The era of high-quality urban development demands refined, digitalized, and visualized scientific research, and 3D point cloud acquisition technology has emerged as a key tool for tree information collection, surveys, assessment, monitoring, and management, which facilitates the innovative development of the landscape architecture industry. While existing applications primarily focus on batch acquisition of point clouds for tree communities, scattered trees in fields such as ancient and famous tree conservation still rely on backpack/handheld LiDAR devices. These devices often require multiple circular scans around individual trees to ensure point cloud completeness, but motion distortion resulting from such multi-circular mobile acquisition has been generally overlooked. Based on Popper’s falsificationism, this study employs three methods: tree parameter comparison, optical porosity analysis, and CloudCompare-based point cloud distance comparison. These methods are used to observe that motion distortion significantly impairs the accuracy of individual tree point cloud data. Results indicate that motion distortion increases point cloud volume and Euclidean distance errors. Notably, a greater number of collection circles induces random variations in tree morphological parameters and a reduction in point cloud optical porosity. Consequently, tripod-mounted LiDAR is recommended over handheld/backpack devices. Significantly, this study is the first to highlight the potential substantial impact of accumulated point cloud motion distortion on landscape tree point cloud data acquisition. It provides a theoretical foundation for enhancing the precision of dynamically acquired tree point cloud data, which in turn improves the accuracy of tree analysis, monitoring, and assessment studies based on tree point cloud data.