The Comparative Bibliometric Analysis of Watershed Ecological Protection and Restoration in the Context of Territorial Spatial Planning: An Overview of Global Research Trends

Research on watershed ecological protection and restoration within the framework of territorial spatial planning serves as a critical approach to ensuring national ecological security and plays a vital role in enhancing ecosystem stability. In recent years, scholarly interest in this topic has grown significantly. However, development trends and optimization strategies remain unclear, especially regarding comparative insights between Chinese and English research articles within the territorial spatial planning paradigm. A comprehensive review is therefore needed to bridge this gap. This study utilizes bibliometric analysis with CiteSpace, based on publications from the Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases, to visualize and compare Chinese and English research articles on watershed ecological protection and restoration. By combining quantitative and qualitative approaches, this study identified research hotspots and trajectories and provided directions for future research. The main findings are as follows: (1) A quantitative analysis indicates that the number of publications has increased significantly since 1998, with growing interdisciplinary and cross-sector collaboration. (2) The qualitative analysis reveals three fundamental theoretical principles: holistic management, multi-scale interactions, and dynamic coordination. (3) The Chinese Academy of Sciences led in research output, while other institutions showed wider geographic coverage, stronger collaboration networks, and a decentralized, multi-core structure. (4) Keyword clustering highlights three major themes: evaluation methodologies for ecological protection and restoration, spatiotemporal evolution and driving mechanisms, and integrated governance system development. (5) Within the territorial spatial planning paradigm, future researchers should employ big data analytics and monitoring technologies to better diagnose and address ecological challenges.