Heikki Korpunen
Research Scientist
Biography
I am a forest engineering researcher with a particular emphasis on wood technology. My work involves traditional forest technology issues, such as studies on forest work time and productivity, research on wood value chains and tree bucking optimization, and studies on forest bioenergy logistics. I have also focused on forest industry production and cost modeling, as well as wood property analyses.
Education: Doctor of science in forestry, major: wood technology at the University of Helsinki, Finland (2015). Master of science in forestry, major: forest engineering and wood technology at the University of Joensuu, Finland (2006).
Abstract
Uneven-aged forests set certain challenges for cut-to-length harvesting work. It is a challenge to cost-effectively remove larger trees while leaving a healthy understory for regrowth. The study’s aim was to evaluate productivity and costs of harvesting two-storied silver birch (Betula pendula Roth) and Norway spruce (Picea abies (L.) H. Karst.) stands by creating time consumption models for cutting, and using existing models for forwarding. Damage to the remaining understory spruce was also examined. Four different harvesting methods were used: 1) all dominant birches were cut; 2) half of them thinned and understory was preserved; compared to 3) normal thinning of birch stand without understory; and 4) clear cutting of two-storied stand. Results showed the time needed for birch cutting as 26–30% lower when the understory was not preserved. Pulpwood harvesting of small sized spruces that prevent birch cutting was expensive, especially because of forwarding of small amounts with low timber density on the strip roads. Generally, when taking the cutting and forwarding into account, the unit cost at clear cuttings was lowest, due to lesser limitations on work. It was noted that with increasing removal from 100 to 300 m3 ha–1, the relative share of initial undamaged spruces after the harvest decreased from 65 to 50% when the aim was to preserve them. During summertime harvesting, the amount of stem damage was bigger than during winter. In conclusion, two-storied stands are possible to transit to spruce stands by accepting some losses in harvesting productivity and damages on remaining trees.
Abstract
No abstract has been registered
Abstract
Butt rot is a main defect in Norway spruce (Picea abies (L.) Karst.) trees and causes large economic losses for forest owners. However, little empirical research has been done on the effects of butt rot on harvested roundwood and the magnitude of the resulting economic losses. The main objective of this study was to characterize the direct economic losses caused by butt rot in Norway spruce trees for Norwegian forest owners. We used data obtained from seven cut-to-length harvesters, comprising ∼400,000 trees (∼140,000 m3) with corresponding stem profiles and wood grade information. We quantified the economic losses due to butt rot using bucking simulations, for which in a first case, defects caused by butt rot were included, and in a second case, all trees were assumed to be free of butt rot. 16% of trees were affected by butt rot, whereby butt rot tended to occur in larger trees. When butt rot was present in a tree, the saw log volume was reduced by 48%. Proportions of roundwood volume affected by butt rot varied considerably across harvested stands. Our results suggest that butt rot causes economic losses upwards of 7% of wood revenues, corresponding to € 18.5 million annually in Norway.
Abstract
The identification of individual tree logs along the wood procurement chain is a coveted goal within the forest industry. The tracing of logs from the sawmill back to the forest would support the legal and sustainable sourcing of wood, as well as increase the resource efficiency and value of harvested timber. In this work, using a dataset of thousands of Scots pine (Pinus sylvestris L.) log end images displaying varying perspectives, lighting, and aging effects, we develop and assess log identification methods based on deep convolutional neural networks. The estimated rank-1 accuracy of our final model on an independent test set of 99 logs is 84 and 91% when allowing for random rotations of the log ends and when keeping each log at approximately fixed orientation, respectively. We estimate the scaling of these methods up to a template pool size of 493 logs, which reveals a weak dependence of accuracy on pool size for logs at fixed orientation. The deep learning approach gives superior results to a classical local binary pattern method, and appears feasible in practice, assuming that pre-filtering of the log database can be leveraged depending on the use case and properties of the queried log image. We make our dataset publicly available.