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ŠUMARSKI LIST 9-10/2014 str. 55     <-- 55 -->        PDF

a classification result with the training data. The overall accuracy, Kappa coefficient and producer’s and user’s accuracies are reported in Tab. 4.
Principal component analysis (PCA) of the original 8 WV2 bands was used with the training dataset only to produce new output bands and to calculate variability between tree species classes explained by different spectral bands (Tab. 2).
3. Results
The confusion matrix (Tab. 3) compares the location and class of each pixel from the testing data set with the corresponding location and class in the classification image. Each of the five columns of the confusion matrix represents a training trees data class for the five tree species and the values in the column correspond to the classification image’s labelling of the training trees data pixels in percent. As it can be seen, 80 % of the pixels of Norway spruce, 50 % of Scots pine, 38 % of European beech, 70 % of Sessile and Pedunculate oak and 1% of Sweet chestnut classes were classified correctly.
The overall accuracy of the classification was 58 % and Kappa Coefficient was 0.431 (Tab. 4). The highest accuracy was for Norway spruce, where producer’s accuracy was 80 % and user’s accuracy was 69 % (Tab. 4). Most S. chestnut crowns were misclassified as Oaks (73 %).
4. Discussion
The purpose of this study was to assess a straightforward method of object-based image analysis (OBIA) (Blaschke, 2010) with a combination of WV2 imagery and LiDAR data for successful classification of individual crowns of five different tree species in the dominant layer of natural, mixed, heterogeneous urban forest in Ljubljana, Slovenia.
Studies by Immitzer et al. (2012) and Puttonen et al. (2009) achieved higher overall accuracy. However, the method of collecting training samples was different. It seems that the approach of those studies was to achieve high classification accuracy with less attention to the practical applicability of the method for potential end-users, such as forest managers usually with significantly less knowledge about remote sensing analyses. For example, the method of collecting only