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ŠUMARSKI LIST 9-10/2014 str. 52     <-- 52 -->        PDF

DCM and DMT. The receiver had a constant GPRS connection with the base station for real-time post processing of the location, which reached up to 40 cm accuracy. That was sufficient to determine the exact location of an individual tree. The crowns of selected trees were delineated by hand in the field manual, to assure the later identification in imagery for digital delineation. For each tree the tree species, diameter at breast height (DBH), within forest stand canopy position and any visible anomalies (illness, injury, dead tree, etc.) were recorded.
In total 608 trees of 15 different tree species were recorded. For further analysis 574 trees were selected – 304 coniferous trees and 270 deciduous trees of tree species whose sample consisted of more than 30 units (Baldeck and Asner, 2014), namely the Norway spruce, Scots pine, European beech, Sessile and Pedunculate oak and Sweet chestnut. Other tree species are sparsely represented in the forest and the method for sampling training tree data did not allow to include enough units for representative learning (Baldeck and Asner, 2014).
The crowns of selected training trees were manually delineated (Fig. 4) on the true orthophoto and DCM to create polygons for classification. The sample was randomly split in half. One half of the crowns were used in the supervised classification process – training data (292 units), and the other half in post-classification for classification accuracy assessment purposes – testing data (calculation of confusion matrix and user’s and producer’s accuracies) (290 units).
The classification polygons were converted into Regions of Interest (ROI) files in ENVI, creating ground truth ROIs for classification. Fig. 5 presents the mean spectral signatures of Norway spruce, Scots pine, European beech, Sessile and Pedunculate oak and Sweet chestnut. The coniferous