Comparative Assessment of Vegetation Removal for DTM Generation and Earthwork Volume Estimation Using RTK-UAV Photogrammetry and LiDAR Mapping

Abstract

Highlights: What are the main findings? Combining a color-based vegetation index with a geometry-based filter greatly enhanced DTM accuracy; the ExGR + Lasground (new) combination achieved RMSE 0.179 m for UAV photogrammetry and 0.165 m for LiDAR. Both UAV-based methods showed reliable earthwork volume accuracy, reaching 100.9% for photogrammetry and 100.3% for LiDAR even in densely vegetated terrain. What are the implications of the main findings? The integrated LiDAR + ExGR + Lasground (new) method is recommended for high-precision terrain modeling and construction surveying. UAV photogrammetry offers a cost-effective and efficient alternative when LiDAR use is limited by budget or operational conditions. Earthwork volume calculation is a fundamental process in civil engineering and construction, requiring high-precision terrain data to assess ground stability encompassing load-bearing capacity, susceptibility to settlement, and slope stability and to ensure accurate cost estimation. However, seasonal and environmental constraints pose significant challenges to surveying. This study employed unmanned aerial vehicle (UAV) photogrammetry and light detection and ranging (LiDAR) mapping to evaluate the accuracy of digital terrain model (DTM) generation and earthwork volume estimation in densely vegetated areas. For ground extraction, color-based indices (excess green minus red (ExGR), visible atmospherically resistant index (VARI), green-red vegetation index (GRVI)), a geometry-based algorithm (Lasground (new)) and their combinations were compared and analyzed. The results indicated that combining a color index with Lasground (new) outperformed the use of single techniques in both photogrammetric and LiDAR-based surveying. Specifically, the ExGR–Lasground (new) combination produced the most accurate DTM and achieved the highest precision in earthwork volume estimation. The LiDAR-based results exhibited an error of only 0.3% compared with the reference value, while the photogrammetric results also showed only a slight deviation, suggesting their potential as a practical alternative even under dense summer vegetation. Therefore, although prioritizing LiDAR in practice is advisable, this study demonstrates that UAV photogrammetry can serve as an efficient supplementary tool when cost or operational constraints are present.