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ŠUMARSKI LIST 9-10/2014 str. 48     <-- 48 -->        PDF

has been a long-term practice (Hladnik and Pirnat, 2011), often with selective thinning. The results are mixed heterogeneous forests stands, with high tree species and stand structure diversity, where field survey approach cannot assess detailed information on tree species distribution over large areas, because it is difficult to identify representative sampling locations. Moreover, such detailed information for a large forest area can highlight important areas, such as minority species presence or locations of a forest health concern (Jurc et al., 2014). Therefore, the use of remotely sensed data seems a promising tool to assess such information in close-to-nature managed urban forests.
Many studies used both multispectral data and laser scanning data for either automated tree isolation or segmentation and classification of tree species, what was found also in a recent review of the aerial laser scanning (ALS) application in the South-East European forestry by Balenović et al. (2013). Leckie et al. (2003) evaluated a combination of high spatial resolution (8.5 cm) multispectral aerial imagery and laser scanning data for isolation of individual trees in even-aged Douglas-fir stands in Canada. Applying a valley-following approach, automated tree isolations of the multispectral imagery achieved 80-90 % consistency with the ground data and the isolation with the lidar data produced 59 % of perfect crown outline delineations. Their main argument was that both types of data could be used to complement each other in order to achieve better automated isolations of individual trees. However, they suggested that research in various forest conditions was needed to improve the method. Popescu and Wynne (2004) used LiDAR data and ATLAS multispectral (visible, near-IR and mid-IR bands) optical data with spatial resolution of 4 m for measuring the heights of individual tress in pine and deciduous stands in Appomattox-Buckingham State Forest in Virginia, United States. They showed that combined multispectral imagery and LiDAR data are able to accurately predict tree heights of interest for forest inventory and assessment. Ali et al. (2008) tested feature-level fusion for modelling individual trees by applying marker-controlled watershed segmentation – using user-specific markers to define local tree-crown tops, and subsequent classification, using tree heights and tree crown signatures derived from light detection and ranging (LiDAR) data and multispectral imagery. Fused data of LiDAR derived height layer, original four-band spectral data and bands created by principal component analysis from original four bands (brightness, redness, greenness, and blue-yellowness), were classified. Classification with only the original four spectral bands had an overall accuracy of 63 % and with a fused ten band image the overall accuracy increased to 86 % due to the LiDAR data and newly derived bands from the multispectral imagery. Voss (2008) analysed the seasonal effect on differentiating tree species in a flat environment of the University of Northern Iowa campus, USA, where trees did not grow in dense conditions and were easily distinguished. They used multi-temporal hyperspectral data, LiDAR data, and ground truth tree species data. Overall hyperspectral data classification accuracy did not defer significantly between summer and autumn data (57 % and 56 %, respectively). The accuracy was increased by 19 % when LiDAR data was applied. Author gives credit to the reduction of the shadow effect (false ‘tree-crowns’ in segmentation process caused by shadows of actual tree-crowns on the ground in-between trees) and the addition of vegetation elevation data to separate low and high vegetation. Puttonen et al. (2009) tested a method called Illumination Dependent Colour Channels (IDCC) to improve individual tree species classification. The method used multispectral data from aerial imagery and LiDAR data dividing sunlit and shaded parts of the crowns and then included calculated indices from the spectral values of those parts to use them for classification using quadratic, linear, and Mahalanobis distance based discrimination functions. The highest overall accuracy achieved was 70.8 % using the linear discriminant analysis function.
Nagendra (2001) evaluated ‘the potential of remote sensing for assessing species diversity’. He concluded that delineation of a large number of species by using spectral data was not yet possible a decade ago. However, in 2009 WorldView-2 (WV2) satellite was launched (DigitalGlobe, 2010), providing 2 m spatial resolution for 8 multispectral bands (Coastal, Blue, Green, Yellow, Red, Red-Edge, near infra-red (NIR) – 1 and NIR – 2) and 0.5 m spatial resolution for panchromatic band. This could facilitate tree species classification also in mixed close-to-nature managed natural urban forests with great diversity of tree species.
In the recent years, several studies have applied WV2 imagery for tree species analyses. The potential of the WV2 for identifying and mapping urban tree species/groups was explored e.g. in the city of Tampa, FL, USA (Pu and Landry, 2012). In comparison to IKONOS satellite imagery, the accuracy of mapping six tree species/groups increased by 16–18% with WV2 imagery. However, the study covered trees/groups in a dense, urban environment, with sparse vegetation, rather than in a forest. Carter (2013) used multi-temporal data from two WV2 images – from June and September 2010 – to classify ash, maple, oak, beech, evergreen and six other tree classification classes in a mixed deciduous forest in Upstate New York. Statistically reducing the dimensionality of multispectral data set and combining ash and maple classes increased classification accuracy to almost 90%. However, training samples were GPS points and not objects (polygons) such as tree crowns. A study by Latif et al. (2012) assessed the potential of WV2 imagery in determining tree species in the Bukit Nanas Forest Reserve, Kuala Lumpur. They compared tree-level spectra extracted by a spectrometer on the ground and spectral data from WV2. The main difficulty they encountered was the delineation of trees of different species growing in a group being of approximately the same height, which is a common