UAV  photogrammetry  offers  a  powerful  and  cheap  methodology  to  reconstruct  the  terrain  geomorphology. The purpose of this study is the application of a generic Structure-from-Mo-tion  workflow  to  properly  elaborate  different  set  of  images  from  multitemporal  surveys  to  perform a surface and volume change detection by meaning of a cloud-to-cloud comparison. The  complex  geomorphology  of  the  site  of  interest,  characterized  by  niches,  asperities,  flat  rock walls with different orientation, debris deposits and isolated rock blocks, challenges the accurate reprojection of the image points into a 3D space. The chronological comparison of the point cloud offers a qualitative and quantitative estimation of distances and volume change between sequential models. For the cloud-to-cloud distance computation, a level of accuracy accounting for different sources of uncertainty was estimated.Seven data sets were available for this study and they were acquired by two different faculties of the University of Ljubljana, Biotechnical Faculty and Faculty of Civil and Geodetic Engineering. Some of the surveys and some of the drone’s flights were performed with different approaches, leading to different accuracy in the final reconstruction of the terrain. The data processing has been performed with the latest versions of Agisoft Metashape and CloudCompare software. UAV  photogrammetry  offers  a  powerful  and  cheap  methodology  to  reconstruct  the  terrain  geomorphology. The purpose of this study is the application of a generic Structure-from-Mo-tion  workflow  to  properly  elaborate  different  set  of  images  from  multitemporal  surveys  to  perform a surface and volume change detection by meaning of a cloud-to-cloud comparison. The  complex  geomorphology  of  the  site  of  interest,  characterized  by  niches,  asperities,  flat  rock walls with different orientation, debris deposits and isolated rock blocks, challenges the accurate reprojection of the image points into a 3D space. The chronological comparison of the point cloud offers a qualitative and quantitative estimation of distances and volume change between sequential models. For the cloud-to-cloud distance computation, a level of accuracy accounting for different sources of uncertainty was estimated.Seven data sets were available for this study and they were acquired by two different faculties of the University of Ljubljana, Biotechnical Faculty and Faculty of Civil and Geodetic Engineering. Some of the surveys and some of the drone’s flights were performed with different approaches, leading to different accuracy in the final reconstruction of the terrain. The data processing has been performed with the latest versions of Agisoft Metashape and CloudCompare software.