## 2 Materials and Methods
### Pre-Processing and Identification of the Forested Areas
#### 2.2.1 Multispectral Dataset  
All multispectral data from the Pleiades satellite is made up of four multispectral (MS) bands (blue = 0.43-0.55 \(\upmu\)m; green = 0.50-0.62; \(\upmu\)m; red = 0.59-0.71 \(\upmu\)m; near-infrared = 0.74-0.94 \(\upmu\)m), with a spatial resolution of 2 m, and a panchromatic band with a 0.5 m resolution that covers wavelengths between 0.47 and 0.83 \(\upmu\)m of the visible spectrum [47].  
The first phase of data preparation involved preprocessing the Pleiades image acquired on 17 November 2018 using PCI Geomatica 2017 software. Atmospheric correction was performed for both the multispectral and panchromatic bands using the ATCOR module, and pansharpening was carried out using the \"High Performance Image Fusion\" algorithm to achieve a 0.5 m resolution and spatial enhancement of the MS bands. Orthorectification of the image was accomplished using the OrthoEngine module with [PERSON]'s mathematical model, utilizing GCPs and the DTM of the metropolitan city of Naples with a 1 m resolution. The processed data were then clipped to the ROI, resulting in the multispectral dataset (MS_DS) used for the subsequent phase.  
In the second phase, a preliminary classification of the area was performed to distinguish trees from the rest of the land cover. The availability of the VHR MS_DS was crucial for this operation because the hyperspectral data alone, with a GSD of 30 m, would pose significant scale limitations in detailing individual tree canopies. Therefore, the MS_DS was segmented using eCognition Essential 1.30 software with the Multiresolution Segmentation algorithm. The segmented areas corresponding to tree canopies were separated from other segments in two steps.  
The first step involved the separation of only vegetated areas, using the normalized difference vegetation index (NDVI) and only selecting areas with NDVI > 0.2, through the Threshold Segmentation/Classification algorithm. NDVI, calculated as a ratio between near-infrared and red spectral reflectance, leverages the way photosynthesizing leaves reflect light. On land, NDVI values range from 0 to 1, where 0 indicates no plant cover, and 1 represents dense, healthy vegetation. Following [48], an NDVI threshold (>0.2) was used to detect all vegetation, including areas with lower photosynthetic activity.  
The second step involved the separation of trees from grasslands and shrubs using the same algorithm but based on the normalized difference between the DSM and the DTM (nDSM) and only selecting vegetated areas with a height greater than 5 m. A DSM is a 3D model of the Earth's surface representing everything visible, including objects such as buildings and vegetation. A DTM is a 3D representation of the Earth's surface including bare-ground topography but excluding objects such as buildings and vegetation. This difference can be used to filter images for analysis, selecting only objects above the terrain. The vector data obtained at the end of this procedure containing polygons of the wooded areas was subsequently rasterized with a spatial resolution of 0.5 m and used as a clipping mask for the hyperspectral dataset (HSI_mask). The HSI_mask is shown in Figure 3.