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.

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 1:10 000 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/).

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 multiscale dataset includes EMODnet seabed substrate maps at a scale of 1:1 500, 1:5 000, 1:10 000, 1:15 000, 1:20 000, 1:25 000, 1:30 000, 1:45 000, 1: 50 000, 1:60 000, 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/).

Maatalousmaa vuonna 2021 aineisto kuvaa mahdollisimman kattavasti maankäytöltään maatalouteen kuuluvia alueita vuonna 2021, sisältäen sekä maataloustukia saavat alueet, että tukien ulkopuoliset alueet. Aineisto on koostettu käyttäen Ruokaviraston tuottamia perus- ja kasvulohkoaineistoja sekä Maanmittauslaitoksen tuottamaa maastotietokantaa. Peruslohkoaineisto on komission asetuksen 796/2004 ja neuvoston asetuksen (EY) N:o 1782/2003 20 artiklassa tarkoitettu viljelylohkojen tunnistusjärjestelmä. Järjestelmää käytetään EU:n pinta-alaperusteisen maataloustuen hallinnoinnissa. Aineisto käsittää vuoden 2021 peruslohkojen tilanteen 31.12.2021. Kasvulohkolla tarkoitetaan yhteen peruslohkoon kuuluvaa yhtenäistä aluetta, jossa kasvatat yhtä kasvilajia, useamman kasvilajin seosta tai jota kesannoidaan tai joka on erityiskäytössä. Yhdellä peruslohkolla voi olla yksi tai useampia kasvulohkoja. Kasvulohko voi kuulua vain yhteen peruslohkoon. Kasvulohkojen rajat ja samalla niiden pinta-alat voivat vaihdella peruslohkon sisällä vuosittain. Peltolohkorekisteristä on aineistoon otettu mukaan ne lohkot joihin yhdistyy kasvulohkoista tieto viljellystä kasvista. Aineistosta on tiputettu pois ei-maatalousaluetta olevat lohkot, esimerkiksi metsäiset alueet. Maanmittauslaitoksen Maastotietokanta on koko Suomen kattava maastoa kuvaava aineisto ja se koostuu erilaisista kohderyhmistä. Maastotietokannan Maatalousmaa -aineisto sisältää Maastotietokannan pellot, ja puutarhat. Niityt ovat erillinen kohdeluokka. Mammuttiprojektia varten MTK kohdeluokat Maatalousmaa (pellot ja puutarhat) ja Niitty yhdistettiin yhdeksi aineistoksi. Kohdeluokat on poimittu vuoden 2021 Maastotietokannasta. Kohdeluokat ja niiden kuvaukset löytyvät: https://www.maanmittauslaitos.fi/sites/maanmittauslaitos.fi/files/attachments/2018/03/Maastotietokohteet_0.pdf Peruslohkoaineistosta ja maastotietokannasta poimitut kohteet on yhdistetty siten, että maatalousmaa muodostetaan ensisijaisesti käyttämällä peruslohkoaineistosta poimittuja peruslohkoja. Tämän joukon ulkopuolelle jäävä maatalousmaa tulee maastotietokannasta. Aineistojen yhdistäminen on kuvattu tarkemmin tuotantokuvauksessa. https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/maatalousmaa2021.pdf https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/Metatietokuvaus_peltolohkorekisteri.pdf Aineisto kuuluu SYKEn avoimiin aineistoihin (CC BY 4.0).

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 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