 DIGITALNA ARHIVA ŠUMARSKOG LISTA
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 ŠUMARSKI LIST 11-12/2014 str. 33     <-- 33 -->        PDF of the area (Milios 2000a). By applying Neyman’s method, which achieves minimum variation in the sample assuming that the cost is the same for each sampling unit (tree), 60 trees were selected randomly from the site type A, 53 from B, and 45 from C. Sampled trees were measured as follows: • Breast height diameter D (in meters with 2 decimals) was measured with a calliper • Total height H (in meters with 0.5m precision) was estimated with a Blume-Leiss altimeter • Form factor f was estimated with a Bitterlich´s Spiegel relaskop. Total volume v (m3) of each tree was calculated using the formula (Van Laar and Akça 2007): . For each sampled tree, distance of the three nearest trees was measured and species of these trees was determined, in an attempt to relate nearest trees establishment to form factor. 2.3 Regression models – Modeli regresije The regression models that were tested for fitting to data are given in Table 1. These models were fitted for each site type separately and for the whole study area. In each case, approximately 80% of the data were used for fitting and the remaining 20% for validation (Ezekiel and Fox 1959, Marquardt and Snee 1975). Regression analysis was performed using the statistical package SPSS v.19.0 (Kitikidou 2005, IBM 2010). The criteria used for comparing the five regression models were (Table 2): 2.4 Nearest neighbor analysis – Analiza metodom najbližih susjeda Nearest neighbor analysis is a method for classifying cases based on their similarity to other cases. In machine learning, it was developed as a way to recognize patterns of data without requiring an exact match to any stored patterns, or cases. Similar cases are near each other and dissimilar cases are distant from each other. Cases that are near each other are called “neighbors” (Weber et al. 1998). In our study, nearest neighbor analysis was performed using the statistical package SPSS v.19.0 (IBM 2010), using the Euclidean metric for distance transformation. The number of nearest neighbors k was set equal to 3, i.e. for each sampled tree (case) the three nearest trees were examined. Three new (theoretical) cases were used as focal identifiers, corresponding to three trees with mean v, D, H and f for each site type.