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  • The Arctic SDI Gazetteer Service is a service that contains authoritative place names data from the arctic area. The service can be used for searching place names and performing reverse geocoding. The service contains about 2.87 million place name locations with about 3.15 million place names. It contains data from following sources: * Canada (Natural Resources Canada, updated: 02/2018) * Denmark (including Greenland) (SDFE, updated: 05/2017) * Finland (National Land Survey of Finland, updated: 04/2017) * GEBCO Undersea feature names gazetteer (updated: 04/2019) * Iceland (National Land Survey of Iceland, updated: 08/2017) * Norway (Norwegian Mapping Authority, updated: 08/2017) * Russia (Russian Mapping Agency, updated: 04/2019) * Sweden (Swedish National Mapping Agency, updated: 05/2017) * USA (US Geological Survey, updated: 05/2017)

  • NLS-FI INSPIRE Download Service (WFS) for Buildings/Polygon is an INSPIRE compliant direct access Web Feature Service. It contains the following INSPIRE feature types: Building The service is based on the NLS-FI INSPIRE Buildings Theme Dataset. The dataset is administrated by the National Land Survey of Finland. The service contains all features from the dataset that are modelled as polygons.

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    Forests of high biodiversity value 2018 (Zonation) datapackage consists of 12 nationwide raster maps from Finland. These 12 maps are all different versions of biodiversity values of Finnish forests. Resolution of these raster maps is 96 meters x 96 meters. Simple instructions for reading the raster maps: The bigger the numeric value the higher the biodiversity value. NAT = National scale analyses of biodiversity values of Finnish forests (6 analysis) REG = Regional scale analyses of biodiversity values of Finnish forests (map looks like one but is in reality a collection of 13 separately done analysis, region = Centre for Economic Development, Transport and the Environment in Finland) (6 analysis) Six different spatial conservation prioritizations were made with Zonation Software (a) so that each new version included everything that had been included in previous, simpler, analysis versions. NAT / REG 1 Decaying wood potential: Version 1 (V1) included the local decaying wood potentials*. Areas with lot of large trees, many tree species and rare forest environments get high local value. NAT / REG 2 Decaying wood potential – penalties: Version 2 = V1 + penalties for forestry operations with negative impact on biodiversity. More realistic local values when taking into account real life changes in forests. NAT / REG 3 Decaying wood potential – penalties + forest connectivity: Version 3 = V2 + connectivity based on ecological similarity, distance and quality between forest patches (attenuation avg. 400m). Unfragmented high value forests areas emerge. NAT / REG 4 Decaying wood potential – penalties + forest connectivity + RL species: Version 4 = V3 + observations of Red List forest species. Red List forest species habitats emerge. NAT / REG 5 Decaying wood potential – penalties + forest connectivity + RL species + FFA 10§: Version 5 = V4 + connectivity to woodland key habitats protected by Finnish Forest Act 10 § (attenuation avg. 200m). Valuable forest areas and landscapes close to protected high biodiversity forest patches emerge. NAT / REG 6 Decaying wood potential – penalties + forest connectivity + RL species + FFA 10§ + PA connectivity: Version 6 = V5 + connectivity to permanent conservation areas (attenuation avg. 2km). Valuable forest areas and landscapes close to protected high biodiversity areas emerge. *Calculation of Decaying wood potential (DWP) DWP was calculated for every strata of tree species in every crown storey class in 2 stages: 1) Decaying wood potential indexes (DWPi) were modelled with MOTTI-program (b, c, d). • 168 tree species, fertility class and latitude combinations 2) DWPis were used for converting diameter and volume into decaying wood potential • Generated for the whole Finland at tree stand level at the resolution of 16 m x 16 m • Eventually combined into 20 tree species & fertility classes and aggregated to 96 m x 96 m resolution. Input data Decaying wood potential was calculated from forest stand level datasets (tree species, diameter, volume, fertility) covering whole Finland. Best possible data was used for every area. - 24 % of Finland covered by state-owned forestry and conservation areas and private conservation areas o Metsähallitus Parks & Wildlife: field and forest stand data (5/2015) o Metsähallitus Forestry Inc.: field and forest stand data (5/2015) o Private owned conservation areas: field and forest stand data (5/2015) - 37 % of Finland covered by privately owned other than protected forest areas: Finnish Forest Centre, forest information (6.5.2005 – 