Effects of a mobile LiDAR-based thinning density assistant (TDA) system on harvester operator performance

Abstract Forestry machines can be equipped with mobile laser scanners that digitally perceive and map the surroundings of the machine. The data collected can be used to assist the machine operator in conducting forest thinning in real-time. The forest machine manufacturer Ponsse Plc has launched a technological concept called the Thinning Density Assistant (TDA), which provides operators with real-time guidance. This advanced driver-assistance system (ADAS) installed in cut-to-length harvesters helps operators manage thinning density, visualise trees that are too close to each other and display the distance to the previous strip road. This study investigated the effect of the TDA system on cutting productivity in forest thinning, the workload experienced by harvester operators and the profitability of the investment. The study involved five experienced operators who thinned four different forest stands in central Finland, totalling an area of 10.5 ha. In the study, we analysed data from 4967 trees and 490 m 3 solid overbark of harvested timber that was collected from the machine’s production data during thinning operations. A comparative time study methodology was used, which initially involved dividing the work cycle into distinct work elements. Subsequently, each element was modelled individually, using either average values or regression techniques. The NASA-TLX questionnaire was used to assess workload. The TDA system led to a modest increase in productivity, with a 1.2% improvement in the first thinnings and a 1.0% improvement in the later thinnings. This new first-generation system did not aid in the selection of specific trees; it only highlighted areas of greater tree density. The study revealed a significant saving in boom-out time (the process of reaching the tree with the harvester head) but with significant differences between operators. The TDA did not influence the time spent during moving. Inexperience in using the assistant might initially reduce productivity, as the operator may instead focus on monitoring the functionality of the device. The observed productivity improvement of approximately 1% does not cover the current acquisition costs of the system for expert operators when viewed solely from a productivity perspective. The TDA is likely to be particularly beneficial for novice operators. Nevertheless, the device is assumed to have other benefits, such as improving the quality of harvesting operations and documenting the logging work at the tree level, as well as the collection of training data for large-scale airborne laser scanning-based surveys at the individual tree level. Further research and improved implementation of the TDA could unlock greater efficiencies and productivity benefits.