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  • Production and Industrial Facilities contain the data set on establishments based on Statistics Finland's Business Register as follows: * Data: location coordinate of the establishment (EUREF), industry according to the Standard Industrial Classification TOL 2008 at the 2-digit level * Industries according to D2.8.III.8 INSPIRE in TOL 2008 industries: - B Mining and quarrying, - C Manufacturing, - D Electricity, gas steam and air conditioning supply, - E Water supply, sewerage, waste management and remediation activities, - F Construction, and - H Transport and storage (excl. 53 Postal and courier activities) * coverage of the data set: establishments with over ten employees * statistical reference year: 2017 The data set is also suitable for viewing the location of industrial establishments. The coverage of the spatial data is about 90 % of the statistical data. The general Terms of Use must be observed when using the data: http://tilastokeskus.fi/org/lainsaadanto/copyright_en.html.

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    Datapaketet Skogsområden med högt biodiversitetsvärde i Finland består av 12 landsomfattande rasterkartor. Dessa 12 kartor är olika versioner av biodiversitetsvärden i Finlands skogar. Rasterkartornas upplösning är 96 x 96 meter. Enkla anvisningar för att läsa rasterkartorna: Ju större numeriskt värde, desto högre biodiversitetsvärde. National = Nationella analyser över biodiversitetsvärden i finska skogar (sex analyser) Regional = Regionala analyser över biodiversitetsvärden i finska skogar (ser ut som en karta men är i själva verket en samling av 13 separata analyser, region = Närings-, trafik- och miljöcentralen i Finland) (sex analyser) Sex olika prioriteringar av naturskydd gjordes med Zonation-programvaran (a) så att varje ny version innefattade allt som fanns med i tidigare, enklare analysversioner. National / Regional 1 Potentiell mängd död ved: Version 1 (V1) innefattade potentiell mängd död ved* på lokal nivå. Områden med många stora träd, många trädslag och ovanliga skogsmiljöer får högt lokalt värde. National / Regional 2 Potentiell mängd död ved och straff: Version 2 = V1 + straff för åtgärder som har negativ inverkan på biodiversiteten. De lokala värdena stämde bättre överens med verkligheten när man tog hänsyn till verkliga förändringar i skogar. National / Regional 3 Potentiell mängd död ved – straff + skogskonnektivitet: Version 3 = V2 + konnektivitet utifrån ekologisk likhet, avstånd och kvalitet mellan skogsområden (genomsnittlig försvagning 400 m). Ofragmenterade skogsområden av hög kvalitet framkommer. National / Regional 4 Potentiell mängd död ved – straff + skogskonnektivitet + RL-arter: Version 4 = V3 + observationer av rödlistade skogsarter. Habitat med rödlistade skogsarter framkommer. National / Regional 5 Potentiell mängd död ved – straff + skogskonnektivitet + RL-arter + skogslagen 10 §: Version 5 = V4 + konnektivitet till särskilt viktiga livsmiljöer enligt skogslagens 10 § (genomsnittlig försvagning 200 m). Värdefulla skogsområden och landskap i närheten av skyddade skogsområden med högt biodiversitetvärde framkommer. National / Regional 6 Potentiell mängd död ved – straff + skogskonnektivitet + RL-arter + skogslagen 10 § + PN-konnektivitet: Version 6 = V5 + konnektivitet till permanenta naturskyddsområden (genomsnittlig försvagning 2 km). Värdefulla skogsområden och landskap i närheten av skyddade områden med högt biodiversitetvärde framkommer. *Uträkning av potentiell mängd död ved (PMDV) PMDV beräknades i två skeden för varje skikt träslag i varje trädskikt: 1) Index för potentiell mängd död ved (PMDVi) togs fram med MOTTI-programmet (b, c, d). • 168 trädslag, fertilitetsklass och latitudkombinationer 2) PMDVi användes för att