This study is a contribution to assess the high resolution digital aerial imagery for semi-automatic analysis
of tree species identification. To maximize the benefit of such data, the object-based classification was
conducted in a mixed forest plantation. Two subsets of an UltraCam D image were geometrically corrected
using aero-triangulation method. Some appropriate transformations were performed and utilized.
Segmentation was conducted stepwise at two levels and a hierarchical image object network was
constructed. The classification hierarchy was developed and Nearest Neighbor classifier, using integration
of different features was performed. Training samples and ground truth map were prepared through
fieldwork. Accuracy assessment of the resulting maps in comparison with reference data showed overall
accuracies and Kappa Index of Agreement of 90.2%, 0.82 (Area1) and 69.8%, 0.49 (Area2), respectively.
Transformed images were advantageous to improve the results. The lower accuracy in Area2 can be
attributed to high diversity and heterogeneous mixture of species. More detailed and accurate mapping of
tree species would be fulfilled applying precise 3D data. The accuracy of detailed vegetation classification
with very high-resolution imagery is highly dependent on the segmentation quality, sample size, sampling
quality, classification framework and ground vegetation distribution and mixture
