Kick-Off Meeting - WP4

Project SLOPE
1
WP 4 – Multi-sensor model-based quality
control of mountain forest production

Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Some thoughts after the first day of kick-off meeting:
1. Complements for all partners for fascinating presentations,
unique know-how and enthusiasm.
2. The forest in mountains is peculiar, and very different than such
of flat lands!!!
3. Trees in mountains are (mostly) BIG…
4. Big/old tree may be or superior quality, or “fuel wood”
5. Trees from mountains might be of really high value
6. We do support with our heart “PROPER LOG FOR PROPER USE”
7. The quality of wood/log/tree is an issue!!!!!
8. But, the quality of wood is not only external dimentions, taper
and diameter…

Wood might not be perfect…

Wood from mountains might be priceless…

The goals of this WP are:
• to develop an automated and real-time grading system for the
forest production, in order to improve log/biomass segregation
and to help develop a more efficient supply chain of mountain
forest products
• to design software solutions for continuous update the pre-
harvest inventory procedures in the mountain areas
• to provide data to refine stand growth and yield models for
long-term silvicultural management

Fine-grained timeline:
4
TRE 4.1
CNR 4.2
BOKU 4.3
CNR 4.4
CNR 4.5
CNR 4.6
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

Interim delivery stages (with dates):
D.4.01 R: Existing grading rules for log/biomass (December 2014)
D.4.02 R: On-field survey data for tree characterization (March 2015)
D.4.03 R: Establishing NIR measurement protocol (April 2015)
D.4.04 R: Establishing hyperspectral imaging measurement protocol (May 2015)
D.4.05 R: Establishing acoustic-based measurement protocol (June 2015)
D.4.06 R: Establishing cutting power measurement protocol (July 2015)
D.4.07 P: Estimation of log/biomass quality by external tree shape analysis (July 2015)
D.4.08 P: Estimation of log/biomass quality by NIR (August 2015)
D.4.09 P: Estimation of log quality by hyperspectral imaging (September 2015)
D.4.10 P: Estimation of log quality by acoustic methods (October 2015)
D.4.11 P: Estimation of log quality by cutting power analysis (November 2015)
D.4.12 P: Implementation and calibration of prediction models for log/biomass quality
classes and report on the validation procedure (July 2016)

Partners’ role and contributions:
Will be explained in presentations of tasks…

Dependences between activities:
•T1.2 (and your comments) vital for proper initiation of work…
•WP4 is strictly related to WP3
•WP4 provides data to WP5

Task 2.3
4.1.
on-field forest survey
GPS
PC/PAD
3D scanner
3D vision
Tasks3.1
4.2-4.3
Marktree
Confirm route of cable crane
GPS
PC/PAD
RFID TAG
RFID reader
Tasks3.2
4.4
Treefelling
Database
NIRQI
H QI
RFID reader
RFID TAG
(if cross cut)
PortableNIR
Hyperspectral
Accellerometers
Oscilloscope
SW QI
Tasks3.3
Cablecrane
Techno carriage
GPRS
RFID reader
WIFI
Skylinelauncher
Load sensor
Intelligent chookers
GPS
PC/PAD
Data logger
Black box access
Controlsystem
M/Minterface
Tasks3.4
4.2-4.3-4.4-4.5-4.6
Processor
de-brunch, cut to length, measures, mark
Load cell for cutting force
Cutting feed sensor
Feed forcesensor
Diameter digital caliper
Length
RFID reader
RFID TAG
PC controlcomp.
GPRS/WIFI
Hyperspectral
NIRscanner
Kinect® (or similar 3D vision)
Microphone/accellerometer
Data logger
Black box access
CodePrinter
Controlsystem
M/Minterface
ID backup
Database
NIRQI + H QI + SW QI + CF QI
Tasks3.5
Truck
RFID tags are only used for identifying trees/logs along the supply chain, not to store information.
Material parameters from sensors are stored in the database
GPS
GPRS
RFID antenna
BUSCAN
Load cell
Logistic Software
ID backup
ID backup
Weight, time
Quality class

