Automatic individual tree based analysis of high spatial resolution aerialimages on naturally regenerated boreal forests

Individual tree based forest surveys are feasible using modern computer tec hnology. The presented approach for analysing high spatial resolution (pixe l size 10 cm) aerial images of naturally regenerated boreal forests is base d on visible significant trees. Sunlight patches on the ground are suppress ed, followed by optimal image smoothing. The problem with inclined illumina tion is handled by adapted thresholding. Each connected threshold segment ( a collection of one or more trees) is further smoothed. A selection of the resulting convex edge segments is used for identifying significant tree cro wn circles. Six complementary image variables are estimated and used for re gression analysis. An evaluation of the ground-truth data in central Sweden gives good results on the stem position estimate (a root mean square (RMS) error of 108 cm) and the stem number estimate (a relative RMS error of 11% ). The complementary variables contribute significantly to the stem diamete r prediction, resulting in the following experimental values: Scots pine (P inus sylvestris L.) (R-2 = 59.5%, s = 4.9 cm, N = 157), Norway spruce (Pice a abies (L.) Karst.) (R-2 = 21.9%, s = 6.4 cm, N = 398), birch (Betula pube scens Ehrh.) (R-2 = 35.4%, s = 5.3 cm, N = 133), and European aspen (Populu s tremula L.) (R-2 = 61.4%, s = 4.6 cm, N = 13). The results indicate stron g species dependence.