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Estimating machine impact on strip roads via close-range photogrammetry and soil parameters: a case study in central Italy
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Estimating machine impact on strip roads via close-range photogrammetry and soil parameters: a case study in central Italy
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Journal Article
Estimating machine impact on strip roads via close-range photogrammetry and soil parameters: a case study in central Italy
2018
Overview
Several studies have been carried out to investigate soil compaction and rutting after logging vehicle traffic, based on time consuming and punctual field measurements. The objective of this study was to measure soil disturbances with two methods: (i) a new, image-based models derived by a structure-from-motion (SfM) photogrammetry approach; and (ii) a traditional soil sampling (bulk density and shear strength). Two trails were selected in a logging area (central Italy), one trafficked by a forwarder (FT) and one trafficked by a skidder (ST). Data collection was conducted before, during and after timber extraction. Image-based models derived by SfM photogrammetry was used to highlight the differences in the shape and distribution of the disturbances along ST and FT. Results showed that the physical parameters of soil significantly changed due to both FT and ST traffic. Machine passes increased bulk density (111% and 31% for FT and ST, respectively), penetration resistance (29% and 24% for FT and ST, respectively) and shear resistance (14% and 6% for FT and ST, respectively), whereas porosity decreased (46% and 9% for FT and ST, respectively). Significant differences between FT and ST were found when comparing ruts removal and bulges with SfM photogrammetry. After logging, FT clearly showed ruts and bulges, whereas in ST ruts and bulges were not visible, but soil displacement in the direction of extraction was evident and measurable. Nevertheless, although our result shows a larger soil disturbance caused by forwarders than skidders, it is not possible to draw any general conclusions about differences between the two machines. Data about the machine passes, or the wood volumes transported over each trial area were not available; therefore, any general conclusion is misleading. SfM photogrammetry give information not available via traditional methods, thus improving impact assessment.
