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The location information of strip roads in thinnings and the numerical variables of strip roads are one aspect of timber harvest quality information. The ideal of automatic quality management for mechanized logging is that the quality of the harvest is calculated and reported based on data collected by forest machines. At present, quality data is collected by means of laborious, manual, field-based, post-harvest measurements. The aim of this study was to develop an automatic method to compute the strip roads of the harvested stands after harvesting, based on the stem-specific location data of the harvester. Subsequently, the strip road variables were computed from the strip road networks. The computed strip road networks were validated with 21 manually recorded field references. The method was also applied to operational stand data, including 544 harvested stands collected from Southern Finland. The results showed that the computation method produces well-located, stand-specific strip road networks from which strip road variables can be accurately determined, covering the whole stand. Thus, the method promotes the automation of quality management and reporting. The computed strip road networks can also support harvester operator work during the harvesting and, later, the automation of harvesting operations.
The location information of strip roads in thinnings and the numerical variables of strip roads are one aspect of timber harvest quality information. The ideal of automatic quality management for mechanized logging is that the quality of the harvest is calculated and reported based on data collected by forest machines. At present, quality data is collected by means of laborious, manual, field-based, post-harvest measurements. The aim of this study was to develop an automatic method to compute the strip roads of the harvested stands after harvesting, based on the stem-specific location data of the harvester. Subsequently, the strip road variables were computed from the strip road networks. The computed strip road networks were validated with 21 manually recorded field references. The method was also applied to operational stand data, including 544 harvested stands collected from Southern Finland. The results showed that the computation method produces well-located, stand-specific strip road networks from which strip road variables can be accurately determined, covering the whole stand. Thus, the method promotes the automation of quality management and reporting. The computed strip road networks can also support harvester operator work during the harvesting and, later, the automation of harvesting operations.
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