Overview of precision forest management in the Nordics and opportunities for Brazil

Overview of precision forest
management in the Nordics
and opportunities for Brazil
Marcos Wichert, MSc
Vice President,
Plantation Forest Management, Stora Enso

Main Topics
2
1. Forest industry in brief – Finland and Sweden
2. Stora Enso in brief
3. Examples precision forestry applied in Stora Enso
4. Opportunities for Brazil
5. Challenges and future needs in Precision Forestry
Extras slides at the end with more examples and informations
Marcos Wichert – Revolução Digital na Floresta Plantada
FinnCham - 17/Jun/2021

Forestry Industry in Brief – Finland and Sweden
Climate, vegetation maps and forest cover
3 Marcos Wichert – Revolução Digital na Floresta Plantada
Winter days/year in the Nordics
Sweden
Finland
Denmark
Iceland
Norway
Artic circle
Productive forests cover = 60% in Finland; 58% in Sweden.
Main forest ownership is with Individual (non-industrial) private
owners, with 60% in Finland and 48% in Sweden.

Forestry Industry in Brief – Finland and Sweden
Example of forestry in Sweden at the time of harvesting
4
Three species are harvested:
• Pine (softwood)
• Spruce (softwood)
• Birch (hardwood)

Forestry Industry in Brief – Sweden
Example of harvesting technologies development over time
5
Cut to length (CTL) harvesting system (harvester + forwarder) was
invented and developed in Sweden and Finland in the late 70’s

Forest Industry - Wood Supply Processes and Operations
Most delivered roundwood volume information comes directly from
the harvester machine
6
Example measurement methods in total commercial roundwood harvested in
2019 in Finland (standing and delivery sales, 51,2 million m3)
Data collected by the harvester
head:
- Data from harvested trees
(*hpr file)
- Sent directly from machines to
database (StanForD standard)
- Given individual tree volume
used to improve growth curves
and inventory volume
estimates
Importance to have a “single source
of truth” related with the data (using
data directly from the machines)

Forest Industry – R&D Cooperatives in the Nordics
Support from Cooperative R&D to Forestry digitalization and
automation
Sweden – Mistra Digital Forest program Norway – Smart Forest program
Finland – UNITE program

Stora Enso in Brief
Stora Enso’s productive forest land areas at end of 2020
Source: Stora Enso 2020 annual report
One of the biggest private forest owner in the world, with 2,34 M ha of productive
forest land. Annual harvested volume of 9,3 M m3. Total land and biological asset
value in balance sheet EUR 7.3 billion.
8
Around 75% final felling
And 25% Thinning

Stora Enso in Brief
One of the largest forest owners and wood supply
organizations in the world
Wood Purchasing
agreements
with private forest owners
24 000
41% of
Tornator
Wood procurement per region,
total 37.1 million m3
3 Mt CO2
Growing Forests
have stored in average every
year 2008–2018
Trees grow back
60–100 years rotation period in the Nordics
Constantly new products
1.4 million
hectares in
Sweden
Nordic wood
supply operations
4.0 million m3
annual harvesting from
own forests in Sweden
9
550
Forest division’s contractor network
in 6 countries (in the Nordics
and Baltics)

Stora Enso in Brief
Stora Enso over 700 years of history. The renewable
materials company – our ambition
10
Everything that’s made with fossil-based materials
today can be made from a tree tomorrow

Trayforma
Paperboard for trays
for food packaging
64% lower carbon footprint
than plastic trays
Cupforma Natura Aqua+™
Renewable paperboard
for paper cups
Designed for full fiber recovery
in a recycling process
Biodegradable straw
with Sulapac
To combat the global
problem of plastic waste
DuraSense™
Biocomposites
replacing plastics
Can reduce the consumption of
fossil-based plastic by up to 60%
Massive wood products
New heights in
sustainable construction
Strong, stable, lightweight
structures with CLT and LVL
Stora Enso in Brief
Examples of new products already in commercial use
11
Cupforma Dairy Barr™
Special paperboard for dairy
(yogurt)
Innovation developed together with
client Vigor in Brazil

Stora Enso in Brief
Our divisions 2021
12
Packaging Solutions
Packaging Materials Biomaterials
Wood Products Paper
Forest
Sales in 2020
EUR 8.6 billion
Employees
23 000
in 15 countries
Sales in
50
countries

Serving markets
around the world
Production unit
Sales office
Design Studio
Innovation Centre
Forests and plantations
Stora Enso in Brief

