Slides from the 2nd Technical Meeting of the Slope Project

1. Work Package 4:
Multi-sensor model-based quality
control of mountain forest production
by CNR: Jakub Sandak
3rd SLOPE project, 19 January 2015, San Michele All’Adige

2. Work Package 4: Multi-sensor model-based quality
control of mountain forest production
The goals of this WP are:
• to develop an automated and real-time grading (optimization)
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
Technical Meeting
19-21 Jan 15

3. Work Package 4: work to be done T4.1
Quality rules &specifications
CNR,TRE:
Develop tool Harvest Simulator
TRE:
Develop models of trees
GRA,TRE:
Compare models with real data
TRE,GRA, TRE:
Link automatic system with visual
TRE,CNR:
Develop 3D qualityindex
TRE, CNR:
Measurement of standing trees
CNR,TRE:
Measurement of felled trees
CNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimalprotocol
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Measure NIR on standing trees
TRE,CNR, FLY:
Measure NIR on felled trees
CNR,GRE:
Measure NIR on processor head
CNR,COM:
Measure NIR on pale of logs
CNR,BOK:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop NIR quality index
CNR,BOK:
Develop provenance NIR models
CNR,BOK:
Design data base of NIR spectra
BOK,CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usability
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Imaging standing trees
BOK,FLY, TRE:
Imaging fallen trees
BOK,GRE:
Imaging on processor head
BOK,COM:
Imaging on pale of logs
BOK,CNR:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop hyperspectral index
CNR,BOK:
Design data base of hyperspectra
BOK,CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimalset-up for the
hyperspectral camera, illumination,
and sample holding
BOK,CNR:
D01.04
Determine optimalset-up for the
NIR sensor, illumination, and
sample holding
CNR,BOK:
Develop report on using SW
CNR:
Develop models for SW quality
CNR:
Test on standing trees
CNR,GRE:
Tests on fallen trees
CNR,GRE:
Tests on processor head
CNR,COM:
Imaging on pale of logs
CNR:
Develop SW quality index
CNR:
Define qualitythresholds
CNR:
Analyze material dependant
factors
CNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimalset-up for the
stress wave measurement,
including time of flight and free
vibrations sensor
CNR:
D01.04
Determine quality requirements for
high-end assortments
CNR:
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
D01.04
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
Link in-field data with cloud
database
CNR:
Compare automatic and visual
grading results
BOK,CNR:
Determine threshold values
CNR:
Develop grading expert system
CNR:
Develop algorithm for data fusion
CNR, COM, TRE:
In field visual qualityassessment
CNR,BOK:
Develop data base for prices of
woody commodities
CNR,BOK:
Reliabilitystudies
BOK:
Economic advantage studies
BOK,CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identifygrading rules for standard
and niche products
CNR:
Prepare state-of-the-art report on
grading rules
CNR:
Technical Meeting
19-21 Jan 15

4. T4.1: Data mining and model integration of
stand quality indicators from on-field survey
Quality rules &specifications
CNR,TRE:
Develop tool Harvest Simulator
TRE:
Develop models of trees
GRA,TRE:
Compare models with real data
TRE,GRA, TRE:
Link automatic system with visual
TRE,CNR:
Develop 3D qualityindex
TRE, CNR:
Measurement of standing trees
CNR,TRE:
Measurement of felled trees
CNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE  31 October 2014
 31.05.2015
the resources planned: 6.5 M/M
the resources utilized: ?.? M/M (CNR: 0.195)
PROBLEMS: Not reported
 

Technical Meeting
19-21 Jan 15

5. Work Package 4: work to be done T4.2
Quality rules &specifications
CNR,TRE:
Develop tool Harvest Simulator
TRE:
Develop models of trees
GRA,TRE:
Compare models with real data
TRE,GRA, TRE:
Link automatic system with visual
TRE,CNR:
Develop 3D qualityindex
TRE, CNR:
Measurement of standing trees
CNR,TRE:
Measurement of felled trees
CNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimalprotocol
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Measure NIR on standing trees
TRE,CNR, FLY:
Measure NIR on felled trees
CNR,GRE:
Measure NIR on processor head
CNR,COM:
Measure NIR on pale of logs
CNR,BOK:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop NIR quality index
CNR,BOK:
Develop provenance NIR models
CNR,BOK:
Design data base of NIR spectra
BOK,CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usability
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Imaging standing trees
BOK,FLY, TRE:
Imaging fallen trees
BOK,GRE:
Imaging on processor head
BOK,COM:
Imaging on pale of logs
BOK,CNR:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop hyperspectral index
CNR,BOK:
Design data base of hyperspectra
BOK,CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimalset-up for the
hyperspectral camera, illumination,
and sample holding
BOK,CNR:
D01.04
Determine optimalset-up for the
NIR sensor, illumination, and
sample holding
CNR,BOK:
Develop report on using SW
CNR:
Develop models for SW quality
CNR:
Test on standing trees
CNR,GRE:
Tests on fallen trees
CNR,GRE:
Tests on processor head
CNR,COM:
Imaging on pale of logs
CNR:
Develop SW quality index
CNR:
Define qualitythresholds
CNR:
Analyze material dependant
factors
CNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimalset-up for the
stress wave measurement,
including time of flight and free
vibrations sensor
CNR:
D01.04
Determine quality requirements for
high-end assortments
CNR:
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
D01.04
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
Link in-field data with cloud
database
CNR:
Compare automatic and visual
grading results
BOK,CNR:
Determine threshold values
CNR:
Develop grading expert system
CNR:
Develop algorithm for data fusion
CNR, COM, TRE:
In field visual qualityassessment
CNR,BOK:
Develop data base for prices of
woody commodities
CNR,BOK:
Reliabilitystudies
BOK:
Economic advantage studies
BOK,CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identifygrading rules for standard
and niche products
CNR:
Prepare state-of-the-art report on
grading rules
CNR:
Technical Meeting
19-21 Jan 15

6. T4.2: Evaluation of NIRS as a tool for
determination of log/biomass quality index
D01.04
Determine optimalprotocol
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Measure NIR on standing trees
TRE,CNR, FLY:
Measure NIR on felled trees
CNR,GRE:
Measure NIR on processor head
CNR,COM:
Measure NIR on pale of logs
CNR,BOK:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop NIR quality index
CNR,BOK:
Develop provenance NIR models
CNR,BOK:
Design data base of NIR spectra
BOK,CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine optimalset-up for the
NIR sensor, illumination, and
sample holding
CNR,BOK:
the resources planned: 11.5 M/M
the resources utilized: ??.? M/M (CNR: 3.325)
PROBLEMS: Delay related to the processor head and final sensor selection
SOLUTIONS: LAB scanner + list of sensor(s) for purchase ready
 


31 October 2014
?!
30.06.2015


Technical Meeting
19-21 Jan 15

7. T4.2: Gantt (original)
Evaluation of near infrared (NIR) spectroscopy as a tool for determination of log/biomass quality index in mountain forests
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
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
T4.2
D.4.03
D.4.08
test sensors avaliable on the market
finalize concept
design/adopt to the processor
test electronic system
assemble hardware
collect reference samples
analyse reference samples
test hardware + software
calibrate system
develop algorithm for NIR qualityindex
integrate NIR quality index with quality grading/optymization (T4.6) D.4.12
D.4.03 Establishing NIR measurement protocol
D.4.08 Estimation of log/biomass quality by NIR
D.4.12 Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure
Technical Meeting
19-21 Jan 15

8. Work Package 4: work to be done T4.3
Quality rules &specifications
CNR,TRE:
Develop tool Harvest Simulator
TRE:
Develop models of trees
GRA,TRE:
Compare models with real data
TRE,GRA, TRE:
Link automatic system with visual
TRE,CNR:
Develop 3D qualityindex
TRE, CNR:
Measurement of standing trees
CNR,TRE:
Measurement of felled trees
CNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimalprotocol
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Measure NIR on standing trees
TRE,CNR, FLY:
Measure NIR on felled trees
CNR,GRE:
Measure NIR on processor head
CNR,COM:
Measure NIR on pale of logs
CNR,BOK:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop NIR quality index
CNR,BOK:
Develop provenance NIR models
CNR,BOK:
Design data base of NIR spectra
BOK,CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usability
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Imaging standing trees
BOK,FLY, TRE:
Imaging fallen trees
BOK,GRE:
Imaging on processor head
BOK,COM:
Imaging on pale of logs
BOK,CNR:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop hyperspectral index
CNR,BOK:
Design data base of hyperspectra
BOK,CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimalset-up for the
hyperspectral camera, illumination,
and sample holding
BOK,CNR:
D01.04
Determine optimalset-up for the
NIR sensor, illumination, and
sample holding
CNR,BOK:
Develop report on using SW
CNR:
Develop models for SW quality
CNR:
Test on standing trees
CNR,GRE:
Tests on fallen trees
CNR,GRE:
Tests on processor head
CNR,COM:
Imaging on pale of logs
CNR:
Develop SW quality index
CNR:
Define qualitythresholds
CNR:
Analyze material dependant
factors
CNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimalset-up for the
stress wave measurement,
including time of flight and free
vibrations sensor
CNR:
D01.04
Determine quality requirements for
high-end assortments
CNR:
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
D01.04
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
Link in-field data with cloud
database
CNR:
Compare automatic and visual
grading results
BOK,CNR:
Determine threshold values
CNR:
Develop grading expert system
CNR:
Develop algorithm for data fusion
CNR, COM, TRE:
In field visual qualityassessment
CNR,BOK:
Develop data base for prices of
woody commodities
CNR,BOK:
Reliabilitystudies
BOK:
Economic advantage studies
BOK,CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identifygrading rules for standard
and niche products
CNR:
Prepare state-of-the-art report on
grading rules
CNR:
Technical Meeting
19-21 Jan 15

