# UAV-Based Evaluation of Rockfall Hazard in the Cultural Heritage Area of Kipinas Monastery, Greece
## 3 Stability Analyses
### Rockfall Susceptibility Analysis  
After the generation of the optimal failure models for each sub-region, the monitoring and, subsequently, the quantification of two (2) important elements for this hazard assessment were made possible. These were the already failed rock blocks and the potentially unstable ones for each sub-region. With the spatial distribution of these blocks monitored, important measures were exported regarding their magnitude (volume, mass) and therefore, the frequency of each magnitude was calculated per sub-region. Additionally, the density of each category of rock blocks in every sub-region was estimated with the combination of each slope's size and the number of the monitored blocks. Figure 11: Kinematic analysis for: (**a**) planar sliding and (**b**) wedge sliding failure in sub-region U2. Figure 12: Kinematic analysis for: (**a**) planar sliding and (**b**) wedge sliding failure in sub-region U3. Figure 13: Kinematic analysis for: (**a**) planar sliding and (**b**) wedge sliding failure in sub-region U4. The followed methodology was based initially on the approach in [69], but it was further developed to utilize the maximum of the detailed UAV-derived dataset's capabilities. Specifically, the proposed methodology started with the processing of every sub-region's point cloud, with the CloudCompareTM software v.2.11.1 (Anoia), in order to perform two separate semi-automated detections for the failed and the likely-to-fail rock blocks, respectively. These detections were based on the visual interpretation of the main discontinuity sets (DSE exports) and their contribution to each slope's failure model (Kinematic Analysis exports). Hence, the geometric characteristics, the spatial distribution, the magnitude and the density of presence of each rock block were specified. Due to the lack of available rock block volumes to be analyzed, the identification and the quantification of the already failed blocks were extracted via the analysis of the fresh surfaces across each sub-region's face slope, that attested to the existence of a prior failure. In total, 455 rock blocks were detected, 326 of them as already detached and 129 as potentially unstable. Their detailed allocation per sub-region is demonstrated in Table 4 and their visual interpretation is shown in Figure 14.  
A supervised automated analysis was then performed for the calculation of each block's volume via the 2.5D Volume Tool, provided by the CloudCompareTM software v.2.11.1 (Anoia). In order to estimate the credibility and highlight potential outliers in these automated calculations, the manual calculation of 20% of the total rock block volumes for each sub-region via the extracted point clouds was decided. The followed methodology was inspired by relevant previous surveys, such as [70], and was further developed. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sub-Region** & **Failed Blocks** & **Potentially Unstable Blocks** \\ \hline U1 & 86 & 32 \\ \hline U2 & 69 & 38 \\ \hline U3 & 99 & 45 \\ \hline U4 & 72 & 14 \\ \hline SUM & 326 & 129 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The distribution of the already failed and the potentially unstable rock blocks per sub-region. Figure 14: Overview of both the already failed (blue) and the potentially unstable (red) blocks in the whole slope. In this particular case, the volumes of both the already failed and the potentially unstable rock blocks varied. In general, their volumes fluctuated in moderate-to-large ranges (\(\leq\)4 m\({}^{3}\)), but it is noteworthy to highlight the existence of some individual cases in which these values were surpassed. Of course, these excesses have not occurred extensively along the slopes, but they comprise isolated cases. In particular, most of these isolated cases were identified in sub-region U3, where many of the potentially unstable block volumes were estimated to have greater values than the average.