1. Terrestrial Laser Scanners for Vegetation Parameter Retrieval Department of Science, IT, Innovation and the Arts Presented by Jasmine Muir Remote Sensing Centre Ecosciences Precinct, Dutton Park Department of Science, IT, Innovation and the Arts

2. Contributors Glenn Newnham 1, John Armston 2,4, Jasmine Muir 2, Nicholas Goodwin 2,Darius Culvenor 1, Kim Calders 3, Kasper Johansen 4,5, Dan Tindall 2, Pyare Püschel 6, Mattias Nyström 7 Affiliations: 1 CSIRO Land and Water; Private Bag 10, Clayton South, VIC 3169, Australia 2 Remote Sensing Centre; Department of Science, Information Technology, Innovation and the Arts; Ecosciences Precinct, 41, Boggo Road, Dutton Park QLD, Australia, 4102 3 Laboratory of Geo-Information Science and Remote Sensing; Wageningen University; Droevendaalsesteeg, Wageningen 6708, PB, The Netherlands 4 Joint Remote Sensing Research Program; School of Geography, Planning and Environmental Management; University of Queensland; Brisbane, Australia, 4072 5 Terrestrial Ecosystem Research Network (TERN) Auscover, School of Geography, Planning and Environmental Management; University of Queensland; Brisbane,Australia, 4072 6 University of Trier, Trier, Germany 7 Swedish University of Agricultural Sciences, Sweden

3. Presentation Outline Purpose Background Study Site and Sampling Design Data Pre-Processing Data Analysis and Evaluation Discussion and Future Research Conclusions Department of Science, IT, Innovation and the Arts

4. The objective of this work was to examine key differences in the data recorded by current commercial Terrestrial Laser Scanners (TLS) when operated in a forest environment. Parameters tested: –Scan resolution –Scan quality Outcomes from the work have been used to inform the purchase decision of a TLS by RSC and TERN for vegetation structure monitoring. Department of Science, IT, Innovation and the Arts Purpose

5. Background – Why Use TLS? Reduced field time for staff Increased data collection ability Provide a reference data set i.e. airborne lidar Different view (looking under the canopy) Measure different parameters

6. Scanner Attributes InstrumentRiegl VZ1000Leica C10Leica HDS7000Faro Focus 3D 120 Supplier CR Kennedy LSS Ranging method Time-of-flight Phase Returns multiplesingle Wavelength 1550nm532nm1500nm905nm Max Zenith Range 100270320 Laser Class 13R1 Range 1.5-1400m 600@20%0.1-300m 134@18%0.3-187m0.6-120m Samples/sec 122000500001016000976000 Scan Configuration 30-130 zenithHemispherical Colour externalintegratedexternalintegrated Weight 10kg13kg10kg5kg Temp Range 0-40C 0-45C5-40C Department of Science, IT, Innovation and the Arts

7. Study Site D’Aguilar National Park (north west of Brisbane) Department of Science, IT, Innovation and the Arts

8. Sampling Design – TLS Placement Department of Science, IT, Innovation and the Arts Stem Measurements Stem diameter (at 1.3m and 0.3m) Crown opacity Crown dimensions (length and width) Tree Height (top and first branch) Total station position (x,y,z) relative to scanner Hemispherical photographs Licor LAI2200

9. Study Site Leica C10 Faro Focus 3D 120 Department of Science, IT, Innovation and the Arts

10. Data Pre-Processing - Proprietary Software and Data Export Each scanner manufacturer has a proprietary data processing software system. Software not sufficient for all of our processing Data exported to ptx format (an ASCII format) except for Riegl which was exported to LAS format To associate multiple returns from the Riegl with a single pulse azimuth and zenith, low-level access to the raw binary files was necessary using Riegl C++ RiVLib library Department of Science, IT, Innovation and the Arts

11. Phase based scanners: –return random ranges in canopy gaps due to sky and direct solar radiation. –are subject to range averaging when the beam intercepts multiple objects. Sky points need to be removed so gaps can be identified. The removal of points that indicate multiple hits would overly inflate gap probability estimates at the stand level, however to determine parameters for individual trees these points must be removed. Data Pre-Processing - Filtering Phase Based Data Department of Science, IT, Innovation and the Arts