9. T4.3: Evaluation of hyperspectral imaging for
the determination of log/biomass quality index
Determine usability
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Imaging standing trees
BOK,FLY, TRE:
Imaging fallen trees
BOK,GRE:
Imaging on processor head
BOK,COM:
Imaging on pale of logs
BOK,CNR:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop hyperspectral index
CNR,BOK:
Design data base of hyperspectra
BOK,CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimalset-up for the
hyperspectral camera, illumination,
and sample holding
BOK,CNR:
D01.04
the resources planned: 13.5 M/M
the resources utilized: ??.? M/M (CNR: 0.835)
PROBLEMS: Delay with Deliverable + setting of the lab scanner + final sensor selection
SOLUTIONS: LAB scanner + collaboration with experts + new solutions for HI sensor(s)




31 January 2015
 ?!
31.07.2015
Technical Meeting
19-21 Jan 15

10. Work Package 4: work to be done T4.4
Quality rules &specifications
CNR,TRE:
Develop tool Harvest Simulator
TRE:
Develop models of trees
GRA,TRE:
Compare models with real data
TRE,GRA, TRE:
Link automatic system with visual
TRE,CNR:
Develop 3D qualityindex
TRE, CNR:
Measurement of standing trees
CNR,TRE:
Measurement of felled trees
CNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimalprotocol
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Measure NIR on standing trees
TRE,CNR, FLY:
Measure NIR on felled trees
CNR,GRE:
Measure NIR on processor head
CNR,COM:
Measure NIR on pale of logs
CNR,BOK:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop NIR quality index
CNR,BOK:
Develop provenance NIR models
CNR,BOK:
Design data base of NIR spectra
BOK,CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usability
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Imaging standing trees
BOK,FLY, TRE:
Imaging fallen trees
BOK,GRE:
Imaging on processor head
BOK,COM:
Imaging on pale of logs
BOK,CNR:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop hyperspectral index
CNR,BOK:
Design data base of hyperspectra
BOK,CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimalset-up for the
hyperspectral camera, illumination,
and sample holding
BOK,CNR:
D01.04
Determine optimalset-up for the
NIR sensor, illumination, and
sample holding
CNR,BOK:
Develop report on using SW
CNR:
Develop models for SW quality
CNR:
Test on standing trees
CNR,GRE:
Tests on fallen trees
CNR,GRE:
Tests on processor head
CNR,COM:
Imaging on pale of logs
CNR:
Develop SW quality index
CNR:
Define qualitythresholds
CNR:
Analyze material dependant
factors
CNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimalset-up for the
stress wave measurement,
including time of flight and free
vibrations sensor
CNR:
D01.04
Determine quality requirements for
high-end assortments
CNR:
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
D01.04
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
Link in-field data with cloud
database
CNR:
Compare automatic and visual
grading results
BOK,CNR:
Determine threshold values
CNR:
Develop grading expert system
CNR:
Develop algorithm for data fusion
CNR, COM, TRE:
In field visual qualityassessment
CNR,BOK:
Develop data base for prices of
woody commodities
CNR,BOK:
Reliabilitystudies
BOK:
Economic advantage studies
BOK,CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identifygrading rules for standard
and niche products
CNR:
Prepare state-of-the-art report on
grading rules
CNR:
Technical Meeting
19-21 Jan 15

11. T4.4: Data mining and model integration of
log/biomass quality indicators from stress-wave
Develop report on using SW
CNR:
Develop models for SW quality
CNR:
Test on standing trees
CNR,GRE:
Tests on fallen trees
CNR,GRE:
Tests on processor head
CNR,COM:
Imaging on pale of logs
CNR:
Develop SW quality index
CNR:
Define qualitythresholds
CNR:
Analyze material dependant
factors
CNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimalset-up for the
stress wave measurement,
including time of flight and free
vibrations sensor
CNR:
D01.04
Determine quality requirements for
high-end assortments
CNR:
the resources planned: 5.5 M/M
the resources utilized: ?.? M/M (CNR: 2.640)
PROBLEMS: Delay related to the processor head and final sensor selection
SOLUTIONS: LAB scanner + collaboration with engineers + list of sensor(s) for purchase
ready



23 December 2014
 ?!
31.08.2015


Technical Meeting
19-21 Jan 15

12. T4.4: Gantt (original)
Data mining and model integration of log/biomass quality indicators from stress-wave (SW) measurements, for the determination of the “SW quality
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
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
T4.4
D.4.05
D.4.10
finalize concept
field tests
design/adopy to the processor
test electronic system
assemble hardware
test hardware + software
callibrate system
develop algorithm for CP Q_index
integrate CP quality index with quality grading/optimization (T4.6) D.4.12
D.4.05 Establishing acoustic-based measurement protocol
D.4.10 Estimation of log quality by acoustic methods
D.4.12 Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure
Technical Meeting
19-21 Jan 15

13. Work Package 4: work to be done T4.5
Quality rules &specifications
CNR,TRE:
Develop tool Harvest Simulator
TRE:
Develop models of trees
GRA,TRE:
Compare models with real data
TRE,GRA, TRE:
Link automatic system with visual
TRE,CNR:
Develop 3D qualityindex
TRE, CNR:
Measurement of standing trees
CNR,TRE:
Measurement of felled trees
CNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimalprotocol
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Measure NIR on standing trees
TRE,CNR, FLY:
Measure NIR on felled trees
CNR,GRE:
Measure NIR on processor head
CNR,COM:
Measure NIR on pale of logs
CNR,BOK:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop NIR quality index
CNR,BOK:
Develop provenance NIR models
CNR,BOK:
Design data base of NIR spectra
BOK,CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usability
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Imaging standing trees
BOK,FLY, TRE:
Imaging fallen trees
BOK,GRE:
Imaging on processor head
BOK,COM:
Imaging on pale of logs
BOK,CNR:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop hyperspectral index
CNR,BOK:
Design data base of hyperspectra
BOK,CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimalset-up for the
hyperspectral camera, illumination,
and sample holding
BOK,CNR:
D01.04
Determine optimalset-up for the
NIR sensor, illumination, and
sample holding
CNR,BOK:
Develop report on using SW
CNR:
Develop models for SW quality
CNR:
Test on standing trees
CNR,GRE:
Tests on fallen trees
CNR,GRE:
Tests on processor head
CNR,COM:
Imaging on pale of logs
CNR:
Develop SW quality index
CNR:
Define qualitythresholds
CNR:
Analyze material dependant
factors
CNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimalset-up for the
stress wave measurement,
including time of flight and free
vibrations sensor
CNR:
D01.04
Determine quality requirements for
high-end assortments
CNR:
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
D01.04
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
Link in-field data with cloud
database
CNR:
Compare automatic and visual
grading results
BOK,CNR:
Determine threshold values
CNR:
Develop grading expert system
CNR:
Develop algorithm for data fusion
CNR, COM, TRE:
In field visual qualityassessment
CNR,BOK:
Develop data base for prices of
woody commodities
CNR,BOK:
Reliabilitystudies
BOK:
Economic advantage studies
BOK,CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identifygrading rules for standard
and niche products
CNR:
Prepare state-of-the-art report on
grading rules
CNR:
Technical Meeting
19-21 Jan 15

14. T4.5: Evaluation of cutting process (CP) for the
determination of log/biomass CP quality index
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
D01.04
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
the resources planned: 6.0 M/M
the resources utilized: ?.? M/M (CNR: 0.480)
PROBLEMS: Delay related to the processor head and final sensor selection/design
SOLUTIONS: LAB scanner + collaboration with engineers + list of sensor(s) for purchase
ready



31.01. 2015

31.09.2015
Technical Meeting
19-21 Jan 15

15. T4.5: Gantt (original)
Evaluation of cutting process (CP) for the determination of log/biomass “CP quality index”
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
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
T4.5
D.4.06
D.4.11
finalize concept
design/adopt to the processor
test electronic system
assemble hardware
test hardware + software
calibrate system
develop algorithm for CP Q_index
integrate CP quality index with quality grading/optymization (T4.6) D.4.12
D.4.06 Establishing cutting power measurement protocol
D.4.11 Estimation of log quality by cutting power analysis
D.4.12 Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure
Technical Meeting
19-21 Jan 15

