## 4 Discussions
### Evaluation on Research Method Selection and Application
This approach aligns more closely with the latest trends in research. The combination of the Theil-Sen median trend analysis and the Mann-Kendall test has proven to be a reliable approach for time series analysis. This method offers several advantages over univariate linear regression analysis, including the lack of requirement for data to follow a normal distribution, reduced sensitivity to outliers and extreme values, the effective detection of sequence trends and change points, and lower susceptibility to errors. Furthermore, this method is relatively straightforward to calculate, enhancing its practical applicability [84; 114]. In their research on vegetation coverage in the Yellow River Basin, [PERSON] [84] also employed the Hurst index. We attempted to use this method as well, but found that the Hurst index is more suitable for studies involving large-scale research areas. To provide a comprehensive and accurate assessment of ecological environment changes within the PA, this study utilized the NDVI as the primary remote sensing evaluation indicator. NDVI is recognized for its ability to quantitatively characterize the growth status and distribution changes of vegetation. Its widespread application in the scientific community also enhances the comparability and credibility of research results [34; 45; 84]. Though the Fraction of Vegetation Cover (FVC) and Remote Sensing Ecological Index (RSEI) were initially tested for their applicability in detecting ecological environment changes, NDVI proved to be more effective. This was due to its superior ability to filter out external environmental interference and accurately extract vegetation information. Conversely, FVC and RSEI were found to be more susceptible to significant errors caused by other environmental factors, making it difficult to extract useful data related to the research objectives. Consequently, NDVI was chosen as the sole remote sensing indicator to detect spatiotemporal changes in the ecological environment of the PA and evaluate the effectiveness of its protection. Our research findings indicated that a higher percentage of vegetation exhibited an improvement trend inside the PA than outside, suggesting that the PA has a certain degree of effectiveness. This conclusion aligns with previous studies, which demonstrated that nature reserves contribute to the improvement of sustainable and stable geographical spaces [34]. In addition, this study leveraged the GEE platform, which has gained prominence in the realm of remote sensing image processing, thanks to the rapid advancement of computer technology. The datasets within the platform require no preprocessing, thereby enhancing research efficiency [47; 115]. Our study highlights the efficacy of remote sensing as a tool for evaluating the conservation effectiveness of PAs. Interestingly, the results derived from remote sensing were found to be almost identical to those obtained through traditional ground-based methods. This congruence underscores the validity and reliability of remote sensing as a method for conservation assessment. However, the advantage of remote sensing over ground-based methods lies in its timeliness and convenience. Remote sensing allows for real-time data collection and analysis, which can significantly enhance the effectiveness of conservation efforts. Furthermore, it provides a convenient and less intrusive means of monitoring PAs, reducing the need for physical intrusion that could potentially disrupt the ecosystems under protection. In this study, we developed a generalizable framework for assessing the conservation effectiveness of PAs by evaluating three key factors: land use, landscape pattern, and NDVI. These three factors apply to various types of PAs and can be visualized to improve the clarity and intuitiveness of the analysis. The framework can effectively compare the conservation effectiveness of different types of PAs, such as forest and wetland types.