Stora Enso in brief
Our approach to digital innovation in our processes
Marcos Wichert – International Harvesting Workshop
Curitiba - 11/Mar/2021
14
Innovation culture Operating model Technology access
https://youtu.be/0mcP_kJqbRE
Dedicated IT team of 258
professionals developing
applications and systems to support
the operations and processes

Stora Enso New Technologies & Innovations
Eco-RFID for packaging tracking – example digital technologies
https://www.youtube.com/watch?v=vSF5oPy6vKE
Radio-Frequency Identification (RFID)

Satellite
Virtual Forest
Mobile solutions
Forest resource data applications
Common Precision
Forestry Services
Condition information
Harvesting
Wood quality
Trestima
Digitalization used to generate a “digital twin” of the forests
Drone
Forest resource data
Public open data
LiDAR
Value creating
applications
WSS, WSR,WSB, WSF
Satellite forest
monitoring
service
Forest Data Platform
Drone tree health
detection service
Data sources
Stem diameter
distribution

ÅF PÖYRY AB / TOWARDS PRECISION FORESTRY STORA ENSO PROJECT
Mechanized harvesting:
Better planning and
monitoring, increased
efficiency and alignment with
end use
Geospatial optimization:
Accessibility and conditions
data; utilisation of sensor
technology in data collection
Site specific management:
Nutrition and maintenance
planning within each stand
based on site specific conditions
Disturbance monitoring:
Use remote sensing to
identify instances of
pest, diseases, fire, etc.
Predictive analytics for yield
maximization: Harness the
power of big data collected
through IoT sensors, UAVs and
traditional data sources to
predict field interventions for
maximizing yield
Forest planning model:
Integrated planning from company-wide
strategic to daily within stand operational
planning
Mechanized silviculture:
Mechanization and automation of soil
preparation, planting and maintenance
for higher efficiency and yield
Digital inventory measurement:
Tree-level digital inventory
measurement using remote
sensing
Value chain optimization:
Production optimization (e.g.
in sawmills) based on tree-
level resource data
Forest infra:
Advanced optimisation to
minimise wood cost in the
long term
17
Stora Enso Precision Forestry
Vision and strategy of precision forestry
Next 5 years road map is to have 100% digital data collection from the field, with
centralized database and utilization of automated analysis and reports, with the use of
“big data” and “predictive analytics”, optimizing the forest value chain process

ÅF PÖYRY AB / TOWARDS PRECISION FORESTRY STORA ENSO PROJECT
18
Stora Enso Precision Forestry
Common forestry data centralized platform, more use of
big-data and analytics (automated reports)
UNITS
Stora Enso
Precision forestry
data
Operational management
Digital purchasing and forest
owner services
Harvester data,
silviculture data, ops
data feedback
AI engine
Simulation and
optimisation platform
Analytics platform
(predictive analytics)
Satellite, Lidar,
photogrammetric and
manual inventory data
Public/national data
sources
Harvest site planning
on the field
Accessibility
DATA
SOURCES
SOLUTIONS
Soil, topography,
weather
Customer portal
Tactical and strategic planning
Decision support in value
maximisation
Ongoing development (forest data service):
- Drone data processing
- Diameter distribution modelling
- Remote sensing data processing
Data monetization
opportunity in the future

Opportunities for Brazil – mechanization and connectivity
Increase the mechanization level in Silviculture with new
equipment with onboard sensors and technologies
Example of a Forestry Research Institute (IPEF) survey with some
Brazilian companies, with 8 levels of mechanization.
Example of planting operation mechanization level
Overall mechanization level of the companies
MANUAL
MANUAL
Level 1 Level 2
Level 1 Level 2
COMPANY
COMPANY
Average Average
SEMI-MECH.
SEMI-MECH.
Level 3 Level 4
Level 3 Level 4
MECHANIZED
MECHANIZED
AUTOMATED
AUTOMATED
Level 5 Level 6
Level 5 Level 6 Level 7 Level 8
Level 7 Level 8
Source: Guerra, S.P.S, IPEF, 2020
https://www.ipef.br/publicacoes/livros/IPEF-PCMAF-LevantamentoMecanizacao.pdf

