The raw materials of forest chips are small-diameter trees from 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 12th national forest inventory (NFI12) measured in the years 2014–2018. 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; http://mela2.metla.fi/mela/tupa/index-en.php). 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 sustained yield (79 mill. m3 in this calculation). The maximum sustained yield was 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 thinnings was calculated separately for all the energywood from thinnings (Stemwood for energy from thinnings) and for material that does not fulfill the size-requirements for pulpwood (Stemwood for energy from thinnings (smaller than pulpwood-sized trees)). Note that the decision whether pulpwood-sized thinning wood is directed to energy or industrial use, is based on the optimisation by MELA. 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. Energywood could be harvested as whole trees or as delimbed. The dry-matter loss in the supply chain was assumed at 5%. The potentials for logging residues and stumps were calculated as follows: The crown 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. Next, technical recovery rates were applied (70% for logging residues and 82-84% for stumps) (Koistinen et al. 2016; Muinonen et al. 2013). Finally, a dry-matter loss of 20% and 5% was assumed for residues and stumps, respectively. 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 2026-2035 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 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)

Tämän aineiston tarkemmat metodikuvaukset löytyvät artikkeleista (Holmberg et al. 2023, Junttila et al. 2023). Tässä on kuvattu aineistoa ja sen valmistelua. Tarkoituksena on ollut tuottaa alueellista tietoa maanpeitteen merkityksestä kasvihuonekaasupäästöihin Suomessa. Lähtöaineisto ja metodit rajoittavat tarkkuutta, mutta aineisto soveltuu paikallisten, esimerkiksi maakuntatason ilmiöiden tarkasteluun. Aineisto edustaa lyhyttä ajanjaksoa. Maanpeiteaineisto perustuu rekisteritietoihin ja kaukokartoitusaineistoon vuosilta 2015-2020, lukuun ottamatta maaperäaineistoa, jokia ja järviä. Aineisto on rasterimuotoista ja tallennettu GeoTiff-formaatissa, joka on yhteensopiva useimpien paikkatieto-ohjelmistojen kanssa. Greenhouse gas net emission intensities by land cover category in Finland The methods related to the data published herein are described in detail in the associated publications (Holmberg et al. 2023, Junttila et al. 2023). This file describes the datasets and the data preparation steps. The aim of this data publication is to provide regional assessments of the role of land cover in greenhouse gas emissions in Finland. The results in the publications are reported for the large administrative divisions, the NUTS 3 regions of mainland Finland (Statistics Finland 2023a). While limited by the accuracy of the methods and source data involved, these data can also be used for more local assessments, e.g., at the scale of municipalities. The data represent a temporal snapshot of land cover. Except for the soil maps, rivers and lakes, all land cover data are from the period 2015-2020 and are based on registry data or remote sensing. Data format. The data are distributed as GeoTiff raster files, which can be read using most GIS-software.

This dataset represents the Integrated biodiversity status assessment for benthic habitats using the BEAT tool. Status is shown in five categories based on the integrated assessment scores obtained in the tool. Biological Quality Ratios (BQR) above 0.6 correspond to good status. The assessment in open sea areas was based on the core indicators ‘State of the soft-bottom macrofauna community’ and ‘Oxygen debt’. Coastal areas were assessed by national indicators, and may hence not be directly comparable with each other. This dataset displays the result of the integrated biodiverity status in HELCOM Assessment unit Scale 4 (Division of the Baltic Sea into 17 sub-basins and further division into coastal and off-shore areas and division of the coastal areas by WFD water types or water bodies). Attribute information: "BQR" = Biological Quality Ratio "Confidence" = Confidence of the assessment "HELCOM_ID" = id of the HELCOM assessment unit "country" = name of the country / opensea "level_2" = HELCOM sub-basins (name of the scale 2 assessment unit) "Name" = Name of the coastal assessment unit on scale 4 "AULEVEL" = scale of the assessment units "type_descr" = Name of the HELCOM scale 4 assessment unit "SAUID" = ID number for the spatial assessment unit "EcosystemC" = Ecosystem component assessed "Confiden_1" = Confidence of the assessment (0-1, higher values mean higher confidence) "Total_numb" = Number of indicators used in assessment "Area_km2" = Area of assessment unit (km2) "Confiden_1" = Confidence level of the assessment (scores < 0.5 = low, 0.5 - 0.75 = intermediate, > 0.75 = high) "STATUS" = Integrated status category (0-0.2 = not good (lowest score), 0.2-0.4 = not good (lower score), 0.4-0.6 = not good (low score), 0.6-0.8 = good (high score), 0.8-1.0 = good (highest score))

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/).

The Regional Stream Water Geochemical Mapping data set gives information on the elemental concentrations in organic sediments of small headwater streams. The samples have been taken from small headwater streams (catchment area under 30 km2) in the late summer of 1990. Sampling has been repeated for about every fourth point during the years 1995, 2000 and 2006. The number of samples was 1162 in 1990 (at a density of one sample / 300 km2), 286 in 1995, 286 in 2000 and 249 in 2006. The data set covers the whole of Finland. Stream water samples have also been taken at the same time. Sampling, processing and analysis methods have been described in the Geochemical Atlas of Finland, Part 3, p. 27 - 30 (Lahermo et. al 1996). Field observations, coordinates and element concentrations determined from samples have been made into a database, in which each record represents one sample point. The data for each sampling year have been recorded on different tables. The method of analysis is referred to with a four-character method code. The codes are as follows: 503H = mercury determination using the cold vapour method 503P = nitric acid extraction in a microwave oven, measurement with ICP-AES 503M = nitric acid extraction in a microwave oven, measurement with ICP-MS 820L = carbon, hydrogen and nitrogen determination with a LECO analyser. The element concentration data include a numerical concentration value (as mg kg-1 or ppm) and possibly a check mark. The concentration is recorded as a variable, which has a name that comprises the chemical symbol for the element and the code for the method of analysis. For example AS_503M is arsenic (As) concentration, which is determined with the ICP-MS method (503M). The next variable has a check mark, for example AS_503MT. If the numerical value following the check mark is ‘>’ or '‘<’ then the number recorded in the concentration field is the determination limit of the chemical analytical method used and the actual concentration is less than this value. If the check mark is an exclamation mark (!), the analytical result is smaller than the determination limit of the analytical method use but the (unreliable) value obtained with the measuring instrument has been entered in the database. There is no data are if the check mark is a 'x'. The original purpose of the Regional Stream Water Geochemical Mapping data set was national general geochemical mapping and the basic assessment of environmental state. Other uses are, for example, the assessment of changes in environmental state and determination of the baseline concentrations of surface water as part of the evaluation of the chemical state of catchment areas in accordance with the Water Framework Directive of the EU.