## 4 Discussion  
The investigation's results demonstrate that the direct use of pansharpening by taking advantage of the coregistered panchromatic data can lead to excellent classification results (OA from 0.64 to 0.78) in urban areas with complex topography, dense settlement patterns, \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Species** & \multicolumn{5}{c}{**HSI (F1)**} & \multicolumn{5}{c}{**HSI2 (F1)**} \\ \hline  & **T1** & **T2** & **T3** & **T4** & **T5** & **Av.** & **T1** & **T2** & **T3** & **T4** & **T5** & **Av.** & **Interval** \\ \hline _Pinus pinea_ & 0.75 & 0.78 & 0.74 & 0.71 & 0.73 & **0.74** & 0.86 & 0.86 & 0.81 & 0.83 & 0.81 & **0.84** & **0.10** \\ \hline _Tilia \(\times\) europaea_ & 0.68 & 0.69 & 0.65 & 0.61 & 0.60 & **0.65** & 0.89 & 0.86 & 0.84 & 0.86 & 0.79 & **0.85** & **0.20** \\ \hline _Platanus \(\times\) acerifolia_ & 0.40 & 0.22 & 0.00 & 0.00 & 0.14 & **0.15** & 0.75 & 0.79 & 0.32 & 0.68 & 0.62 & **0.63** & **0.48** \\ \hline _Quercus lix_ & 0.41 & 0.00 & 0.00 & 0.00 & 0.18 & **0.12** & 0.50 & 0.47 & 0.11 & 0.29 & 0.00 & **0.27** & **0.15** \\ \hline _Eucalyptus spp._ & 0.40 & 0.50 & 0.00 & 0.15 & 0.00 & **0.21** & 0.64 & 0.40 & 0.53 & 0.48 & 0.25 & **0.46** & **0.25** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: This table refers to the test phase and shows the F1 values by class obtained with HSI1 and HSI2 in each trial (T), the relative average values for each HSI and the interval between HSI1 and HSI2 metric averages. Figure 5: Classification results obtained in all trials (T) using HSI1 and HSI2. and fragmented green areas [45]. The UA and PA values can be increased, and both the commission error and the omission error can be reduced by using the improved hyperspectral data. The result is an improvement in the reliability of the classification map. These key findings clearly show that the operation alone significantly improves the quality of the classification, given that the same conditions and software are used. For the classes under investigation with a larger number of GTs, the F1 value increased on average from 0.74 to 0.84 for the _Pinus pinea_ class, and even more significantly went from 0.65 to 0.85 for the _Tilia \(\times\) europaea_ class. The classification of these two species benefits from a high number of GTs, and therefore the results confirm the efficiency of the proposed method. It can be stated that the application of pansharpening in an urban environment can lead to a considerable improvement in classification accuracy. Although other species with fewer GTs show even more pronounced improvements, these are less significant due to the smaller sample size of GTs. The improved accuracy metrics (OA, K, UA, PA, and F1) show that pansharpening can be an affordable alternative for high spatial resolution hyperspectral data. The proposed method enhances spatial resolution by applying pansharpening to the hyperspectral bands in the range covered by the panchromatic band (400-700 nm), leaving the values recorded by the hyperspectral sensor in the other bands unchanged. The reconstruction of the details of the spectral signature and the spatial patterns is definitely more effective with the use of multispectral data over several intervals of the spectrum ([PERSON], 2024 [39]). However, the pansharpening operation allowed the spatial distribution of the different surfaces to be highlighted without altering the shape of the spectral signature curve, especially in the mixed pixels of the 30 m resolution data. This process resulted in a better distinction of the spectral signature of the studied classes and improved classification accuracy. Thus, to improve the spatial resolution of the data without using laborious techniques, pansharpening the PRISMA data with the coregistered panchromatic band is a viable and accessible option. One of the most promising applications of remote sensing technology is environmental resource and biodiversity monitoring.