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Terrestrial Laser Scanners for Vegetation Parameter Retrieval
- 1. Department of Science, IT, Innovation and the Arts Terrestrial Laser Scanners for Vegetation Parameter Retrieval Presented by Jasmine Muir Remote Sensing Centre Ecosciences Precinct, Dutton Park Department of Science, IT, Innovation and the Arts
- 2. Contributors Glenn Newnham1, John Armston2,4, Jasmine Muir2, Nicholas Goodwin2,Darius Culvenor1, Kim Calders3, Kasper Johansen4,5, Dan Tindall2, Pyare Püschel6, Mattias Nyström7 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. Department of Science, IT, Innovation and the Arts Presentation Outline • Purpose • Background • Study Site and Sampling Design • Data Pre-Processing • Data Analysis and Evaluation • Discussion and Future Research • Conclusions
- 4. Department of Science, IT, Innovation and the Arts Purpose • 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.
- 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. Department of Science, IT, Innovation and the Arts Scanner Attributes Instrument Riegl VZ1000 Leica C10 Leica HDS7000 Faro Focus 3D 120 Supplier CR Kennedy CR Kennedy CR Kennedy LSS Ranging method Time-of-flight Time-of-flight Phase Phase Returns multiple single single single Wavelength 1550nm 532nm 1500nm 905nm Max Zenith Range 100 270 320 320 Laser Class 1 3R 1 3R Range 1.5-1400m 600@20% 0.1-300m 134@18% 0.3-187m 0.6-120m Samples/sec 122000 50000 1016000 976000 Scan Configuration 30-130 zenith Hemispherical Hemispherical Hemispherical Colour external integrated external integrated Weight 10kg 13kg 10kg 5kg Temp Range 0-40C 0-40C 0-45C 5-40C
- 7. Department of Science, IT, Study Site Innovation and the Arts D’Aguilar National Park (north west of Brisbane)
- 8. Department of Science, IT, Sampling Design – TLS Placement 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. Department of Science, IT, Study Site Innovation and the Arts Leica C10 Faro Focus 3D 120
- 10. Department of Science, IT, Data Pre-Processing - Proprietary Software Innovation and the Arts 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
- 11. Department of Science, IT, Data Pre-Processing - Filtering Phase Based Data Innovation and the Arts • 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.
- 12. Department of Science, IT, Data Analysis and Evaluation - Point Cloud Innovation and the Arts Artefacts Faro Focus Leica 3D 120 HDS7000 Riegl Leica C10 VZ1000
- 13. Department of Science, IT, Data Pre-Processing - Filtering Phase Based Data Innovation and the Arts • 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. HDS7000 Non-Filtered Default Filtering Range Kernel Filtering
- 14. Department of Science, IT, Data Pre-Processing - DEM Generation Innovation and the Arts • 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).
- 15. Department of Science, IT, Innovation and the Arts Data Pre-Processing - Vertical Foliage Profiles • 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)
- 16. Department of Science, IT, Data Analysis and Evaluation - Range Innovation and the Arts Summaries • 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)
- 17. Department of Science, IT, Data Analysis and Evaluation - DEM Validation Innovation and the Arts Example DEM surfaces ALS Leica C10 Faro 3D 120 Leica HDS7000 Riegl VZ1000
- 18. Department of Science, IT, Data Analysis and Evaluation - Foliage Profile Innovation and the Arts Comparison
- 19. Department of Science, IT, Data Analysis and Evaluation - Foliage Profile Innovation and the Arts Comparison • 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
- 20. Department of Science, IT, Data Analysis and Evaluation - Foliage Profile Innovation and the Arts Comparison Riegl VZ1000 First return only Weighted returns
- 21. Department of Science, IT, Data Analysis and Evaluation - Discussion Innovation and the Arts • 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
- 22. Department of Science, IT, Data Analysis and Evaluation - Future Innovation and the Arts • 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.
- 23. Department of Science, IT, Data Analysis and Evaluation - Future Innovation and the Arts • 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
- 24. Department of Science, IT, Conclusions Innovation and the Arts • 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.
