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  • The 1:250 000 data on the soft and hard areas of the seabed supplements the seabed substrate data produced by the Geological Survey of Finland (GTK) for areas for which actual survey data is unavailable. The data covers two categories; hard and soft seabed areas. The substrate types categorised as hard seabed areas cover types ranging from gravel to boulders and exposed rock, and the substrate types for soft seabed areas cover types from silt to sand. The model is based on the marine geological survey data of GTK, the substrate observations made by the Finnish Environment Institute (SYKE) and Metsähallitus, and on the environment variable data produced by the Finnish Inventory Programme for the Underwater Marine Environment, VELMU, particularly on depth and seabed openness models. The data has undergone statistical evaluation and the ultimate model is based on expert estimates and modelling. At its most precise, the data is at a scale of 1:250 000 and the areas with a size less than 0.3 square kilometres have been removed. The substrate type information pertaining to restricted areas has been removed from the final data. A permit (AK15246) for publishing data with a similar scale, i.e. the EMODnet data, was received from the Defence Command of the Finnish Defence Forces on 28 July 2014.

  • Seabed substrate maps of the European marine areas including (e.g. the Baltic Sea, the Greater North Sea, the Celtic Sea, the Iberian Coast, and the Mediterranean Sea within EU waters). The maps are collated and harmonized from seabed substrate information within the EMODnet-Geology III project. Where necessary, the existing seabed substrate classifications (of individual maps) have been translated to a scheme that is supported by EUNIS. This EMODnet reclassification scheme includes at least five seabed substrate classes. Four substrate classes are defined on the basis of the modified Folk triangle (mud to sandy mud; sand; coarse sediment; and mixed sediment) and one additional substrate class (rock and boulders) was included by the project team. If the original seabed substrate dataset has enabled more detailed substrate classification, classifications with 7 and 16 substrate classes might be available. The EMODnet-Geology III project started in 2017 with 39 marine departments of the geological surveys of Europe (from 30 countries), with an objective to assemble marine geological information from all European sea areas.

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    The Finnish Forest Research Institute (Metla) developed a method called multi-source national forest inventory (MS-NFI). The first operative results were calculated in 1990. Small area forest resource estimates, in here municipality level estimates, and estimates of variables in map form are calculated using field data from the Finnish national forest inventory, satellite images and other digital georeferenced data, such as topographic database of the National Land Survey of Finland. Six sets of estimates have been produced for the most part of the country until now and five sets for Lapland. The number of the map form themes in the most recent version, from year 2011, is 45. In addition to the volumes by tree species and timber assortments, the biomass by tree species groups and tree compartments have been estimated. The first country level estimates correspond to years 1990-1994. The most recent versions are from years 2005, 2007, 2009 and 2011. The maps from 2011 is the second set of products freely available. The new set of the products will be produced annually or biannually in the future. The maps are in a raster format with a pixel size of 20mx20m and in the ETRS-TM35FIN coordinate system. The products cover the combined land categories forest land, poorly productive forest land and unproductive land. The other land categories as well as water bodies have been delineated out using the elements of topographic database of the Land Survey of Finland.

