## 2 Materials and Methods
### Methodology  
In order to effectively assess the evolution of the western coastal zone of Lefkada Island, as well as to examine the influence of seismicity on the slope instabilities, we applied a complementary approach based on multi-sensor remote sensing data and in situ field observations (Figure 6). Specifically, medium resolution imagery acquired from the Landsat satellites was utilized to detect the areas displaying large alterations in vegetation before and after the last two strong earthquakes. In this framework, we computed the normalized difference vegetation index (NDVI) using Landsat imagery in the GEE platform. The  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sampling Periods** & **Sediment Weight** & **Precipitation (mm)** & **Earthquakes in R** &
\begin{tabular}{c} **Earthquake Energy** \\ **Release (Joules)** \\ \end{tabular} \\ \hline
3 November 2018–30 April 2019 1 st semester & 3206 g/or 534 g/month & 856.50 & 3.4, 3.7, 3.5, 4.1, 3 & \(10.63\times 10^{-10}\) \\ \hline
30 April 2019–9 9 November 2019 2 nd semester & 1393 g/or 232 g/month & 263.9 & 3.4 & \(1.36\times 10^{-10}\) \\ \hline
9 November 2019–17 March 2020 3 rd semester & 1202 g/or 240.4 g/month & 712.50 & 3.1, 3.5 & \(2.4\times 10^{-10}\) \\ \hline
17 March 2020–3 October 2020 4 th semester & 2790 g/or 429.2 g/month & 468.8 & 3.6, 3.2, 3, 4.1 & \(7.47\times 10^{-10}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sampling periods and sediment weight of the sedimentary trap in the Egremni site.  
Figure 5: Photos during sieving showing the maximum clast collected from the trap during the four sampling semesters.  
NDVI represents the photosynthetic activity occurring in a pixel and is calculated using Equation (1):  
\[\text{NDVI}=\frac{(\text{NIR }-\text{ Red})}{(\text{NIR }+\text{ Red})}. \tag{1}\]  
The index ranges between \(-1\) and \(1\), where a higher NDVI corresponds to a higher level of photosynthetic activity. Specifically, bands 3 and 4 of the Landsat 8 mission were utilized to compute NDVI indices before and after the 2015 earthquake. Afterwards, these NDVI indices were imported into a GIS environment, where they were subtracted to identify vegetation changes (Equation (2)):  
\[\text{dNDVI}=\text{NDVI}_{\text{before}}-\text{NDVI}_{\text{after}}. \tag{2}\]  
Areas which displayed large vegetation changes were further investigated using multi-sensor high-resolution data. To validate these vegetation differences, we used Pleiades multispectral data. In particular, a band combination (4, 3, and 2), corresponding to the near infrared, red, and green parts of the spectrum, was applied to highlight the vegetation canopy. Moreover, to identify topographic changes, we performed slope calculation and statical interpretation over these areas utilizing high-precision DSMs (Table 1).  
At the same time, a sediment trap was placed within the area of interest in order to define the influence of seismicity and precipitation on slope integrity during the interseismic era. The collected samples were analyzed via the sieving technique.  
More details concerning the applied methodology are provided in the following section.