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INTELECTUAL OUTPUTS

Virtual Forest Twins based on Marteloscope point cloud data from different forest ecosystems type and its associated teaching workload

The main task in a sustainable silviculture and ecological forest management is to decide which tree shall be harvested and which one will be allowed to grow as a future tree. Changing ecological conditions and climate change triggered by the industrial revolution, influence working and learning conditions in forestry. So far foresters are informed by observation of actual forest stands – decisions are often based on assumptions and guesswork about prior management decision. Foresters lack real world laboratories for forest decision making and sustainable management. In the scope of this project, a real forests stands and a selected marteloscope sites will turn into full 3D realistic virtual forest twins and digital examples of open blended learning sites for forest higher education as well as for crowd sourcing and citizen science activities of stakeholders. Such virtual 3D forest twin integrates typical augmented reality means like the reproduction of specific scenarios which go beyond the pure theoretical dimension into virtual forest ecosystems and virtual timber production sites.

for the forestry industry as well as citizen science projects – the idea offers a wide scope for transfer beyond higher education. Traditional stand inventories on an individual tree level are inefficient and time consuming. Today, remote sensing (RS) technologies represent an effective and state of the art way to acquire such digital information of typical tree parameters. For example, the advent of wearable, movable LiDAR sensor technology and structure-from-motion photogrammetry (SfM) allow mobile, high-resolution scanning of 3D survey parameters. The innovation of the digital forest twin is the single tree database which allows standard forest stand simulations based on real individual trees from the virtualized stand. Each individual tree in a virtualized marteloscope or other virtualized forest training site is registered with its characteristic parameters such as species and sub-species, a spatial coordinate, DBH, Crown height and diameter and others. On this basis, empirical data from real forest sites are mirrored in an information-rich virtual dimension and providing waypoints for exploratory data analysis, practical training, and decision- or event-chain simulations. For example, innovative sustainable forest management concepts such as climate change adaptation and resilience measures can be tested and simulated on the scale of individual trees which is an innovative didactical tool in forestry education. The latest developments of augmented reality technologies, especially in the gaming sector has uncovered new opportunities for learning in formal and non-formal educational contexts. But adjacent educational didactics for virtual environments and a proper theoretical-methodological framework for their integration into the higher forestry education system in Europe is still missing. Virtual forest twins and digitized Martelsocopes of the VirtualForst project offer the development and evaluation of an AR-supported learning system with associated learning scenarios in order to use errors and mistakes "made" by a learner as integral part of the individual competence acquisition without negative consequences in the real world. Importantly, the VirtualForests consortium will have at its disposal a series marteloscopes established during a preceding Erasmus+ Capacity Building Project “BioEcoN”. These marteloscopes are in temperate (EUSD), temperate-Mediterranean (UVa) and tropical (VNU-HUS) forests, with an additional long-term teaching/monitoring plot available in the boreal forest of interior Finland. A data sharing agreement is in place to ensure the forest plots and associated attribute data are maintained beyond the lifespan of the project. Building on these efforts, the VirtualForests project will add the acquisition and analysis of 3D point cloud and other related RS-derived data into the fold, to be integrated with other project outputs into an emergent system for innovative digital and blended learning environments in forest ecosystem management studies. Consortium members will jointly coordinate efforts to train students on point cloud data collection and processing and share this data as well as data from subsequent inventories freely among participants. The result is a reference database which will be integrated into higher education in forestry across Europe and is open to scientific research, including empirical examples from different forest ecotypes and management situations.

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