Abstract
Monitoring and preserving biodiversity in mountain agroecosystems are critically important because these systems provide several services such as carbon sequestration, food and wood supply, as well as offering habitat diversity for different species. For example, extensively managed alpine pastures in South Tyrol, Italy, host rare and endemic plant species and represent one of the few examples of traditional management in the area. Remote sensing techniques can provide useful information on biodiversity over large areas. In this context, we aim to explore the promising potential of the hyperspectral sensor on the Italian Space Agency's PRISMA mission, providing data in 239 spectral bands.
In this study, we exploit PRISMA images to estimate species diversity in the grasslands of the Sciliar Natural Park in South Tyrol (Italy). Specifically, we verify the existence of a direct relationship between remotely sensed reflectance values and species diversity derived from field data by using the Spectral Variation Hypothesis. According to this hypothesis, areas showing pronounced spectral variation in an image are often indicative of high environmental heterogeneity, thus serving as a powerful indicator to estimate species diversity. A field data collection campaign was carried out during summer 2023 to quantify species diversity indices, e.g., the Shannon Diversity Index and the Pielou´s Evenness Index. From two PRISMA images, acquired during the summer and autumn of 2023, spectral diversity indices like the Rao's Q Index were calculated. To verify the relationship between in situ and remotely sensed data, we compare the satellite-based spectral variation with the ground-based species diversity by regression analysis. Results provide interesting insights into the strengths of PRISMA hyperspectral data, such as spectral resolution, and their limitations, such as low spatial resolution and availability of images.