## 2 Materials and Methods
### Auxiliary Features  
To enhance classification accuracy and capture the complex landscape characteristics of the study areas, a range of spatial features were derived from spectral bands or acquired from various datasets. This study focused on the Blue, Green, Red, NIR, SWIR1, and SWIR2 bands of Sentinel-2 imagery, excluding other spectral channels to optimize computational efficiency while maintaining relevant spectral information. Band 1 (Coastal Aerosol) was omitted due to its primary application in studying coastal waters and atmospheric aerosol properties, which were not pertinent to the objectives of this study.  
Topographic features, including elevation and slope, were extracted from the Shuttle Radar Topography Mission (SRTM) digital elevation dataset provided by NASA (Washington, DC, USA). These features contribute valuable information about the terrain characteristics that can influence land cover patterns. Principal Component Analysis (PCA) was applied to the spectral bands to reduce data dimensionality and enhance information content. PCA is widely used in classification and change detection analyses, particularly in unsupervised classification, as it eliminates redundant information and improves data representation. The first three PC levels were utilized in this study, providing a compact representation of the most significant spectral variations in the imagery.  
Five spectral indices (Table 3) were derived from the Sentinel-2 multispectral bands to enhance specific land cover characteristics [42]. These indices were chosen due to their prominent use in LULC classification studies [43; 44] and their ability to differentiate between various land features, particularly forest classes, from other types of land covers.  
Textural information was incorporated using the Gray-Level Co-occurrence Matrix (GLCM), an effective method for extracting textural indices even from grayscale images [50]. The texture attributes used in this study included Homogeneity (_'idm'_), Angular  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Usage** & **Formula** & **Reference** \\ \hline Normalized Difference & Assessing vegetation & & \\ Vegetation Index (NDVI) & health and density. & & \\ Normalized Difference & Delineating open & & \\ Water Index (NDWI) & water features. & & \\ Normalized Difference & Mapping urban areas. & & \\ Built-up Index (NDBI) & Assessing vegetation & & \\ Moisture Index (NDMI & moisture content. & & \\ Soil-Adjusted & For improving & & \\ Vegetation Index (SAVI) & vegetation signal in areas with & & \\  & sparse vegetation cover. & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Spectral indices and related formulas used in this study.  
Second Moment (_'asm'_), Sum Average (_'savg'_), Entropy (_'ent'_), Contrast (_'contrast'_), and Correlation (_'corr'_).  
Lastly, to account for seasonal variations in land cover, a multi-temporal seasonal image collection from the wet season was also included. Maximum and median values were extracted from this collection for each year, along with NDVI and NDWI values, to capture seasonal dynamics that might influence land cover classification.