## 4 Discussion  
This research presents a novel approach to forest detection and classification using remote sensing data and machine learning techniques, specifically the Support Vector Machine (SVM) algorithm. The study focuses on integrating vegetation indices and multispectral bands as input parameters for the SVM classification. Including these indices, which capture specific relationships between spectral bands and vegetation properties, significantly enhanced forest detection accuracy. The initial classification, which used Sentinel-2 bands as input parameters, yielded an accuracy of 98.18%. However, when individual vegetation indices were incorporated, the accuracy ranged from 98.23% to 98.56%. The accuracy was further improved by selecting the best-performing vegetation indices to 99.01%. The improvement in accuracy demonstrated the effectiveness of vegetation indices in enhancing the quality of forest detection. While the enhancement in accuracy may have appeared marginal at less than 1%, it was crucial to interpret this advancement within its broader context and implications. In remote sensing and forest detection, even minor increments in accuracy could yield substantial outcomes [10; 97; 98]. For example, a 1% augmentation in accuracy could equate to precisely \begin{table}
\begin{tabular}{l c} \hline \hline  & **Data Combinations** & **Accuracy (\%)** \\ \hline Test Data 1 + Test Data 3 (mcnari, rendvi, ndi45, gndvi, ndii) & 99.01 \\ \hline Test Data 1 + Test Data 3 (mcnari, rendvi, ndi45, gndvi, ndii) + ndmi & 98.79 \\ \hline Test Data 1 + Test Data 3 (mcnari, rendvi, ndi45, gndvi, ndii) + ndvi & 98.84 \\ \hline Test Data 1 + Test Data 3 (mcnari, rendvi, ndi45, gndvi, ndii) + sui & 98.90 \\ \hline Test Data 1 + Test Data 3 (mcnari, rendvi, ndi45, gndvi, ndii) + ireci & 98.95 \\ \hline Test Data 1 + Test Data 3 (mcnari, rendvi, ndi45, gndvi, ndii) + tcb & 98.95 \\ \hline Test Data 1 + Test Data 3 (mcnari, rendvi, ndi45, gndvi, ndii) + tvi & 98.95 \\ \hline Test Data 1 + Test Data 3 (mcnari, rendvi, ndi45, gndvi, ndii) + ireci + tcb & 98.90 \\ \hline Test Data 1 + Test Data 3 (mcnari, rendvi, ndi45, gndvi, ndii) + ireci + tcb + tvi & 98.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The overall classification accuracy is performed for: Test Data 1 + Test Data 3 + additional VI’s marked with S. No. 5 in Table 5.  
Figure 12: The most significant bands that improved the classification accuracy. identifying several square kilometres of forest that might otherwise be inaccurately classified. This precision can be pivotal for deforestation monitoring, conservation planning, or carbon stock estimation applications. Consequently, the endeavour invested in boosting the model's accuracy, despite yielding a seemingly modest improvement, could be warranted considering the potential ramifications of these significant applications. The study also emphasised the importance of carefully selecting and evaluating vegetation indices for specific classification tasks. Some indices positively impacted the classification accuracy, whereas others had no effect or even degraded the accuracy. The five indices that contributed the most significantly to the increase in classification accuracy were MCARI, RENDVI, NDI45, GNDVI, and NDII. The SVM, a supervised machine learning technique, identifies an optimal hyperplane that separates different classes based on the training data. Selecting appropriate parameters, such as the gamma and C coefficients, proves crucial for achieving accurate results. The C coefficient is set at 500 through empirical determination, whereas the gamma coefficient is established at 3. The findings of this study align with other research in the field, such as [1; 3; 4; 5; 18; 19; 20; 21], which also explored the use of remote sensing data and machine learning for vegetation classification and monitoring. These studies underscore the potential of remote sensing techniques for assessing deforestation, land cover changes, and monitoring vegetation degradation.