Accurate timber assortment information is required before cuttings to optimize wood allocation and logging activities. Timber assortments can be derived from diameter-height distribution that is most often predicted from the stand characteristics provided by forest inventory. The aim of this study was to assess and compare the accuracy of three different pre-harvest inventory methods in predicting the structure of mainly Scots pine-dominated, clear-cut stands. The investigated methods were an area-based approach (ABA) based on airborne laser scanning data, the smartphone-based forest inventory Trestima app and the more conventional pre-harvest inventory method called EMO. The estimates of diameter-height distributions based on each method were compared to accurate tree taper data measured and registered by the harvester’s measurement systems during the final cut. According to our results, grid-level ABA and Trestima were generally the most accurate methods for predicting diameter-height distribution. ABA provides predictions for systematic 16 m × 16 m grids from which stand-wise characteristics are aggregated. In order to enable multimodal stand-wise distributions, distributions must be predicted for each grid cell and then aggregated for the stand level, instead of predicting a distribution from the aggregated stand-level characteristics. Trestima required a sufficient sample for reliable results. EMO provided accurate results for the dominating Scots pine but, it could not capture minor admixtures. ABA seemed rather trustworthy in predicting stand characteristics and diameter distribution of standing trees prior to harvesting. Therefore, if up-to-date ABA information is available, only limited benefits can be obtained from stand-specific inventory using Trestima or EMO in mature pine or spruce-dominated forests.

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Forest harvesting activities can cause soil damage and disturbance through soil compaction, rut formation and soil mixing. These affect the soil structure and functions and forest productivity. Soil compaction results for instance in increased bulk density and decreased porosity, affecting soil moisture, water infiltration and aeration. The effects of timber forwarding on soil physical properties have gained little attention in boreal forests. These issues will become more important in the future since harvesting operations on unfrozen soils are getting more common due to the anticipated climate warming. In this study, the changes of forest soil physical properties (bulk density, moisture content and porosity) after 1 to 10 forwarder passes on two fine-grained mineral soil sites in southern Finland were analysed. Penetration resistance and rut formation were also measured. The measurements were performed in three periods with different soil moisture conditions. The test drives were carried out with a conventional 8-wheeled forwarder with total mass of 29.8 tons. Soil bulk density increased and porosity decreased after the machinery passes. However, soil moisture content increased on one site and mainly decreased on another. The first three passes caused the greatest compaction and rutting, the first pass having the strongest impact. After the first and third pass 34-55 % and over 70 % of the total mean rut depth was formed, respectively. Further passes only had slight effects. The compaction and changes of soil physical properties appeared to be greater in dry conditions. Rut formation and soil mixing Highlights-Traffic by heavy machinery has significant impacts on soil properties-First passes cause the strongest impacts-Impacts depend highly on soil characteristics and actual site conditions

The strength of soil is known to be dependent on water content but the relationship is strongly affected by the type of soil. Accurate moisture content – soil strength models will provide forest managers with the improved ability to reduce soil disturbances and increase annual forest machine utilization rates. The aim of this study was to examine soil strength and how it is connected to the physical properties of fine-grained forest soils; and develop models that could be applied in practical forestry to make predictions on rutting induced by forest machines. Field studies were conducted on two separate forests in Southern Finland. The data consisted of parallel measurements of dry soil bulk density (BD), volumetric water content (VWC) and penetration resistance (PR). The model performance was logical, and the results were in harmony with earlier findings. The accuracy of the models created was tested with independent data. The models may be regarded rather trustworthy, since no significant bias was found. Mean absolute error of roughly 20% was found which may be regarded as acceptable taken into account the character of the penetrometer tool. The models can be linked with mobility models predicting either risks of rutting, compaction or rolling resistance.

Factors affecting soil disturbance caused by harvester and forwarder were studied on mid-grained soils in Finland. Sample plots were harvested using a one-grip harvester. The harvester operator processed the trees outside the strip roads, and the remaining residues were removed to exclude the covering effect of residues. Thereafter, a loaded forwarder made up to 5 passes over the sample plots. The average rut depth after four machine passes was positively correlated to the volumetric water content at a depth of 0–10 cm in mineral soil, as well as the thickness of the organic layer and the harvester rut depth, and negatively correlated with penetration resistance at depths of both 0–20 cm and 5–40 cm. We present 5 models to predict forwarder rut depth. Four include the cumulative mass driven over a measurement point and combinations of penetration resistance, water content and the depth of organic layer. The fifth model includes harvester rut depth and the cumulative overpassed mass and provided the best fit. Changes in the penetration resistance (PR) were highest at depths of 20–40 cm. Increase in BD and VWC decreased PR, which increased with total overdriven mass. After four to five machine passes PR values started to stabilize.