## 4 Proposed LU/LC Prediction Using Vision Transformer-Based Bi-LSTM Model
The high-temperature values indicate less vegetation, and the low-temperature value indicates a high-vegetation area. The LST (\(LST_{I}\)) and the LU/LC (\(LU_{I}\)) classification map was used as an input for predicting the LU/LC map of Javadi Hills. We combined the time-series features of LST and the LU/LC map of Javadi Hills. The impact of LST on the LU/LC map provides good results during the prediction process. For a better understanding, we show the impact of a few \(LST\) and LU/LC features in Figure 19, and we show the values in Table 4. The impact on the LST and LU/LC map strengthens our proposed predicted model with good validation results. Figure 18: Validation of LU/LC classified map for the area of Javadi Hills. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training Feature Value** & **Longitude** & **Latitude** & **Class Value** & **Class Label** \\ \hline
1 & 78.829746 & 12.581815 & 1 & High Vegetation \\
241 & 78.81025 & 12.58796 & 2 & Less Vegetation \\
1785 & 78.818244 & 12.580221 & 2 & Less Vegetation \\
733 & 78.849159 & 12.576782 & 1 & High Vegetation \\
6640 & 78.81107 & 12.57028 & 2 & Less Vegetation \\
6277 & 78.83463 & 12.576789 & 1 & High Vegetation \\
12,354 & 78.851079 & 12.59151 & 1 & High Vegetation



From the input LU/LC and LST features of 2012 and 2015, we predicted the LU/LC map of 2018 by using the Bi-LSTM model with the tuning of different parameters. The validated result provides good accuracy for our proposed model. We used the inputs of the LU/LC map of 2012 and 2015, along with the predicted LU/LC map of 2018 for predicting the LU/LC map for the year 2021. The short-term prediction was performed till the year 2027 for our study area. The working process of the Bi-LSTM model was explained in Section 3.4. The parameter used during the training process of the Bi-LSTM model is shown in Table 5. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature Value** & **Longitude** & **Latitude** & **Class Value** & **Temperature Value** & **Class Label** \\ \hline
1 & 78.82975 & 12.58182 & 1 & 32.183754 & High Vegetation \\
241 & 78.81025 & 12.58796 & 2 & 37.755061 & Less Vegetation \\
1785 & 78.81824 & 12.58022 & 2 & 37.755061 & Less Vegetation \\
733 & 78.84916 & 12.57678 & 1 & 31.708773 & High Vegetation \\
6640 & 78.81107 & 12.57028 & 2 & 34.998298 & Less Vegetation \\
6277 & 78.83463 & 12.57679 & 1 & 31.708773 & High Vegetation \\
12,354 & 78.85108 & 12.59151 & 1 & 30.273344 & High Vegetation \\
12,179 & 78.80721 & 12.58024 & 2 & 38.20916 & Less Vegetation \\
20,163 & 78.81167 & 12.5669 & 2 & 34.998298 & Less Vegetation \\
30,759 & 78.84193 & 12.59148 & 1 & 32.607521 & High Vegetation \\
24,465 & 78.84046 & 12.59148 & 1 & 32.183754 & High Vegetation \\
28,861 & 78.80598 & 12.58023 & 2 & 38.20916 & Less Vegetation \\
35,655 & 78.83613 & 12.5915 & 1 & 31.708773 & High Vegetation \\
33,638 & 78.81246 & 12.58019 & 2 & 34.533323 & Less Vegetation \\
63 & 78.81674 & 12.60167 & 2 & 36.842331 & Less Vegetation \\
39,388 & 78.81276 & 12.58634 & 2 & 38.20916 & Less Vegetation \\ \hline \hline \end{tabular}
\end{table} 
Table 4: LST and LU/LC values for the area of Javadi Hills. Figure 19: Impact of LST features with the LU/LC classes for Javadi Hills, India. The combined features of the LU/LC and LST map were used as the training features during the process of the Bi-LSTM training. Each pixel value was identified through the latitude and longitudinal coordinates of Javadi Hills manually through the combined features of the LU/LC and LST map. Each pixel holds either high or less vegetation for its defined coordinate system. The few combined values were shown in Table 4. For better understanding, we show the combined features map in Figure 20. The accuracy results for the prediction model are shown in Section 5.2. The results were also cross-verified with the time-series Google Earth Engine for acquiring the validation accuracy of our model. With the impact of the LST map with the LU/LC map, good validation accuracy was obtained with a lower misclassification rate.