Estimating plot-level tree height and volume of eucalyptus grandis plantations using small-footprint, discrete return lidar data

Progress in Physical Geography

Volumn 34, Issue 4, 2010, Pages 515-540

  Tesfamichael, S G ^a   van Aardt, J A N ^b   Ahmed, F ^a  

^a UNIVERSITY OF KWAZULU NATAL   (South Africa)

^b ROCHESTER INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Eucalyptus grandis; Plot level tree height; Plot level tree volume; Small footprint lidar

Indexed keywords

FOREST CANOPY; LIDAR; PLANTATION FORESTRY; REMOTE SENSING; TREE;

EUCALYPTUS GRANDIS;

EID: 77954823314     PISSN: 03091333     EISSN: None     Source Type: Journal    
DOI: 10.1177/0309133310365596     Document Type: Article

References (113)

1. Ackermann, F. 1999: Airborne laser scanning - present status and future expectations. ISPRS Journal of Photogrammetry and Remote Sensing 54, 64-67.
2. Andersen, H.-E., McGaughey, R.J. and Reutebuch, S.E. 2005: Estimating forest canopy fuel parameters using lidar data. Remote Sensing of Environment 94, 441-49.
3. Anderson, E.S., Thompson, J.A. and Austin R.E. 2005: Lidar density and linear interpolator effects on elevation estimates. International Journal of Remote Sensing 26, 3889-900.
4. Avery, T.E. and Burkhart, H.E. 2001: Forest Measurements (fifth edition). New York: McGraw-Hill, 456 pp.
5. Baltsavias, E.P. 1999: A comparison between photogrammetry and laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing 54, 83-94.
6. Bortolot, Z.J. and Wynne, R.H. 2005: Estimating forest biomass using small footprint lidar data: an individual tree-based approach that incorporates training data. ISPRS Journal of Photogrammetry and Remote Sensing 59, 342-60.
7. Boyd, D.S. and Danson, F.M. 2005: Satellite remote sensing of forest resources: three decades of research development. Progress in Physical Geography 29, 1-26.
8. Brandtberg, T. 2007: Classifying individual tree species under leaf-off and leaf-on conditions using airborne lidar. ISPRS Journal of Photogrammetry and Remote Sensing 61, 325-40.
9. Brandtberg, T. and Walter, F. 1998: Automated delineation of individual tree crowns in high spatial resolution aerial images by multiple-scale analysis. Machine Vision and Applications 11, 64-73.
10. Brandtberg, T., Warner, T.A., Landenberger, R.E. and McGraw, J.B. 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 Sensing of Environment 85, 290-303.
11. Brown, A.G., Nambiar, E.K.S. and Cossalter, C. 1997: Plantations for the tropics - their role, extent, and nature. In Nambiar, E.K.S. and Brown, A.G., editors, Management of soil, nutrients, and water in tropical plantation, Canberra: ACIAR, 1-23.
12. Brown, S. 2002: Measuring carbon in forests: current status and future challenges. Environmental Pollution 116, 363-72.
13. Chen, J.M. and Cihlar, J. 1996: Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sensing of Environment 55, 153-62.
14. Chen, Q., Baldocchi, D., Gong, P. and Kelly, M. 2006: Isolating individual trees in a savanna woodland using small-footprint lidar data. Photogrammetric Engineering and Remote Sensing 72, 923-32.
15. Chen, Q., Gong, P., Baldocchi, D. and Tian, Y.Q. 2007: Estimating basal area and stem volume for individual trees from lidar data. Photogrammetric Engineering and Remote Sensing 73, 1355-65.
16. Chubey, M.S., Franklin, S.E. and Wulder, M.A. 2006: Object-based analysis of IKONOS-2 imagery for extraction of forest inventory parameters. Photogrammetric Engineering and Remote Sensing 72, 383-94.
17. Clark, M.L., Clark, D.B. and Roberts, D.A. 2004: Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape. Remote Sensing of Environment 91, 68-89.