omvandla diameter och volym till potentiell mängd död ved • Genererades för hela Finland enligt bestånd med en upplösning på 16 x 16 m • Kombinerades sedan i 20 trädslag och fertilitetsklasser och förenades till 96 x 96 m upplösning. Inmatade data Den potentiella mängden död ved beräknades från beståndsdata (trädslag, medeldiameter, volym, vegetationsklass) vilket omfattade hela landet. Bästa möjliga data användes för varje område. - 24 % av Finland täcks av statligt ägda skogs- och naturskyddsområden och privata naturskyddsområden. o Forststyrelsens Naturtjänster: data om fält- och bestånd (5/2015) o Forststyrelsens Skogsbruk: data om fält- och bestånd (5/2015) o Privatägda naturskyddsområden: data om fält- och bestånd (5/2015) - 37 % av Finland täcks av privatägd skog som inte är naturskyddsområden: Skogscentralen, skogsdata (6.5.2005–6.5.2015) - 39 % av Finland täcks av o Naturresursinstitutet: Nationella skogsinventariedata som är tillverkat med skogsinventeringsmetod som utnyttjar information om riksskogstaxeringens provytor och satellitbilder 2013 (volym, trädslag, vegetationsklass och medeldiameter) Spatiella data om skogsbruk med negativ effekt på biodiversitet (till exempel fällning, gallring och dikning) (uppdaterades 10/2017) - Lantmäterivärket och Finlands miljöcentral SYKE: dikning i finsk torvmark (SOJT_09b1) - Forststyrelsens Skogsbruk: utförda anmälningar om användning av skog och dikningsfigurer - Skogscentralen: anmälningar om användning av skog och dikningsfigurer - University of Maryland/Dept. of Geographica Sciences: Global Forest Change/Forest Cover Loss 2000-2014 Observationer av skogsarter som har rödlistats av IUCN (sedan 1990): Finländska miljödatabasen HERTTA Spatiella data om särskilt viktiga livsmiljöer enligt skogslagens 10 § (uppdaterades 10/2017) - Forststyrelsens Skogsbruk och Skogscentralen Spatiella data om permanenta naturskyddsområden (uppdaterades 2/2018) - Forststyrelsens Naturtjänster: databas över naturskyddsområden SATJ Bakgrund Områden som är viktiga för skogens biodiversitet identifierades runt om i Finland för att främja hållbar markanvändning genom planering och naturskydd på lokal, regional och nationell nivå genom att informera markägare, ministerier och skogtjästemän. Vikten av sådana analyser beror på ökad användning av naturresurser och skadliga effekter på biodiversiteten tillsammans med begränsade naturskyddsresurser. Dessa betonar vikten av att utveckla kostnadseffektiv, ekologiskt hållbar markanvändning som dessa spatiella prioriteringar av naturskydd för skogar som görs för första gången för hela Finland. Prioriteringsmetoden Zonation användes för att hitta nya skogsområden med potentiellt högt skyddsvärde. Det övergripande målet var att tillämpa rikstäckande prioriteringsanalyser utifrån skogsdata relaterade till biodiversitet och markanvändningsdata som hade samlats in på beståndsnivå. De data som primärt tillämpades på skogsstruktur och -kvalitet (vegetationsklass, trädslag, volym och diameter) gav ekologiskt användbara ersättningar för skyddsvärde i barrskog. Resultaten visar att en betydande andel skog med högt biodiversitetsvärde finns utanför det aktuella nätverket för finska naturskyddsområden. Eftersom största delen av det finska skogsområdet är kommersiellt kan nätverket för naturskyddsområden inte stoppa den pågående nedgången av biodiversitet i skogarna. Nyckelord: biodiversitet, död ved, GIS, Handlingsplanen för den biologiska mångfalden i skogarna i södra Finland METSO, markanvändning, värdering, prioritering, skogar, skogarnas biodiversitet, skogsbruk, skogsskydd, spatiell prioritering av naturskydd,Zonation-programvara Datapaketet innefattar 12 rasterkartor och en .lyr-fil. .lyr-filen innehåller färgade symboler och beskrivningar av olika analysversioner. .lyr-filen är troligen endast