## 4 Discussion
Research has been conducted evaluating the accuracy of image classification based on various data sources, including very high resolution imagery, nDSM, and HSI [2,11]. Recent advancements in spectroscopy satellite missions, such as PRISMA, have made this last option much more accessible. However, existing open-access satellites provide images at a relatively coarse resolution (30 m or lower), which works quite well in larger natural environments [34]. However, a finer resolution is necessary in urban environments where images are typically composed of spectrally mixed pixels [35], and canopies are fragmented and often made up of different species grouped close together [2]. Satellite HSIs typically have a spatial resolution of about 30 m and consist primarily of spectrally mixed pixels in urban contexts [35]. Hyperspectral data with higher spatial resolution can be acquired from airplane field missions with sensors such as CASI with a 2-5 m spatial resolution, depending on altitude [27], or restricted access satellite missions, such as TacSat-3 with a 4 m spatial resolution [61]. However, both of these sources pose data availability and accessibility problems for entities involved in biodiversity monitoring (government bodies, universities, non-profit organizations, etc.). Airplane missions only capture a small field of view and private or military high-resolution satellites are either too costly or too classified to be acquired. Thus, HSI remains one of the best options, if it can be refined to a higher resolution. Viable techniques to address these limitations and enhance HSI for urban classification include pansharpening and hypersharpening, which improve spatial resolution by fusing data with higher-resolution sources. In fact, the validity of the pansharpening or hypersharpening methods to enhance HSI data from PRISMA while preserving the original spectral characteristics has been demonstrated [39] by comparing the enhancement result with hyperspectral data acquired at the origin from higher resolution aircraft sensors(CASI/SASI). However, each one of these techniques has advantages and drawbacks. This study availed itself of pansharpening for the specific reasons explained below. Research shows that using pansharpening to adjust the reflectance of hyperspectral data improves the spatial resolution of the new pixels. However, this process only adjusts the intensity of the hyperspectral signature based on the panchromatic information, without adding any new radiometric information [38; 39; 41]. Approaches proposed in other studies [37; 38; 39], which use hypersharpening with Sentinel data, would likely yield even more accurate results, particularly in flat areas where orthorectification of non-coregistered images is less challenging. In such a context, Hypersharpening may have some additional potential compared to single-band pansharpening. Hypersharpening regards the fusion of higher spatial resolution multispectral data with hyperspectral data. Each of the multispectral bands is used as a sharpening band, as opposed to pansharpening, where only the panchromatic band is used. Hypersharpening allows the corresponding hyperspectral bands to be grouped using the wavelength range of each multispectral band. Hyperspectral enhancement is thus determined as a function of the values recorded by the multispectral sensor in different spectral regions, instead of just once for the entire panchromatic range, as in pansharpening. However, while this technique certainly has advantages in terms of spectral signature reconstruction, it also has drawbacks. These are related to the use of scenes acquired by different satellites, at different times, at different angles, and using a mismatched orthorectification. This method relies on images taken from different satellites in different moments and thus demands high precision in orthorectification to prevent spatial misalignments and the overlapping of acquisition dates.