## 3 Results and Discussion
### Model Monitoring Results and Analysis
#### 3.4.5 Comparative Analysis of Models
Figure 22: The comparison of the monitoring results of various models in the ecological restoration areas in 2022: (**a**) random forest model; (**b**) PT-Former model; (**c**) _kNDVI_-PT-Former model. solid technical foundation for establishing a long-term ecological protection mechanism and promoting the sustainable development of grassland and sandy land ecosystems. Therefore, in complex environments, the use of _kNDVI_ combined with the Transform model for vegetation change monitoring has significant advantages, which can capture long-term vegetation dynamics and support ecological monitoring and climate change research. In this paper, however, no comparative experiments were conducted using other models. In the future, it may be considered to apply more advanced models to vegetation change monitoring in ecological restoration to enhance model diversity. This study has achieved good results in the monitoring of vegetation change in grassland desertification ecological restoration, but it still needs to be refined and improved in the algorithm of extracting vegetation change characteristics in the network layer, so as to provide ideas for future research on grassland vegetation change monitoring. While the _kNDVI_-PT-Former model demonstrates consistent performance improvements across cross-validation folds (e.g., 11% _CPA_ gain over PT-Former, Section 3.4.5), the absence of formal statistical tests reflects the study's focus on methodological innovation rather than exhaustive comparative inference. Meanwhile, although this study is based on single-phase data, cross-seasonal validation may enhance the model's robustness. In future research, multi-seasonal imagery data--such as those collected during the spring green-up period and the autumn biomass peak period--will be integrated with correlation analysis of climatic factors to enhance the comprehensiveness and accuracy of monitoring. The study also plans to incorporate additional quantitative validation experiments and analyses, with the aim of strengthening the applicability and reliability of the monitoring model across diverse environments and conditions. However, vegetation change monitoring via remote sensing imagery also has certain limitations. Key limitations include the following aspects: First, optical remote sensing data are significantly perturbed by cloud cover, atmospheric scattering, and climatic influences (e.g., precipitation events), which introduce noise into vegetation index calculations. Furthermore, the compromised temporal continuity of time-series datasets often limits the ability to capture rapid vegetation growth phases during critical phenological windows (e.g., spring budburst or monsoon-driven biomass accumulation). Most critically, low-spatial-resolution remote sensing data (e.g., \(>\)10 m) are increasingly inadequate for high-precision vegetation monitoring requirements, while high-resolution datasets (e.g., 0.5 m resolution) remain prohibitively expensive and lack universal accessibility for large-scale applications. Therefore, how to utilize remote sensing techniques for more efficient and precise vegetation change monitoring remains a research topic warranting in-depth investigation. Additionally, this study identifies several commonalities with prior research. First, the integration of multi-source data enriches the dataset and establishes a robust foundation for subsequent research. For example, [PERSON] et al. (2024) achieved effective dynamic vegetation cover monitoring in rare earth mines by utilizing multi-source remote sensing data [23]. Second, model comparisons highlight the superiority of specific frameworks. For instance, [PERSON] et al. (2024) evaluated four object detection models--classical SSD, RetinaNet, YOLOv3, and Faster R-CNN--for tree detection and found that the SSD model demonstrated the best balance between inference speed and performance on their dataset, positioning it as a powerful target detection solution [33].