Paul McLean
Research Scientist
Authors
Seija Tuulentie Therese Bjärstig Inger Hansen Unni Støbet Lande Paul McLean Jani Pellikka Rainer Peltola Jasmine ZhangAbstract
• Sustainable forest management approaches, regardless of whether they involve continuous cover forestry (CCF) or rotation forestry (RF), require a holistic landscape perspective that acknowledges the multiple interests, values, and uses that depend on the locally relevant economic, ecological, and socio-cultural circumstances. These must be considered alongside the use of forests and forest landscapes as a resource for rural development. • Forests provide a wide range of goods and services. Those addressed here (i.e. tourism, recreation, health, grazing, non-timber forest products, and societal protection from natural hazards) are a subset of all of those potential services that are already considered to be of special signifcance for the Nordic region. • Most recreational users consider variation in the forest landscape and longdistance views as visually attractive but think that clearcuttings and soil tilling are harmful. • In general, CCF favours bilberries, while lingonberries and some mushrooms benefit from even-aged forestry. • Owing to the many and varied demands relating to forests and forest landscapes in Norway, Sweden, and Finland, CCF-supported multiple-use strategies and planning will need to consider stakeholder requirements more, now and in the future, than is currently the case.
Abstract
Key message This study compares the measured radial variation in wood stiffness, strength, and density of noble fir, Norway spruce, western hemlock, and western red cedar by developing mixed-effects models for each property using age as the explanatory variable. These models could be used to simulate the effect of rotation length and species choice on sawn wood properties. Context Timber production in Great Britain relies primarily on Sitka spruce. The use of multiple species is desirable to mitigate against biotic and abiotic risks posed to a single species. When considering alternative species, quantifying and modeling radial variation in wood properties is important to determine the potential for sawn timber production at a given rotation length. Aims To build empirical models for the radial variation in wood properties that can account for species. Methods Clear-wood samples were produced along radial transects in trees from four conifer species: Abies procera Rehder, Picea abies (L.) Karst, Tsuga heterophylla (Raf.) Sarg., Thuja plicata Donn. ex D.Don. Modulus of Elasticity, Modulus of Rupture, and density were measured on each species according to established standards. Mixed-effects models were built using ring numbers from the pith and species as explanatory variables. Results The same model forms could be used across the four species. Nonlinear models were developed for the Modulus of Elasticity and density. For the Modulus of Rupture, a linear model was most appropriate. The effect of species in the models was significant. Conclusion At similar rotation lengths, noble fir, Norway spruce, and western hemlock can produce timber with comparable properties to Sitka spruce. Overall, western red cedar would have worse properties for structural use. Keywords MOE, MOR, Radial variation, Tree growth, Alternative species
Abstract
Information on tree height-growth dynamics is essential for optimizing forest management and wood procurement. Although methods to derive information on height-growth information from multi-temporal laser scanning data already exist, there is no method to derive such information from data acquired at a single point in time. Drone laser scanning data (unmanned aerial vehicles, UAV-LS) allows for the efficient collection of very dense point clouds, creating new opportunities to measure tree and branch architecture. In this study, we examine if it is possible to measure the vertical positions of branch whorls, which correspond to nodes, and thus can in turn be used to trace the height growth of individual trees. We propose a method to measure the vertical positions of whorls based on a single-acquisition of UAV-LS data coupled with deep-learning techniques. First, single-tree point clouds were converted into 2D image projections, and a YOLOv5 (you-only-look-once) convolutional neural network was trained to detect whorls based on a sample of manually annotated images. Second, the trained whorl detector was applied to a set of 39 trees that were destructively sampled after the UAV-LS data acquisition. The detected whorls were then used to estimate tree-, plot- and stand-level height-growth trajectories. The results indicated that 70 per cent (i.e. precision) of the measured whorls were correctly detected and that 63 per cent (i.e. recall) of the detected whorls were true whorls. These results translated into an overall root-mean-squared error and Bias of 8 and −5 cm for the estimated mean annual height increment. The method’s performance was consistent throughout the height of the trees and independent of tree size. As a use case, we demonstrate the possibility of developing a height-age curve, such as those that could be used for forecasting site productivity. Overall, this study provides proof of concept for new methods to analyse dense aerial point clouds based on image-based deep-learning techniques and demonstrates the potential for deriving useful analytics for forest management purposes at operationally-relevant spatial-scales.
Division of Forest and Forest Resources
A Decision Support System for emerging forest management alternatives
This project aims to develop advanced tree growth models using LiDAR-derived, high-density point cloud data to improve the simulation of forest dynamics under close-to-nature silvicultural practices. By modeling tree-level growth in structurally complex and heterogeneous stands, these models will support more accurate, spatially explicit forest simulations and inform sustainable and diversified forest management decisions.
Division of Environment and Natural Resources
Precilience: Precision climate resilience for agriculture and forestry sectors in the European boreal regions
Precilience will develop precision solutions with farmers, foresters, landowners, and other actors to increase climate resilience in the Nordic-Baltic regions of Denmark, Estonia, Finland, Norway and Sweden.
Division of Forest and Forest Resources
SFI SmartForest: Bringing Industry 4.0 to the Norwegian forest sector
SmartForest will position the Norwegian forest sector at the forefront of digitalization resulting in large efficiency gains in the forest sector, increased production, reduced environmental impacts, and significant climate benefits. SmartForest will result in a series of innovations and be the catalyst for an internationally competitive forest-tech sector in Norway. The fundamental components for achieving this are in place; a unified and committed forest sector, a leading R&D environment, and a series of progressive data and technology companies.