Marselis 2014 Vegetation Structure mapping with LiDAR for forest fire research

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Assessing the three dimensional vegetation structure is important in fire management. Manually mapping forest structural characteristics is time consuming and hence expensive and automated methods should prove beneficial. In this research I investigated the use of airborne light detection and ranging (LiDAR) for mapping vegetation height and canopy cover and to derive information on the understory. Airborne LiDAR data provided good quality information on both vegetation height and canopy cover, but understory information was more uncertain. The use of automated hand-held LiDAR data collection to obtain information on the understory and to complement the airborne LiDAR data was investigated and looks to have strong potential.

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  • Bunyip State Forest,
    East of Melbourne, Victoria
    February 7, 2009
    © AAP 2009
  • Albert: What picture of field assessment doe you suggest? / have ?
  • False color – R: infra-red G: green B: blue
  • I agree … the colours on the scale bare are not so well visible… hmm.. You want me to change that colorbar? I think I can do that. But I am not sure I I can get the exact same one back.
  • Botanical gardens and powerline clearing
  • south facing gullies -> less evapotranspiration, so better growth.
  • and also botanical gardens very clear, inc. rain froest gully
  • and also botanical gardens very clear, inc. rain froest gully
  • Albert: a picture of what?
  • Commission (red) and omission (yellow) errors. What is what again? – it also hurts my eyes but I can’t really help it.
  • This is the pearson correlation coefficient. Is that wrong?
  • 1:1 line
  • What do you like to put about the costs?
  • Depending on scale, desired resolution
  • Marselis 2014 Vegetation Structure mapping with LiDAR for forest fire research

