Non-destructive evaluation of wood stiffness and fiber coarseness, derived from SilviScan data, via near infrared hyperspectral imaging

Ma, Tongmei; Inagaki, Toru; Tsuchikawa, Satoru

doi:10.1177/0967033518808053

Cited by 14 publications

(6 citation statements)

References 31 publications

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“…Although applying NIR spectroscopy to detect changes within a sample is not a new concept, the application to rock samples is innovative and timely. In fact NIR has been used as a non-destructive method to evaluate mechanical properties in wood, 30 flax fibres 31 and rubber. 32 It has also been used to evaluate the change in shape of rubber latex during swelling.…”

Section: Introduction: Principles and Methodology Of Strain Measurementsmentioning

confidence: 99%

Near infrared spectroscopic measurement of strain in rocks

Butcher¹,

Gibson²,

Benson³

et al. 2019

Journal of Near Infrared Spectroscopy

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The measurement of strain is a fundamental and widely studied parameter in engineering, rock mechanics, construction and materials testing. Contact sensors often used in these fields require contact with the target surface throughout the duration of a strain event. Non-contact methods typically require that that the measurement surface is prepared and often coated prior to testing. This paper considers the potential application of near infrared spectroscopy as a non-contact technique for the measurement of strain on natural surfaces. Excellent correlation was found between surface measurements of visible-NIR spectra and longitudinal strain taken during indirect Brazilian Disc Test for samples of sandstone, marble and basalt.

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Section: Introduction: Principles and Methodology Of Strain Measurementsmentioning

confidence: 99%

Near infrared spectroscopic measurement of strain in rocks

Butcher¹,

Gibson²,

Benson³

et al. 2019

Journal of Near Infrared Spectroscopy

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show abstract

“…Examples of this application are provided by Stängle et al (2014), who compared Terrestrial Laser Scanning (TLS) with X-ray computed tomography (CT) for stem and branch scars analysis, Uner et al (2009), who used X-ray CT to highlight the effects of thinning on timber density, and Ma et al (2018), who applied NIR spectroscopy to assess wood stiffness and fibre coarseness of boards. X-ray CT is also a powerful imaging procedure for measuring density distributions and water content in the xylem with high spatial resolution, representing a link with xylem physiology and wood anatomy (Tognetti et al 1996;Fromm et al 2001).…”

Section: Timber Production and Transformationmentioning

confidence: 99%

A new generation of sensors and monitoring tools to support climate-smart forestry practices

et al. 2021

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Climate-smart forestry (CSF) is an emerging branch of sustainable adaptive forest management aimed at enhancing the potential of forests to adapt to and mitigate climate change. It relies on much higher data requirements than traditional forestry. These data requirements can be met by new devices that support continuous, in-situ monitoring of forest conditions in real time. We propose a comprehensive network of sensors, i.e. a wireless sensor network (WSN), that can be part of a world-wide network of interconnected uniquely addressable objects, an Internet of Things (IoT), which can make data available in near real time to multiple stakeholders, including scientists, foresters, and forest managers, and may partially motivate citizens to participate in big data collection. The use of in-situ sources of monitoring data as ground-truthed training data for remotely sensed data can boost forest monitoring by increasing the spatial and temporal scale of the monitoring, leading to a better understanding of forest processes and potential threats. Here, some of the key developments and applications of these sensors are outlined, together with guidelines for data management. Examples are given of their deployment to detect early warning signals (EWS) of ecosystem regime-shifts in terms of forest productivity, health and biodiversity. Analysis of the strategic use of these tools highlights the opportunities for engaging citizens and forest managers in this new generation of forest monitoring.

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“…Development of NIR systems now allows the collection of NIR data at higher spatial resolution. Recent studies have used either fiber optic probes [11,37] or NIR-HSI [32,33,59,60] with both approaches utilizing linear translation systems to collect data across samples. Both permit the investigation of within-tree variation of wood properties at the ring, or within-ring level and the production of more detailed maps [60].…”

Section: Modelsmentioning

confidence: 99%

Mapping and modeling within-tree variation for loblolly pine pulp yield and lignin content

Schimleck

Antony

Mora³

et al. 2021

SN Appl. Sci.

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We examined the within-tree variation of pulp yield and lignin content for loblolly pine (Pinus taeda L.) trees aged 13 and 22 years. Radial trends in pulp yield (increase) and lignin (decrease) were consistent with what would be expected for loblolly pine as were changes in properties related to maturation. Maps, based on the average of 18 trees at each age, depicting pulp yield variation within-tree were similar to loblolly pine maps reported for microfibril angle and stiffness, while lignin maps resembled the inverse of those reported for density and related properties. Mixed-effects models for both properties were developed with the base model for pulp yield explaining 64% of the observed variation, with the inclusion of tree height improving the model slightly, whereas models for lignin content explained 44% of the variability. The models could be incorporated into growth and yield prediction systems, or procurement model systems that predict within-tree wood properties based on age and tree size.

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Non-destructive evaluation of wood stiffness and fiber coarseness, derived from SilviScan data, via near infrared hyperspectral imaging

Cited by 14 publications

References 31 publications

Near infrared spectroscopic measurement of strain in rocks

Near infrared spectroscopic measurement of strain in rocks

A new generation of sensors and monitoring tools to support climate-smart forestry practices

Mapping and modeling within-tree variation for loblolly pine pulp yield and lignin content

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