- 25. Department of Science, IT, Innovation and the Arts Acknowledgements CR Kennedy and Faro for providing the TLS demonstrations. Contact Details Glenn.Newnham@csiro.au John.Armston@derm.qld.gov.au Jasmine.Muir@derm.qld.gov.au
Notes de l'éditeur
- Introduction Background Scanner Selection Study Site and Sampling Design Site Description Field Measurements Data Pre-Processing Proprietary Software and Data Export Filtering Phased Based Data DEM Generation Vertical Foliage Profiles Data Analysis and Evaluation Point Cloud Artefacts Range Summaries DEM Validation Foliage Profile Comparison Discussion and Future Research
- Stress that any outcomes have only been validated in terms of forest structural attributes… Tested scan resolution and quality within a specified time…
- Differences to point out: Zenith (Riegl two scans to get complete hemisphere) – only did one during testing Ranging Method Multiple Returns Laser Class Range Colour Weight Cost
- eucalyptus forest stand with high grass cover, fine litter and woody debris present. Although some large stems have been removed in past logging, the site includes many mature trees with heights ranging up to 35m
- Permanent monthly monitoring site 2 TLS scan sites at which a number of scans were performed… to test variation in scan quality and resolution setting achievable within a defined time frame….
- The most important feature for vegetation analysis is the ability to export the data in a format that can be easily interpreted and incorporated into other dedicated software systems. All include facility for data viewing, filtering of points, cropping of the point cloud to specific features and exporting Cartesian point coordinates in text and binary formats. The vegetation structure analysis in this study requires the spherical geometry of the scan. This includes the zenith and azimuth angles for each pulse and range for each return. Scanners except Riegl only give x,y,z co-ordinates in .ptx format – post processing to get 3d scan geometry (azimuth and zenith) For our applications really need low-level access – can get with the riegl library – others more black box These software systems are focused on built structure surveys and are not specifically designed for vegetation analysis. Additional limitations of the Riegl ASCII and LAS export formats include: There was no information for pulses that had no return (i.e. canopy gaps); Small fluctuations of rotation speed and laser pulse rate resulted in an irregular scan pattern and prevented the data being sorted into scan order; The origin of the sensor coordinate system is different to the laser pulse origin. This resulted in different zenith and azimuth angles recorded for returns from the same pulse, particularly at near range. Gaps – needed to export as ptx to find returns with gaps Needed to predict azimuth/zenith angles of pulses where there was no return.
- The FARO Scene and Leica Cyclone software include a number of filters to deal with what they might consider spurious points in their phase based scanner data. Both include simple threshold filters for range and intensity. In addition they have also implemented similar textural filters that specify the minimum proportion of neighbouring points that must fall within a given distance from each point. For the HDS7000 the default is to apply intensity and range thresholds, as well as range textural filtering on import of the data into Cyclone. For the FARO Focus 3D, two filters are applied in hardware which are described as “clear sky” and “clear contour”.
- The two phase based scanners (FARO Focus and Leica HDS) show a scatter of points that fall between the two trunks where both trunks have intercepted a single pulse. The Riegl scanner shows a similar range averaging effect between the two trunks. However, this is likely due to the default waveform processing algorithm, which is unable to separate overlapping returns if they are separated by less than 0.8m in range. The Riegl output data file provides a measure of deviation in shape from the transmitted pulse that can be used to remove these returns if detailed structural analysis is required. The Leica C10 data has no visible range averaging effects between overlapping features. Given that multiple returns are likely to still be occurring, the proprietary algorithm is likely to be recording the first return above a given return intensity threshold or a single range to the highest intensity return. This ranging algorithm is fixed in hardware and its details are not available to the user.
