## 2 Materials and Methods
### Datasets
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Subsystem**} & \multirow{2}{*}{**Band Number**} & **Wavelength Range (\(\upmu\)m)** & **Band Central Wavelength (\(\upmu\)m)** & **Bandwidth (nm)** & **Spatial Resolution (m)** \\ \hline \multirow{8}{*}{VNIR} & \multirow{2}{*}{1} & 0.43–0.45 & 0.443 (Coastal & \multirow{2}{*}{20} & \multirow{2}{*}{60} \\  & & & & aerosol) & \\ \cline{1-1} \cline{3-5}  & 2 & 0.46–0.52 & 0.490 (_BLUE_) & 65 & \\ \cline{1-1} \cline{3-5}  & 3 & 0.54–0.58 & 0.560 (_GREEN_) & 35 & \\ \cline{1-1} \cline{3-5}  & 4 & 0.65–0.68 & 0.665 (_RED_) & 30 & \\ \cline{1-1} \cline{3-5}  & 5 & 0.70–0.71 & 0.705 (Vegetation _RED_ edge) & 15 & \\ \cline{1-1} \cline{3-5}  & 6 & 0.73–0.75 & 0.749 (Vegetation _RED_ edge) & 15 & 20 \\ \cline{1-1} \cline{3-5}  & 7 & 0.77–0.79 & 0.783 (Vegetation _RED_ edge) & 20 & \\ \cline{1-1} \cline{3-5}  & 8 & 0.78–0.90 & 0.842 (_NIR_) & 115 & 10 \\ \cline{1-1} \cline{3-5}  & 8a & 0.86–0.88 & 0.865 (Vegetation _RED_ edge) & 20 & 20 \\ \cline{1-1} \cline{3-5}  & 9 & 0.93–0.95 & 0.945 (Water vapor) & 20 & \\ \cline{1-1} \cline{3-5}  & 10 & 1.37–1.39 & 1.375 (_SWIR_–Cirrus) & 30 & \\ \hline \multirow{2}{*}{SWIR} & \multirow{2}{*}{11} & 1.57–1.66 & 1.610 (_SWIR_) & 90 & \\ \cline{1-1} \cline{3-5}  & 12 & 2.10–2.28 & 2.190 (_SWIR_) & 180 & \\ \hline \hline \end{tabular} Note: _VNIR_, visible and near infrared; _SWIR_, short-wave infrared; _NIR_, near infrared.  
\end{table} 
Table 1: Band specification of the Sentinel-2 image. Step 1: Preprocessing--In this step, the optical remote sensing image was preprocessed for noise reduction, radiometric correction and calibration, atmospheric correction, and spatial resampling. The noise persists in most of the satellite images due to sensor malfunctioning and heating, and environmental influences reduce the quality of the data. Hence, suppression of noise contents in an image is essential to enhance the quality of the data. The distributed remote sensing images usually contain the digital number, which is a calibrated radiance value by sensor-specific gain and offset. Thus, radiometric correction and calibration are required to convert digital numbers to radiance. Further, atmospheric correction is performed to reduce the effects of atmospheric scattering and absorptions in the radiance data and convert the radiance into reflectance data. The Sentinel-2 image contains different spatial resolution in various spectral bands. Spatial resampling was used to achieve a single spatial resolution from different spatial resolutions in different spectral bands. In preprocessing, noise reduction and radiometric correction were performed using image statistics-based algorithms developed using the gain and offset of the sensor. A combined flat-field and dark object subtraction method was developed for atmospheric correction and conversion of a radiometric image into a reflectance image. The Gram-Schmidt pan sharpening algorithm was used to achieve the entire spectral bands into a single high spatial resolution. Step 2: Forest \(FVC\) calculation--In this step, the forest \(FVC\) at each pixel level was calculated from optical remote sensing imagery using the \(FVC\) model proposed by [PERSON] et al. (2016) as follows:  
Suppose spectral information of pure vegetation pixel and pure soil pixel are represented as \(S_{v}\) and \(S_{s}\). Then spectral information (\(S\)) of an image at each pixel is composed of spectral information of vegetation cover (\(S_{vgg}\)) and nonvegetation cover (\(S_{o}\)). If forest \(FVC\) in a pixel is represented as \(F_{c}\), then a fraction of nonvegetation cover should be \(1-F_{c}\). Thus, mathematically, forest FVC at a pixel level can be calculated as follows:  
\[S_{vgg}=S_{v}\times F_{c}, \tag{1}\]  
\[S_{o}=S_{s}\times(1-F_{c}) \tag{2}\]  
\[S=\big{(}S_{vgg}+S_{o}\big{)}\leftrightarrow\{(S_{v}\times F_{c})+(S_{s}\times (1-F_{c}))\}, \tag{3}\]  
\[\text{hence, }F_{c}=\frac{S-S_{s}}{S_{v}-S_{s}} \tag{4}\]  
Figure 2: Flow diagram of the proposed methodology.