European Journal of Forest Research

, Volume 129, Issue 4, pp 749–770 | Cite as

Retrieval of forest structural parameters using LiDAR remote sensing

  • Martin van LeeuwenEmail author
  • Maarten Nieuwenhuis


In this paper, a literature overview is presented on the use of laser rangefinder techniques for the retrieval of forest inventory parameters and structural characteristics. The existing techniques are ordered with respect to their scale of application (i.e. spaceborne, airborne, and terrestrial laser scanning) and a discussion is provided on the efficiency, precision, and accuracy with which the retrieval of structural parameters at the respective scales has been attained. The paper further elaborates on the potential of LiDAR (Light Detection and Ranging) data to be fused with other types of remote sensing data and it concludes with recommendations for future research and potential gains in the application of LiDAR for the characterization of forests.


LiDAR Forest inventory Remote sensing 



We would like to thank COFORD (the Irish National Council for Forest Research and Development) for funding the FORESTSCAN project, through which this research was made possible, and two anonymous reviewers for their constructive feedback.


  1. Alados CL, Escos J, Emlen JM, Freeman DC (1999) Characterization of branch complexity by fractal analyses. Int J Plant Sci 160:s147–s155PubMedCrossRefGoogle Scholar
  2. Andersen H-E, McGaughey RJ, Reutebuch SE (2005) Estimating forest canopy fuel parameters using LiDAR data. Remote Sens Environ 94:441–449CrossRefGoogle Scholar
  3. Anderson JE, Plourde LC, Martin ME, Braswell BH, Smith M-L, Dubayah RO, Hofton MA, Blair JB (2008) Integrating waveform LiDAR with hyperspectral imagery for inventory of a northern temperate forest. Remote Sens Environ 112:1856–1870CrossRefGoogle Scholar
  4. Aschoff T, Spiecker H (2004) Algorithms for the automatic detection of trees in laser scanner data. Int Arch Photogramm Remote Sens Spat Inf Sci XXXVI, part 8/W2:71–75Google Scholar
  5. Bienert A, Maas H-G, Scheller S (2006a) Analysis of the information content of terrestrial laserscanner point clouds for the automatic determination of forest inventory parameters. ISPRS WG VIII/11 & EARSeL joint Conference ‘3D Remote Sensing in Forestry’, Vienna, Austria, 14–15 FebruaryGoogle Scholar
  6. Bienert A, Scheller S, Keane E, Mullooly G, Mohan F (2006b) Application of terrestrial laser scanners for the determination of forest inventory parameters. In: Maas H-G, Schneider D (eds) Proceedings of ISPRS commission V symposium ‘Image engineering and vision metrology’, ISPRS, Dresden, Germany, 25–27 SeptemberGoogle Scholar
  7. Bienert A, Scheller S, Keane E, Mohan F, Nugent C (2007) Tree detection and diameter estimations by analysis of forest terrestrial laser scanner point clouds. ISPRS Workshop on Laser Scanning 2007 and SilviLaser 2007, Espoo, Finland, pp 50–55, 12–14 September 2007Google Scholar
  8. Blair JB, Rabine DL, Hofton MA (1999) The laser vegetation imaging sensor: a medium-altitude, digitisation-only, airborne laser altimeter for mapping vegetation and topography. ISPRS J Photogramm Remote Sens 54:115–122CrossRefGoogle Scholar
  9. Boudon F, Godin C, Pradal C, Puech O, Sinoquet H (2006) Estimating the fractal dimension of plants using the two-surface method: an analysis based on 3D digitized tree foliage. Fractals 14:149–163CrossRefGoogle Scholar
  10. Brandtberg T, Warner TA, Landenberger RE, McGraw JB (2003) Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America. Remote Sens Environ 85:290–303CrossRefGoogle Scholar
  11. Breidenbach J, Næsset E, Lien V, Gobakken T, Solberg S (2010) Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data. Remote Sens Environ 114:911–924CrossRefGoogle Scholar
  12. Brolly G, Király G (2009) Algorithms for stem mapping by means of terrestrial laser scanning. Acta Silv Lignaria Hung 5:119–130Google Scholar