- These default filtering methods were found to be non-ideal for the D’Aguilar National Park data. For the HDS7000, default software filtering is too severe, removing sky points but also edges of trunks and branches, as well as clumps of foliage (Figure 5b). For the FARO Focus 3D, hardware filtering only appears to detect large gaps in the canopy, while spurious ranges are still reported in smaller gaps (Figure 5e). Both of these issues can be addressed in software with the application of other software filtering methods. However, to ensure fair comparison between the data we implemented a range based kernel filter to allow consistent batch processing of all phase based data. This filter removed a point if more than 20% of its neighbouring points were separated by greater than 1m in range[m1] . The kernel size used for the processing was a 5 by 5 pulse window.
- In the case of flat terrain this may be as simple as adding the height of the instrument optical centre to the vertical (Z) coordinate in the data. In more complex terrain access to a digital elevation model (DEM) is required. There are many possible sources for these data but in the ideal case an accurate DEM would be generated from the TLS data itself. However, TLS does not provide the ideal perspective from which to record points on the ground surface, as occlusion by vegetation, ground cover, woody debris and the topography itself increases with horizontal distance from the scanning position. DEMs generated using an adaptation of the Zhang et al. (2003) method, which was originally developed for filtering airborne lidar data. Minimum elevation points within 1m by 1m pixels were located and the progressive morphological filter applied to these points to determine ground returns. This dataset was acquired in 2009 by AAM Hatch with the Leica ALS50-II sensor with a flying elevation of 1800m, and recording at a maximum scan angle of 18 degrees off nadir. Pulse repetition frequency of 126.2 kHz produced an average pulse density of 2.8 pulses m-2. The airborne DEM used only returns classified as ground points by the contractor. To be consistent with the TLS data, the airborne DEM was produced at a spatial resolution of 1m using the natural neighbour algorithm. DEM generation – ideally get all our DEM info from the scan – however topography occlusion was more of a problem than scanner differences/accuracy – need multiple scans registered – increase field time – unless can assume flat plane
- Not going into detail…if anyone wants to know I can talk to them after… There is no separation of foliage and woody vegetation in this work, so we assume plant area index is equal to LAI.
- The figures represent a probability density function where the number of ranges returned within 1 metre increments from the instrument is normalised by the total number of shots in the scan. The FARO Focus 3D showed a significant difference in range distributions, with the medium resolution, high quality (signal integration time) scans returning substantially fewer ranges that could be associated with vegetation components, when compared to the lower quality scans. This may be associated with an increase in ambient light interference with increasing integration time. The other three scanners showed very consistent distributions of returned range for all scanner settings. Range distributions are heavily skewed toward smaller values. This is due to near field objects that intercept the beam and obscure objects in the far field. Within a forest (or any medium) of randomly distributed vegetation components the expected distribution of returned ranges is an exponential curve. Deviation from this exponential decay is due to both the many return ranges from the ground surface in the lower hemisphere of the scan and the clumping of vegetation components into discrete trees. Unlike the other instruments in the trial, which only record a single return per outgoing pulse, the range distributions for the Riegl VZ1000 in Figure 7 include all recorded returns (a maximum of four per pulse is imposed by Riegl waveform processing). Multiple ranges are only returned when the beam is partially intercepted by more than one object. For example when an outgoing beam hits the edge of one tree and the non-intercepted portion of the beam continues on to be intercepted by a tree at a greater range. There is a finite range limit within which the default ranging algorithms in the Riegl do not distinguish between multiple interceptions and an average range is recorded (see Figure 6). In these cases there may be possibilities of post-processing waveform data to distinguish these multiple ranges. The impact of multiple returns on the distribution of range data from the Riegl VZ1000 can be seen in Figure 8. When only first returns are considered, the range distribution has a greater bias toward shorter ranges. With the addition of second returns, a greater number of ranges are recorded across the full distribution. The addition of third and fourth returns has only a small impact and influences the distribution more as ranges increase.
