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

MLS-CAPS: Data and Code for Optimising Mobile Laser Scanning Path Spacing in Vegetation Surveys This repository contains the datasets and Python code supporting the study “MLS-CAPS: Optimising Path Spacing for Mobile Laser Scanning of Vegetation Structure via Completeness Analysis”, submitted to Remote Sensing. The repository provides: Mobile laser scanning (MLS) point cloud datasets collected across three eucalypt forest types in south-eastern Australia (closed forest, open woodland, and sub-alpine woodland), representing a gradient of vegetation structural complexity. Derived products, including trajectory segments, voxel occupancy grids, digital elevation models (DEMs), canopy height models (CHMs), and completeness decay profiles. The MLS-CAPS Python workflow, an open-source tool for quantifying how vegetation structural information decays with distance from MLS trajectories and for translating these decay relationships into recommended path spacing for future surveys. MLS-CAPS implements a data-driven approach in which a dense pilot scan is used as a reference to assess reconstruction completeness for key structural metrics (voxel occupancy, DEM, and CHM) as a function of lateral distance from the scanning path. The workflow enables users to define metric-specific error or completeness thresholds and derive site-specific path spacing rules that account for vegetation structural complexity. All data and code are provided to support reproducibility, method evaluation, and reuse by researchers and practitioners interested in ecological applications of mobile laser scanning, survey design, and vegetation structure analysis.