  13. Bucksch A, Fleck S (2009) Automated detection of branch dimensions in woody skeletons of leafless fruit tree canopies. SilviLaser 2009, Austin, Oct 14–16Google Scholar
  14. Bucksch A, Lindenbergh R (2008) CAMPINO—a skeletonization method for point cloud processing. ISPRS J Photogramm Remote Sens 63:115–127CrossRefGoogle Scholar
  15. Chen Q, Baldocchi D, Gong P, Kelly M (2006) Isolating individual trees in a savanna woodland using small footprint lidar data. Photogramm Eng Remote Sens 72:923–932Google Scholar
  16. Coops NC, Hilker T, Wulder M, St-Onge B, Newnham G, Siggins A, Trofymow JA (2007) Estimating canopy structure of Douglas-fir forest stands from discrete-return LiDAR. Trees 21:295–310CrossRefGoogle Scholar
  17. Côté J, Widlowski J, 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
  18. Danson FM, Hetherington D, Morsdorf F, Koetz B, Allgöwer B (2007) Forest canopy gap fraction from terrestrial laser scanning. IEEE Geosci Remote Sens Lett 4:157–160CrossRefGoogle Scholar
  19. Donoghue DNM, Watt PJ, Cox NJ, Wilson J (2007) Remote sensing of species mixtures in conifer plantations using LiDAR height and intensity data. Remote Sens Environ 110:509–522CrossRefGoogle Scholar
  20. Drake JB, Weishampel JF (2000) Multifractal analysis of canopy height measures in a longleaf pine savanna. For Ecol Manag 128:121–127CrossRefGoogle Scholar
  21. Drake JB, Dubayah RO, Clark DB, Knox RG, Blair B, Hofton MA, Chazdon RL, Weishampel JF, Prince SD (2002) Estimation of tropical forest structural characteristics using large-footprint LiDAR. Remote Sens Environ 79:305–319CrossRefGoogle Scholar
  22. Flamant PH (2005) Atmospheric and meteorological LiDAR: from pioneers to space applications. Comptes Rendus Phys 6:864–875CrossRefGoogle Scholar
  23. Fleck S, Van der Zande D, Schmidt M, Coppin P (2004) Reconstructions of tree structures from laser-scans and their use to predict physiological properties and processes in canopies. Int Arch Photogramm Remote Sens Spat Inf Sci XXXVI part 8/W2:118–123Google Scholar
  24. Fleck S, Obertreiber N, Schmidt I, Brauns M, Jungkunst HF, Leuschner C (2007) Terrestrial LiDAR measurements for analysing canopy structure in an old-growth forest. Int Arch Photogramm Remote Sens Spat Inf Sci XXXVI part 3/W52:125–129Google Scholar
  25. Fröhlich Z, Mettenleiter M (2004) Terrestrial laser scanning—new perspectives in 3D surveying. Int Arch Photogramm Remote Sens Spat Inf Sci XXXVI part 8/W2:7–13Google Scholar
  26. Gaveau DLA, Hill RA (2003) Quantifying canopy height underestimation by laser pulse penetration in small-footprint airborne laser scanning data. Can J Remote Sens 29:650–657Google Scholar
  27. Gitelson AA (2004) Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J Plant Physiol 161:165–173PubMedCrossRefGoogle Scholar
  28. Goodwin NR, Coops NC, Culvenor DS (2006) Assessment of forest structure with airborne LiDAR and the effects of platform altitude. Remote Sens Environ 103:140–152CrossRefGoogle Scholar
  29. Gorte B, Winterhalder D (2004) Reconstruction of laser-scanned trees using filter operations in the 3D raster domain. Int Arch Photogramm, Remote Sens Spat Inf Sci XXXVI part 8/W2: 39–44Google Scholar
  30. Gougeon FA (2005) The individual tree crown (ITC) suite. Canadian Forest Service, VictoriaGoogle Scholar
  31. Green AA, Berman P, Switzer P, Craig MD (1988) A transform for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Trans Geosci Remote Sens 26:65–74CrossRefGoogle Scholar
  32. Harding DJ, Carabajal CC (2005) ICESat waveform measurements of within-footprint topographic relief and vegetation vertical structure. Geophys Res Lett 32:L21s10CrossRefGoogle Scholar
  33. Henning JG, Radtke PJ (2006) Detailed stem measurements of standing trees from ground-based scanning LiDAR. For Sci 52:67–80Google Scholar
