MEASURING*WOOD*WITH* HYPERSPECTRAL*IMAGING€¦ · · 2016-01-18outlook! • what!wood!is(...
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MEASURING WOOD WITH HYPERSPECTRAL IMAGING J. Sandak , A. Sandak, M. Riggio, G. Picchi, J. Burger + Trees and Timber Ins-tute/Na-onal Research Council, IVALSA/CNR, Via Biasi 75, San Michele All’Adige, Italy + BurgerMetrics, Ciņu iela 7, Rīga, Latvia 1
Transcript of MEASURING*WOOD*WITH* HYPERSPECTRAL*IMAGING€¦ · · 2016-01-18outlook! • what!wood!is(...
MEASURING WOOD WITH HYPERSPECTRAL IMAGING
J. Sandak, A. Sandak, M. Riggio, G. Picchi, J. Burger +
Trees and Timber Ins-tute/Na-onal Research Council, IVALSA/CNR, Via Biasi 75, San Michele All’Adige, Italy
+BurgerMetrics, Ciņu iela 7, Rīga, Latvia
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• aaa
Manfred Schwanninger 1963-‐2013
James Burger 1963-‐2014
My gurus… missing so much…
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outlook
• what wood is (hyperspectral imaging perspecDve)
• quality grading of logs in the forest • how we could use hyperspectral imaging (wood technology perspecDve)
• problems of scanning wood in forest • case studies with James Burger • further challanges 3
Forests… • are one of the most important renewable resources in Europe and are essenDal for the conservaDon of European nature
• cover approximately 177 million ha in the EU (over 40 % of the territory), of which 130 million ha are available for wood supply
• area covered by forests in the EU-‐28 is slowly increasing, in contrary to the other parts of the World
• Is this a “market” for hyperspectral imaging???
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wood is…
• a natural material widely used in numerous applicaDons (construcDon, furniture, paper, bio-‐energy…)
• a complex matrix of several polymers (lignin, cellulose, hemicelluloses + extracDves and minerals)
• heterogeneous & anisotropic • a highly hygroscopic material, responding to the humidity changes of surrounding air
The variability of wood properDes is very high in the level of one wood species, same forest, single tree, or even within one piece of wood.
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• aaa
Inspired by artwork by Mark Harrington (http://www.nzwood.co.nz)
wood at different scales…
Note: it is possible to use hyperspectral imaging
at all levels!!! 6
value of log
geometric characterisUcs • length • diameter(s)
quality characterisUcs • presence of defect(s) • material proper-es
dimension class
quality class
Commercial value
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Umber grading • is an important step in the producDon chain, when the resource properUes and the quality of the product are assigned
• proper grading process leads to accurate definiDon of the Umber value on the market
• grading logs in forest is the first assessment of the technical quality of wood and it is a starDng point for following acDons in the fields of Dmber trade and its valorizaDon
• is an indispensable and essenUal procedure in the modern forest producDon process
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Defects on the cross secDon
crack(s) stain eccentric pith
root resin pockets compression wood 9
Defects on the log side
knots(s) spiral grain
taper sweep
insect holes
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why hyperspectral imaging for wood?
