## 2 Materials and Methods
### Land Use/Cover Change Analysis
#### 2.2.2 GIS Processing  
To trace the processes of peri-urban development over the years in the study area, the cartographic documentation, covering about two hundred years, was analysed. As a first step, the hard copies of the maps were scanned as raster files in TIFF format at 300 dpi resolution. Then, the raster datasets were imported into ArcMap of ESRI(r) for the georeferencing process, which results into a coordinate transformation algorithm; a first order polynomial was used. The algorithm links the scanned historical map to the current reference projection system (UTM-WGS84, zone 33 N [23]. In order to associate the historic maps with a common geo-referencing system, the ground control points (GCPs) method was used (ArcMAP georeferencig tool), basing on georeferenced topographic regional maps (TRM, 2004-2005). This method consists of establishing a mathematical/statistical relationship between the coordinates of the pixels on the acquired raster map and those of the reference map by means of a set of ground control points (GCPs). The number of GCPs used was high enough to achieve a satisfactory level of transformation accuracy. The average root mean square (RMS) value was calculated for each map, and the associated error to the transformation was obtained by the link table in the ESRI ArcMAP software. Then, the MapAnalyst Visual open-source Java application ([PERSON] and [PERSON] 2011) was used to assess the accuracy of the historical maps. MapAnalyst calculates the mean position error (MPE) and standard deviation (SD), obtaining the mean deviation error for all of the control points on the old maps. The MPE and SD lower values are referred to higher values in terms of the accuracy of the old maps. Data processing and georeferencing accuracy assessment are reported in [PERSON] et al., 2016 [9]. Then, historical maps of the same date were patched together with the mosaic raster tool of the ArcMap toolset. The manual vectorization procedure was preferred, because it does not require specific software and if performed accurately, it can guarantee considerable precision in terms of the interpretation of map contents. The entities were visually recognized and reconstructed according to the work of [PERSON] et al. (2016) [9]. For the two topographic maps of 1817 and 1907, the meaning of symbolism was derived by [PERSON] [24], for instance, the label of agricultural use was generally highlighted by irregular polygons and small circles along the sides, urban areas were generally black rectangles and sparse vegetation was depicted by patchy narrow lines (Figure 4). The maps of 1960 and 2009 were thematic, so classes were visually recognized according to the attribute clearly reported on the legend. In order to perform a multi-temporal direct comparison of paired land use maps, it was necessary to apply thematic generalization [9; 22; 23; 25]. Starting from the 1817 map labels, the classes considered were the following: agricultural use, vineyards, urban, sparse vegetation, forests and waters. The water class was omitted in the subsequent evaluation of the Vesuvian landscape, as it was not of interest within the perimeter of the study area. The choice to not combine the classes of vineyards and agricultural use was made in order not to lose the information on vineyards which has been reported with a very clear symbolism already, starting with the cartography of 1817. The symbol used for vineyards was the same in the two maps of 1817 and 1907, represented as a wavy line with a small trait on the bottom part, and it was clearly different from other type of rows of plants (Figure 4). For olive groves, the other historically widespread class of agricultural use in the area, however, it was not possible to introduce a separate class since no dedicated symbolism was found on the map sheets of 1817. The thematic generalization for the 1817, 1907 and 1960 map legends was derived by [PERSON] et al.