The aim of this study is to demonstrate the advantages of using micro drones in the study of large slow-moving landslides, which are widespread along the northwestern coast of Malta. In particular, attention was given to the inventory and analysis of gravity-induced joints and megaclast deposits at four study sites selected due to the presence of remarkable examples of lateral spreads evolving into block slides. The research was carried out by means of Google Earth (GE) image analysis and uncrewed aerial vehicle digital photogrammetry (UAV-DP). UAV-DP outputs enabled the identification and characterization of tens of persistent joints (locally exceeding 150 m) and permitted the size categorization of thousands of blocks. With reference to gravity-induced joints, a favorable agreement was found between existing datasets (mainly based on the integration of GE analysis and field survey) and UAV-DP outputs in terms of the identification of joints and their persistence. Conversely, the use of the UAV-DP technique showed significant advantages in terms of joint aperture determination (even exceeding 1 m) and distribution setting. Regarding the extensive megaclast deposits, UAV-DP enabled the identification of 8943 individuals which, compared with the 5059 individuals identified by GE analysis, showed an increase in the total population of 76%. This is related to the high accuracy of DP-derived orthomosaics and 3D models, which are particularly useful for identifying detached blocks. The inexpensive technique used in this research highlights its potential for being extended to other rocky coastal areas affected by slow-moving landslides.

The photogrammetric method is widely used in coastal areas and in submerged environments. Time-lapse images collected with unmanned aerial vehicles are used to reproduce the emerged areas, while images taken by divers are used to reproduce submerged ones. Conversely, 3D models of natural or human-made objects lying at the water level are severely affected by the difference in refractive index between air and seawater. For this reason, the matching of 3D models of emergent and submerged coasts has been very rarely tested and never used in Earth Sciences. The availability of a large number of time-lapse images, collected at the intertidal zone during previous snorkel surveys, encouraged us to test the merging of 3D models of emerged and submerged environments. Considering the rapid and effective nature of the aforementioned program of swim surveys, photogrammetric targets were not used during image acquisition. This forced us to test the matching of the independent models by recognizing prominent landforms along the waterline. Here we present the approach used to test the method, the instrumentation used for the field tests, and the setting of cameras fixed to a specially built aluminum support console and discuss both its advantages and its limits compared to UAVs. 3D models of sea cliffs were generated by applying structure-from-motion (SfM) photogrammetry. Horizontal time-lapse images, collected with action cameras while swimming parallel to the coastline at nearly constant velocity, were used for the tests. Subsequently, prominent coastal landforms were used to couple the independent models obtained from the emergent and submerged cliffs. The method was pilot tested in two coastal sites in the north-eastern Adriatic (part of the Mediterranean basin). The first site was a 25 m sea wall of sandstone set within a small harbor, while the second site was a 150 m route below plunging limestone cliffs. The data show that inexpensive action cameras provide a sufficient resolution to support and integrate geomorphological field surveys along rocky coastlines.