Harvesting machines weigh about 20 tons and require good bearing capacity of the terrain to avoid the machines from getting stuck. Only sufficient soil conditions prevent damaging the topsoil of the forest floor and negatively impacting the biosphere.
Up to 80% Finish forest land have been classified by Finnish Forest center (Metsäkeskus) based on airborne laser scanning data. Such information can be used in assessing sufficient conditions for forest trafficability. Six classes are available, where some require dry summer and some winter conditions for harvesting to occur.
The new Harvester Seasons -service by Finish Meteorological Institute (FMI) extends this classification by helping to estimate for up to several months ahead if and when good trafficability conditions occur. While for summer dry conditions are best suitable, in winter adequate frost and snow conditions are required.
“The service has a map interface showing if there will be high probability of having favourable conditions. Correspondingly if probabilities are low the map will show warning about unfavourable conditions.” explains Mikko Strahlendorff from FMI.
When probability is neither high nor low the trafficability classification is shown as is.
When weather stays typical to seasons the trafficability estimates can be considered very useful at least two months ahead. Caution should be applied in case of exceptional weather events as they can fall outside the service’s ability handle properly.
Read the full press release
More information:
Mikko Strahlendorff, Development Manager, Finnish Meteorological Institute, tel. +35850 359 3795, This email address is being protected from spambots. You need JavaScript enabled to view it.
Asko Poikela, Senior Researcher, Metsäteho Oy, tel +35840 530 7159, This email address is being protected from spambots. You need JavaScript enabled to view it.
Harvester Seasons -service free to use during one year trial period lasting until 04/2021
The service can be found at https://harvesterseasons.com/.
The e-shape project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 820852