Risks and mitigating actions:
To keep focus on practical applications and not pure (fascinating for
us) research; 2-monts progress reporting, contributions/comments
of SLOPE partners
Properly define real user expectations; contribution of the
development of WP1, discussions with stake holders, foresters,
users of forest resources
Technologies provided will not be appreciated by “conservative”
forest users; demonstrate financial (and other) SLOPE advantages
Difficulties with integration of some sensors with forest machinery;
careful planning, collaboration with SLOPE engineers

TreeMetrics
“PROVIDE MORE END PRODUCT FROM LESSTREES”

WP 4.1: Data Mining and Model Integration of Stand
Quality Indicators
• Stem Taper Variation
• Stem Quality Variation
– Straightness
– Branching
– Internal wood quality
• Stem Bucking Simulation Systems

Log Quality: Straightness (Sweep),Taper,
Branching ,Rot,

Terrestrial Laser Scanning Forest Measurement System
(AutoStem Forest)
Automated 3D Forest
Measurement System

New Stand Analytics – Log distribution

Harvest Modelling
• ‘Cutting to Value’ (Value Optimisation)
• ‘Cutting to Demand’ (Keep the market satisfied)
– Manage the trade off’s
– Combinatorial problem
– Constraint Modelling

The Problems
• Productive Area
• Stratification
• Stocking
• Stem Taper Variation
• Stem Quality Variation

The Products
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cm
Pulp
7cm
PulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?
• Taper Variation
• Straightness
• Branching
• Rot etc.

The Products: GeneralValues
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cm
Pulp
7cm
PulpPulp = €20 per M3
Large Sawlog = €60 per M3
Small Sawlog = €40 per M3

The Problem - “The Collision of Interests”
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cm
Pulp
7cm
PulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?

MaximiseValue
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cm
Pulp
7cm
PulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?

MaximiseValue: Sawlog Lengths
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cm
Pulp
7cm
PulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?
3.7mOption 1

14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cm
Pulp
7cm
PulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?
4.3mOption 2

14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cm
Pulp
7cm
PulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?
4.9mOption 3

Better Management
 Targets
 Incentives
 Monitoring

Competitive Advantage
“the stronger the LINKAGES between the primary and
secondary producers the greater the source of
competitive advantage”
Michael Porter, Harvard Business School

Summary
• ekeane@treemetrics.com
• www.treemetrics.com

Task 4.2
Evaluation of near infrared (NIR) spectroscopy
as a tool for determination of log/biomass
quality index in mountain forests

Task 4.2: Partners involvement
Task Leader: CNR
Task Partecipants: KESLA, BOKU, FLY, GRE
CNR: Project leader,
•will coordinate all the partecipants of this task
•will evaluate the usability of NIR spectroscopy for characterization of bio-
resources along the harvesting chain
•will provide guidelines for proper collection and analysis of NIR spectra
•will develop the “NIR quality index”; to be involved in the overall log and biomass
quality grading
Boku: will support CNR with laboratory measurement and calibration transfer
Kesla, Greifenberg and Flyby: will support CNR in order to collect NIR spectra at
various stages of the harvesting chain

 evaluating the usability of NIR spectroscopy for
characterization of bio-resources along the
harvesting chain
 providing guidelines for proper collection and
analysis of NIR spectra
 The raw information provided here are near infrared
spectra, to be later used for the determination of
several properties (quality indicators) of the sample
4.2 Objectives

Electromagnetic spectrum
Kick-off Meeting
8-9/jan/2014
The study of the interactions between electromagnetic radiation (energy, light) and matter

Source of spectra
TwistingWaggingRocking
Scissoring
asimmetric
stretching
simmetric
stretching
Spectra represents molecular vibrations of chemical molecules
exposed to infrared light.
http://en.wikipedia.org/wiki/Infrared_spectroscopy

NIR technique
 No need special sample preparation
 Non-destructive testing
 Relatively fast measurement
 No residues/solvents to waste
 Possibility for determination of many components
simultaneously
 High degree of precision and accuracy
 Direct measurement with very low cost
 Overlapping of spectral peaks
 Needs sophisticated statistics methods for data analysis
 Moisture sensitive
 Calibration transfer from lab equipment into field equipment

