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FIN Aineiston tarkoituksena on: -Identifioida tie- ja rata-alueet, joiden varrella esiintyy uhanalaisia ja silmälläpidettäviä lajeja -Identifioida tie- ja rata-alueet, joiden varrella esiintyy hyviä elinvoimaisia niittyindikaattorilajeja (hyönteisten mesi- ja ravintokasveja) -Identifioida tie- ja rata-alueet, joiden varrella esiintyy suojelualueita -Identifioida tie- ja rata-alueet, joiden varrella esiintyy komealupiinia tai kurtturuusua -Identifioida tie- ja rata-alueet, joiden varrella esiintyy komealupiinia tai kurtturuusua uhanalaisten lajien lisäksi -> Löytää herkät alueet ja paikallistaa vieraslajien uhka Tieto esitetään 1 kilometrin ruuduissa. Aineistosta on julkaistu kaksi erillistä versiota. -VaylanvarsienVieraslajitJaArvokkaatElinymparistot_avoin: Avoin versio, jonka lajitietoa on karkeistettu mahdollisista herkistä lajeista johtuen. Aineisto kuuluu SYKEn avoimiin aineistoihin (CC BY 4.0) ja sitä saa käyttää lisenssiehtojen mukaisesti -VaylanvarsienVieraslajitJaArvokkaatElinymparistot_kayttorajoitettu: Alkuperäinen karkeistamaton versio. Tämä versio on vain viranomaiskäyttöön eikä kyseistä aineistoa saa jakaa Aineistosta on tehty tarkempi menetelmäkuvaus https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/VierasVayla_Menetelmakuvaus.pdf sekä muuttujaseloste https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/VierasVayla_VariableDescription.xlsx ENG The purpose of the material is to: -Identify road and rail areas that have nearby observations of endangered and near threatened species -Identify road and rail areas with good meadow indicator plant species -Identify road and rail areas along which there are protected areas -Identify the road and rail areas along which there are observations of Lupinus polyphyllus or Rosa rugosa observations -Identify the road and rail areas along which there are Lupinus polyphyllus or Rosa rugosa observations in addition to sensitive species -> Finds sensitive areas and identify the overall threat of alien species The data is presented in 1-kilometer square grid cells. There are two separate versions of the data. -VaylanvarsienVieraslajitJaArvokkaatElinymparistot_avoin: Open access version, in which its species-related parts have been simplified due to data restriction issues. The material belongs to Syke's open materials (CC BY 4.0) and may be used in accordance with the license terms. -VaylanvarsienVieraslajitJaArvokkaatElinymparistot_kayttorajoitettu: Original version. This version is only for official use and the material in question may not be shared. A more precise description about the data procedures can be found from (In Finnish) https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/VierasVayla_Menetelmakuvaus.pdf Furthermore, all the variables in the data are explained in this bilingual variable description https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/VierasVayla_VariableDescription.xlsx This dataset was updated with the newest species observations on 10/2023 and 11/2024 Process code for this can be found from https://github.com/PossibleSolutions/VierasVayla_SpeciesUpdate
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FIN Järvien vesikasvillisuusvyöhykettä kuvaava aineisto 1971 suomalaisesta järvivesimuodostumasta. Aineisto on polygonivektorimuodossa, jossa yksittäisen järven vesikasvivyöhyke esitetään moniosaisena polygonina. Vesikasvillisuusvyöhyke koostuu ilmakuvilta erottuvasta vedenpinnan yläpuolisesta (ilmaversoinen ja kelluslehtinen) ja aivan vedenpinnan tasolle yltävästä uposlehtisestä kasvillisuudesta. Vesikasvillisuusvyöhykkeen ja järven 0–3 metrin syvyysvyöhykkeen perusteella järville on laskettu kasvittumisaste-niminen tunnusluku, jota käytetään järvien ekologisen tilan arvioinnissa kuvaamaan rehevöitymisen aiheuttamaa kasvillisuuden runsastumista. Vesikasvillisuusvyöhyke on analysoitu Picterra-yrityksen koneoppimismalleilla Maanmittauslaitoksen hallinnoimista väri-infra- eli vääräväriortokuvista vuosilta 2012-2023. Vyöhykkeen analysointi on rajattu 1.7.