Fusion of Airborne, SLAM-Based, and iPhone LiDAR for Accurate Forest Road Mapping in Harvesting Areas

Evangelia Siafali, Vasilis Polychronos and Petros A. Tsioras ()
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Evangelia Siafali: Laboratory of Mechanical Science and Topography, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece
Vasilis Polychronos: Geosense, Geoinformatics Company, 570 13 Oreokastro, Greece
Petros A. Tsioras: Laboratory of Forest Utilization, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece

Land, 2025, vol. 14, issue 8, 1-19

Abstract: This study examined the integraftion of airborne Light Detection and Ranging (LiDAR), Simultaneous Localization and Mapping (SLAM)-based handheld LiDAR, and iPhone LiDAR to inspect forest road networks following forest operations. The goal is to overcome the challenges posed by dense canopy cover and ensure accurate and efficient data collection and mapping. Airborne data were collected using the DJI Matrice 300 RTK UAV equipped with a Zenmuse L2 LiDAR sensor, which achieved a high point density of 285 points/m 2 at an altitude of 80 m. Ground-level data were collected using the BLK2GO handheld laser scanner (HPLS) with SLAM methods (LiDAR SLAM, Visual SLAM, Inertial Measurement Unit) and the iPhone 13 Pro Max LiDAR. Data processing included generating DEMs, DSMs, and True Digital Orthophotos (TDOMs) via DJI Terra, LiDAR360 V8, and Cyclone REGISTER 360 PLUS, with additional processing and merging using CloudCompare V2 and ArcGIS Pro 3.4.0. The pairwise comparison analysis between ALS data and each alternative method revealed notable differences in elevation, highlighting discrepancies between methods. ALS + iPhone demonstrated the smallest deviation from ALS (MAE = 0.011, RMSE = 0.011, RE = 0.003%) and HPLS the larger deviation from ALS (MAE = 0.507, RMSE = 0.542, RE = 0.123%). The findings highlight the potential of fusing point clouds from diverse platforms to enhance forest road mapping accuracy. However, the selection of technology should consider trade-offs among accuracy, cost, and operational constraints. Mobile LiDAR solutions, particularly the iPhone, offer promising low-cost alternatives for certain applications. Future research should explore real-time fusion workflows and strategies to improve the cost-effectiveness and scalability of multisensor approaches for forest road monitoring.

Keywords: handheld mobile laser scanning; topography; forest operations; forest road maintenance and wearing course damage; sensor performance; logistics and transportation infrastructure (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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