Multisensoral UAV-Based Reference Measurements for Forestry Applications

Transcription

1 Multisensoral UAV-Based Reference Measurements for Forestry Applications Research Manager D.Sc. Anttoni Jaakkola Centre of Excellence in Laser Scanning Research

2 2 Outline UAV applications Reference level UAS measurements Low-cost LiDAR High-end LiDAR Hyperspectral camera FMCW radar Multrispectral LiDAR Future outlook of UAV LiDAR

3 History 3

4 4 Strengths of UAS Measurements Small area mapping Corridor mapping Multitemporal measurements Air/spaceborne sensor Simulation Validation Reference data collection

5 Alternative Platforms 5

6 Mapping: Urban Planning 6

7 Corridors: Roads 7

8 Corridors: Power Lines 8

9 Corridors: Rivers 9

10 Multitemporal Data 10

11 UAV-Based Reference Data Collection 11 Evo test site Southern Finland Boreal forest Pine Spruce Birch 91 test plots 32x32 m

12 Low-Cost LiDAR 12 Velodyne VLP-16 Lite Novatel SPAN-IGM S1 40 m AGL 10 m line spacing 8 m/s Up to 800 pts/m 2 Single plot

13 Low-Cost LiDAR 13 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 Hz Horizontal accuracy 1 cm Profile frequency 16 x 20 Hz Vertical accuracy 2 cm Range accuracy ±3 cm Roll/Pitch accuracy Beam divergence 3 mrad Heading accuracy 0.080

14 Low-Cost LiDAR 14

15 Low-Cost LiDAR 15 Bias Bias (%) RMSE RMSE (%) R Tree height (m) DBH (cm) Basal area (m 2 ) Volume (m 3 ) Biomass (Mg)

16 16 High-End LiDAR Riegl VQ-480-U Novatel SPAN-LCI 75 m AGL Single overpass >100 pts/m 2

17 17 High-End LiDAR Riegl VQ-480-U Size Weight Measurement range Pulse repetition rate Profile frequency Range accuracy Beam divergence Ø183 x 348 mm 7.5 kg m Hz 150 Hz ±2.5 cm 0.3 mrad

18 High-End LiDAR 18

19 High-End LiDAR 19

20 High-End LiDAR 20

21 21 High-End LiDAR Non-calibrated Calibrated Height DBH Basal area Volume Biomass Bias (%) RMSE (%) R

22 22 Hyperspectral Camera Fabry-Pérot interferometric hyperspectral camera RGB camera GNSS L1 receiver 90 m AGL 4 m/s

23 23 Hyperspectral Camera Rikola/Senop FPI camera nm 1010 x 1010 pixels Up to 30 fps Samsung NX300 APS-C, 24 mpix

24 Hyperspectral Mosaics 1 2 3,

25 Individual tree classification Reference data: - fieldwork data (location & species) Classification features 3D features of individual trees (RGB pointcloud) Lastools Classification Feature Selection Classification training and evaluation Weka Spectral features from hyperspectral FPI mosaics Matlab Classification of detected trees Photoscan Photogrammetric point cloud from RGB imagery FUSION Software (Pacific Northwest Research Station) ) Digital Surface Model (DSM) Digital terrain model (DTM) from NLS ALS data Canopy Height Model (CHM) = DSM DTM Individual tree detection using local maxima method

26 Hyperspectral Camera 26 True Class Precision Pine Spruce Birch Larch Classified as Recall Pine Spruce Birch Larch All Features Spectral Features Feature Selection All Features (no norm. spectra) Spectral Features (no norm. spectra) Feature Selection (no norm. spectra) 3D Features Accuracy (%) Kappa

27 27 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, m AGL

28 FMCW Radar 28

29 FMCW Radar 29

30 30 FMCW Radar (a) B E db db A (b) C D

31 FMCW Radar 31

32 32 FMCW Radar mean height (m) mean DBH (cm) Observed 20 Observed Mean height (m) Mean DBH (cm) Basal area (m 2 /ha) Volume (m 3 /ha) Biomass (Mg/ha) Bias Bias (%) RMSE RMSE (%) R Observed Observed Predicted Predicted 50 2 basal area (m /ha) Predicted 250 biomass (Mg/ha) Observed /ha) volume (m Predicted Predicted

33 33 Multispectral LiDAR Optech Titan

34 34 Multispectral LiDAR Chan1 Chan2 Chan

35 35 Multispectral LiDAR Confusion matrix based on intensity features Reference Predicted Producer Pine Spruce Birch Pine Spruce Birch User Overall = 86.81%

36 36 Multispectral LiDAR Confusion matrix based on point cloud and intensity features Reference Predicted Producer Pine Spruce Birch Pine ,55 Spruce ,10 Birch ,43 User Overall = 88.36%

37 37 Future Outlook of LiDAR Technology Automotive industry Solid state / flash LiDAR technology Multispectral LiDAR Single photon LiDAR

38 38 Automotive Industry Driving the development of low-cost light-weight LiDARs Range accuracy or beam divergence are not the primary targets

39 39 Solid-State Technology Automotive applications require low cost and high reliability Optical beam steering

40 40 Flash LiDAR Low volume and high price of InGaAs components Frame based, but low resolution

41 41 Multispectral LiDAR Optech Titan or multiple scanners, e.g. Faro S120 + X330 Currently heavy and expensive

42 42 Single photon LiDAR Currently available for full-scale airborne laser scanning

43 43 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