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.
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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
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DOI: https://doi.org/10.1007/978-3-642-02998-1_34
Publisher Name: Springer, Berlin, Heidelberg
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