# UAV-Based Evaluation of Rockfall Hazard in the Cultural Heritage Area of Kipinas Monastery, Greece
## 6 Conclusions  
Rockfall hazard and risk assessments consist some of the most elaborate and challenging surveys in geoengineering. The complexity of the event's nature, combined with the importance of what is comprised in a potential failure, automatically pose their assessment as a top priority. With the introduction of remote sensing technologies, such as UAVs, some of the previous formidable deterring factors have been eased. The construction of qualitative and quantitative analyses for rockfall events became more precise and their results produce a more accurate simulation of the reality. In general, UAVs constitute effective and efficient low-cost tools for data collection in many aspects of engineering geology [33]. Due to their ability to provide detailed information about the surface ground conditions, especially on difficult-to-access sites, many prior limitations in hazard and risk assessments can be now surpassed. With the use of digital photogrammetric techniques in the UAV-derived data, it is now possible to produce ultra-high spatial resolution datasets of hazardous regions, combined with the elimination of human risk. The further appropriate computer-based analysis of these datasets could lead to the construction of a detailed 3D visual interpretation (3D point cloud) of the investigated region that allows real-time applications and measurements of the rockfall phenomenon, which would not be attainable in that scale otherwise. This study primarily aims to take advantage of the aforementioned capabilities that Unmanned Aerial Vehicles have to offer, in order to produce a hazard and risk assessment, with a qualitative approach, via the proposition of a new rating system. The qualitative approach was selected over a quantitative one, in order to cover a wider range of applications in areas susceptible to rockfall events with negligible to non-existent data from former rockfall-related events. The followed methodology led to the extraction of all the necessary quantitative data that can be used for rockfall hazard and risk assessment of any natural slope, via the implementation of a new rating system. The area of application of the proposed methodology is the cultural heritage location of Kipinas Monastery in Epirus, Greece, an area characterized by the absence of pre-existing data regarding previous rockfall events, their spatial distribution along the cliff, their frequency (or the return period) of occurrence and their impact in the area of interest. Hence, this method can be suggested as a powerful approach to conduct a detailed hazard and risk assessment, in areas where negligible to no data from former rockfall-related events exist. Initially, traditional field survey methods have been applied at the lower accessible part of the rock face, aiming to define the geometrical characteristics of the joints. Simultaneously, a photogrammetry survey via a manually flown UAV was performed. A total of 362 images were acquired from the UAV in order to cover the whole slope face. Then, the utilization of Structure from Motion (SfM) methodology enabled the production of ultra-high-resolution orthomosaic by intersecting the matched features between the overlapping offset images and interpreting them as detailed 3D point clouds. This 3D point cloud model, which consists of about 104 million points with a ground resolution of 1.27 cm/pixel, was georeferenced using the UAV sensor GPS data and a set of five artificial GCPs, and then segmented into four (4) sub-regions in order to proceed to the structural analysis. The structural analysis was performed, in a supervised, automated manner, with the open-source DSE software [66]. The result of this analysis was the extraction of the principal discontinuity sets and their main characteristics (dip, dip direction, persistence, spacing). With the geometrical characteristics of the four slopes and from the principal discontinuity sets known, a kinematic analysis was performed in order to estimate the optimal failure model for each sub-region.