genomförbar med GIS-programmet som tillhandahålls av ESRI Inc. Datapaketet kan hämtas från: http://www.syke.fi/en-US/Open_information/Spatial_datasets High Biodiversity Value Forests 2018 (Zonation) nationwide High Biodiversity Value Forests 2018 (Zonation) regional Detailjerad poster på engelska: http://www.syke.fi/en-US/Research__Development/Ecosystem_services/Specialist_work/Zonation_in_Finland/Zonation_materials/Posters eller http://www.syke.fi/download/noname/%7B771FF5A4-DAB6-45EE-8246-F38FC0090CAD%7D/138289 Detailjerad rapport på finska: http://hdl.handle.net/10138/234359 Mikkonen et al. 2018. Suomen ympäristökeskuksen raportteja 9/2018. Monimuotoisuudelle tärkeät metsäalueet Suomessa - Puustoisten elinympäristöjen monimuotoisuusarvojen Zonation-analyysien loppuraportti. Andra källor: a) Moilanen et al. 2014. Zonation–Spatial Conservation Planning Methods and Software. Version 4. User Manual. See also www.syke.fi/Zonation/en b) Hynynen et al. 2015. Eur. J. For. Res. 134/3. Long-term impacts of forest management on biomass supply and forest resource development: a scenario analysis for Finland. c) Hynynen et al. 2014. Metlan työraportteja 302. Scenario analysis for the biomass supply potential and the future development of Finnish forest resources. d) Salminen et al. 2005. Comput. electron. agr. 49/1. Reusing legacy FORTRAN in the MOTTI growth and yield simulator. Användar lisens: Creative Commons 4.0. © SYKE Datasources: Finnish Forest Centre, Metsähallitus, Natural Resources Institute Finland, National Land Survey of Finland, Hansen/UMD/Google/USGS/NASA

  • 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.

  • The technical harvesting potential of small-diameter trees can be defined as the maximum potential procurement volume of small-diameter trees available from the Finnish forests based on the prevailing guidelines for harvesting of energy wood. The potentials of small-diameter trees from early thinnings have been calculated for fifteen NUTS3-based Finnish regions covering the whole country (Koljonen et al. 2017). To begin with the estimation of the region-level potentials, technical harvesting potentials were estimated using the sample plots of the eleventh national forest inventory (NFI11) measured in the years 2009–2013. First, a large number of sound and sustainable management schedules for five consecutive ten-year periods were simulated for each sample plot using a large-scale Finnish forest planning system known as MELA (Siitonen et al. 1996; Redsven et al. 2013). MELA simulations consisted of natural processes and human actions. The ingrowth, growth, and mortality of trees were predicted based on a set of distance-independent tree-level statistical models (e.g. Hynynen et al. 2002) included in MELA and the simulation of the stand (sample plot)-level management actions was based on the current Finnish silvicultural guidelines (Äijälä et al. 2014) and the guidelines for harvesting of energy wood (Koistinen et al. 2016). Simulated management actions for the small-tree fraction consisted of thinnings that fulfilled the following stand criteria: • mean diameter at breast height ≥ 8 cm • number of stems ≥ 1500 ha-1 • mean height < 10.5 m (in Lapland) or mean height < 12.5 m (elsewhere). Energy wood was harvested as delimbed (i.e. including the stem only) in spruce-dominated stands and peatlands and as whole trees (i.e. including stem and branches) elsewhere. When harvested as whole trees, a total of 30% of the original crown biomass was left onsite (Koistinen et al. 2016). Energy wood thinnings could be integrated with roundwood logging or carried out independently. Second, the technical energy wood potential of small trees was operationalized in MELA by maximizing the removal of thinnings in the first period. In this way, it was