Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass

Canadian Journal of Remote Sensing

Volumn 29, Issue 5, 2003, Pages 564-577

  Popescu, Sorin C ^a,c   Wynne, Randolph H ^a   Nelson, Ross F ^b  

^a VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

^b NASA GODDARD SPACE FLIGHT CENTER   (United States)

^c TEXAS A AND M UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOMASS; OPTICAL RADAR; PHOTOGRAMMETRY; POLYNOMIALS; REGRESSION ANALYSIS; REMOTE SENSING;

BIOMASS ESTIMATION; LIDAR DATSETS;

FORESTRY;

EID: 0141885851     PISSN: 07038992     EISSN: 17127971     Source Type: Journal    
DOI: 10.5589/m03-027     Document Type: Article

References (52)

1. Avery, T.E., and Burkhart, H.E. 1994. Forest Measurements. 4th ed. McGraw-Hill, Inc., New York.
2. Barbezat, V., andJacot, J. 1999. The CLAPA project: automated classification of forest with aerial photographs. In Proceedings of the International Forum on Automated Interpretation of High Spatial Resolution Digital Imagery for Forestry, 10-12 February 1998, Victoria, B.C. Pacific Forestry Center, Canadian Forest Service, Natural Resources Canada. pp. 345-356.
3. Biging, G.S., and Dobbertin, M. 1995. Evaluation of competition indices in individual tree growth models. ForestScience, Vol. 41, No. 2, pp. 360-377.
4. Blair, B.J., Rabine, D.L., and Hofton, M.A. 1999. The laser vegetation imaging sensor: a medium-altitude, digitisation-only, airborne laser altimeter for mapping vegetation and topography. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 54, No. 2-3, pp. 115-122.
5. Clark, A., III, Phillips, D.R., and Frederick, D.J. 1986. Weight, volume, and physical properties of major hardwood species in the Piedmont. Southeastern Forest Experiment Station Research, USDA Forest Service. Technical paper SE-255.
6. Clutter, J.L., Fortson, J.C., Pienaar, L.V., Brister, G.H., and Bailey, R.L. 1983. Timber management: a quantitative approach. John Wiley & Sons Inc., New York.
7. Corvallis Microtechnology, CMTProducts - HP-GPS-L4. Available from [accessed 30 October 2001].
8. Crow, T.R. 1971. Estimation of biomass in an even-aged stand — Regression and “mean tree” techniques. In Proceedings of the 15th IUFRO Congress, Section 25, Forest Biomass Studies, 15-20 March 1971, Gainesville, Fla. University of Maine, Orono, Maine. pp. 35-48.
9. Daley, N.M.A, Burnett, C.N., Wulder, M., Niemann, L.O., and Goodenough, D.G. 1998. Comparison of fixed-size and variable-sized windows for the estimation of tree crown position. In Proceedings ofIGARSS ‘98, 6-10 July 1998, Seattle, Wash. Geoscience and Remote Sensing Society, IEEE, New York. pp. 1323-1325.
10. EarthData, Inc. 2003. Shedding new light on the Earth’s terrain: Lidar. Available from accessed 17 February 2003
11. Erdas, Inc. 1997. Erdas Imagine - Erdas Field Guide. 4th ed. [computer program]. Erdas, Inc., Atlanta, Ga. p. 192.
12. Eyre, F.H. 1980. Forest cover types of the United States and Canada. Society of American Foresters, Bethesda, Md.
13. Gill, S.J., Biging, G.S., and Murphy, E.C. 2000. Modeling conifer tree crown radius and estimating canopy cover. Forest Ecology and Management, Vol. 126, pp. 405-416.
14. Gillett, P. 1984. Calculus and analytic geometry. 2nd ed. D.C. Heath and Company, Boston. p. 188.
15. Gougeon, F. 1999. Automatic individual tree crown delineation using a valley-following algorithm and a rule-based system. In Proceedings of the International Forum on Automated Interpretation of High Spatial Resolution Digital Imagery for Forestry, 10-12 February 1998, Victoria, B.C. Pacific Forestry Center, Canadian Forest Service, Natural Resources Canada. pp. 11-23.