- Digital Elevation Models generated using the procedure described in Section 3.3 are shown in Figure 10. Spurious points can be seen to have a major influence on the FARO Focus and Leica HDS DEMs. It may be possible to remove these artefacts by employing more sophisticated filtering and smoothing methods. In general the Leica C10 and Riegl VZ1000 provided a much smoother surface, without the need for significant filtering and smoothing. The difference between each TLS DEM and the airborne lidar DEM were assessed within a horizontal radius of 100 metres from the instrument location. As TLS scans were not registered to map coordinates, each DEM was rotated until a minimum difference was found between the TLS and airborne data. Differences were then summarised as mean deviation within increasing circular areas with radii of 10, 25, 50 and 95 metres (see Table 3). The Leica C10 and Riegl VZ1000 instruments produced the lowest mean deviation, particularly at ranges less than 50m. The FARO Focus 3D produced the largest error relative to the airborne DEM. The hill shade images support these results with the Leica C10 producing a smoother surface which agrees closely with the airborne DEM. The DEM generated using the FARO Focus 3D data showed comparatively high amount of spurious depressions in the surface. A road shown in the south-west corner of the airborne DEM at a distance greater than 70m from the TLS scan location, is not evident in any of the TLS derived DEM surfaces. The comparison of DEM surfaces also revealed different error characteristics between the north and south sub-plots (Table 2). The DEM profiles show a high level of agreement at the south sub-plot out to 50-75m from the sensor (Figure 11). In contrast, a systematic bias is evident at the north sub-plot, with the positive bias in the TLS DEM elevations increasing with range. The ability of TLS instruments to record a return from the ground surface may have differed between sub-plots due to occlusion by topography and vegetation. This is supported by the distribution of DEM residuals (airborne DEM minus TLS DEM). Errors as high as 8m are evident correspond to depressions in the landscape (Figure 12a and b). Furthermore, a cross-sectional profile shown in Figure 13 shows that there is little evidence of ground returns being returned from horizontal ranges greater than 30m. Additional research to optimise the classification of ground returns and provide an adequately smooth surface for the correction of the complete point cloud is required and important if structure is to be assessed as a function of true height above the ground surface. Although ideally an accurate DEM could be generated from a single TLS scan, other options for correction of the data include access to external data or the derivation of an approximate DEM, such as estimation of a best fit planar surface. Time of flight instruments produced smoother and more accurate digital terrain elevation model (DEM) surfaces than the phase based instruments. Differences in Faro range
- Lai = total one sided leaf area/ ground area (m2/per unit area) x,y FAVD = leaf area per unit volume– incorporates height x,y,z m2/m3 For high and low res scans not a lot of difference Time of flight scanner resolution had negligible impact on profiles Fix up scan res in legend
- The distinct peaks that appear in the uncorrected profiles, appear less distinct after DEM correction. This is likely due to the merging of the effect of distinct crowns which may truly be part of the same forest strata.
- The weighted multiple return profile is picking up more foliage in the upper canopy and less in the lower canopy compared the first return profile. The maximum FAVD is also a little lower. Max heigth increasaed. Increased info in riegl Units for FAVD PAVD?
- The objective of this work was to demonstrate key differences in the data recorded by current commercial Terrestrial Laser Scanners (TLS) when operated in a forest environment. The instruments used included two scanners that use phase-shift ranging and two that use a time-of-flight ranging method, which is more conventional in TLS for deriving vegetation structural parameters. However, there are clear distinctions, when used in a forest environment in which soft targets (i.e. tree leaves) make up a large proportion of the intercepted material. An increase in scan quality (signal integration time) may increase range precision but will also increase exposure to solar radiant flux, thus decreasing the signal-to-noise ratio. More sophisticated DEM generation algorithms that are tuned to the radial sampling pattern of terrestrial scanners may improve these results in future. Multiple scans needed for DEM generation, assumption of a planar surface or use of external DEM source may be required for optimal vegetation structure assessment in areas of significant slope or undulating terrain. Pulse density has a negligible impact on return range distributions, DEM surfaces and vertical foliage profiles. Quality of filtering of data from the phase-shift scanners directly impacts the variance in metrics derived from gap fraction such as leaf area index. signal-to-noise ratio that can be achieved is highly dependent on levels of ambient radiant flux at the wavelength where the sensor operates. Size and weight decrease essential direction for tls in the future…