## 3 Results and Discussions
Moreover, if we consider that the Basilicata region is considered one of the European areas with High Natural Value farmland [42], the provision of an APW management tool is even more important. \begin{table}
\begin{tabular}{c c c} \hline \hline
**APS Class** & **Cells Numbers** & **\%** \\ \hline
0–2 & 113 & 23.64 \\
2–10 & 222 & 46.44 \\
10–30 & 122 & 25.52 \\
30–75 & 21 & 4.39 \\ \hline Total & 478 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Number of hexagonal cells for each of the APS percentage classes shown in Figure 7.  
Figure 7: APS expressed as a percentage for each hexagonal cell. Cells where the value is equal to 0 were not represented. In addition, the use of the GIS environment is essential for the quantification of APSs for countries where there are only agricultural statistics and censuses in tabular format. Censuses and agricultural statistics allow an indirect APS estimation, which may not be up-to-date and by administrative areas (region, province, and municipality). However, for continuous monitoring for sustainable planning purposes, it is necessary to have spatially accurate and constantly updated data. Indeed, it is essential to geolocate APSs, because this represents the main dataset for subsequent quantifications of APW [15; 16] and for complex spatial analyses essential for the implementation of spatial decision support systems [43]. GIS-based methodologies, particularly FOSS (Free and Open-Source Software) methodologies, allow for the integration of different techniques, geographic tools, and different types of geodata, so that the same work environment can be used to perform all analyses and assessments of agricultural activities in general [44]. In this paper, we have used digital images that can be easily processed even for those who do not have high technical skills. Orthophotos allow an effective and truthful classification and distinction of plastics, typologies, and rural context. New satellite imagery, and in particular, that of the Sentinel-2 mission allow the implementation of continuous and standardized monitoring of plastics [45]. Obviously, for regional-scale applications and land-use planning, the use of satellite imagery can be challenging for local authorities, but the index employed in this study can be a valuable support to those operationally involved in APS management. The overall accuracy recorded with the application of the RPGI index is about 55%, which was obtained by comparison with APS visual classification for the entire study area. This not-high value is due both to the resolution of the satellite images (as it does not allow identification of some very small plots) and to the fact that the application of the RPGI index is not a real supervised classification of the territory, so it only provides a rough indication of the presence of plastics and needs further investigation to improve its exclusive usability. As reported by [PERSON] et al. [30], the RPGI index is less sensitive than other classification methodologies because it is related to the type of crop, and if the plastic greenhouses are painted or not during their use. Moreover, greenhouse plastics have high correspondences to bare soil spectra, but still allow a preliminary detection of APSs [30]. In addition, a 5% incidence of classification errors emerged. From the analysis of APS distribution data, it appears that, in the perspective of detailed planning in areas where there is not a continuous extension of plastics in the fields, it is necessary to use other methods of spatial aggregation of APSs different from the use of simple administrative boundaries [18]. Operating on grids, besides providing data that can be easily used in a spatial decision-support system useful for plastic-waste management, it is possible to perform analyses that also include different aspects such as the evaluation of visual impact or ecosystem services [46; 47].