Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Management and Production Engineering Review

The Journal of Production Engineering Committee of Polish Academy of Sciences and Polish Association for Production Management

4 Issues per year


CiteScore 2016: 0.48

SCImago Journal Rank (SJR) 2016: 0.126
Source Normalized Impact per Paper (SNIP) 2016: 0.551

Open Access
Online
ISSN
2082-1344
See all formats and pricing
More options …

Automated airborne lidar-based assessment of timber measurements for forest management

Marek B. Zaremba / Frédérik Doyon / Jean-François Senécal
Published Online: 2012-10-23 | DOI: https://doi.org/10.2478/v10270-012-0027-8

Abstract

This paper presents processing and analysis techniques to apply LiDAR data to estimate tree diameter at breast height (DBH) - a critical variable applied in a large number of forest management tasks. Our analysis focuses on the estimation of DBH using only LiDAR-derived tree height and tree crown dimensions, i.e., variables accessible from aerial observations. The modeling process was performed using 161 white and red pine trees from four 3850 m2 plots in the Forˆet de l’Aigle located in southwestern Quebec. Segments of the LiDAR data extracted for DBH estimation were obtained using the Individual Tree Crown (ITC) delineation method. Regression models were investigated using height as well as crown dimensions, which increased the precision of the model. This study demonstrates that DBH can be modeled to acceptable accuracy using altimetry data and automated data processing procedures and then be used in high-precision timber volume assessment.

Keywords: forest mensuration; LiDAR; remote sensing,terrain modeling; tree crown; timber volume modeling; white pine.

  • [1] Lim K., Treitz P., Baldwin K., Morrison I., Green J., Lidar remote sensing of biophysical properties of tolerant hardwood forests, Canadian J. Remote Sensing, 29, 5, 658-678, 2003.Google Scholar

  • [2] Magnussen S., Eggermont P., LaRiccia V.N., Recovering tree heights from airborne laser scanner data, Forest Science, 45, 3, 407-422, 1999.Google Scholar

  • [3] Maltamo M., Eerikäinen K., Pitkäten J., Hyyppä J., Vehmas M., 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, 2004.Google Scholar

  • [4] Andersen H.-E., McGaughey R.J., Reutebuch S.E., Estimating forest canopy fuel parameters using LIDAR data. Remote Sensing of Environment, 94, 441-449, 2005.Google Scholar

  • [5] Means J., Acker S., Fitt B., Renslow M., Emerson L., Hendrix C., Predicting forest stand characteristics with airborne scanning Lidar, Photogrammetric Engineering and Remote Sensing, 66, 1367-1371, 2000.Google Scholar

  • [6] Pasher J., King D.J., Development of a forest structural complexity index based on multispectral airborne remote sensing and topographic data, Canadian J. Forestry Research, 41, 44-58, 2011.Google Scholar

  • [7] Gougeon F.A., St-Onge B.A., Wulder M., Leckie D.G., Synergy of airborne laser altimetry and digital videography for individual tree crown delineation, Proc. 23rd Canadian Symposium on Remote Sensing (CD-ROM), Sainte-Foy, Qu´ebec, 2001.Google Scholar

  • [8] Hyyppä J., Mielonen T., Hyyppä H., Maltamo M., Yu X., Honkavaara E., Kaartinen H., Using Individual Tree Crown approach for forest volume extraction with aerial images and laser point clouds, Proc. ISPRS Workshop “Laser scanning 2005”, Enschede, the Netherlands, pp. 144-149, 2005.Google Scholar

  • [9] Leckie D., Gougeon F., Hill D., Quinn R.L., Shreenan R., Combined highdensity lidar and multispectral imagery for individual tree crown analysis, Can. J. of Remote Sensing, 29, 5, 633-649, 2003.Google Scholar

  • [10] Popescu S.C., Estimating biomass of individual pine trees using airborne lidar, Biomass and Bioenergy, 31, 9, 646-655, 2007.Google Scholar

  • [11] Kozak A., A variable-exponent taper equation, Canadian J. Forest Research, 18, 1363-1368, 1988.Google Scholar

