Speaker
Description
According to our Roadmap 2035 Vision for Forest Machine Data and Sensor Technologies in Finland, forest resource data in 2035 will be substantially more accurate in terms of tree volume, quality, changes, harvestability and soil information. Accurate, single-tree interpreted forest resource data will provide high-quality pre-operational information for cost- and energy-efficient, climate-resilient, low-carbon planning and implementation of the wood supply process. These pre-operational data include information collected in advance via national airborne laser scanning programs, drones and other unmanned aerial vehicle platforms. By 2035, development accelerated by artificial intelligence (AI) will have introduced more automated functions into the wood supply planning systems, thereby enabling optimal selection and timing of harvesting sites. Digital forest twins will allow AI-optimised advanced planning of forest machine operations, taking into account harvesting conditions and seasonality. They will also enable the transfer of optimised harvesting plans with single tree maps to systems that assist machine operators.
By 2035, precision positioning will be the standard for all harvesters in Finland. Forest machine sensors incorporating laser scanning and machine vision will increasingly enrich the pre-operational data sent to machines. While they are not yet universal across the entire Finnish fleet, these technologies are becoming fundamental to modern forest operations. Pre-operational data will also be augmented in real time using StanForD 2010-based forest machine data. Simultaneously, forest machine manufacturers will provide automated assistant systems that will enhance operator performance, improve harvesting quality and support operator well-being. These systems will also help address the shortage of skilled operators. For example, tasks such as cutting strip road networks on harvesting sites, monitoring thinning intensity in real time, identifying poor-quality or diseased trees, preserving biodiversity-relevant trees and planning forwarder payloads will be almost fully automated, requiring minimal attention from the operator, who will act more as a process supervisor.
By 2035, the quality of harvesting operations will be transparently reported to forest owners and authorities. Authorities will no longer conduct nationwide field inventories themselves; instead, field data will be automatically generated during harvesting and forest management operations with single-tree accuracy. After forest operations, harvested timber data will be transferred seamlessly to mill customers, thereby enabling batch-level traceability of industrial roundwood. Guidelines and rules regarding ownership and use of forest machine and sensor data will be updated, and data will be actively collected, shared and utilised according to established recommendations in Finland.
Implementation of the Roadmap 2035 Vision requires coordinated actions from multiple stakeholders across the wood supply value chain. Key actors include forest companies, forest machine contractors and manufacturers, forest owners, and forest and environmental authorities and research scientists. To measure and document forest biodiversity and assist the operators that use machine and sensor data, actions and investments are needed to define metrics, develop and standardise technologies and processes, and ensure competence. Many technologies can be adopted relatively quickly for full-scale operational use. Some technologies, however, will require prominent RDI investment to be ready within the ten-year target. Achieving this Vision demands commitment and a forward-looking mindset from all stakeholders.
| Keywords | Automation; Assistance; Operator; Harvesting |
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