Speaker
Description
Despite increasing fully mechanized timber harvesting, motor-manual operations are still required, for example for the selective removal of high-value individual trees, in sensitive stands or difficult terrain, in calamity patches, or in small private forests. Besides physical strain, these motor-manual tasks entail an elevated accident risk for workers, caused for instance by deadwood, limited experience in assessing trees, or unsuitable choice of tools. While the flow of information along the wood value chain is already digitally supported in harvester–forwarder systems through standards such as StanForD2010, ELDATsmart, or papiNet, many steps in motor-manual harvesting are still recorded by hand and are frequently passed on with media discontinuities.
The research project SmaSiKaFE (Smart and Safe Calamity Area Harvesting) addresses these challenges in motor-manual timber harvesting and designs a continuous digital process. Forest workers are provided with a smartphone app that supplies the necessary information and offers recommendations for safe felling and value-optimized bucking. Using the app, work progress can be documented and transmitted to downstream actors.
The technological basis for data exchange is the Smart Forestry concept, which follows the Industry 4.0 approach. A so‑called Asset Administration Shell (AAS) enables standard-compliant data exchange along the entire value chain. The AAS represents physical objects (forest stands, machines, stem sections etc.) as digital twins, encapsulating their data, functions, and parameters and thus enabling manufacturer-independent interoperability. Data is exchanged in a decentralized, trustworthy, and protected way via a messaging hub, the Smart Systems Service Infrastructure (S³I).
Digital work orders can be created using standard software connected to the AAS. In addition to the task description, aspects of work organization as well as emergency numbers and rescue points can be specified, which users can directly view or dial in an emergency.
For the safe felling of backward-leaning trees with wedges or winches, estimating the tree’s center of gravity is crucial. The proposed method records a point cloud of the stand via terrestrial laser scanning, either in advance of harvesting or continuously during routine work. Individual trees are extracted from the point cloud, followed by spatial recognition of stems and branches and automatic detection of possible damage (e.g., crown or stem breakage). The stem is then decomposed into segments to which typical density values are assigned, allowing the center of gravity to be derived. This information is provided in the app via the tree’s digital twin, enabling the calculation and display of the required forces, attachment heights, and insertion depths of auxiliary equipment. As an alternative to laser scanning, photo-optical methods for determining tree parameters are considered, which may be particularly advantageous in small private forests.
Based on the tree data, precise suggestions for assortment bucking can be generated from the taper curve and actual stem form, analogous to harvester-based optimization. After felling and processing, stem sections and work progress are recorded in the app; the data is transmitted back, updates the virtual stand, and forms the basis for subsequent extraction and logistics.
| Keywords | Forestry-4.0; digital-workflow; digital-twin; point-cloud-analysis |
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