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.

Persistent Scatterers Interferometry (PSI) techniques are widely employed in geosciences to detect and monitor landslides with high accuracy over large areas, but they also suffer from physical and technological constraints that restrict their field of application. These limitations prevent us from collecting information from several critical areas within the investigated region. In this paper, we present a novel approach that exploits the results of PSI analysis for the implementation of a statistical model for landslide susceptibility. The attempt is to identify active mass movements by means of PSI and to avoid, as input data, time-/cost-consuming and seldom updated landslide inventories. The study has been performed along the northwestern coast of Malta (central Mediterranean Sea), where the peculiar geological and geomorphological settings favor the occurrence of a series of extensive slow-moving landslides. Most of these consist in rock spreads, evolving into block slides, with large limestone blocks characterized by scarce vegetation and proper inclination, which represent suitable natural radar reflectors for applying PSI. Based on geomorphometric analyses and geomorphological investigations, a series of landslide predisposing factors were selected and a susceptibility map created. The result was validated by means of cross-validation technique, field surveys and global navigation satellite system in situ monitoring activities. The final outcome shows a good reliability and could represent an adequate response to the increasing demand for effective and low-cost tools for landslide susceptibility assessment.

This paper presents the results of geomorphological investigations carried along the north-western coast of the Island of Malta. Field surveys, accompanied by aerial photo-interpretation, have led to the production of a geomorphological map at 1:7500 scale which outlines the main processes and related landforms. The latter are the result of the complex interplay of structural, gravitational, coastal and karst processes. Particular attention was devoted to the recognition, identification and mapping of landslides which affect large coastal sectors of the study area, locally giving rise to hazardous conditions.