seen for blocks R1, R4, R6, R7, R8, R9, R10, R11, R12, R14, R15, R16, and R17 represent equant features that involve different size and volume are present different run out distance based onto the geometry of the blocks.Apart from this, R12 block has a continuous slope pro le; however, the block could not progress a long distance due to the different shape (real long).A rock block with equant geometry has a runout distance of 53.1-126.9m, whereas a at or long geometry has a runout distance of 34-122.9m.In the case of the rock block R8, whose value is 122.9 m, the trajectory was carried over long distances due to the continuous steep slopes (Fig. 19).It should be noted that the residential houses shown in Fig. 10 are mostly demolished and thus some trajectories are rolling over the remnants of these buildings.Modeling of blocks R1, R4, R7, R8, R9 and R10 show the majority involve low hazard, while a small portion involve moderate degree of hazard (Fig. 10).

Conclusions
In this study, rockfall risks in Hacıabdullah village where rockfall events occur continuously were assessed using high-resolution DMS, eld measurements, and 3D rockfall simulations.Based on RAMMS analysis, the kinetic energy varies between 90.6 and 3476 kJ, the velocity between 8.17 and 23.1 m/s, and the jump height between 1.99 and 14.57 meters.These values show variation according to the general slope values for the study area and trajectory of the modelled blocks the slope pro le and geomorphology of the study area, and the geometry and volume of the blocks.Rock blocks may travel very long distances in regions with slope values below 46°, while blocks stop on slope breaks without traveling long distances when slope values are from 46-90°.Some of the results rockfalls modelling shows the trajectories that channelled into valleys ended within the valley, while some of the results rockfalls that found on ridges show large areas are threatened.As a result of the shape and volume of the blocks, as well as the slope features, rocks display differences in their runout distances after falls.A rock block with equant geometry has a runout distance of 53.1-126.9m, whereas a at or long geometry has a runout distance of 34-122.9m.It should be noted that the residential houses are mostly demolished and thus some trajectories are rolling over the remnants of these buildings.According to the hazard map, R6, R12, R13, R14, R15, R16 and R17 involve high and moderate levels of risk for settlement units.R1, R4, R7, R8, R9, and R10 show that the majority of them involve low risk, while a small portion is moderate risk.Taking the necessary precautions for the determined risk areas has vital importance due to the frequent experience of rockfalls in the area.The model outcomes should be considered when taking the necessary precautions.In conclusion, in Hacıabdullah village where rockfall events are actively experienced, risk for settlement areas and the spatial distribution of the possible risk were accurately revealed by eld study results and rockfall models completed with a high-resolution digital elevation model produced with the aid of a UAV.These results have great importance in terms of taking the necessary precautions based on rockfall hazard maps.Several views of the study area affected by rockfall events occurred in the past

Figures Figure 1 a
Figures

Figure 2 General views of the village and falling rock blocks Figure 3 Page 22 / 29
Figure 2

Figure 4 a
Figure 4
Vick et al. 2019)port by the Ministry of Interior Disaster and Emergency Management Presidency (AFAD), analysis is required due to the high hazard and risk status of rockfalls.From this aspect, modeling studies have great importance (MaryVick et al. 2019).For rockfall modelling studies, geomorphometric approaches and modelling based on high-resolution DSM data provide more accurate and sensitive results(Loye etal.2009, Zhang et al. 2019, Pérez-Rey et al. 2019, Francioni et al. 2020, Rodriguez et al. 2020, Akın et al. 2021).Within this scope, UAVs offer signi cant advantages in producing DSM and orthophoto data for large areas in a short duration and gain great importance for monitoring the distribution of natural disasters, especially, in spatial and temporal terms (Feng and Röshoff 2004, Abellán et al. 2006, Armesto et al. 2009, Alejano et al. 2013, Giordan et al. 2015).High-resolution DSM and orthophoto data are very effective for accurate and sensitive trajectory modeling and the detection of source zones in rockfall prone areas.For this reason, rockfall studies in recent years have moved beyond traditional methods (Colomina and Molina 2014, Boccardo et al. 2015, Gomez and Purdie 2016, Manconi et al. 2019) and begun to use high-resolution DSM and orthophoto images obtained with UAVs (Matasci et al. 2015, Török et al. 2017, Byrne 2018, Manconi et al. 2019). Accurate

Table 1
(Dewez et al. 2016, Riquelme et al. 2017, 2018, Valkaniotis et al.tatistical parameters using RMSE, MSE, MAD, MAPE.Discontinuity analysis, which is used in the identi cation of fracture and crack joints in rock surface, is important to understand behavior of the blocks.Discontinuities are explained by creating contour diagrams depending on the angle of the extension of the cracks and fractures in the rock units with the north(Zhang 2006, Riquelme et al. 2018).In recent years, LIDAR, TLS, and UAV platforms, which are actively used and highly preferred in the creation of digital surface data (DSM-DTM) and play a major role in the creation of discontinuities(Dewez et al. 2016, Riquelme et al. 2017, 2018, Valkaniotis et al.

Table 2
The summary of the discontinuities in the study area based on qFacet plugins

Table 6
Rockfall parameters obtained by 3D rockfall modeling (H: House, T: Tree, R: Road) To map the hazard of rockfalls, Crosta and Agliardi (2003) used the "Rockfall Hazard Index" (RHI).In this study, the RAMMS program was used to determine the kinetic energy, jump height, and block count parameters to create rockfalls hazard map.Using the hazard map, modeling of rocks R6, R12, R13, R14, R15, R16, and R17 indicates that settlement areas constitute most of the high and moderate risk areas.