## 3 Results and Discussion
The forest \(AGB\) evaluation by the [PERSON] et al. model was performed by \(NDVI\)-based \(CPA\) whereas \(AGB\) estimation by the proposed approach is achieved by \(MSAVI\)-based \(FVC\). Similarly, a random forest-based regression model was developed for forest \(AGB\) valuation by [PERSON] et al. [9]. This model developed a relation between RED-edge-derived vegetation index and ground \(AGB\) using a random forest algorithm [9]. These results likewise revealed that the selection of the vegetation indices might also be important parameters in forest \(AGB\) estimation with accuracy. There could be a reason that \(MSAVI\) is more accurate compared to \(NDVI\) for vegetation cover recognition in the forest. Moreover, precise \(CPA\) estimation is difficult compared to \(FVC\) estimation from optical remote sensing data could be the other reason. Furthermore, the [PERSON] et al. model revealed that not only robust algorithm selection is valuable, but rather precise vegetation identification and evaluation are also crucial in forest \(AGB\) model development. However, the developed model considered vegetation index selection for precise forest cover quantification before forest \(AGB\) model development. The developed approach is straight forward and simple to use for accurate assessment of forest \(AGB\). In addition, a generalized forest \(AGB\) approach is presented so that the developed approach can be applied to other optical remote sensing data for forest \(AGB\) estimation from different study locations. Overall, these results and analysis from the study area show that Sentinel-2 has \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Ground \(AGB\)** & **Image \(AGB\)** & **Image \(AGB\)** & **Image \(AGB\)** \\
**Location** & **(in kg)** & **(Proposed)** & **([PERSON] et al. Model)** & **([PERSON] et al. Model)** \\ \hline SL1 & 18659.89 & 18840.10 & 17989.54 & 18469.38 \\ \hline SL17 & 29352.34 & 30196.83 & 34432.89 & 32746.29 \\ \hline SL18 & 13470.94 & 14310.09 & 13934.32 & 15583.64 \\ \hline SL19 & 17159.58 & 17551.79 & 16930.83 & 17184.53 \\ \hline SL20 & 27933.05 & 28272.18 & 31030.86 & 30734.47 \\ \hline SL21 & 20521.26 & 19412.03 & 24418.61 & 21435.69 \\ \hline SL22 & 17844.75 & 18221.16 & 20692.22 & 18974.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison and validation of the image retrieved forest \(AGB\) with ground forest \(AGB\). remarkable potential for estimation of the forest \(AGB\). Based on the developed approach and the Sentinel-2 image, the average forest \(AGB\) obtained was 463.66 tons per hectare from the study area, which effectively agreed with the ground observation-based estimates. One of the key aims of this work was to develop a novel method for precise, accurate, and quick evaluation of the forest above ground biomass from satellite optical remote sensing data. Sentinel-2 satellite data was utilized for this purpose. The ground \(FVC\) from optical remote sensing imagery at each ground sample location was derived. Apart from various available vegetation indices, a new method has been developed for precise, accurate, and rapid evaluation and validation of above ground forest biomass estimation using optical remote sensing in Tundi reserved forest area, India. This forest area typically represents a subtropical forest type found abundantly in the region for which the study was undertaken. The ground validation and comparisons with the developed \(CPA\) were validated. This method demonstrated better results than any other known method developed so far. Using this method, \(AGB\) was derived to be 463 tons per hectare for the study area and further, the satellite-derived data were also compared with the ground estimated data. Finally, the desired level of accuracy was achieved using the model developed. This leads to understanding and appreciating the value of the research outcome from this work.