Purpose of Review: Mechanized logging operations with ground-based equipment commonly represent European production forestry but are well-known to potentially cause soil impacts through various forms of soil disturbances, especially on wet soils with low bearing capacity. In times of changing climate, with shorter periods of frozen soils, heavy rain fall events in spring and autumn and frequent needs for salvage logging, forestry stakeholders face increasingly unfavourable conditions to conduct low-impact operations. Thus, more than ever, planning tools such as trafficability maps are required to ensure efficient forest operations at reduced environmental impact. This paper aims to describe the status quo of existence and implementation of such tools applied in forest operations across Europe. In addition, focus is given to the availability and accessibility of data relevant for such predictions. Recent Findings: A commonly identified method to support the planning and execution of machine-based operations is given by the prediction of areas with low bearing capacity due to wet soil conditions. Both the topographic wetness index (TWI) and the depth-to-water algorithm (DTW) are used to identify wet areas and to produce trafficability maps, based on spatial information. Summary: The required input data is commonly available among governmental institutions and in some countries already further processed to have topography-derived trafficability maps and respective enabling technologies at hand. Particularly the Nordic countries are ahead within this process and currently pave the way to further transfer static trafficability maps into dynamic ones, including additional site-specific information received from detailed forest inventories. Yet, it is hoped that a broader adoption of these information by forest managers throughout Europe will take place to enhance sustainable forest operations.

Spatiotemporal modeling is an innovative way of predicting soil moisture and has promising applications that support sustainable forest operations. One such application is the prediction of rutting, since rutting can cause severe damage to forest soils and ecological functions. In this work, we used ERA5-Land soil moisture retrievals and several topographic indices to model variations in the in situ soil water content by means of a random forest model. We then correlated the predicted soil moisture with rut depth from different trials. Our spatiotemporal modeling approach successfully predicted soil moisture with Kendall's rank correlation coefficient of 0.62 (R² of 64 %). The final model included the spatial depth-to-water index, topographic wetness index, stream power index, as well as temporal components such as month and season, and ERA5-Land soil moisture retrievals. These retrievals were shown to be the most important predictor in the model, indicating a large temporal variation. The prediction of rut depth was also successful, resulting in Kendall's correlation coefficient of 0.61. Our results demonstrate that by using data from several sources, we can accurately predict soil moisture and use this information to predict rut depth. This has practical applications in reducing the impact of heavy machinery on forest soils and avoiding wet areas during forest operations.

Timber harvesting operations using heavy forest machinery frequently results in severe soil compaction and displacement, threatening sustainable forest management. An accurate prediction of trafficability, considering actual operating conditions, minimizes these impacts and can be facilitated by various predictive tools. Within this study, we validated the accuracy of four terramechanical parameters, including Cone Index (MPa, Penetrologger), penetration depth (cm, Penetrologger), cone penetration (cm blow⁻¹, dual-mass dynamic cone penetrometer) and shear strength (kPa, vane meter), and additionally two cartographic indices (topographic wetness index and depth-to-water). Measurements applying the four terramechanical approaches were performed at 47 transects along newly assigned machine operating trails in two broadleaved dominated mixed stands. After the CTL thinning operation was completed, measurement results and cartographic indices were correlated against rut depth. Under the rather dry soil conditions (29 ± 9 vol%), total rut depth ranged between 2.2 and 11.6 cm, and was clearly predicted by rut depth after a single pass of the harvester, which was used for further validations. The results indicated the easy-to-measure penetration depth as the most accurate approach to predict rut depth, considering coefficients of correlation (r_P = 0.44). Moreover, cone penetration (r_P = 0.34) provided reliable results. Surprisingly, no response between rut depth and Cone Index was observed, although it is commonly used to assess trafficability. The relatively low moisture conditions probably inhibited a correlation between rutting and moisture content. Consistently, cartographic indices could not be used to predict rutting. Rut depth after the harvester pass was a reliable predictor for total rut depth after 2–5 passes (r_P = 0.50). Rarely used parameters, such as cone penetration or shear strength, outcompeted the highly reputed Cone Index, emphasizing further investigations of applied tools.

Recently, forest operations are facing unfavorable climatic conditions more frequently. In Central Europe, machine trafficability and induced soil disturbances are negatively affected by periods of high precipitation and less intensive frost during ground-based harvesting operations. Winch-assist technology is assumed to reduce soil disturbance by forest machines in steep terrain. Still, the potential positive effects of winches to assist traction of forest machines in flat terrain have rarely been surveyed. In this study, a field trial was conducted in flat terrain (slope < 5%). There, a forwarder with an integrated traction-assist was monitored during six consecutive machine passes on a permanent machine operating trail. The first section of the machine operating trail was passed without traction-assist, the remaining section with traction-assist, resulting in two treatment groups. To assess soil impacts, three test plots were positioned per treatment group. Within these plots, pre- and post-operational soil bulk density, soil displacement and rutting were determined. Additionally, wheel slippage and cable tensile force were examined, using incremental rotary encoders and a flexible tensile force measurement kit, respectively. It was observed, that wheel slippage responded inversely with tensile force. Traction-assist technology reduced wheel slippage from 5.3 ± 11.9% to 0.37 ± 10.19% along the section of the machine operating trail, as compared to results from the unassisted section. Although wheel slippage was significantly reduced, no mitigating effect by the usage of traction-assist technology on soil disturbance could be observed, probably due to the low volumetric water content of 27%.