16. Work Package 4: work to be done T4.6
Quality rules &specifications
CNR,TRE:
Develop tool Harvest Simulator
TRE:
Develop models of trees
GRA,TRE:
Compare models with real data
TRE,GRA, TRE:
Link automatic system with visual
TRE,CNR:
Develop 3D qualityindex
TRE, CNR:
Measurement of standing trees
CNR,TRE:
Measurement of felled trees
CNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimalprotocol
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Measure NIR on standing trees
TRE,CNR, FLY:
Measure NIR on felled trees
CNR,GRE:
Measure NIR on processor head
CNR,COM:
Measure NIR on pale of logs
CNR,BOK:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop NIR quality index
CNR,BOK:
Develop provenance NIR models
CNR,BOK:
Design data base of NIR spectra
BOK,CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usability
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Imaging standing trees
BOK,FLY, TRE:
Imaging fallen trees
BOK,GRE:
Imaging on processor head
BOK,COM:
Imaging on pale of logs
BOK,CNR:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop hyperspectral index
CNR,BOK:
Design data base of hyperspectra
BOK,CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimalset-up for the
hyperspectral camera, illumination,
and sample holding
BOK,CNR:
D01.04
Determine optimalset-up for the
NIR sensor, illumination, and
sample holding
CNR,BOK:
Develop report on using SW
CNR:
Develop models for SW quality
CNR:
Test on standing trees
CNR,GRE:
Tests on fallen trees
CNR,GRE:
Tests on processor head
CNR,COM:
Imaging on pale of logs
CNR:
Develop SW quality index
CNR:
Define qualitythresholds
CNR:
Analyze material dependant
factors
CNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimalset-up for the
stress wave measurement,
including time of flight and free
vibrations sensor
CNR:
D01.04
Determine quality requirements for
high-end assortments
CNR:
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
D01.04
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
Link in-field data with cloud
database
CNR:
Compare automatic and visual
grading results
BOK,CNR:
Determine threshold values
CNR:
Develop grading expert system
CNR:
Develop algorithm for data fusion
CNR, COM, TRE:
In field visual qualityassessment
CNR,BOK:
Develop data base for prices of
woody commodities
CNR,BOK:
Reliabilitystudies
BOK:
Economic advantage studies
BOK,CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identifygrading rules for standard
and niche products
CNR:
Prepare state-of-the-art report on
grading rules
CNR:
Technical Meeting
19-21 Jan 15

17. T4.6: Implementation of the log/biomass
grading system
Link in-field data with cloud
database
CNR:
Compare automatic and visual
grading results
BOK,CNR:
Determine threshold values
CNR:
Develop grading expert system
CNR:
Develop algorithm for data fusion
CNR, COM, TRE:
In field visual qualityassessment
CNR,BOK:
Develop data base for prices of
woody commodities
CNR,BOK:
Reliabilitystudies
BOK:
Economic advantage studies
BOK,CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identifygrading rules for standard
and niche products
CNR:
Prepare state-of-the-art report on
grading rules
CNR:
the resources planned: 8.0 M/M
the resources utilized: ?.? M/M (CNR: 1.821)
PROBLEMS: Delay related to other tasks
SOLUTIONS: LAB scanner + prototype software developed in lab


31 October 2014

31.03.2016

Technical Meeting
19-21 Jan 15

18. T4.6: Gantt (original)
Implementation of the log/biomass grading system
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
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
T4.6
D.4.01
D.4.12
surveys
literature research
test quality measuring systems
develop software for integration of quality indexes
test software
calibrate system
validate the algorithm/system
D.4.01 Existing grading rules for log/biomass
D.4.12 Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure
Technical Meeting
19-21 Jan 15

19. fulfillment of the project work plan:
related deliverables
WP4 6th reporting period
task
delive
rable
title
type of
deliverable
lead
particip
ant
due date
foreseen or actual
delivery date
comment
T4.1
D4.2 on field survay data for tree characterization report TRE 31.10.2014 31.10.2014 draft
D4.7
estimation of log/biomass quality by external tree shape
analysis
software tool TRE 31.05.2015 same as planed
T4.2
D4.3 establisghing NIR measurement protocol report CNR 31.10.2014 31.10.2014 draft
D4.8 estimation of log/biomass quality by NIR software tool CNR 30.06.2015 same as planed
T4.3
D4.4
establisghing hyperspectral imaging measurement
protocol
report BOK 30.11.2014 DELAY
foreseen
31.01.2015
D4.9 estimation of log/biomass quality by hyperspectral imaging software tool BOK 31.07.2015 same as planed
T4.4
D4.5 establishing acoustic-based measurement protocol report CNR 31.12.2014 31.12.2014 draft
D4.10 estimation of log/biomass quality by acoustic methods software tool CNR 31.08.2015 same as planed
T4.5
D4.6 establisghing cutting power measurement protocol report CNR 31.01.2015 same as planed
D4.11 estimation of log/biomass quality by cutting power analysis software tool CNR 30.09.2015 same as planed
T4.6
D4.1 existing grading rules for log/biomass report CNR 31.10.2014 31.10.2014 draft
D4.12
implementatio and callibration of prediction models for
log/biomass quality classes
software tool CNR 31.03.2016 same as planed
Technical Meeting
19-21 Jan 15

20. Resources used (1° year):
only CNR data available
reporting period
task deliverable 1st 2nd 3rd 4th 5th 6th used
foreseen
CNR % (CNR)
foreseen
all
T4.1 D4.2 0,000 0,000 0,000 0,000 0,012 0,183 0,195 1 19,5 6,5
D4.7 0,000 0,000 0,000 0,000 0,000 0,000 0,000
T4.2 D4.3 0,000 0,000 0,025 0,573 1,725 1,003 3,325 3,5 95,0 11,5
D4.8 0,000 0,000 0,000 0,000 0,000 0,000 0,000
T4.3 D4.4 0,000 0,000 0,000 0,617 0,000 0,218 0,835 3,5 23,9 13,5
D4.9 0,000 0,000 0,000 0,000 0,000 0,000 0,000
T4.4 D4.5 0,000 0,000 0,000 0,120 0,814 1,705 2,640 4 66,0 5,5
D4.10 0,000 0,000 0,000 0,000 0,000 0,000 0,000
T4.5 D4.6 0,000 0,000 0,027 0,000 0,000 0,453 0,480 4 12,0 6,0
D4.11 0,000 0,000 0,000 0,000 0,000 0,000 0,000
T4.6 D4.1 0,000 0,000 0,000 0,057 0,949 0,815 1,821 2 91,0 8,0
D4.12 0,000 0,000 0,000 0,000 0,000 0,000 0,000
0,000 0,000 0,052 1,367 3,500 4,376 9,296 18 51
Technical Meeting
19-21 Jan 15

21. Work Package 4: Multi-sensor model-based quality
control of mountain forest production
Planning actions for all activities and deliverables to be executed
in M13-18:
Finalize + close: D04.1, D04.2, D04.04, D04.05
Deliver + finalize + close: D04.03, D04.6
Initiate + deliver: D04.07, D04.08
Build lab scanner at CNR + purchase sensors + install sensors
Perform field tests with portable instruments
Collaborate with WP3 (and others) in hardware develeopment
Technical Meeting
19-21 Jan 15

22. Work Package 4: Multi-sensor model-based quality
control of mountain forest production
the expected potential impact in scientific, technological,
economic, competition and social terms, and the beneficiaries'
plan for the use and dissemination of foreground.
Technical Meeting
19-21 Jan 15

23. Work Package 4: Multi-sensor model-based quality
control of mountain forest production
Risks and mitigating actions:
Significant delay related to changes in the consortium:
•lack of the practical expertise of the processor head engineers;
technical meetings, new partners/collaborators
•the selection, purchase, set-up of the new processor hear is
delayed; development of the laboratory scanner capable to
simulate log scanning
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 (+outside) engineers
Technical Meeting
19-21 Jan 15

24. Work Package 4: Multi-sensor model-based quality
control of mountain forest production
criticalities, recommendations for partners/consortium
How about demonstrations?
•New schedule?
•Contribution of WP4 already during the first demo?
•All sensors are expected to work during first demo?
How to deal with the overall delay?
•Need to update Gantt(s) ?
How to deal with the new DoW?
•Need to update list of activities ?
The communication between partners is not optimal… how to
change it?
Technical Meeting
19-21 Jan 15

25. Work Package 4: Multi-sensor model-based quality
control of mountain forest production
Thank you! – Grazie!
Technical Meeting
19-21 Jan 15

26. T4.1- 3D Quality Indexing
Delive- rable
Number
61
Deliverable Title
Lead benefi-
ciary
number
Estimated
indicative
person-
months
Nature 62
Dissemi-
nation level
63
Delivery date 64
D4.01 Existing grading rules for log/biomass 2 1.00 R PU 10
D4.02 On-field survey data for tree characterization 9 3.00 R PU 10
D4.03 EstablishingNIR measurement protocol 2 5.00 R PU 10
D4.04 Establishinghyperspectral imaging
measurementprotocol
6 6.00 R PU 11
D4.05 Establishingacoustic-based measurement
protocol
2 2.00 R PU 12
D4.06 Establishing cutting power measurement
protocol
2 2.00 R PU 13
D4.07 Estimation of log/biomass quality by external
tree shape analysis
9 5.50 P PU 17
D4.08 Estimation of log/biomass quality by NIR 2 8.00 P PU 18
D4.09 Estimation of log quality by hyperspectral
imaging
6 11.00 P PU 19
Technical Meeting
19-21 Jan 15

