Title
Multisensorial Close-Range Sensing Generates Benefits for Characterization of Managed Scots Pine (Pinus sylvestris L.) Stands.
Abstract
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 (H-g) and mean stem volume (V-mean). Most notably, the root-mean-square-error (RMSE) in H-g 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, V-mean, H-g, 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.
Year
DOI
Venue
2020
10.3390/ijgi9050309
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
Keywords
DocType
Volume
terrestrial laser scanning,unmanned aerial vehicle,image matching,remote sensing,forest inventory
Journal
9
Issue
Citations 
PageRank 
5
0
0.34
References 
Authors
0
10
Name
Order
Citations
PageRank
Tuomas Yrttimaa100.68
ninni saarinen263.53
Ville Kankare3659.21
Niko Viljanen400.34
Jari Hynynen500.34
Saija Huuskonen600.34
Markus Holopainen735740.95
Juha Hyyppä843966.75
Eija Honkavaara922027.92
Mikko Vastaranta1029834.91