## 3 Results and Discussion  
The forest _AGB_ evaluation results from the study area using the methodology and experimental analysis described in Section 2 are produced below. The estimated forest _FVC_ images based on _NDVI_, _MVI_, _SAVI_, and _MSAVI_, respectively, are shown in Figure 3. The ground and image _FVC_ calculated from field data and Sentinel-2 respectively, for each vegetation indices extracted from the corresponding locations, are shown in Table 3. The \(R\) and _MAE_ were analyzed between the ground _FVC_ and the _FVC_ estimated using vegetation indices from Sentinel-2. The estimated \(R\) and _MAE_ values are shown in Table 4. The results show that _MSAVI_-based image _FVC_ delivered the highest correlation and lowest _MAE_. Based on the _AGB_ empirical model [27], the calculated ground forest _AGB_ from field data and the selected image forest _FVC_ are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample Location** & _FVC (GROUND)_ & _FVC (NDVI)_ & _FVC (MVI)_ & _FVC (SAVI)_ & _FVC (MSAVI)_ \\ \hline SL1 & 0.29 & 0.58 & 0.56 & 0.57 & 0.26 \\ \hline SL2 & 0.99 & 0.98 & 0.97 & 0.97 & 0.77 \\ \hline SL3 & 0.67 & 0.78 & 0.77 & 0.77 &  
Figure 3: Calculated forest fraction of vegetation cover (_FVC_) images based on (**a**) normalized difference vegetation index (_NDVI_), (**b**) modified vegetation index (_MVI_), (**c**) soil-adjusted vegetation index (_SAVI_), and (**d**) modified soil-adjusted vegetation index (_MSAVI_), from the study area. As per the derived results from the experimentation using the satellite data together with ground-based primary data, the image-based forest \(FVC\) was calculated through _MSAVI_. This demonstrated the highest correlation and least mean absolute error with respect to ground \(FVC\) compared to the other vegetation indices used in this study (Table 4; Figure 3). These comparisons revealed that the _MSAVI_ was the best vegetation index for image-based forest \(FVC\) calculation. Further, image-based forest \(FVC\) demonstrated a linear relation with a high correlation value of 0.9995 derived using Equation (13). The empirical model (Equation (14)) was useful in calculating ground forest \(AGB\). Further, the linear model between the ground forest \(AGB\) and selected image-based forest \(FVC\) showed a good correlation value of 0.75 (Equation (15)). The derived model was applied to the image-based forest \(FVC\) to calculate image-based \(AGB\) of the study area (Figure 4). This model was trained using 15 among the 22 samples collected from the field. The accuracy of the Sentinel-2-based forest \(AGB\) was assessed by comparing the remaining seven field measurements to the corresponding locations. The standard deviation was 3166.50 kg per plot, which was calculated from the forest \(AGB\) estimate from all 22 locations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Vegetation** & **NDVI** & **MVI** & _SAVI_ & _MSAVI_ \\ \hline \(R\) & 0.9696 & 0.9599 & 0.9677 & 0.9981 \\ \hline _MAE_ & 0.1941 & 0.1729 & 0.1793 & 0.0947 \\ \hline \end{tabular}
\end{table} 
Table 4: Correlation coefficient (\(R\)) and mean absolute error (\(MAE\)) of image-based forest \(FVC\) from different vegetation indices with ground forest \(FVC\).