## 3 Materials and Methods  
Given that this study covers a period of six decades, the consequent changes in available materials and research techniques have conditioned the development of this research. Due to this, three different and complementary data sources have been used. The first source used were aerial photographs and orthophotographs from the IGN (Instituto Geografico Nacional) [37] from years 1956, 1983, 1989, 2002, 2004, 2008, 2010, 2014 and 2017. Those from the first two study years were black and white aerial photographs, whereas those from years 1989-2004 were color aerial photographs and those from 2008 onwards were orthophotographs (Table 1). Based on them, variations in the different environments located in Corrubedo were analyzed. The second data source was the topographic survey performed in this area in February 2001. This survey had a planimetric accuracy around 5 cm and an altimetric accuracy around 8 cm for recorded points and 20 cm for interpolated points. All data were obtained either from the main station or from auxiliary stations, always avoiding linking different auxiliary stations together. Moreover, this research used LiDAR data [37] from years 2010 and 2015. This information had a resolution of 0.5 points/m\({}^{2}\) and was obtained from the IGN. Additionally, data from our 2008 LiDAR flight, with a resolution of 0.3 points/m\({}^{2}\), were employed (Table 2). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Code** & **Date** & **Scale** & **Program** \\ \hline - & 1956 & 1/32,000 & American Flight \\ - & 1983 & 1/30,000 & National Flight \\ - & 1989 & 1/5000 & Coastal Flight \\ P1 & 08/01/2002 & 1/30,000 & SIGPAC 1  \\ P2 & 09/21/2004 & 1/30,000 & PNOA 2  \\ P3 & 08/05/2008 & 1/30,000 & PNOA 2 [FOOTNOTE:2]Footnote 2: Sist  
Different methodologies and software were used depending on the type of analysis. ArcGIS 10.5 (license USC) was employed for geospatial processing and R software [43] was used for statistical analysis and for the majority of graphs. To analyze variations in the mobile dune front, it was mapped for each date for which images were available. These lines were used as a proxy to quantify variations through DSAS (Digital Shoreline Analysis System) [44], whose validity was further corroborated by multiple studies about coastline evolution [26; 45; 46]. For this analysis, transects were generated every 5 m and variations were calculated in three different periods: first, between 1956 and 2017, in order to determine the changes throughout the whole study period; a second timeframe between 1989 and 2017, to determine the differences between the present and a moment when the configuration of the sector was already similar to the current one; and, finally, variations between 2004 and 2017 were calculated to understand the recent evolution and to estimate differences with a higher accuracy. Variations in pathways were analyzed based on the images listed in Table 1. In this case, the detectable paths were first drawn and their length was then calculated within a 134.9 ha area around the mobile dune (Figure 1, orange line). This analysis aims to highlight the importance of human activities in this space since 1956. As for plant coverage, the edges of the plant-covered areas were mapped for all years with available images (Table 1). Based on the generated polygons, the area covered by vegetation within the area delimited in Figure 1 was estimated and surface variations between dates were then calculated. For the analysis of LiDAR data (2008-2015), Geomorphic Change Detection (GCD) implemented in ArcGIS [47] was used. In this case, a Limit of Detection (LoD) of 0.5 m, related to RMSE values of data acquisition, was estimated for the analysis of LiDAR data from 2008, while for data from 2010 to 2015, the LoD was established in 0.2 m, following specifications by the IGN [37]. This limit sets the minimum threshold for a variation to be considered as an actual change, therefore removing possible errors related to analysis uncertainty.