The interest of the scientific community about geotourism is abruptly increasing, as well as that on geoparks. According to UNESCO, geoparks should define management policies addressed to increasing the awareness of local people and tourists about Earth’s dynamics to reduce the impact of climate change and natural disasters. With this aim in mind, we tried to provide a solid scientific approach to geotourism that could be useful to the development of a geotourism strategy in the Cilento, Vallo di Diano and Alburni (CVDA) Geopark, in Southern Italy. Starting from the official inventory of the CVDA Geopark, we defined the potential Education Value (EV) and potential Touristic Value (TV) of each of the 160 sites listed by applying the Brilha method. Then we selected 20 geosites and geomorphosites with high values of both the EV and TV, and we included them in two geoitineraries. The two geoitineraries move in the inner sector of the Geopark (i.e., from the Paestum archaeological area to the Vallo di Diano basin) and along a portion of the coastal stretch (i.e., from Punta Telegrafo cape to the Lambro and Mingardo rivers’ mouths). Selected sites are representative of several geoscience disciplines (e.g., geomorphology, structural geology, quaternary geology, hydrogeology), thus suggesting that the CVDA Geopark is an ideal place where dissemination of geoscience concepts may be carried out. The latter point enhances the high geotourism potential of the area. This kind of approach was not tried before in the CVDA Geopark and can be a useful example of how to promote touristic development strategies in the area.

Coastal dune ecosystems are highly threatened, and one of the strongest pressures is invasive alien plants (IAPs). Mitigating the negative effects of IAPs requires development of optimal identification and mapping protocols. Remote sensing offers innovative tools that have proven to be very valuable for studying IAPs. In particular, unmanned aerial vehicles (UAVs) can be very promising, especially in the study of herbaceous invasive species, yet research in UAV application is still limited. In this study, we used UAV images to implement an image segmentation approach followed by machine learning classification for mapping a dune clonal invader (Carpobrotus sp. pl.), calibrating a total of 27 models. Our study showed that: (a) the results offered by simultaneous RGB and multispectral data improve the prediction of Carpobrotus; (b) the best results were obtained by mapping the whole plant or its vegetative parts, while mapping flowers was worse; and (c) a training area corresponding to 20% of the total area can be adequate for model building. Overall, our results highlighted the great potential of using UAVs for Carpobrotus mapping, despite some limitations imposed by the particular biology and ecology of these taxa.