Log end face image and stem tapering indicate maximum bow height on Norway spruce bottom logs

Raatevaara, Antti; Korpunen, Heikki; Mäkinen, Harri; Uusitalo, Jori

doi:10.1007/s10342-020-01309-0

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…The DiscBot system developed by Scion was designed to measure wood property variation both radially and in circumference (Schimleck et al 2019). Raatevaara et al (2020) used RGB images of the end faces of Norway spruce (Picea abies) logs to extract out-of-roundness and pith eccentricity data which they combined with stem taper data to predict maximum bow height.…”

Section: Introductionmentioning

confidence: 99%

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Image Analysis To Assess Wood Variability In Longleaf Pine Cross-Sectional Disks

Raut,

Dahlen

2023

WFS

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Image analysis is an important method for rapidly measuring wood property variation, but it is infrequently applied to disks collected from forestry studies. The objective of this study was to compare image-estimated wood and bark volumes and diameters to reference measurements, and to extract more information from the images including the shape (out-of-round index, eccentric pith) and the amount and location of severe compression wood. A total of 1120 disks were cut from multiple height levels of 48 defect-free and 56 defect-containing (forking, excessive sweep, and ramicorn branching) longleaf pine (Pinus palustris) trees from 16 stands across Georgia (U.S.). Disks were machined on one transverse surface using a computer numeric controlled router to prepare a clean surface for imaging. Three images, one under white light, the second under blue light, and the third under blue light with a green longpass filter, were taken for each disk. Volumes and diameters estimated from images were in close agreement with reference methods. Linear models fitted as measured vs image volumes for wood and bark had coefficient of determination (R 2 ) values of .0.99 and 0.96. Linear models fitted as measured vs image diameters had R 2 values of .0.99. Out-of-round index and pith eccentricity values calculated from images showed a moderate positive correlation (R 5 0.43). Algorithms developed were able to correctly identify severe compression wood, but not mild-to-moderate compression wood. Severe compression wood was moderately correlated to out-of-round index (R 5 0.54) and pith eccentricity (R 5 0.48). More than 98% of the disks having severe compression wood came from defect-containing trees.

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Section: Introductionmentioning

confidence: 99%

“…learning with neural networks that use various convolutions to provide a hierarchical representation of data could provide more accurate estimates than existing, conventional methods(Raatevaara et al 2020;Hwang and Sugiyama 2021). However, one drawback of deep learning methods is the need for large datasets when training the models(Hwang and Sugiyama 2021).…”

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confidence: 99%

Image Analysis To Assess Wood Variability In Longleaf Pine Cross-Sectional Disks

Raut,

Dahlen

2023

WFS

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“…Other uses of the images is to implement a fingerprinting approach for log traceability and authentications (Schraml et al 2020). The scanning with cameras can be performed on the log crosscut end (Raatevaara et al 2020) (Fig. 9.10i) or on the side of the log (Shenga et al 2015) along its circumference (Fig.…”

Section: Sensors For Timber Quality Assessmentmentioning

confidence: 99%

Climate-Smart Forestry in Mountain Regions

Climate Change and Law Collection

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This book is dedicated to the memory of Professor Giustino Tonon: our friend, colleague, and a co-author of many of these chapters. Giustino conveyed great passion and commitment for forests and conservation; it showed in his words, his actions, and the intensity of his listening. Yet his calm and steadfast manner allowed him to work with everyone. His work brought a rigorous scientific approach to understanding the challenges faced by humans and nature in a time of rapid change. He was unfailingly caring and considerate of others, and his positive outlook on life invariably lifted the mood of those in his company. When he passed away on 7 July 2021, he was in a place he loved, the mountain forests of the Dolomites.vii PrefaceThis book is one of the outputs of the COST Action CA15226, Climate-Smart Forestry in Mountain Regions (CLIMO). Funded by the EU's Horizon 2020 COST Action programme, CLIMO has been developing in the last few years as a new concept, which is central to the changes in the way forestry resources are used by the European community. While climate change is increasingly filling the policy agenda at global level, mountain regions are extremely vulnerable to its effects. Because climate change increases the frequency and intensity of ecosystem imbalances, the economic value and adaptive capacity of these regions is jeopardised, which has led to a call for changes in forestry polices and management.Initiated in October of 2016 and finished in April of 2021, CLIMO addressed the complex issue of forest management, which plans for the long term, while dealing with uncertainties related to the productivity and health of forest ecosystems, and their adaptation to short-term environmental changes. The establishment of this network, which mobilized more than two hundred researchers from 28 countries, focused on mountain environments, considered as a climate change hotspot. Given the growing pressure on mountain regions by climate change, there is a need to emphasise forest production systems that are resilient to climate-driven disturbances. These climate targets can be mainstreamed through multidisciplinary Climate-Smart Forestry, paying attention to regional circumstances, opportunities and challenges. This multidisciplinary approach, from tree to landscape and with a variety of tools, was made possible through networking and stakeholders' engagement.A three-dimensional approach was presented to enhance adaptation and resilience to climate change within forest ecosystems, optimising the provision of ecosystem services. A new definition of Climate-Smart Forestry advocated by this COST Action, supported by the development of indicators, allowed a balanced understanding of adaptation and mitigation potentials of mountain forests facing climate change. CLIMO also contributed to the debate concerning the resilience of forest and the provision of ecosystem services. This way, the seeds of progress in forest practices were sown, which hopefully will germinate into results that will allow for a more sustainable ...

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Smart Harvest Operations and Timber Processing for Improved Forest Management

Picchi

Sandak²,

Grigolato

et al. 2021

Managing Forest Ecosystems

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Climate-smart forestry can be regarded as the evolution of traditional silviculture. As such, it must rely on smart harvesting equipment and techniques for a reliable and effective application. The introduction of sensors and digital information technologies in forest inventories, operation planning, and work execution enables the achievement of the desired results and provides a range of additional opportunities and data. The latter may help to better understand the results of management options on forest health, timber quality, and many other applications. The introduction of intelligent forest machines may multiply the beneficial effect of digital data gathered for forest monitoring and management, resulting in forest harvesting operations being more sustainable in terms of costs and environment. The interaction can be pushed even further by including the timber processing industry, which assesses physical and chemical characteristics of wood with sensors to optimize the transformation process. With the support of an item-level traceability system, the same data could provide a formidable contribution to CSF. The “memory” of wood could support scientists to understand the response of trees to climate-induced stresses and to design accordingly an adaptive silviculture, contributing to forest resilience in the face of future changes due to human-induced climate alteration.

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Log end face image and stem tapering indicate maximum bow height on Norway spruce bottom logs

Cited by 5 publications

References 30 publications

Image Analysis To Assess Wood Variability In Longleaf Pine Cross-Sectional Disks

Image Analysis To Assess Wood Variability In Longleaf Pine Cross-Sectional Disks

Climate-Smart Forestry in Mountain Regions

Smart Harvest Operations and Timber Processing for Improved Forest Management

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