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coniferous tree species group since the presence of the 
deciduous trees is more easily recognizable in the aerial images. 
Despite the theoretical advantages of the segment-based 
approach, the features extracted for segments did not perform 
well in the estimation procedure. There are some possible 
reasons for this. First, the field data was measured per sample 
plots and not per segments. Because of this the areas of the field 
measurement and the extracted remote sensing features 
correspond to each other best in the feature set Grid. 
Furthermore, the automatic segmentation often produces 
segments that are irregularly shaped, i.e. not compact, and in 
forest stands with large trees the segment borders are typically 
located in gaps between trees, in which case the variation within 
the segments may be more significant than the variation 
between segments. On the other hand, using geographically 
larger segments in extracting the features typically resulted in 
lower estimation accuracy compared to other feature sets, which 
indicates that the larger the units are the more internal variation 
they have. 
Based on the results of this study the most feasible inventory 
procedure utilizing ALS and aerial image data seems to be the 
following: 1) estimation based on ALS data and aerial imagery 
for the systematic grid elements, 2) automatic segmentation 
utilizing ALS height, ALS intensity and aerial imagery, 3) 
deriving the estimates for image segments on the basis of the 
estimates of grid elements and 4) manual combination of image 
segments for deriving spatial units for forest management 
purposes. 
Forestry Sciences, vol. 76. (pp. 111-123). Kluwer Academic 
Publishers. 
Suvanto, A., Maltamo, M., Packalen, P. and Kangas, J., 2005. 
Kuviokohtaisten puustotunnusten ennustaminen 
laserkeilauksella. Metsdtieteen aikakauskirja, 4/2005, 413-428. 
Tokola, T., Pitkanen, J., Partinen, S. and Muinonen, E., 1996. 
Point accuracy of a non-parametric method in estimation of 
forest characteristics with different satellite materials. 
International Journal of Remote Sensing, Vol. 17, No. 12, pp. 
2333-2351. 
ACKNOWLEDGEMENTS 
The authors wish to thank M.Sc. Risto Viitala at the HAMK 
University of Applied Sciences and Lie.Sc. Juho Heikkila at 
Forestry Development Centre Tapio for providing the field and 
remote sensing materials for this study. 
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