6.5.2015) - 39 % of Finland covered by o Natural Resources Institute Finland: Multi-source national forest inventory data of Finland 2013(volume, tree species, fertility class, diameter) Spatial data on forestry operations with negative impact on biodiversity (e. g. fellings, thinning and ditching) (updated 10/2017) - National Land Survey of Finland & Finnish Environment Institute SYKE: Ditching state of Finnish peatlands (SOJT_09b1) - Metsähallitus Forestry Inc.: Executed forest operations of forest operations from field and forest stand data and ditching status - Finnish Forest Centre: Forest declariations and ditching status - University of Maryland / Dept. of Geographica Sciences: Global Forest Change / Forest Cover Loss 2000-2014 Observations of IUCN Red List forest species (since 1990): Finnish Environmental database HERTTA Spatial data on woodland key habitats protected by The Finnish Forest Act 10§ (updated 10/2017) - Finnish Forest Centre: woodland key habitats protected by Finnish Forest act 10§ Spatial data on permanent conservation areas (updated 2/2018) - Metsähallitus Parks & Wildlife: Conservation area database SATJ Background Areas important to forest biodiversity were identified throughout Finland to support sustainable land using planning and nature conservation at local, regional and national level by informing land owners, ministries and forestry stakeholders. Importance of analyzes like this rise from increased usage of natural resources and consequent harmful impacts on biodiversity together with limited resources for conservation. These highlight the importance of developing cost-effective, ecologically sustainable land use planning approaches such as these spatial conservation prioritizations of forests made for a first time for the whole Finland. Prioritization approach, Zonation, was used to find new forest areas of potential high conservation value. The overall aim was to implement nationwide prioritization analyses based on biodiversity-related forest data and land use data recorded at the level of forest stand. Primarily employed data on forest structure and quality (vegetation class, tree species, volume and diameter) provided ecologically useful surrogates for conservation value in boreal forest. Results show that a significant portion of high biodiversity value forests lay outside the current Finnish protected area (PA) network. As most of the Finnish forest area is under commercial management, PA network cannot halt the on-going decline of forest biodiversity. Keywords: biodiversity value, decaying wood, forest biodiversity, Forest Biodiversity Programme for Southern Finland (METSO), forest conservation, forestry, geographical information system (GIS), land use, spatial conservation prioritization, Zonation software Datapackage includes all 12 raster maps and a .lyr -file. .lyr -file contains coloured symbology and descriptions of different analysis versions. .lyr -file is probably operable only with GIS-software prvided by ESRI Inc. Datapackage can be loaded from: http://www.syke.fi/en-US/Open_information/Spatial_datasets High Biodiversity Value Forests 2018 (Zonation) nationwide High Biodiversity Value Forests 2018 (Zonation) regional Detailed poster available: http://www.syke.fi/en-US/Research__Development/Ecosystem_services/Specialist_work/Zonation_in_Finland/Zonation_materials/Posters OR http://www.syke.fi/download/noname/%7B771FF5A4-DAB6-45EE-8246-F38FC0090CAD%7D/138289 Detailed report (only in Finnish): 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. Other references: 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. Availability: Aineisto kuuluu SYKEn avoimiin aineistoihin (CC BY 4.0) Creative Commons 4.0. © SYKE Datasources: Finnish Forest Centre, Metsähallitus, Natural Resources Institute Finland 2015, National Land Survey of Finland, Hansen/UMD/Google/USGS/NASA

  • The Topographic map series is a dataset depicting the terrain of all of Finland. The key elements in it are the road network, buildings and constructions, geographic names, waterways, land use and elevation. The more precise levels of the Topographic map series consist of the same map objects and map symbols depicted in the same way as in the familiar Basic map. Basic map raster is applicable to be used, for instance, as a base map for planning land use or for excursion and outdoor recreational purposes in mobile devices and in various Internet services associated with nature. When going over to the more general datasets in the Topographic map series, the number and visualisation of objects and map symbols changes. The generalised small-scale Topographic maps raster are applicable to be used as approach maps in e.g. mobile devices and Internet services. 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

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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

