Flagship Initiatives

Forestry 4.0 Initiative

Supply chains have evolved significantly in recent decades and are on the edge of more changes driven by the digital revolution. Currently, Canada's resource sector is challenged to be part of this revolution, and the biggest gaps are the lack of adequate communication networks reaching remote locations and the limited technology levels of forest contractors.. To close these gaps, it is necessary to continue developing technologies that will enable the generation and access to real-time process input/output data relevant to supply chain management and adapted to Canadian conditions. Several enabling technologies must be developed, implemented in the Canadian context, and coordinated, including elements such as the Internet of Things (IoT), real-time communication systems, and remote sensing, among others.

The Forestry 4.0 initiative aims at bringing the upstream part of the forest value chain to fully leverage the agility and power of Industry 4.0. One of the key goals of the initiative is to assist contractors of the forest sector value chain to be more reactive and resilient by adopting connected technologies.

Four themes have been defined for the Forestry 4.0 initiative that will help establish the foundations of a new “connected” value chain for the forest sector. These themes are:

  1. Real Environment: This theme is dedicated to developing technologies necessary to feed the value chain with foundational data on which monitoring, analysis, and decisions can be taken along the supply chain assembler function).
  2. Internet of Forests (IoF): Analogous to the IoT, this theme is the critical link in many remote and large areas of Canadian forests. The initiative activities involved represent the key enablers in the exchange of realtime data between forest sector contractors and mills.
  3. NextGen Fibre Supply Chain: Under this theme, new production systems will be developed, tested, and implemented. This theme is seen as the accelerator of the value chain, using data of the Real Environment to adapt actions based on user needs.
  4. Data Analytics: This theme serves as an integrator of the complete upstream process to implement “smart harvesting” and artificial intelligence to the management of the Canadian forest sector value chain.

The illustration below shows how the four components fit together along the value chain.

Click on each title below for theme details

Theme 1: Real Environment

Theme objective:

Develop foundational data on which monitoring, analysis, and decisions can be taken along the supply chain.

The theme has three focus areas:
  • Forest resource assessment
  • Operating environment
  • Monitoring outcomes

1. Forest resource assessment

Developed by the Canadian Wood Fibre Centre, the concept of enhanced forest inventory using light detection and ranging (Lidar) information is the focus of improved forestry supply information. While inventory products based on enhanced forest inventory are still being developed, additional or alternative sources of information can be collected from current processes to improve knowledge about the forest resource.

Development of a Geostatistical Method for Computing Forest Biomass VolumeBiomass supply analyses currently forecast the distribution of total wood volume in each period of the planning horizon. There is a need to refine these strategic analyses to predict the volume available by the desired fibre quality attribute.

  • Use enhanced forest inventory, terrain classification, and new provincial light detection and ranging acquisition to accurately calculate and locate volume to generate large-scale, value-based inventory.

2. Operating environment

Data collected from machines can represent foundational data on which to establish connections with biophysical data (stand, terrain, weather, etc.). It can be acquired from production monitoring systems (onboard data loggers, on-board computers) or from external sensors.

Automated Production Data Collection in Harvesting Systems

Real-time capture of harvesting production data can provide decision-support data for wood flow and inventory management. Cut-to-length machines generate data-rich production files that need to be captured and processed into useful information and then reported to the contractor, manager, and their customers in a timely, semi-automated process. Some of the work involves testing and implementing existing original equipment manufacturer mobile device-based transfer solutions to ensure timeliness and usefulness of the data.

  • Develop and test production data collection systems that use sensors/captors for full-tree equipment connected with on-board data loggers, such as FPInnovations’ FPDat system.
  • Develop and test production data collection for cut-to-length equipment
Mobile Ground LiDAR for Machine Vision

Point cloud data generated by terrestrial laser scanners provide detailed and accurate 3-D data and have the potential to reconstruct terrain, stand, and tree structure. Reconstruction algorithms for trees developed by the Canadian Wood Fibre Centre have shown some potential for implementation in an operational setting, but the feasibility of using this type of system in real time on a moving machine to help make decisions is still to be proven and implemented.

