## 4 Proposed LU/LC Prediction Using Vision Transformer-Based Bi-LSTM Model  
This research work aimed to identify the LU/LC changes in the forest-covered (high vegetation) and non-forest-covered (less vegetation) regions of the proposed study area. The flow of LU/LC change for our study area is shown in Figure 16. The proposed flow of this work is described in the following steps,  
* The LISS III satellite images for the years 2012 and 2015 of Javadi Hills, India, were collected from Bhuvan-Thematic Services of the National Remote Sensing Centre (NRSC), Indian Space Research Organization (ISRO). * The Landsat satellite images for the years 2012 and 2015 of Javadi Hills, India, were collected from the United States Geological Survey (USGS), United States. * Atmospheric, geometric, and radiometric corrections were performed to provide better visibility in the acquired LISS-III and Landsat images. * The proposed Vision Transformers for classifying LU/LC classes were successfully performed for the years 2012 and 2015 of the LISS-III image. * An LST map was calculated for the years 2012 and 2015 from Landsat TIRS images for extracting the spatial features. * The relationship between the spatial features of the LST map with the LU/LC classification map were used to provide good validation results during the prediction process. * The Bi-LSTM model was successfully applied to forecast the future LU/LC changes of Javadi Hills for the years 2018, 2021, 2024, and 2027. * The LU/LC changes that occurred in our study area will assist the urban planners and forest department to take proper actions in the protection of the environment through XAI. Figure 15: Explainable AI interface through Google Earth Engine platform. Algorithm to Construct the Vision Transformer-Based Bi-LSTM Model for LU/LC Prediction  
Our research is based on the Vision Transformer-based Bi-LSTM model for LU/LC Prediction of Javadi Hills, India. From the brief analysis and validation, we found that the impact of the TIRS LST map with the LU/LC classified provides a good percentage of results with a lower misclassification rate. The detailed steps of our proposed model are presented in Algorithm 1. Each process in our proposed algorithm provides the different aspects of LU/LC information of Javadi Hills. A brief explanation of the input data, training data, parameter settings, and accuracy assessment of our proposed model is explained in Section 5. ``` Inputs (\(I_{P}\)): The LISS-III multispectral satellite images for the years 2012 and 2015 (\(I_{1}\), \(I_{2}\)), and Landsat bands for the years 2012 and 2015 (\(IR_{1}\), \(IR_{2}\)) Output (\(O_{P}\)): Predicted LU/LC images 2018, 2021, 2024, and 2027 (\(PR_{1}\), \(PR_{2}\), \(PR_{3}\), \(PR_{4}\)) Begin
1 Input data (\(I_{P}\)):
2 Initialize the input data
3 Extract LISS-III multispectral image (\(M=I_{1}\), \(I_{2}\))
4 Extract Landsat bands (\(T=IR_{1}\), \(IR_{2}\))
5 Return input data (\(I_{P}\))
6
7 Preprocessed data (\(PR_{I}\)):
8 Initialize the input data for performing the preprocessing for the input data \(I_{P}\) of \(M\) and \(T\)
9 For each initialized input image of \(M\) and \(T\)

Figure 16: Proposed flow of LU/LC prediction using Vision Transformer–based Bi-LSTM model. ## 5 Results and Discussion

The problematic study on LU/LC prediction in Javadi was presented in this research work. The LISS-III multispectral, Landsat TIRS, RED, and NIR satellite images were used for predicting the vegetation in the forest- and non-forest-covered regions of the Javadi Hills. All the research experiments were processed on the Intel Xeon processor 2.90 GHz CPU, along with 128 GB RAM in Windows 10 (64-bit) environment. The needed libraries and packages of Python of version 3.10.2 developed by Python Software Foundation ([[https://www.python.org/](https://www.python.org/)]([https://www.python.org/](https://www.python.org/))) were installed for implementing the proposed model of our research.