This document discusses using big data for forestry applications. It aims to evaluate existing and new big data sources through a SWOT analysis to create decision support models. These models would help with efficient, sustainable, and valuable forestry operations. The document demonstrates potential models including trafficability maps, logging trail visualizations, harvester scheduling, and models to improve efficiency in silviculture operations. It concludes that big data opens possibilities for research if standardization of data collection can occur.

1. Big Data applications
Efforte Business Forum
Erik Willén, Isabelle Bergkvist

2. Objectives
Describe potentials in using, refining and linking existing and new
Big Data sources in forestry applications, and evaluate the identified
sources of data through a SWOT analysis.
Based on these data, create decision-support models for
efficient, sustainable and value-creating forestry and forest operations.
Demonstrate the potential in keeping and refining information
throughout subsequent operations in the forestry process.
Validation and pilot tests of project results in relevant
environment (hosted by partner companies).

3. Outlook on Big Databases - forest planning
Detailed
digital
elevation
model
Soil
maps
VHR
imagery
Mobile
laser
scanning
Weather
data and
models
Lidar
based
forest
estimates
Road
databases
Forest
machine
data

4. Demonstrated decision support models
 Trafficability maps
 Operational logging trafficability mapping
 Logging trail visualizations and port-harvest quality
control
 Models for operational planning of forest operations
 Yield and wood property forecasting
 Models to improve efficiency in silviculture operations

5. Trafficability maps
Depth to Water map
5 26.6.2019

6. 6 26.6.2019
Static Trafficability map

7. Dynamic Trafficability map - SpatHy
7 26.6.2019

8. Layout of main extraction routes
Slope and terrain
Volume
Digital terrain model

10. 10 26.6.2019
Logging trail visualizations and port-harvest quality control
2D LiDAR for continous rut depth measurement

11. -15
-10
-5
0
5
10
15
20
25
30
-15 -10 -5 0 5 10 15 20 25 30
Manual
Laser
LiDAR vs manual: per 5m test blocks and
pass
Left LiDAR vs
manual
Right LiDAR vs
manual
Vihti study Kuru study

12. 12 26.6.2019Ala-Ilomäki et. al Harvester CAN-bus
Harvester CAN-bus data for site trafficability
mapping
GPS/Glonass -
positioning
Harvester followed by a
loaded fwd up to five passes
Harvester courtesy of Ponsse Plc
First harvester with and without tracks on two parallel test
tracks

13. 13 26.6.2019Ala-Ilomäki et. al Harvester CAN-bus
Motion resistance, rut depth of harvester
and forwarder on 1st and 2nd pass

14. Harvester scheduling

15. Preferred
areas
Home
Performance
Assortiment
Supply by
roads
Type of
forest
cutting
Quantity
Assortiment
Quantity
Radie for
actions
Priority
Average
stem
Forwarder
distance
Labour time
Target
volumes

16. 1011110010101011100001010101
000101010101111111001010
101111001010101110
00010101
Wood property modelling
Wood property models using harvester data for each log
Validated in x-rays at sawmills
Blue: model
Yellow: reference
Example:
Basic density
Fiberwall thickness
Fiber length
….

17. Models to improve efficiency
in silviculture operations
Plant order tool
 Based on harvester data
 Actual growth conditions
 Root rot frequency
 Site index

19. Conclusions
 Operational use of trafficability maps and tools to extract main extraction
routes
 Digital elevation model spatial resolution critical
 Important to act pro-actively to secure data collection
 Interesting potential to use Can-Bus data to for trafficability mapping – other
options also availible (drones, machine-mounted equipment, ..)
 Big data applications opens a wide range of possibilites for research and
applications – standardisation crucial!
 Previous research now possible to implement with Big Data Streams
 Forest machine data key for many applications – start collect standardised
data!