possible to pick out all small tree fellings simulated in the first period despite, for example, the profitability of the operation. However, a single logging event was rejected if the energy wood removal was lower than 25 m³ha-1 or the industrial roundwood removal of pine, spruce, or birch exceeded 45 m³ha-1. The potential calculated in this way contained also timber suitable for industrial roundwood. Therefore, two estimates are given: • potential of trees below 10.5 cm in breast-height diameter • potential of trees below 14.5 cm in breast-height diameter. Subsequently, the region-level potentials were spread on a raster grid at 1 km × 1 km resolution. Only grid cells on Forests Available for Wood Supply (FAWS) were considered in this operation. In this study, FAWS was defined as follows: First, forest land was extracted from the Finnish Multi-Source National Forest Inventory (MS-NFI) 2013 data (Mäkisara et al. 2016). Second, restricted areas were excluded from forest land. The restricted areas consisted of nationally protected areas (e.g. nature parks, national parks, protection programme areas) and areas protected by the State Forest Enterprise. In addition, some areas in northernmost Lapland restricted by separate agreements between the State Forest Enterprise and stakeholders were left out from the final data. Furthermore, for small trees, FAWS was further constrained by the stand criteria presented above to represent similar stand conditions for small-tree harvesting as in MELA. Finally, the region-level potentials were distributed to the grid cells by weighting with MS-NFI stem wood biomasses. References Äijälä O, Koistinen A, Sved J, Vanhatalo K, Väisänen P (2014) Metsänhoidon suositukset [Guidelines for sustainable forest management]. Metsätalouden kehittämiskeskus Tapion julkaisuja. Hynynen J, Ojansuu R, Hökkä H, Salminen H, Siipilehto J, Haapala P (2002) Models for predicting the stand development – description of biological processes in MELA system. The Finnish Forest Research Institute Research Papers 835. Koistinen A, Luiro J, Vanhatalo K (2016) Metsänhoidon suositukset energiapuun korjuuseen, työopas [Guidelines for sustainable harvesting of energy wood]. Metsäkustannus Oy, Helsinki. Koljonen T, Soimakallio S, Asikainen A, Lanki T, Anttila P, Hildén M, Honkatukia J, Karvosenoja N, Lehtilä A, Lehtonen H, Lindroos TJ, Regina K, Salminen O, Savolahti M, Siljander R (2017) Energia ja ilmastostrategian vaikutusarviot: Yhteenvetoraportti. [Impact assessments of the Energy and Climate strategy: The summary report.] Publications of the Government´s analysis, assessment and research activities 21/2017. Mäkisara K, Katila M, Peräsaari J, Tomppo E (2016) The Multi-Source National Forest Inventory of Finland – methods and results 2013. Natural resources and bioeconomy studies 10/2016. Redsven V, Hirvelä H, Härkönen K, Salminen O, Siitonen M (2013) MELA2012 Reference Manual. Finnish Forest Research Institute. Siitonen M, Härkönen K, Hirvelä H, Jämsä J, Kilpeläinen H, Salminen O, Teuri M (1996) MELA Handbook. Metsäntutkimuslaitoksen tiedonantoja 622. ISBN 951-40-1543-6.

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    The Bio-geographical regions are internally homogeneous biogeographical regions of Finland. The number of regions is 21. The regions were spatially defined by an expert committee in 1930 as collections of municipalities. Consequently, the province boundaries follow the delineation of of municipalities in the 1930's including some enclaves, exclaves, and narrow stripes as the province boundaries have not been changed or updated since then excluding the cession of territory after the Second World War. In the "Extended" data set regions have names and abbreviations in Finnish, Swedish, and Latin. No other attribute data is available.

