Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Tuomas Yrttimaa
* ORCID logo ,
Ninni Saarinen
ORCID logo ,
Ville Kankare
,
Niko Viljanen
ORCID logo ,
Jari Hynynen
,
Saija Huuskonen
,
Markus Holopainen
,
Juha Hyyppä
,
Eija Honkavaara
ORCID logo ,
Mikko Vastaranta
ORCID logo
Version 1 : Received: 25 March 2020 / Approved: 27 March 2020 / Online: 27 March 2020 (02:30:55 CET)

A peer-reviewed article of this Preprint also exists.

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. 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.

Abstract

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 all trees and especially the 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 cloud is complemented with a photogrammetric point cloud 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 was considered feasible especially in 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 0.88 m to 0.58 m and the bias improved from -0.75 m to -0.45 m with the multisensorial close-range sensing approach. However, in managed Scots pine stands the mere TLS captured also 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 a relative RMSE less than 5.5% for all of the sample plots. Although the multisensorial close-range sensing approach mainly enhanced characterization of 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.

Keywords

terrestrial laser scanning; unmanned aerial vehicle; image matching; remote sensing; forest inventory

Subject

Biology and Life Sciences, Forestry

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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