12. Data Analysis and Evaluation - Point Cloud Artefacts Faro Focus 3D 120 Leica C10 Leica HDS7000 Riegl VZ1000 Department of Science, IT, Innovation and the Arts

13. Phase scanners provide inbuilt hardware and software filtering options – appeared non-ideal Used a range based kernel filter to allow consistent batch processing and remove points in canopy gaps. Data Pre-Processing - Filtering Phase Based Data Department of Science, IT, Innovation and the Arts HDS7000 Non-FilteredDefault FilteringRange Kernel Filtering

14. DEM generation from the scan allows vegetation structure to be analysed in terms of height relative to the ground surface, rather than relative to the origin of the sensor coordinate system. A DEM from each scan was derived at a scale of 1m. Each DEM generated was validated using an equivalent DEM generated using airborne laser scanning (ALS). Data Pre-Processing - DEM Generation Department of Science, IT, Innovation and the Arts

15. Range Summaries Gap probability (Pgap) Leaf area index (LAI) or plant area index (PAI) as a cumulative profile foliage profile, sometimes referred to as the foliage area volume density (FAVD) Data Pre-Processing - Vertical Foliage Profiles Department of Science, IT, Innovation and the Arts

16. Range distribution for points recorded by each scanner Similar for all resolutions for all scanners except for Faro General pattern is the same between scanners although some difference with the Riegl (no data >30deg. zenith) Data Analysis and Evaluation - Range Summaries Department of Science, IT, Innovation and the Arts

17. Data Analysis and Evaluation - DEM Validation Department of Science, IT, Innovation and the Arts ALSLeica C10Leica HDS7000Riegl VZ1000Faro 3D 120 Example DEM surfaces

18. Data Analysis and Evaluation - Foliage Profile Comparison Department of Science, IT, Innovation and the Arts

19. Correcting for terrain height is necessary for analysis of vegetation structure in areas of varied topography. Assume planar surface in flat areas. Maximum height decrease at both sites Bimodal canopy response to unimodal canopy response Data Analysis and Evaluation - Foliage Profile Comparison Department of Science, IT, Innovation and the Arts

20. First return onlyWeighted returns Data Analysis and Evaluation - Foliage Profile Comparison Riegl VZ1000 Department of Science, IT, Innovation and the Arts

21. Findings include: –Pulse density has a negligible impact. –Quality of phase-shift data filtering directly impacts the variance in metrics derived from gap fraction. –signal-to-noise ratio that can be achieved is highly dependent on levels of ambient light. Occlusion by near-range terrain and vegetation has a greater impact on DEM error than sensor properties or scan settings. Phase-shift scanners: –needed filtering applied to accurately detect canopy gaps –range averaging when there are multiple targets in beam –higher scan integration time decreased signal-to-noise ratio –Faro size and weight make field operation easy Time of flight scanners: –relatively clean data (i.e. no range averaging) –Riegl multiple returns/waveform increases the information available Data Analysis and Evaluation - Discussion Department of Science, IT, Innovation and the Arts

22. Development of data filtering and ground return classification algorithms for phase-based data. Improved estimation of gap fraction to account for terrain, wood area and volume fractions, clumping and to assess sensitivity to different leaf area projection functions. Linking airborne and ground-based estimates of structural measurements for calibration and validation of larger area mapping from lidar. Department of Science, IT, Innovation and the Arts Data Analysis and Evaluation - Future

23. Compare scanner results to actual field measurements of DBH, height, biomass, LAI, canopy cover, foliage profile. Absolute field truth??? Stand attributes vs individual trees Average from each scan rather than registration of multiple scans Department of Science, IT, Innovation and the Arts Data Analysis and Evaluation - Future

24. Conclusions 4 scanners tested at Brisbane Forest Park: –FARO Focus 3D 120 –Leica HDS7000 –Leica C10 and –Riegl VZ1000 Time-of-flight instruments are currently providing the best characterisation of vegetation structure, particularly foliage measurements in the upper parts of the canopy, where multiple beam interceptions are not accommodated well by the phase-shift scanners. Department of Science, IT, Innovation and the Arts

25. Acknowledgements CR Kennedy and Faro for providing the TLS demonstrations. Department of Science, IT, Innovation and the Arts Contact Details Glenn.Newnham@csiro.au John.Armston@derm.qld.gov.au Jasmine.Muir@derm.qld.gov.au