# Visibility Assessment of New Photovoltaic Power Plants in Areas with Special Landscape Value
## 4 Results and Discussion  
In order to evaluate the values of the _GAPT_ variable in the region under study, the stages described above in Section 2 have been followed:  
1. Using a GIS tool and considering the cell size of the DSM, equidistant nodes were generated every 25 m (\(d_{n}\) = 25 m) along each route of the Way of St. James. The points  
Figure 3: Way of Saint James through La Rioja (Spain). corresponding to each of the five routes were collected in different vector data layers, in whose attribute tables, each node had associated data such as the average height above the ground of the observer's eyes (1.61 m in this study), and the maximum height \(H_{pv}\) above the ground of the PV facility (\(H_{pv}\) = 6 m);
2. GIS tools were used to obtain the value of the pilgrim's walking speed \(ws_{n}\) associated with each node \(n\). This value was obtained applying expression (2). The value of walking speed was calculated for all the nodes of the five routes in the studied area;
3. The average observation time, \(t_{n}\), was calculated for each node, dividing the real distance between consecutive nodes \(D_{n}\) by the value of the observer's walking speed \(ws_{n}\) at that node;
4. With the suitable GIS tool, a Euclidean distance raster map was generated for each node \(n\), what allows to generate a new raster dataset containing the weight factor \(w_{i,n}\);
5. Several repeated visibility analyses were performed to calculate the visible height factor \(fhw_{i,n}\) of the future PV plant placed in the cell \(i\), following the methodology described in Section 2. Note that the length of the segment used in this case study was 1 m (\(h_{seg}\) = 1 m) and, therefore, a set of six binary raster maps was obtained. Afterwards, using map algebra GIS tools, expression (3) was applied, obtaining a raster map with the values of the \(fhw_{i,n}\) factor as a result, which represents the fraction of the PV facility located in the cell \(i\), that can be seen from node \(n\);
6. The skyline index \(Isk_{i,n}\) was evaluated using the expression (4). For each node \(n\), two raster maps were obtained, each one storing the values of \(\theta h_{i,n}\) and \(\theta f_{i,n}\), respectively, in cell \(i\). Then, using map algebra techniques, the \(Isk_{i,n}\) index was determined, which made it possible to give more weight to the visibility of future PV plants that could be seen above the skyline. Next, taking into account the annual number of walkers traveling all the nodes of each of the five routes, \(NOY_{k}\), the expression (6) was applied, obtaining a raster map in which each cell \(i\) represented the value of the accumulated perception time \(APT_{i,k}\), achieved by the PV plant placed in that cell. The volume of data in the raster format obtained in the calculation process was so high that R functions [45] were used to speed up this process and to automate access to the geo-algorithms integrated in QGIS from outside QGIS [46]. Finally, the expression (7) was applied to obtain the global value of accumulated perception time in each cell \(i\) (\(GAPT_{i}\)). Applying the described methodology to the selected study area, the \(GAPT\) map shown in Figure 4 was obtained. In the area of study, the urban centres are represented by yellow polygons and the different routes of the Way of St James are represented by red lines. The cells that show the DEM as background (green and grey cells at the top and bottom of Figure 4) represent the areas that have a zero \(GAPT\) value and correspond to sites where the PV facilities are not visible from the routes, or to sites that are at a greater distance than the established limit of vision (10 km in our case study). These cells will be the best areas for the location of this type of solar facilities, from the point of view of their low observability from the routes of the Way of Saint James.