## 5 Conclusions
Furthermore, the collection of GTs presents an interesting opportunity for municipalities to involve other stakeholders, such as educational institutions, associations, and citizens [64]. This study's limitations are related to its focus on verifying the utility of pansharpening on PRISMA data by classifying tree species in an urban environment. While it successfully demonstrated that hyperspectral data with a 30 m resolution is useful not only in the field of forestry but also in urban areas, it was constrained by the availability of spaceborne hyperspectral data. Although easier to obtain than the other sources mentioned above, it still remains challenging for interested parties to acquire usable images in their ROI, under the conditions that they require (season, cloud cover, etc.). There is currently a very high demand for image acquisitions from users and a concentration of requests limiting the frequency of data availability [65]. The high demand for image acquisitions often makes it difficult to request new data as many users must wait their turn according to their priority status. Future investigations could certainly start from the classification and spatial distribution of species carried out here and look more in depth at such phenomena as fragmentation and ecological connectivity in urban environments [48]. Furthermore, the obtained results suggest that future analyses could identify individual species to uncover specific vegetation characteristics and detect potential diseases. Such an approach would likely yield statistically significant results and could provide a valuable foundation for urban, peri-urban, and rural forest inventories. Furthermore, hyperspectral data could be used to compare specimens of the same species that are in different contexts in order to study phenological differences, or health status in urban environments or as a response to climate change. Today's environmental, climate, and biodiversity challenges require innovative solutions that can be implemented at various scales with limited resources. Technologies like remote sensing and AI offer precise tools for monitoring and management. International policies, such as Agenda 2030 and the Sustainable Development Goals, increasingly rely on data-driven and adaptive management approaches. PRISMA and other hyperspectral satellite missions can significantly contribute to these global efforts by providing data that, when enhanced through techniques such as pansharpening, become more applicable in urban environments. For effective urban tree management and improved policy implementation, it is crucial to leverage such advancements to ensure accurate, efficient, and actionable environmental monitoring. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpicom/article/10.3390/rs16193730/s1](https://www.mdpicom/article/10.3390/rs16193730/s1)]([https://www.mdpicom/article/10.3390/rs16193730/s1](https://www.mdpicom/article/10.3390/rs16193730/s1)), File S1: Error matrices with accuracy metrics calculated for trial 1 to 5 using HSI1 and HSI2. **Author Contributions:** Conceptualization, M.P., G.D., C.F., A.P., E.C. and L.B. ; methodology, M.P., G.D., C.F. and L.B. ; software, M.P., G.D. and L.B. ; validation, M.P., G.D., C.F., A.P. and E.C. ; formal analysis, M.P. and G.D.; investigation, M.P. and G.D.; resources, M.P., G.D. and L.B. ; data curation, M.P. and G.D.; writing--original draft preparation, M.P., G.D. and C.F. ; writing--review and editing, M.P., G.D. and C.F. ; visualization, M.P. ; supervision, L.B. ; project administration, L.B. ; funding acquisition, L.B. All authors have read and agreed to the published version of the manuscript. **Funding:** This paper is part of the National Biodiversity Future Center (NBFC) project, funded under the National Recovery and Resilience Plan (NRRP).