  34. Heurich M (2008) Automatic recognition and measurement of single trees based on data from airborne laser scanning over the richly structured natural forests of the Bavarian Forest National Park. For Ecol Manag 255:2416–2433CrossRefGoogle Scholar
  35. Hilker T, Wulder MA, Coops NC (2008) Update of forest inventory data with LiDAR and high spatial resolution satellite imagery. Can J Remote Sens 34:5–12Google Scholar
  36. Holmgren J, Persson Å (2004) Identifying species of individual trees using airborne laser scanner. Remote Sens Environ 90:415–423CrossRefGoogle Scholar
  37. Holmgren J, Nilsson M, Olsson H (2003) Estimation of tree height and stem volume on plots using airborne laser scanning. For Sci 49:419–428Google Scholar
  38. Hopkinson C, Chasmer L, Young-Pow C, Treitz P (2004) Assessing forest metrics with a ground-based scanning LiDAR. Can J Remote Sens 34:573–583Google Scholar
  39. Hough PVC (1962) Method and means for recognizing complex patterns. US Patent 3,069,654Google Scholar
  40. Hug C, Ullrich A, Grimm A (2004) Litemapper-5600—a waveform-digitizing LiDAR terrain and vegetation mapping system. Int Arch Photogramm Remote Sens Spat Inf Sci XXXVI part 8/W2: 24–29Google Scholar
  41. Jupp DLB, Culvenor DS, Lovell JL, Newnham GJ, Strahler AH, Woodcock CE (2009) Estimating forest LAI profiles and structural parameters using a ground-based laser called ‘Echidna®’. Tree Physiol 29:171–181PubMedCrossRefGoogle Scholar
  42. Kalliovirta J, Laasasenaho J, Kangas A (2005) Evaluation of the laser-relascope. For Ecol Manag 204:181–194CrossRefGoogle Scholar
  43. Kato A, Moskal LM, Schiess P, Swanson ME, Calhoun D, Stuetzle W (2009) Capturing tree crown formation through implicit surface reconstruction using airborne lidar data. Remote Sens Environ 113:1148–1162CrossRefGoogle Scholar
  44. Király G, Brolly G (2007) Tree height estimation methods for terrestrial laser scanning in a forest reserve. Int Arch Photogramm Remote Sens Spat Inf Sci XXXVI part 3/W52:211–215Google Scholar
  45. Kirchhof M, Jutzi B, Stilla U (2008) Iterative processing of laser scanning data by full waveform analysis. ISPRS J Photogramm Remote Sens 63:99–114CrossRefGoogle Scholar
  46. Koetz B, Sun G, Morsdorf F, Ranson KJ, Kneubühler M, Itten K, Allgöwer B (2007) Fusion of imaging spectrometer and LiDAR data over combined radiative transfer models for forest canopy characterization. Remote Sens Environ 106:449–459CrossRefGoogle Scholar
  47. Kumar L, Schmidt K, Dury S, Skidmore A (2002) Imaging spectrometry and vegetation science. In: Van der Meer FD, De Jong SM (eds) Imaging spectrometry. Springer, Netherlands, pp 111–156CrossRefGoogle Scholar
  48. Leckie D, Gougeon F, Hill D, Quinn R, Armstrong L, Shreenan R (2003) Combined high-density LiDAR and multispectral imagery for individual tree crown analysis. Can J Remote Sens 29:633–649Google Scholar
  49. Lee AC, Lucas RM (2007) A LiDAR-derived canopy density model for tree stem and crown mapping in Australian forests. Remote Sens Environ 111:493–518CrossRefGoogle Scholar
  50. Lefsky M, Harding DJ, Cohen WB, Parker GG, Shugart HH (1999) Surface LiDAR remote sensing of basal area and biomass in deciduous forests of eastern maryland, USA. Remote Sens Environ 67:83–98CrossRefGoogle Scholar
  51. Lefsky MA, Cohen WB, Spies TA (2001) An evaluation of alternate remote sensing products for forest inventory, monitoring, and mapping of Douglas-fir forest in Western Oregon. Can J Remote Sens 31:78–87Google Scholar
  52. Lefsky MA, Harding DJ, Keller M, Cohen WB, Carabajal CC, Espirito-Santo FDB, Hunter MO, de Oliveira JR (2005a) Estimates of forest canopy height and aboveground biomass using ICESat. Geophys Res Lett 32:L22S02CrossRefGoogle Scholar
  53. Lefsky MA, Hudak AT, Cohen WB, Acker SA (2005b) Geographic variability in LiDAR predictions of forest stand structure in the Pacific Northwest. Remote Sens Environ 95:532–548CrossRefGoogle Scholar
