Background and Purpose: Coppice forests have a particular socio-economic and ecological role in forestry and environmental management. Their production sustainability and spatial stability become imperative for forestry sector as well as for local and global communities. Recently, integrated forest inventory and remotely sensed data analysed with non-parametrical statistical methods have enabled more detailed insight into forest structural characteristics. The aim of this research was to estimate forest attributes of beech coppice forest stands in the Sarajevo Canton through the integration of inventory and Sentinel S2A satellite data using machine learning methods. Materials and Methods: Basal area, mean stand diameter, growing stock and total volume data were determined from the forest inventory designed for represented stands of coppice forests. Spectral data were collected from bands of Sentinel S2A satellite image, vegetation indices (difference, normalized difference and ratio vegetation index) and biophysical variables (fraction of absorbed photosynthetically active radiation, leaf area index, fraction of vegetation cover, chlorophyll content in the leaf and canopy water content). Machine learning rule-based M5 model tree (M5P) and random forest (RF) methods were used for forest attribute estimation. Predictor subset selection was based on wrapping assuming M5P and RF learning schemes. Models were developed on training data subsets (402 sample plots) and evaluations were performed on validation data subsets (207 sample plots). Performance of the models was evaluated by the percentage of the root mean squared error over the mean value (rRMSE) and the square of the correlation coefficient between the observed and estimated stand variables. Results and Conclusions: Predictor subset selection resulted in a varied number of predictors for forest attributes and methods with their larger contribution in RF (between 8 and 11). Spectral biophysical variables dominated in subsets. The RF resulted in smaller errors for training sets for all attributes than M5P, while both methods delivered very high errors for validation sets (rRMSE above 50%). The lowest rRMSE of 50% was obtained for stand basal area. The observed variability explained by the M5P and RF models in training subsets was about 30% and 95% respectively, but those values were lower in test subsets (below 12%) but still significant. Differences of the sample and modelled forest attribute means were not significant, while modelled variability for all forest attributes was significantly lower (p<0.01). It seems that additional information is needed to increase prediction accuracy, so stand information (management classes, site class, soil type, canopy closure and others), new sampling strategy and new spectral products could be integrated and examined in further more complex modelling of forest attributes.

Utjecaj gljiva truležnica roda Heterobasidion i Armillaria na pojavu truleži na stablima obične smreke provedeno je na Šumskogospodarskom području “Gornjebosansko”, gospodarska jedinica “Gornja Stavnja”, odjeljenje 65. Utvrđivanje prisutnosti truleži vršeno je na srušenim stablima obične smreke na premjernim površinama koje su raspoređene u sistematski postavljenoj mreži 100 m x 100 m. Uzorci su prikupljeni sa tri mjesta na dijelu debla zahvaćenom procesom truleži (početak zone truleži, sredina i vršna zona truleži). Na mjestima uzorkovanja uzimani su kolutovi drva debljine 5 cm. Analize uzoraka su provedene u laboratorijima Šumarskog fakulteta Univerziteta u Sarajevu. Za izravnu izolaciju gljivične DNA su pripremljeni uzroci drva mase 10-20 mg. Za amplifikaciju ciljanog segmenta DNK korištene su tubice s pripremljenim reagensima, proizvod ReadyToGo PCR beads tvrtke Amersham, Bioscience. Za amplifikaciju ciljanog segmenta ITS regije za rod Heterobasidion su korišteni početnice MJ-F, MJ-R, KJ-F i KJ-R, pomoću kojih je utvrđena međuvrsna raznolikost (tablica 2). Za amplifikaciju ciljanog segmenta ITS regiona za rod Armillaria su korišteni početnice ITS-1 i ITS-4, pomoću kojih je utvrđena samo pripadnost rodu. Za međuvrsnu raznolikost je vršena amplifikacija ciljanog segmenta IGS regiona za rod Armillaria pomoću početnice LR12 i O-1, te razgradnja endonukleazom AluI (tablica 3). Uspješnost amplifikacije je provjerena elektroforezom na agaroznom gelu. Interpretacija profila je izvršena pomoću molekularnog markera poznate veličine (100 bp) (slika 1). Na osnovi provedenih istraživanja unutar istraživane sastojine utvrđeno je 9 stabala s gljivom H. parviporum, 2 stabla s gljivom A. ostoyae, 1 stablo s gljivom A. cepistipes, te 17 stabala čiju trulež su uzrokovale ostale gljive truležnice (tablica 4). Gljiva H. annosum je uspješno identificirana iz plodišta. Na osnovi svega navedenog, može se zaključiti da je molekularnim analizama moguće utvrđivati međuvrsnu raznolikost gljiva ova dva roda iz uzoraka drveta sa truleži i plodišta gljiva.

