Connecting the dots: airborne laser scanning from an Unmanned Aircraft System (UAS)
Arko Lucieer, Luke Wallace, Richard Ballard, Steve Harwin, Darren Turner, Christopher Watson, Jon Osborn
Surveying and Spatial Sciences Group School of Land & Food
Outline
• Overview of the UTAS TerraLuma project
• UAS LiDAR sensor and system integration
• Geometric accuracy assessment
• Individual Tree Detection
• Canopy cover
• Change detection (pruning)
• UAS LiDAR vs SfM
• New Velodyne prototype
TerraLuma UAS
Sensors
Applications
UAS LiDAR – Dr Luke Wallace
LiDAR Sensor
• Ibeo LUX Laser Scanner
• 904 nm wavelength
• 4 Scanning Layers
• 3 returns per pulse
• 12.5 Hz Scan Frequency
• Up to 22,000 pulses s-1
• 200 m effective range
• <1 kg
Wallace, L., Lucieer, A., Watson, C., & Turner, D. (2012). Development of a UAV-LiDAR System with Application to Forest Inventory. Remote Sensing (UAV special issue), 4(6), 1519-1543. doi:10.3390/rs4061519
Point cloud Generation
Spatial Accuracy
Wallace et al., 2012. Development of a UAV-LiDAR System with Application to Forest Inventory. Remote Sensing, 4(6), pp.1519–1543.
The accuracy was assessed by measuring 32 Targets with RTK-GPS and with UAV-LiDAR in 8 flights (130 total targets) RMSE ± 30cm Horizontal ± 10cm Vertical
Point Density Comparison
LiDAR UAV: 80 pts/m2 Manned LiDAR: ~8 pts/m2
Forestry study area
Effect of Above Ground Flying Height
- Repeatable measurements up to 50 m - Loss of information for flights above 50 m
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830 m 50 m 70 m 90 m
Tree detection algorithms • Point cloud detection and delineation (PDD)
• Voxel space detection and delineation (VDD)
• CHM detection and delineation (CDD)
• Hybrid: CHM detection and point cloud delineation (CDPD)
Wallace, L., Lucieer, A., and Watson, C. (2014). Evaluating tree detection and segmentation routines on very high resolution UAV LiDAR data. IEEE Transactions on Geoscience and Remote Sensing, 52(12): 7619-7628. doi:10.1109/TGRS.2014.2315649
Tree detection
Repeatability at Tree Level
30 Tree Heights measured manually giving RMSE 13 cm
Repeatability vertical distributions
α-shape crown area and volume
Detection of pruning
LiDAR vs SfM trial
SfM vs LiDAR
UAS SfM vs LiDAR
Commercial developments
Dual antenna, dual frequency GNSS: 2 – 4 cm
position, 0.15° heading
“Devourer” X8 heavy-lift multi-rotor
Intel NUC data logger
Brushless gimbal for sensor stabilisation
Velodyne VLP-16 ‘Puck’ Machine vision camera Spatial Dual GNSS/IMU
Electronics: voltage regulation and
synchronisation
Video
Potree online viewer
http://www.uas4rs.org.au/potree/examples/Springfield_UAV1.html
Conclusions • UAS are an effective tool to capture a scale niche
• UAS LiDAR is feasible and accurate
• UAS LiDAR can collect extremely dense point clouds: 100 – 1000 pts/m2
• UAS LiDAR allows experimentation with tree identification and change detection algorithms
• SfM techniques are promising, however, LiDAR superior when it comes to canopy penetration
• Still many problems to solve: prototype to operations
• The future of UAS LiDAR is exciting!
Acknowledgements
• Dr Luke Wallace (RMIT)
• Mr Richard Ballard, Dr Steve Harwin, Dr Darren Turner, Dr Josh Kelcey, Mr Tony Veness, Dr Zbynek Malenovsky, Dr Colin McCoull, Mr Deepak Gautam, Mr Iain Clarke, Drs Christopher Watson & Jon Osborn (UTAS)
• Forest and Wood Products Association (FWPA)
• Dr Christine Stone (DPI NSW)
• Australian Antarctic Division (AAD)
• Australian Research Council (ARC)
• Central Science Lab (CSL) & Engineering workshop
• School of Land and Food and UTAS support
Web: http://www.lucieer.net Web: http://www.terraluma.net Email: [email protected] https://twitter.com/TerraLuma @TerraLuma
Processing workflow
Source: http://www.phoenix-aerial.com/
