Paul McLean
Research Scientist
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.
Abstract
Management of Scots pine (Pinus sylvestris L.) in Norway requires a forest growth and yield model suitable for describing stand dynamics of even-aged forests under contemporary climatic conditions with and without the effects of silvicultural thinning. A system of equations forming such a stand-level growth and yield model fitted to long-term experimental data is presented here. The growth and yield model consists of component equations for (i) dominant height, (ii) stem density (number of stems per hectare), (iii) total basal area, (iv) and total stem volume fitted simultaneously using seemingly unrelated regression. The component equations for stem density, basal area, and volume include a thinning modifier to forecast stand dynamics in thinned stands. It was shown that thinning significantly increased basal area and volume growth while reducing competition related mortality. No significant effect of thinning was found on dominant height. Model examination by means of various fit statistics indicated no obvious bias and improvement in prediction accuracy in comparison to existing models in general. An application of the developed stand-level model comparing different management scenarios exhibited plausible long-term behavior and we propose this is therefore suitable for national deployment.
Abstract
The British forestry sector lacks reliable dynamic growth models for stands of improved Sitka spruce, the most important commercial forest type in Great Britain. The aim of this study is to fill this gap by trialling a new modelling framework and to lay the foundations of a future dynamic growth simulator for that forest type. First, we present single tree diameter and height increment models that are climate sensitive and include explicit competition effects. The predictions from the increment models are pooled to project diameter and height at a given age. These projections are then used as inputs to an integrated taper model from which stochastic tree volume predictions are obtained. Retrospective data from over 1400 trees collected in two extensive genetic trials in Scotland and Wales were used for the purposes of this study. Diameter increment and height increment predictions were highly accurate and diameter and height projections proved consistent. The predicted volume at the time of harvesting also exhibited a high degree of accuracy, which shows the robustness of our approach. Further data will be needed in the future to recalibrate the present models and extend their range of validity to the whole of Great Britain.
Abstract
Stand-level growth and yield models are important tools that support forest managers and policymakers. We used recent data from the Norwegian National Forest Inventory to develop stand-level models, with components for dominant height, survival (number of survived trees), ingrowth (number of recruited trees), basal area, and total volume, that can predict long-term stand dynamics (i.e. 150 years) for the main species in Norway, namely Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and birch (Betula pubescens Ehrh. and Betula pendula Roth). The data used represent the structurally heterogeneous forests found throughout Norway with a wide range of ages, tree size mixtures, and management intensities. This represents an important alternative to the use of dedicated and closely monitored long-term experiments established in single species even-aged forests for the purpose of building these stand-level models. Model examination by means of various fit statistics indicated that the models were unbiased, performed well within the data range and extrapolated to biologically plausible patterns. The proposed models have great potential to form the foundation for more sophisticated models, in which the influence of other factors such as natural disturbances, stand structure including species mixtures, and management practices can be included.
Authors
Johannes Schumacher Håvard Toft Larsen Paul McLean Marius Hauglin Rasmus Astrup Johannes BreidenbachAbstract
The number of people affected by snow avalanches during recreational activities has increased over the recent years. An instrument to reduce these numbers are improved terrain classification systems. One such system is the Avalanche Terrain Exposure Scale (ATES). Forests can provide some protection from avalanches, and information on forest attributes can be incorporated into avalanche hazard models such as the automated ATES model (AutoATES). The objectives of this study were to (i) map forest stem density and canopy-cover based on National Forest Inventory and remote sensing data and, (ii) use these forest attributes as input to the AutoATES model. We predicted stem density and directly calculated canopy-cover in a 20 Mha study area in Norway. The forest attributes were mapped for 16 m × 16 m pixels, which were used as input for the AutoATES model. The uncertainties of the stem number and canopy-cover maps were 30% and 31%, respectively. The overall classification accuracy of 52 ski-touring routes in Western Norway with a total length of 282 km increased from 55% in the model without forest information to 67% when utilizing canopy cover. The F1 score for the three predicted ATES classes improved by 31%, 9%, and 6%.
Abstract
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