## 3 Results  
The current research aimed to propose an effective approach for LULC identification. The proposed methodology is divided into four major parts. First, Landsat images were obtained, followed by training and testing data. After that, we introduced standard bands, as well as new classification variables. The images were then classified using the RF classification technique. Finally, we assessed the accuracy of the landcover map and its implications for the study area's forest cover dynamics using test data. Figure 3 and Table 3 show that high to very-high vegetation density comprises areas with an altitude higher than 1700 m and medium-high vegetation density comprises about 44%, 22%, and 33% of the whole area from 1980, 2000, and 2020. Medium vegetation density was observed mostly from 1250-1700 m altitude in areas of 22%, 26%, and 18% for 1980, 2000, and 2020, while the majority of areas with low to very dense vegetation density cover are found at elevations of less than 1250 m. The percentage of land covered by low to very low vegetation was calculated to be 33 percent, 52 percent, and 49.2 percent in the year 1980, 2010, and 2020, respectively. Dense vegetation cover ranges from high in the northwest to medium in the south and from extremely low in the southeast to low in the southeast were observed, as demonstrated in Figure 3. Table 3 illustrates the distribution of vegetation cover from very high to high, medium to low, and low to very low vegetation cover and its variation and distribution with the digital elevation model. We attempted to determine 8 features for the Landsat 1-3 time series data for the year 1980, and 33 features for the Landsat 4-5 time series data for the years 1985, 1990, and 1995. A total of 34 features were determined for the Landsat 7 image for the years 2000, 2005, and 2010. A total of 25 features for the Landsat 8 time series images for the years 2015 and 2020 were determined. In terms of the Landsat 5 images, B4, B5, DEM, and slope were the four variables that had the most significant impact. High-importance factors comprised DEM and B1 for the time series data obtained from Landsat 7, and B4, B11, and DEM for the time series data obtained from Landsat 8. However, to some extent, B3, DBI, and NDVI assisted in classifying all datasets. The overall outcomes indicate that DEM, B4, B5, B11, and VCP are the most critical factors for accurate RF classification, while GNDVI, EVI, and SAVI are less significant in the classification approach. The function plot (modelRF) was \begin{table}
\begin{tabular}{l c c} \hline \hline
**Land Cover Classes** & **Training Pixels** & **Validation Pixels** \\ \hline Forest cover & 21,434 & 9356 \\ \hline Woodland cover & 32,224 & 13,410 \\ \hline Other land cover & 16,342 & 7234 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Indicates the total number of training and validation pixels of each land cover type. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**V Vegetation Cover** & **NDVI** & **VCP** & **SAVI** & **DEM** \\ \hline High to very High & \textgreater{}0.45 & \textgreater{}0.75 & \textgreater{}0.45 & \textgreater{}1700 \\ \hline Medium & 0.35–0.55 & 0.55–0.75 & 0.28–0.53 & 1250–1700 \\ \hline Low to very low & \textless{}0.25 & \textless{}0.55 & \textless{}0.33 & 800–1250 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Illustrates the distribution of vegetation indices values, ranging from low to high levels of vegetation cover, in relation to changes in elevation. used to determine the optimal tree size (ntree) based on the OOB error rate. Inaccuracies in the OOB ranged from 0.021 to 0.0484. For all classes, OOB errors were exceptionally high between 22 trees. After 25 trees, the OOB errors remained constant. The OOB errors in the years 1980, 1990, and 2010 were the least, followed by 2005, 2015, and 2020 as shown in Figure 4. OOB errors were more prevalent in 1985 and 1995.