  • Seabed substrate 1:250 000 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 1:250 000 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), which does not cover the whole Finnish marine area yet. The seabed substrate data will be updated with a new interpreted data on a yearly basis.The data has been harmonized and reclassified into five Folk substrate classes (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 (1:250 000). The smallest smallest cartographic unit within the data is 0.3 km2 (30 hectares). Further information about the EMODnet-Geology project is available on the portal (http://www.emodnet-geology.eu/). Permission (AK15246) to publish the material was obtained from the Finnish Defence Office 28.07.2014

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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 raw materials of forest chips in Biomass Atlas are small-diameter trees from first thinning fellings and logging residues and stumps from final fellings. The harvesting potential consists of biomass that would be available after technical and economic constraints. Such constraints include, e.g., minimum removal of energywood per hectare, site fertility and recovery rate. Note that the techno-economic potential is usually higher than the actual availability, which depends on forest owners’ willingness to sell and competitive situation. The harvesting potentials were estimated using the sample plots of the 11th and 12th national forest inventory (NFI11 and NFI12) measured in the years 2013–2017. 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; Hirvelä et al. 2017). 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). Future potentials were assumed to materialize when the industrial roundwood fellings followed the level of maximum sustainable removals (80.7 mill. m3 in this calculation). The maximum sustainable removals were defined such that the net present value calculated with a 4% discount rate was maximized subject to non-declining periodic industrial roundwood and energy wood removals and net incomes, and subject to the saw log removal remaining at least at the level of the first period. There were no constraints concerning tree species selection, cutting methods, age classes, or the growth/drain ratio in order to efficiently utilize the dynamics of forest structure. The potential for energywood from first thinnings was calculated separately for all the wood from first thinnings (Small-diameter trees from first thinnings) and for material that does not fulfill the size-requirements for pulpwood (Small-diameter trees from first thinnings, smaller than pulpwood). The minimum top diameter of pulpwood in the calculation was 6.3 cm for pine (Pinus sylvestris) and 6.5 cm for spruce (Picea abies) and broadleaved species (mainly Betula pendula, B. pubescens, Populus tremula, Alnus incana, A. glutinosa and Salix spp.). The minimum length of a pulpwood log was assumed at 2.0 m. The potentials do not include branches. The potentials for logging residues and stumps were calculated as follows: The biomass removals of clear fellings were obtained from MELA. According to harvesting guidelines for energywood (Koistinen et al. 2016) mineral soils classified as sub-xeric (or weaker) and peatlands with corresponding low nutrient levels were left out from the potentials. Finally, technical recovery rates were applied (70% for logging residues and 82-84% for stumps) (Koistinen et al. 2016; Muinonen et al. 2013) The techno-economical harvesting potentials were first calculated for nineteen Finnish regions and then distributed on a raster grid at 1 km × 1 km resolution by weighting with Multi-Source NFI biomasses as described by Anttila et al. (2018). The potentials represent time period 2025-2034 and are presented as average annual potentials in solid cubic metres over bark. References Äijälä O, Koistinen A, Sved J, Vanhatalo K, Väisänen P. 2014. Metsänhoidon suositukset. [Guidelines for sustainable forest management]. Metsätalouden kehittämiskeskus Tapion julkaisuja. Anttila P., Nivala V., Salminen O., Hurskainen M., Kärki J., Lindroos T.J. & Asikainen A. 2018. Regional balance of forest chip supply and demand in Finland in 2030. Silva Fennica vol. 52 no. 2 article id 9902. 20 s. https://doi.org/10.14214/sf.9902 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, 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]. Tapion julkaisuja. 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. Siitonen M, Härkönen K, Hirvelä H, Jämsä J, Kilpeläinen H, Salminen O et al. 1996. MELA Handbook. 622. 951-40-1543-6.

  • 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 Finnish Forest Research Institute (Metla) developed a method called multi-source national forest inventory (MS-NFI). The first operative results were calculated in 1990. The first country level estimates correspond to years 1990-1994. 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. Nine sets of estimates have been produced for the most part of the country until now and eight sets for Lapland. These three themes have been produced for production of the CORINE2006. 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 the topographic database of the Land Survey of Finland. The original map data can be downloaded from http://kartta.luke.fi/