    1. 1. Vegetation structure mapping with airborne and ground-based laser scanning to advance forest fire research Suzanne Marselis1,2,3 June 11th, 2014 Prof. Dr. Albert van Dijk1,2 Dr. Marta Yebra1,2 Tom Jovanovic2 Dr. Harry Seijmonsbergen3 1: Australian National University 2: CSIRO 3: University of Amsterdam
    2. 2. Acknowledgements • Bushfire and Natural Hazards CRC • ACT Parks and Conservation Service • Earth Observation and Informatics Transformational Capability Platform (CSIRO) • Terrestrial Ecosystem Research Network (TERN)
    3. 3. Content • Introduction • Aim of research • Airborne LiDAR • Limitation of Airborne LiDAR • Ground-based LiDAR opportunities • Summary • Recommendations for forest fire research
    4. 4. Bunyip State Forest, Victoria, 7 February 2009 © AAP 2009
    5. 5. Introduction • Need for monitoring • Two important aspects • Fuel flammability • Fuel load • Problem: Field fuel assessments can be • Time consuming • Costly • Slightly subjective • Solution: Remote sensing? Phil Zylstra & Marta Yebra, January & April 2014
    6. 6. Aim of my research • Study the potential of using remote sensing data to map forest structural characteristics that describe the fuel load.
    7. 7. Project Vesta Fuel assessment Forest Surface Near- surface Elevated Canopy Continuity of litter: LiDAR Available fuel: LiDAR Amount of decomposition Continuity of fuel Proportion of dead material Percentage cover Amount of fuel (t/ha) Continuity of fuel Amount of fuel (t/ha) Fraction of dead material Type of bark based on tree species Canopy cover Canopy height Assigning hazard scores Information needed for fuel hazard scores Division in layers SF.FHS SF.depth.mm EF.FHS NSF.ht.cm NSF.FHS EF.ht.cm BK.FHS Canopy.PC Canopy.ht.m
    8. 8. Remote sensing • Any data collected from a distance • Active and Passive remote sensing • Optical - Hyperspectral • Light Detection and Ranging (LiDAR) Aranxta Cabello-Leblich, June 2014 Hyperspectral data for Black Mountain, collected March 2014
    9. 9. Light Detection and Ranging (LiDAR) • Airborne LiDAR • Point cloud Airborne LiDAR data (Source: Blair et al. 1999) Full-waveform LiDAR signal Source: Wagner et al. 2008 p1 p2 p3
    10. 10. LiDAR LiDAR point cloud for 1 isolated tree LiDAR point cloud for Black Mountain Nature Reserve • Point cloud • x,y,z value
    11. 11. LiDAR – 2 datasets • Research areas • Black Mountain Nature Reserve • Mulligans Flat Nature Reserve • Point cloud: height classification • Ground • Understory (z < 0.3 meter, noise?) • Midstory (0.3 < z < 2 meter) • Canopy (z > 2 meter) Black Mountain Mulligans Flat
    12. 12. Tree dimensions • Isolated trees on Mulligans Flat
    13. 13. Tree dimensions R² = 0.8889 0 5 10 15 20 25 30 0 10 20 30 40 LiDARcalculatedtopheight(meter) Field measured top height (meter) Canopy Top Height Individual trees R² = 0.7034 0 2 4 6 8 10 12 0 2 4 6 8 10 12 14 LiDARcalculatedbaseheight(meter) Field measured base height (meter) Canopy Base Height Individual trees Source: Wagner et al. 2008
    14. 14. Spatial Maps – Black Mountain • Canopy height • Canopy base height • Canopy cover
    15. 15. > 20 > 20
    16. 16. Canopy cover Canopy cover, Black Mountain
    17. 17. How about understory and midstory? Adam Leavesley, March 2014
    18. 18. Source: Wagner et al. 2008
    19. 19. Limitations • It seems to work … • But can we actually ground-truth this? • Required: • High resolution, reliable understory information • Is this possible? YES!
    20. 20. Ground-based LiDAR - Zebedee Tom Jovanovic (CSIRO) preparing the Zebedee for data collection
    21. 21. Data collection in Mulligans Flat
    22. 22. Result: in 15 minutes a floating point cloud
    23. 23. Data collection in Mulligans Flat • 3 field sites
    24. 24. Zebedee data ‘floating in space’ Airborne LiDAR data, plot Tom Un-georeferenced Zebedee LiDAR data, plot Tom
    25. 25. Georeferencing Zebedee point cloud Rotation
    26. 26. Matching two datasets Airborne Ground-based Merged
    27. 27. Compare Zebedee with Airborne LiDAR • Create same classification for Zebedee • Ground • Understory (z < 0.3 meter, noise?) • Midstory (0.3 < z < 2 meter) • Canopy (z > 2 meter) Zebedee dataset, classified in three classes based on heights
    28. 28. Understory presence: z< 0.3 meter PLOT 1 Airborne Airborne 0 1 Total Zebedee 0 1336 97 1433 Zebedee 1 463 220 683 Total 1799 317 2116 PLOT 2 Airborne Airborne 0 1 Total Zebedee 0 577 131 708 Zebedee 1 720 563 1283 Total 1297 694 1991 PLOT TOM Airborne Airborne 0 1 Total Zebedee 0 1028 202 1230 Zebedee 1 2354 640 2994 Total 3382 842 4224 Zebedee Airborne Omission error Commission error
    29. 29. Midstory presence: 0.3 < z < 2 meter PLOT 1 Airborne Airborne 0 1 Total Zebedee 0 1524 2 1526 Zebedee 1 532 58 590 Total 2056 60 2116 PLOT 2 Airborne Airborne 0 1 Total Zebedee 0 1179 3 1182 Zebedee 1 789 20 809 Total 1968 23 1991 PLOT TOM Airborne Airborne 0 1 Total Zebedee 0 1476 6 1482 Zebedee 1 2481 261 2742 Total 3957 267 4224 Zebedee Airborne Omission error Commission error
    30. 30. What else can we do with Zebedee data? • Interpolate tree heights, 1x1 meter resolution Airborne LiDAR Zebedee LiDAR
    31. 31. Height difference Airborne - Zebedee = height difference meter meter Reclassified Height Difference (meter)
    32. 32. Height difference (meter) Zebedee point density
    33. 33. Calculate canopy cover Zebedee Airborne Plot nr. R2 R2 – restriction* Plot 1 0.438 0.851 Plot 2 0.143 0.557 Plot Tom 0.368 0.649 - Fractional cover, 1x1 meter resolution - Airborne: straightforward - Zebedee: occupied grid cells within larger grid cell *Only cells with more than 20 Zebedee points included in analyses
    34. 34. Calculate DBH – Slice at 1.3 – 1.35 meter Slice Raw slice Selection of the stems
    35. 35. Automating this processing? • Need for good classification Height classification Understory, midstory & canopy Understory, midstory, canopy & stem Application in different area
    36. 36. Calculating grass volumes The total volume of grass: 33.12 m3 Area: 234 m2 Average volume: 0.14 m3/m2.
    37. 37. Summary of findings Dataset Pro’s Con’s Airborne - Covers large areas - Canopy height - Canopy base height - Canopy cover - Applicability for understory/midstory evaluations Zebedee - Easy data collection - Understory volume - Shrub dimensions - DBH calculations - Processing times - Algorithm availability - Small-scale
    38. 38. Recommendations for fire research • Depending on the needs it would be better to invest in either: • Airborne LiDAR data collection to large areas • Research on using Zebedee data and data sampling
    39. 39. Thank you • For having me at ANU • For all the assistance • For the funds • And for listening to my story – I hope you enjoyed • I definitely did!
    40. 40. Questions? Mourad Bandjee, 2014 Questions?
    41. 41. Clumping
    42. 42. Automating stem extraction 18 out of 29 stems = 62.07 %.
    43. 43. Automating stem extraction clustering of 14/18 = 77.78 % of the stems
    44. 44. Automating stem extraction R² = 0.9483 0 5 10 15 20 25 30 35 0 5 10 15 20 25 30 35 CalculatedDBH Measured DBH Correlation between Calculated & measured DBH
    45. 45. DBH frequency distributions
    46. 46. Calculating shrub & grass dimensions Measure Field – Measured LiDAR - Calculated Error (meter) Grass1 NZ 0.45 0.82 -0.37 EW 0.55 0.72 -0.17 Height 0.45 0.4 0.05 Grass2 NZ 0.8 0.85 -0.05 EW 0.85 0.88 -0.03 Height 0.5 0.675 -0.18 Shrub1 NZ 1.2 1.1418 0.06 EW 1.44 1.199 0.24 Height 2.5 2.2596 0.24 Shrub 3 NZ 1.2 0.7868 0.41 EW 1.18 1.1169 0.06 Height 1.45 1.4114 0.04 Shrub 4 NZ 1.4 1.1158 0.28 EW 1.4 1.1744 0.23 Height 1.8 1.7294 0.07
    47. 47. Canopy cover & height error
    48. 48. Biomass estimations • Tree recognition • DBH calculations • Height calculations • Allometric equation: Calculate biomass.

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