Abstract
In forestry, it is important to be able to accurately determine the volume of timber in a harvesting site and the products that could potentially be produced from that timber. We describe new terrestrial scanning technology that can produce a greater volume of higher quality data about individual trees. We show, however, that scanner data still often produces an incomplete profile of the individual trees. We describe Cabar, a case-based reasoning system that can interpolate missing sections in the scanner data and extrapolate to the upper reaches of the tree. Central to Cabar’s operation is a new asymmetric distance function, which we define in the paper. We report some preliminary experimental results that compare Cabar with a traditional approach used in Ireland. The results indicate that Cabar has the potential to better predict the market value of the products.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Boston, K., Murphy, G.: Value recovery from two mechanized bucking operations in the Southeastern United States. Southern Journal of Applied Forestry 27(4), 259–263 (2003)
Murphy, G.: Mechanization and value recovery: worldwide experiences. In: Forest Engineering Conference: Forest Engineering Solutions for Achieving Sustainable Forest Resource Management – An International Perspective, pp. 23–32 (2002)
Kivinen, V.P.: Design and testing of stand-specific bucking instructions for use on modern cut-to-length harvesters. PhD thesis, University of Helsinki (2007)
Marshall, H.D.: An Investigation of Factors Affecting the Optimal Log Output Distribution from Mechanical Harvesting and Processing Systems. PhD thesis, Oregon State University (2005)
Gordon, A., Wakelin, S., Threadgill, J.: Using measured and modelled wood quality information to optimise harvest scheduling and log allocation decisions. New Zealand Journal of Forestry Science 36(2/3), 198–215 (2006)
Newnham, R.M.: Variable-form taper functions for Alberta tree species. Canadian Journal of Forest Research 22, 210–223 (1992)
Lappi, J.: A multivariate, nonparameteric stem-curve prediction method. Canadian Journal of Forest Research 36(4), 1017–1027 (2006)
Uusitalo, J.: Pre-harvest meaurement of pine stands for sawlog production planning. Department of Forest Resuorce Management Pubications, University of Helsinki, Finland (1995)
Curtis, R.O.: Height-diameter and height-diameter-age equations for second growth Douglas fir. Forest Science 13(4), 365–375 (1967)
Nieunwenhuis, M.: The development and validation of pre-harvest inventory methodologies for timber procurement in Ireland. Silva Fennica 36(2), 535–547 (2002)
Nasberg, M.: Mathematical programming models for optimal log bucking. PhD thesis, Linköping University (1985)
Bobrowski, P.M.: Branch-and-bound strategies for the log bucking problem. Decision Sciences 21(4), 1–13 (1990)
Sessions, J., Layton, R., Guangda, L.: Improving tree bucking decisions: a network approach. The Compiler 6(1), 5–9 (1988)
Bienert, A., Scheller, S., Keane, E., Mohan, F., Nugent, C.: Tree detection and diameter estimations by analysis of forest terrestrial laserscanner point clouds. In: ISPRS Workshop on Laser Scanning 2007, pp. 50–55 (2007)
Bienert, A., Scheller, A., Keane, E., Mulloly, G., Mohan, F.: Application of terrestrial laser scanners for the determination of forest inventory parameters. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 36(5) (2006)
Goodenough, D.G., Charlebois, D., Bhogal, A.S., Matwin, S., Daley, N.: Automated forestry inventory update with SEIDAM. In: Procs. of the IEEE Geoscience and Remote Sensing Symposium, pp. 670–673 (1997)
Kelly, M., Cunningham, P.: Building competent compact case-bases: A case study. In: Procs. of the Ninth Irish Conference in Artificial Intelligence & Cognitive Science, pp. 177–185 (1998)
Amishev, D., Murphy, G.: Implementing resonance-based acoustic technology on mechanical harvesters/processors for real-time wood stiffness assessment: Opportunities and considerations. International Journal of Forest Engineering 19(2), 49–57 (2008)
Nummi, T.: Prediction of stem characteristics for pinus sylvestris. Scandinavian Journal of Forest Research 14, 270–275 (1999)
Liski, E., Nummi, T.: Prediction of tree stems to improve efficiency in automatized harvesting of forests. Scandinavian Journal of Statistics 22, 255–269 (1995)
Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. International Journal of Computer Vision 40(2), 99–121 (2000)
Balakrishnan, N., Basu, A.P.: The Exponential Distribution: Theory, Methods, and Applications. Gordon and Breach, New York (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nugent, C., Bridge, D., Murphy, G., Øyen, BH. (2009). Case-Based Support for Forestry Decisions: How to See the Wood from the Trees. In: McGinty, L., Wilson, D.C. (eds) Case-Based Reasoning Research and Development. ICCBR 2009. Lecture Notes in Computer Science(), vol 5650. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02998-1_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-02998-1_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02997-4
Online ISBN: 978-3-642-02998-1
eBook Packages: Computer ScienceComputer Science (R0)