27. T4.1- 3D Quality Indexing
Document 4.02 : (Complete By Treemetric sM10)
ON FIELD SURVEY FOR THE DETERMINATION OF 3D QUALITY INDEX
PRESENTATION:
Part 1: Overview of process for 3d model creation
Part2: TLS Quality Indicators
Part3: Harvest Simulation
Participants: Graphitech, CNR, FLYBY
D4.01 Existing Grading Rules (Complete by CNR)
Very Detailed complex grading rules identified
Technical Meeting
19-21 Jan 15

28. T4.1 Overview
Task 4.1 - Data mining and model integration of
stand quality indicators from on-field survey for
the determination of the tree “3D quality index”.
The Task 4.1 aims at evaluating the effectiveness/reliability, as quality
indicators, of single and combined parameters related to the external
characteristics of the standing tree, such as tree height, diameter, stem
taper, straightness, sweep and lean, branchiness, branch length, thickness
and dimension of the live crown
Technical Meeting
19-21 Jan 15

29. Overview 3d model creating
Most basic parameters: Manually measured in field
Diameter at breast height (DBH)
Total tree height (h) RECORDED MANUALLY QUALITY INDICATORS
stem extraction from the point cloud 3D model of one sample tree
TLS
Technical Meeting
19-21 Jan 15

30. TLS Analysis
3D Model Creation
Steps:
Pre-Processing: Filtering points and eliminating noise.
TLS point cloud filtered
Technical Meeting
19-21 Jan 15

31. TLS Analysis
Step 2: Local DTM generation
Autostem software generates a best fit plane for the local DTM based on point cloud data.
Once Local DTM is established, tree profiles are defined relative to this.
Step 3: Single tree detection:
Autostem, Profile disks are fitted around cylinders in the point cloud data at every 10cm.
If insufficient points in the cloud a specific height, a disk is interpolated between nearest
disks above and below.
Upper section of tree is calculated using local taper equations.
Technical Meeting
19-21 Jan 15

32. TLS Analysis
Tree detection after filter
Technical Meeting
19-21 Jan 15

33. TLS Analysis
Tree location
Tree position for a sample plot in Autostem
Technical Meeting
19-21 Jan 15

34. TLS Analysis
Stemfiles are generated that fully support the Standard for "Forestry Data and
Communication" (StanForD) standard in a widely accepted file with ".stm" extension. The
allows for storing of x,y,z and diameter for each decimetre disk on the stem.
**This format does not allow for extra stem quality information to be stored. **
The extra information should either be stored in a linked file to the .stm file or a new
approach that does not support the StanForD standard can be used.
STEM FILE GENERATION
Technical Meeting
19-21 Jan 15

35. TLS Quality Indicators
1-straight log;; 3 - maximum deviation (d) exceeds 1 cm over 1 m;
2- maximum deviation (d) does not exceed 1
cm over 1 m
4 - bow in more than
one direction.
Straightness
Technical Meeting
19-21 Jan 15

36. TLS Quality Indicators
Branchiness
Branch inter-whorl distance: Some species of trees have branch patterns called whorls.
The distance between whorls gives an important indicator of internal quality attributes
Technical Meeting
19-21 Jan 15

37. TLS Quality Indicators
Forked Stem
TLS can be used to spot stem damage and defects. It can identify multi-stems and forks
Technical Meeting
19-21 Jan 15

38. Harvest Simulation
Cutting instructions: Log quality specifications
• Length: Targeted length of the log.
Small End Diameter (Min + Max SED)
Large End Diameter (Min + Max LED)
Straightness: Maximum deviation
Technical Meeting
19-21 Jan 15

39. Harvest Simulator
Log ID/name LOG1 LOG2 LOG3 LOG4 LOG5 LOG6
Length 3m 3.1m 3.1m 4.9m 4.9m 4.9m
Min SED 7cm 12cm 12cm 16cm 16cm 16cm
Max SED 36cm 36cm
Min LED 13cm
Max LED 38cm
Straightness 20cm/m 2cm/m 2cm/m 1cm/m 1cm/m 1cm/m
Sample Log Constraints (Quality Limitations)
Technical Meeting
19-21 Jan 15

40. Cutting Simulation
Forest Warehouse
SED Range 16-20cm 20-24cm 24-30cm 30cm+
Weighting 500 700 600 500
Example of a range of weightings based on SED
Technical Meeting
19-21 Jan 15

41. Harvest Simulation
Optimising Waste Logs: waste log has a value of zero
Technical Meeting
19-21 Jan 15

42. Conclusions
The main conclusions about the stand quality indicators and harvest simulation are the
following:
• The MAIN indicators to define the log can be easily measured using the stem 3D model
created using TLS data.
• Additional quality indicators can be measured and applied to the log constraints.
• Internal quality of the timber can be estimated by using quality models. These models
will be applied to the 3D stem profile and used in the harvest simulation. This will be
described in deliverable D4.07
Technical Meeting
19-21 Jan 15

43. TASK 4.2
Evaluation of NIR spectroscopy as a tool for determination of
log/biomass quality index in mountain forest
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Task leader: Anna Sandak (CNR)
Technical Meeting
19-21 Jan 15

44. Task 4.2: Partners involvement
Task Leader: CNR
Task Partecipants: 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
Greifenberg and Flyby: will support CNR in order to collect NIR spectra at various
stages of the harvesting chain
Technical Meeting
19-21 Jan 15

45. • 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
Technical Meeting
19-21 Jan 15

46. 4.2 Deliverables
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 M10, October 2014
Estimated person Month= 5
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 M18, June 2015
Estimated person Month= 8
Technical Meeting
19-21 Jan 15

47. 4.2 Timing
Evaluation of near infrared (NIR) spectroscopy as a tool for determination of log/biomass quality index in mountain forests
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
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
T4.2
D.4.03
D.4.08
test sensors avaliable on the market
finalize concept
design/adopt to the processor
test electronic system
assemble hardware
collect reference samples
analyse reference samples
test hardware + software
calibrate system
develop algorithm for NIR qualityindex
integrate NIR quality index with quality grading/optymization (T4.6) D.4.12
Technical Meeting
19-21 Jan 15

48. Deliverable 4.03
This report contains a recommended protocol for proper collection of NIR spectra
within SLOPE project.
Brief presentation of currently available hardware, listing their advantages and
disadvantages.
Basic information regarding mathematical algorithms for spectra pre-processing
and data evaluation are provided.
Detailed procedure, potential obstacles and important considerations related to
measurement of NIR along the whole harvesting scenario according to SLOPE
approach are discussed here.
Brief description of various forest operation steps and information regarding
quality indexes obtained at varying harvesting chain stages are provided.
Brief description of wood properties and log defects that can be measured and
detected by means of NIR spectroscopy.
Technical Meeting
19-21 Jan 15

49. Spectrophotometers
laboratory
in-field
Technical Meeting
19-21 Jan 15

50. NIR spectrophotometers
cameras FT-NIR DA LVF DM AOTF MEMS
Spectral range limited full limited limited full limited limited
Scanning time (s) cont. 30 1 0.5 10 1 1
resolution high very high high limited high limited limited
cost N/A high middle low middle middle middle
Signal/noise high high limited limited high limited limited
Calibrations
transfer
limited very
good
good good very
good
good limited
Shock resistance yes no yes yes no yes yes
Suitable for SLOPE       
Technical Meeting
19-21 Jan 15

51. Mathematical methods and algorithms suitable for NIR
spectroscopic evaluation of log/wood quality in SLOPE scenario
Algorithms for pre-processing of spectra
•Averaging
•Derivative
•Smoothing normalization
•Baseline correction
•Multiplicative Scatter Correction
Algorithms for NIR data post-processing and data mining
•Cluster Analysis (CA)
•Principal Component Analysis (PCA)
•Identity Test (IT)
•Quick Compare (QC)
•Partial Least Squares (PLS)
Technical Meeting
19-21 Jan 15

52. – 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
Activities: Feasibility study and specification of the
measurement protocols for proper NIR data acquisition
Technical Meeting
19-21 Jan 15

53. Detailed procedure related to measurement of NIR along
the whole harvesting scenario
Forest modeling
NIR quality index #1 will be related directly to the health status, stress status and to the
productivity capabilities of the tree(s) foreseen for harvest
Tree marking
Direct measurement of the NIR spectra by means of portable instruments (DA and LVF) will
be performed in parallel to the tree marking operation. The spectra will be collected and
stored for further analysis (NIR quality index #2)
Cutting of tree
testing the possibility of collecting sample of wood in a form of the triangular slice being a
part of the chock cut-out from the bottom of the log (NIR quality index #3)
Processor head
NIR sensors will be integrated with the processor head (NIR quality index #4). All the
sensors will be positioned on a lifting/lowering bar on the head processor near the cutting
bar. The cutting bar will be activated in two modes: automatic and manual
Technical Meeting
19-21 Jan 15

