## 2 Materials and Methods
### Feature Generation  
Vegetation indices for assessing vegetative covers have been developed and implemented by researchers during the past half century [41; 42]. Spectral measurements are used as the basis for the generation of such indices [43]. This investigation made use of both spectral and temporal features in their respective forms. Landsat collection bands and  
Figure 2: Flow chart of the methodology for the land cover and vegetation assessment using Landsat and DEM. features taken from raw and secondary data make up these features (e.g., DEM, slope, and aspect). Mention is made of specifics regarding features taken from the Landsat archive in an effort to improve the accuracy of the land use and land cover (LULC) classification. By using the vegetation index, we may gain an idea of how much greenery is present on the planet's surface and how important it is for global climate change mitigation efforts. Consequently, the vegetation index is a helpful method for evaluating different types of vegetation cover in addition to forest cover. The NDVI's maximum value indicates a forest, and its lowest value indicates little to no vegetation [44]. The values range from \(-1\) to \(1\), where a negative number indicates an area that is devoid of vegetation or water bodies, and a positive value signifies the presence of vegetation. When the value of NDVI is high, it indicates the presence of a forest; when it is low, it reflects a lack of vegetation [45]. In 1998, [PERSON] introduced the soil-adjusted vegetation index to limit the effect of soil background on the vegetation signal. He did this by introducing a constant soil adjustment factor L into the denominator of the NDVI model, where L changes based on the reflectance values of the soil. The soil-adjusted vegetation index (SAVI) was developed to reduce the influence of soil background on the vegetation signal. The vegetation condition proportion VCP is used in this study to indicate the levels of vegetation cover that are the highest, medium, and lowest over the course of the study periods. This is crucial for maximizing classification accuracy. In order to separate the NDVI's short-term climate signal from its long-term ecological signal, a pixel-based normalization called the vegetation condition proportion (VCP) is used. It is more accurate than NDVI at assessing vegetation stress [46; 47]. The vegetation was categorized as: high to very high, medium, and low to very low using the following vegetation indices: normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), and vegetation cover proportion (VCP), respectively. Out of these, VCP gives more realistic results as shown in Figure 3. The greenness of vegetation can be measured with EVI, much like the normalized difference vegetation Index (NDVI). However, the enhanced vegetation index EVI is more sensitive in places with extensive vegetation and can account for some atmospheric conditions and canopy noise levels. An L value accounts for the canopy background, while C values are used as factors for the Earth's atmospheric resistance, and blue band values are used to account for atmospheric conditions (B) [48]. Equations (1)-(3) were used for the estimation of shows NDVI, SAVI, and VCP. Barren land is indicated by higher dry built-up index (DBI) values, which can range from \(-2\) to \(+2\). When mapping areas of bare soil and areas with other types of vegetation, it is possible to use a suitable threshold for the non-vegetated class. A DBSI of 0.26 or higher was designated as soil surface for the research area, which depends on a test with a collection of soil pixels, while areas with lower values were classified as other classes. This was determined by a test with a collection of bare ground pixels [49].