Integrating UAV photogrammetry with terrestrial laser scanning to characterize managed forest stands

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Tuomas Yrttimaa, Ninni Saarinen, Ville Kankare, Niko Viljanen, Jari Hynynen, Saija Huuskonen, Markus Holopainen, Juha Hyyppä , Eija Honkavaara, Mikko Vastaranta


Terrestrial laser scanning (TLS) provides detailed three-dimensional representation of the surrounding forest structure. However, due to close-range hemispherical scanning geometry the ability of TLS technique to comprehensively characterize the upper parts of forest canopy is often limited. To overcome challenges in upper canopy characterization, TLS point cloud were complemented with a point cloud acquired from above the canopy using UAV photogrammetry. The use UAV point cloud data was considered feasible especially in tree segmentation. With multi-sensoral approach 98.8% of all the 2102 Scots pine trees on the 27 sample plots were automatically detected. Root-mean-square-error (RMSE) in tree height estimates was 1.47 m (7.4%) with 0.33 m (1.7%) of underestimation. Plot-level forest inventory attributes were estimated with a relative RMSE less than 5.5% with the multi-sensoral approach. The results showed that in managed Scots pine forests the multi-scan TLS captures also the upper parts of the forest canopy and improvement in tree height measurement accuracy was obtained with the use of photogrammetric UAV point clouds. The RMSE in basal area-weighted mean height improved 34% (from 0.88 m to 0.58 m) and the bias improved 40% (from -0.75 m to -0.45 m) when UAV data was utilized. However, in this case the accuracy of TLS measurement was already high. In single-species, single-layer forest conditions, multi-sensoral approach generated benefits especially for forest height characterization. However, characterization of complex forest structures may benefit even more from point clouds that have been collected using sensors with different measurement geometries.



Forest Sciences, Geography, Life Sciences, Remote Sensing, Social and Behavioral Sciences


remote sensing, forest sciences, point cloud, Data Fusion


Published: 2020-03-03 21:45

Last Updated: 2020-03-05 06:36

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CC BY Attribution 4.0 International

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