• very good experiences with NIR & wood, (but clear limitaDons of the spectroscopy)
• in contrast to FT-‐NIR, large number of spaDally resolved spectra is generated during each scanning:
• HI provides spaDally resolved informaDon • HI provides spectrally resolved informaDon
• available algorithms/sobware tools for extracDon of unique informaDon
• … scienUfic curiosity (also of Manfred and Jim)
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PotenDal of HI to detect defects
Sensor ty
pe
Multispe
ctral cam
eras fo
r rem
ote sensing (satellite)
Multispe
ctral cam
eras fo
r rem
ote sensing (UAV
)
3D laser scann
er and
cloud
of p
oints
Near Infrared spectrom
eter (laboratory)
Near Infrared spectrom
eter (in-‐fie
ld)
Hype
rspe
ctral imaging VIS
Hype
rspe
ctral imaging NIR
Ultrasou
nd se
nsor
Free vibratio
ns m
eter
Cutting forces m
eters (de
-‐branching)
Acou
stic emiss
ion sensor
Cutting resis
tance of cross cut se
nsor
Vision
CCD
cam
era on
side
of log
3D cam
era on
side
of log
Log-‐geom
etry se
nsors (diam
eter f(length))
Condition of forest area ü ü Health condition of tree ü ü ü ü ? ? Foliar index ü ü Crown damage ü ü ? ? Tree species recognition ü ü ü ? ? ü Branch index ? ? ü ü M
acro prope
rties
of th
e forest area
or th
e who
le tree
knots ü ü ü ü ? ? ü ü ü ? resin pocket ü ü ü ü twist ? ? ? ü eccentric pith ü ü ? compression wood ü ü ü ü ? ? ? sweep ? ü taper ? ü
shakes ü ü ü ? ? ü ü insects ü ? ü ü ü ? ü dote ü ? ? ü rot ü ü ü ü ü ? ü ü stain ü ? ü ü
Log de
fects a
ccording to
EN
1927
-‐1:200
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lignin ü ? ü cellulose ü ? ü hemicellulose ü ? ü extractives ü ? ü microfibryl angle ü ? ? calorific value ü ? ü heartwood/sapwood ü ü ü ü
density ü ü ? ü ü ? ? ? ü mechanical properties ü ü ? ü ü ü ü ü ü moisture content ü ü ? ü ? ? ? provenance ü ? ? wood tracking ü ? ü ü ? ? bottom-‐end diameter ü ü ü ü ü top-‐end diameter ü ü ü ü ü external shape of log ü ü ü log diameter without bark ü ü
log volume ü ü ü
Other woo
d prop
ertie
s/characteristics
resonance wood ü ? ? ü ü ü ?
Suita
bility for
detection of re
sources
for n
iche
produ
cts
Sensor ty
pe
Multispe
ctral cam
eras fo
r rem
ote sensing (satellite)
Multispe
ctral cam
eras fo
r rem
ote sensing (UAV
)
3D laser scann
er and
cloud
of p
oints
Near Infrared spectrom
eter (laboratory)
Near Infrared spectrom
eter (in-‐fie
ld)
Hype
rspe
ctral imaging VIS
Hype
rspe
ctral imaging NIR
Ultrasou
nd se
nsor
Free vibratio
ns m
eter
Cutting forces m
eters (de
-‐branching)
Acou
stic emiss
ion sensor
Cutting resis
tance of cross cut se
nsor
Vision
CCD
cam
era on
side
of log
3D cam
era on
side
of log
Log-‐geom
etry se
nsors (diam
eter f(length))
Condition of forest area ü ü Health condition of tree ü ü ü ü ? ? Foliar index ü ü Crown damage ü ü ? ? Tree species recognition ü ü ü ? ? ü Branch index ? ? ü ü M
acro prope
rties
of th
e forest area
or th
e who
le tree
knots ü ü ü ü ? ? ü ü ü ? resin pocket ü ü ü ü twist ? ? ? ü eccentric pith ü ü ? compression wood ü ü ü ü ? ? ? sweep ? ü taper ? ü
shakes ü ü ü ? ? ü ü insects ü ? ü ü ü ? ü dote ü ? ? ü rot ü ü ü ü ü ? ü ü stain ü ? ü ü
Log de
fects a
ccording to
EN
1927
-‐1:200
8
lignin ü ? ü cellulose ü ? ü hemicellulose ü ? ü extractives ü ? ü microfibryl angle ü ? ? calorific value ü ? ü heartwood/sapwood ü ü ü ü
density ü ü ? ü ü ? ? ? ü mechanical properties ü ü ? ü ü ü ü ü ü moisture content ü ü ? ü ? ? ? provenance ü ? ? wood tracking ü ? ü ü ? ? bottom-‐end diameter ü ü ü ü ü top-‐end diameter ü ü ü ü ü external shape of log ü ü ü log diameter without bark ü ü
log volume ü ü ü
Other woo
d prop
ertie
s/characteristics
resonance wood ü ? ? ü ü ü ?
