## 3 Results  
Based on the results presented in Table 4 and Figure 8, it can be observed that the detected forest area increased as the dataset changes. Test Data 1 resulted in a detected area of 700.81 km\({}^{2}\) (57.54%), whereas Test Data 2 yielded a slightly larger area of 705.06 km\({}^{2}\) (57.89%). However, the largest detected forest area of 706.30 km\({}^{2}\) (57.99%) was recorded with Test Data 3. These findings indicated that Test Data 3 provided additional features that enhanced the accuracy of forest detection, making it the most effective dataset for this purpose. The consistent increase in detected forest areas across the different data variations further validated the reliability of Test Data 3 in accurately identifying forested regions. The accuracy evaluation of the acquired results included assessing the classification accuracy performed on the Test Data 1 classification (ten Sentinel-2 bands) in the first step. In the second step, an accuracy assessment was performed for the classification, where individual vegetation indices (Test Data 2) were added to Sentinel-2 bands (Table 5). In the final step, an accuracy assessment was performed for the classification, for which index groups (Test Data 3) were added to Test Data 1 as input data for the classification process (Table 6). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Set** & **Detected Forest Area (km\({}^{2}\))** & **Detected Forest Area (\%)** \\ \hline Test Data 1 & 700.81 & 57.54 \\ Test Data 2 & 705.06 & 57.89 \\ Test Data 3 & 706.30 & 57.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Impact of Data Variations on Detected Forest Areas. The overall accuracy of classification performed using Test Data 1 was 98.18%. This result was a reference point for comparison with other classification results. The overall classification accuracy is performed using Test Data 1 and each index from Test Data 2 individually. S. No. column presents the accuracy assessment quality group. Figure 8: AOI Test Data 2 result and Detected Forest Areas across Different Datasets: Visualising part of the Area of Interest. Copyright: Authors, contains modified Copernicus Sentinel data 2021. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Index Reference to** & **S. No.** & **Data Combinations** & **Accuracy (\%)** \\
**Table 2 Equation No.** & & **(Test Data 1 + Test Data 2)** & **(Test Data 1 + Test Data 2)** \\ \hline (7) & 1 & Test Data 1 + MCARI & 98.56 \\ \hline (15) & & Test Data 1 + RENDVI & 98.56 \\ \hline (9) & 2 & Test Data 1 + NDI45 & 98.40 \\ \hline (5) & 3 & Test Data 1 + GNDVI & 98.29 \\ \hline (10) & 4 & Test Data 1 + NDII & 98.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The overall classification accuracy is performed using Test Data 1 and each index from Test Data 2 individually. S. No. column presents the accuracy assessment quality group. The accuracy varied when the SVM classification procedure included individual vegetation indices from Test Data 2. The improved accuracy ranged from 98.56 to 98.23% (Table 5). Five indices increased the classification accuracy (S. No. 1-4), whereas the other indices had no positive effect (S. No. 5) or had a negative effect (S. No. 6-10) on classification accuracy (Table 5 and Figure 9). The classification results of Test Data 1 and 2 combinations gave a new potential to the multispectral analysis of satellite imagery. These positive results confirmed the initial hypothesis of this research, so the next step consisted of creating groups of vegetation indices (Test Data 3). The first Test Data 3 group was created using five vegetation indices that enhanced the accuracy of Test Data 1 (Table 5 Serial Numbers 1-4). Test Data 3 combinations were added to Test Data 1 and included in the SVM procedure. The accuracy assessment of these data combinations indicated that the improvement in classification accuracy with the vegetation indices was not unnoticeable or negligible.