16. s Congres de l’Association quebecoise de teledetection, 21-24 August 2001, Quebec, Que. CASI, Ottawa, Ont
17. Harding, D.J., Blair, J.B., Garvin, J.B., and Laurence, W.T. 1994. Laser altimetry waveform measurement of vegetation canopy structure. In Proceedings of the International Geoscience and Remote Sensing Symposium - IGARSS ‘94. ESA Scientific & Technical Publication, Noordwijk, Netherlands. pp. 1251-1253.
18. Holmgren, J., Nilsson, M., and Olsson, H. 2003. Estimation of tree height and stemvolume on plots using airborne laser scanning. Forest Science, Vol. 49, No. 3, pp. 409-418.
19. Lefsky, M.A., Cohen, W.B., Acker, S.A., Spies, T.A., Parker, G.G., and Harding, D. 1997. Lidar remote sensing of forest canopy structure and related biophysical parameters at the H.J. Andrews experimental forest, Oregon, U.S.A. In Natural resources management using remote sensing and GIS. Edited by J.D. Greer. ASPRS, Washington, D.C. pp. 79-91.
20. Lefsky, M.A., Harding, D., Cohen, W.B., Parker, G., and Shugart, H.H. 1999. Surface lidar remote sensing of basal area biomass in deciduous forests of eastern Maryland, U.S.A. Remote Sensing of Environment, Vol. 67, pp. 8398.
21. Maclean, G.A., and Krabill, W.B. 1986. Gross-merchantable timber volume estimation using an airborne LIDAR system. Canadian Journal of Remote Sensing, Vol. 12, No. 1, pp. 7-18.
22. Magnussen, S., and Boudewyn, P. 1998. Derivations of stand heights from airborne laser scanner data with canopy-based quantile estimators. Canadian Journal of Forest Research, Vol. 28, pp. 1016-1031.
23. Magnussen, S., Eggermont, P., and LaRiccia, V.N. 1999. Recovering tree heights from airborne laser scanner data. Forest Science, Vol. 45, No. 3, pp. 407-422.
24. 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, Vol. 49, No. 3, pp. 457-466.
25. 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, Vol. 67, pp. 298-308.
26. Myers, R.H. 1990. Classical and modern regression with applications. The Duxbury advanced series in statistics and decision sciences. PWS-Kent, Boston. pp. 171-178, 370.
27. Nesset, E. 1997a. Determination of mean tree height of forest stands using airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 52, pp. 49-56.
28. Nesset, E. 1997b. Estimating timber volume of forest stands using airborne laser scanner data. Remote Sensing of Environment, Vol. 61, No. 2, pp. 246-253.
29. Nesset, E., and Bjerknes, K.-O. 2001. Estimating tree heights and number of stems in young forest stands using airborne laser scanner data. Remote Sensing of Environment, Vol. 78, pp. 328-340.
30. Nesset, E., and 0kland, T. 2002. Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve. Remote Sensing of Environment, Vol. 79, pp. 105-115.
31. Nelson, R.F. 1997. Modeling forest canopy heights: the effects of canopy shape. Remote Sensing of Environment, Vol. 60, pp. 327-334.
32. Nelson, R.F., Krabill, W.B., and Maclean, G.A. 1984. Determining forest canopy characteristics using airborne laser data. Remote Sensing of Environment, Vol. 15, pp. 201-212.
33. Nelson, R.F, Swift, R., and Krabill, W. 1988a. Using airborne lasers to estimate forest canopy and stand characteristics. Journal of Forestry, Vol. 86, pp. 31-38.
34. Nelson, R.F, Krabill, W., and Tonelli, J. 1988b. Estimating forest biomass and volume using airborne laser data. Remote Sensing of Environment, Vol. 24, pp. 247-267.
35. Pinz, A. 1999. Tree isolation and species classification. In Proceedings of the International Forum on Automated Interpretation of High Spatial Resolution Digital Imagery for Forestry, 10-12 February 1998, Victoria, B.C. Pacific Forestry Center, Canadian Forest Service, Natural Resources Canada. pp. 127-139.