  • Determine the acquisition feasibility and data quality from mobile light detection and ranging (LiDAR) sensors
  • Determine whether available algorithms could be used directly for terrain and stand feature extraction on the data collected to facilitate operator decisions

The outcome of this activity will help generate a road map for the implementation of more advanced systems to support operator decisions, facilitate more advanced terrain navigation, and automate repetitive or nonstrategic tasks

Advanced Roadbed Engineering Modelling

There is a lack of understanding about the impact of heavy vehicles on frozen roadbeds. Road managers need to design roadbed structures using models that account for the behaviour of frozen pavement under load. Canadian regulators do not have an adequate engineering pavement analysis procedure to evaluate the bearing capacity of frozen pavement. In 2016, FPInnovations leveraged its membership in the NSERC i3C Chair on pavement (at Université Laval) to commission advanced pavement modelling and testing in state-ofthe-art, large-scale laboratory facilities. Pavement responses to vehicle loading were monitored on frozen pavement structures and used to validate and calibrate empirical models.

  • Provide Canadian transportation ministries, in general, with improved, science-based roadbed modelling tools.

3. Monitoring outcomes

Post-harvest surveys are normally done to ensure that biomass resources have been used to their full potential and that conditions are suitable for the establishment of a desirable forest stand. With the developing field of remote sensing, images are now providing detailed and accurate information that should lead to full area census much more efficiently than with traditional ground-based methods.

Harvest Residue Assessments With UAS

Given the ease of deployment and comparably low-cost acquisition of very high resolution data in small or inaccessible areas, images based on unmanned aerial systems (UAS) could potentially be an alternative solution to conventional waste and biomass surveys. This technology has raised a lot of interest with FPInnovations’ stakeholders, who hope to reduce the intensity of cruising for logging residue assessments while maintaining or even improving the quality of information generated.

  • Validate image segmentation techniques combined with quantitative GIS tools on UAS-based image mosaics to provide spatially explicit, piece-size details of dispersed residue on the ground.
Forest Establishment and Free-To-Grow Status Assessment With UAS

Currently, regeneration assessment from remote sensing imagery is done through human interpretation. This process is tedious, subjective, and prone to errors. The development of automated processes could save a lot of money while providing information on areas that would be difficult to cover.

  • Explore the potential of unmanned aerial systems (UAS) products to replace ground establishment and free-to-grow surveys.
  • Develop and calibrate image analysis models in various stand and acquisition conditions.
  • Test algorithms’ robustness for softwood regeneration.
  • Develop algorithms or models to automate image analysis of regeneration assessments for mixedwood and free-to-grow stands.
Monitoring Treatment Results and Stand Vigour With UAS

Miniaturized sensors based on unmanned aerial systems (UAS), such as light detection and ranging (LiDAR), infra-red, and multi-spectral, offer the flexibility needed for monitoring treatment results over time. Thermal regime, normalized difference vegetation index, or other indices from multi-spectral images could also help assess stand vigour after a silvicultural treatment.

  • Provide a complete stand inventory, including canopy and subcanopy elements, to help assess current and successional potential in managed stands
  • Determine whether high-resolution, multi-temporal LiDAR acquisition could be an effective method to assess stand growth and site quality
  • Assess stand vigour and effectiveness of silvicultural interventions with spectral indices.

Theme 2: Internet of Forests

Theme objective:

Develop and implement communication systems in resource operations that enable the implementation of the Industry 4.0 standard.

The overall approach is to find, test, and/or develop communication systems to determine whether they meet the requirements for vehicle-to-vehicle and machine-to-machine (M2M) technology and data flow of remote forest operations. Furthermore, the terminal part of a forest-wide communication network is the fleet of equipment operating in the forest. These machines must be able to act as nodes for the efficient transmission of data among them, or from them to the office/network and vice versa.

For all the systems, FPInnovations will test different performance criteria, including operational range, bandwidth, robustness, and affordability.

  • Test communication systems related to extending cell networks, meshing, or clustering communication systems to determine applicability, as well as technical and cost effectiveness in rural areas.
  • Continue work with the electrical and computer engineering department of the University of British Columbia on two initiatives:
    • Development of dedicated short-range communications technology for the resource sectors and investigation of the feasibility of such systems to support autonomous vehicle operation in confined rural applications.
    • Investigation and development of the potential for ad hoc communication networks that can manage and optimize the use of the communication networks that will be available to the resource sector.
  • Further develop the M2M technology in the FPDat system to develop additional applications that allow data exchange between machines, data transfer to the office, and data transfer from the office to specific machines in the field (e.g., for configurations).
  • Use enhanced forest inventory, terrain classification, and new provincial light detection and ranging acquisition to accurately calculate and locate volume to generate large-scale, value-based inventory.

Theme 3: NextGen Fibre Supply Chain

Theme objective:

Develop and implement next-generation equipment, hardware, software, and models to allow forest contractors to run to an Industry 4.0 standard by being automated, connected, and accessed in real time.

