Concepts Towards Nation-Wide Individual Tree Data and Virtual Forests

Individual tree data could offer potential uses for both forestry and landscape visualization but has not yet been realized on a large scale. Relying on 5 points/m2 Finnish national laser scanning, we present the design and implementation of a system for producing, storing, distributing, querying, and viewing individual tree data, both in a web browser and in a game engine-mediated interactive 3D visualization, “virtual forest”. In our experiment, 3896 km2 of airborne laser scanning point clouds were processed for individual tree detection, resulting in over 100 million trees detected, but the developed technical infrastructure allows for containing 10+ billion trees (a rough number of log-sized trees in Finland) to be visualized in the same system. About 92% of trees wider than 20 cm in diameter at breast height (corresponding to industrial log-size trees) were detected using national laser scanning data. Obtained relative RMSE for height, diameter, volume, and biomass (stored above-ground carbon) at individual tree levels were 4.5%, 16.9%, 30.2%, and 29.0%, respectively. The obtained RMSE and bias are low enough for operational forestry and add value over current area-based inventories. By combining the single-tree data with open GIS datasets, a 3D virtual forest was produced automatically. A comparison against georeferenced panoramic images was performed to assess the verisimilitude of the virtual scenes, with the best results obtained from sparse grown forests on sites with clear landmarks. Both the online viewer and 3D virtual forest can be used for improved decision-making in multifunctional forestry. Based on the work, individual tree inventory is expected to become operational in Finland in 2026 as part of the third national laser scanning program

Agroforestry · Data science · Environmental resource management · Geography · Computer Science · Environmental Science · Forest Ecology and Biodiversity Studies · Forest ecology and management · Mathematics · Remote Sensing and LiDAR Applications · Forestry

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