Speaker
Description
In Finland, thinning operations are carried out annually on approximately 0.5 million hectares. Due to the labour‑intensive and costly nature of field inspections, official harvesting quality assessments produced by the Finnish Forest Centre are conducted on less than 1% of this area. For many years, the aspect with the greatest need for improvement has been thinning intensity. Harvester operators also require real‑time information and feedback systems, because feedback on thinning quality is given in many cases after weeks or even months after the operation. It is assumed that harvesting quality can be improved through tree‑level operator guidance, while at the same time enabling the collection of large amounts of forest‑related data.
This study evaluates the feasibility of conducting follow-up harvesting quality assessments using only digital data sources: StanForD 2010-based harvested production (hpr) messages and stand-level tree maps derived from Ponsse Thinning Density Assistant (TDA) point clouds combined with external forest information databases (Metsakanta.fi service and Metsaan.fi data provided by the Finnish Forest Centre). A new methodology is introduced to automatically assess harvesting quality indicators, including residual stand structure, thinning intensity, and strip road geometry along machine trails.
The objective is to determine whether harvesting quality can be digitalised and assessed at scale without field measurements, enabling coverage of all harvested stands instead of small inspection samples. Additionally, the study examines whether the use of TDA-based data collection supports improved harvesting quality through operator guidance and feedback. By linking quality assessment with machine-generated data streams, the proposed framework represents a step towards scalable, transparent, and adaptive monitoring of forest harvesting operations.
| Keywords | Thinning; MLS; Intensity; StanForD2010 |
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