Development of Advanced Automated Vehicles for Rural Road Networks and Forest Operations

The Society of Automotive Engineers (SAE) has categorized driving automation technology into six levels, from 0 (no automation) to 5 (full automation). Technologies at SAE level 1 (driver assistance) and level 2 (partial automation) are on the market now, or soon will be, and must be tested for their applicability to rural and resource roads. There are also opportunities to take advantage of the remote and controlled environment of forest operations by developing niche vehicles at levels 4 (high automation) and 5 (full automation), which can be implemented without the restrictions that apply to highway transportation.

  • Evaluate and monitor technology related to vehicle automation, driver assistance, and remote control of off-road machines
  • Continue development of a fully automated yard truck and other vehicles specific to the forest sector.
  • Identify opportunities for adapting new technologies to forest harvesting and for assembling a team of partners and collaborators.
Development of a Resource Road Traffic Simulation Model

Having a robust traffic model that tracks individual vehicles provides opportunities to study many "what if” scenarios on resource roads. The focus of the project is to collect data at a case study location to replicate real-world traffic through calibration, and then consider how changes to parameters, such as traffic flow, traffic scheduling, or adding pullout locations, can change metrics that are important to safety. This decision-making tool will be valuable for transportation managers to improve safety on high-volume, multi-sector resource roads.

  • Continue development of a microsimulation traffic model specific to resource roads by extending the functionality of Aimsun, a popular highway traffic simulation software.
Cloud-Based Feedstock Management Systems

This project will investigate cloud-based systems for tracking biomass feedstock properties and explore new technologies to monitor and reduce biomass moisture content.

  • Conduct a trial with Pineland Forest Nursery in Manitoba on wood chip piles for combined heat and power (CHP) applications. Pile hotspot temperatures, moisture, and emissions will be monitored using Sentroller devices and infra-red smartphone camera. Biomass quality will be modelled using weather monitoring data.
  • Conduct a trial in partnership with Skogforsk (Sweden) to assess the effectiveness of the Drinor mechanical dewatering press.
  • Evaluate new technologies, such as X-ray and radar, to monitor moisture of comminuted biomass and improve real-time decision-making in industrial CHP applications.

Theme 4: Data Analytics

Theme objective:

Develop and adapt analytics, algorithms, and methods that enable creating predictive models for forecasting and optimizing production systems.

Few decision-support tools exist that can be used in a practical and cost-effective way. This theme proposes to bring the implementation of these technologies closer to reality by leveraging available data streams into integrated solutions.

Geospatial Link Between Enhanced Forest Inventory and Production Data Streams

Production files from cut-to-length harvesting machines provide detailed stem information, including diameter changes over the length of the stems (taper), and are, in effect, extraordinary “cruising” systems. This project focuses on connecting forest inventory information and production data obtained from machine on-board computers to help calibrate the inventory information and enable some predictive ability through correlation of cruise versus actual data. Forest inventory information will be validated using geospatial referencing, and predictive functions will be generated and linked with other FPInnovations modeling and decision-support tools. This work represents one of the early steps of connecting some of these complex and separate data streams to provide a richer information set than the individual data flows can.

  • Validate forest inventory information based on geospatial referencing data from the harvesting machines.
  • Develop prediction algorithms that can be linked with other FPInnovations tools.
Decision-Support Models for Forest Supply Chains

Existing planning tools for harvesting and allocation of biomass to manufacturing plants do not explicitly account for the capacities of downstream facilities. This can result in plans that are either not feasible or unrealistic. This project continues ongoing testing and prototyping, as well as developing new approaches for several existing and new FPInnovations technologies.

  • Investigate the opportunity to move from an Access database in the FPInnovations cost model(FPInterface) to an SQL database to be able to process larger datasets.
  • Adapt and validate a tool developed by a university partner (FORAC Research Consortium) that uses a mathematical solution to explore the flexibility of optimal scenarios and test the trade-offs of obtaining less precise solutions but doing so more quickly.
  • Develop a way to link FPInnovations decision-support models to currently implemented management systems (e.g., web service, API for MaxTour, and other tools).
  • Develop a methodology for evaluating supply chain scenarios for producing new bioproducts integrated in current forest supply chains.
  • Prototype the ForestPlan tool as a strategic planning decision-support tool and explore its integration with GIS-based software for spatial representation of the forest inventory (FPInterface) and test its capabilities for scenario analysis.