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Open AccessEditor’s ChoiceArticle

Multisensorial Close-Range Sensing Generates Benefits for Characterization of Managed Scots Pine (Pinus sylvestris L.) Stands

1
School of Forest Sciences, University of Eastern Finland, 80101 Joensuu, Finland
2
Department of Forest Sciences, University of Helsinki, 00014 Helsinki, Finland
3
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of Finland (NLS), 02431 Masala, Finland
4
Natural Resources Institute Finland (Luke), 00790 Helsinki, Finland
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(5), 309; https://doi.org/10.3390/ijgi9050309
Received: 25 March 2020 / Revised: 24 April 2020 / Accepted: 5 May 2020 / Published: 7 May 2020
(This article belongs to the Special Issue UAV in Smart City and Smart Region)
Terrestrial laser scanning (TLS) provides a detailed three-dimensional representation of surrounding forest structures. However, due to close-range hemispherical scanning geometry, the ability of TLS technique to comprehensively characterize all trees, and especially upper parts of forest canopy, is often limited. In this study, we investigated how much forest characterization capacity can be improved in managed Scots pine (Pinus sylvestris L.) stands if TLS point clouds are complemented with photogrammetric point clouds acquired from above the canopy using unmanned aerial vehicle (UAV). In this multisensorial (TLS+UAV) close-range sensing approach, the used UAV point cloud data were considered especially suitable for characterizing the vertical forest structure and improvements were obtained in estimation accuracy of tree height as well as plot-level basal-area weighted mean height (Hg) and mean stem volume (Vmean). Most notably, the root-mean-square-error (RMSE) in Hg improved from 0.8 to 0.58 m and the bias improved from −0.75 to −0.45 m with the multisensorial close-range sensing approach. However, in managed Scots pine stands, the mere TLS also captured the upper parts of the forest canopy rather well. Both approaches were capable of deriving stem number, basal area, Vmean, Hg, and basal area-weighted mean diameter with the relative RMSE less than 5.5% for all the sample plots. Although the multisensorial close-range sensing approach mainly enhanced the characterization of the forest vertical structure in single-species, single-layer forest conditions, representation of more complex forest structures may benefit more from point clouds collected with sensors of different measurement geometries. View Full-Text
Keywords: terrestrial laser scanning; unmanned aerial vehicle; image matching; remote sensing; forest inventory terrestrial laser scanning; unmanned aerial vehicle; image matching; remote sensing; forest inventory
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Yrttimaa, T.; Saarinen, N.; Kankare, V.; Viljanen, N.; Hynynen, J.; Huuskonen, S.; Holopainen, M.; Hyyppä, J.; Honkavaara, E.; Vastaranta, M. Multisensorial Close-Range Sensing Generates Benefits for Characterization of Managed Scots Pine (Pinus sylvestris L.) Stands. ISPRS Int. J. Geo-Inf. 2020, 9, 309.

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