Suita
bility for
detection of re
sources
for n
iche
produ
cts
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What hyperspectral imaging may tell about the log quality (only cross secDon)
diameter(s) of wood without bark
external diameter(s)
reacDon wood
pith posiDon
diameter(s) of hearthwood
among others
wood decay
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our challenges (SLOPE project)
• dealing with wood (anizotropy, heterogenity, biological material, high effect of moisture)
• using costly/sophisDcated scanners in the filed (forest)
• install scanners directly on the processor • determine wood quality in automaDc system
hgps://www.youtube.com/watch?v=c3zY5rFLBUk
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Working condiDons of hysperspectral sensor (SLOPE project scenario)
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Case studies with James Burger 28 July, 2014 – 1 August, 2014
BurgerMetrics HyperPro 2.2 VNIR instrument: • 320 x 256 pixel extended InGaAs sensor array • spectral range of 512.3 – 1768.0 nm • 256 wavelength channels • the camera has a field of view of 56 mm • an effecDve pixel width of 175 microns • the step size of the sample delivery system was matched to obtain square 175 x 175 micron pixels
• 10 images were acquired at each linescan step and averaged to improve signal/noise
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Case study #1: “demo wood”
• Goal: (preliminary) test exploring capacity of hyperspecral imaging for wood characterizaDon
• Material: slice of Norway spruce (picea abies) wood containing various features (bark, carbonized wood, pencil mark)
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Case study #1: “demo wood”
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Case #1: “demo wood”
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Case #1: “demo wood”
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Case study #2: “effect of roughness”
• Goal: to use hyperspectral imaging data to determine set of wavelengths most suitable for detecDon of wood rot + to invesDgate effect of surface roughness on the image quality
• Material: 10mm thick slice of Norway spruce (picea abies) wood containing wood decay in a central part of the log
Please visit poster by Andreas Zitek et al. for addiDonal details
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Case study #2: “effect of roughness”
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Case #2: “effect of roughness”
Single band: 48 (~747nm)
Dry wood, surface aber chain saw
Dry wood, surface aber sanding 23
Case study #3: “paper”
• Goal: to use hyperspectral imaging data to discriminate different papers + to confront hyperspectral imaging approach (thousands of low resoluDon spectra) with FT-‐NIR (few, but “high quality” spectra)
• Material: 41 paper samples handmade from different species of wood (sobwood, hardwood, eucaliptus) or recycled pulp, with different addiDves (starch, resin, glue)
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Case study #3: “paper”
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Paper 1
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Paper 27
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Paper 32
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selecDvity S
D
R1
R2
21 RRDS+
=
S > 1 S = 1 S < 1 29
SelecDvity S between pairs of papers
PC1 PC1 + PC2 PC1 + PC2 + PC3
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conclusions
• forest/wood sector is a great “opportunity” to apply HI • it was shown that HI has a great potenDal for (at least):
• assessment of wood properDes • assisDng quality grading of logs • discriminaDon of ligno-‐cellulosic materials
• it has sDll to be researched if; • HI (many low resoluDon spectra) > FR-‐NIR (few detailed spectra)
• the technical complexity and cost of HI are sDll limiDng factors for wide use of this technology (not only in the wood industry)
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future steps (SLOPE project)
• understand beger hyperspectral images of wood (lab trials, different samples, algorithms)
• determine in-‐field strategy for acquiring (hyper)spectral images
• determine procedures for automaDc quality grading of logs
• test the system on the pilot demonstraDons
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Acknowledgement We would like to dedicate this work to the memory of two
great researchers: Dr James Burger
and Dr Manfred Schwanninger
Special thanks to Ingunn Burud for support in analysis of the
hyperspectral images!
Part of this research has been conducted within frame of the SLOPE project Integrated proceSsing and controL systems fOr sustainable forest ProducDon in mountain arEas (FP7 -‐ 604129)
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