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<p>The recent explosion in availability of high resolution remote sensing technologies and, crucially, the tools to analyse the 3D data they produce is leading to substantial interest in using them for widespread forest structural monitoring. The level of detail contained in the entire 3D shape of trees, fully captured within these data, can generate a wide range of metrics of interest to ecologists, but the potential metrics of interest and their uncertainties have not been fully explored. In particular, the value of different technologies - whether passive or active sensors, and from the ground or the air - for accurately deriving different metrics is not well known.</p> <p> </p> <p>Working across a range of European forest ecosystems, we have constructed a unique 3D dataset of European forest structural properties from passive and active sensors. We segment individual trees from concurrent and co-located Structure from Motion photogrammetry (SfM) (passive sensor), and UAV LiDAR, and terrestrial laser scanning (active sensors) campaigns, and use these to compute tree structural metrics. We compare the ability of these different technologies to accurately measure key tree properties across a diversity gradient in multiple biomes.</p>

Table 1: species -α linear mixed model (equation 1) showing relationship between tree species and α for all 2472 individual trees. Species are listed from low -high drought tolerance, with the exception of P. pinaster, for which drought tolerance index has not been calculated in the literature.