multisensoral uav-based reference measurements for...

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

2

History3

Strengths of UAS Measurements

• Small area mapping• Corridor mapping• Multitemporal measurements• Air/spaceborne sensor

• Simulation• Validation

• Reference data collection

4

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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