, Volume 24, Issue 5, pp 819–832 | Cite as

Comparing canopy metrics derived from terrestrial and airborne laser scanning in a Douglas-fir dominated forest stand

  • Thomas HilkerEmail author
  • Martin van Leeuwen
  • Nicholas C. Coops
  • Michael A. Wulder
  • Glenn J. Newnham
  • David L. B. Jupp
  • Darius S. Culvenor
Original Paper


Accurate estimates of vegetation structure are important for a large number of applications including ecological modeling and carbon budgets. Light detection and ranging (LiDAR) measures the three-dimensional structure of vegetation using laser beams. Most LiDAR applications today rely on airborne platforms for data acquisitions, which typically record between 1 and 5 “discrete” returns for each outgoing laser pulse. Although airborne LiDAR allows sampling of canopy characteristics at stand and landscape level scales, this method is largely insensitive to below canopy biomass, such as understorey and trunk volumes, as these elements are often occluded by the upper parts of the crown, especially in denser canopies. As a supplement to airborne laser scanning (ALS), a number of recent studies used terrestrial laser scanning (TLS) for the biomass estimation in spatially confined areas. One such instrument is the Echidna® Validation Instrument (EVI), which is configured to fully digitize the returned energy of an emitted laser pulse to establish a complete profile of the observed vegetation elements. In this study we assess and compare a number of canopy metrics derived from airborne and TLS. Three different experiments were conducted using discrete return ALS data and discrete and full waveform observations derived from the EVI. Although considerable differences were found in the return distribution of both systems, ALS and TLS were both able to accurately determine canopy height (Δ height < 2.5 m) and the vertical distribution of foliage and leaf area (0.86 > r 2 > 0.90, p < 0.01). When using more spatially explicit approaches for modeling the biomass and volume throughout the stands, the differences between ALS and TLS observations were more distinct; however, predictable patterns exist based on sensor position and configuration.


LiDAR Terrestrial LiDAR Canopy architecture Leaf area Canopy volume Echidna EVI Fluxnet Full waveform LiDAR 



Past and current members of the Integrated Remote Sensing Studio, Faculty of Forest Research management, University of British Columbia are thanked for their assistance with the field work. This work is partially funded by an NSERC Discovery grant to Coops. Additional funding support was also received from the Canadian Wood Fibre Centre, of the Canadian Forest Service of Natural Resources Canada.


