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More precise wood supply through forest big data

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

Publicado en: Datos y análisis
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More precise wood supply through forest big data

  1. 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. 2. Metsäteho is a wood procurement development company building a better future for the forest industry JARMO HÄMÄLÄINEN / METSÄTEHO 14.10.2019 2
  3. 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 3
  4. 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 € 4
  5. 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 5
  6. 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 6
  7. 7. Forest inventory system of Finnish Forest Centre JARMO HÄMÄLÄINEN / METSÄTEHO 14.10.2019 7 Aerial laser scanning & aerial photographs Forest resource data for grid (16 * 16 m) and pattern level
  8. 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 JARMO HÄMÄLÄINEN / METSÄTEHO 14.10.2019 8
  9. 9. New big data sources in the future – automatic work quality measurement in harvesting JARMO HÄMÄLÄINEN / METSÄTEHO 14.10.2019 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 9
  10. 10. Road conditions data through machine vision and sensor fusion Source: Vaisala & Metsäteho JARMO HÄMÄLÄINEN / METSÄTEHO 14.10.2019 10
  11. 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 11
  12. 12. Examples of applications JARMO HÄMÄLÄINEN / METSÄTEHO 14.10.2019 12
  13. 13. An example of grid data utilization – a terrain trafficability map for timber harvesting JARMO HÄMÄLÄINEN / METSÄTEHO 14.10.2019 Source: Arbonaut Ltd. & Finnish Forest Centre Trafficability Map availability 10/2019 13
  14. 14. Logging track planning tool for a harvester operator JARMO HÄMÄLÄINEN / METSÄTEHO 14.10.2019 14
  15. 15. Gravel road trafficability prediction JARMO HÄMÄLÄINEN / METSÄTEHO 14.10.2019 Source data: • Soil type • Wetness index • Ditch depth • Radiation index → Information for timber transport and road maintenance Source: Arbonaut Ltd. & Metsäteho 15
  16. 16. Virtual forest application by Stora Enso JARMO HÄMÄLÄINEN / METSÄTEHO 14.10.2019 Source: 16
  17. 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 JARMO HÄMÄLÄINEN / METSÄTEHO 14.10.2019 17
  18. 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. 19. jarmo.hamalainen@metsateho.fi CREATING OPPORTUNITIES 19 Thank you!

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