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    This assessment was part of project Baltic ForBio funded by the Interreg Baltic Sea Region Programme ( The project was carried out in 2017-2020. The harvesting potentials in Finland were calculated for the following assortments: • Stemwood for energy from 1st thinnings, pine • Stemwood for energy from 1st thinnings, spruce • Stemwood for energy from 1st thinnings, broadleaved • Stemwood for energy from 1st thinnings (smaller than pulpwood-sized trees), pine • Stemwood for energy from 1st thinnings (smaller than pulpwood-sized trees), spruce • Stemwood for energy from 1st thinnings (smaller than pulpwood-sized trees), broadleaved • Logging residues, pine • Logging residues, spruce • Logging residues, deciduos • Stumps, pine • Stumps, spruce. 1.1 Decision support system used in assessment Regional energywood potentials were calculated with MELA forest planning tool (Siitonen et al. 1996; Hirvelä et al. 2017). 1.2 References and further reading Anttila P., Muinonen E., Laitila J. 2013. Nostoalueen kannoista jää viidennes maahan. [One fifth of the stumps on a stump harvesting area stays in the ground]. BioEnergia 3: 10–11. Anttila P., Nivala V., Salminen O., Hurskainen M., Kärki J., Lindroos T.J. & Asikainen A. 2018. Re-gional balance of forest chip supply and demand in Finland in 2030. Silva Fennica vol. 52 no. 2 article id 9902. 20 p. Hakkila, P. 1978. Pienpuun korjuu polttoaineeksi. Summary: Harvesting small-sized wood for fuel. Folia Forestalia 342. 38 p. Hirvelä, H., Härkönen, K., Lempinen, R., Salminen, O. 2017. MELA2016 Reference Manual. Natural Resources Institute Finland (Luke). 547 p. Hynynen, J., Ojansuu, R., Hökkä, H., Siipilehto, J., Salminen, H. & Haapala, P. 2002. Models for predicting stand development in MELA System. Metsäntutkimuslaitoksen tiedonantoja 835. 116 p. Koistinen A., Luiro J., Vanhatalo K. 2016. Metsänhoidon suositukset energiapuun korjuuseen, työopas. [Guidelines for sustainable harvesting of energy wood]. Metsäkustannus Oy, Helsinki. ISBN 978-952-5632-35-4. 74 p. Mäkisara, K., Katila, M., Peräsaari, J. 2019: The Multi-Source National Forest Inventory of Finland - methods and results 2015. Muinonen E., Anttila P., Heinonen J., Mustonen J. 2013. Estimating the bioenergy potential of forest chips from final fellings in Central Finland based on biomass maps and spatially explicit constraints. Silva Fennica 47(4) article 1022. Natural Resources Institute Finland. 2019. Industrial roundwood removals by region. Available at: Accessed 22 Nov 2019. Ruotsalainen, M. 2007. Hyvän metsänhoidon suositukset turvemaille. Metsätalouden kehittämiskeskus Tapio julkaisusarja 26. Metsäkustannus Oy, Helsinki. 51 p. ISBN 978-952-5694-16-1, ISSN 1239-6117. Siitonen M, Härkönen K, Hirvelä H, Jämsä J, Kilpeläinen H, Salminen O et al. 1996. MELA Handbook. 622. 951-40-1543-6. Äijälä, O., Kuusinen, M. & Koistinen, A. (eds.). 2010. Hyvän metsänhoidon suositukset: energiapuun korjuu ja kasvatus. Metsätalouden kehittämiskeskus Tapion julkaisusarja 30. 56 p. ISBN 978-952-5694-59-8, ISSN 1239-6117. Äijälä, O., Koistinen, A., Sved, J., Vanhatalo, K. & Väisänen, P. (eds). 2014. Metsänhoidon suositukset. Metsätalouden kehittämiskeskus Tapion julkaisuja. 180 p. ISBN 978-952-6612-32-4. 2. Output considered in assessment Valid for scenario: Maximum sustainable removal Main output ☒Small-diameter trees ☒Stemwood for energy ☒Logging residues ☒Stumps ☐Bark ☐Pulpwood ☐Saw logs Additional information Stemwood for energy from 1st thinnings. Part of this potential consists of trees smaller than pulpwood size. This part is reported as Small-diameter trees. Forecast period for the biomass supply assessment Start year: 2015 End year: 2044 Results presented for period 2025-2034 3. Description of scenarios included in the assessments Maximum sustainable removal The maximum sustainable removal is defined by maximizing the net present value with 4% discount rate subject to non-declining periodic total roundwood removals, energy wood removals and net incomes, further the saw log removals have to remain at least at the level of the first period. There are no sustainability constraints concerning tree species, cutting methods, age classes or the growth/drain -ratio in order to efficiently utilize the dynamics of forest structure. Energy wood removal can consist of stems, cutting residues, stumps and roots. According to the scenario the total annual harvesting potential of industrial roundwood is 80.7 mill. m3 (over bark) for period 2025-2034. In 2018 removals of industrial roundwood in Finland totaled 68.9 mill. m3 (Natural Resources… 2019). 4. Forest data characteristics Level of detail on forest description ☒High ☐Medium ☐Low NFI data with many and detailed variables down to tree parts. Sample plot based ☒Yes ☐No NFI sample plot data from 2013-2017. Stand based ☐Yes ☒No Grid based ☒Yes ☐No Multi-Source NFI data from 2015 (Mäkisara et al. 2019) utilized when distributing regional potentials to 1 km2 resolution. 5. Forest available for wood supply: Total forest area defined as in: FAO. 2012. FRA 2015, Terms and Definitions. Forest Resources Assessment Working Paper 180. 36 p. Available at: Forest and scrub land 22 812 000 ha Forest land 20 278 000 ha and scrub land 2 534 000 ha Forest area not available for wood supply Forest and scrub land 2 979 000 ha Forest land 1 849 000 ha and scrub land 1 130 000 ha Partly available for wood supply Forest and scrub land 2 553 000 ha (includes in FAWS, below) Forest land 1 149 000 ha and scrub land 1 404 000 ha. Forest Available for wood supply (FAWS) Forest and scrub land 19 833 000 ha Forest land 18 429 000 ha and scrub land 1 404 000 ha In MELA calculations all the scrub land belonging to the FAWS belongs to the category “Partly available for wood supply”, but there are no logging events on scrub land regardless or the category. 6. Temporal allocation of fellings Valid for scenario: Maximum sustainable removal Allocation method ☐Optimization based without even flow constraints ☒Optimization based with even flow constraints ☐Rule based with no harvest target ☐Rule based with static harvest target ☐Rule based with dynamic harvest target See item 3 above (max NPV with 4 % discount rate). 7. Forest management Valid for scenario: Maximum sustainable removal Representation of forest management ☐Rule based ☒Optimization ☐Implicit Treatments, among of the optimization makes the selections, are based on management guidelines (e.g. Äijälä etc 2014) 7.2 General assumptions on forest management Valid for scenario: Maximum sustainable removal ☒Complies with current legal requirements ☐Complies with certification ☒Represents current practices ☐None of the above ☐ No information available Forest management follows science-based guidelines of sustainable forest management (Ruotsalainen 2007, Äijälä et al. 2010, Äijälä et al. 2014). 7.3 Detailed assumptions on natural processes and forest management Valid for scenario: Maximum sustainable removal Natural processes ☒Tree growth ☒Tree decay ☒Tree death ☐Other? Tree-level models (e.g. Hynynen et al., 2002). Silvicultural system ☒Even-aged ☐Uneven-aged Click here to enter text. Regeneration method ☒Artificial ☒Natural Regeneration species ☐Current distribution ☒Changed distribution Optimal distribution may differ from the current one. Genetically improved plant material ☐Yes ☒No Cleaning ☒Yes ☐No Thinning ☒Yes ☐No Fertilization ☐Yes ☒No 7.4 Detailed constraints on biomass supply Volume or area left on site at final felling ☒Yes ☐No 5 m3/ha retained trees are left in final fellings. Final fellings can be carried out only on FAWS with no restrictions for wood supply. Constraints for residues extraction ☒Yes ☐No ☐N/A Retention of 30% of logging residues onsite (Koistinen et al. 2016) Constraints for stump extraction ☒Yes ☐No ☐N/A Retention of 16–18% of stump biomass (Muinonen et al. 2013; Anttila et al. 2013) 8. External factors Valid for scenario: Maximum sustainable removal External factors besides forest management having effect on outcomes Economy ☐Yes ☒No Climate change ☐Yes ☒No Calamities ☐Yes ☒No Other external ☐Yes ☒No