  1. Blair J, Coyle D, Bufton J, Harding D (1994) Optimization of an airborne laser altimeter for remote sensing of vegetation and tree canopies. vol II, pp 939–941Google Scholar
  2. Boudreau J, Nelson RF, Margolis HA, Beaudoin A, Guindon L, Kimes DS (2008) Regional aboveground forest biomass using airborne and spaceborne LiDAR in Quebec. Remote Sens Environ 112:3876–3890CrossRefGoogle Scholar
  3. Chasmer L, Hopkinson C, Treitz P (2004) Assessing the 3D frequency distribution of airborne and ground-based LiDAR data for red pine and mixed deciduous plots. ISPRS Workshop Laser-Scanners for Forest and Landscape Assessment, Freiburg, pp 66–70Google Scholar
  4. Chen JM (1996) Canopy architecture and remote sensing of the fraction of photosynthetically active radiation absorbed by boreal conifer forests. IEEE Trans Geosci Remote Sens 34:1353–1368CrossRefGoogle Scholar
  5. Chen J, Black T (1992) Defining leaf area index for non-flat leaves. Plant Cell Environ 15:421–429CrossRefGoogle Scholar
  6. Chen JM, Cihlar J (1995) Plant canopy gap-size analysis theory for improving optical measurements of leaf-area index. Appl Opt 34:6211–6222CrossRefGoogle Scholar
  7. Chen J, Rich P, Gower S, Norman J, Plummer S (1997) Leaf area index of boreal forests: theory, techniques, and measurements. J Geophys Res 102:429–443Google Scholar
  8. Coops NC, Hilker T, Wulder MA, St-Onge B, Newnham G, Siggins A, Trofymow JA (2007) Estimating canopy structure of douglas-fir forest stands from discrete-return LiDAR. Trees Struct Funct 21:295–310Google Scholar
  9. Cote JF, Widlowski JL, Fournier RA, Verstraete MM (2009) The structural and radiative consistency of three-dimensional tree reconstructions from terrestrial LiDAR. Remote Sens Environ 113:1067–1081CrossRefGoogle Scholar
  10. Dubayah R, Bergen K, Hall FG, Hurtt G, Houghton R, Kellndorfer J, Lefsky M, Moorcroft P, Nelson R, Saatchi S, Shugart H, Simard M, Ranson J, Blair JB (2008) Global vegetation structure from NASA’s DESDynI mission: an overview. AGU, San FranciscoGoogle Scholar
  11. Duncanson LI, Niemann KO, Wulder MA (2008) Estimating forest canopy height and terrain relief from GLAS waveform metrics. Remote Sens Environ 114:138–154CrossRefGoogle Scholar
  12. Frazer GW, Fournier RA, Trofymow JA, Hall RJ (2001) A comparison of digital and film fisheye photography for analysis of forest canopy structure and gap light transmission. Agric For Meteorol 109:249–263CrossRefGoogle Scholar
  13. Hilker T, Coops NC, Culvenor DS, Newnham G, Jupp DLB, Siggins A, Bater CW (2010) Merging multiple terrestrial LiDAR data for vegetation analysis: extracting terrain information. ISPRS J Photogramm Remote Sens (submitted)Google Scholar
  14. Hopkinson C, Chasmer L (2009) Testing LiDAR models of fractional cover across multiple forest ecozones. Remote Sens Environ 113:275–288CrossRefGoogle Scholar
  15. Jonckheere I, Fleck S, Nackaerts K, Muys B, Coppin P, Weiss M, Baret F (2004) Review of methods for in situ leaf area index determination: part I. Theories, sensors and hemispherical photography. Agric For Meteorol 121:19–35CrossRefGoogle Scholar
  16. Jupp DLB, Lovell JL (2007) Airborne and ground-based LiDAR systems for forest measurement: background and principles. CSIRO Marine and Atmospheric Research Paper 017, CSIRO Marine and Atmospheric ResearchGoogle Scholar
  17. Jupp DLB, Culvenor DS, Lovell JL, Newnham GJ, Strahler AH, Woodcock CE (2008) Estimating forest LAI profiles and structural parameters using a ground-based laser called ‘Echidna’. Tree Physiol 29:171–181CrossRefPubMedGoogle Scholar
  18. Kraus K, Pfeifer N (1999) Determination of terrain models in wooded areas with airborne scanner data. ISPRS J Photogrammetry Remote Sens 54:193–203Google Scholar
  19. Lefsky MA, Cohen WB, Acker SA, Parker GG, Spies TA, Harding D (1999) LiDAR remote sensing of the canopy structure and biophysical properties of douglas-fir western hemlock forests. Remote Sens Environ 70:339–361CrossRefGoogle Scholar
  20. Lefsky MA, Cohen WB, Harding DJ, Parker GG, Acker SA, Gower ST (2002a) LiDAR remote sensing of above-ground biomass in three biomes. Global Ecol Biogeogr 11:393–399CrossRefGoogle Scholar
  21. Lefsky MA, Cohen WB, Parker GG, Harding DJ (2002b) LiDAR remote sensing for ecosystem studies. Bioscience 52:19–30CrossRefGoogle Scholar
  22. Lefsky M, Turner D, Guzy M, Cohen W (2005a) Combining LiDAR estimates of aboveground biomass and landsat estimates of stand age for spatially extensive validation of modeled forest productivity. Remote Sens Environ 95:549–558CrossRefGoogle Scholar
  23. Lefsky MA, Harding DJ, Keller M, Cohen WB, Carabajal CC, Espirito-Santo FD, Hunter MO, de Oliveira R (2005b) Estimates of forest canopy height and aboveground biomass using ICESat. Geophys Res Lett 32Google Scholar
