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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%.

Identifying tree locations is a basic step in the derivation of other tree parameters using remote sensing techniques, particularly when using airborne laser scanning. There are several techniques for identifying tree positions. In this paper, we present a raster-based method for determining tree position and delineating crown coverage. We collected data from nine research plots that supported different mixes of species. We applied a raster-based method to raster layers with six different spatial resolutions and used terrestrial measurement data as reference data. Tree identification at a spatial resolution of 1.5 m was demonstrated to be the most accurate, with an average identification ratio (IR) of 95% and average detection ratio of 68% being observed. At a higher spatial resolution of 0.5 m, IR was overestimated by more than 600%. At a lower spatial resolution of 3 m, IR was underestimated at less than 44% of terrestrial measurements. The inventory process was timed to enable evaluation of the time efficiency of automatic methods.

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.

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 INTEGRAL project has received funding from the European Union’s Seventh Programme for research, technological development and demonstration under grant agreement No. 282887. http://www. integral-project.eu/. Moreover, financial support by the Transnational Access to Research Infrastructures activity in the 7th Framework Programme of the EC under the Trees4Future project (No. 284181) for conducting the research is gratefully acknowledged. This research has also received funding from the European Union H2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 691149 (SuFoRun). Researcher Jordi Garcia-Gonzalo was supported by a “Ramon y Cajal” research contract from the MINECO (Ref. RYC-2013-14262) and has received funding from CERCA Programme/Generalitat de Catalunya. This paper could be achieved thanks to support of EFIATLANTIC donors: Conseil regional d’Aquitaine, Ministère de l’agriculture et de la forêt.

In the work, a fully automatic approach for vegetation delineation using ALS data is presented. Nowadays, in Slovakia, aerial images and satellite scenes are used for this purpose. For vegetation identification, the measurement of local transparency and roughness directly in 3D point cloud was used. The aim was the identification of groups of trees with area bigger than 0.1 ha and individual trees. On the experimental area, 33 polygons representing groups of trees and 120 individual trees were identified. For groups of trees the accuracy of identification was 100%. For comparison, an area with reference polygons, which were manually vectorised by the operator on the orthophotos with spatial resolution 30 cm, was used. The average difference in the area was –0.26%, with standard deviation ±8.17%. The distance of borders of reference polygons and polygons derived from ALS data was also compared, average distance for border parts that fall inside the reference polygons was 2.24 m with standard deviation of ±2.8 m. The average distance for borders parts that fall outside of the reference polygons was 1.84 m with standard deviation ±2.04 m. The accuracy of individual trees identification was 98%.

The article deals with the issue of the aerial survey of forest transportation (road mapping and calculation of skidding distance), via methods of remote sensing of the Earth and geographical information system (GIS) applications. Road network mapping was carried out by the manual vectorisation of linear objects based on terrain models created from laser scanning data. Based on the roads and digital terrain model derived from airborne laser scanning data, the average skidding distance for every forest stand was calculated. An algorithm created in the ArcGIS environment was used for calculation, which fully automated the calculation of the skidding distance. The versatility of the suggested method was verified on the study area of the University Forest Enterprise of the Technical University in Zvolen. The results indicated that it is possible to identify the permanent and, under certain conditions, temporary network of forest roads from airborne laser scanning data, including the case of roads situated und...

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 info:eu-repo/semantics/publishedVersion