More precise wood supply through forest big dataJarmo Hämäläinen (Research Director, Metsä-teho)

1. More precise wood supply
through forest big data
Jarmo Hämäläinen
Metsäteho Oy
The European Big Data Value Forum
14-16 October 2019, Helsinki

2. Metsäteho is a wood procurement development company
building a better future for the forest industry
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3. R&D focus areas 2018–2025
JARMO HÄMÄLÄINEN / METSÄTEHO 14.10.2019
INFORMATION ECOSYSTEMS AND
DECISION SUPPORT SYSTEMS
OCCUPATIONAL SAFETY,
WELLBEING AND COMPETENCESUSTAINABILITY
RESOURCE- AND ENERGY-
EFFICIENCY
IMPROVING WOOD
PRODUCTION EFFICIENCY
TRANSPORTATION
SYSTEMS
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4. Finnish wood supply in a nutshell
JARMO HÄMÄLÄINEN / METSÄTEHO 14.10.2019
Forest inventory
• Finnish Forest
Centre
• ALS & aerial
photographs
Forest management
• 600 000 forest owners
• 100 000 wood trades/year
Harvesting
• 1000 companies
• 2000 harvesters
in cuttings
Transport
• 450 truck companies
• 1400 trucks
+ Railroad and
waterway transport
Annual domestic wood deliveries about 70 Mm3 and turnover 3 billion €.
Forest industry
• 110 large mills
• Hundreds of
smaller plants
• Turnover 30 billion €
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5. Development drivers in wood supply
JARMO HÄMÄLÄINEN / METSÄTEHO 14.10.2019
• Customer focus – raw material needs derived from mill production plans
• The amount and quality of wood
• Just-in-time
• Cost control – raw material price at the mill
• Flexibility in changing situations and conditions
• Operational reliability
• High work quality in the forest
• Sustainability
Key role in big data production
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6. Application development (continuing)
Utilization concepts (POC)
Legislation and rules (→ data availability)
Data management and analysing
Data transfer and fusion
Data acquisition and modelling
Vision and targets
Vision:
”More precise and cost-effective wood supply through
improved data and advanced decision support systems”
2014 towards the vision with large R&D efforts 2019
JARMO HÄMÄLÄINEN / METSÄTEHO 14.10.2019
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7. Forest inventory system of Finnish Forest Centre
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Aerial laser scanning & aerial photographs
Forest resource
data for grid (16 * 16 m)
and pattern level

8. Harvester data offers huge potential
Location of strip roads (machine
tracks) and key figures
Location and basic attributes
of harvested trees
Measured stem profiles and the
cutting data of logs
Key figures of wood removal on harvesting
object
Stem diameter distributions
Forest stand boundaries and other
location data
Management and
monitoring of
harvesting operations
• automatic systems
and self-control
• verification of
sustainability
Update of national
forest resource data
Use as ground truth in
remote sensing
Management and
control of cross-cutting
Development of
descriptive models of
trees and stock
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9. New big data sources in the future – automatic work quality
measurement in harvesting
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Thinning intensity
• Laser/camera
Strip road density
• Harvester data
Fig. Metsäteho
Fig. Jyry Eronen, UEF
Tree damages
• Camera
Rut depth
• Time-of-flight imaging or laser
Fig. Lari Melander, TUT
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10. Road conditions data through machine vision
and sensor fusion
Source: Vaisala & Metsäteho
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11. Forest Data Platform accelerating data usage
• Main target is to boost forest sector’s data
utilization.
• Platform’s role is the fusion, enrichment and
delivery of data from different sources to
applications.
• The target is:
• to make application and service
development easier and more cost-
effective
• to improve the flexibility to implement new
data sources
• Productization has started by The Finnish
Forest Centre. A similar concept test of road
data platform ongoing.
JARMO HÄMÄLÄINEN / METSÄTEHO 14.10.2019
Source: Metsäteho, CGI, Tampere University
ForestJson
query language
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12. Examples of applications
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13. An example of grid data utilization – a terrain trafficability
map for timber harvesting
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Source: Arbonaut Ltd. & Finnish Forest Centre
Trafficability
Map availability 10/2019
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14. Logging track planning tool for a harvester operator
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15. Gravel road trafficability prediction
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Source data:
• Soil type
• Wetness index
• Ditch depth
• Radiation index
→ Information for timber
transport and road maintenance
Source: Arbonaut Ltd. & Metsäteho
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16. Virtual forest application by Stora Enso
JARMO HÄMÄLÄINEN / METSÄTEHO 14.10.2019
Source:
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17. Great potential in Forest Big Data
• At least 100 M€ annually for the forest sector actors
• Added value in wood usage
• Cost, resource and capital efficiency
• Sustainability & climate targets
• Precision services for customers
• New business
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18. Summary
• Excellent drive in wood supply digitalization and big data utilization
• Great benefits for the whole sector within reach
• A common Forest Big Data vision as a key element
• Large R&D programs have been critical boosters
• Big data based decision support systems in focus now
JARMO HÄMÄLÄINEN / METSÄTEHO 14.10.2019 18

19. jarmo.hamalainen@metsateho.fi
CREATING OPPORTUNITIES 19
Thank you!