Spaceborne canopy height products should be complemented with airborne laser scanning data: Towards a European canopy height model

Vítězslav Moudrý, Ruben Remelgado, Matthias Forkel, Michele Torresani, Gaia Vaglio Laurin, Eliska Sarovcova, Virginia E. Garcia Millan, Fabian Jörg Fischer, Tommaso Jucker, Michal Gallay, Patrick Kacic, Christopher R. Hakkenberg, Žiga Kokalj, Krzysztof Stereńczak, Yousef Erfanifard, Duccio Rocchini, Jiří Prošek, Stephanie Roilo, Katerina Gdulova, Anna F. Cord, Michela Perrone, Juan Alberto Molina-Valero, Jiří Šmída, Peter Surovy, Zlatica Melichová, Marco Malavasi, Rudolf Urban, Martin Štroner, Dominik Seidel, Szilárd Szabó, László Bertalan, Anette Etner, Roberto Cazzolla Gatti, Ján Kaňuk, Vojtech Barták, Daniel Franke, Benjamin Brede, Qian Song, Mikhail Urbazaev, W. Daniel Kissling

Measuring and mapping vegetation structure is essential for understanding the functioning of terrestrial ecosystems and for informing environmental policies. Recent years have seen a growing demand for high-resolution data on vegetation structure, driving their prediction at fine resolutions (1 m - 30 m) at state, continental, and global spatial extents by combining satellite data with machine learning. As these initiatives expand, it is crucial to actively discuss the quality and usability of these products. Here, we briefly summarize current efforts to map vegetation structure with spaceborne lidar missions and show that predictions from continental-to-global canopy height models (CHMs) exhibit significant errors in canopy heights compared to national airborne laser scanning (ALS) data. We recommend that regions with abundant ALS data, such as Europe, prioritize using ALS-based canopy height metrics rather than relying on less accurate predictions from satellite products. Despite variations in ALS data characteristics such as temporal inconsistencies and differences in point density and classification accuracy, the generation of spatially contiguous canopy height products in raster format at fine spatial resolution is necessary and feasible. This requires coordinating efforts for data and survey harmonization, developing standardized processing pipelines and continent-wide ALS products, and ensuring free access for scientific research and environmental policy. Beyond numerous applications in forestry, ecology and conservation, such datasets are crucial for calibrating future Earth Observation missions, making them essential for producing reliable and accurate global, fine-resolution vegetation structure data