  54. Lefsky M, Keller M, Pang Y, de Camargo PB, Hunter MO (2007) Revised method for forest canopy height estimation from geoscience laser altimeter system waveforms. J Appl Remote Sens 1:013537CrossRefGoogle Scholar
  55. Liang X, Litkey P, Hyyppä J, Kukko A, Kaartinen H, Holopainen M (2008) Plot-level trunk detection and reconstruction using one-scan-mode terrestrial laser scanning data. 2008 International workshop on earth observation and remote sensing applications, IEEE, Beijing, 30 June–2 JulyGoogle Scholar
  56. Lillesand TM, Kiefer RW, Chipman JW (2004) Remote sensing and image interpretation. Wiley, Hoboken, NJGoogle Scholar
  57. 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
  58. Lindenmayer DB (2000) Indicators of biodiversity for ecologically sustainable forest management. Conserv Biol 14:941–950CrossRefGoogle Scholar
  59. Lovell JL, Jupp DLB, Newnham GJ, Coops NC, Culvenor DS (2005) Simulation study for finding optimal LiDAR acquisition parameters for forest height retrieval. For Ecol Manag 214:398–412CrossRefGoogle Scholar
  60. Maas H-G, Bienert A, Scheller S, Keane E (2008) Automatic forest inventory parameter determination from terrestrial laser scanner data. Int J Remote Sens 29:1579–1593CrossRefGoogle Scholar
  61. MacArthur RH, Horn HS (1969) Foliage profile by vertical measurements. Ecol 50:802–804CrossRefGoogle Scholar
  62. Maltamo M, Eerikäinen K, Pitkänen J, Hyppä J, Vehmas M (2004) Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions. Remote Sens Environ 90:319–330CrossRefGoogle Scholar
  63. McCombs JW, Roberts SD, Evans DL (2003) Influence of fusing LiDAR and multispectral imagery on remotely sensed estimates of stand density and mean tree height in a managed loblolly pine plantation. For Sci 49:457–466Google Scholar
  64. Middleton WEK, Spilhaus AF (1953) Meteorological instruments. University of Toronto Press, TorontoGoogle Scholar
  65. Moorthy I, Miller JR, Hu B, Chen J, Li Q (2008) Retrieving crown leaf area index from an individual tree using ground-based lidar data. Can J Remote Sens 34:320–332Google Scholar
  66. Morsdorf F, Meier E, Koetz B, Itten KI, Dobbertin M, Allgöwer B (2004) LIDAR-based geometric reconstruction of boreal type forest stands at single tree level for forest and wildland fire management. Remote Sens Environ 92:353–362CrossRefGoogle Scholar
  67. Morsdorf F, Koetz B, Meier E, Itten KI, Allgöwer B (2006) Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction. Remote Sens Environ 104:50–61CrossRefGoogle Scholar
  68. Morsdorf F, Frey O, Meier E, Itten KI, Allgöwer B (2008) Assessment of the influence of flying altitude and scan angle on biophysical vegetation products derived from airborne laser scanning. Int J Remote Sens 29:1387–1406CrossRefGoogle Scholar
  69. Morsdorf F, Nichol C, Malthus T, Woodhouse IH (2009) Assessing forest structural and physiological information content of multi-spectral LiDAR waveforms by radiative transfer modelling. Remote Sens Environ 113:2152–2163CrossRefGoogle Scholar
  70. Murphy G (2008) Determining stand value and log product yields using terrestrial lidar and optimal bucking: a case study. J For 106:317–324CrossRefGoogle Scholar
  71. Myneni R, Nemani R, Running S (1997) Estimation of global leaf area index and absorbed par using radiative transfer models. IEEE Trans Geosci Remote Sens 35:1380–1393CrossRefGoogle Scholar
  72. Næsset E (2009a) Effects of different sensors, flying altitudes, and pulse repetition frequencies on forest canopy metrics and biophysical stand properties derived from small-footprint airborne laser data. Remote Sens Environ 113:148–159CrossRefGoogle Scholar
  73. Næsset E (2009b) Influence of terrain model smoothing and flight and sensor configurations on detection of small pioneer trees in the boreal-alpine transition zone utilizing height metrics derived from airborne scanning lasers. Remote Sens Environ 113:2210–2223CrossRefGoogle Scholar
