Estimation of Cut and Fill and Removed Tree Volume for Forest Road Design Using Fused Aerial and Terrestrial Laser Scans: A Case Study from Oregon, USA
Year: 2026
Type: article
Abstract: Efficient planning of forest roads is essential to sustaining uninterrupted forestry activities and supporting sustainable forest management. Roads must be designed in concert with existing infrastructure while conforming to operational requirements for timber extraction, accessibility, and environmental compliance. Advances in high-resolution remote sensing and computational geomatics enable acquisition of high-precision, three-dimensional descriptions of forest structure integrable into geographic information systems (GISs) for route optimization and terrain modeling. Despite this progress, a formal procedure for designing forest roads from point clouds derived by combining aerial and terrestrial laser scanning is not yet established. This study addresses that gap by presenting a complete, reproducible workflow for forest road design using fused airborne and near-ground laser scanning point clouds. The workflow encompasses three stages: LiDAR acquisition and derivation of topographic and vegetation attributes, optimal route identification, and estimation of standing timber volume within the proposed corridor. The procedure is demonstrated on a 200 m test section within the McDonald-Dunn Research Forest of Oregon State University, where 133 trees were inventoried and cut and fill volumes quantified. The results confirm that LiDAR-based planning achieves sub-0.12 m accuracy while enabling precise quantification of standing stock to be removed, reducing field time, improving cost efficiency, and supporting data-driven environmental decision-making.
Source: Remote Sensing
DOI: 10.3390/rs18203581
Authors: Ender Bugday, Bogdan M. Strimbu
Institutions: Çankırı Karatekin University, Oregon State University
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
OpenAlex ID: W7222965491