How it works?
+
calibration (PLS)
0,3
0,4
0,5
0,6
0,7
0,3 0,4 0,5 0,6 0,7
gęstość referencja (g/cm3
)
gęstośćestymacja(g/cm3
)
r2 = 64,94
RMSECV = 0,039
RPD = 1,69
density
45
45,5
46
45 45,5 46
celuloza referencja (%)
celulozaestymacja(%)
r2 = 84,98
RMSECV = 0,0638
RPD = 2,58
cellulose
26
27
28
29
30
26 27 28 29 30
lignina referencja (%)
ligninaestymacja(%)
r2 = 98,67
RMSECV = 0,102
RPD = 8,86
lignin
R
2
= 0.984
0
10
20
30
40
50
60
0 10 20 30 40 50 60
reference stress (MPa)
predictedstress(MPa)
Tensile strength
spectra reference data

Identity test
Compare the unknown spectrum with all reference spectra, the result of comparison between two spectra is the
spectral distance called hit quality. The better spectra match the smaller is spectral distance; HQ for
identical spectra is 0
Model sample1
HQ1
> treshold1
Model sample3
HQ3
> treshold3
Model samplen
HQn
> tresholdn
Model sample2
HQ2
< treshold2
???
sample

 NIR spectra will be collected at various stages of the harvesting chain
 measurement procedures will be provided for each field test
 In-field tests will be compared to laboratory results
4.2 Activities: Feasibility study and specification of the
measurement protocols for proper NIR data acquisition

• spectra pre-processing, wavelength selection, classification,
calibration, validation, external validation (sampling –
prediction – verification)
• prediction of the log/biomass intrinsic “quality indicators”
(such as moisture content, density, chemical composition,
calorific value) (CNR).
• classification models based on the quality indicators will be
developed and compared to the classification based on the
expert’s knowledge.
• calibrations transfer between laboratory instruments
(already available) and portable ones used in the field
measurements in order to enrich the reliability of the
prediction (BOKU).
4.2 Activities: Development and validation of
chemometric models.

4.2 Deliverables
Kick-off Meeting
8-9/jan/2014
Deliverable D.4.03 Establishing NIR measurement protocol
evaluating the usability of NIR spectroscopy for characterization of bio-resources
along the harvesting chain, providing guidelines for proper collection and analysis
of NIR spectra.
Delivery Date M16 April 2015
Deliverable D.4.08 Estimation of log/biomass quality by NIR
Set of chemometric models for characterization of different “quality indicators” by
means of NIR and definition of “NIR quality index”
Delivery Date M20 August 2015
Estimated person Month= 3.45

 Development of “provenance models”. The set of
spectra collected from selected samples (of known
provenance and silvicultural characteristics) along the
supply chain will be also processed in order to verify
applicability of NIR spectroscopy to traceability of
wood (CNR).
4.2 Additional deliverable

Wood provenance & NIRS
2163 trees of Norway spruce
from 75 location
in 14 European countries
2163 samples measured
x 5 spectra/sample
= 10815 spectra

TASK 4.5
Evaluation of cutting process (CP) for the
determination of log/biomass “CP quality index”

Task 4.5: Cutting Process (CP) for the determination of
log/biomass “CP quality index”
Task Leader: CNR
Task Partecipants: Kesla
Starting : October 2014
Ending: November2015
Estimated person-month = 4.00 (CNR) + 2.00 (Kesla)
CNR : will coordinate the research necessary, develop the knowledge base linking process and wood
properties, recommend the proper sensor, develop software tools for computation of the CP quality
index
Kesla : will provide expertise in regard to sensor selection and integration with the processor head +
extensive testing of the prototype

Task 4.5: cutting process quality index
Deliverables
D.4.06 Establishing cutting power measurement protocol
Report: This deliverable will contain a report and recommended protocol for collection of
data chainsaw and delimbing cutting process.
Delivery Date: July 2015 (M.19)
D.4.11 Estimation of log quality by cutting power analysis
Prototype: Numerical procedure for determination of “CP quality index” on the base of
cutting processes monitoring
Delivery Date: November 2015 (M.23)

Timing
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
11.11.21.31.41.522.12.22.32.42.533.13.23.33.43.53.6
4
4.1
4.2
4.3
4.4
4.5
4.655.15.25.35.45.566.16.26.36.477.17.27.37.488.18.28.38.48.58.68.78.899.19.29.3

Objectives
The goals of this task are:
• to develop a novel automatic system for estimation of the
cutting resistance of wood processed during harvesting
• to use this information for the determination of log/biomass
quality index

Theory
The value of cutting forces is
related to:
• wood density
• cutting conditions
• selected mechanical
properties of wood
(i.e. fracture toughness
and shear modulus).

