Under the dual pressures of climate change and rapid urbanization, a comprehensive analysis of vegetation’s spatiotemporal patterns and their driving forces plays a pivotal role for addressing global ecological challenges. However, systematic bibliometric analyses in this field remain limited. This study involved a comprehensive bibliometric analysis of 18,270 related publications from 1989 to 2024 retrieved from the Web of Science SCI-Expanded database, elucidating research trends, methodologies, and key thematic areas. Utilizing the bibliometrix and biblioshiny tools, the results reveal an annual average growth rate of 17.62% in the number of published research articles, indicating rapid research expansion. Climate change emerged as the core driving force, with high-frequency keywords such as “vegetation”, “dynamics”, and “variability”. China (18,687 papers), the United States (14,502 papers), and Germany (3394 papers) are the leading contributors in this domain, with China showing the fastest growth in research output, albeit with relatively lower average citation rates. Core journals, including Remote Sensing of Environment and Global Change Biology, have played pivotal roles in advancing vegetation dynamics research, with remote sensing techniques dominating the field. The study highlights a shift in vegetation dynamics research from single-variable analyses (e.g., temperature, precipitation) to multi-scale and multidimensional approaches around 2010. Regional studies, such as those focusing on the Loess Plateau, are gaining importance, while advancements in remote sensing and machine learning technologies have enhanced the precision and scalability of research. This study provides a comprehensive summary of the current state and development trends in vegetation spatiotemporal dynamics and their driving forces, offering valuable insights for future research in the field.

This paper presents the mechanical design and simulation-based validation of a novel compact and low-cost 3-DOF dual-arm robotic system tailored for space-constrained applications such as rescue robotics. The proposed design achieves a fully folded footprint of 366 × 226.3 × 100 mm through an orthogonal joint configuration and modular structure, while maintaining a hemispherical workspace for each arm. Key innovations include the following: (1) A cost-optimized architecture with only 3 motors per arm (total system cost ~£2000), enabled by hybrid manufacturing (laser-cut acrylic hull and 3D-printed ASA-CF reinforced links with 3740 MPa flexural modulus); (2) a custom Python-based skeleton modeling tool that automates D-H parameter generation and kinematic analysis, supporting rapid design iteration; (3) verified collision-free operation via point cloud analysis, demonstrating successful target grasping (50 mm objects) and dual-arm coordination despite a 5–20 mm deflection tolerance. The system addresses critical limitations in affordable, deployable manipulators, with future work focusing on 3D printing and part manufacturing in industry applications.