MLS-CAPS: Optimising Path Spacing for Mobile Laser Scanning of Vegetation Structure via Completeness Analysis

This study presents a data-driven approach, MLS-CAPS (mobile laser scanning completeness analysis for path spacing), for defining optimal path spacing (walking path) for backpack or handheld MLS surveys in ecological applications. By using an initial high-density pilot scan of the site, captured with the same MLS system intended for subsequent full surveys, MLS-CAPS quantifies how voxel occupancy completeness, digital terrain model accuracy, and canopy height model accuracy vary with lateral distance from individual walking trajectories. This provides an objective basis for translating local vegetation structure into path spacing recommendations for subsequent MLS surveys. Case study results showed that structural complexity strongly influences the rate of decay, with denser and more complex vegetation requiring closer path spacing to maintain data completeness. While this meets expectations, what has previously been lacking is a way to quantify it in a manner that directly supports data acquisition decision making. MLS-CAPS, provided as a Python tool, allows users to define thresholds aligned with their metrics of interest, recognising that no single spacing is universally sufficient across all structural attributes. The framework therefore enables MLS operators to plan surveys that balance efficiency with accuracy, while maintaining transparency in the trade-offs between path spacing and completeness. By formalising what has previously been a trial-and-error process, this method offers a practical tool to support ecological applications of MLS across diverse environments.