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ŠUMARSKI LIST 9-10/2014 str. 56     <-- 56 -->        PDF

sun-lit pixels of the crowns by Immitzer et al. (2012) requires more detailed knowledge of interpretation of satellite imagery. The second study (Puttonen et al., 2009) divided sunlit and shaded part of the crowns and included calculated indices from the spectral values of those parts to use them for classification. However, the results of Puttonen et al. (2009) should be compared with even extra caution due to different change in elevation (only 30 m), different geographical latitude causing different angles of the solar illumination, different forest type (not as dense and with fewer tree species) and not least – different type of spectral data. Krahwinkler and Rossmann (2013) achieved an average accuracy of more than 80 % by using laser scanning data, airborne red, green, and blue true colour imagery (RGB) and colour infrared data (CIR) imagery, SPOT and RapidEye satellite data for classification of trees in a mixed species forest, also using support vector machine classifier. However, the comparison is limited since their aerial imagery and laser scanning data had a sub-half meter spatial resolution and even the authors argue whether higher classification accuracy was caused by the multitemporal information, additional spectral bands, better suitability of the acquisition dates, or better homogeneity of the RapidEye and SPOT satellites data sets. According to Ke and Quackenbush (2011), lower classification accuracy could be caused by non-optimal segmentation of trees growing close to each other and often overlapping.
Accuracy assessment made by inspecting the detailed true orthophoto image of the classified area showed that the object-based classification produced fair estimation of tree species distribution and composition in the park (Fig. 9). Distribution of the presence of the analysed cases of tree species was mainly consistent with the actual allocation in nature. The proportion of classified Norway spruce cases increases towards the southwest, Scots pine to the northwest, European beech was the most present in the south-eastern part, the oaks in the southern and eastern parts. According to forest management plans (ZGS, 2007) the eastern part has more diverse tree species composition, including planted non-autochthonous oak – Quercus rubra. Second, on average Sweet chestnut achieves the minimum height among all of the classified tree species in the study. Moreover, its crown is not as contrasting as those of the other four tree species. Isolating deciduous tree species also with LiDAR data remains difficult task due to their complex structure and overlapping of the crowns (Chen et al., 2012). Other likely causes for over-classification of the oaks in the eastern part and no classifications of Sweet chestnut are harder to explain.
5. Conclusions
The object-based image analysis method used in this study showed promising applicability of fused WorldView-2 and laser scanning data for dense, tree-species-rich, highly heterogeneous natural urban forest stands where crowns of trees often intertwine. However, the accuracy of the proportions of individual tree species that form the forest stand canopy was lower than in some other studies. The distinction between deciduous and coniferous tree species was the most reliable. This was expected based on the reports of previous studies and the spectral signatures of the reference sample data for this study. However, segmentation of individual tree crowns was (mostly) not achieved. Most of the segments were either parts of the crowns or conglomerates of parts of crowns and interspace.
Complete visible parts of the tree crowns were selected for ground-truth tree data. In this way, not just the spectral signature but also texture served as an attribute for classification. In a dense forest stand canopy – especially the one formed by the deciduous trees – the intertwining of adjacent crowns makes delineation of neighbouring tree crowns very difficult, both in manual digitization and automated segmentation and classification of objects.
This study offers new evidence on how the application of remote sensing data offers an opportunity to reduce the time of assessment of tree species inventory by using a straightforward method of object-based image analyses. The next project will compare this method using the same data for the same forest to remove the effect of variability of forest conditions which has a high impact on the accuracy of the results (Kaartinen et al., 2012).
We therefore recommend further research that would provide more evidence on the optimal combination of spectral, spatial and temporal resolution of the data to achieve the optimal cost – benefit ratio for forest management practice.
6. Acknowledgments
We thank dr. Milan Kobal from the Slovenian Forestry Institute for his help with tree-sampling design and dr. Aleksander Marinšek for translation of forest communities. We thank the City of Ljubljana for orthophoto imagery and laser scanning data. We thank also to Matej Rupel, Samo ­Grbec and Matevž Triplat for help with fieldwork. We thank Gašper Okršlar to assist in the digitization. We thank Tadej Reissner for providing English language editing services and dr. Silvija Krajter Ostoić for translation into Croatian language. The Centre of Excellence for Space Sciences and Technologies SPACE-SI is an operation partly financed by the European Union, European Regional Development Fund and Republic of Slovenia, Ministry of Higher Education, Science and Technology. The research was performed in the frame of the research program Anthropological and Spatial Studies (P6-0079) financed by the Slovenian Research Agency and a PhD research study partly financed by the European Union, the European Social Fund.