54. 
the scanning bar #1  with NIR sensor
Sensor position in the intelligent processor head
Technical Meeting
19-21 Jan 15

55. CRio
NIR spectra (USB)
Control system
Technical Meeting
19-21 Jan 15

56. Detailed procedure related to measurement of NIR along
the whole harvesting scenario
Pile of logs
The cross section of logs stored in piles is easily accessible for direct measurement. Such
measurements will be repeated periodically in order to monitor the quality depreciation
and to determine the most optimal scanning frequency. The result of measuring NIR
spectra of logs stored in piles will be NIR quality index #5
Laboratory
Samples collected in the forest will be measured instantaneously after arrival in the
laboratory (at the wet state and with rough surface) by using the bench equipment
(NIR quality index #6). However, samples will be conditioned afterward and their surfaces
prepared (smoothed) in order to eliminate/minimize effects of the moisture variations and
light scatter due to excessive roughness on the evaluation results of fresh samples.
Technical Meeting
19-21 Jan 15

57. Collection of NIR spectra and flow of samples/data at
different stages of the harvesting process chain
(optional)
prepare samples #1
measurement of infrared
spectra (wet state)
prepare samples #2
condition samples
chemometric models for wet
wood and/or in field
chemometric models for
dry/conditioned wood (lab)
measurement of infrared
spectra
collect sample #1:
chip of axe
collect sample #2:
core ~30mm deep
collect sample #3:
chips after drilling core
collect sample #4:
triangular slices
measurement NIR profile
or hyperspectral image
measurement profile
of infrared spectra
consider approach: max
slope, pith position, WSEN
compute NIR
quality index#2
compute NIR
quality index#3
compute NIR
quality index#4
measurement profile
of infrared spectra
consider approach:
pith position, defects
compute NIR
quality index#5
tree marking
cutting tree
processor head
pile of logs
expert system & data base
refresh sample surface
measurement of infrared
spectra (dry state)
compute dry wood
NIR quality index#6compute the log quality
class (optimize cross-cut)
estimated tree quality
forest models
update the forest database
compare results of wet and
dry woods
combine all available char-
acteristics of the log
lab
Calibration transfer
f(MC, surface_quality)
3D tree
quality index
hyperspectral
HI quality index
stress wave
SW quality index
cutting force
CF quality index
compute NIR
quality index#1
Technical Meeting
19-21 Jan 15

58. Protocol for NIR measurement of logs/wood
Procedure for logs:
• turn on instruments
• warm up detector
• measure white reference
• measure black reference
• measure series of spectra
• save results
• post processing of spectra
• in field data mining
(assuming availability of
previously developed
chemometric models)
Procedure for wood:
• turn on instrument
• warm up detector
• perform instrument validation
• PQ (Performance Qualification)
• OQ (Operational Qualification)
• measure background
• measure series of spectra
• save results
• post-process the spectra
• develop calibration models
• perform calibration transfer
(if required)
Technical Meeting
19-21 Jan 15

59. Important considerations
Logs:
•Resolution
•Measurement time
•Number of measurements
•Effect of ruggedness (effect of moisture, temperature and vibrations)
Wood:
•Number os scanes per averaging
•Number of measurements
•Selection of scanning zones (wood section, early/late wood)
•Effect of roughness and surface preparation
•Effect of moisture
•Effect of time (surface deactivation)
Technical Meeting
19-21 Jan 15

60. Potential for detection of defects and determination of material
properties as measured by means of various NIR sensors
Instrument type FT-NIR dispersive linear variable filter MicroNIR
Moisture content of sample wet dry wet dry wet dry
Surface of sample smooth rough smooth rough smooth rough smooth rough smooth rough smooth rough
knots            
resin pocket            
twist            
eccentric pith            
compression wood      ?    ?  
sweep            
taper            
shakes            
insects ?   ? ?  ? ? ?  ? ?
dote  ?  ?  ?  ? ? ? ? ?
rot            
WooddefectsaccordingtoEN
1927-1:2008
stain  ?   ? ?  ? ? ?  ?
lignin ? ?   ? ?   ? ?  ?
cellulose ? ?   ? ?   ? ?  ?
hemicellulose ? ?   ? ?   ? ?  ?
extractives ? ?   ? ?   ? ?  ?
microfibryl angle ? ?     ? ?   ? ?
calorific value ? ?   ? ?  ? ? ?  ?
heartwood/sapwood       ? ?   ? ?
density      ?  ?  ?  ?
mechanical properties      ?  ?  ?  ?
moisture content            
provenance    ?        
resonance wood ? ?  ? ? ? ? ? ? ? ? ?
Otherwoodproperties/characteristics
Technical Meeting
19-21 Jan 15

61. • 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).
Development and validation of chemometric models.
Technical Meeting
19-21 Jan 15

62. Thank you very much
Technical Meeting
19-21 Jan 15

63. SLOPE
Integrated proceSsing and controL systems fOr sustainable forest Production in mountain arEas
WORK PACKAGE 4: DEFINITION OF
REQUIREMENTS AND SYSTEM ANALYSIS
T.4.3 – EVALUATION OF HYPERSPECTRAL IMAGING (HI) FOR THE
DETERMINATION OF LOG/BIOMASS “HI QUALITY INDEX”
THEME:
Integrated processing and Control Systems for
Sustainable Production in Farms and Forests
Duration: 36 Months
Partners: 10
Coordinating institution: Fondazione Graphitech
Coordinator: Dr. Raffaele De Amicis
Task leader: BOKU
Participants: GRAPHITECH, CNR, KESLA, FLY,
GRE
Prepared by: Andreas Zitek, Katharina Böhm,
Ferenc Firtha, Barbara Hinterstoisser
Technical Meeting
19-21 Jan 15

64. WT 4.3 - Aims
• Evaluating the usability of hyperspectral imaging for
characterization of bio-resources along the harvesting chain
and providing guidelines for proper collection and analysis of
data.
• Intensive laboratory tests and transfer to field conditions will
be tested and solutions ranked for their applicability in field
• System calibration and calibration transfer
• Both visible range cameras and near infrared scanners will be
investigated.
• Postprocessing of data with different chemometric approaches
• Development of the “HI quality index” for quality grading
• from the set of characteristics as a result of image
processing/data mining
• Indices compared to “expert“ judgements
Technical Meeting
19-21 Jan 15

65. Task 4.3 – D 4.04. and D 4.09
Task 4.3 Evaluation of hyperspectral imaging (HI) for
the determination of log/biomass “HI quality index”
• Deliverables under lead of BOKU
• D 4.04 - Establishing hyperspectral measurement
protocol (planned: month 11 – November 2014;
asked extension of deadline to January 2015 due
to team reformation after death of Manfred
Schwanninger)
• D4.09 Estimation of log quality by hyperspectral
imaging (planned: month 19 – July 2015)
Project meeting
19-22/jan/2015

66. Task 4.3 - NIR vs. VIS/NIR
hyperspectral imaging
Project meeting
19-22/jan/2015
Spectra at on one spot Spectra at each pixel
• Ranges within the electromagentic spectrum
• Visible wavelength range ~ 390-700 nm
• NIR wavelength range ~ 780 nm and 3 μm
Advantages:
• remote sensing
• segmentation of object
• scanning non-homogeneous surface
Disadvantages:
• non-izolated (lower signal to noise ratio)
• setup dependant calibration (distance, lens, illumination)
• indefinite geometry (illumination/observation angle)
• huge amount of data must be processed
Specific software needed (control system, calibration, data
processing)

67. Task 4.3 - HSI background
RGB – 3 colours
Project meeting
19-22/jan/2015
After: BURGER, J. & KAUŠAKYTĖ, A., 2013. Visual Chemometrics –
Interactive Software for Hyperspectral Image Exploration and
Analysis. 27 March, 2013 Gembloux, BE: SIA BurgerMetrics, Riga,
Latvia.

68. Task 4.3 - HSI background
Multispectral – 4-10 wavelenghts
Project meeting
19-22/jan/2015
After: BURGER, J. & KAUŠAKYTĖ, A., 2013. Visual Chemometrics –
Interactive Software for Hyperspectral Image Exploration and
Analysis. 27 March, 2013 Gembloux, BE: SIA BurgerMetrics, Riga,
Latvia.

69. Task 4.3 - HSI background
Hyperspectral – hypercube > 100 wavelengths, quasi-
continous, nm steps
After: BURGER, J. & KAUŠAKYTĖ, A., 2013. Visual Chemometrics –
Interactive Software for Hyperspectral Image Exploration and
Analysis. 27 March, 2013 Gembloux, BE: SIA BurgerMetrics, Riga,
Latvia.
Project meeting
19-22/jan/2015

70. Task 4.3 HSI – general setups
• Whiskbroom imaging : During whiskbroom imaging the sample is scanned pixel
per pixel in the x–y–spatial direction in a sequential manner.
• Staring (staredown) imaging: Staring imaging is done by a two-dimensional
camera capturing the spectral information in each pixel x-, y-plane at once.
• Pushbroom imaging: Pushbroom imaging as a line scanning system acquires the
information for each pixel in the line at once.
Project meeting
19-22/jan/2015
From: BOLDRINI, B.,
KESSLER, W., REBNER, K.
& KESSLER, R. W., 2012.
Hyperspectral imaging: a
review of best practice,
performance and pitfalls
for inline and online
applications. Journal of
Near Infrared
Spectroscopy, 20 (5):
438-508.

