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

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 density of all IMO registered ships operating in the Baltic Sea. Density is defined as the number of ships crossing a 1 x 1km grid cell. It is based on HELCOM AIS (Automatic Identification System) data. The HELCOM AIS network hosts all the AIS signals received by the Baltic Sea States since 2005. The AIS Explorer allows to compare density maps of different ship types per month: http://maps.helcom.fi/website/AISexplorer/ The data was processed to produce density maps and traffic statistics. All scripts are available in GitHub: https://github.com/helcomsecretariat. The production of these maps have been carried out 2016-2017 through the HELCOM project on the assessment of maritime activities in the Baltic Sea. The underlying AIS data processing work has been co-financed by EU projects Baltic Scope (2015-2017 EASME/EMFF/2014/1.2.1.5) and Baltic Lines (2016-2019, Interreg Baltic Sea Region). In addition, the Ministry of the Environment of Finland supported the work with a special contribution in view of the use of the results in the HOLAS II process.

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 1:70 000 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/).

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/

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.

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