While sustainable forestry in Europe is characterized by the provision of a multitude of forest ecosystem services, there exists no comprehensive study that scrutinizes their sensitivity to forest management on a pan-European scale, so far. We compile scenario runs from regionally tailored forest growth models and Decision Support Systems (DSS) from 20 case studies throughout Europe and analyze whether the ecosystem service provision depends on management intensity and other co-variables, comprising regional affiliation, social environment, and tree species composition. The simulation runs provide information about the case-specifically most important ecosystem services in terms of appropriate indicators. We found a strong positive correlation between management intensity and wood production, but only weak correlation with protective and socioeconomic forest functions. Interestingly, depending on the forest region, we found that biodiversity can react in both ways, positively and negatively, to increased management intensity. Thus, it may be in tradeoff or in synergy with wood production and forest resource maintenance. The covariables species composition and social environment are of punctual interest only, while the affiliation to a certain region often makes an important difference in terms of an ecosystem service's treatment sensitivity.
For forest sustainability and vulnerability assessment, the landscape scale is considered to be more and more relevant as the stand level approaches its known limitations. This review, which describes the main forest landscape simulation tools used in the 20 European case studies of the European project "Future-oriented integrated management of European forest landscapes" (INTEGRAL), gives an update on existing decision support tools to run landscape simulation from Mediterranean to boreal ecosystems. The main growth models and software available in Europe are described, and the strengths and weaknesses of different approaches are discussed. Trades-offs between input efforts and output are illustrated. Recommendations for the selection of a forest landscape simulator are given. The paper concludes by describing the need to have tools that are able to cope with climate change and the need to build more robust indicators for assessment of forest landscape sustainability and vulnerability.
The paper presents an application of structural analysis in search of key drivers and barriers of forest management in two Slovak regions: Podpoľanie and Kysuce. A comparison with factors identified in selected European regions is also presented. First, various relevant factors affecting forest management were selected for both regions. The selections draw on the pool of primary data (structured in-person interviews) and secondary data (qualitative analysis of national and European documents). Second, factors were grouped according to the STEEP categories (Society, Technology, Economy, Ecology, and Policy). Subsequently, factors were rigorously assessed by the regional stakeholders in participatory workshops, and their answers were analysed by structural analysis with the help of Parmenides EIDOS™ software. The results show that in both Slovak regions political, economic, and ecological factors dominated over social and technological factors. The comparison with selected European regions revealed that in the Slovak and other European regions, the Policy category dominated due to having the highest number of factors and their overall impact on forest management. In contrast, the least important societal domain was Technology in both the Slovak and other European regions. However, while stakeholders across the selected European regions perceived the Society domain as significant, stakeholders in both Slovak regions perceived the Economy and Ecology domains as more significant.
The presented paper discusses the potential of low point density airborne laser scanning (ALS) data for use in forestry management. Scanning was carried out in the Rožnava Forest enterprise zone, Slovakia, with a mean laser point density of 1 point per 3 m2. Data were processed in SCOP++ using the hierarchic robust filtering technique. Two DTMs were created from airborne laser scanning (ALS) and contour data and one DSM was created using ALS data. For forest stand height, two normalised DSMs (nDSMs) were created by subtraction of the DSM and DTM. The forest stand heights derived from these nDSMs and the application of maximum and mean zonal functions were compared with those contained in the current Forest Management Plan (FMP). The forest stand heights derived from these data and the application of maxima and mean zonal functions were compared with those contained in the current Forest management plan. The use of the mean function and the contour-derived DTM resulted in forest stand height being underestimated by approximately 3% for stands of densities 0.9 and 1.0, and overestimated by 6% for a stand density of 0.8. Overestimation was significantly greater for lower forest stand densities: 81% for a stand density of 0.0 and 37% for a density of 0.4, with other discrepancies ranging between 15 and 30%. Although low point density ALS should be used carefully in the determination of other forest stand parameters, this low-cost method makes it useful as a control tool for felling, measurement of disaster areas and the detection of gross errors in the FMP data. Through determination of forest stand height, tree felling in three forest stands was identified. Because of big differences between the determined forest stand height and the heights obtained from the FMP, tree felling was verified on orthoimages
(1) This study focused on the derivation of basic stand characteristics from airborne laser scanning (ALS) data, aiming to elucidate which characteristics (mean height and diameter, dominant height and diameter) are best approximated by the variables obtained using ALS data. The height of trees of different species in four permanent plots located in the Slovak Republic was derived from the normalised digital surface model (nDSM) representing the canopy surface, using an automatic approach to identify local maxima (individual treetops). Tree identification was carried out using four different spatial resolutions of the nDSM (0.5 m, 1.0 m, 1.5 m, and 2.0 m) and the number of trees identified was compared with reference data obtained from field measurements. The highest percentage of tree detection (69-75%) was observed at the spatial resolutions of 1.0 and 1.5 m. Absolute differences of tree height between reference and ALS datasets ranged from 0 to 36% at all spatial resolutions. The smallest difference in mean height was obtained using the higher spatial resolution (0.5 m), while the smallest difference in the dominant height of the relative number of thickest trees (h10% and h20%) was observed using the lower spatial resolution (2 m). The same trends also apply to diameters. The average errors at resolution of 1.0 and 1.5 m was 8.7%, 5.9% and 9.7% for mean height, h20% and h10%, respectively. ALS-derived diameters (obtained using regression models from reference data and ALS-derived individual height as predictor) showed absolute errors in the range 0-48% at all spatial resolutions. The deviation in mean diameter at a resolution of 0.5 m ranged from -12.1% to 15.3%.
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