Environmental Monitoring and Assessment

, Volume 151, Issue 1–4, pp 117–125 | Cite as

Using LiDAR technology in forestry activities

  • Abdullah Emin AkayEmail author
  • Hakan Oğuz
  • Ismail Rakip Karas
  • Kazuhiro Aruga


Managing natural resources in wide-scale areas can be highly time and resource consuming task which requires significant amount of data collection in the field and reduction of the data in the office to provide the necessary information. High performance LiDAR remote sensing technology has recently become an effective tool for use in applications of natural resources. In the field of forestry, the LiDAR measurements of the forested areas can provide high-quality data on three-dimensional characterizations of forest structures. Besides, LiDAR data can be used to provide very high quality and accurate Digital Elevation Model (DEM) for the forested areas. This study presents the progress and opportunities of using LiDAR remote sensing technology in various forestry applications. The results indicate that LiDAR based forest structure data and high-resolution DEMs can be used in wide-scale forestry activities such as stand characterizations, forest inventory and management, fire behaviour modeling, and forest operations.


DEM Forestry activities LiDAR Remote sensing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Akay, A. E. (2004). A new methodology in designing forest roads. The Turkish Journal of Agriculture and Forestry, 28, 273–279.Google Scholar
  2. Akay, A. E. & Sessions, J. (2005). Applying the Decision Support System, TRACER, to Forest Road Design. Western Journal of Applied Forestry, 20(3), 184–191.Google Scholar
  3. Akay, A. E., Karas, I. R., Sessions, J., Yuksel, A., Bozali, N., & Gundogan, R. (2004). Using high-resolution digital elevation model for computer-aided forest road design, The Proceedings for XXth International Society for Photogrammetry and Remote Sensing (ISPRS) Congress, Istanbul, Turkey, July.Google Scholar
  4. Andersen, H. A. (1999). The use of airborne laser scanner data (LiDAR) for forest measurement applications. Seattle: Precision Forestry Cooperative, College of Forest Resources, University of Washington, 32p.Google Scholar
  5. Aruga, K., Sessions, J., Akay, A. E. (2005). Application of an airborne laser scanner to forest road design with accurate earthwork volumes. The Japanese Forest Society. Journal of Forest Research, 10(2), 113–123.CrossRefGoogle Scholar
  6. Brandtberg, T., Warner, T. A., Landenberger, R. E., & McGraw, J. B. (2002). 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.CrossRefGoogle Scholar
  7. Chung, W. (2003). Optimization of Cable Logging Layout Using a Heuristic Algorithm for Network Programming. Ph.D. Thesis. Oregon State University, College of Forestry, Department of Forest Engineering, Corvallis, Oregon, 198 p.Google Scholar
  8. Fernandes, A. M., Utkin, A. B., Lavrov, A. V., & Vilar, R. M. (2005). Design of committee machines for classification of single-wavelength lidar signals applied to early forest fire detection. Pattern Recognition Letters, 26, 625–632.CrossRefGoogle Scholar
  9. Hofton, M. A., Rocchio, L. E., Blair, J. B., & Dubayah, R. (2002). Validation of vegetation canopy lidar sub-canopy topography measurements for a dense tropical forest. Journal of Geodynamics, 34, 491–502.CrossRefGoogle Scholar
  10. Hyde, P., Dubayah, R., Peterson, B., Blair, J. B., Hofton, M., Hunsaker, C., et al. (2005). Mapping forest structure for wildlife habitat analysis using waveform lidar: Validation of montane ecosystems. Remote Sensing of Environment, 96, 427–437.CrossRefGoogle Scholar
  11. Maltamo, M., Eerikainen, K., Pitkanen, J., Hyyppa, J., & 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, 319–330.CrossRefGoogle Scholar
  12. Means, J. E., Acker, S. A., Harding, D. J., Blair, J. B., Lefsky, M. A., Cohen, W. B., et al. (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.CrossRefGoogle Scholar
  13. Morsdorf, F., Meier, E., Kotz, B., Itten, K. I., Dobbertin, M., & Allgower, 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, 353–362.CrossRefGoogle Scholar
  14. Patenaude, G., Hill, R. A., Milne, R., Gaveau, D. L. A., Briggs, B. B. J., & Dawson, T. P. (2004). Quantifying forest above ground carbon content using LiDAR remote sensing. Remote Sensing of Environment, 93, 368–380.CrossRefGoogle Scholar
  15. Reutebuch, S. E., Andersen, H., & McGaughey, R. J. (2005). Light Detection and Ranging (LIDAR): An emerging tool for multiple resource inventory. Journal of Forestry, 103(6), 286–292.Google Scholar
  16. Reutebuch, S. E., McGauhey, R. J., Andersen, H. E., & Carson, W. W. (2003). Accuracy of high-resolution LIDAR terrain model under a conifer forest canopy. Canadian J. of Remote Sensing, 29, 527–535.Google Scholar
  17. Riano, D., Meier, E., Allgower, B., Chuvieco, E., & Ustin, S. L. (2003). Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behaviour modeling. Remote Sensing of Environment, 86, 177–186.CrossRefGoogle Scholar
  18. Roberts, S. D., Dean, T. J., Evans, D. L., McCombs, J. W., Harrington, R. L., & 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.CrossRefGoogle Scholar
  19. Sessions, J. & Chung, W. (2003). An approach to optimizing helicopter logging operations. (pp. 81–86). In proceedings of the 2000 Systems Analysis Symposium in Forest Resources, Aspen, Colorado, Sept 28–30. Volume 7, Managing Forest Ecosystems, Kluwer Press.Google Scholar
  20. Takahashi, T., Yamamoto, K., Senda, Y., & Tsuzuku, M. (2005a). Estimating individual tree heights of sugi (Cryptomeria japonica D. Don) plantations in mountainous areas using small-footprint airborne LiDAR. Journal of Forest Research, 10, 135–142.CrossRefGoogle Scholar
  21. Takahashi, T., Yamamoto, K., Senda, Y., & Tsuzuku, M. (2005b). Predicting individual stem volumes of sugi (Cryptomeria japonica D. Don) plantations in mountainous areas using small-footprint airborne LiDAR. Journal of Forest Research, 10, 305–312.CrossRefGoogle Scholar
  22. Zimble, D. A., Evans, D. L., Carlson, G. C., Parker, R. C., Grado, S. C., & Gerard, P. D. (2003). Characterizing vertical forest structure using small-footprint airborne LiDAR. Remote Sensing of Environment, 87, 171–182.CrossRefGoogle Scholar
  23. Zutter, B. R., Oderwald, R. G., Murphy, P. A., & Farrar Jr., R. M. (1986). Characterizing diameter distributions with modified data types and forms of the Weibull distribution. Forest Science, 32, 37–48.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Abdullah Emin Akay
    • 1
    Email author
  • Hakan Oğuz
    • 1
  • Ismail Rakip Karas
    • 2
  • Kazuhiro Aruga
    • 3
  1. 1.Department of Forest Engineering, Faculty of ForestryKahramanmaras Sutcu Imam UniversityKahramanmarasTurkey
  2. 2.Department of Geodetic and Photogrammetric EngineeringGebze Institute of TechnologyKocaeliTurkey
  3. 3.Department of Forest ScienceUtsunomiya UniversityTochigiJapan

Personalised recommendations