The estimation of forest attributes representing aspects of structural biodiversity from vast areas is difficult not only because of the lack of agreement on what constitutes a structurally diverse forest, but also due to difficulties in the detection of small trees. However, a structurally diverse forest should have large variation in tree size (i.e., height and diameter), as well as some clustering of trees, as regular tree pattern is typical for managed forests. In this work we developed a framework for building structurally representative tree maps using airborne laser scanning data and a limited number of ground measurements using data from two locations in Finland. In the proposed method, an individual tree detection algorithm is optimized so that the number of undetected trees and false detections is minimized. The ground measurements are then used to train models for predicting the number and location of undetected trees and false detections by resampling the attributes of undetected trees in the training data. This model can then be applied to other areas in the proximity of the ground measurements to build tree maps, with the location, height, and diameter for each tree. The methodology was shown to reproduce the number of trees, the height distribution, and the spatial pattern of the trees with sufficient accuracy for practical use in large-scale mapping of forest attributes. For example, the individual tree detection could find at best about 54% of the trees with an additional 7% of false detections, resulting in a bias of approximately −30%, while with the new method the bias was reduced to ±10% of the stem density. Further research is needed on how to account for tree species when building tree maps. 

Recreating structurally realistic tree maps with airborne laser scanning and ground measurements

To investigate the effects of stand conditions and climatic factors on branch growth and the dynamics of branch growth over time, we constructed a mixed-effects model on branch annual length growth using data from 803 branches of 70 sample trees in Korean pine (Pinus koraiensis Sieb. et al. Zucc.) plantations in Heilongjiang Province, China. The results showed that the introduction of branch age, branch depth into crown, branch diameter with bark, height-diameter ratio of the tree, stand average diameter at breast height, summer mean temperature and summer precipitation significantly improve the model fitting effect. The increase in summer mean temperature promoted branch growth, while the increase in the branch depth into the crown, height-diameter ratio of the tree, stand average diameter at breast height, and summer precipitation negatively impacted branch growth. We found that in the second year, the annual length growth reached a maximum, decreased with increasing branch age and stabilized when the branch age was greater than 20. Younger branches responded more strongly to competitive changes. The influence of summer precipitation on branch growth was greater than that of summer mean temperature. Moreover we found that within the appropriate range, climatic factors will not have a significant impact on branch growth until the summer mean temperature is lower than 16 °C and the summer precipitation exceeds 400 mm, which will have a significant inhibitory effect on branch growth. The introduction of random effects significantly improved the predictive ability of the model, and it was reasonable to select 6 branch annual length growths per branch to estimate random parameters. Our research indicated that branch growth is influenced by the combined effects of climate and competition, which helps to make reasonable forest management decisions under global climate change. 

Modelling branch growth of Korean pine plantations based on stand conditions and climatic factors

Continuous cover forestry (CCF) is forest management based on ecological principles and this management type is currently re-visited in many countries. CCF woodlands are known for their structural diversity in terms of tree size and species and forest planning in CCF needs to make room for multiple forest development pathways as opposed to only one management target. As forest management diversifies and management types such as CCF become more common, models used for projecting forest development need to have a generic and flexible bottom-up design. They also need to be able to handle uncertainty to a larger extent and more comprehensively than is necessary with single, traditional forest management types. In this study, a spatial tree model was designed for analyzing a data set involving 18 plots from CCF stands in Southern Finland. The tree model has specific ingrowth, growth and mortality model components, each including a spatially explicit competition effect involving neighboring trees. Approximations were presented that allow inference of the model components operating in annual steps based on time-series measurements from several years. We employed Bayesian methodology and posterior predictive distributions to simulate forest development for short- and long-term projections. The Bayesian approach allowed us to incorporate uncertainties related to model parameters in the projections, and we analyzed these uncertainties based on three scenarios: (1) known plot and tree level random effects, (2) known plot level random effects but unknown tree level random effects, and (3) unknown random effects. Our simulations revealed that uncertainties related to plot effects can be rather high, particularly when accumulated across many years whilst the length of the simulation step only had a minor effect. As the plot and tree effects are not known when tree models are applied in practice, in such cases, it may be possible to significantly improve model projections for a single plot by taking one-off individual-tree growth measurements from the plot and using them for calibrating the model. Random plot effects as used in our tree model are also a way of describing environmental conditions in CCF stands where other traditional descriptors based on stand height and stand age fail to be suitable any more. 

