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  • Buildings 3D

    The 3D vectors for buildings are three-dimensional instances of the Building target category in the National Topographic Database (KMTK). The 3D vectors are produced with a high degree of automation from laser scanning data 5 p based on 2D vectors for buildings in KMTK. For the time being, data is available only from a few example areas, but the coverage will be extended to the whole of Finland as the laser scanning programme proceeds. The product belongs to the open data of the National Land Survey of Finland.

  • EMODnet Seabed substrate 1:20 000 – Europe

    The EMODnet (European Marine Observation and Data network) Geology project collects and harmonizes marine geological data from the European sea areas to support decision making and sustainable marine spatial planning. The partnership includes 39 marine organizations from 30 countries. The partners, mainly from the marine departments of the geological surveys of Europe (through the Association of European Geological Surveys-EuroGeoSurveys), have assembled marine geological information at various scales from all European sea areas (e.g. the White Sea, Baltic Sea, Barents Sea, the Iberian Coast, and the Mediterranean Sea within EU waters). This dataset includes EMODnet seabed substrate maps at a scale of 1:20 000 from the European marine areas. Traditionally, European countries have conducted their marine geological surveys according to their own national standards and classified substrates on the grounds of their national classification schemes. These national classifications are harmonised into a shared EMODnet schema using Folk's sediment triangle with a hierarchy of 16, 7 and 5 substrate classes. The data describes the seabed substrate from the uppermost 30 cm of the sediment column. Further information about the EMODnet Geology project is available on the portal (http://www.emodnet-geology.eu/).

  • Integrated biodiversity status assessment - pelagic habitats 2018

    This dataset represents the Integrated biodiversity status assessment for pelagic 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. Open sea areas were assessed based on the core indicators ‘Zooplankton mean size and total stock’ and ‘Chlorophyll-a’, as well as the pre-core indicator ‘Cyanobacterial bloom index’ . Coastal areas were assessed by national indicators. This dataset displays the result of the integrated biodiversity 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" = Code of the HELCOM assessment unit "Country" = Country of coastal assessment unit/ open sea "Level2" = HELCOM sub-basins (name of the scale 2 assessment unit) "Name" = Name of the HELCOM scale 4 assessment unit "Area_km2" = Area of assessment unit "AULEVEL" = scale of the assessment unit "HID" = assessment unit ID by country "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 "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))

  • Integrated biodiversity status assessment - fish 2018

    This dataset represents the Integrated biodiversity status assessment for fish used in State of the Baltic Sea – Second HELCOM holistic assessment 2011-2016. Status is shown in five categories based on the integrated assessment scores obtained in the BEAT tool. Biological Quality ratios (BQR) above 0.6 correspond to good status. The assessment is based on core indicators of coastal fish in coastal areas, and on internationally assessed commercial fish in the open sea. The open sea assessment includes fishing mortality and spawning stock biomass as an average over 2011–2016. Open sea results are given by ICES subdivisions, and are not shown where they overlap with coastal areas. Coastal areas results are given in HELCOM Assessment unit Scale 3 (Division of the Baltic Sea into 17 sub-basins and further division into coastal and off-shore areas) Attribute information: "COUNTRY" = name of the country / opensea "Name" = Name of the coastal assessment unit, scale 3 (empty for ICES open sea units) "HELCOM_ID" = ID of the HELCOM scale 3 assessment unit (empty for ICES open sea units) "EcoystemC" = Ecosystem component analyzed "BQR" = Biological Quality Ratio "Conf" = Confidence (0-1, higher values mean higher confidence) "Total_indi" = Number of HELCOM core indicators included (coastal assessment units) "F__of_area = % of area assessed "D1C2" = MSFD descriptor 1 criteria 2 "Number_of" = Number of open sea species included "Confidence" = Confidence of the assessment "BQR_Demer" = Demersal Biological Quality Ratio "F_spec_Deme" = Number of demersal species included "Conf_Demer" = Confidence for demersal species "BQR_Pelagi" = Pelagic Biological Quality Ratio "F_specPela" = Number of pelagic species included "Conf_Pelag" = Confidence for pelagic species "ICES_SD" = ICES Subdivision number "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))

  • Integrated biodiversity status assessment - seals 2018

    This dataset represents the Integrated biodiversity status assessment for seals (grey seal, harbour seal and ringed seal). 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 status of the seals was assessed using four core indicators: population trends and abundance of seals, distribution of Baltic seals, nutritional status of seals, and reproductive status of seals. In the latter two only grey seals are considered for the 2018 State of the Baltic Sea report. The assessment is based on the one-out-all-out approach, i.e. the species reflecting the worst status in each assessment unit. This dataset displays the result of the integrated biodiversity status in HELCOM Assessment unit Scale 2 (Division of the Baltic Sea into 17 sub-basins). Attribute information: "HELCOM_ID" = ID of the HELCOM scale 2 assessment unit "level_2" = Name of the HELCOM scale 2 assessment unit "EcosystemC" = Ecosystem component analyzed "BQR" = Biological Quality Ratio "Conf" = Confidence of the assessment "Total_indi" = Number of indicators used "% of area assessed" = Share of the total assessed area "D1CX" = MSFD descriptor 1 criteria X "conf_D1CX" = Confidence for MSFD descriptor criteria X "Confidence" = Conifdence of the assessment ("high"/ "moderate"/ "low") "STATUS" = Status of the assessment (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 1:100 000

    Seabed substrate 1:100 000 is one of the products produced in the EMODnet (European Marine Observation and Data network) Geology III EU project. Project provided seabed geological material from the European maritime areas. The EMODnet Geology III 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 39 marine organizations from 30 countries. This data includes the EMODnet seabed substrate map at a scale of 1:100 000 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 (1:100 000). The smallest smallest cartographic unit within the data is 0.05 km2 (5 hectares). Further information about the EMODnet-Geology project III is available on the portal (http://www.emodnet-geology.eu/). Permission (AN17367) to publish the material was obtained from the Finnish Defence Office 29.9.2017.

  • Landscape feature on agricultural land 2022

  • Agricultural parcel containing spatial data 2020

  • HELCOM MPAs

    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

  • Categories  

    Energywood potentials

    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