71. Task 4.3 – available systems
At CORVINUS university
HeadWall Photonics® push-
broom hyperspectral system
(Xenics NIR camera: 320*256
matrix, 14 bit A/D, 5 nm
resolution, 250 mm Y-table
gear, stable diffuse 45/0
illumination).
Project meeting
19-22/jan/2015

72. Task 4.3 – available systems
At BOKU university
Setup of BOKU HSI
system – Zeutec
system with
updated
Xeneth Camera
(Xenics LuxNIR
InGaAs camera:
320*256 matrix, 12
bits, linear-table
gear ISEL).
Project meeting
19-22/jan/2015

73. Task 4.3 – available systems
Project meeting
19-22/jan/2015

74. Project meeting
19-22/jan/2015
Task 4.3 – available software
ARGUS data aquisition software
F. Firtha, CuBrowser hyperspectral data processing
algorithm ftp://fizika2.kee.hu/ffirtha/Argus-
CuBrowser.pdf, (2012).
Cubrowser data browsing and
pre-processing software
F. Firtha, Argus hyperspectral acquisition software,
ftp://fizika2.kee.hu/ffirtha/Argus-CuBrowser.pdf,
(2010)
CAMO: Unscrambler
Eigenvector: PLS_toolbox, MIA,
Model_exporter
BRUKER: OPUS

75. Task 4.3 – First results
First results – dry, wetted,
fungi, normal wood
Project meeting
19-22/jan/2015
Normal

76. Task 4.3 – CUBE wavelength scroll
Project meeting
19-22/jan/2015

77. Task 4.3 – First results summary
• Fungi and/or structural abnormalities could be clearly identified on the
dry and wet wood
• The influence of wood surface roughness was negligible
• HSI and NIR provided comparable results providing explanatory model
Project meeting
19-22/jan/2015
@ IASIM Conference 3.-5.December 2014

78. Task 4.3 – training & classification
GELADI, P., SETHSON, B., NYSTRÖM, J., LILLHONGA, T., LESTANDER, T. &
BURGER, J., 2004. Chemometrics in spectroscopy: Part 2. Examples.
Spectrochimica Acta Part B: Atomic Spectroscopy, 59 (9): 1347-1357.
Project meeting
19-22/jan/2015

79. WT 4.3 Workflow
Sampling of wood logs
Austria, BOKU
Sampling of wood logs
Italy, CNR Ivalsa
Hyperspectral imaging BOKU
NIR measurements CNR
NIR measurements BOKU
Sample exchange? Sample storage? (20°C, 60 % air mositure)?
Questions
Coverage of deficits?
Number of samples total
Number of samples along the stem of one tree
Number of samples per deficit
Number of tree species
Data exchange?
NIR data (WT. 4.2)
existing data from J.
Burger measurements
Interferences?
Oil, soil, water, surface
roughness, lightning
Prediction of quality by
multivariate model –
Index of log/biomass quality
Training on known deficits and
interferences – statistical model
evaluation
FieldLaboratoryField
Calibration transfer to sensors
used in the field? Rugged field conditions?
Position of sensor?
Type of sensor?
Multispectral with filters?
Hyperspectral sensors?
Online processing of data
Combination with other data
Judgement of log quality
Quality communication
Action selection
Model integration?
Task 4.6 and D4.12 – Implementation and calibration of
predicition models for log/biomass quality classes and
report on validation procedure (CNR)
Field application and integrated system?
WP 5 (Forest information system), WP6 (System
integration), WP7 (Pilot of SLOPE demonstrator) -time loss?

80. HSI sensors-latest developments
IMEC (Belgium) sensor combined with camera
• Imec has developed a process for depositing
hyperspectral filters directly on top of CMOS image
sensors
• VISNX (http://www.visnx.com/) built first consumer
ready made mini HSI system
‘wedge design’ ‘per pixel design’ ‘area design’
Project meeting
19-22/jan/2015
(“Precision farming“)

81. HSI sensors-latest developments
BaySpec, Inc., San Jose, CA
• Push-broom (OCITM-U-1000) - “true push-broom” fast
hyperspectral imaging, simply by using your hand to move
the imager or sample (600-1000 nm, 100 spectral bands).
• Snapshot (OCITM-U-2000) hyperspectral cube data can be
captured at video or higher rates (600-1000 nm, 20 spectral
bands).
Project meeting
19-22/jan/2015

82. HSI sensors-latest developments
EVK Graz Austria
• Fully integrated systems for harsh conditions
with algorithms on board
Project meeting
19-22/jan/2015

83. Project meeting
19-22/jan/2015
HSI – processor head and sensor
MIETTINEN, M., KULOVESI, J., KALMARI, J. & VISALA, A. 2010. New Measurement Concept for
Forest Harvester Head. In: HOWARD, A., IAGNEMMA, K. & KELLY, A. (eds.) Field and Service
Robotics. Springer Berlin Heidelberg.
Möller, 2011

84. Task 4.3 & WP 8 – Hyperspectral
Imaging Workshop Tulln 20.3.2015
Talks and topics
• Hyperspectral imaging – general introduction (Rudolf
Kessler, DE)
• Hyperspectral imaging of food (Ferenc Firtha, HU)
• Hyperspectral imaging of biomaterials in agriculture
and other fields (Philippe Vermeulen, B)
• Hyperspectral imaging of wood (Ingunn Burud, NO)
• Integrated HSI solutions (EVK, AT)
• Chemometrics course (Eigenvector, USA)
• Workshop is dedicated to the SLOPE challenges &
questions with regard to hyperspectral imaging
Project meeting
19-22/jan/2015

85. Thank you for your attention
ANDREAS ZITEK
KATHARINA BÖHM
andreas.zitek@boku.ac.at
katharina.boehm@boku.ac.at
Phone: +43 676 780 65 15
Fax: +43 1 47654 6029
University of
Natural Resources
and Life Sciences,
Vienna
Department of Material Sciences
and Process Engineering
BOKU - University of Natural Resources
and Life Sciences, Vienna (BOKU-UFT),
Dept. of Chemistry, Division of
Analytical Chemistry, Konrad-Lorenz-
Straße 24, 3430 Tulln, AUSTRIA
https://viris.boku.ac.at/WShyperspectral2015/WShyperspectral2015/HOME.html
Project meeting
19-22/jan/2015

86. TASK 4.4
Data mining and model integration of
log/biomass quality indicators from
stress-wave (SW) measurements,
for the determination of the
“SW quality index”
Work Package 4: Multi-sensor model-
based quality of mountain forest
production
Technical Meeting
19-21 Jan 15

87. The objectives of this task is to optimize testing procedures and
prediction models for characterization of wood along the harvesting
chain, using acoustic measurements (i.e. stress-wave tests).
A part of the activity will be dedicated to the definition of optimal
procedures for the characterization of peculiar high-value assortments,
typically produced in mountainous sites, such as resonance wood.
Task Leader: CNR
Task Participants: Greifenberg, Compolab
WP4: T 4.4 Data mining and model integration of log/biomass
quality indicators from stress-wave (SW) measurements, for the
determination of the “SW quality index”
Objectives
Technical Meeting
19-21 Jan 15

88. WP4: T 4.4 Deliverables
D4.05) Establishing acoustic-based measurement protocol: This deliverable
contains a report and protocol for the acoustic-based measurement procedure
Starting Date: August 2014 - Delivery Date: December 2014
D4.10) Estimation of log quality by acoustic methods: Numerical procedure for
determination of “SW quality index” on the base of optimized acoustic velocity
conversion models.
Starting Date: January 2015 - Delivery Date: August 2015
Estimated person Month= 6.00
Technical Meeting
19-21 Jan 15

89. Preliminary tests
Testing protocol
Lab scanner tests
Validation
Measurements
Prediction models
Quality index
WP4: T 4.4 Schedule
Technical Meeting
19-21 Jan 15

90. D: 4.5 Establishing acoustic-based
measurement protocol
1 Introduction
2 Determination of log quality indicators from stress-wave (SW)
3 Stress-wave data acquisition and analysis
3 Protocol of acoustic measurement within SLOPE for the determination of the quality
indicators
4 Determination of SW quality index
6 Test plan for on-line SW measurement in the processor head
Table of content
22.12.14: First draft submitted
16:01.15: Revised version submitted
Technical Meeting
19-21 Jan 15

91. D: 4.5 Establishing acoustic-based
measurement protocol
1.1 Application of stress wave-based techniques in forestry and wood characterization
1.2 Material-dependent factors affecting acoustic measurements
1.2.1 Anisotropy and heterogeneity
1.2.2 Moisture content
1.2.3 Temperature
1.3 Methodology-dependent factors affecting acoustic measurements
1.3.1 Frequency
1.3.2 Transmission modes
1.3.3 Coupling
TOC: chapter 1
Technical Meeting
19-21 Jan 15

