## 4 Proposed LU/LC Prediction Using Vision Transformer-Based Bi-LSTM Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**LU/LC Classification**} & \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**Reference Class**} \\ \cline{3-5}  & & **2012** & & **2015** \\ \cline{3-5}  & & **High Vegetation** & **Less Vegetation** & **High Vegetation** & **Less Vegetation** \\ \hline \multirow{2}{*}{Actual Class} & High Vegetation & 694 & 4 & 689 & 6 \\  & Less Vegetation & 7 & 303 & 8 & 305 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: LU/LC confusion matrix. \begin{table}
\begin{tabular}{c c c} \hline \hline
**LU/LC Classification** & **2012** & **2015** \\ \hline Overall Accuracy & 0.9891 & 0.9861 \\ Precision & 0.9901 & 0.9885 \\ Recall & 0.9942 & 0.9913 \\ F1-Score & 0.9921 & 0.9898 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: LU/LC accuracy assessment for the proposed Vision Transformer model. 98.38%, and the testing accuracy is 96.84% for our prediction model. We infer that the impact of the LST spatial variable from TIRS bands with the classified LU/LC map provides a good percentage of results. The computational complexity defines the total time taken by the computer for running an algorithm. The computational complexity of the Vision Transformer model is \(O\left(nC\right)\), where \(n\) is the size of input, and \(C\) is the number of classified LU/LC classes. The computational complexity of the Bi-LSTM prediction model is \(O\left(nkC+1\right)\), where \(k\) is the size of the spatial maps (LST) associated with input data \(n\). Hence, the total computational time of our proposed algorithm \(C_{c}\) is the arithmetic sum of the classification and prediction model, which is given in Equation (28). \[C_{c}=O\left(nC\right)+O\left(nkC+1\right) \tag{28}\]

Although the proposed Vision Transformer-based Bi-LSTM prediction model shows significant performance, its training phase requires the determination of class values associated with spatial maps for each pixel in the \(n\) images, and this is computationally expensive. ### Growth Pattern of the LU/LC Area of Javadi Hills

The growth patterns of LU/LC change in the area of Javadi Hills were performed between the years 2012 to 2027, and the results are shown in Table 9. In 2012, the LU/LC multispectral classified map was found to be 1651.04 ha (hectare) of the high vegetation and 736.85 ha of less vegetation. In 2015, the LU/LC multispectral classified map was found to be 1601.22 ha of vegetation and 786.67 ha of less vegetation. In 2018, the LU/LC predicted map was found to be 1621.18 ha of high vegetation and 766.71 ha of less vegetation. In 2021, the LU/LC predicted map was found to be 1596.04 ha of high vegetation and 791.85 ha of less vegetation. In 2024, the LU/LC predicted map was found to be 1568.23 ha of high vegetation and 819.66 ha of less vegetation. In 2027, the LU/LC predicted map was found to be 1553.17 ha of high vegetation and 834.72 ha of less vegetation. It was observed that the LU/LC changes have been frequently happening every three years in the area of Javadi Hills. The results of the LU/LC change that occurred between the years 2012 to 2027 are shown in Table 10. The comparison chart of LU/LC area statistics for the time-series data from 2012 to 2027 is shown in Figure 21. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Input Map (Year)**} & **Training Feature Map** & **Train—Validation** & **Test Data (Google** & **Predicted Map** & **Validation Accuracy** & **Testing Accuracy** \\  & **(256 \(\times\) 200 Pixels)** & **Split (82) Data** & **Earth Image)** & **Predicted Map** & **Validation Accuracy** & **Testing Accuracy** \\ \hline LU/LC & & & & & & \\ Classification— & 51,200 & 40,960–10,240 & 51,200 & 2018 & 0.9865 & 0.9696 \\ LST Map (2012, & & & & & \\ and 2015) & 51,200 & 40,960–10,240 & 51,200 & 2021 & 0.9811 & 0.9673 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Validation and testing process of the proposed Vision Transformer–based Bi-LSTM Prediction Model.