Variability of wood properties using airborne and terrestrial laser scanning

: Pyörälä, Jiri; Saarinen, Ninni; Kankare, Ville; Coops, Nicholas C.; Liang, Xinlian; Wang, Yunsheng; Holopainen, Markus; Hyyppä, Juha; Vastaranta, Mikko

Publisher: ELSEVIER SCIENCE INC

: NEW YORK

: 2019

: Remote Sensing of Environment

: REMOTE SENSING OF ENVIRONMENT

: REMOTE SENS ENVIRON

: 111474

: 235

: 14

: 0034-4257

: 1879-0704

DOI: https://doi.org/10.1016/j.rse.2019.111474

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 R-2 = 0.25, maximum branch diameter R-2 = 0.03) but were suited to predicting stem and crown dimensions from stand, tree, and competition factors (diameter at breast height and sawlog volume R 2 = 0.5, and live crown base height R-2 = 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.