%0 Research article
%T Predicting tree attributes and quality characteristics of Scots pine using airborne laser scanning data
%A Maltamo, Matti
%A Peuhkurinen, Jussi
%A Malinen, Jukka
%A Vauhkonen, Jari
%A Packalén, Petteri
%A Tokola, Timo
%D 2009
%J Silva Fennica
%V 43
%N 3
%R doi:10.14214/sf.203
%U https://silvafennica.fi/article/203
%X The development of airborne laser scanning (ALS) during last ten years  has provided new possibilities for accurate description of the living  tree stock. The forest inventory applications of ALS data include both  tree and area-based plot level approaches. The main goal of such  applications has usually been to estimate accurate information on timber  quantities. Prediction of timber quality has not been focused to the  same extent. Thus, in this study we consider here the prediction of both  basic tree attributes (tree diameter, height and volume) and  characteristics describing tree quality more closely (crown height,  height of the lowest dead branch and sawlog proportion of tree volume)  by means of high resolution ALS data. The tree species considered is  Scots pine (Pinus sylvestris), and the field data originate from 14  sample plots located in the Koli National Park in North Karelia, eastern  Finland. The material comprises 133 trees, and size and quality  variables of these trees were modeled using a large number of potential  independent variables calculated from the ALS data. These variables  included both individual tree recognition and area-based  characteristics. Models for the dependent tree characteristics to be  considered were then constructed using either the non-parametric k-MSN  method or a parametric set of models constructed simultaneously by the  Seemingly Unrelated Regression (SUR) approach. The results indicate that  the k-MSN method can provide more accurate tree-level estimates than  SUR models. The k-MSN estimates were in fact highly accurate in general,  the RMSE being less than 10% except in the case of tree volume and  height of the lowest dead branch.