  74. Næsset E, Gobakken T (2008) Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser. Remote Sens Environ 112:3079–3090CrossRefGoogle Scholar
  75. Næsset E, Økland T (2002) Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve. Remote Sens Environ 79:105–115CrossRefGoogle Scholar
  76. Næsset E, Bollandsås OM, Gobakken T (2005) Comparing regression methods in estimation of biophysical properties of forest stands from two different inventories using laser scanner data. Remote Sens Environ 94:541–553CrossRefGoogle Scholar
  77. Nelson RF (2008) Model effects on GLAS-based regional estimates of forest biomass and carbon. SilviLaser 2008, 17–19 September, Edinburgh, pp 207–215 Google Scholar
  78. Nelson R, Jimenez J, Schnell CE, Hartshorn GS, Gregoire TG, Oderwald R (2000) Canopy height models and airborne lasers to estimate forest biomass: two problems. Int J Remote Sens 21:2153–2162CrossRefGoogle Scholar
  79. Noss RF (1999) Assessing and monitoring forest biodiversity: a suggested framework and indicators. For Ecol Manag 115:135–146CrossRefGoogle Scholar
  80. Ørka HO, Næsset E, Bollandsås OM (2009) Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data. Remote Sens Environ 113:1163–1174CrossRefGoogle Scholar
  81. Parker GG, Harding DJ, Berger ML (2004) A portable LiDAR system for rapid determination of forest canopy structure. J Appl Ecol 41:755–767CrossRefGoogle Scholar
  82. Parkes D, Newell G, Cheal D (2003) Assessing the quality of native vegetation: the ‘habitat hectares’ approach. Ecol Manag Restor 4(supplement):s29–s38CrossRefGoogle Scholar
  83. Pfeifer N, Briese C (2007) Geometrical aspects of airborne and terrestrial laser scanning. Int Arch Photogramm Remote Sens Spat Inf Sci XXXVI part 3/W52: 311–319Google Scholar
  84. Pfeifer N, Winterhalder D (2004) Modelling of tree cross sections from terrestrial laser scanning data with free-form curves. Int Arch Photogramm Remote Sens Spat Inf Sci XXXVI part 8/W2: 76–81Google Scholar
  85. Popescu S, Wynne RH (2004) Seeing the trees in the forest: using lidar and multispectral data fusion with local filtering and variable window size for estimating tree height. Photogramm Eng Remote Sens 70:589–604Google Scholar
  86. Popescu SC, Wynne RH, Nelson RF (2002) Estimating plot-level tree heights with LiDAR: local filtering with a canopy-height based variable window size. Comput Electron Agric 37:71–95CrossRefGoogle Scholar
  87. Popescu SC, Wynne RH, Nelson RF (2003) Measuring individual tree crown diameter with LiDAR and assessing its influence on estimating forest volume and biomass. Can J Remote Sens 29:564–577Google Scholar
  88. Rahman MZA, Gorte BGH (2009) Tree crown delineation from high resolution airborne lidar based on densities of high points. Int Arch Photogramm Remote Sens Spat Inf Sci XXXVIII part 3/W8:123–128Google Scholar
  89. Ranson KJ, Sun G, Kovacs K, Kharuk VI (2004a) Landcover attributes from ICESat GLAS data in central Siberia. In: Proceedings of international geoscience and remote sensing symposium 2004, 20–24 September. IEEE International, Anchorage, pp 753–756 Google Scholar
  90. Ranson KJ, Sun G, Kovacs K, Kharuk VI (2004b) Use of ICESat GLAS data for forest disturbance studies in central Siberia. In: Proceedings of international geoscience and remote sensing symposium 2004, 20–24 September. IEEE International, Anchorage, pp 1936–1939 Google Scholar
  91. Ranson KJ, Kimes D, Sun G, Nelson R, Kharuk V, Montesano P (2007) Using MODIS and GLAS data to develop timber volume estimates in central Siberia. In: International Conference on Geoscience and Remote Sensing Symposium 2007, 23–28 July. IEEE, Barcelona, pp 2306–2309 Google Scholar
  92. Reese H, Nilsson M, Sandström P, Olsson H (2002) Applications using estimates of forest parameters derived from satellite and forest inventory data. Comput Electron Agric 37:37–55CrossRefGoogle Scholar