A Bayesian approach to projecting forest dynamics and related uncertainty: An application to continuous cover forests

Pre-commercial thinning in Alberta is a potential option for increasing the growth rate and shortening the rotation age of regenerating forests. Previous studies have focused on the evaluation of either the immediate- or long-term response to thinning after many decades. Our current study compares the pre-commercial thinning response in lodgepole pine for a 10-year period immediately after thinning, and again 38-45 years after thinning, based on 22 paired plots of precommercially thinned trials in Alberta. The first group of plots was pre-commercially thinned in 1984, measured in 1985 and 1995 (short-term) and the second group was pre-commercially thinned between 1962 and 1969 and measured in 2007 and 2017 (long-term). In the short-term, individual tree DBH growth was 56% greater in pre-commercially thinned plots, while in the long-term plots, there were no measured growth differences between pre-commercially thinned and unthinned plots. Small- and medium-sized trees benefited more from pre-commercial thinning than larger trees. However, at the stand level, the number and volume of merchantable-sized trees (≥13.5 cm DBH) were higher in pre-commercially thinned plots than in unthinned plots in both the short- and long-term. Although the growth response of thinning appeared to be a short-term response (number and volume of larger trees), yield at the end of the long-term measurement period was still higher in pre-commercially thinned than in unthinned plots.

Growth response to pre-commercial thinning of lodgepole pine is short-term but the effects on size distribution persist for decades

The prediction of tree biomass and stem volume is necessary for monitoring and assessing of national forest biomass, carbon stock and sustainable forest management. Such predictions of biomass or carbon stocks are generated by using either allometric models or applying biomass expansion factors (BEFs), where the former is a better method. Volume models are generated by using allometric models. The data for development of the new volume and biomass models for Iceland presented in this paper were collected between years 2000-2021, from 48 locations in even aged stands that were planted between 1942-1983. Because of the young age of forest plantations in Iceland, existing biomass and volume models need to be updated regularly as trees get older and larger. The new models use the same independent variables as the previous ones for Iceland but use a wider approach and have a different form than the older ones. Using the previous Icelandic models outside their data range results in underestimation for both aboveground biomass and stem volume.

Stem volume and above ground biomass models for Sitka spruce (Picea sitchensis (Bong.) Carr.) in Iceland

Abstract The volume models that have been used in Finland for the last 40 years, while generally well thought-out, exhibit an illogical behaviour for small trees. In recent studies, tree stem form was observed to have changed in time and also involve spatial variation attributable to environmental factors. It is yet unclear how the stem taper has actually changed. To overcome these problems, we fitted a completely new set of volume and taper curve models and examined whether this change is attributable to the changes in management and environmental factors rather than to measurement errors in the previously used datasets. For the latter, we added a dataset into the analysis, which was smaller but of higher quality due to the destructive nature of the stem taper measurements. We aim at (1) developing a new non-linear variable form factor volume function that works with trees of all sizes, (2) improving the description of the variation of the stem form in time and space by including temperature sum and soil type as predictors, (3) understanding the changes in the stem form by fitting new taper curve models and (4) improving the statistical properties of the predictions by using mixed model techniques and by addressing the effect of parameter uncertainty. To assess the impact of renewing the models, we (5) predicted the mean volume and its confidence interval with each model for forest inventory data at country level. The results show that the tree stem form has a spatial trend that can be described with the temperature sum. Moreover, the changes in stem form also have a spatial trend, with largest changes in Lapland. The difference is mostly observable in the lowest part of the stem, and it is especially large in the largest pines. We conclude that environmental variables can help to improve national stem taper functions in countries with pronounced environmental gradients. 

Mixed linear and non-linear tree volume models with regional parameters to main tree species in Finland

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Mixed linear and non-linear tree volume models with regional parameters to main tree species in Finland