–10.9. otettuihin ortokuviin. Lisäksi analysointi on rajattu seuraaviin vesienhoidon suunnittelun 3. suunnittelukaudella määritettyihin järvityyppeihin: • Pienet humusjärvet • Keskikokoiset humusjärvet • Runsashumuksiset järvet • Matalat humusjärvet • Matalat runsashumuksiset järvet Aineisto sisältää 698 järvivesimuodostumalta ilmakuvatulkinnan useammalta vuodelta. Havaittu kasvittumisaste on laskettu niille 977 järvivesimuodostumalle, joilta oli saatavissa tieto 0–3 metrin syvyysvyöhykkeestä. Aineistoon on jätetty järviä ilman syvyysaineistoa ja siten kasvittumisasteen laskentaa siinä tarkoituksessa, jotta aineistoa voidaan tarvittaessa hyödyntää muuhunkin kuin kasvittumisaste-muuttujaan perustuvaan tila-arviointiin. Aineistolle on tehty silmämääräinen tarkastus virheellisten havaintojen poistamiseksi. Aineisto voi silti sisältää väärintulkintoja. Kasvittumisasteen luontaisen vaihtelun mallintamisesta saadut tunnusluvut, kuten odotetut kasvittumisasteet ja kasvittumisasteeseen perustuva ekologinen tilaluokka, ovat ympäristöhallinnon asiantuntijoiden katseltavissa Pisara-järjestelmässä. Käyttötarkoitus: Ympäristöhallinnon tehtävien tueksi vesien tilan arviointiin. Järvien ekologisen tilan arviointia tekevät asiantuntijat käyttävät paikkatietoaineistoa ilmakuvatulkinnan laadun arvioimiseen yksittäisellä järvellä. Asiasanat: kaukokartoitus, ilmakuvat, vesikasvillisuus, seuranta, ekologinen tila Lisätietoja: https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/Jarvien_vesikasvillisuusvyohykkeet.pdf https://vesi.fi/aineistopankki/koneoppimispohjaiseen-ilmakuvatulkintaan-perustuva-jarvien-vesikasvillisuuden-tilanarviointi/ ENG This data describes lake macrophyte zone on 1971 Finnish lake waterbodies. The spatial features are represented as multi-part polygons. The attributes are in Finnish. The zone represents emergent and floating-leaved vegetation plus submerged vegetation just above the surface of water. Together with lake bathymetric data, the percentage of vegetated littoral (PVL) was calculated. The PVL is applied in ecological status assessment. Lake macrophyte zone was detected from color-infrared aerial orthophotos administered by the National Land Survey of Finland. The detections were performed with the help of a custom machine learning model trained using Picterra. The detections were applied to orthophotos in 2012-2013 which were filmed between 1st of July and 10th of September. The detections were limited to humic and humic-rich lake waterbodies. There are detections from multiple years for 698 lake waterbodies. Observed PVL were calculated on 977 lake waterbodies which have bathymetric data to identify the 0 to 3 meters deep littoral zone. To potentially utilize the data for more than just the PVL-based approach, the data also have detections on waterbodies without bathymetric data and therefore observed PVL. A visual inspection of the data has been performed to remove erroneous detections. The data may still contain misinterpretations. Purpose of use: Support of environmental administration in ecological status assessment. More information: https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/Jarvien_vesikasvillisuusvyohykkeet.pdf https://vesi.fi/aineistopankki/koneoppimispohjaiseen-ilmakuvatulkintaan-perustuva-jarvien-vesikasvillisuuden-tilanarviointi/
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FIN Suomen hiekkarantoja ja niiden taustatekijöitä kuvaava aineisto. Datan taustalla olevan hankkeen pääasiallisena tarkoituksena on hiekkarantojen identifioiminen parhaasta käytettävissä olevasta tiedosta, näiden rantojen ominaispiirteiden kuvaaminen, ympäristöllisen arvon arvioiminen sekä hoitotarpeessa olevien rantojen löytäminen. Aineistosta on julkaistu kaksi erillistä versiota. -HiekkarantojenOminaisuudet_avoin: Avoin versio, jonka lajitietoa on karkeistettu mahdollisista herkistä lajeista johtuen. Aineisto kuuluu SYKEn avoimiin aineistoihin (CC BY 4.0) ja sitä saa käyttää lisenssiehtojen mukaisesti -HiekkarantojenOminaisuudet_kayttorajoitettu: Alkuperäinen karkeistamaton versio. Tämä versio on vain viranomaiskäyttöön eikä kyseistä aineistoa saa jakaa” Aineistosta on tehty tarkempi menetelmäkuvaus https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/RantaPutte_Menetelmakuvaus.pdf sekä muuttujaseloste https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/RantaPutte_VariableDescription.xlsx ENG This data describes Finnish sandy beaches and their background factors. The main purpose of the project underlying the data is to identify sandy beaches from the best available information, to describe the characteristics of these beaches, to assess their environmental value and to find beaches in need of conservation There are two separate versions of the data. -HiekkarantojenOminaisuudet_avoin: Open access version, in which its species-related parts have been simplified due to data restriction issues. The material belongs to Syke's open materials (CC BY 4.0) and may be used in accordance with the license terms. -HiekkarantojenOminaisuudet_kayttorajoitettu: Original version. This version is only for official use and the material in question may not be shared. A more precise description about the data procedures can be found from (In Finnish) https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/RantaPutte_Menetelmakuvaus.pdf All the variables in the data are explained in this bilingual variable description https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/RantaPutte_VariableDescription.xlsx