36. Popescu, S.C. 2002. Estimating plot-levelforest biophysical parameters using small-footprint airborne lidar measurements. Ph.D. dissertation, Virginia Polytechnic Institute and State University, Blacksburg, Va
37. Popescu, S.C., and Wynne, R.H. 2001. Estimating tree heights and stand density with high performance lidar: Initial results from a case study in the Virginia Piedmont. In Proceedings of the American Society for Photogrammetry and Remote Sensing 2001 Annual Conference, 2327 April 2001, St. Louis, Mo
38. Popescu, S.C., and Wynne, R.H. 2003. Seeing the trees in the forest: using lidar and multispectral data fusion with local filtering and variable window size for estimating tree height. Photogrammetric Engineering & Remote Sensing. In press
39. Popescu, S.C., Wynne, R.H., and Nelson, R.H. 2000. Estimating forest vegetation biomass using airborne lidar measurements. In Proceedings of the Second International Conference on Geospatial Information in Agriculture and Forestry, 10-12 January 2000, Lake Buena Vista, Fla. ERIM International, Inc., Ann Arbor, Mich. pp. 346-353.
40. Popescu, S.C., Wynne, R.H., and Nelson, R.H. 2002. Estimating plot-level tree heights with lidar: local filtering with a canopy-height based variable window size. Computers and Electronics in Agriculture, Vol. 37, No. 1-3, pp. 71-95
41. Powell, D.S., Faulkner, J.L., Darr, D.R., Zhou, Z., and MacCleerry, D.W. 1993. Theforestresourcesinthe UnitedStates, 1992. USDAForestService General Technical Report RM-234. 132 pp.
42. Press, W.H., Teukolsky, S.A., Vetterling, W.T., and Flannery, B.P. 1992. Numerical recipes in C: the art of scientific computing. 2nd ed. Cambridge University Press, New York. p. 676.
43. St-Onge, B.A., and Cavayas, F. 1995. Estimating forest stand structure from high resolution imagery using the directional variogram. International Journal of Remote Sensing, Vol. 16, No. 11, pp. 1999-2021.
44. Saucier, R.J., and Clark, A., III. 1985. Tables for estimating total tree and product weight and volume of major southern tree species and species groups. Southwide Energy Committee, American Pulpwood Association, Inc., Rockville, Md
45. Schroeder, P., Brown, S., Mo, J., Birdsey, R., and Cieszewski, C. 1997. Biomass estimation for temperate broadleaf forests of the United States using inventory data. Forest Science, Vol. 43, pp. 424-434.
46. Schumacher, F.X., and Hall, F.D.S. 1933. Logarithmic expression of timber-tree volume. Journal of Agriculture Research, Vol. 47, pp. 719-734.
47. Sharma, M., and Oderwald, R.G. 2001. Dimensionally compatible volume and taper equations. Canadian Journal of Forest Research, Vol. 31, No. 5, pp. 797-803.
48. Spurr, S.H. 1960. PhotogrammetryandPhoto-Interpretation with a Section on Applications to Forestry. Ronald Press, New York. p. 366.
49. U.S. Department of Agriculture - Forest Service. 2001. Forest inventory and analysis national core field guide, Volume 1: Field data collection procedures for phase 2 plots, Version 1.5. U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis, Washington Office. Internal report.
50. Weishampel, J.F., Harding, D.J., Boutet, J.C. Jr., and Drake, J.B. 1997. Analysis of laser altimeter waveforms for forested ecosystems of central Florida. In Proceedings of the SPIE Conference on Advances in Laser Remote Sensing for Terrestrial and Oceanographic Applications, 2125 April, Orlando, Fla. SPIE, Bellingham, Wash. Vol. 3059, pp. 184-189.
51. 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 SensingofEnvironment, Vol. 73, pp. 103-114.
52. Wynne, R.H. 2003. Forest mensuration with remote sensing: A retrospective with a vision for the future. Southern Forest Science: Past, Present, Future. U.S. Government Printing Office, Washington, D.C. In press