DIGITALNA ARHIVA ŠUMARSKOG LISTA
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method (1982). Microclimate elements air temperature (°C) and relative air humidity (%) were measured at the centre point of the forest gap. Microclimate elements soil temperature (°C) and volumetric water content (%) were measured in the centre of the forest gap, at the midpoint to the forest gap edge in the cardinal point directions, and in the gap edge area. The same microclimate elements were measured in control plots (canopy-covered stands) at three positions. At sites where the microclimate elements were measured, the number of small seedlings was counted on 1.5x1.5 m plots, in order to determine the relationship between microclimate and the density of small seedlings of the forest tree species. A total of 24 plots were laid out in the large gap, small gap and in the control plots. Air temperature and relative air humidity were measured at 1.5 m from the ground. Soil temperature and volumetric soil water content were measured at a depth of 10 cm. Measurements of forest soil microclimate were carried out in undisturbed soil using the microclimate stations “Rotronic” and “Spectrum” once per hour during 2007 and 2008 years. Calibration of microclimate stations and sensors was performed by the Meteorological and Hydrological Service of the Republic of Croatia. According to official reports of the Meteorological and Hydrological Service of the Republic of Croatia, the years 2007 and 2008 were extremely hot compared to the 30-year reference period, though the overall annual temperature percentiles and precipitation percentiles for these two years were with the normal range. Summer 2007 was extremely hot and very dry, and the autumn was cold with common precipitation characteristics. Winter 2007/8 was warm and dry, while spring 2008 was warm with common precipitation characteristics, while summer 2008 was extremely hot and dry. Figure 1 shows the experimental design in the forest gaps.
Spatial interpolation implies the prediction of values of the primary variable at points within the same range as the input points (Burrough and McDonnell 1998). In this study, ESRI ArcGIS 9.3 Desktop was used for data visualization and interpolation. Spatial interpolation of variables measured in the field (e.g. temperature, volumetric water content) was performed using default regularized Spline function (Spatial Analyst extension, Interpolation toolset). This tool interpolates a raster surface from points using a two-dimensional minimum curvature spline technique resulting with smooth surface passes exactly through the input points. The predicted values are very close to the values being interpolated and errors are relatively small. However, accuracy of all spatial interpolation methods mostly depends on sampling (size, distribution, density etc.), nature and quality of the collected data.