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Number of population by statistical units. The number of population on the last day of the reference year (31.12.). Statistical areas are municipalities, sub-regional units, regions, major regions, Regional State Administrative Agencies (AVI) and Centres for Economic Development, Transport and the Environment (ELY). The statistical variables in the data are: total population (vaesto), share of the total population (vaesto_p), number of men (miehet), men's share of the population in an area (miehet_p) and women (naiset), women's share (naiset_p), those aged under 15: number (ika_0_14), share (ika_0_14p), those aged 15 to 64: number (ika_15_64), share (ika_15_64p), and aged 65 or over: number (ika_65_), share (ika_65_p). The data is suitable for the examination of population distribution and making various regional analyses. The general Terms of Use must be observed when using the data: https://tilastokeskus.fi/org/lainsaadanto/copyright_en.html. In addition to the national version, an INSPIRE information product is also available from the data.
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The Baltic Sea Impact Index is an assessment component that describes the potential cumulative burden on the environment in different parts of 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. Cumulative impacts are calculated for each assessment unit (1 km2 grid cells) by summing all pressures occurring in the unit for each ecosystem component. Highest impacts are found from the cells where both are abundant, but high impacts can be caused also by a single pressure if there are diverse and sensitive habitats in the grid cell. All data sets and methodologies used in the index calculations are approved by all HELCOM Contracting Parties in review and acceptance processes. This data set 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.
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NLS-FI INSPIRE View Service for Administrative Units Theme is an INSPIRE compliant Web Map Service. It contains the following harmonized INSPIRE map layers: AdministrativeUnit, AdministrativeBoundary, Baseline, TerritorialSea, ExclusiveEconomicZone, MaritimeBoundary.InternalWaters, MaritimeBoundary.TerritorialSea ,MaritimeBoundary.ExclusiveEconomicZone. The service is based on the NLS-FI INSPIRE Administrative Units dataset. The dataset is administrated by the National Land Survey of Finland.
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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
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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. 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. Seven sets of estimates have been produced for the most part of the country until now and six sets for Lapland. The number of the map form themes in the most recent version, from year 2015, is 45. In addition to the volumes by tree species and timber assortments, the biomass by tree species groups and tree compartments have been estimated. The first country level estimates correspond to years 1990-1994. The most recent versions are from years 2005, 2007, 2009, 2011, 2013 and 2015. The maps from 2015 is the fourth set of products freely available. It is also the second set produced by the Natural Resources Institute Finland. A new set of the products will be produced annually or biannually in the future. The maps are in a raster format with a pixel size of 16m x 16m (from 2013) and in the ETRS-TM35FIN coordinate system. 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.
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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.
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The Bio-geographical provinces 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. All regions have names and abbreviations in Finnish, Swedish, and Latin. No other attribute data is available.
Paikkatietohakemisto