###### Abstract  
As a crucial component of the ecological security pattern, ecological source (ES) plays a vital role in providing ecosystem service value (ESV) and conserving biodiversity. Previous studies have mostly considered ES only from either landscape change pattern or ecological function perspectives, and have ignored their integration and spatio-temporal evolutionary modeling. In this study, we proposed a multi-perspective framework for the spatio-temporal characteristics of ES by ESV incorporating landscape aesthetics, carbon sink characteristics, ecological quality, and kernel NDVI (kNDVI). By integrating the revised ESV and the kernel normalized difference vegetation index as a foundation, we employed the spatial priority model to identify ES. This improvement aims to yield a more practical and specific ESV result. Applying this framework to the Three-River Headwaters Region (TRHR), a significant spatio-temporal change in ecological sources has been observed from 2000 to 2020. This performance provided a reference for ecological conservation in the TRHR. The results indicate that this ecological source identification framework has reliable accuracy and efficiency compared with the existing NRs in the TRHR. This method could reveal more precise spatio-temporal distributions of ES, enhancing ecosystem integrity and providing technical modeling support for developing cross-scale spatial planning and management strategies for nature reserve boundaries. The framework proposed in our research could serve as a reference for building ecological networks in other ecologically fragile areas. ecological sources; ecosystem services value; vegetation productivity; Three-River Headwaters Region 2024 16 1258 1 Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road,  
Haidian District, Beijing 100094, China; [EMAIL_ADDRESS]. The most accessible and straightforward approach is the nature reserve method. Early studies have designated nature reserves as ecological sources [2; 14; 15]. However, constrained by their random distribution and subjective delineation, this method impedes objective differentiation based on diverse land use and landscape types. To compensate for the shortcomings of the nature reserves approach, some scholars have also employed the Remote Sensing Ecological Index (RSEI) to comprehensively identify ecological source areas [16]. However, it may not directly capture ecosystem functional patterns and characteristics of ecosystem services. Along with the developments of landscape morphology theory, Morphological Spatial Pattern Analysis (MSPA) is widely employed in ecological source extraction [10; 11]. However, the theory concentrates on landscape connectivity while ignoring the community organization of the ecological space [17]. Under the background of ecological conservation [18], scholars are incorporating ecological importance and sensitivity features into the identification framework, considering ecological pressure and ecological quality [13], and using machine learning [19] to construct an ES identification framework. Although multiple processes in ecosystems are considered in this complex method, ESV, a crucial aspect of the ecosystem, has not received sufficient attention. Identifying ES based on ESV is another significant method. Diverging from the previously discussed methods, ESV offers a clear understanding of the coupling mechanism between humans and nature within an ecosystem, effectively illustrating the ecological function of the designated source areas [20; 21]. Simultaneously, the incorporation of ESV enhances the visualization of the spatio-temporal evolution of ecosystems [22; 23]. In summary, current ES extraction methods still face big challenges, and more in-depth and comprehensive identification methods are not yet available. First, most current research adopted a single perspective to identify ES.