92. D: 4.5 Establishing acoustic-based
measurement protocol
TOC: chapter 2
2 Determination of log quality indicators from stress-wave (SW)
2.1 Defectiveness indicators
2.1.1 Slope of grain
2.1.2 Compression wood
2.1.3 Knottiness
2.2 Decay indicators
2.2.1 Insect decay
2.2.2 Rot
2.3 Density/stiffness indicator
Technical Meeting
19-21 Jan 15

93. D: 4.5 Establishing acoustic-based
measurement protocol
TOC: chapter 3
3 Stress-wave data acquisition and analysis
3.1 Test setup
3.2 Signal processing and data mining
Technical Meeting
19-21 Jan 15

94. D: 4.5 Establishing acoustic-based
measurement protocol
TOC: chapter 4
4 Protocol of acoustic measurement within SLOPE for the determination of the quality
indicators
4.1 Preliminary analysis: SW measurement on standing trees
4.2 Preliminary analysis: SW measurement on trees after felling
4.3 SW analysis on de-branched logs
4.4 Measurement of visible defects
4.5 Direct measurement of wood material properties correlated with SW data
Technical Meeting
19-21 Jan 15

95. D: 4.5 Establishing acoustic-based
measurement protocol
v
Technical Meeting
19-21 Jan 15

96. D: 4.5 Establishing acoustic-based
measurement protocol
TOC: chapter 5
5 Determination of SW quality index
5.1 Stress-wave velocity conversion models
5.1.1 Stress wave and relation with measurement position in the stem
5.1.2 Stress wave and relation with log diameter
5.1.3 Stress wave and relation with density
5.1.4 Stress wave and relation with moisture content
5.1.5 Stress wave and relation with mechanical properties
5.2 Incorporation of parameters from other types of measurements
Technical Meeting
19-21 Jan 15

97. D: 4.5 Establishing acoustic-based
measurement protocol
TOC: chapter 6
Test plan for on-line SW measurement in the processor head
Features and functionality to test
Types of testing
Functional testing
Data testing
Usability testing
Performance testing
Quality testing
Interface testing
Regression testing
Other testing
To be developed in the frame of WP3 – T3.04
CONTRIBUTION OF COMPOLAB IS NEEDED TO FINALIZE THIS CHAPTER!
Technical Meeting
19-21 Jan 15

98. WP4: T 4.4 Data mining and model integration of log/biomass
quality indicators from stress-wave (SW) measurements, for the
determination of the “SW quality index”
Technical Meeting
19-21 Jan 15

99. WP4: T 4.4 Data mining and model integration of log/biomass
quality indicators from stress-wave (SW) measurements, for the
determination of the “SW quality index”
Accelerometer #1
Accelerometer #2
Laser displacement
sensor
SW generator
TOF
Resonance method
Local
characterization
Global
characterization
Technical Meeting
19-21 Jan 15

100. Conclusions
Many factors influence SW propagation in wood.
Parameters measured with the other NDT methods will be incorporated in the SW
prediction models
Multiple linear regression analysis will be implemented for the definition of the
importance of the different parameters (regression t-values) for the model.
The further development of Task 4.4 is based on the implementation of the lab scanner
(i.e. purchase of sensors)
For the implementation of the methodology in the real case scenario, some practical issues
(e.g. coupling-decoupling of sensors, etc.) have to be considered in combination with
activity of Task 3.4
Technical Meeting
19-21 Jan 15

101. TASK 4.5
Evaluation of cutting process (CP) for the
determination of log/biomass “CP quality index”
Work Package 4: Multi-sensor model-
based quality control of mountain
forest production
Technical Meeting
19-21 Jan 15

102. Task 4.5: cutting process quality index
Objectives
The goals of this task are:
• to develop a novel automatic system for measuring of the
cutting resistance of wood processed during harvesting
• to use this information for the determination of log/biomass
quality index
Technical Meeting
19-21 Jan 15

103. Task 4.5: Cutting Process (CP) for the determination of
log/biomass “CP quality index”
Task Leader: CNR
Task Partecipants: Compolab
Starting : October 2014
Ending: November2015
Estimated person-month = 4.00 (CNR) + 2.00 (Compolab) (to be confirmed)
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
Compolab: will provide expertise in regard to sensor selection and integration with the processor head
+ extensive testing of the prototype
Technical Meeting
19-21 Jan 15

104. 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: January 2015 (M.13)
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: September 2015 (M.21)
Technical Meeting
19-21 Jan 15

105. T4.5: Evaluation of cutting process (CP) for the
determination of log/biomass CP quality index
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
Technical Meeting
19-21 Jan 15

106. Task 4.5: cutting process quality index
Timing
Evaluation of cutting process (CP) for the determination of log/biomass “CP quality index”
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
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
T4.5
D.4.06
D.4.11
finalize concept
design/adopt to the processor
test electronic system
assemble hardware
test hardware + software
calibrate system
develop algorithm for CP Q_index
integrate CP quality index with quality grading/optymization (T4.6) D.4.12
D.4.06 Establishing cutting power measurement protocol
D.4.11 Estimation of log quality by cutting power analysis
D.4.12 Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure
Technical Meeting
19-21 Jan 15

107. Task 4.5: cutting process quality index
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.
Sensors to be tested on the lab scanner:
•Electrical power consumption of feed
•Electrical power consumption of chain saw
•Tensionmeters for deformations due to forces
•Load cells directly measuring cutting forces
•Optional: Acoustic Emission sensor
•Optional: microphone array
Technical Meeting
19-21 Jan 15

108. Task 4.5: cutting process quality index
Delimbing system



Schematic of the de-branching system; cutting knives  and hydraulic actuator 
Technical Meeting
19-21 Jan 15

109. Task 4.5: cutting process quality index
Chainsaw

the scanning bar #1  and the chain saw  in the working positions
Technical Meeting
19-21 Jan 15

110. Task 4.5: cutting process quality index
control system
CRio
cutting force
saw “push” force
feed force
Technical Meeting
19-21 Jan 15

111. Task 4.5: cutting process quality index
Comments
The working principles of the selected processor head (upscaled
ARBRO 1000) allows direct measurement of the cutting/feed force
as related to (just) the cutting-out branches.
The output of this task provide a “quality map of log” for grading
(and assisting operator in cutting decision?)
Technical Meeting
19-21 Jan 15

112. Task 4.5: cutting process quality index
Challenges
No prototype developed due to delays; lab scanner under
construction.
What sensors are appropriate for measuring cutting forces in
processor head?
load cell? tensometer? oil pressure? electrical current? microphone? AE sensor?
How to physically 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?
Technical Meeting
19-21 Jan 15

113. Thank you very much
Technical Meeting
19-21 Jan 15

114. TASK 4.6
Implementation of the log/biomass grading
system
Work Package 4: Multi-sensor model-
based quality control of mountain
forest production
Technical Meeting
19-21 Jan 15

115. Task 4.6: Implementation of the log/biomass grading system
Task Leader: CNR
Task Participants: GRAPHITECH, COMPOLAB ,MHG, BOKU, GRE, TRE
Starting : June 2014
Ending: July 2016
Estimated person-month = 1.50 (GRAPHITECH) + 2.0 (CNR) + 1.00 (COMPOLAB) + 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, COMPOLAB: 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
Technical Meeting
19-21 Jan 15

116. 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: October 2014 (M.10)
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: March 2016 (M.27)
Technical Meeting
19-21 Jan 15

117. Task 4.6: Implementation of the grading system
Timing
Implementation of the log/biomass grading system
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
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T4.6
D.4.01
D.4.12
surveys
literature research
test quality measuring systems
develop software for integration of quality indexes
test software
calibrate system
validate the algorithm/system
Technical Meeting
19-21 Jan 15

118. Deliverable 4.01
• Introduction and purpose of the report
• General overview on the recent wood market in relation to SLOPE
• Wood market within and beside legal norms and regulation
• “Quality of wood” from perspectives of various players
• Potential advantages of wood from mountain areas
• List of legal norms, standards and regulations in relation to log grading
– Global level
– European Union level
– Specific countries
– Specific regulation in various wood industries
Technical Meeting
19-21 Jan 15

119. Currently used logs grading practice
• Currently used log grading practices
• Procedure for estimation of the log’s geometrical characteristics and volume
• Visual grading procedures
• Machine grading systems of logs
• Detailed criteria of Norway spruce (Picea abies) quality sorting according to
EN 1927-1:2008
– List of wood/log defects: knots, resin pocket, twist (or spiral grain), eccentric
pith, compression wood, sweep, taper, shakes, checks and splits, insect or worm
holes, dote, rot, stain
– Round wood quality classes according to EN 1927-1:2008
– Criteria of classification
Technical Meeting
19-21 Jan 15

