## 5 Conclusions  
This study aimed to achieve both a technical and a practical objective: to evaluate the contribution of pansharpening to improve image classification accuracy using hyperspectral data, so that it is also useful in urban settings; and, to apply this method to a real-world problem, specifically urban tree monitoring, through a simple pansharpening application that only uses the more easily accessible panchromatic band. In order to carry out these objectives, the classification accuracy results for some tree species obtained with HSII (GSD 30 m) and HSI2 (GSD 5 m) were compared. The results showed that pansharpening improves classification quality in dense urban areas with complex topography. In fact, pansharpened data led to significantly higher classification accuracy for all the examined species. Through its investigation, this study made contributions by:  
* introducing a novel application using pansharpening techniques to improve classification performance in urban environments by enhancing the spatial resolution of PRISMA hyperspectral imaging data;
* demonstrating its efficiency by applying the method to a practical real-world use of pansharpened image classification for urban tree monitoring. Thus, dissemination of this method could improve HSI classification performance to help public administrations that are currently struggling to meet international and national policy demands to monitor biodiversity at various scales of governance. In fact, the study's results confirm one of the objectives of the PRISMA mission, which is intended as a precursor to the applied mission: to evaluate the benefits of using coregistered panchromatic data [30; 42]. To the best of our knowledge, there are no specific evaluations of pansharpening for PRISMA data based on the use of GTs for classification purposes in urban environments. Based on the results of this initial study, it could be said that the PRISMA mission has confirmed the usefulness of a panchromatic band sensor on hyperspectral satellite platforms. Based on these findings, the following recommendations can be made. Firstly, it seems that hyperspectral data have potential to be used to differentiate the spectral signatures of trees in complex urban environments. Indeed, this technique addresses the needs of public administrations seeking to follow international and national urban forestry policies, recognizing the many contributions made by urban trees to health and wellbeing, ecosystem functions, biodiversity conservation, and sustainable development. In Italy, national legislation such as the _Regulations for the Development of Urban Green Spaces_ (Italian Law n. 10, 14 January 2013) [62] and the _Minimum Environmental Criteria (CAM) for Public Green Space Management Services and the Supply of Green Care Products_ (Ministerial decree n. 63 of 10 March 2020) [63] require municipal administrations to keep up-to-date georeferenced tree inventories specifying the number, species, health, and location of specimens and a plan for their management. This study was fortunate enough to be able to make use of an extensive inventory provided by the Public Greenery Department of the municipality of Naples. However, not all cities are able to keep up with policy demands. In fact, the same CAM legislation mentioned above was conceived to nudge municipalities into compliance by making up-to-date tree inventories and management plans a mandatory step in public procurement processes. The problem regards the limited number of public employees with the specialized skills necessary to create and update such an inventory and the huge amount of time such a feat requires. The approach proposed by this study could clearly be of great use to speed up this enterprise. By using hyperspectral data for initial classification, it is possible to provide a preliminary classification and then have field personnel focus solely on verifying changes over time, which can be identified using GIS techniques.