Variability of wood properties using airborne and terrestrial laser scanning

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http://hdl.handle.net/10138/309060

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Pyörälä , J , Saarinen , N , Kankare , V , Coops , N C , Liang , X , Wang , Y , Holopainen , M , Hyyppä , J & Vastaranta , M 2019 , ' Variability of wood properties using airborne and terrestrial laser scanning ' , Remote Sensing of Environment , vol. 235 , 111474 . https://doi.org/10.1016/j.rse.2019.111474

Title: Variability of wood properties using airborne and terrestrial laser scanning
Author: Pyörälä, Jiri; Saarinen, Ninni; Kankare, Ville; Coops, Nicholas C.; Liang, Xinlian; Wang, Yunsheng; Holopainen, Markus; Hyyppä, Juha; Vastaranta, Mikko
Contributor: University of Helsinki, Laboratory of Forest Resources Management and Geo-information Science
University of Helsinki, Forest Health Group
University of Helsinki, Forest Health Group
University of Helsinki, Forest Health Group
University of Helsinki, Finnish Geospatial Research Institute FGI
Date: 2019-12-15
Language: eng
Number of pages: 14
Belongs to series: Remote Sensing of Environment
ISSN: 0034-4257
URI: http://hdl.handle.net/10138/309060
Abstract: Information on wood properties is crucial in estimating wood quality and forest biomass and thus developing the precision and sustainability of forest management and use. However, wood properties are highly variable between and within trees due to the complexity of wood formation. Therefore, tree-specific field references and spatially transferable models are required to capture the variability of wood quality and forest biomass at multiple scales, entailing high-resolution terrestrial and aerial remote sensing methods. Here, we aimed at identifying select tree traits that indicate wood properties (i.e. wood quality indicators) with a combination of terrestrial laser scanning (TLS) and airborne laser scanning (ALS) in an examination of 27 even-aged, managed Scots pine (Pinus sylvestris L.) stands in southern Finland. We derived the wood quality indicators from tree models sampled systematically from TLS data and built prediction models with respect to individual crown features delineated from ALS data. The models were incapable of predicting explicit branching parameters (height of the lowest dead branch R2 = 0.25, maximum branch diameter R2 = 0.03) but were suited to predicting stem and crown dimensions from stand, tree, and competition factors (diameter at breast height and sawlog volume R2 = 0.5, and live crown base height R2 = 0.4). We were able to identify the effect of canopy closure on crown longevity and stem growth, which are pivotal to the variability of several wood properties in managed forests. We discussed how the fusions of high-resolution remote sensing methods may be used to enhance sustainable management and use of natural resources in the changing environment.
Subject: 4112 Forestry
Lidar
Data fusion
Precision forestry
Scots pine
SCOTS PINE
FIBER ATTRIBUTES
MECHANICAL-PROPERTIES
BRANCH DIAMETER
LODGEPOLE PINE
TIMBER QUALITY
RADIAL GROWTH
NORWAY SPRUCE
DOUGLAS-FIR
TREE HEIGHT
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