## 3 Results and Discussion
### Model Monitoring Results and Analysis
#### 3.4.5 Comparative Analysis of Models  
The random forest model, PT-Former model and _kNDVI_-PT-Former model were verified and analyzed by the vegetation change monitoring dataset of grassland desertification ecological restoration in the study area of Wuzhumuqin. Random forest was chosen as a representative machine learning method to establish a baseline, while PT-Former--sharing the Transformer architecture--allows direct comparison of _kNDVI_ feature integration. This highlights the synergistic effect of spectral indices (_kNDVI_) and spatio-temporal modeling. Figure 15 and Table 8 are the accuracy comparison of the three models. The accuracy of the _kNDVI_-PT-Former model is the best in these models, followed by the PT-Former model, and the performance of the random forest model is relatively poor. Therefore, the PT-Former model based on deep learning and the _kNDVI_-PT-Former model can extract complex features from multi-spectral remote sensing data more accurately than the random forest model based on machine learning. In particular, the _kNDVI_-PT-Former model can effectively identify small vegetation changes, and the overall model monitoring accuracy is improved by 11%. Therefore, the use of the _kNDVI_-PT-Former model for vegetation change monitoring in complex environments has significant advantages and more accurate monitoring results. Figure 14: _kNDVI_-PT-Former model output results of the study area. Combined with the area and types of grassland desertification ecological restoration projects of Xiwuzhumuqin Banner Forestry and Grassland Bureau over the years, field investigation and verification were carried out in mid-November 2024, and some ecological restoration projects were selected for monitoring and verification in this study area every year. In the study area, the local relevant departments carried out fencing and artificial grass ecological restoration measures on 2.13 km\({}^{2}\) of sandy land at the beginning of 2021. As shown in Figure 16, the restoration vegetation is growing well and the ecological restoration effect is remarkable. Figure 16: Ecological restoration effect of partial grassland desertification in 2021: (**a–c**) all represent ecological restoration outcomes. Figure 15: Comparison of output results of different models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**MODEL** & **CPA** & **IoU** \\ \hline Random Forest & 0.5860 & 0.5831 \\ PT-Former & 0.6194 & 0.6157 \\ _kNDVI_-PT-Former & 0.7295 & 0.7228 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Model accuracy comparison. At the beginning of 2022, fencing and artificial afforestation ecological restoration measures were carried out on 1.33 km\({}^{2}\) of sandy land. As shown in Figure 17, the vegetation gradually grew and the ecological restoration effect was obvious. At the beginning of 2023, artificial afforestation ecological restoration measures were carried out on 0.691 km\({}^{2}\) of sandy land. As shown in Figure 18, due to the short vegetation, it is difficult to identify the existence of vegetation through remote sensing images, and only part of the vegetation can be monitored. At the beginning of 2024, artificial afforestation ecological restoration measures were carried out on 0.4 km\({}^{2}\) sandy land, as shown in Figure 19. The vegetation restored by 2024 is shorter and smaller, and it is difficult to identify vegetation in remote sensing images. Only a small part of vegetation can be monitored. Therefore, it is difficult to monitor the presence of vegetation within 2 years after ecological restoration in grassland desertification areas, especially the use of vegetation such as Caragana korshinskii and saplings for ecological restoration, and the effect is not obvious in the short term.