In forest harvesting, operators must visually monitor the terrain, machinery, the stand and the trees they are cutting in order to plan, evaluate and adjust their tasks. To exploit increasing opportunities to automate these tasks and create decision support systems it is essential to understand not only what forestry workers do, but also what they look at and why they focus on specific aspects during specific tasks. This knowledge may also aid operator training and knowledge transfer between age and experience groups. Eye-tracking (ET) is therefore a potentially valuable technique that may facilitate both extraction of implicit knowledge and elucidation of operators' information acquisition strategies. However, real world ET-recordings are sensitive to environmental variations and analyzing them is time consuming. Thus, the aims of this study were to examine the utility of a head-mounted eye-tracking system in forest harvesting machines in a natural setting and obtain information on operators' visual behavior (gaze patterns) during harvesting. The output from the eye-tracker was affected by large head movements, changes in illumination and (possibly) vibrations. The gaze pattern analysis revealed that the operators looked at the harvester head or forest most of the time, but their gaze behaviors varied during different harvesting operations. They looked at the monitor, canopy and falling trees less frequently during first thinning than during second thinning and final felling. The results suggest that some harvesting information is gathered in advance to get an overview and plan the work, but most eye movements closely follow actions.
Globally, almost 2 billion cubic meters of industrial roundwood are harvested yearly. Two of the most common methods of harvest and extraction are cut-to-length (CTL) and full-tree or tree-length (FT/TL). The aim of this study was to compile data on annual volumes of industrial roundwood harvested by the main methods in forestry countries. To quantify the effect of potential explanatory variables, the data were subjected to linear regression analysis, using shares of roundwood volumes harvested by fully mechanized CTL and/or FT/TL as response variables. Generally, high diesel price and Gross Domestic Product appear to favor CTL, while high shares of steep terrain (>20°) in forest land decrease the leve l of both mechanization and CTL, and low Social Security Rate (SSR) favor FT/TL. Two models were created for CTL, one with an R 2 of 0.64 and another more complex with an R 2 of 0.75. A separate model for mechanization (CTL and FT/TL together) showed an R 2 0.57. The CTL models could potentially be used to predict shares of roundwood volumes harvested by CTL in countries not included in this study. Predictions for countries with large harvested volumes, e.g. China and India, are presented here, but they require validation, as does the model's applicability for countries with small harvested volumes. Countries with less than 10% of steep slope forests are almost exclusively mechanized according to the model. For FT/TL, the proposed model is probably not sufficiently robust for prediction, but it highlights SSR as one important explanatory variable.
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