  • [12] Peng C., Zhang L., Huang S., Zhou X., Parton J., Woods M., Developing ecoregion-based heightdiameter models for jack pine and black spruce in Ontario, Forest Research Report No. 159, 2001, Ontario Forest Research Institute, Sault Ste. Marie, Ontario.Google Scholar

  • [13] Kalliovirta J., Tokola T., Functions for estimating stem diameter and tree age using tree height, crown width and existing stand database information, Silva Fennica, 39, 2, 227-248, 2005.Google Scholar

  • [14] Schroeder P., Brown S., Mo J., Birdsay R., Ciszewski C., Biomass estimation for temperate broadleaf forests in the United States using inventory data, Forest Science, 43, 424-434, 1997.Google Scholar

  • [15] Krajicek J.E., Brinkman K.A., Gingrich S.F., Crown competition - A measure of density, Forest Science, 7, 1, 555-570, 1961.Google Scholar

  • [16] Barbezat V., Jacot J., The CLAPA project: Automated classification of forest with aerial photographs, Proc. International Forum: Automated Interpretation of High Spatial Resolution Digital Imagery for Forestry, Victoria B.C., pp. 345-356, 1999.Google Scholar

  • [17] Dub´e P., Hay G.J., Marceau D.J., Vorono¨ı diagrams, extended area stealing interpolation and tree crown recognition: a fuzzy approach, In: Automated Interpretation of High Spatial Resolution Digital Imagery for Forestry. Canadian Forest Service, Victoria B.C., pp. 115-125, 1998.Google Scholar

  • [18] Wulder M., Niemann K.O., Goodenough D.G., Local maximum filtering for the extraction of tree locations and basal area from high spatial resolution imagery, Remote Sensing of Environment, 73, 103- 114, 2000.Google Scholar

  • [19] Gougeon F.A., A crown-following approach to the automatic delineation of individual tree crowns in high spatial resolution aerial images, Canadian J. of Remote Sensing, 21, 3, 274-284, 1995.Google Scholar

  • [20] Palenichka R.M., Zaremba M.B., Multiscale Isotropic Matched Filtering for Individual Tree Detection in LiDAR Images, IEEE Trans. on Geoscience and Remote Sensing, 45, 12, 2864-2879, 2007.Web of ScienceGoogle Scholar

  • [21] Popescu S.C., Wynne R.H., Nelson R.F., Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass, Can. J. Remote Sensing, 29, 5, 564- 577, 2003.Google Scholar

  • [22] Zhang L., Cross-validation of non-linear growth functions for modelling tree height-diameter relationships, Ann. Bot., 79, 251-257, 1997.Google Scholar

  • [23] Huang S., Titus S.J., Wiens D.P., Comparison of nonlinear height-diameter functions for major Alberta tree species, Canadian J. Forestry Research, 22, 1297-1304, 1992.CrossrefGoogle Scholar

  • [24] Peng C., Nonlinear Height-Diameter Models for Nine Boreal Forest Tree Species in Ontario, Forest Research Report No. 155, Ontario Forest Research Institute, Sault Ste. Marie, Ontario, 1999.Google Scholar

  • [25] Bozdogan H., Model selection and Akaike’s information criterion (AIC): the general theory and its analytical extensions, Psychometrika, 52, 3, 345-370, 1987.CrossrefGoogle Scholar

  • [26] Akaike H., Information theory and an extension of the maximum likelihood principle, In B.N. Petrov and F. Csaki (Eds.), Second Int. Symp. on Information Theory, Budapest, pp. 267-281, 1973.Google Scholar

  • [27] Huang S., Ecoregion-based individual tree height - diameter models for lodgepole pine in Alberta, West. J. Appl. Forestry, 14, 186-193, 1999.Google Scholar

About the article

Published Online: 2012-10-23

Published in Print: 2012-10-01


Citation Information: Management and Production Engineering Review, ISSN (Online) , DOI: https://doi.org/10.2478/v10270-012-0027-8.

Export Citation

This content is open access.

Comments (0)

Please log in or register to comment.
Log in