18. Clarke, C.R.E. and Wessels, A.M. 1995: Variation and measurement of pulp properties in eucalypts. In Potts, B.M., Borralho, N.M.G., Reid, J.B., Tibbits, W.N. and Raymond, C.A., editors, Eucalypt plantations: improving fibre yield and quality, Proceedings of the CRC-IUFRO Conference, Hobart: CRC for Temperate Hardwood Forestry, 93-100.
19. Coetzee, J. 1992: A revised tree volume table for short rotation Eucalyptus grandis timber crops. ICFR Bulletin 9/92. Pietermaritzburg: Institute for Commercial Forestry Research, 5 pp.
20. Coetzee, J. 1999: The use of tree volume equations stratified by height classes instead of a general single format for short rotation applications with Eucalyptus grandis. ICFR Bulletin 6. Pietermaritzburg: Institute for Commercial Forestry Research, 9 pp.
21. Coops, N.C., Wulder, M.A., Culvenor, D.S. and St-Onge, B. 2004: Comparison of forest attributes extracted from fine spatial resolution multispectral and lidar data. Canadian Journal of Remote Sensing 30, 855-66.
22. Drake, J.B., Dubayah, R.O., Clark, D.B., Knox, R.G., Blair, J.B., Hofton, M.A., Chazdon, R.L., Weishampel, J.F. and Prince, S.D. 2002: Estimation of tropical forest structural characteristics using large-footprint lidar. Remote Sensing of Environment 79, 305-19.
23. Dralle, K. and Rudemo, M. 1996: Stem number estimation by kernel smoothing of aerial photos. Canadian Journal of Forest Research 26, 1228-36.
24. Dye, P. and Versfeld, D. 2007: Managing the hydrological impacts of South African plantation forests: an overview. Forest Ecology and Management 251, 121-28.
25. Esler, W.K. 2004: The basics of forest mensuration. Unpublished internal document. Pietermaritzburg: Mondi South Africa, 13 pp.
26. Falkowski, M.J., Evans, J.S., Martinuzzi, S., Gessler, P.E. and Hudak, A.T. 2009: Characterizing forest succession with lidar data: an evaluation for the Inland Northwest, USA. Remote Sensing of Environment 113, 946-56.
27. Falkowski, M.J., Smith, A.M.S., Gessler, P.E., Hudak, A.T., Vierling, L.A. and Evans, J.S. 2008: The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data. Canadian Journal of Remote Sensing34 (Supplement 2), 338-50.
28. Foody, G.M. and Curran, P.J. 1994: Estimation of tropical forest extent and regenerative stage using remotely-sensed data. Journal of Biogeography 21, 223-44.
29. Franco-Lopez, H., Ek, A.R. and Bauer, M.E. 2001: Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbours method. Remote Sensing of Environment 77, 251-74.
30. García, O. 1998: Estimating top height with variable plot sizes. Canadian Journal of Forest Research 28, 1509-17.
31. Godsmark, R. 2009: The South African forestry and forest products industry 2008. Pretoria: Forestry South Africa.
32. Goetz, S., Steinberg, D., Dubayah, R. and Blair, B. 2007: Laser remote sensing of canopy habitat heterogeneity as a predictor of bird species richness in an eastern temperate forest, USA. Remote Sensing of Environment 108, 254-63.
33. Gonçalves, J.L.M., Barros, N.F., Nambiar, E.K.S. and Novais, R.F. 1997: Soil and stand management for short rotation plantations. In Nambiar, E.K.S. and Brown, A.G., editors, Management of soil, nutrients, and water in tropical plantation, Canberra: ACIAR, 379-417.
34. Gougeon, F.A. 1995: A crown-following approach to the automatic delineation of individual tree crowns in high spatial resolution aerial images. Canadian Journal of Remote Sensing 21, 274-84.
35. Graf, R.F., Mathys, L. and Bollmann, K. 2009: Habitat assessment for forest dwelling species using lidar remote sensing: Capercaillie in the Alps. Forest Ecology and Management 257, 160-67.
36. Hall, R.J., Skakun, R.S., Arsenault, E.J. and Case, B.S. 2006: Modeling forest stand structure attributes using Landsat ETM+ data: application to mapping of above-ground biomass and stand volume. Forest Ecology and Management 225, 378-90.
