The Arctic SDI Geoportal provides access to geospatial data and services available via the Arctic SDI to support and facilitate monitoring, management and decision making, and support sustainable development in the Arctic. Specifically, the Arctic SDI Geoportal facilitates the discovery, visualization, evaluation, download and integration of geographic data from a variety of sources for the Arctic. The Arctic SDI Geoportal is the result of cooperative efforts between the National Mapping Agencies (NMAs) of the eight Arctic Council Member countries - Canada, Denmark, Finland, Iceland, Norway, Russia, Sweden and the United States. The Arctic SDI Geoportal includes reference data (such as the Arctic SDI topographic basemap or Pan-Arctic Digital Elevation Model) and thematic data from various sources. Thematic data section includes themes such as oceans, climatology and geoscientific information. Most of the data covers the Arctic or the involved Arctic countries, but new data sources with a smaller or larger geographical extent may be accepted. The Geoportal allows searching placenames via a circumpolar gazetteer, and embedding interactive maps to any website. Some of the features require registration.

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.

This dataset contains integrated eutrophication status assessment 2011-2016. The assessment is done using the HEAT 3.0 by combining assessment unit-specific results from various indicators by three MSFD criteria groups (C1: Nutrient levels, C2: Direct effect, C3: Indirect effect). The assessment is done on HELCOM Assessment Unit level 4: HELCOM Subbasins with coastal WFD water type or water bodies. The HEAT 3.0 has been applied for open sea assessment units using HELCOM core indicators and for coastal areas using national WFD indicators. In case of Denmark, the WFD results were used directly, displaying different classification as obtained from HEAT. For more information about the methodology, see the State of the Baltic Sea report and HELCOM Eutrophication assessment manual. Attribute information: "HELCOM_ID": ID of the HELCOM Level 4 Assessment unit "Country": Country/ Opensea "level_2": Name of the HELCOM Level 2 Assessment unit "Name": Name of the HELCOM Level 4 Assessment unit "Area_km2": Area of assessment unit "C1_N": MSFD criteria 1, number of indicators used for calculating Eutrophication Ratio (ER) "C1_ER": MSFD Criteria 1, ER "C1_SCORE": MSFD Criteria 1, Confidence of ER "C2_N": MSFD Criteria 2, number of indicators used for calculating ER "C2_ER": MSFD Criteria 2, ER "C2_SCORE": MSFD Criteria 2, Confidence of ER "C3_N": MSFD Criteria 3, number of indicators used for calculating ER "C3_ER": MSFD Criteria 3, ER "C3_SCORE": Criteria 3, Confidence of ER "N": Number of criteria used for calculating overall ER "ER": Overall ER "SCORE": Status confidence "STATUS": Status classification (Good (classes 0-0.5 & 0.5-1.0), Not Good (classes 1.0-1.5, 1.5-2.0 & >2.0), Not assessed) "CONFIDENCE": Final confidence class (< 50% = low, 50-74 % = Moderate, = 75 % = High) "AULEVEL": Level of assessment units

KUVAUS: Jätehuoltomääräysten biojätteen velvoitealue. Tampereen keskustaajaman alue esitetään kartalla keltaisella. Tampereen keskustaajamassa velvoite on ollut voimassa 1.9.2023 alkaen. Biojätteen velvoitealueen laajennusalueet esitetään kartalla sinisellä. Laajennusalueiden velvoite tulee voimaan siirtymäajan jälkeen, 30.9.2025 mennessä. PÄIVITYS: Satunnainen (vain tarvittaessa). YLLÄPITOSOVELLUS: Tampereen kaupungin tiedostopalvelin ja PostGIS-tietokanta KOORDINAATTIJÄRJESTELMÄ: Aineisto tallennetaan ETRS-GK24FIN (EPSG:3878) tasokoordinaattijärjestelmässä GEOMETRIA: vektori (alue) SAATAVUUS: Aineisto on tallennettu Postgis-tietokantaan. JULKISUUS: Aineisto on nähtävillä julkisesti kaikille käyttäjille Oskari-karttapalvelussa. TIETOSUOJA: Aineistoon ei liity tietosuojakysymyksiä. AINEISTOSTA VASTAAVA TAHO: Tampereen kaupunki, Alueellinen jätehuoltolautakunta, jatehuoltolautakunta@tampere.fi

This dataset contains borders of the HELCOM MPAs (former Baltic Sea Protected Areas (BSPAs). The dataset has been compiled from data submitted by HELCOM Contracting Parties. It includes the borders of designated HELCOM MPAs stored in the http://mpas.helcom.fi. The designation is based on the HELCOM Recommendation 15/5 (1994). The dataset displays all designated or managed MPAs as officially reported to HELCOM by the respective Contracting Party. The latest related HELCOM publication based on MPA related data is http://www.helcom.fi/Lists/Publications/BSEP148.pdf The dataset contains the following information: MPA_ID: Unique ID of the MPA as used in HELCOM Marine Protected Areas database Name: Name of the MPA Country: Country where MPA is located Site_link: Direct link to site's fact sheet in the http://mpas.helcom.fi where additional information is available MPA_status: Management status of the MPA Date_est: Establishment date of the MPA Year_est: Establishment year of the MPA

The Baltic Sea Pressure Index is a calculation of quantity and spatial distribution of potential cumulative impacts on the Baltic Sea. The BSII is based on georeferenced datasets of human activities (36 datasets), pressures (18 datasets) and ecosystem components (36 datasets), and on sensitivity estimates of ecosystem components (so-called sensitivity scores) that combine the pressure and ecosystem component layers, created in <a href="http://www.helcom.fi/helcom-at-work/projects/holas-ii" target="_blank">HOLAS II</a> project. The assessment can be applied with a focus on pressures only by using the Baltic Sea Pressure Index (BSPI) which shows the anthropogenic pressures/human activities in the defined assessment units without including ecosystem components. The BSPI however includes a weighting component in order to grade the effect of the pressures on the ecosystem in a generalized perspective. Cumulative impacts are calculated for each assessment unit (1 km2 grid cells) by summing all impacts occurring in the unit. All datasets and methodologies used in the index calculations are approved by all HELCOM Contracting Parties in review and acceptance processes. This dataset covers the time period 2011-2016. Please scroll down to "Lineage" and visit <a href="http://stateofthebalticsea.helcom.fi/cumulative-impacts/" target="_blank">State of the Baltic Sea website</a> for more info.

NLS-FI INSPIRE View Service for Hydrography Theme is an INSPIRE compliant Web Map Service. It contains the following harmonized INSPIRE map layers: Land-water Boundary, Waterbodies, Man-made Objects, Hydro Point of Interest. The service is based on the NLS-FI INSPIRE Hydrography Physical Waters dataset. The dataset is administrated by the National Land Survey of Finland.