  93. Reitberger J, Krzystek P, Stilla U (2008) Analysis of full waveform LIDAR data for the classification of deciduous and coniferous trees. Int J Remote Sens 29:1407–1431CrossRefGoogle Scholar
  94. Reitberger J, Schnörr C, Krzystek P, Stilla U (2009) 3D segmentation of single trees exploiting full waveform LIDAR data. ISPRS J Photogramm Remote Sens 64:561–574CrossRefGoogle Scholar
  95. Riaño D, Meier E, Allgoewer 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
  96. Ritchie JC (1996) Remote sensing applications to hydrology: airborne laser altimeters. Hydrol Sci J 41:625–636CrossRefGoogle Scholar
  97. Roberts G (1998) Simulating the vegetation canopy LiDAR: an investigation of the waveform information content. Masters, University College London, LondonGoogle Scholar
  98. Roberts JW, Tesfamichael S, Gebreslasie M, Van Aardt J, Ahmed FB (2007) Forest structural assessment using remote sensing technologies: an overview of the current state of the art. South Hemisph For J 69:183–203CrossRefGoogle Scholar
  99. Rosette JAB, North PRJ, Suárez JC (2008) Vegetation height estimates for a mixed temperate forest using satellite laser altimetry. Int J Remote Sens 29:1475–1493CrossRefGoogle Scholar
  100. Sasaki T, Imanishi J, Ioki K, Morimoto Y, Kitada K (2008) Estimation of leaf area index and canopy openness in broad-leaved forest using an airborne laser scanner in comparison with high-resolution near-infrared digital photography. Landsc Ecol Eng 4:47–55CrossRefGoogle Scholar
  101. Schaepman ME (2007) Spectrodirectional remote sensing: from pixels to processes. Int J Appl Earth Obs Geoinf 9:204–223CrossRefGoogle Scholar
  102. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22:888–905CrossRefGoogle Scholar
  103. Simard M, Rivera-Monroy VH, Mancera-Pineda JE, Castaneda-Moya E, Twilley RR (2008) A systematic method for 3D mapping of mangrove forests based on shuttle radar topography mission elevation data, ICEsat/GLAS waveforms and field data: application to Ciénaga Grande de Santa Marta, Colombia. Remote Sens Environ 112:2131–2144CrossRefGoogle Scholar
  104. Smith B, Knorr W, Widlowski J-L, Pinty B, Gobron N (2008) Combining remote sensing data with process modelling to monitor boreal conifer forest carbon balances. For Ecol Manag 255:3985–3994CrossRefGoogle Scholar
  105. St-Onge B, Vega C, Fournier RA, Hu Y (2008) Mapping canopy height using a combination of digital stereo-photogrammetry and LiDAR. Int J Remote Sens 29:3343–3364CrossRefGoogle Scholar
  106. Strahler AH, Jupp DLB, Woodcock CE, Schaaf CB, Yao T, Zhao F, Yang X, 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®’. Can J Remote Sens 34:S426–S440Google Scholar
  107. Sun G, Ranson KJ, Kimes DS, Blair JB, Kovacs K (2008) Forest vertical structure from GLAS: an evaluation using LVIS and SRTM data. Remote Sens Environ 112:107–117CrossRefGoogle Scholar
  108. Takahashi T, Kazukiyo Y, Senda Y, Tsuzuku M (2005) Estimating individual tree heights of sugi (Cryptomeria japonica D. Don) plantations in mountainous areas using small-footprint airborne LiDAR. J For Res 10:135–142CrossRefGoogle Scholar
  109. Tansey K, Selmes N, Anstee A, Tate NJ, Denniss A (2009) Estimating tree and stand variables in a Corsican Pine woodland from terrestrial laser scanner data. Int J Remote Sens 30:5195–5209CrossRefGoogle Scholar
  110. Teobaldelli M, Zenone T, Puig D, Matteucci M, Seufert G, Sequeira V (2007) Structural tree modelling of aboveground and belowground poplar tree using direct and indirect measurements: terrestrial laser scanning, WGROGRA, AMAPmod and JRC-3D Reconstructor®. Functional Structural Plant Models, Napier, New Zealand, November 4–9Google Scholar
  111. Thies M, Spiecker H (2004) Evaluation and future prospects of terrestrial laser scanning for standardized forest inventories. Int Arch Photogramm Remote Sens Spat Inf Sci XXXVI part 8/W2:192–197Google Scholar