  24. Lim K, Treitz P, Wulder M, St-Onge B, Flood M (2003) LiDAR remote sensing of forest structure. Prog Phys Geogr 27:88–106CrossRefGoogle Scholar
  25. Lovell JL, Jupp DLB, Culvenor DS, Coops NC (2003) Using airborner and ground-based ranging LiDAR to measure canopy structure in Australian forests. Can J Remote Sens 29:607–622Google Scholar
  26. Miller JB (1964) An integral equation from phytology. J Aust Math Soc 4:397–402CrossRefGoogle Scholar
  27. Miller JB (1967) A formula for average foliage density. Aust J Bot 15:141–144CrossRefGoogle Scholar
  28. Morgenstern K, Black TA, Humphreys ER, Griffis TJ, Drewitt GB, Cai TB, Nesic Z, Spittlehouse DL, Livingstone NJ (2004) Sensitivity and uncertainty of the carbon balance of a Pacific Northwest Douglas-Fir forest during an El Nino La Nina cycle. Agric For Meteorol 123:201–219CrossRefGoogle Scholar
  29. Næsset E (1997) Determination of mean tree height of forest stands using airborne laser scanner data. ISPRS J Photogrammetry Remote Sens 52:49–56CrossRefGoogle Scholar
  30. Ni-Meister W, Jupp DL, Dubayah R (2001) Modeling LiDAR waveforms in heterogeneous and discrete canopies. Geosci Remote Sens 39:1943–1957CrossRefGoogle Scholar
  31. Pang Y, Lefsky M, Andersen HE, Miller ME, Sherrill K (2008) Validation of the ICEsat vegetation product using crown-area-weighted mean height derived using crown delineation with discrete return LiDAR data. Can J Remote Sens 34:S471–S484Google Scholar
  32. Parker GG, Harding DJ, Berger ML (2004a) A portable LiDAR system for rapid determination of forest canopy structure. J Appl Ecol 41:755–767CrossRefGoogle Scholar
  33. Parker GG, Harmon ME, Lefsky MA, Chen JQ, Van Pelt R, Weis SB, Thomas SC, Winner WE, Shaw DC, Frankling JF (2004b) Three-dimensional structure of an old-growth pseudotsuga–tsuga canopy and its implications for radiation balance, microclimate, and gas exchange. Ecosystems 7:440–453CrossRefGoogle Scholar
  34. Peddle D, Hall F, LeDrew E (1999) Spectral mixture analysis and geometric optical reflectance modeling of boreal forest biophysical structure. Remote Sens Environ 67:288–297CrossRefGoogle Scholar
  35. Riano D, Meier E, Allgower B, Chuvieco E, Ustin SL (2003) Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling. Remote Sens Environ 86:177–186CrossRefGoogle Scholar
  36. Richards P (1983) The three dimensional structure of tropical rain forest. Blackwell, OxfordGoogle Scholar
  37. Ross JK (1981) The radiation regime and architecture of plant stands. Dr. W. Junk Publishers, The HagueGoogle Scholar
  38. Schaaf CB, Li XW, Strahler AH (1994) Topographic effects on bidirectional and hemispherical reflectances calculated with a geometric–optical canopy model. IEEE Trans Geosci Remote Sens 32:1186–1193CrossRefGoogle Scholar
  39. Strahler AH, Jupp DLB, Woodcock CE, Schaaf CB, Yao T, Zhao F, Yang XY, Lovell J, Culvenor D, Newnham G, Ni-Miester W, Boykin-Morris W (2008) Retrieval of forest structural parameters using a ground-based LiDAR instrument (Echidna (R). Canadian Aeronautics Space Inst, pp S426–S440Google Scholar
  40. Van der Zande D, Hoet W, Jonckheere L, van Aardt J, Coppin P (2006) Influence of measurement set-up of ground-based LiDAR for derivation of tree structure. Agric For Meteorol 141:147–160CrossRefGoogle Scholar
  41. Warren Wilson J (1965) Stand structure and light penetration. I. Analysis by point quadrats. Appl Ecol 2:383–390CrossRefGoogle Scholar
  42. Welles JM, Cohen S (1996) Canopy structure measurement by gap fraction analysis using commercial instrumentation. J Exp Bot 47:1335–1342CrossRefGoogle Scholar
  43. Whitehead D, Grace J, Godfrey M (1990) Architectural distribution of foliage in individual Pinus radiata D. Don crowns and the effects of clumping on radiation interception. Tree Physiol 7:135–155PubMedGoogle Scholar
  44. Wulder MA, Bater CW, Coops NC, Hilker T, White JC (2008) The role of LiDAR in sustainable forest management. For Chronicle 84:807–826Google Scholar
  45. Zwally HJ, Schutz B, Abdalati W, Abshire J, Bentley C, Brenner A, Bufton J, Dezio J, Hancock D, Harding D, Herring T, Minster B, Quinn K, Palm S, Spinhirne J, Thomas R (2002) ICESat’s laser measurements of polar ice, atmosphere, ocean, and land. J Geodyn 34:405–445CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Thomas Hilker
    • 1
    Email author
  • Martin van Leeuwen
    • 1
  • Nicholas C. Coops
    • 1
  • Michael A. Wulder
    • 4
  • Glenn J. Newnham
    • 2
  • David L. B. Jupp
    • 3
  • Darius S. Culvenor
    • 2
  1. 1.Faculty of Forest Resources ManagementUniversity of British ColumbiaVancouverCanada
  2. 2.CSIRO Sustainable EcosystemsClayton SouthAustralia
  3. 3.CSIRO Marine and Atmospheric ResearchCanberraAustralia
  4. 4.Canadian Forest Service (Pacific Forestry Centre), Natural Resources CanadaVICCanada

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