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

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Yrttimaa , T , Saarinen , N , Kankare , V , Viljanen , N , Hynynen , J , Huuskonen , S , Holopainen , M , Hyyppa , J , Honkavaara , E & Vastaranta , M 2020 , ' Multisensorial Close-Range Sensing Generates Benefits for Characterization of Managed Scots Pine (Pinus sylvestris L.) Stands ' , ISPRS International Journal of Geo-Information , vol. 9 , no. 5 , 309 . https://doi.org/10.3390/ijgi9050309

Title: Multisensorial Close-Range Sensing Generates Benefits for Characterization of Managed Scots Pine (Pinus sylvestris L.) Stands
Author: Yrttimaa, Tuomas; Saarinen, Ninni; Kankare, Ville; Viljanen, Niko; Hynynen, Jari; Huuskonen, Saija; Holopainen, Markus; Hyyppa, Juha; Honkavaara, Eija; Vastaranta, Mikko
Contributor organization: Laboratory of Forest Resources Management and Geo-information Science
Department of Forest Sciences
Forest Health Group
Forest Ecology and Management
Date: 2020-05
Language: eng
Number of pages: 14
Belongs to series: ISPRS International Journal of Geo-Information
ISSN: 2220-9964
DOI: https://doi.org/10.3390/ijgi9050309
URI: http://hdl.handle.net/10138/317817
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.
Subject: terrestrial laser scanning
unmanned aerial vehicle
image matching
remote sensing
forest inventory
INDIVIDUAL TREE DETECTION
POINT CLOUDS
FOREST INVENTORY
LASER
BIOMASS
PHOTOGRAMMETRY
DENSITY
LIDAR
UAV
IDENTIFICATION
4112 Forestry
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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