A1 Refereed original research article in a scientific journal
High-resolution harvester data for estimating rolling resistance and forest trafficability
Authors: Salmivaara, Aura; Holmström, Eero; Kulju, Sampo; Ala-Ilomäki, Jari; Virjonen, Petra; Nevalainen, Paavo; Heikkonen, Jukka; Launiainen, Samuli
Publisher: Springer Nature
Publication year: 2024
Journal: European Journal of Forest Research
Journal name in source: European Journal of Forest Research
Volume: 143
Issue: 6
First page : 1641
Last page: 1656
ISSN: 1612-4669
eISSN: 1612-4677
DOI: https://doi.org/10.1007/s10342-024-01717-6
Web address : https://link.springer.com/article/10.1007/s10342-024-01717-6
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/457258661
Information on terrain conditions is a prerequisite for planning environmentally and economically sustainable forest harvesting operations that avoid negative impact on soils. Current soil data are coarse, and collecting such data with traditional methods is expensive. Forest harvesters can be harnessed to estimate the rolling resistance coefficient (μRR), which is a proxy for forest trafficability. Using spatio-temporal data on engine power used, speed travelled, and machine inclination, μRR can be computed for harvest areas. This study describes an extensive, high-resolution data on μRR collected in a boreal forest landscape in Southern Finland during the non-frost period of 2021, covering roughly 50 km of harvester routes. We report improvements in removing some of the previous restrictions on calculating μRR on steeper slopes, enabling the calculation within a -10∘ to +10∘ slope range with a speed range of 0.6–1.2 ms-1. We characterise the variation in μRR both between and within 11 test sites harvested during the April-August period. The site mean μRR varies from ∼ 0.14 to 0.19 and shows significant differences between the sites. Using simulations of the hydrological state of the soil and open spatial data on forest and topography, we identify features that best explain the extremes of μRR within the sites. Several wetness-related indices, such as the depth-to-water index with varying thresholds, explain the μRR extremes, while biomass-related stand attributes indirectly explain these through their linkage to site and soil characteristics. Obtaining μRR from actual operational data extends the capabilities of large-scale harvester-based data collection and paves the way for building data-driven models for trafficability prediction.
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Funding information in the publication:
Open access funding provided by Natural Resources Institute Finland. This research was funded by the Research Council of Finland (funding decisions 332172 and 332171) and the GreenFeedBack project from the EU Horizon Europe Framework Programme for Research and Innovation (Grant No. 101056921). In addition, this work was supported by the Research Council of Finland Flagship “Forest–Human–Machine Interplay—Building Resilience, Redefining Value Networks and Enabling Meaningful Experiences” (funding decision 337655).