37. Hall, S.A., Burke, I.C., Box, D.O., Kaufmann, M.R. and Stoker, J.M. 2005: Estimating stand structure using discrete return lidar: an example from low density, fire prone ponderosa pine forests. Forest Ecology and Management 208, 189-209.
38. 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. Forest Ecology and Management 255, 2416-33.
39. Holmgren, J. 2004: Prediction of tree height, basal area, and stem volume in forest stands using airborne laser scanning. Scandinavian Journal of Forest Research 19, 543-53.
40. Holmgren, J., Nilsson, M. and Olsson, H. 2003: Estimation of tree height and stem volume on plots using airborne laser scanning. Forest Science 49, 419-28.
41. Hyyppä, J., Hyyppä, H., Leckie, D., Gougeon, F., Yu, X. and Maltamo, M. 2008: Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests. International Journal of Remote Sensing 29, 1339-66.
42. Hyyppä, J., Kelle, O., Lehikoinen, M. and Inkinen, M. 2001: A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners. IEEE Transactions on Geoscience and Remote Sensing 39, 969-75.
43. Jensen, J.L.R., Humes, K.S., Vierling, L.A. and Hudak, A.T. 2008: Discrete return lidar-based prediction of leaf area index in two conifer forests. Remote Sensing of Environment 112, 3947-57.
44. Kato, A., Moskal, L.M., Schiess, P., Swanson, M.E., Calhoun, D. and Stuetzle, W. 2009: Capturing tree crown formation through implicit surface reconstruction using airborne lidar data. Remote Sensing of Environment 113, 1148-62.
45. Koch, B., Heyder, U. and Welnacker, H. 2006: Detection of individual tree crowns in airborne lidar data. Photogrammetric Engineering and Remote Sensing 72, 357-63.
46. Koch, B., Straub, C., Dees, M., Wang, Y. and Weinacker, H. 2009: Airborne laser data for stand delineation and information extraction. International Journal of Remote Sensing 30, 935-63.
47. Kotze, H. 2000: Growth and yield functions. Unpublished Mensuration and Modeling Consortium (MMRC) report. Pietermaritzburg: MMRC, 70 pp.
48. Leboeuf, A., Beaudoin, A., Fournier, R.A., Guindon, L., Luther, J.E. and Lambert, M.-C. 2007: A shadow fraction method for mapping biomass of northern boreal black spruce forests using Quickbird imagery. Remote Sensing of Environment 110, 488-500.
49. Lefsky, M.A., Cohen, W.B., Acker, S.A., Parker, G.G., Spies, T.A. and Harding, D. 1999: Lidar remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests. Remote Sensing of Environment 70, 339-61.
50. Lefsky, M.A., Cohen, W.B., Parker, G.G. and Harding, D.J. 2002: Lidar remote sensing for ecosystem studies. Bioscience 52, 19-30.
51. Lefsky, M.A., Cohen, W.B. and Spies, T.A. 2001: An evaluation of alternate remote sensing products for forest inventory, monitoring, and mapping of Douglas-fir forests in western Oregon. Canadian Journal of Forest Research 31, 78-87.
52. LeMaitre, D.C., van Wilgen, B.W., Gelderblom, C.M., Bailey, C., Chapman, R.A. and Nel, J.A. 2002: Invasive alien trees and water resources in South Africa: case studies of the costs and benefits of management. Forest Ecology and Management 160, 143-59.
53. Lim, K., Treitz, P., Wulder, M., St-Onge, B. and Flood, M. 2003: Lidar remote sensing of forest structure. Progress in Physical Geography 27, 88-106.
54. Louw, J.H. and Scholes, M. 2002: Forest site classification and evaluation: a South African perspective. Forest Ecology and Management 171, 153-68.
55. Lovell, J.L., Jupp, D.L.B., Newnham, G.J., Coops, N.C. and Culvenor, D.S. 2005: Simulation study for finding optimal lidar acquisition parameters for forest height retrieval. Forest Ecology and Management 214, 398-412.
56. Magnusson, M., Fransson, J.E.S. and Holmgren, J. 2007: Effects on estimation accuracy of forest variables using different pulse density of laser data. Forest Science 53, 619-26.
