UDK: 630*52/*56:519.8(497.6) The aim of this research was to evaluate estimates of the current annual increment of volume (CAIv) variability considering growing stock (V) as structural variable and topographic conditions and Landsat 8 spectral response as environmental variables on hilly and mountainous mixed forests in the northeast Bosnia using multiple linear regressions based on ordinary least squares (MLR) and geographically weighted regression (GWR). Sample data contains geo-referenced forest inventory data, CAIv (m3/ha/year) and V (m3/ha), extracted values from digital terrain model (altitude, slope and aspect) and derived principal components values from Landsat 8 satellite image for forest stands of the management unit located on hilly and mountain positions in protected area Konjuh, Kladanj. Here are applied MLR and GWR using stepwise procedure. MLR and GWR analyses resulted with global coefficients of significant predictors on hilly position. This was expected due to homogenous vegetation and environmental conditions on hilly position. It was found that growing stock affected CAIv the most. Significant improvement of regression modeling is achieved by GWR appliance on sample from mountainous position. There were obtained local influence of growing stock and the first principle component related to green biomass on CAIv. The highest improvement is found for broadleaves CAIv where quantification of local variability of growing stock increased adjusted coefficient of determination about 11% and reduced relative root mean square error for 6%. Local character of green biomass related to conifers CAIv did not improve regression estimation significantly. The broadleaves root mean square error based on GWR was 1.60 m3/ha/year (coefficient of variation more than 30%) which is still high so further modeling including other structural characteristics (stems number, basal area, mixture) as predictors is required. 

UDK: 630*52:311 Regular forest inventory on state owned forest delivers plenty of data and information enabling detailed insight in forest structure and quantities. Current methodology for forest assessment on private properties considers time-consuming, low-intensive terrestrial measurement and observation on scattered small forest stands distributed on hilly and plane position around complex of state owned forests. Here are evaluated two modeling techniques: ordinary least square (OLS) regression and geographically weighted regression (GWR) estimating growing stock quantities of point sample inside the smallest state owned forest stands (area less then 10 ha). Used material contained forest attributes local estimates from regular inventory distributed in unique management class: beech and fir mixed forest on deep silicate soil, environmental and transformed spectral Landsat 8 data. Obtained results pointed out statistical significance of normalized standardized spectral radiance of NIR and SWIR Landsat bands in regression models. The GWR estimates achieve up to almost 30% higher variability explanation then OLS models. Also, GWR showed wider range then OLS estimates with smaller prediction errors. Evaluation on sample stand level resulted in reliable estimates of particular species or groups and total mean growing stock for all small stands. Further research about potential of GWR and other geo-statistical techniques for forest attribute estimates on more intensive point sample inside small spatial unit and/or whole spatial unit is recommended.

In the traditional forest management the non-living woody biomass in forests was perceived negatively. Generally, deadwood was removed during the silvicultural treatments to protect forests against fire, pests and insects attacks. In the last decades, the perception of forest managers regarding forest deadwood is changing. However, people’s opinions about the presence of deadwood in the forests have been few investigated. In view of this gap, the aim of the paper is to understand the tourists’ perception and opinions towards the deadwood in mountain forests. The survey was carried out in two study areas: the first one in Italy and the second one in Bosnia-Herzegovina. A structured questionnaire was administered to a random sample of visitors ( n =156 in Italy; n =115 in Bosnia-Herzegovina). The tourists’ preferences were evaluated through a set of images characterized by a different amount of standing dead trees and lying deadwood. The collected data were statistically analyzed to highlight the preferred type of forests related to different forms of management of deadwood (unmanaged forests, close-to-nature forests, extensive managed forests and intensive managed forests). The results show that both components of deadwood are not perceived negatively by tourists. More than 60% of respondents prefer unmanaged forests and close-to-nature managed forests, 40% of respondents prefer intensive managed forests in which deadwood is removed during the silvicultural treatments.

UDK: 630*52/*56:528.8(234.422 Igman) Information about quantitative and qualitative forest attributes are the base for successful forest planning and management. Forest inventories collect number of data used for different estimations from large (management unit level) to small (forest stand) scales. Then, control sampling has to be done in order to confirm regularity of terrestrial work. Such sample becomes data source too.  Recent approach for forest characterization includes all available information as sources for additional non-standard insight. Here were used available data about wood volume and increment from control sample for high forest on mountain Igman. Also, recent Landsat TM image from vegetation period was available and used in this research. Here is applied k nearest neighbor’s estimation method. Five nearest neighbors and Euclidian distance is chosen for estimation and mapping. Biases for all forest attributes were non-significant. Obtain results show non significant differences between means and observed and estimated distributions of wood volume and increment. It is estimated higher mean wood volume and increment of broadleaves while means for conifers and totals are lower. That higher wood volume and increment is estimated in all diameter classes for broadleaves while lower quantities are estimated for conifers. Spatial mapping presents distribution of wood volume and increment respecting variability of vegetation in high forest on Igman.