## 3 Results and Discussion
### Model Monitoring Results and Analysis
#### 3.4.5 Comparative Analysis of Models
Taking the result map of vegetation monitoring change in the study area output by the _kNDVI_-PT-Former model as an example, as shown in Figure 20, the area of ecological restoration projects in recent years is marked in the map, and then the monitoring effect maps of each model in the ecological restoration project area are extracted, respectively, and then the monitoring effect of each model is verified by comparative analysis. Among them, the yellow box indicates the ecological restoration project area in 2021; the blue box indicates the ecological restoration project area in 2022; the purple box and orange box  
Figure 17: Ecological restoration effect of partial grassland desertification in 2022: (**a–c**) all represent ecological restoration outcomes. Figure 18: Ecological restoration effect of partial grassland desertification in 2023: (**a–c**) all represent ecological restoration outcomes. represent the ecological restoration project area in 2023 (only the restoration measures are different); and the light blue box indicates the ecological restoration project area in 2024. The three model monitoring results of the ecological restoration area in 2021 are extracted, respectively, as shown in Figure 21. (a) is the result of the random forest model output, (b) is the result of the PT-Former model output, and (c) is the result of the _kNDVI_-PT-Former model output. Comparing the three maps, it can be seen that the _kNDVI_-PT-Former model has the best monitoring effect. Figure 19: Ecological restoration effect of partial grassland desertification in 2024: (**a–c**) all represent ecological restoration outcomes. Figure 20: Output Map of Ecological Restoration in the Study Area by _kNDVI_-PT-Former Model. The three model monitoring results of the ecological restoration area in 2022 are extracted, respectively, as shown in Figure 22. (a) is the result of the random forest model output, (b) is the result of the PT-Former model output, and (c) is the result of the _kNDVI_-PT-Former model output. Comparing the three maps, it can be seen that the _kNDVI_-PT-Former model has the best monitoring effect. The areas repaired in 2023 and 2024 are no longer compared due to the relatively small amount of vegetation that can be monitored. The time series map maintains the same geographical range, projections, and color schemes to ensure spatial comparability. The coordinate system for all the above model output figures is WGS84 (EPSG: 4326). The scale of Figures 9-15 and 20 is 1:200,000, while the scale of Figures 21 and 22 is 1:20,000. Through comparative analysis with random forest and PT-Former, the _kNDVI_-PT-Former model, based on an advanced deep learning architecture, integrates time-series analysis and spatial feature extraction techniques. It can break through the limitations of traditional monitoring methods and achieve high-precision dynamic monitoring of vegetation for grassland and sandy land ecological restoration. In practical applications, this model, with its high-resolution remote sensing image processing capability, can cover vast grassland and sandy areas, quickly identify the vegetation changes at different succession stages, and accurately capture the tiny fluctuations in vegetation coverage and biomass, as well as the evolution trend of community structure. Whether it is the assessment of the vegetation restoration process in the ecological restoration project area or the early warning of potential desertification areas, the _kNDVI_-PT-Former model can provide detailed and reliable data support. This enables relevant departments and research institutions to promptly grasp the effectiveness of ecological restoration, scientifically adjust strategies for combating desertification, and rationally allocate human and material resources, laying a  
Figure 21: The comparison of the monitoring results of various models in the ecological restoration areas in 2021: (**a**) random forest model; (**b**) PT-Former model; (**c**) _kNDVI_-PT-Former model.