  112. Thies M, Pfeifer N, Winterhalder D, Gorte BGH (2004) Three-dimensional reconstruction of stems for assessment of taper, sweep, and lean based on laser scanning of standing trees. Scand J For Res 19:571–581CrossRefGoogle Scholar
  113. Turner W, Spector S, Gardiner N, Fladeland M, Sterling E, Steininger M (2003) Remote sensing for biodiversity science and conservation. Trends Ecol Evol 18:306–314CrossRefGoogle Scholar
  114. Van Leeuwen M, Coops NC, Wulder MA (2010) Canopy surface reconstruction from a LiDAR point cloud using hough transform. Remote Sens Lett 1:125–132Google Scholar
  115. Wagner W, Ullrich A, Ducic V, Melzer T, Studnicka N (2006) Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner. ISPRS J Photogramm Remote Sens 60:100–112CrossRefGoogle Scholar
  116. Wagner W, Hollaus M, Briese C, Ducic V (2008) 3D vegetation mapping using small-footprint full-waveform airborne laser scanners. Int J Remote Sens 29:1433–1452CrossRefGoogle Scholar
  117. Watt PJ, Donoghue DNM (2005) Measuring forest structure with terrestrial laser scanning. Int J Remote Sens 26:1437–1446CrossRefGoogle Scholar
  118. Wehr A, Lohr U (1999) Airborne laser scanning—an introduction and overview. ISPRS J Photogramm Remote Sens 54:68–82CrossRefGoogle Scholar
  119. Weinacker H, Koch B, Heyder U, Weinacker R (2004) Development of filtering, segmentation and modelling modules for LIDAR and multispectral data as a fundament of an automatic forest inventory system. Int Arch Photogramm Remote Sens Spat Inf Sci XXXVI part 8/W2:50–55Google Scholar
  120. Wezyk P, Tompalski P, Szostak M, Glista M, Pierzchalski M (2008) Describing the selected canopy layer parameters of the Scots pine stands using ALS data. SilviLaser 2008, Edinburgh, Sept 17–19Google Scholar
  121. Wulder MA, Bater CW, Coops NC, Hilker T, White JC (2008) The role of LiDAR in sustainable forest management. For Chron 84:807–826Google Scholar
  122. Xu H, Gossett N, Chen B (2007) Knowledge and heuristic-based modeling of laser-scanned trees. ACM Trans Graph 26:19:1–19:13Google Scholar
  123. Yong G, Zengyuan L, Sun G, Lefsky M, Xinfang Y (2006) Model based terrain effect analyses on ICEsat GLAS waveforms. In: Proceedings of IEEE international conference on geoscience remote sensing symposium 2006, 31 July–4 Aug. IEEE, Denver, pp 3232–3235Google Scholar
  124. Yu X, Hyppä J, Hyppä H, Maltamo M (2004) Effects of flight altitude on tree height estimation using airborne laser-scanning. Int Arch Photogramm Remote Sens Spat Inf Sci XXXVI part 8/W2:96–101Google Scholar
  125. Yu X, Hyyppä J, Kaartinen H, Maltamo M, Hyyppä H (2008) Obtaining plotwise mean height and volume growth in boreal forests using multi-temporal laser surveys and various change detection techniques. Int J Remote Sens 29:1367–1386CrossRefGoogle Scholar
  126. Zhao K, Popescu S (2009) Lidar-based mapping of leaf area index and its use for validating GLOBCARBON satellite LAI product in a temperate forest of the southern USA. Remote Sens Environ 113:1628–1645CrossRefGoogle Scholar
  127. Zhao K, Popescu S, Nelson R (2009) LiDAR remote sensing of forest biomass: a scale invariant estimation approach using airborne lasers. Remote Sens Environ 113:182–196CrossRefGoogle Scholar
  128. Zimble DA, Evans DL, Carlson GC, Parker RC, Grado SC, Gerard PD (2003) Characterizing vertical forest structure using small-footprint airborne LiDAR. Remote Sens Environ 87:171–182CrossRefGoogle Scholar

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© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Department of Forest Resources ManagementUniversity of British ColumbiaVancouverCanada
  2. 2.UCD Forestry, Agriculture and Food Science CentreUniversity College DublinBelfield, DublinIreland

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