## 1 Introduction  
The evolution of the relief in seismically and tectonically active regions is dominated by the occurrence of earthquakes. These events contribute to the destabilization of the slope, triggering topographic changes or landslides. Earthquake-induced mass movements can cause casualties, economic loss, and severe damages to the natural and man-made environment [1, 2]. Over the past few years, extensive studies have been performed on determining the parameters of earthquake-induced landslides either through laboratory simulations and numerical modeling [3, 4, 5, 6] or by investigating the instability factors, the distribution of mass movements, and the topography [7, 8]. In this framework, remote sensing enables the effective detection and monitoring of landslides on either a local or regional scale due to the large-area observations and the reduced survey completion time. Hence, multispectral (Landsat, WorldView-2, GeoEye-1, etc. ), as well as synthetic aperture radar (SAR) (TerraSAR-X, COSMO-SkyMed, ALOS-2 PALSAR-2, Sentinel-1), data have been used for the identification and analysis of landslides with high precision [9, 10, 11, 12]. Nowadays, unmanned aerial systems (UAS) photogrammetry constitutes the most widely used method for landslide investigations [13, 14, 15]. As technology and processing methods develop, new approaches are emerging, relying on soft computing algorithms in order to detect earthquake-induced landslides or create susceptibility maps. Specifically, an automated methodology was proposed to map co-seismic landslides. Its methodology was based on multi-scale extended morphological profiles, machine learning algorithms, and unsupervised change detection methods [16]. Moreover, high-resolution images, acquired using Planet satellite, were utilized for the detection of landslides after the Mw6.6 Tomakomai earthquake, while the extracted inventory database was used to generate susceptibility maps via logistic regression and support vector machine modeling [17]. In a respective study, the importance of inventory quality was highlighted by examining different databases of earthquake-induced landslides to create susceptibility models for a given area [18]. Other researchers have investigated how multiple susceptibility algorithms affect the prediction of landslides related to the Hokkaido earthquake [19]. However, the majority of these previous studies mainly focused on either the instantaneous mapping of earthquake-induced topographic changes or on the creation of landslide susceptibility over tectonically active regions [12; 15; 16; 17; 18; 19]. Despite the immediate effects of an earthquake on a slope, there are areas that, while they do not move, have a disturbed internal structure, and they can be activated by smaller earthquakes or other external factors, such as rainfall [5]. In light of this, this study aimed to examine the long-term evolution of the western cliffs of Lefkada Island following the occurrence of the last two strong earthquakes, on 14 August 2003 and 17 November 2015, respectively. Specifically, we obtained multi-sensor remote sensing data, covering a period from the years 2000 to 2016, to investigate the evolution of the western cliffs of Lefkada Island after the occurrence of the last strong earthquakes as well as during the inter-seismic era. It is worth mentioning that previous studies have been conducted on inspecting shoreline evolution over a 73-year period using diverse remote sensing data [20] and assessing the evolution of mass movements between 2018-2021 through field measurements and unmanned aerial vehicle (UAV) campaigns [21]. However, the main objective of the current research was to determine the factors (seismicity and precipitation) that reduce the integrity of the cliffs in the western part of Lefkada Island.