57. Mäkelä, H. and Pekkarinen, A. 2001: Estimation of timber volume at the sample plot level by means of image segmentation and Landsat TM imagery. Remote Sensing of Environment 77, 66-75.
58. Maltamo, M., Eerikäinen, K., Pitkänen, J., Hyyppä, J. and Vehmas, M. 2004: Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions. Remote Sensing of Environment 90, 319-30.
59. Maltamo, M., Hyyppä, J. and Malinen, J. 2006: A comparative study of the use of laser scanner data and field measurements in the prediction of crown height in boreal forests. Scandinavian Journal of Forest Research 21, 231-38.
60. McAllister, D.M. and Valeo, C. 2007: A robust new method for the remote estimation of LAI in montane and boreal forests. International Journal of Remote Sensing 28, 1891-905.
61. McCombs, J.W., Roberts, S.D. and Evans, D.L. 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. Forest Science 49, 457-66.
62. Means, J.E., Acker, S.A., Harding, D.J., Blair, J.B., Lefsky, M.A., Cohen, W.B., Harmon, M.E. and McKee, W.A. 1999: Use of large-footprint scanning airborne lidar to estimate forest stand characteristics in the western cascades of Oregon. Remote Sensing of Environment 67, 298-308.
63. Morsdorf, F., Meier, E., Kötz, B., Itten, K.I., Dobbertin, M. and Allgöwer, B. 2004: Lidar-based geometric reconstruction of boreal type forest stands at single tree level for forest and wildland fire management. Remote Sensing of Environment 92, 353-62.
64. Müller, J. and Brandl, R. 2009: Assessing biodiversity by remote sensing in mountainous terrain: the potential of lidar to predict forest beetle assemblages. Journal of Applied Ecology 46, 897-905.
65. Næsset, E. 2002: Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sensing of Environment 80, 88-99.
66. Næsset, E. 2004: Practical large-scale forest stand inventory using a small-footprint airborne scanning laser. Scandinavian Journal of Forest Research 19, 164-79.
67. Næsset, E. 2007: Airborne laser scanning as a method in operational forest inventory: status of accuracy assessments accomplished in Scandinavia. Scandinavian Journal of Forest Research 22, 433-42.
68. Nelson, R. 1997: Modeling forest canopy heights: the effects of canopy shape. Remote Sensing of Environment 60, 327-34.
69. Nemani, R.R., Pierce, L., Running, S. and Band, L. 1993: Forest ecosystem processes at the watershed scale: sensitivity to remotely-sensed leaf area index estimates. International Journal of Remote Sensing 14, 2519-34.
70. Oliver, M.A. 1990: Kriging: a method of interpolation for Geographical Information Systems. International Journal of Geographic Information Systems 4, 313-32.
71. Ørka, H.O., Næsset, E. and Bollandsås, O.M. 2009: Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data. Remote Sensing of Environment 113, 1163-74.
72. Ozdemir, I. 2008: Estimating stem volume by tree crown area and tree shadow area extracted from pan-sharpened Quickbird imagery in open Crimean juniper forests. International Journal of Remote Sensing 29, 5643-55.
73. Pallett, R.N. and Sale, G. 2004: The relative contributions of tree improvement and cultural practice toward productivity gains in Eucalyptus pulpwood stands. Forest Ecology and Management 193, 33-43.
74. Patenaude, G., Hill, R.A. and Milne, R. 2004: Quantifying forest above-ground carbon content using lidar remote sensing. Remote Sensing of Environment 93, 368-80.
75. Persson, Å., Holmgren, J. and Södermann, U. 2002: Detecting and measuring individual trees using an airborne laser scanner. Photogrammetric Engineering and Remote Sensing 68, 925-32.
