Fusion of Airborne, SLAM-Based, and iPhone LiDAR for Accurate Forest Road Mapping in Harvesting Areas
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area Description
2.2. Aerial Laser Scanning Acquisition Data and Processing
2.3. Mobile Laser Scanning Data Collection and Preprocess
2.4. iPhone-Based Data Collection and Preprocessing
2.5. Data Fusion of All Point Clouds
2.6. Data Collection Validation
3. Results
3.1. Comparative Analysis of Point Cloud Data
3.1.1. Aerial Laser Scanning Data Generation
3.1.2. Comparison of Measurement Methods for Forest Road DEMs
3.1.3. Sensor Performance Analysis for Forest Road Cross Sections
3.1.4. Sensor Performance Analysis Across Different Terrain Conditions
3.2. Accuracy Analysis of Forest Road DEMs Based on GNSS RTK Measurements
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
LiDAR | Light Detection and Ranging |
SLAM | Simultaneous Localization and Mapping |
HPLS | Handheld Laser Scanner |
DEM | Digital Elevation Model |
DSM | Digital Surface Model |
TDOM | True Digital Orthophoto Map |
ALS | Airborne Laser Scanner |
TLS | Terrestrial Laser Scanning |
UAV | Unmanned Aerial Vehicle |
GNSS | Global Navigation Satellite System |
RTK | Real-Time Kinematic |
GCP | Ground Control Point |
IDW | Inverse Distance Weighting |
CHM | Canopy Height Model |
MAE | Mean Absolute Error |
RMSE | Root Mean Square Error |
Appendix A
Parameter | Equipment | ||
---|---|---|---|
DJI Zenmuse L2 LiDAR Sensor | Leica BLK2GO HPLS | iPhone 13 Pro Max LiDAR | |
Manufacturer | DJI | Leica Geosystems | Apple |
Sensor Type | Livox LiDAR module (non-repetitive scanning) | Multi-line (High-Speed) Pulsed Laser Scanner | VCSEL-based ToF LiDAR |
Laser Class | Class 1 (Eye-safe) | Class 1 (Eye-safe) | Class 1 (Eye-safe) |
Wavelength | 905 nm | 830 nm | ~940 nm |
Range | ≤450 m (reflectivity-dependent) | Up to 25 m | Up to ~5 m (typical use) |
Accuracy | ±5 cm @ 150 m | 10–20 mm @ 10 m | ~±1–2 cm (short-range, under ideal conditions) |
Field of View | 70.4° × 77.2° (FOV of scanner) | 360° horizontal, ~270° vertical | ~120° horizontal |
Scan Rate/Points per Second (pts/s) | Max 240,000 pts/s | Up to 420,000 pts/s | ~600,000 pts/s (raw capture) |
Measurement Principle | Hybrid Time-of-Flight with APD receiver | Direct time-of-flight pulsed laser scanning | Indirect ToF (modulated VCSEL) |
Inertial Measurement Unit (IMU) | Integrated high-accuracy IMU | High-grade SLAM IMU for 3D path tracking | Built-in phone IMU (consumer-grade) |
SLAM Capability | Supported (RTK/IMU assisted) | Yes (real-time 3D mapping) | Software-based, limited (ARKit) |
Weight | ~905 g (sensor only) | ~775 g | Entire phone ~238 g |
Power Source | Powered by drone | Internal rechargeable battery (~45 min runtime) | Phone battery |
Primary Use Case | Aerial LiDAR mapping with drone integration | Mobile indoor/outdoor mapping, construction | AR apps, room scanning, small-scale modeling |
Data Output | Point cloud (LAS), intensity, GPS/IMU data | Point cloud (E57, LAS, etc.) | Depth map (ARKit), .USDZ, or .OBJ via apps |
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Road class * | B |
Type of material | Gravel and soil |
Construction year | 1994 |
Year of last maintenance | 2023 |
Length (m) | 608 |
Road width (m) | 4–6 |
Maximum longitudinal downhill slope (%) | 8 |
Maximum longitudinal uphill slope (%) | 6 |
Radius of curvature in maneuvers (m) | 20 |
Average slope (%) | 6 |
Method | MAE | RMSE | Relative Error (%) |
---|---|---|---|
HPLS | 0.507 | 0.542 | 0.123 |
iPhone | 0.229 | 0.279 | 0.056 |
ALS + HPLS | 0.346 | 0.487 | 0.084 |
ALS + iPhone | 0.011 | 0.011 | 0.003 |
iPhone + HPLS | 0.380 | 0.490 | 0.092 |
Sensor/Interpolation Method Combination | Mean Correlation | Standard Deviation (m) | Range (m) | Local Variation (m) |
---|---|---|---|---|
ALS/IDW-Kriging | 0.7796 | 0.414 | 0.1392 | 0.0107 |
ALS + HPLS/IDW-Kriging | 0.4378 | 0.0231 | 0.0839 | 0.0093 |
ALS + iPhone/IDW-Kriging | 0.7194 | 0.0771 | 0.2032 | 0.0131 |
HPLS/IDW-Kriging | 0.5556 | 0.0243 | 0.0958 | 0.0100 |
iPhone/IDW | 0.4820 | 0.0076 | 0.0273 | 0.0021 |
iPhone/Kriging | 0.4875 | 0.0075 | 0.0248 | 0.0021 |
iPhone + HPLS/IDW-Kriging | 0.7570 | 0.0438 | 0.1532 | 0.0104 |
Terrain Class | Slope Range | Elevation Measurement Consistency | Calculation Accuracy | Performance Characteristics |
---|---|---|---|---|
Flat terrain | 0–1% | High | Very high | Optimal performance |
Gentle slopes | 1–3% | Good | High | Good performance |
Moderate slopes | 3–5% | Moderate | Moderate | Requires attention |
Steep slopes | >5% | Variable | Challenging | Needs special consideration |
Method | Mean Error (m) | Std Dev (m) | RMSE (m) |
---|---|---|---|
ALS/Full area | −0.028 | 0.056 | 0.057 |
ALS/Forest road | 0.021 | 0.025 | 0.029 |
ALS + HPLS | 0.028 | 0.020 | 0.032 |
ALS + iPhone | 0.036 | 0.118 | 0.108 |
HPLS | 0.119 | 0.163 | 0.179 |
iPhone | 0.036 | 0.121 | 0.111 |
iPhone + HPLS | 0.054 | 0.105 | 0.106 |
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Siafali, E.; Polychronos, V.; Tsioras, P.A. Fusion of Airborne, SLAM-Based, and iPhone LiDAR for Accurate Forest Road Mapping in Harvesting Areas. Land 2025, 14, 1553. https://doi.org/10.3390/land14081553
Siafali E, Polychronos V, Tsioras PA. Fusion of Airborne, SLAM-Based, and iPhone LiDAR for Accurate Forest Road Mapping in Harvesting Areas. Land. 2025; 14(8):1553. https://doi.org/10.3390/land14081553
Chicago/Turabian StyleSiafali, Evangelia, Vasilis Polychronos, and Petros A. Tsioras. 2025. "Fusion of Airborne, SLAM-Based, and iPhone LiDAR for Accurate Forest Road Mapping in Harvesting Areas" Land 14, no. 8: 1553. https://doi.org/10.3390/land14081553
APA StyleSiafali, E., Polychronos, V., & Tsioras, P. A. (2025). Fusion of Airborne, SLAM-Based, and iPhone LiDAR for Accurate Forest Road Mapping in Harvesting Areas. Land, 14(8), 1553. https://doi.org/10.3390/land14081553