  • NLS orthophotos are an aerial photo data set covering the whole of Finland. The geometry of the orthophotos corresponds to a map. NLS orthophotos are updated every 3 to 10 years. The product is a part of the open data of the National Land Survey of Finland. More information: Acquisition of open data http://www.maanmittauslaitos.fi/en/maps-and-spatial-data/expert-users/topographic-data-and-how-acquire-it

  • Seabed substrate 1:1 000 000 is one of the products produced in the EMODnet (European Marine Observation and Data network) Geology EU project. Project provided seabed geological material from the European maritime areas. The EMODnet Geology project (http://www.emodnet-geology.eu/) collects and harmonizes geological data from the European sea areas to support decision-making and sustainable marine spatial planning. The EMODnet Geology partnership has included 36 marine organizations from 30 countries. This data includes the EMODnet seabed substrate map at a scale of 1:1 000 000 from the Finnish marine areas. It is based on the data produced on a scale of 1:20 000 by the Geological Survey of Finland (GTK). The data has been harmonized and reclassified into five Folk substrate classes (clay + silt (mud), sandy clays, clayey sands, coarse sediments, mixed sediments) and bedrock. The data describes the seabed substrate from the uppermost 30 cm of the sediment column. The data have been generalized into a target scale (1:1 000 000). The smallest cartographic unit within the data is 4 km2. Further information about the EMODnet-Geology project is available on the portal (http://www.emodnet-geology.eu/).

  • This assessment was part of project Baltic ForBio funded by the Interreg Baltic Sea Region Programme (https://www.slu.se/en/departments/forest-economics/forskning/research-projects/baltic-forbio/). 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. https://doi.org/10.14214/sf.9902 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. https://doi.org/10.14214/sf.1022. Natural Resources Institute Finland. 2019. Industrial roundwood removals by region. Available at: http://stat.luke.fi/en/industrial-roundwood-removals-by-region. 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: http://www.fao.org/3/ap862e/ap862e00.pdf. 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 Topographic database is a dataset depicting the terrain of all of Finland. The key objects in the Topographic database are the road network, buildings and constructions, administrative borders, geographic names, land use, waterways and elevation. Aerial photographs, scanning data and data provided by other data providers are utilised in updating the Topographic database. The updating is done in close cooperation with the municipalities. Field checks in the terrain are also needed to some extent, mostly as regards the classification of features. The topographic database is used in the production of other map products and in various optimisation tasks. The product belongs to the open data of the National Land Survey of Finland. More information: Topographic data and how to acquire it http://www.maanmittauslaitos.fi/en/maps-and-spatial-data/expert-users/topographic-data-and-how-acquire-it.

  • The GTK’s Mineral Deposit database contains all mineral deposits, occurrences and prospects in Finland. Structure of the new database was created in 2012 and it is based on global geostan-dards (GeoSciML and EarthResourceML) and classifications related to them. The database is in Oracle, data products are extracted from the primary database. During 2013 GTK’s separate mineral deposit databases (Au, Zn, Ni, PGE, U, Cu, Industrial minerals, FODD, old ore deposit database) were combined into a single entity. New database contains extensive amount of information about mineral occurrence feature along with its associated commodities, exploration activities, holding history, mineral resource and re-serve estimates, mining activity, production and geology (genetic type, host and wall rocks, min-erals, metamorphism, alteration, age, texture, structure etc.) Database will be updated whenever new data (e.g. resource estimate) is available or new deposit is found. Entries contain references to all published literature and other primary sources of data. Also figures (maps, cross sections, photographs etc.) can be linked to mineral deposit data. Data is based on all public information on the deposits available including published literature, archive reports, press releases, companies’ web pages, and interviews of exploration geologists. Database contains 33 linked tables with 216 data fields. Detailed description of the tables and fields can be found in separate document. (http://tupa/metaviite/MDD_FieldDescription.pdf) The data products extracted from the database are available on Mineral Deposits and Exploration map service (http://gtkdata.gtk.fi/MDaE/index.html) and from Hakku -service (http://hakku.gtk.fi).