76. Peuhkurinen, J., Maltamo, M., Malinen, J., Pitkänen, J. and Packalén, P. 2007: Preharvest measurement of marked stands using airborne laser scanning. Forest Science 53, 653-61.
77. Pienaar, B. and Harrison, W.M. 1988: A stand table projection approach to yield prediction in unthinned even-aged stands. Forest Science 34, 804-808.
78. Pienaar, B. and Kotze, H. 1998: Growth and yield functions within the growth and yield simulator. Unpublished Mensuration and Modeling Consortium (MMRC) report. Pietermaritzburg: MMRC, 58 pp.
79. Pollock, R. 1998: Individual tree recognition based on a synthetic tree crown image model. In Proceedings of the International Forum on Automated Interpretation of High Spatial Resolution Digital Imagery for Forestry, 10-12 February, Pacific Forestry Centre, Canada Forest Service, Victoria, BC: Natural Resources Canada, 25-34.
80. Popescu, S.C., Wynne, R.H. and Nelson, R.F. 2002: Estimating plot-level tree heights with lidar: local filtering with a canopy height based variable window size. Computers and Electronics in Agriculture 37, 71-95.
81. Popescu, S.C., Wynne, R.H. and Nelson, R.F. 2003: Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass. Canadian Journal of Remote Sensing 29, 564-77.
82. Pouliot, D.A., King, D.J., Bell, F.W. and Pitt, D.G. 2002: Automated tree crown detection and delineation in high resolution digital camera imagery of coniferous forest regeneration. Remote Sensing of Environment 82, 322-34.
83. Reitberger, J. Schnörr, Cl., Krzystek, P. and Stilla, U. 2009: 3-D segmentation of single trees exploiting full waveform lidar data. ISPRS Journal of Photogrammetry and Remote Sensing 64, 561-74.
84. Reutebuch, S.E., Andersen, H.-E. and McGaughey, R.J. 2005: Light detection and ranging (lidar): an emerging tool for multiple resource inventory. Journal of Forestry 103, 286-92.
85. Rietz, D.N. and Smith, C.W. 2004: A preliminary study of above-ground allometric relationships of Eucalyptus nitens (Deane and Maiden) plantations in South Africa. ICFR Bulletin 14. Pietermaritzburg: Institute for Commercial Forestry Research, 34 pp.
86. Roberts, S.D., Dean, T.J., Evans, D.L., McCombs, J.W., Harrington, R.L. and Glass, P.A. 2005: Estimating individual tree leaf area in loblolly pine plantations using lidar-derived measurements of height and crown dimensions. Forest Ecology and Management 213, 54-70.
87. Sader, S.A., Waide, R.B., Lawrence, W.T. and Joyce, A.T. 1989: Tropical forest biomass and successional age class relationships to a vegetation index derived from Landsat TM data. Remote Sensing of Environment 28, 143-56.
88. Schönau, A.P.G. and Boden, D.I. 1982: Preliminary biomass studies in young eucalypts. South African Forestry Journal 120, 24-28.
89. Schreuder, H.T., Gregoire, T.G. and Wood, G.B. 1993: Sampling methods for multiresource forest inventory. New York: Wiley, 464 pp.
90. Schulze, R.E. 1997: South African atlas of agrohydrology and climatology. Report TT82/96. Pretoria: Water Research Commission, 286 pp.
91. Schumacher, F.X. and Hall, F.D.S. 1933: Logarithmic expression of timber-tree volume. Journal of Agricultural Research 47, 719-34.
92. Smith, S.L., Holland, D.A. and Longley, P.A. 2004: The importance of understanding error in lidar digital elevation models. International Archives of Photogrammetry, Remote Sensing, and Spatial Information Sciences 35, 996-1001.
93. Song, C. and Dickinson, M.B. 2008: Extracting forest canopy structure from spatial information of high resolution optical imagery: tree crown size versus leaf area index. International Journal of Remote Sensing 29, 5605-22.
94. Steininger, M.K. 2000: Satellite estimation of tropical secondary forest above-ground biomass: data from Brazil and Bolivia. International Journal of Remote Sensing 21, 1139-57.