Forest productive attributes changes over time in native forests has been recognized as crucial challenge for management of uneven aged mixed forests in Bosnia and Herzegovina since middle of the last century. Experimental study has been carried out on set of experimental plots established in mixed stands on mountain Igman in central Bosnia. The most important forest productivity attributes changes based on repeated measures have been monitored over time. The aim of this research was to conduct the post-hoc power analysis for monitored forest attributes: basal area per ha (BA), growing stock per ha (GS) and current annual increment per ha (CAIv). Here are used repeated measures conduced on the 10 experimental plots in two types of mixed stands: fir-spruce and fir-spruce-beech plots (five plots per each type) measured in five (BA and GS) and four (CAIv) occasions in periods between 10–20 years. Analyses of variance (ANOVA) within and within-between repeated measures were applied and power analysis was performed. ANOVA within forest type over time showed highly significant differences for all attributes (α = 0.05, p < 0.001). Here, power analysis for comparison of stand attributes resulted in observed high power values ranged from 82% to 99% (very low risk of Type II errors). Then, ANOVA between two forest types over time showed different significances for forest attributes (α = 0.05, pBA = 0.25, pGS = 0.23 and pCAIv = 0.02). The risks of Type II errors were high for BA and GS (from 66% to 72%) while conclusions for CAIv could be accepted with very low risk (4%). So, the post-hoc power analysis of comparisons of stand attributes between forests types found low power for BA (28%) and GS (34%) and high power for CAIv (96%). These findings confirm importance of proper forest species composition planning in mixed stands related to highest wood productivity and other forest characteristics as biodiversity.

Forest resources in Bosnia and Herzegovina present rich sites of various non-wood forest products. In the concept of sustainable use of forest resources and assurance the economic, environmental and social effects, non-wood forest products have great importance, especially in the strategic commitments of rural development. However, information on the potentials of non-wood forest products in Bosnia and Herzegovina is very scant, whereas institutional and procedural framework relevant to this sector is underdeveloped. In this paper value chain analysis of non-wood forest products in Bosnia and Herzegovina have been carried out in order to identify the participants in the value chain, their mutual relations, and the analysis of organizational and institutional issues that affect the economic aspects of certain stages of the value chain. Survey method was used for primary data collection in the Federation of Bosnia and Herzegovina, where the relevant information from participants in the chain of non-wood forest products has been obtained. The study included a sample of 156 collectors, who had continuity in the collection and delivery, and 18 companies engaged in purchasing, processing and distribution, which have agreed to participate in the research. For data processing and interpretation of the results classical methods of analysis, synthesis, induction, deduction and comparison, and statistical methods of trend analysis were used. Technique of SWOT analysis was used in order to identify the positive and negative factors, as the basis for defining the strategic direction of non-wood forest products sector development. The obtained results indicate on the presence of numerous problems in the value chain. The share of individual groups of non-wood forest products in the analyzed period is: 50% of berries, 40% of medicinal and aromatic plants and 10% of mushrooms. The average annual growth rate of purchased and processed amounts of medicinal and aromatic plants was 17%, 28% of forest berries, and

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.

book May 6 -7, 2016 Prague, Czech Republic

UDK: 630*44:632.25(234.422 Igman)              Silver Fir in Bosnia and Herzegovina is the important coniferous species of trees in term of forestry and biodiversity. Numerous harmful factors have the impact to its health and vitality. One of them is different damages of standing trees by machinery. These injuries represent suitable entering openings for microorganisms which afterwards cause decay of the wood. Wood affected by this process has a decreased quality or it becomes completely unusable. Infection and development of decay are in correlation with the size of the injuries and its position on the tree. In recent times the methods of analysis of decay based on the flow of electric energy or sound through the wood were developed. One of these methods is the sound tomography which gives us the possibility to review the condition of the tree without the need to cut it or damage it significantly. It is performed by the device called tomograph. In this paper, by the method of tomography, the presence of signs of decay of wood on injured trees of Silver Fir was identified.