120. Wood defects and possibilities of their
detection/identification (focus on SLOPE sensors)
Sensor type
Multispectral cameras for remote sensing (satellite)
Multispectral cameras for remote sensing (UAV)
3D laser scanner and cloud of points
Near Infrared spectrometer (laboratory)
Near Infrared spectrometer (in-field)
Hyperspectral imaging VIS
Hyperspectral imaging NIR
Ultrasound sensor
Free vibrations meter
Cutting forces meters (de-branching)
Acoustic emission sensor
Cutting resistance of cross cut sensor
Vision CCD camera on side of log
3D camera on side of log
Log-geometry sensors (diameter f(length))
Conditionofforestarea
Healthconditionoftree??
Foliarindex
Crowndamage??
Treespeciesrecognition??
Branchindex??
Macro properties
of the forest area
or the whole tree
knots???
resinpocket
twist???
eccentricpith?
compressionwood???
sweep?
taper?
shakes??
insects??
dote??
rot?
stain?
Log defects according to EN
1927-1:2008
lignin?
cellulose?
hemicellulose?
extractives?
microfibrylangle??
calorificvalue?
heartwood/sapwood
density????
mechanicalproperties?
moisturecontent????
provenance??
woodtracking???
bottom-enddiameter
top-enddiameter
externalshapeoflog
logdiameterwithout
bark

logvolume
Other wood properties/characteristics
resonancewood???
Suitability for
detection of resources
for niche products
Technical Meeting
19-21 Jan 15

121. Missing contributions to D04.01
1. Grading rules from Austria
2. Final discussion with TreeMetrics about strategy for automatic
calssifications
3. Final conclussions
Foreseen deadline for conclussion: January 31st
Technical Meeting
19-21 Jan 15

122. Task 4.6: Implementation of the grading system
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 (value) wood from less trees
Technical Meeting
19-21 Jan 15

123. Task 4.6: Implementation of the grading system
The concept (logic)
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)
Technical Meeting
19-21 Jan 15

124. Task 4.6: Implementation of the grading system
The concept (diagram)
Measure 3D shape of
several trees
Measure NIR spectra of
tree X in forest
Extract 3D shape of
tree X
Compute 3D quality in-
dexes for log X.1 … X.n
Measure NIR spectra of
tree X on processor
Measure NIR spectra of
tree X on the pale
Compute NIR quality in-
dex for tree X
Compute NIR quality in-
dexes for log X.1 … X.n
Compute NIR quality in-
dexes for log X.1 … X.n
Data base for harvest
data
Data base for Forest In-
formation System
Determine quality grade
for log X.1 … X.n
T4.1
T4.2
Measure hyperspectral
image of tree X in forest
Measure cross section
image of log X.1 … X.n
Measure NIR spectra of
tree X on the pale
Compute HI quality index
for tree X
Compute HI quality in-
dexes for log X.1 … X.n
Compute HI quality in-
dexes for log X.1 … X.n
T4.3
Measure stress waves on
tree X in forest
Measure stress waves of
tree X on processor
Measure stress waves of
log X.1 …X.n on the pale
Compute SW quality in-
dex for tree X
Compute SW quality in-
dexes for log X.1 … X.n
Compute SW quality in-
dexes for log X.1 … X.n
T4.4
Measure delimbing force
on log X.1 … X.n
Measure cross-cutting
force on log X.1 … X.n
Compute CF quality in-
dexes for tree X
Compute CF quality in-
dexes for log X.1 … X.n
T4.5
Technical Meeting
19-21 Jan 15

125. Task 4.6: Implementation of the grading system
The concept (diagram)#1
Measure 3D shape of
several trees
Measure NIR spectra of
tree X in forest
Extract 3D shape of
tree X
Compute 3D quality in-
dexes for log X.1 … X.n
Measure NIR spectra of
tree X on processor
Measure NIR spectra of
tree X on the pale
Compute NIR quality in-
dex for tree X
Compute NIR quality in-
dexes for log X.1 … X.n
Compute NIR quality in-
dexes for log X.1 … X.n
Data base for harvest
data
Determine quality
for log X.1 …
T4.1
T4.2
Measure hyperspectral
image of tree X in forest
Measure cross section
image of log X.1 … X.n
Measure NIR spectra of
tree X on the pale
Compute HI quality index
for tree X
Compute HI quality in-
dexes for log X.1 … X.n
Compute HI quality in-
dexes for log X.1 … X.n
T4.3
Technical Meeting
19-21 Jan 15

126. Task 4.6: Implementation of the grading system
The concept (diagram)#2
Measure stress waves on
tree X in forest
Measure stress waves of
tree X on processor
Measure stress waves of
log X.1 …X.n on the pale
Compute SW quality in-
dex for tree X
Compute SW quality in-
dexes for log X.1 … X.n
Compute SW quality in-
dexes for log X.1 … X.n
T4.4
Measure delimbing force
on log X.1 … X.n
Measure cross-cutting
force on log X.1 … X.n
Compute CF quality in-
dexes for tree X
Compute CF quality in-
dexes for log X.1 … X.n
T4.5
Technical Meeting
19-21 Jan 15

127. Task 4.6: Implementation of the grading system
The concept (diagram)#3
Compute 3D quality in-
dexes for log X.1 … X.n
Compute NIR quality in-
dex for tree X
Compute NIR quality in-
dexes for log X.1 … X.n
Compute NIR quality in-
dexes for log X.1 … X.n
Data base for harvest
data
Data base for Forest In-
formation System
Determine quality grade
for log X.1 … X.n
Compute HI quality index
for tree X
Compute HI quality in-
dexes for log X.1 … X.n
Compute HI quality in
Technical Meeting
19-21 Jan 15

128. Task 4.6: Implementation of the grading system
The concept (data flow & hardware)
NI CompactRio master
Database
NI CompactRio client Wifi (in field)
FRID
weight
fuel
???
Wifi (home)
Wifi (home)
HD
or
GPRMS
Black box
CP
NIR
HI
SW
camera
kinect
Wifi (in field)
Wifi (home)
Wifi (home)
Technical Meeting
19-21 Jan 15

129. Task 4.6: Implementation of the grading system
The “real world” actions: lab scanner
Technical Meeting
19-21 Jan 15

130. Task 4.6: Implementation of the grading system
The “real world” actions: processor
Technical Meeting
19-21 Jan 15

131. Task 4.6: Implementation of the grading system
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?
Some same as in before, but the final answer possible only after demonstrations
Technical Meeting
19-21 Jan 15

132. Thank you very much
Technical Meeting
19-21 Jan 15

133. Task 4.1: 3D quality index
Objective: the use of laser scanners for 3D mapping of standing
trees and experiences of Treemetrics in 3D data evaluation
on order to determine 3D quality index on standing trees
Method: detailed methodology for the hardware set-up and
measurement procedure has been described in Deliverable 4.2.
Algorithm: The custom software developed at TreeMetrics will
be used for visualization of the tree shape and for computation
of the stem dimensions (including diameters, taper, tree height).
The quality index will include the estimated quality class,
recommended cross-cut pattern and suggested market for each
measured log.
Technical Meeting
19-21 Jan 15

134. Task 4.2: NIR quality index
Objective: the use of NIR for determination of selected wood
properties, detection of some wood defects and determination
of the NIR quality index on both standing trees and logs.
Method: NIR technique will be used for measuring spectra at
varying stages of the harvesting chain: tree marking, tree feeling,
processing on the processor and storing in the stack. NIR sensor
will be integrated with the processor head in order to allow in-line
acquisition of spectra and determination of the log quality
Algorithm: two approaches will be tested: laboratory and in field
Technical Meeting
19-21 Jan 15

135. Task 4.3: hyperspectral imaging quality index
Objective: the use of near HI for determination of selected wood
properties, detection of selected wood defects and determination
of the HI quality index on standing trees and harvested/processed
logs and wooden samples extracted from trees/logs.
Method: HI will be used for measuring spectral hypercubes at
varying stages of the harvesting chain: tree marking, tree feeling,
processing on the processor and storing in the stack. HI sensor will
be also integrated with the processor head
Algorithm: two approaches will be tested: laboratory and in field
Technical Meeting
19-21 Jan 15

136. Task 4.4: stress wave quality index
Objective: the use of SW propagation parameters for pre-grading
logs in forest on the base of correlated mechanical and physical
properties of the wood material (dynamic modulus of elasticity,
density) and the presence of damage, decay or other defects
Method: SW measurements will be carried out at various stages
of the harvesting chain: on the standing tree, felled tree and
debranched logs. Additional measurements in lab, to calibrate
measurements and determine correlation with selected material
properties and standard commercial classes.
Algorithm: two approaches will be tested: laboratory and in field
Technical Meeting
19-21 Jan 15

137. Task 4.5: cutting forces quality index
Objective: to develop on line system for determination of selected
wood properties and technical characteristics by measurement of
cutting forces associated with processing of logs in forest
Method: Two strategies will be tested:
•Cutting resistance of the chain saw cross-cutting the log
(The hydraulic pressure and oil flow will be measured. It will be later regressed
against wood density and cross-section geometry)
•Resistance of the de-barking knives as measured during the stroke
(The hydraulic pressure, mechanical deformations of the knife holder, acoustic
emission associated with the cutting out branches will be monitored. The presence,
number of branches and its health will be detected as a result)
Algorithm: 2 set of info will be extracted: wood density and branch idx
Technical Meeting
19-21 Jan 15