UAV-Based LiDAR Survey for Accurate 3D Terrain Mapping and Volumetric Analysis

Abstract

Unmanned aerial vehicle (UAV)-based Light Detection and Ranging (LiDAR) has emerged as an effective technology for high-resolution three-dimensional (3D) terrain mapping and volumetric analysis. This study presents an integrated UAV-LiDAR framework that connects aerial data acquisition, sensor calibration, trajectory enhancement, point-cloud preprocessing, ground-surface extraction, digital terrain model (DTM) generation, 3D terrain reconstruction, and volumetric estimation. The proposed approach addresses key factors affecting the reliability of UAV-LiDAR products, including three-dimensional point errors, positioning and trajectory quality, environmental conditions, and surface reconstruction procedures. The reviewed literature demonstrates the application of UAV-LiDAR across forest inventory, DTM generation, complex terrain morphology, urban modelling, and stockpile volume estimation. Quantitative evidence reported in the supplied literature further demonstrates the influence of survey configuration and acquisition geometry on volumetric accuracy. In an experimental stockpile assessment reported by Alsayed et al. (2025), a reference volume of 3.00 m³ was reconstructed with errors ranging from −0.7% to 5.0% across the evaluated UAV-LiDAR configurations, with the lowest reported error of 0.3% obtained using a three-drone configuration at a 1.5 m survey altitude. These findings reinforce the importance of integrating acquisition planning, geometric processing, terrain reconstruction, and quantitative validation rather than evaluating LiDAR performance solely from point-cloud density or visual quality. Overall, UAV-LiDAR provides a flexible and detailed basis for accurate 3D terrain representation and volumetric assessment, provided that sensor configuration, trajectory quality, point-cloud processing, and validation procedures are appropriately controlled.