  • The marine habitat type data concerns the modelling work carried out within the Finnish Inventory Programme for the Underwater Marine Environment (VELMU) in spring 2015. The task was done in cooperation between the Geological Survey of Finland (GTK) and Åbo Academi University (ÅA). The work included the modelling of the marine habitats included in the Annex 1 of the Habitats Directive: reefs (1170) and sandbanks, which are slightly covered by sea water all the time (1110). The aforementioned marine habitat types are specified on the basis of seabed substrate type and topographic form and they can overlap one another. The objective was to produce comprehensive maps of the occurrences of reefs and sandbanks throughout the entire marine area of Finland based on the best data available. The criteria to determine the marine habitats were discussed with the responsible bodies and the instructions (version 5.1), which include more precise criteria for determining marine habitat types than the Natura 2000 Habitats Manual (Airaksinen & Karttunen 2001), for a Natura 2000 inventory were utilised. On the basis of different criteria and test analyses, a decision was made to model the following entireties: - Potential rocky reefs - detail-scale sites that are likely to have reef occurrences. - Potential rocky reef environments - larger sites that are likely to have reef occurrences. - Potential sandbanks - detail-scale sites that are likely to have sandbank occurrences. - Potential sandbank environments - larger sites that are likely to have sandbank occurrences. The data concerning the marine habitats of restricted areas has been removed.

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    Field biomass sidestreams GIS data describes the maximum harvestable sidestream potential based on current tillage. Sidestreams has been calculated by crop statistics, cultivation area, solid content and harvest index. Harvest index describes the part of the plant that is utilized as a crop. Rest of the plant is considered sidestream. In many cases the maximum sidestream cannot be necessarily utilized as whole, because of technical and economical constraints for harvest. Part of the sidestream is also wise to plough in to field to maintain its fertility. Field crop data is conducted from Luke's crop production statistics. The crop statistics in ELY centre level is divided into the Biomass Atlas grid weighting by the crop area of that certain plant. Crop area is from IACS-register, used to manage subsidies in agriculture. Farmers report their cultivation plans there every spring. Crop area and amount are from same year, usually previous year.

  • The Luke download service for MS-NFI is an Atom feed, that provides data from the Luke Multisource National Forest Inventory (MS-NFI) starting from year 2006. The data is in the topic areas decribed in Appendix II and III of the EU INSPIRE Directive. The service uses the data downloadable from the Luke file service for publicly available data (, The data are managed by Luke. The service is free of charge and no authentication is required. The data are part of the open data of Luke.

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    Lorry parking areas at E18 road in Finland. Data is in Esri shapefiles, in ETRS-TM35FIN coordinates. Data is maintained and provided by FTA (Finnish Transport Agency).

  • Harvester Seasons is a service designed to help with estimating evolving trafficability conditions in forested terrain based on weather and model forecast information. The full service is currently provided for the geographical area of Finland.

  • Statistics Finland's INSPIRE data Web Service is a WMS interface service through which the following data required by INSPIRE and national legislation on geographic information are available: 1) Statistical units: Regional divisions (municipality, major region, region, sub-regional unit, Regional State Administrative Agency (AVI), Centre for Economic Development, Transport and the Environment (ELY), electoral district, working area) and grids 1 km x 1 km and 5 km x 5 km. 2) Non-profit and public services: Educational institutions (comprehensive schools, upper secon-dary general schools) 3) Production and industrial facilities: Production and industrial facilities The data are administered by Statistics Finland. The service is free of charge and does not require authentication or identification with a user ID and password. The general Terms of Use must be observed when using the data: