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

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 1:1 500 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/).

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.

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 1:15 000 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/).

This dataset contains points of information describing the location and size of spills of mineral oil observed during aerial surveillance flights by HELCOM Contracting Parties during 1998-2023. The data covers detections from fixed-wing aircraft only. Since 2014 Contracting Parties have also reported spills of other substances and unknown substances. The purpose of the regional aerial surveillance is to detect spills of oil and other harmful substances and thus prevent violations of the existing regulations on prevention of pollution from ships. Such illegal spills are a form of pollution which threatens the marine environment of the Baltic Sea area. Further information on detected spills in the Baltic Sea area and HELCOM aerial surveillance activities can be found at http://www.helcom.fi/baltic-sea-trends/maritime/illegal-spills/ and https://helcom.fi/action-areas/response-to-spills/aerial-surveillance/ The dataset contains the following information: Country Year Spill_ID = A unique code which will enable each individual spill to be individually identified FlightType = The type of flight the detection was made during: National = "N", CEPCO = "C", Super CEPCO = "SC", Tour d’Horizon = “TDH” Date = The date of the detection (dd.mm.yyyy) Time_UTC = The time of the detection in UTC (hh:mm) Wind_speed = The wind speed at the time of the detection (m/s) Wind_direc = The wind direction in degrees at the time of the detection (degrees) Latitude = The latitude of the detection (decimal degrees, WGS84) Longitude = The longitude of the detection (decimal degrees, WGS84) Length__km = The length of the detection (km) Width__km = The width of the detection (km) Area__km2_ = The area of the detection (km2) Spill_cat = Spill/pollution category: Mineral Oil = “Oil", Other Substance = "Other substance" , "Unknown substance" = “Unknown” EstimVol_m = If Spill_cat="Oil", then estimated min. volume of oil spill. Volume of the detection confirmed/observed as mineral oil as calculated using the Bonn Agreement Oil Appearance Code using the lower figure (BAOAC minimum) in m3. Vol_Category = Category of the detection: <0,1m3 = “1”, <0,1-1m3 = “2”, 1-10 m3 = “3”, 10-100 m3 = “4”, >100 m3 = “5” Type_substance = If Spill_cat="Other substance" or "Unknown. Product name or type of OS or GAR substances that could be identified (in case of known polluter, or via visual identification - cf. BAOAC Atlas). - Examples for OS: vegetable oils (palm oil sun flower oil, soya oil etc.), fish oil, molasses, various chemicals (methanol, biodiesels/FAME, toluene, paraffines etc.); Examples of GAR: solid cargo residues (e.g. coal residues), plastics, fish nets, … OR "Unknown" (in case the type of substance could not be identified) Polluter = Type of polluter source: Offshore Installation = “Rig”, Vessel = “Ship”, Other Polluter or source (e.g. land based source) = “Other”, Unknown = “Unknwon” (in case of an “orphan” spill that cannot be linked to a polluter) Remarks = Any additional information to inform on particular situations Description of marine litter sightings

The technical harvesting potential of logging residues and stumps from final fellings can be defined as the maximum potential procurement volume of these available from the Finnish forests based on the prevailing guidelines for harvesting of energy wood. The potentials of logging residues and stumps have been calculated for fifteen NUTS3-based Finnish regions covering the whole country (Koljonen et al. 2017). The 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). Final fellings consisted of clear cutting, seed tree cutting, and shelter-wood cutting, but only the clear-cutting areas were utilized for energy wood harvesting. As both logging residues and stumps are byproducts of roundwood removals, the technical potentials of chips have to be linked with removals of industrial roundwood. Future potentials were assumed to materialize when the industrial roundwood fellings followed the level of maximum sustainable removals. The maximum sustainable removals were 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 felling behaviour of the forest owners was not taken into account either. For the present situation in 2015, the removal of industrial roundwood was assumed to be the same as the average level in 2008–2012. Fourth, the technical harvesting potentials were derived by retention of 30% of the logging residues onsite (Koistinen et al. 2016) and respectively by retention of 16–18% of stump biomass (Muinonen et al. 2013). Next, the regional potentials were allocated to municipalities proportionally to their share of mature forests (MetINFO 2014). Subsequently, the municipality-level potentials were spread evenly 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. Here, 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). 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. 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 Fenn 47. 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.

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