Principles
The indicators of cutting forces:
• energy demand
• hydraulic pressure in the saw feed piston
• power consumption
will be collected on-line and regressed to the known log
characteristics.
http://www.youtube.com/watch?v=bZoq7PkyO-c
http://www.youtube.com/watch?v=XzaPvftspg0

Chainsaw

Delimbing systems

Comments
The average density and mechanical resistance will be a result of the
analysis of the chainsaw cutting process.
Estimation of the “CP-branch indicator” will be computed only in
the case of delimbing on the processor head. In this case, it will be
correlated to the “3D-branch indicator” determined from the 3D
stem model of the original standing tree (T4.1).
The information will be forwarded to the server in real-time and will
support final grading of logs.

Challenges
What sensors are appropriate for measuring cutting forces in
processor head?
load cell? tensometer? oil pressure? electrical current?
How to install sensors on the processor?
How reliable will be measurement of cutting forces in forest?
What is an effect of tool wear?
How to link cutting force (wood density) with recent quality sorting
rules?
Delimbing or debarkining?

TASK 4.6
Implementation of the log/biomass grading
system

Task 4.6: Implementation of the log/biomass grading
system
Task Leader: CNR
Task Participants: GRAPHITECH, KESLA, MHG, BOKU, GRE, TRE
Starting : June 2014
Ending: July 2016
Estimated person-month = 1.50 (GRAPHITECH) + 2.0 (CNR) + 1.00 (Kesla) + 1.00 (MHG)
+ 1.00 (BOKU), 0.50 (GRE) + 1.00 (TRE)
CNR: will coordinate the research necessary, develop the software tools (expert systems)
and integrate all available information for quality grading
TRE, GRE, KESLA: incorporate material parameters from the multisource data extracted
along the harvesting chain
GRAPHITECH: integration with the classification rules for commercial assortments, linkage
with the database of market prices for woody commodities
MHG: propagate information about material characteristics along the value chain (tracking)
and record/forward this information through the cloud database
BOKU: validation of the grading system

Task 4.6: Implementation of the grading system
Deliverables
D.4.01 Existing grading rules for log/biomass
Report: This deliverable will contain a report on existing log/biomass grading criteria and
criteria gap analyses
Delivery Date: December 2014 (M.12)
D.4.12 Implementation and calibration of prediction models for log/biomass quality classes
and report on the validation procedure
Prototype: This deliverable will contain a report on the validation procedure, and results of
the quality class prediction models, and integration in the SLOPE cloud data base
Delivery Date: July 2016 (M.31)

Timing
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
11.11.21.31.41.522.12.22.32.42.533.13.23.33.43.53.6
4
4.1
4.2
4.3
4.4
4.5
4.655.15.25.35.45.566.16.26.36.477.17.27.37.488.18.28.38.48.58.68.78.899.19.29.3

Objectives
The goals of this task are:
• to develop reliable models for predicting the grade (quality
class) of the harvested log/biomass.
• to provide objective/automatic tools enabling optimization of
the resources (proper log for proper use)
• to contribute for the harmonization of the current grading
practice and classification rules
• provide more wood from less trees

The concept
3D quality index (WP 4.1)
NIR quality index (WP 4.2)
HI quality index (WP 4.3)
SW quality index (WP 4.4)
CP quality index (WP 4.5)
Data from harvester
Other available info
Quality class
Threshold values and
variability models of
properties will be
defined for the
different end-uses
(i.e. wood processing
industries, bioenergy
production).
(WP5)

Results
Several grading rules are in use in different regions and/or niche
products: a systematic database of these rules will be developed for
this purpose.
• The performance
• Reliability
• Repetability
• Flexibility
of the grading system will be carefully validated in order to quantify
advantages from both economic and technical points of view.
at different stages of the value chain.

Challenges
What sensors set is optimal (provide usable/reliable information)?
How to merge various types of indexes/properties?
Can the novel system be accepted by “conservative” forest (and
wood transformation) industry?
How the SLOPE quality grading will be related to established
classes?

Kick-Off Meeting - WP4

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Kick-Off Meeting - WP4