multisensoral uav-based reference measurements for...
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Multisensoral UAV-Based Reference
Measurements for Forestry Applications
Research ManagerD.Sc. Anttoni Jaakkola
Centre of Excellence in Laser Scanning Research
Outline• UAV applications• Reference level UAS measurements
• Low-cost LiDAR• High-end LiDAR• Hyperspectral camera• FMCW radar• Multrispectral LiDAR
• Future outlook of UAV LiDAR
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History3
Strengths of UAS Measurements
• Small area mapping• Corridor mapping• Multitemporal measurements• Air/spaceborne sensor
• Simulation• Validation
• Reference data collection
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Alternative Platforms5
Mapping: Urban Planning6
Corridors: Roads7
Corridors: Power Lines8
Corridors: Rivers9
Multitemporal Data10
UAV-Based Reference Data Collection
• Evo test site
• Southern Finland
• Boreal forest
• Pine
• Spruce
• Birch
• 91 test plots
• 32x32 m
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Low-Cost LiDAR
• Velodyne VLP-16 Lite• Novatel SPAN-IGM S1
• 40 m AGL• 10 m line spacing• 8 m/s• Up to 800 pts/m2
• Single plot
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Velodyne Puck LITE Novatel IGM-S1
Size Ø103 x 72 mm Size 152 x 142 x 51 mm
Weight 590 g Weight 540 g
Measurement range 100 m Measurement rate 125 Hz
Pulse repetition rate 300 000 Hz Horizontal accuracy 1 cm
Profile frequency 16 x 20 Hz Vertical accuracy 2 cm
Range accuracy ±3 cm Roll/Pitch accuracy 0.015°
Beam divergence 3 mrad Heading accuracy 0.080°
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Low-Cost LiDAR
Low-Cost LiDAR14
Low-Cost LiDARBias Bias (%) RMSE RMSE (%) R
Tree height (m) 0.02 0.08 1.02 5.16 0.92
DBH (cm) 0.02 0.07 2.55 10.40 0.88
Basal area (m2) 0.00 0.56 0.01 19.73 0.84
Volume (m3) 0.00 -0.02 0.09 19.26 0.88
Biomass (Mg) 0.12 0.05 40.81 17.35 0.89
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High-End LiDAR• Riegl VQ-480-U• Novatel SPAN-LCI
• 75 m AGL• Single overpass• >100 pts/m2
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Riegl VQ-480-U
Size Ø183 x 348 mm
Weight 7.5 kg
Measurement range 200-1500 m
Pulse repetition rate 550 000 Hz
Profile frequency 150 Hz
Range accuracy ±2.5 cm
Beam divergence 0.3 mrad
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High-End LiDAR
High-End LiDAR18
High-End LiDAR19
High-End LiDAR20
High-End LiDAR
Non-calibrated Calibrated
Height DBH Basal area Volume Biomass
Bias (%) 0.28 -4.56 1.06 0.19 0.57
RMSE (%) 4.54 8.88 16.91 17.14 16.77
R 0.97 0.97 0.81 0.90 0.88
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Hyperspectral Camera• Fabry-Pérot interferometric
hyperspectral camera• RGB camera• GNSS L1 receiver
• 90 m AGL• 4 m/s
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Hyperspectral Camera• Rikola/Senop FPI camera• 500-900 nm• 1010 x 1010 pixels• Up to 30 fps
• Samsung NX300• APS-C, 24 mpix
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Hyperspectral Mosaics1 2 3, 4 5 8
6 7 9 10 11
Individual tree classification
Digital Surface Model (DSM)
Digital terrain model (DTM) from NLS ALS data
Photogrammetric point cloud from RGB imagery
Reference data:- fieldwork data
(location & species)
Individual tree detection using local maxima method
Canopy Height Model (CHM) = DSM – DTM
Photoscan FUSION Software (Pacific Northwest Research Station))
3D features of individual trees (RGB pointcloud)
Lastools
Spectral features from hyperspectral FPI mosaics