95. Suárez, J.C., Ontiveros, C., Smith, S. and Snape, S. 2005: Use of airborne lidar and aerial photography in the estimation of individual tree heights in forestry. Computers and Geosciences 31, 253-62.
96. Tesfamichael, S.G., Ahmed, F. and van Aardt, J.A.N. 2010: Investigating the impact of discrete return lidar point density on estimations of mean and dominant plot-level tree height in Eucalyptus grandis plantations. International Journal of Remote Sensing, in press.
97. Tesfamichael, S.G., Ahmed, F., van Aardt, J.A.N. and Blakeway, F. 2009: A semi-variogram approach for estimating stems per hectare in Eucalyptus grandis plantations using discrete return lidar height data. Forest Ecology and Management 258, 1188-99.
98. Thomas, V., Treitz, P., McCaughey, J.H. and Morrison, I. 2006: Mapping stand-level forest biophysical variables for a mixedwood boreal forest using lidar: an examination of scanning density. Canadian Journal of Forest Research 36, 34-47.
99. Thomas, V., Treitz, P., McCaughey, J.H., Noland, T. and Rich, L. 2008: Canopy chlorophyll concentration estimation using hyperspectral and lidar data for a boreal mixedwood forest in northern Ontario, Canada. International Journal of Remote Sensing 29, 1029-52.
100. Tickle, P.K., Lee, A., Lucas, R.M., Austin, J. and Witte, C. 2006: Quantifying Australian forest floristics and structure using small footprint lidar and large scale aerial photography. Forest Ecology and Management 223, 379-94.
101. Turner, D.P., Cohen, W.B., Kennedy, R.E., Fassnacht, K.S. and Briggs, J.M. 1999: Relationship between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites. Remote Sensing of Environment 70, 52-68.
102. van Aardt, J.A.N., Wynne, R.H. and Oderwald, R.G. 2006: Forest volume and biomass estimation using small-footprint lidar distributional parameters on a per-segment basis. Forest Science 52, 636-49.
103. von Gadow, K. and Bredenkamp, B. 1992: Forest management. Pretoria: Academica, 164 pp.
104. Wang, L., Gong, P. and Biging, G.S. 2004: Individual tree crown delineation and treetop detection in high spatial resolution aerial imagery. Photogrammetric Engineering and Remote Sensing 70, 351-58.
105. Wehr, A. and Lohr, U. 1999: Airborne laser scanning - an introduction and overview. ISPRS Journal of Photogrammetry and Remote Sensing 54, 68-82.
106. Woodcock, C.E., Strahler, A.H. and Jupp, D.L.B. 1988a: The use of variograms in remote sensing: I. Scene models and simulated images. Remote Sensing of Environment 25, 323-48.
107. Woodcock, C.E., Strahler, A.H. and Jupp, D.L.B. 1988b: The use of variograms in remote sensing: II. Real digital images. Remote Sensing of Environment 25, 349-79.
108. Wu, Y. and Strahler, A.H. 1994: Remote estimation of crown size, stand density, and biomass on the Oregon transect. Ecological Applications 4, 299-312.
109. Wulder, M. 1998: Optical remote sensing techniques for the assessment of forest inventory and biophysical parameters. Progress in Physical Geography 22, 449-76.
110. Wulder, M., Niemann, K.O. and Goodenough, D.G. 2000: Local maximum filtering for the extraction of tree locations and basal area from high spatial resolution imagery. Remote Sensing of Environment 73, 103-14.
111. Wynne, R.H., Oderwald, R.G., Reams, G.A. and Scrivani, J.A. 2000: Optical remote sensing for forest area estimation. Journal of Forestry 98, 31-36.
112. Zhao, K., Popescu, S. and Nelson, R. 2009: Lidar remote sensing of forest biomass: a scale-invariant estimation approach using airborne lasers. Remote Sensing of Environment 113, 182-96.
113. Zimble, D.A., Evans, D.L., Carlson, G.C., Parker, R.C., Grado, S.C. and Gerard, P.D. 2003: Characterizing vertical forest structure using small-footprint airborne lidar. Remote Sensing of Environment 87, 171-82.