## 5 Discussion
### Comparison of the Proposed Method and Previous Methods  
Compared with the ESV assessments in previous studies [44; 81], our revised results were consistent with the results: \"_ESV decreasing from southeast to northwest in the TRHR_\". To validate the simulated ES results, we compared the ES with [PERSON] et al. [76]. Our results are similar to the previous results in the southeast and middle zones, indicating that the identification results are reliable. However, it is noteworthy that some differences were observed in parts of the northwest. We believe these differences may be attributed to variations in ESV assessment methods and data resolution, and our identification results reveal more intricate patterns.  
Specifically, to understand the difference across conservation objectives, we identified and mapped ESs that would be missed if conservation efforts were to focus solely on the ESV or kNDVI. We have considered three scenarios: 1 only considered the kNDVI, 2 only considered the ESV, and 3 combined the ESV and kNDVI into identification. We used the same spatial priority methods and analyzed the spatial distribution and temporal differences in three scenarios. From a spatial perspective, there are also some benefits and drawbacks to ES identification using the kNDVI or ESV, respectively (Figure 9). Identifying ESs through the kNDVI can reveal the ecological contribution of the Yangtze River Headwaters Basin in the southeast of the TRHR. However, the kNDVI is unable to identify the lakes in ESs, due to the theory of vegetation index (the red solid box in Figure 9). Meanwhile, ES identification emphasized the northwest ecological function but neglected the middle areas (the black dotted box in Figure 9). To detect ES, it is crucial to integrate the kNDVI and ESV. Thus, we decided to combine the ES identification results from both the kNDVI and ESV to exact more comprehensive ESs.  
Given the difficulty in obtaining identification results from other authors, we quantitatively present a comparison of source site results using only the ESV (the traditional method employed by previous studies) and incorporating the kNDVI (the method used in our study). According to previous studies [53], we used landscape metrics calculated by Fragstats4.0 to compare the ESs identified using different approaches.  
The Split Index represents the total degree of landscape dispersion, whereas the Division Index represents the degree of landscape separation. The high Split and Division Indices indicate terrain fragmentation in research areas, which does not facilitate energy flow between ESs. As depicted in Figure 10, the Split Index and Division Index of ESs identified by the proposed method were significantly lower than those of the traditional method.  
The Largest Patch Index (LPI) characterizes the strength of landscape fragmentation, heterogeneity, and human disturbance [82]. A high LPI can reflect the stability of the total  
Figure 10: Landscape metrics of ecological sources identified by different approaches.  
Figure 9: Ecological source identification differences between (**a**) only the ESV indicator, (**b**) only the kNDVI indicator, and (**c**) ESV and kNDVI indicators.  
region. The Patch Cohesion Index (COHESION) and the Contagion index (CONTAG) further indicate natural connectivity and a concentrated distribution of ESs. Stronger ES identification efficiency and accuracy were demonstrated by the high LPI, COHESION, and CONTAG. ESs identified by our method were slightly higher than the traditional way, which indicates that the degree of ES aggregation improved following identification by the proposed method, as well as the existence of dominant patch types with high connectivity in the landscape [83].