MatlabClassification of detected trees
WekaFeature Selection
Classification features Classification
Classification training and evaluation
Hyperspectral Camera26
Classified as RecallPine Spruce Birch Larch
True
Cla
ss
Pine 2584 41 0 2 0.984Spruce 122 692 2 6 0.842Birch 13 8 558 1 0.962Larch 11 1 5 105 0.861
Precision 0.947 0.933 0.988 0.921
All Features Spectral Features
Feature Selection
All Features (no norm. spectra)
Spectral Features (no
norm. spectra)
Feature Selection (no
norm. spectra)
3D Features
Accuracy (%) 95.11 94.65 94.89 94.84 94.29 92.58 72.01
Kappa 0.91 0.90 0.90 0.90 0.89 0.86 0.39
FMCW Radar• Frequency 14.0 GHz (Ku)• Sweep frequency 1000 Mhz• Range resolution 15 cm• Beam width 6°• Polarization HH, VV, HV, VH• Weight 14 kg (UAV version 6 kg)• Single line, 60-80 m AGL
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FMCW Radar28
FMCW Radar29
FMCW Radar30
(a)
(b)
A
B
D
E
C
dBdB
FMCW Radar31
15 20 25 30
Predicted
10
15
20
25
30
35
Obs
erve
d
2096
mean height (m)
10 20 30 40
Predicted
10
20
30
40
50
Obs
erve
d
mean DBH (cm)
1041
10 20 30 40
Predicted
0
10
20
30
40
50
Obs
erve
d
basal area (m 2 /ha)
100 200 300 400 500
Predicted
0
100
200
300
400
500
600
Obs
erve
d
volume (m 3 /ha)
50 100 150 200
Predicted
0
50
100
150
200
250
Obs
erve
d
biomass (Mg/ha)
FMCW Radar32
Bias Bias (%) RMSE RMSE (%) R
Mean height (m) -0.09 -0.42 2.83 12.99 0.78Mean DBH (cm) -0.02 -0.07 5.59 21.25 0.65Basal area (m2/ha) 0.10 0.35 5.89 21.34 0.61Volume (m3/ha) -0.80 -0.28 67.19 23.61 0.77Biomass (Mg/ha) -0.19 -0.14 33.05 23.39 0.69
Multispectral LiDAR33
• Optech Titan
Multispectral LiDAR34
135
140
145
150
155
135
140
145
150
155
Chan1
Chan2
Chan3
Multispectral LiDAR35
Predicted Producer
Pine Spruce Birch
Refe
renc
e Pine 623 12 16 95.70
Spruce 32 180 27 75.31
Birch 47 18 197 75.19
User 88.75 85.71 82.08 Overall = 86.81%
• Confusion matrix based on intensity features
Multispectral LiDAR• Confusion matrix based on point cloud and intensity features
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Predicted Producer
Pine Spruce Birch
Refe
renc
e Pine 622 14 15 95,55
Spruce 18 201 20 84,10
Birch 46 21 195 74,43
User 90.67 85.17 84.78 Overall = 88.36%
Future Outlook of LiDAR Technology
• Automotive industry• Solid state / flash LiDAR technology• Multispectral LiDAR• Single photon LiDAR
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Automotive Industry• Driving the development of low-cost light-weight LiDARs• Range accuracy or beam divergence are not the primary targets
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Solid-State Technology• Automotive applications require low cost and high reliability• Optical beam steering
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Flash LiDAR• Low volume and high price of InGaAs components• Frame based, but low resolution
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Multispectral LiDAR• Optech Titan or multiple scanners, e.g. Faro S120 + X330• Currently heavy and expensive
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Single photon LiDAR• Currently available for full-scale airborne laser scanning
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Summary• UAV-based measurements can be used as reference data
for some applications• Efficiency compared to manual measurements may be
higher by an order of magnitude• Current LiDAR sensors are still heavy, expensive,
inaccurate and/or limited in measurement range• Development towards new sensor technologies is rapid
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