## 2 Materials and Methods
### Methods
#### 2.3.2 Feature Extraction  
GF, Landsat, and DEM had different resolutions, and, before feature calculations, it was necessary to unify the resolution of all data. We used the resampling method to unify the spatial resolution of the data to the lowest resolution among various images. For example, when selecting GF-1 (16 m), DEM (10 m) resampled to 16 m and the Shapefile was converted to a 16 m resolution raster. **Extraction. ** We extracted 50 features comprising terrain, spectral, texture, and vegetation index information, as shown in Table 1. Among these features, terrain features were obtained using DEM, whereas the remaining features were obtained using GF-1 data. A detailed summary of the obtained features is shown in Table 2. Topographic features included 11 bands, such as slope, aspect, etc. Spectral features included 4 bands. Texture features were extracted with a grayscale level cogeneration matrix (GLCM), including 8 bands, such as the mean, variance, etc., which were extracted for 4 bands of GF-1 for a total of 32 features. Vegetation index features included the normalized difference vegetation index (NDVI), difference vegetation index (DVI), and ratio vegetation index (RVI) for a total of 3 features, as shown in Equations (1)-(3), where \(\rho_{NIR}\) is the NIR band and \(\rho_{RED}\) is the red band. \[NDVI=\frac{\rho_{NIR}-\rho_{RED}}{\rho_{NIR}+\rho_{RED}} \tag{1}\]  
\[RVI=\frac{\rho_{NIR}}{\rho_{RED}} \tag{2}\]  
\[DVI=\rho_{NIR}-\rho_{RED} \tag{3}\]  
**Selection. ** The 50 features completed by extraction were normalized to eliminate the difference in magnitude, and principal component analysis (PCA) [26] was performed to identify principal components. The first principal component demonstrated high contribution, accounting for 89.88%, and the cumulative contribution of the first three principal components was nearly 100% (as shown in Table 2). Directly using all 50 feature descriptors was not feasible due to redundancies in feature information between needleleaf and broadleaf forest areas. Therefore, a comparison was conducted to select the most prominent feature bands to be merged and used for feature extraction in the model. The feature comparison of needleleaf and broadleaf forests is shown in Figures 2 and 3, which shows that the 1 st (PCA Component-1), 2 nd (PCA Component-2), 18 th (PCA Component-18), 82 nd (GLCM Entropy-2), 83 rd (GLCM Second Moment-2), and 90 th (GLCM Entropy-3) feature bands were strongly distinguished, so they were combined into two sets of images for verification and comparison. Our pre-processing pipeline for the image dataset involved several steps. First, we converted the images to an 8-bit unsigned format to ensure a consistent data range. Subsequently, we synthesized the images as RGB 3-channel to enable color representation. Following that, we clipped the images to 256 \(\times\) 256 size to attain uniformity in dimensions. Finally, we utilized the pre-processed dataset as a training input in Pix2 Pix. The two models were the PCA-Pix2 Pix model consisting of the 1 st, 2 nd, and 18 th bands and the F-Pix2 Pix model consisting of the 82 nd, 83 rd, and 90 th bands. Figure 3: Sample interpretation comparison. Notes: before is the image combined with the first three bands of GF-1, PCA is the image combined with the 1 st, 2 nd, and 18 th principal components, and Feature is the image combined with the 14 th, 15 th, and 22 nd features of the gray-level cooccurrence matrix (GLCM). Figure 2: Statistics of different forest samples’ texture spectral features. Notes: the band number and name of the feature layers are shown.