Opportunities for Brazil
Forestry Sector Further Developments
20
• Increase mechanization of silviculture operations, this is first step to add digital tools
• Main harvesting machine suppliers to also have silviculture equipment (planters, etc.)
• For remaining manual operations add GPS antenna and system to collect the geo-
referenced field production data
• Connectivity is an issue for real time machine monitoring and sending data from the field
(from field mobile offices also). This is important also for safety and fire prevention (faster
reporting of fire spots).
• Use more machine data (e.g. Harvested volume from harvester report, and also to
generate productivity map from crop harvested)
• Artificial Intelligence (AI), analytics and automated reporting (dashboard KPI’s without using
excel reports and controls)
• Link silviculture data with harvesting and have data communication and feedback with
database
• Improve more collaborative R&D programs focused to support digitalization and
automation for the future (as it is done in the Nordics)

Precision Forestry - Challenges and future needs
Critical components to successfully implement new
technologies
• Right skills and competencies
• Capability building
• Change management
• Transform more data available into intelligence, improving decision making
• Critical thinking and complex problem solving capacity
• After sales technical support and solutions development (new tech. provider)
20%
30%
50%
Precision technologies (digitalization)
Processes
People
• Integration in current business processes and IT database systems (cloud)
• Data quality, availability and updating schedule
• Data analytics (algorithms, big data, machine learning, AI)
• Data and computation platform (data collection and transmission)
• Connectivity to send data
• Visualization, analysis and restitution tools
• Data engineering

New technologies increase exponentially and new skills are
needed
Source: Dominguez, J., Sep 2018. Engineering Education for XXI.
1. Complex problem solving
2. Coordinating with others
3. People management
4. Critical thinking
5. Negotiation
6. Quality control
7. Service orientation
8. Judgement and decision making
9. Active listening
10. Creativity
World Economic Forum (2015) World Economic Forum (2020)
Difficult for Universities to catch up with the
speed that new technologies are introduced.
As the world becomes more complex, new skills and competencies are required from the
workforce for the successful performance of the companies in the future.
Covid19 impact: Adaptability and Resilience!

Main technology drivers for the future of precision forestry
Robots &
automation
Increased autonomous machine operation in the field and
robots in the nurseries
Cloud
systems
Real time communication between operational
systems. Big databases in open systems
Augmented
reality
Support from augmented reality for
machine maintenance, and display of
operational standards/manuals
Cyber-security
Guarantee data safety in open systems
and integrated networks, and also data
transfer from machines
IoT and
connected
sensors
Network of smart forestry sensors with
machines. Multi-directional communication
between networked objects
Optimization &
simulation
Optimization systems utilizing real-time data updates from database.
Simulation of best solution considering the impact in the whole value chain
Big-data &
analytics
Real time decision making support and
optimization based on full evaluation of
available data from all systems (e.g ERP, SCM,
CRM, etc.) and machine data
Source: Adapted from McKinsey & Company, June 2018. Precision forestry a revolution in the woods.
Data
integration &
monetization
Horizontal and vertical data integration based
on data transfer standards. Needed before a
fully automated value chain implementation is
done and also monetization of databases in
the value chain will be possible

The renewable future
grows in the forest
Thank you!

marcos.wichert@storaenso.com
Ever thought about what a tree can do? See our film.

Stora Enso Precision Forestry Examples
Drone and LiDAR for forest and log yard inventories
Forest health monitoring
Log piles stocked volume
measurement - update
26
https://www.youtube.com/watch?v=bHL
R5oqXsWQ&t=11s

Faster truck volume measurement with TimSpect project
27
https://www.youtube.com/watch?v=prk674TWHoI

Woodforce harvesting machines on-board system
28
• WoodForce is designed and developed in
Finland in joint project with Trimble, Stora Enso
and some other Finnish forest companies.
• It is designed for collecting and reporting data
from the contractor’s harvesting machines in the
Baltic region. Improving the data collection and
reporting digitally, regardless the brand of the
harvesting equipment.

Stora Enso New Technologies & Innovations
Autonomous unmanned truck driving trial
29
Proof-of-concept project testing autonomous unmanned truck driving at Enocell Uimaharju wood terminal
area in Finland, transporting wood chips inside the mill area.

The skill shift happening with more digitalization and
automation
Source: McKinsey & Company, May 2018
The table below shows how the labor market skills are expected to shift, under 5 main categories and in different
industries and job sectors, as a result of adoption of artificial intelligence (AI) and various automations.

Main topics of publications and articles about precision
forestry in the internet
Top 4 main topics from published
articles and papers in the internet are:
1. Remote sensing & imagery data
analysis
2. Digital forestry solutions
3. LiDAR scanning
4. Forest fire monitoring and
management

Overview of precision forest management in the Nordics and opportunities for Brazil

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