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Sustainable forest management is driving the development of forest decision support systems (DSSs) to include models and methods concerned with climate change, biodiversity and various ecosystem services (ESs). The future development of forest landscapes is very much dependent on how forest owners act and what goes on in the wider world; thus, models are needed that incorporate these aspects. The objective of this study is to assess how nine European state-of-the-art forest DSSs cope with these issues. The assessment focuses on the ability of these DSSs to generate landscape-level scenarios to explore the output of current and alternative forest management models (FMMs) in terms of a range of ESs and the robustness of these FMMs in the face of increased risks and uncertainty. Results show that all DSSs assessed in this study can be used to quantify the impacts of both stand- and landscape-level FMMs on the provision of a range of ESs over a typical planning horizon. DSSs can be used to assess how timber price trends may impact that provision over time. The inclusion of forest owner behavior as reflected by the adoption of specific FMMs seems to be also in the reach of all DSSs. Nevertheless, some DSSs need more data and development of models to estimate the impacts of climate change on biomass production and other ESs. Spatial analysis functionality needs to be further developed for a more accurate assessment of the landscape-level output of ESs from both current and alternative FMMs. European Commission Horizon 2020 Portuguese Science Foundation

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.