Project

PRECISION

Goal: The fundamental idea behind PRECISION is that the information collected by harvesters, combined with remotely sensed and existing data, can form the backbone in developing a precision forestry framework aimed at reducing losses to root and butt rot (RBR) in the short- and long-term. The project focuses on improving the foundation for site-specific management related to two of the most fundamental decisions in silviculture and forest management; (a) site-specific optimal harvest age given risk of RBR, and (b) the regeneration strategy for harvested stands with RBR infection.

Date: 1 January 2018 - 31 December 2021

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Project log

Annika Susanna Kangas
added a research item
• Key message Root and rot (RBR) caused by Heterobasidion parviporum Niemelä & Korhonen and Heterobasidion annosum (Fr.) Bref. damages Fennoscandian spruce stands. In case the rot infection and its severity are unknown, the mere risk of infection should seldom affect the harvest timing. When it does, the gains by harvesting earlier are minimal. • Context It has been suggested that stands infected by RBR should be harvested earlier than the healthy ones. Yet, we must decide on harvest timing decisions without reliable information on the infection. • Aims We studied if harvesting earlier pays off under RBR uncertainty. • Methods We structured the uncertainty with a decision tree and calculated the optimal rotations based on expected net present values. We compared rotation lengths to those of healthy stands and calculated gains from earlier harvesting. • Results The inclusion of RBR-related uncertainty in the model changed the rotation length of only 14–23% of the stands. The average reduction was 1.3–4.7 years. Yet, the gain from harvesting earlier was too low to be considered. • Conclusion In the absence of information on the extent and severity of RBR, it seldom pays off to advance harvests. The value growth in healthy trees tends to compensate for the value reduction due to rot.
Kjersti Holt Hanssen
added a research item
Based on data from long-term experimental fields with Norway spruce ( (L.) H. Karst.), we developed new stem taper and bark functions for Norway. Data was collected from 477 trees in stands across Norway. Three candidate functions which have shown good performance in previous studies (Kozak 02, Kozak 97 and Bi) were fitted to the data as fixed-effects models. The function with the smallest Akaike Information Criterion (AIC) was then chosen for additional analyses, fitting 1) site index-dependent and 2) age-dependent versions of the model, and 3) fitting a mixed-effects model with tree-specific random parameters. Kozak 97 was found to be the function with the smallest AIC, but all three tested taper functions resulted in fairly similar predictions of stem taper. The site index-dependent function reduced AIC and residual standard error and showed that the effect of site index on stem taper is different in small and large trees. The predictions of the age-independent and age-dependent models were very close to each other. Adding tree-specific random parameters to the model clearly reduced AIC and residual variation. However, the results suggest that the mixed-effects model should be used only when it is possible to calibrate it for each tree, otherwise the fixed-effects Kozak 97 model should be used. A model for double bark thickness was also fitted as fixed-effects Kozak 97 model. The model behaved logically, predicting larger relative but smaller absolute bark thickness for small trees. Picea abies
Tyrone Nowell
added a research item
Root and Butt-Rot (RBR) is having a significant economic impact on the forest industry and is expected to increase with climate change. The current management strategies are becoming less effective, and little data on RBR distribution is available to develop new ones. In Europe, approximately half of the timber production is using Cut-To-Length timber harvesters which store a considerable amount of data on each tree. Being able to supplement this data with the presence and quantity of RBR in the tree would add significant value to both the forest industry and to the scientific community in developing new strategies for RBR management. This Master’s thesis explored the feasibility of embedding a computer vision system on the harvester for autonomous rot detection and quantification using state of the art Convolutional Neural Networks (CNNs). Among the potential applications of this system, this study assessed the possibilities to (1) provide real time feedback of this information to the harvester operator for faster, more accurate categorisation of the timber quality and (2) enable the collection of big data on RBR distribution for high spatial resolution mapping for the development of new management strategies. The model developed to detect RBR achieved an F1 score of 97.1% accuracy (precision of 95.2% and recall of 99.0%) which is a significant improvement over previous techniques with an F1 score of 90.8% accuracy (precision of 90.8% and recall of 90.8%). Prediction of the RBR quantity as a percentage of the surface area attained an RMSE of 6.88%, and was reduced to 6.17% when aggregated with the RBR detector. Evaluating the misclassifications of the detection system indicated that the model performance is at least on par with that of the author. These results indicate that there is significant potential in developing this technology further for both economic and environmental gains.
Bruce Talbot
added a research item
Root and butt-rot (RBR) has a significant impact on both the material and economic outcome of timber harvesting, and therewith on the individual forest owner and collectively on the forest and wood processing industries. An accurate recording of the presence of RBR during timber harvesting would enable a mapping of the location and extent of the problem, providing a basis for evaluating spread in a climate anticipated to enhance pathogenic growth in the future. Therefore, a system to automatically identify and detect the presence of RBR would constitute an important contribution to addressing the problem without increasing workload complexity for the machine operator. In this study, we developed and evaluated an approach based on RGB images to automatically detect tree stumps and classify them as to the absence or presence of rot. Furthermore, since knowledge of the extent of RBR is valuable in categorizing logs, we also classify stumps into three classes of infestation; rot = 0%, 0% < rot < 50% and rot ≥ 50%. In this work we used deep-learning approaches and conventional machine-learning algorithms for detection and classification tasks. The results showed that tree stumps were detected with precision rate of 95% and recall of 80%. Using only the correct output (TP) of the stump detector, stumps without and with RBR were correctly classified with accuracy of 83.5% and 77.5%, respectively. Classifying rot into three classes resulted in 79.4%, 72.4%, and 74.1% accuracy for stumps with rot = 0%, 0% < rot < 50%, and rot ≥ 50%, respectively. With some modifications, the developed algorithm could be used either during the harvesting operation to detect RBR regions on the tree stumps or as an RBR detector for post-harvest assessment of tree stumps and logs.
Rasmus Astrup
added a research item
Unmanned aerial vehicles (UAVs) are increasingly used as tools to perform a detailed assessment of post-harvest sites. One of the potential use of UAV photogrammetric data is to obtain tree-stump information that can then be used to support more precise decisions. This study developed and tested a methodology to automatically detect, segment, classify, and measure tree-stumps. Among the potential applications for single stump data, this study assessed the possibility (1) to detect and map root- and butt-rot on the stumps using a machine learning approach, and (2) directly measure or model tree stump diameter from the UAV data. The results revealed that the tree-stumps were detected with an overall accuracy of 68–80%, and once the stump was detected, the presence of root- and butt-rot was detected with an accuracy of 82.1%. Furthermore, the root mean square error of the UAV-derived measurements or model predictions for the stump diameter was 7.5 cm and 6.4 cm, respectively, and with the former systematically under predicting the diameter by 3.3 cm. The results of this study are promising and can lead to the development of more cost-effective and comprehensive UAV post-harvest surveys.
Rasmus Astrup
added a project goal
The fundamental idea behind PRECISION is that the information collected by harvesters, combined with remotely sensed and existing data, can form the backbone in developing a precision forestry framework aimed at reducing losses to root and butt rot (RBR) in the short- and long-term. The project focuses on improving the foundation for site-specific management related to two of the most fundamental decisions in silviculture and forest management; (a) site-specific optimal harvest age given risk of RBR, and (b) the regeneration strategy for harvested stands with RBR infection.